Proceedings on Engineering Sciences

6

Authors: Preeti Khanwalkar

Recent advancements in wearable smart devices, medical internet of things, cloud computing, wireless communications, and AI-based technologies have enabled personalized remote health care for patients with chronic diseases. Covid pandemic period has exposed the shortfall of the healthcare system, where there was a massive shortage of doctors, nurses, medical supplies, hospital beds, and other healthcare infrastructure, which has affected many patients with chronic diseases who needed constant monitoring and consultation with doctors. This has affirmed the necessity of remotely monitoring the patients to predict their requirements for medicines, treatment, etc., and to avoid any unusual severe condition. In this work, we presented the Patient Model to monitor the patient's activities and remotely identify and fulfil their treatment requirements. The framework monitors the patients and depending on the diagnosis provides personalized remote health care services such as telemedicine, medical tests, diet plans, etc., along with an ambulance facility if needed. The proposed framework uses a CNN and other machine learning algorithm to predict the required personalized healthcare service requirements. The simulation results show that the proposed framework, using the patient’s model, and CNN algorithm significantly improves the precision and recall of the prediction and reduces the time to predict the requirements.

6

Authors: Ghazi M. Magableh, Tala B. Shbail, Sondos A. Al-Namarneh, Rahaf L. Azaizeh, Noorhan J. Hayajneh

Storage systems and warehouse processing in factories are integral to manufacturing operations and logistics procedures. Proper factory layout, storage locations, and procedures save time and reduce cost and wastes. This study examines a real case scenario of a metal industry and sheet metal factory manufacturing commercial easy-to-install metal racks. We collected data, investigated the current situation of the factory, and specified the difficulties and problems regarding the procedures, storage system, and distribution of machines. Furthermore, several proposed solutions were presented and analyzed using simulation, followed by the selection of the best solution. Then, we applied 5S methodology to improve productivity and reduce waste. The findings reveal that reducing waste and time, enhancing productivity, and better space utilization can be achieved significantly. Moreover, this study indicates the feasibility of the proposed solutions, which can be adopted by similar factories and other industries.

8

Authors: Sudheer Reddy J, Vikram Kedambadi Vasu

To better comprehend the machinability studies during MMCs, the effect of cutting tool while machining MMCs, the cutting forces that induce and importantly the influence of cutting tool were carried out. Polycrystalline diamond (PCD) and uncoated tungsten carbide (K10) cutting tools were used to machine a eutectic (Al-12wt% Si alloy) and a hypereutectic (Al-17wt%Si) matrix material with Silicon Carbide (SiCP) and Aluminium Oxide (Al2O3P) in the particulate form at 0, 5, 7.5, and 10 volume percent volume fractions as MMCs. To further characterize the machinability of the composites, SEM analysis of chips created during machining and the cutting inserts was performed. Based on the results of the MMC machining operations, the Built-up Edge (BUE) development on the K10 insert has caused it to exert significantly higher cutting forces than the PCD. Machining of SiCp and Al2O3P reinforced Al-Si alloys utilizing K10 insert showed a comparatively bigger difference in cutting forces, indicating that the cutting forces exerted by PCD did not significantly alter with varied composite composition.

8

Authors: Jayanta Chakraborty, Sushil Kumat Maurya, Nitendra Kumar

Startups face challenges in investing large amounts of money in business promotion due to resource constraints and operational size. Hence, business promotion strategies like advertising, public relations, sales promotions, billboards, and event sponsorship are generally avoided by startup companies. Instead, Startups tend to employ social media strategies to run promotional campaigns on platforms like Facebook, Twitter, Instagram, and YouTube, among others. However, there is a paucity of research to understand whether such promotional campaigns are delivering the desired value and able to achieve the objectives set by the startups. The present study makes an endeavor to explore whether promotions through social media are effective in delivering significant outcome to startups. Therefore, this study aims to empirically investigate whether Facebook Promotion is delivering value to Indian startups. To conduct the research, cross-sectional data has been collected from 100 startup located in different cities in India. The data has been analyzed following the “Partial Least Squared-Structural Equation Modelling (PLS-SEM)” technique. The findings from this study may contribute towards the literature on understanding the importance of social media strategies in marketing campaigns for startups. Furthermore, this study may enhance clarity about the value obtained by startups, particularly during the early stage of their existence. Consequently, the study findings may help startups plan their investments in promoting their products and services on social media, in general, and Facebook, in particular.

8

Authors: Jovan Vukman, Mijodrag Milosevic, Aco Antic, Dejan Bozic, Vladimir Todic, Dejan Lukic

Due to their homogeneity and excellent ratio between load capacity and weight, thin-walled aluminum structures are used as structural parts in the aerospace, automotive and military industries. The manufacture of these thin-walled structures is mainly done by removing a large amount of material from full raw pieces, sometimes up to 95% of their initial mass. Because of a large volume of material removing, it is necessary to achieve high productivity, which is limited by the lack of rigidity of the thin walls of these structures. As a result, errors occur, while reducing accuracy and machining quality. The main subject and objective of this paper is related to the optimization of high-speed machining parameters of linear thin-walled structures made of aluminum alloy Al7075 from the aspect of surface quality of processed superficial as a goal function. For this purpose, experiments were carried out based on which conclusions were made of the influence of input parameters on the surface roughness.

8

Authors: Muxtar M. Karabayev, Nilufar M. Nabiyeva, Andrei A. Lezhebokov, Alexandra V. Ryattel

Different methodological approaches to identification of the essence of the digital economy and the formation of the need for scientific personnel and specialists according to each approach are considered, the main competencies that become more popular in the context of the digital economy are outlined, the necessity for the transformation of the educational and scientific environment through the opening of new scientific directions, specialities, educational programmes, etc. is emphasised, and the key role of science and education in the ability to ensure quality transformation of social processes for the development of a competitive digital economy is established. State programmes and initiatives, aimed at the creation of conditions for academic technological leadership of Russia in the global digital economy, are described. Special attention is paid to the goals and features of the programmes Priority 2030, Digital Department, Digital Professions, Ready for Digit, CDO, Personnel for the Digital Economy, etc. Organisational features of the development of scientific potential are described, and the necessity for balancing administrative and organisational measures to implement the programmes of strategic technological academic leadership of Russia in the conditions of the digital economy is substantiated. The value of this work lies in the comparison of the processes of implementing national programmes in the sphere of development of education and science for the achievement of strategic academic leadership in the digital economy with the real trends and ranking of Russia, which leads to the need for reconsideration of the mechanisms and tools of this policy realisation.

8

Authors: Akmaljon A. Ermatov, Shaxrinoz A. Kurbonova, Shokhistakhon N. Akhunova, Aglaya D. Busalova

This work presents a study of the processes of digitalization of the economy in the context of assessing the role of investments in the development of digital technologies as the key drivers of the development of information systems management. Theoretical aspects of the formation and development of information systems are considered, their classification and general characteristics are given, general trends of transformation of information systems under the influence of digital technologies are established, and directions and volumes of investments in the digital economy from the position of their impact on the development of information systems are determined. The research methodology is based on the systemic approach and is characterised by the "blurring of the boundaries" of certain research spheres within such concepts as "digital economy”, “information system”, and “venture investment and start-ups”. The methodological tools of this study include analysis and synthesis, specific historical methods, expert analyses, observation, and generalisation. This research is a stage in substantiating the connection between the factors of investments in the digital economy and the reflection of their impact on the management and development of information systems. The empirical study of the connection between the considered processes and justification of the key trends and directions for their development form a theoretical basis for mathematical models that are aimed at determining concrete sets of factors of investment effect on the designated range of the elements of the digital economy with further reflection of this effect in the form of economic results of companies’ activities.

8

Authors: Davlatbay X. Kudbiyev, Durdona A. Muxamadjonova, Nasiba A. Sherkuziyeva, Natalia A. Chetverikova

We analysed the directions for state regulation of technologies of the digital economy in the selected countries and a supranational union (EU). The goal of this work was to reveal the features of state regulation of the management of knowledge and information systems in the digital economy in the selected countries. The presented approaches to the regulation of the considered problem sphere are being formed, which is connected with discussions regarding the need for an increase or decrease in state interference in the companies’ activities in the sphere of information and communication technologies (ICT). The government's understanding of the necessity and productiveness of mass transition to digital technologies in various sectors of the economy and infrastructure, as well as spheres of human life activities, leads to the need to create an environment that is acceptable for the growth of the country's innovative capital through the development of business and R&D sector. The considered characteristics of the formation of the regulatory framework in the EU showed that national legal regimes must have competencies for further creation of an attractive climate for the development of entrepreneurship and the scientific and technical sector in the context of implementing new solutions in the sphere of the management of knowledge and information systems. The scientific novelty of this research consists in the identification of models for regulating the creation, implementation, and use of the technologies of knowledge and information systems management in the conditions of digitalization.

8

Authors: Olim K. Abdurakhmanov, Andrey V. Kuklin, Abdumalik M. Kadirov

In this paper, we considered and characterised the features of information systems management of agricultural production processes, which are based on agricultural technologies to support and increase food security in the domestic and external markets. We revealed management models in this category, which are used in the agrarian sector of Canada and Vietnam. Differences between these two models are connected with several factors, including socioeconomic development, quality of arable land, availability of resources and their cost, level of labour resources' readiness to the implementation of new ICT, and farms’ adopting the necessity of transition to precise farming. The revealed advantages and problems of implementing Agritech at the level of the two countries demonstrate that the focus on sustainable and precise farming has become a new trend in this sphere. The goal of this paper was to establish the features of the formation of approaches to information systems management in Agritech to ensure the food security of countries that produce agricultural products. The scientific novelty of this paper lies in the determination of approaches to the implementation and management of information systems in the considered category, which facilitate the creation of conditions for the growth of efficiency of agricultural productions under the influence of threats of climate change.

8

Authors: Shohyora K. Otakhonova, Diana R. Galoyan, Dilorom A. Kholmatova, Tatul M. Mkrtchyan

In this research, we study the types and features of digital automatized solutions which are connected with knowledge and information systems management in various sectors of the economy. It is demonstrated that at the current stage of the formation of the digital economy, there have formed various directions (vectors) that cover e-commerce, high-tech industry, logistics, transport, etc. Despite many advantages of automatization in the considered directions, there are still problems with adapting ICTs and a global threat of the growth of unemployment. A comprehensive solution to this problem will determine the further socioeconomic development of countries. The objective of this research is to systematise the key directions (vectors) of automatization in the management of knowledge and information systems in the conditions of digitalization of economic sectors and infrastructure. The methodological framework of this research is comprised of frame analysis, statistical method, and trend method. The scientific novelty of this research is connected with the substantiation of perspectives of further development of automatized digital solutions in the digital economy given the existing social threats.

8

Authors: Elena G. Popkova, Mavjuda K. Abdullaeva, Elena V. Karanina, Nikita O. Stolyarov

We set a research hypothesis on the influence of the formation of international economic relations of countries on the effectiveness of knowledge and information systems management in the digital economy. We substantiate the features of the connection between these two components, which confirm the hypothesis. We consider the results of the manifestation of two forms of cooperation, which emerged due to the effect of the digital economy and due to the improvement of international economic relations between partner countries. Growth of investments in the implementation of these forms of interaction allows achieving significant results in case of favourable conditions for financial activities and R&D in countries in which businesses of partner countries are located (taxation, terms of rent, infrastructure, etc.). It is shown that activities of partner countries at the political level towards the creation of advantages for the integration of international business ensure mutual implementation of bilateral obligations in support and interaction. The goal of this paper is to identify the connection between international economic relations and the level of implementing digital technologies in the sphere of knowledge and information systems management. To reach this goal, we use the following research methods: the complex method, the comparative method, the statistical method, and the trends method. The scientific novelty of this research consists in determining the features of new forms of cooperation in the business environment in the conditions of transformation of international economic relations.

8

Authors: L N P Kumar Rallabandi, Ravindranath Vandrangi

Maintaining consistency is a crucial element in ensuring the reliability of pairwise comparisons provided by experts in the Analytic Hierarchy Process (AHP) and Fuzzy AHP. Many researchers have put forth various definitions concerning the consistency of fuzzy Pairwise Comparison Matrices (PCMs) using fuzzy set theory. However, in most applications of fuzzy AHP, fuzzy PCM consistency is evaluated by defuzzifying the fuzzy comparisons, similar to the approach used for crisp PCMs. This paper introduces a novel method for transforming fuzzy comparisons into crisp comparisons through defuzzification, specifically utilizing the geometric mean. Furthermore, it proposes a method to enhance the consistency of the PCM. The presented methodology is applied to several problems previously addressed in the literature.

10

Authors: Sanjay Nayak, R. K. Jain, Deepa Joshi, Radhika Menon, Vinayak Kale

Continuously Reinforced Concrete Pavement (CRCP) is a preferred choice in highway and roadway construction due to its durability and reduced maintenance needs. This study employs Finite Element Analysis (FEA) to investigate CRCP's static behavior, shedding light on its structural response. A comprehensive numerical model within a commercial software package is utilized, considering complex interactions among concrete, longitudinal reinforcement, and subgrade layers. This model accounts for nonlinear material behavior by incorporating concrete and steel reinforcement constitutive models. Various loading scenarios, including wheel loads and temperature variations, are simulated to assess their impact on CRCP's static behavior. The analysis focuses on essential performance metrics such as load-carrying capacity, stress distribution, and deflection patterns. It systematically examines variable parameters like reinforcement spacing, concrete strength, and slab thickness. The results are compared against experimental data and design guidelines to validate the numerical model's accuracy and applicability. This research enhances our understanding of CRCP's static behavior, emphasizing its sensitivity to different parameters. The findings underscore the significance of proper reinforcement spacing, concrete strength, and slab thickness in optimizing CRCP performance and longevity. The implications of this study extend to pavement designers, engineers, and transportation agencies involved in CRCP construction and maintenance. The validated numerical model serves as a valuable tool for optimizing designs, assessing structural integrity, and predicting long-term performance.

10

Authors: Bojan Z. Knezevic, Radovan Jalic, Dragan Erceg

The main goal of this paper was to connect two controllers of the older generation into one robotic cell, then software implement their synchronized work on servicing and welding certain objects. For this purpose, two controllers manufactured by Yaskawa Motoman were used with the associated manipulators. The hardware solution involved connecting the controllers via General I/O circuit board, where the output of one controller is connected to the input of another and vice versa. The software solution involved parallel programming of two robots for synchronized operation on a common task. During programming, it was necessary to create macro jobs that implemented repetitive actions, such as calling and waiting for robots, grabbing and releasing objects, as well as the actions of starting and stopping the program. The conclusion of presented research is that the robotic cell, formed by two robots that are not intended for synchronization, meets the requirements that until now could only be solved with robots of the newer generation. Proposed solution was confirmed by experimental verification.

10

Authors: Asha Sapna A.P, Anbalagan C

The urbanization development in India over the last few decades has been fast, very unequal, and not sustainable. The UN projects that by 2050, it will be one of the top three nations in terms of urbanization. India's urban population is expected to grow from 481 million to 814 million by 2050 an added 333 million. There will be significant effects on energy consumption patterns and GHG emissions when India's urban population increases from 34.93% now to 57.7% in 2050. Conventional construction materials have a high embodied energy, and buildings account for most of the world's energy usage. The production of today's standard materials accounts for 20% of global CO2 emissions and 25% of global energy usage. Rapid building is required to satisfy the need for housing, but if it is done wrongly, it will only meet the demand for housing temporarily and at the expense of livability, sustainability, pollution, resilience, and ultimately economic competitiveness. The Compressed Stabilized Earth Blocks (CSEB) have the potential to revolutionize environmentally friendly construction in India. CSEBs provides the perspective of energy-efficient, cost-reduction, and environmentally-friendly building materials, overall contributing to sustainable development, at a time when there is a growing concern of awareness regarding environmental issue. Since CSEBs are unburned materials, no coal or other burning fuel is required in their manufacturing. The paper delves into the history of CSEB, passive architecture, and circular economy in Indian residences, incorporating many strategies into one plan, the potential to reduce a building's overall energy consumption while simultaneously improving its cost-effectiveness, usability, and social cohesion. The goal of this study is to demonstrate that stabilized earth walls may be utilized in low and medium-rise structures when paired with a frame constructed of today's standard materials, lowering total use while achieving density requirements.

10

Authors: Khushboo Jha, Aruna Jain, Sumit Srivastava

This paper evaluates the effectiveness of feature-level fusion through the concatenation method, of two independent and emerging modalities, speech and face. The major benefit of face modality (physiological) is that the data acquisition does not require much user cooperation or awareness, as seen in airports or public places in mass. Speech (physiological and behavioural) based recognition, for disabled and illiterate people, is the most convenient and reliable user identification technique due to the ease with which a contactless speech-receiving device can be accessed. Furthermore, it should be noted that adverse conditions, such as low illumination for facial recognition and a noisy environment for speech recognition during data acquisition, are not interdependent and function autonomously. Consequently, the acoustic and distinctive facial features are the paramount (fused) features in achieving higher user identification accuracy. This paper aims to explore the state-of-the-art techniques for data fusion, dimensionality reduction, feature extraction (speech-face) and classifier. Based on the above findings, we have proposed an efficient feature level fusion of speech and face cues with the deep convolutional neural network as a classifier for the VidTIMIT database. We have tested the effectiveness of the proposed approach in terms of identification accuracy with different training sample sizes and numbers of users. The proposed user identification approach achieves an accuracy of 97.31%, an EER of 3.62% and outperforms the unimodal biometric system for speech and face by 3.83% and 1.59 % respectively. Additionally, the proposed approach outperformed a few existing methodologies. Thus, we can infer that even in the presence of adverse conditions, such an approach can ameliorate the user identification-based solution.

10

Authors: Shakhlo T. Ergasheva, Zhanna V. Gornostaeva, Anastasia A. Sozinova, Gordey V. Borodin

This paper considered theoretical aspects of the concept of knowledge management as a factor in the technological and innovative development of the country and organisation. In this context, the essence of knowledge and its role in the economic and technological development were disclosed, the specifics and character of the process of knowledge management were considered, and the expedience and necessity of managing knowledge to ensure high competitiveness of organisations in the conditions of the market environment and technological conditions that change quickly were studied. The research methodology is based on the systemic approach, the general scientific methods of analysis, synthesis, generalisation, and systematisation, content analysis, and correlation analysis of table data. The theoretical basis of the research is philosophical and conceptual provisions of the cognition of reality and capabilities of the human brain, which are implemented through the tools of the management of knowledge and intellectual capital, and the e-HRM and HR Tech concepts. The value of this work lies in the generalisation of scientific approaches to the use of digital HRM from the position of knowledge management in the conditions of the state programme “The Decade of Science and Technology” in the Russian Federation and the development of a model of knowledge management that characterises the structure of the system and determines the points and capabilities of digital processes and tools to raise the level of organisations’ competitiveness.

10

Authors: Anastasia A. Sozinova, Nadegda K. Savelyeva, Dmitriy V. Chuprakov, Victoria А. Saidakova

This paper elaborates on the issue of digitalisation of labour, development of digital competencies, and integration of digital technologies into production and managerial processes of companies in combination with the problem of knowledge management in high-tech companies. In the context of this research, the problems of hiring and involving employees with ICT competencies are analysed, the problem of involving intellectual resources by companies of the high-tech sector is considered, and expectations from digital transformation of the labour market in the context of the development of digital programme solutions, which can partially replace workforce, are studied. The research methodology is based on the concepts of digital personnel from the position of a combination of digital technologies and human labour (digitalisation of labour, digital workplace, automatized digital system, autonomous intelligent platform, etc.). It involves the study of the environment of the development of digital personnel and knowledge management in high-tech companies, which are based on the methods of statistical and mathematical analysis, as well as general scientific methods of observation and generalisation. The main contribution of this work lies in the systemic consideration of the conditions of digitalisation of companies' work from the position of search and hiring of employees, substantiation of the need for high-tech production for intelligent resources, and description of trends and expectations from the integration of digital technologies and systems of knowledge management into the systems of high-tech companies’ management.

10

Authors: Aziza A. Kurmanbekova, Aybek X. Tashpulatov, Alexey V. Sysolyatin, Karina A. Chernitsova

This paper elaborates on the role of higher education in knowledge management in the conditions of the digital economy, considers challenges and prospects connected with the digital transformation of economy and society and the necessity for the formation of new competencies with employees, analyses the trends of development of the digital economy in the Russian Federation and the level of digital literacy of the population and the state of personnel training for the digital sector, substantiates the importance of adapting the system of higher education to new realities through the integration of digital technologies into the educational process and development of digital competencies of students and teachers, and emphasizes the necessity for the system approach to the development of the digital economy in the Russian Federation, in particular, in the context of import substitution, investments in science and education, and commercialisation of R&D. The research methodology is founded on theoretical approaches to the educational process and its transformation under the influence of modern digitalisation trends. It covers the provisions of connectivism, constructivism, and the theory of active training and socio-cultural theory. These theoretical concepts are analysed in the context of Industry 4.0 and University 4.0, which involves a close integration of digital technologies into production and educational systems. General scientific methods of analysis, synthesis, observation, literature review, graphical method, etc. are used to formulate theoretical conclusions. The main value of the paper lies in substantiation of the necessity to create an adaptive organisational model of knowledge management, which combines academic traditions and values with the flexibility of information systems and the potential of digital technologies, as well as the generalisation of the adaptive measures of the higher education system, which are a reaction to current trends inherent to the educational environment.

10

Authors: Ilmidin T. Yormatov, Nurzhan D. Kenzhebekov, Abdumalik M. Kadirov, Maria G. Umnova

This paper considers the current problems of knowledge and information systems management, which are solved with the help of digital institutes for the purpose of the development of high-tech production in the conditions of Industry 4.0. The paper identifies the role of knowledge in the innovative development of industrial production, characterises the specifics of the influence of digital technologies on the technological development of companies and production processes, and focuses on the sensitivity of high-tech production to the level of intellectual support of production processes. Emphasis is made on the necessity of using comprehensive digital institutes, which can expand the boundaries of production companies and use the potential of digital platforms and systems in full. The research methodology combines traditional and new scientific directions and institutional theories in disciplines that study the research problem in the context of an increase in the intellectualisation of labour in the conditions of Industry 4.0. The research methodology covers general scientific methods of synthesis, analysis, the monographic method, observation, generalisation, and comparison. The paper contains important thoughts regarding the features of digital institutes from the position of their formation and influence on knowledge management in high-tech companies. It considers the structure and functions of the digital institutes of knowledge and information systems management in the conditions of Industry 4.0 and characterises selected institutes in the context of their influence on the digital transformation of the economy and the improvement of the state of knowledge management.

10

Authors: Bobir O. Tursunov, Elena G. Popkova, Nadejda V. Palesheva, Elena S. Petrenko

This paper presents the assessment of the formation of economic integration in the EAEU as the basis for improving the management of knowledge and information systems in the digital economy. The goal of the paper was to find the specifics of the influence of economic integration on the level of formation of information systems and knowledge management in the digital economy in countries of the EAEU and the EAEU on the whole. In the course of the research, we studied the factual state, problems, and perspectives of the influence of the tools of the digital economy on the level of economic integration in the EAEU member countries. It was also determined that the programme principles of the EAEU influence the support for the intellectualisation of partnership (unification of knowledge management and information systems). We proved the hypothesis about the direct dependence between the formation of conditions of economic integration and the level of the digital economy in the EAEU member countries, which demonstrated the necessity of improving these components as the precondition of economic growth. The methods of classification, comparison, and ranking analysis were used, as well as the factor method. Based on the conducted analysis, we determined the effectiveness of interaction of the EAEU member countries, the perspective of improvement of the digital environment given the main sectors, and the possibility of adapting the experience of each member country given the similarity of the systems of state management.

10

Authors: K Ganesh Kadiyala, K. Jagadeesh, Ch. Geetika, I. Lakshmi Lavanya, B. Kanaka Durga, J. Suresh Kumar, Bachina Kiran, B. N. Suresh Varma Dendukuri

Molecular docking simulations were conducted to analyze the interactions between eight lead molecules with AR and PSA proteins. The lead molecules included Enzalutamide, Abiraterone, Docetaxel, Apalutamide, Cabazitaxel, Bicalutamide, Curcumin, Galeterone, Resveratrol, and Darolutamide. For the Androgen Receptor (AR), Enzalutamide displayed the most favorable docking energy of -10.96Kcal/mol, followed by Galeterone (-10.52Kcal/mol) and Darolutamide (-9.97Kcal/mol). The binding affinities of these compounds to AR suggest potential inhibitors. On the other hand, resveratrol exhibited the strongest interaction with the AR protein (-8.02Kcal.mol) among the natural compounds studied (Resveratrol and Curcumin). In the case of Prostate Specific Antigen (PSA), Abiraterone showed a docking energy of -9.14 kcal/mol, indicating a potential interaction with PSA. The docking results suggest that Enzalutamide, Galeterone, and Darolutamide, hold promise as potential inhibitors for the Androgen Receptor in prostate cancer treatment. Abiraterone, Enzalutamide, Apalutamide ligands shown a significant interaction on Prostate Specific Antigen, hinting at its potential as a dual-target agent.

10

Authors: Zow Afshan, Sherin Zafar, Safdar Tanweer

The protection of the water's quality is an essential component of the planning, control, prevention, and preventative actions that must be taken to reduce the pollution of the water and the deterioration of aquatic habitat. Monitoring the water's quality on a consistent basis is an essential activity that contributes to keeping track of the hydrological particulars of any body of water. Many surviving systems and methods have been shown to be ineffective in achieving the same purpose, and only a small handful of other conventional methods have shown a remarkable outcome that has helped to reinstate and keep up water resources from piles of nutrients and organic composites that cause quality degradation. However, the figures collected from all corners of the world suggest that there is an immediate demand for an upgrade in the monitoring system for water quality. Based on the findings of the survey, the concept presented here entails the suggestion of a unique system that is able to maintain tabs on the quality of water, hence assisting the ongoing pollution control trends.. In this paper, a methodical approach is suggested for collecting the parametric quality indicators, most of which are chemical, which then leads to the production of a characteristic drawing for each of the variables relevant to the specific water resource that is the subject of the observation. The information that was gathered is, to put it another way, coupled with clouds. The suggested approach eliminates the most significant drawbacks of the present techniques, which results in an increase in the effectiveness of monitoring water quality and a reduction in water pollution.

12

Authors: Nagarathna R, A R Aswatha

In digital circuits, particularly space signal applications, the detection/estimation of phase (angle) like milli degree is challenging and involves many complex operations. To estimate milli degree (10-3) or more, floating point operations (like double precision adders, subtractions, multiplications, and divisions) are the significant components and power and area consumption; delays are more in existing works. Existing works mainly concentrate on clock-based synchronous operations, which require more hold, and clock distribution is another major problem due to extra circuitry requirements. The proposed pipelined and clock-efficient distribution system (CEDS) is incorporated in the Floating-Point Unit (FPU) design to address these issues. It includes a double-precision adder, multiplication, division, and subtraction. These FPU and CEDS can be used to detect Milli Degree for GHz frequency in Space applications more accurately. The floating points are essential in phase shift operation used in the space-borne system for effective correction of error checking rather than execution of error analysis in the real-time scenario. Modern digital circuits can hardly improve due to limitations of physical area consumption. The substitution for these limitations, the new digital ternary logic (0, 1, 2), is the solution because of its higher number of digital circuits involved in floating points. The ternary logic is the best solution for the minimization of number bits to optimize memory utilization, and ternary logic has Negative Ternary Inverter (NTI), Ternary Decrement Cycling Inverter (DCI), Standard Ternary Inverter (STI,) and Positive Ternary Inverter (PTI). The proposed design is successfully designed and validated on Zybo Z7-10 (XC7Z010-1CLG400C) FPGA development board. The results show a 45% reduction in power consumption, delay, and area utilization.

12

Authors: Ramesh Babu Gurujukota, Gokuldhev M

In agricultural informatics, the accurate prediction of tomato diseases is crucial for optimizing yield and maintaining quality. This study introduces an innovative hybrid algorithm that synergistically combines the meta-heuristic Cuckoo Search (CS) with the gradient boosting capabilities of XG Boost. The proposed model aims to predict five distinct states of tomato health: No Disease, Early Blight, Late Blight, Leaf Mold, and Tomato Yellow Leaf Curl Virus. By fusing CS's prowess in optimized feature selection with XG Boost's robustness in classification, the hybrid model endeavors to enhance the predictive precision. A comparative analysis was conducted against benchmark algorithms, namely KNN, SVM, Random Forest, standalone XG Boost, and Cat Boost. Preliminary results, evaluated based on standard metrics like accuracy and F1-score, indicate that the hybrid CS-XG Boost algorithm manifests a marked improvement in prediction accuracy and computational efficiency. This research underscores the potential of integrating meta-heuristic search algorithms with gradient boosting models, providing a new avenue for advancements in agricultural disease prediction.

12

Authors: V L N Murthy G, Ramprasad Naik D, Anjankumar M, Gireesh Kumar Pala

Traffic Simulating and Evaluating Traffic Patterns at heterogeneous traffic Situations in the Indian Context is extremely increasing. Many researchers from all over the world are trying to explore driving behavior and simulate traffic flow conditions under heterogeneous traffic environments. Simulating Traffic at a Microscopic Level with VISSIM is used in this study identifying every element that contributes to traffic at various traffic stages. Further, the VISSIM model was calibrated based on the desired safety distance and car-following theory. Traffic metrics including flow volume, speed, acceleration, and deceleration were all input parameters in the VISSIM model for simulation purposes. Furthermore, the analysis part of different cases of driving behavior models, such as the default case, and calibration values based on measurement were compared to find out if there was a significant improvement. Moreover, the linear regression model was proposed to understand the calibrated model versus the default case parameters, and it was identified that it is significant when plotting the data. The proposed study results highlighted driving behavior under heterogeneous traffic conditions using VISSIM at Toll Plaza.

12

Authors: Suman Lata, Neha Kishore, Pardeep Sangwan

The analysis of emotions expressed in spoken language holds a pivotal role in human communication, artificial intelligence, and human-computer interaction. While emotion recognition in text has seen considerable advancements, recognizing emotional states in spoken speech presents distinct challenges and opportunities. This research introduces an innovative approach that harnesses Mel Frequency Cepstral Coefficients (MFCC) and Long Short-Term Memory (LSTM) networks to facilitate deep emotion recognition in spoken speech signals. This study explores the profound potential of the MFCC-LSTM framework, a combination of established audio feature extraction and deep learning. Mel Frequency Cepstral Coefficients offer a powerful representation of spectral features over time, while LSTM networks excel at modeling temporal dependencies. This system classifies emotional states such as sadness, angry, neutral, and happiness from the speaker's utterances. Several performance assessments were carried out on the suggested MFCC-LSTM model. There is significant improvement in the recognition rates when compared with other models that are currently available. The proposed hybrid model reached 96 % recognition success.

14

Authors: Abbarapu Ashok, Nadiminti Nagamani

Estimating a common parameter is the most essential and quite fascinating task across various probability distributions. This article addresses the challenge of estimating this parameter through the application of Maximum Likelihood Estimation (MLE). Numeric determination of common parameters is conducted for several distributions, including the Lomax distribution, Gamma distribution, Rayleigh distribution, and Weibull distribution. In cases where distributions lack a closed-form solution, estimation of MLEs is achieved using the Newton-Raphson technique. Furthermore, asymptotic confidence intervals are computed utilizing the Fisher information matrix tailored to each distribution. The performance evaluation of these estimators centers on the assessment of bias and mean squared error. To enable a numerical comparison of these estimators, the Monte Carlo simulation method is employed. Finally, these techniques are applied to real-time rainfall data to assess parameter estimates for each distribution.

6

Authors: Qasim O. Kadhim, Mahmoud A. Hassan, M. Abdulrazzaq

Nickel and Tantalum alloys have a wide range of capabilities biomedical applications due to its high degree of biocompatibility, Favorable mechanical properties and high corrosion resistance The possibility of Osseointegration. In this study, all the alloys were prepared by powder metallurgy Then the alloying element (indium as a binder with different structural composition) in different groups (0.6, 0.7, and 0.8 wt.%) to the main alloy (75 wt.% Ni-25wt% Ta) for to study the effect of these elements on microstructure characterization (XRD, SEM, EDS, XRF and light optical microscopy) and mechanical properties (compression, hardness, Wet sliding corrosion tests) for this alloy. The compact pressure was determined as 800MPa and the green alloyed sintered at 700 oC for 5h in inert gas (Argon), then the samples cooled in the furnace.

6

Authors: Vinay Agrawal, Aman Jain, Tanmay Shandilya, Rajesh Gupta

Worldwide, annual glass waste production surpasses 200 million tonnes, urging exploration of sustainable reuse avenues. This study evaluates the incorporation of waste glass powder (WGP) into concrete as a partial replacement for fine aggregate. Thirty concrete mixes, varying water-cement ratios and WGP levels, underwent comprehensive analysis. Findings reveal that as WGP percentage in sand increases, the slump of concrete rises marginally until reaching maximum glass powder replacement, with workability remaining within the 50 to 100 mm range. At a 10% replacement level, early and later age strength minimally impacts compressive strength. WGP's workability and strength hinge on particle geometry, indicating increased concrete strength when mixed with WGP featuring higher surface area and improved cement paste bonding. To enhance solid waste management, conserve sand, and bolster sustainability, WGP can replace up to 10% of sand weight in concrete at lower water-cement ratios. Positive correlation exists between slump and WGP percentage replaced. Both water-cement ratio and replacement amount significantly influence compressive strength, as confirmed by ANOVA. However, long-term mechanical strength and durability research is essential before recommending WGP for environmentally friendly and sustainable concrete applications, promising increased understanding through additional data collection and analysis.

8

Authors: Milan Sljivic, Ana Pavlovic, Cristiano Fragassa

Additive Manufacturing (AM), commonly known as 3D printing, represents a groundbreaking technology driving the advancement of the fourth industrial revolution (Industry 4.0). The production of models, rapid prototypes, components, and final-use parts using 3D printing is significantly more cost-effective compared to traditional manufacturing methods. The primary objective of this paper is to highlight the role of sustainable development in three-dimensional (3D) printing technology for effectively implementing Industry 4.0 amidst the green and digital transition. With the capability to realize virtually any desired object, 3D printing finds applications across various industries including construction, architecture, automotive, aviation, household goods, and medical fields. The analysis of Additive Manufacturing 3D printing technologies involves categorization based on process type, purpose, production equipment, energy consumption, materials, and other factors that influence sustainable development. The implementation of green initiatives and digital transformation is fully integrated into these innovative 3D technologies.

8

Authors: Sasa Zivanovic, Goran Vasilic, Zoran Dimic, Nikola Vorkapic, Branko Kokotovic, Nikola Slavkovic

This paper presents programming methods and program verification for the 3-axis reconfigurable hybrid kinematics machine MOMA V3, which represents an educational desktop milling machine with a horizontal position of the main spindle. The paper considers the different programming and program verification methods. For programming used CAD/CAM system PTC Creo, specialized CAM software CUT3D and new programming method based on STEP-NC. Program verification is based on tool path simulation, material removal simulation and virtual machine simulation. The paper presented the virtual machine in the programming system and the virtual machine integrated with the LinuxCNC control system. Final verification was achieved during the testing on machine by machining the selected workpieces which programmed with different programming methods.

8

Authors: Bade Sudarshan Chakravarthy, B. Uma Rani, K. Karunakaran

In the ever-evolving landscape of digital marketing, the role of social media in shaping effective communication strategies is paramount. This research paper delves into the world of data-driven insights to examine how social media channels contribute to the success of digital communication efforts. This paper focuses on exploring the multifaceted aspects of social media's impact on brand visibility, engagement, and customer interaction. The data was analyzed quantitatively using descriptive statistics while Analysis of Variance was used to establish associations between variables. The study revealed that major factors influencing the social media's effectiveness in digital communication are Content, Customer Interaction and Social Media metrics. As a result of comprehensive analysis, this research paper not only provides valuable insights into the current state of social media's effective-ness in digital communication but also offers practical recommendations for businesses looking to optimize their social media strategies. In an era where data reigns supreme, this research findings serve as a guide for harnessing the power of social media as a dynamic and influential tool in the digital communication arsenal.

8

Authors: Meshel Q. Alkahtani

Landslides are a growing threat in steep regions of the world, taking lives and damaging property. The recent damages caused by landslides demand that authorities pay attention to catastrophe risk mitigation strategies. One crucial risk reduction strategy is the creation of an efficient landslide early warning system (LEWS), which will allow authorities and the public at large to be informed in advance of any landslide incidents. In order to construct a system of early warning for landslides, a wireless sensing network may collect data on the geological features and a few physical surroundings characteristics. The recommended system's primary objective is to predict when a landslide could happen and alert authorities to prevent or at least minimize casualties. In this study, we show how Internet of Things-based sensors (temperature, soil moisture ,humidity) may be used to observe and alert authorities to impending landslide danger. An advanced landslide tracking system built on Internet of Things infrastructure is shown here. The system is comprised of a group of self-sufficient wearable sensors, each of which wears a sensor costume designed for tracking landslides, and a microprocessor that aggregates data from a wide variety of sensors.

8

Authors: Ruchi Varshney, Amit Dixit

Nuclear reactors, as a class, exhibit nonlinear and higher-order system characteristics, posing a consistent challenge for researchers in the design of effective controllers. The specific focus of this study is on the Pressurized Heavy Water Reactor (PHWR), a representative example of such intricate systems. Given the inherent complexity of higher-order system dynamics, this work tackles the challenge by employing a reduced-order modeling approach, capturing the essence of the original system's behavior. In the context of this research, a novel approach is taken in the design of a controller for the PHWR system. The method involves the use of an optimization-based Fractional Order Proportional Integral Derivative (FOPID) controller tailored for the lower-order model of the PHWR. The reduced-order model is derived through the application of the Balanced Truncation method, which enables the creation of a simplified yet representative model that faithfully emulates the behavior of the original higher-order system. The optimization of the FOPID controller parameters is achieved through the adoption of the Grey Wolf Optimization (GWO) algorithm. To substantiate the efficacy of the proposed controller, a comprehensive performance analysis is conducted. Various performance indices are employed to evaluate the controller's robustness and overall effectiveness in regulating the PHWR system.

8

Authors: Ram Kumar Kuncha, Durgansh Sharma

Failure to innovate in this era of rapid IT growth is a significant obstacle to the modernization and growth of industries and increases the competitiveness of such organizations in the market. Strong innovation and competence are more than necessary to turn innovative ideas into reality, gain new competitive advantages and achieve sustainable long-term growth. Innovation is not only an important tool for companies to increase their competitiveness, it is also an important driver of long-term economic growth for a country. Regular engagement in high-quality innovation activities should be mandatory for organizations that intend to successfully adapt to today's fast-paced digital economy. If companies want to improve their chances of survival in the coming years and continue to grow, they need to invest in their innovation capabilities. Many companies now operate under the assumption that updating their accounting systems with advanced software will provide better results than relying on old, time-honored methods. Concrete steps are needed, such as developing powerful data-driven tools to improve how individuals, organizations and governments spend their money. Beginners still have to put in the effort to learn new skills because they often have trouble imagining using a device they've never used before to accomplish a task. Due to the increased automation of this financial system, the risk of error has increased; So it is very important. In fact, you can manage your needs exactly with this tool.

10

Authors: Lishmah Dominic M, S. Sowmiya, P.S. Venkateswaran

The Micro, Small, and Medium Enterprise (MSME) sector stands as a pivotal pillar within the Indian economy, contributing to 48% of the nation's production output and providing employment to a staggering 110 million individuals. The pervasive impact of the pandemic has inevitably reverberated across the economic landscape, although its full extent remains shrouded in uncertainty. Particularly, the small-scale enterprises that play a crucial role in India's manufacturing and service domains have borne the brunt of this impact. The year 2020 witnessed an unprecedented deceleration in revenue, with the MSME sector grappling with severe challenges in maintaining operational continuity. In response to this crisis, the government took affirmative action aimed at rejuvenating the beleaguered sector, offering targeted support to MSMEs. This concerted intervention proved to be a significant impetus, infusing hope and vitality into these enterprises. Consequently, a comprehensive evaluation of the barriers encountered, a candid exposition of these hurdles, and the formulation of a robust strategy to surmount the pandemic-induced turmoil have become imperative. In light of this exigency, the researcher undertakes a rigorous analytical investigation into the government's policies crafted to ensure the sustainable growth of MSMEs in India. By distilling insights from diverse sources, a holistic understanding of the challenges faced by MSMEs and the efficacy of governmental interventions is pursued.

10

Authors: Vinod B. Ingale, E. Saikiran

Recommendation Systems (RS) are systems that propose products for customers to view. Since the turn of the millennium, these kinds of technologies have been increasingly common, and now almost all online apps use them to make suggestions to their users in an effort to keep and increase their engagement with the apps. The challenge of idea generation is tackled in a variety of ways by the various RS kinds. These strategies have progressed to the point where both complicated and straightforward algorithms can be used to implement them. In spite of the availability of numerous recommendation algorithms, some may be more suitable than others for specific tasks. This paper discusses a variety of recommendation algorithms, some of which are quite complex.

10

Authors: Sudaryanto Sudaryanto, Anifatul Hanim, Tatok Endhiarto, Bambang Irawan, Ivana Rosediana Dewi

Unplanned purchasing involves buying goods or services influenced by the external environment, which creates low-quality purchasing. This research was conducted towards consumers of the Muslim clothing store in Jember East Java, Indonesia. Accidentally, sampling was used to draw 125 respondents to address the questionnaire. The result shows that hedonic shopping, fashion involvement, and store environment influence low purchasing quality in Muslim fashion stores. Most consumers were mature female consumers and government officials with bachelor’s degrees. Moreover, hedonic shopping, fashion involvement and store environment significantly influence low-quality purchase decisions. The research implied that consumers’ shopping behavior would underline the quality of buying decisions.

10

Authors: Mohammad Yahya Samaana, Anna N. Polukhina

This work studied the role of tourism digitalization in achieving sustainable development of the territories. First, the basic concepts of sustainable development were defined. Then, a number of leading countries in the field of sustainable development were studied, such as Denmark, Norway, Sweden, and Germany, and the methods used in digitizing tourism to achieve sustainable development were analyzed. Then the level of sustainable development in Russia was studied, as it is one of the countries that seeks to achieve sustainable development for the country as a country with regions. The situation and importance of tourism in Russia was analyzed. Then the level of technology in Russia was analyzed. Then the level of sustainable development in Russia was studied. In addition, the Tatarstan region was analyzed as an example of one of the regional regions that has achieved growth in the level of sustainable development.

10

Authors: Dmitry Napolskikh

The study elaborates on the model of innovative hypercluster platforms in environmental management, which is regarded as a unique kind of innovation cluster developing on the basis of digital environments. A cluster analysis of the technological developments of subsurface management industries is presented, along with the analysis of the main problems arising from the synchronisation of technological development in resource-exctracting industries. In the context of the digital transformation of subsurface management industries, the interaction between various cluster types, digital platforms, and ecosystems is discussed. The primary economic systems and businesses that act as hubs for innovation promotion and serve as the basis for technical synchronisation within the context of an innovative hypercluster in subsurface management are identified for resource-extracting territories. The main directions for synchronising the technological development of these territories are systematized. The emphasis is on digital and intelligent technologies that intersect to establish innovative hyperclusters. In the context of subsurface management, two primary development vectors for innovative hyperclusters have been identified: innovative hyperclusters of resource-saving and sustainable mining businesses and innovative hyperclusters of digital green technologies.

10

Authors: Samarth Sharma, Pallavi Sharda Garg, Archana Singh

The fifth generation of mobile networks is going to transform the way we live, work, and communicate. The shift from 4G to 5G would be a crucial enabler for the growth of smart cities due to its superior speed and minimal latency. Extant literature indicates that 5G technology holds immense potential in implementing and managing smart cities and would provide benefit to citizens and corporates alike. The purpose of this study is to examine the potential of 5G computing as a smart city accelerator, in terms of enabling data driven decisions and its effects on various economic sectors. Based on primary data collected from 392 respondents based in Delhi NCR, the study attempts to explore the attitude of people towards 5G technology with respect to Connectivity, Infrastructure and Application for development of smart cities in India.

10

Authors: Vidushi Nain, Hari Shankar Shyam

This study provides an empirical evaluation of AI's function in the field of human resources (HR) hiring and selection. Human resources departments may save time and effort by using sophisticated algorithms and machine learning to efficiently sort through piles of applicants and make well-informed decisions. The potential for AI to lessen the prevalence of unconscious bias in the recruiting process is an especially particular advantage. Furthermore, the incorporation of AI into the recruiting process has improved the candidate experience via the use of real-time interaction technologies such as chatbots. Despite its benefits, integrating AI into HR is not without its share of difficulties, most notably protecting employee data and avoiding becoming too dependent on digital tools. The research highlights the need of a unified strategy, which strikes a balance between the efficacy of AI and that of human judgment.

10

Authors: Nitendra Kumar, Priyanka Agarwal, Sanjeev Bansal, Vinay Kumar Yadav, Dhrubajyoti Bhowmik

In the current digital era, the humongous amount of data being generated has been impacting public lives in one or the other ways. Sentiment analysis, also known as opinion mining, is related to contextual mining of texts which helps in identification and extraction of subjective information from the source material. Sentiment analysis is being used for brand monitoring and reputation management across different market segments. It helps to understand how the public perceive a particular brand, product or service that is highly useful for different tech companies, marketing agencies, media organizations, fashion brands etc. In today’s scenario we have been suffering with data overload which makes it impossible to analyze public sentiments without any sort of error or bias. Sentiment analysis provides better insights into the public reviews as it can be automated which ultimately helps in decision making. There are various deep learning and machine learning methods and models as well as natural language processing tools which help in examining and analyzing public opinions with low time complexity. However, deep learning methods have become highly popular in recent times as these models provide high efficiency and accuracy. In this review paper we have provided a complete overview of the common deep learning frameworks being employed for sentiment classification and analysis. This paper discusses various learning models, evaluation, text representations and other metrics in deep learning architectures. The key findings of different authors have been discussed in detail. This paper will help other researchers in understanding the deep learning techniques being used for sentiment analysis.

12

Authors: Andrei I. Stoicescu, Adrian Predescu

A viscous coupling is a simple device that transmits torque by shearing an oil film between one or more pairs of discs that are moving in relation to one another. Due to their many advantages, viscous couplings are studied in many applications such as fan transmissions in vehicles, pumps, winches, belt conveyors, wind turbines, tunnel boring machines. Using a CFD Software, this study examined how grooves affected the performance of a viscous coupling. The main objective of the reported work is to study the flow of the fluid film, the total temperature and the torque of a viscous coupling that is operating in different conditions.

12

Authors: Fanfei Meng, Lele Zhang, Yu Chen, Yuxin Wang

Federated learning (FL) is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. The learning scheme may be horizontal, vertical or hybrid (both vertical and horizontal). Most existing research work with deep neural network (DNN) modeling is focused on horizontal data distributions, while vertical and hybrid schemes are much less studied. In this paper, we propose a generalized algorithm FedEmb, for modeling vertical and hybrid DNN-based learning. The idea of our algorithm is characterized by higher inference accuracy, stronger privacy-preserving properties, and lower client-server communication bandwidth demands as compared with existing work. The experimental results show that FedEmb is an effective method to tackle both split feature & subject space decentralized problems. To be specific, there are 0.3% to 4.2% improvement on inference accuracy and 88.9 % time complexity reduction over baseline method.

12

Authors: Karunakar T, F T Josh, J Jency Joseph, J Jayakumar, P S Mayurappriyan

Increased demand in renewable power move towards the balancing conventional dependent energy source lead the increase in technical development in reliable power generation. This model contains the wind speed variation, capacity, uncertainty, and production are considered in system evaluations to bring the wind power to more sustainable energy. More reliable VSC HVDC technology in power transmissions for longer distances is preferred to transmit the renewable generated energy to remote end areas. Authors introduced particle swarm optimization method into the wind integrated HVDC System. The system is developed in MATLAB/Simulink to evaluate the wind power generation performance and Transmission performance. The simulation results are correlated and validated with the existing VSC HVDC link parameters. The supremacy in wind energy with particle swarm optimization performance is validated and more viable to integrate wind energy system with VSC HVDC system to transmit the reliable power to remote areas.

12

Authors: Helen Grace Gonzales

The Intelligent Nursing Assistant Robot (INA) emerged as an innovative solution with COVID straining the healthcare systems and over 180 million cases globally by April 2021. INA protects overworked and exposed staff by remotely monitoring patients in quarantine. This robot boasts features like a thermal camera for precise temperature readings and a high-definition camera for visual checks. Facial recognition aids in patient identification, while its mobile control via an Android app and Pixy camera with Arduino connection offers flexibility. Built with durable Cagebot materials, INA provides a safe and versatile tool for overwhelmed healthcare professionals during this critical time.

14

Authors: Ugochi Sandra Uzoho, Frank Alaba Ogedengbe, Francis Sylvanus Udoh

Successful project delivery is essential for economic growth and infrastructure development. This study looked at construction companies’ management of materials. The improper handling of materials affects how well projects are completed. Inadequate material management could seriously hinder the realisation of projects. 291 employees from Habitat and Building Concepts Limited and Dantata & Sawoe Construction Company (Nigeria) Limited made up both the study's population and sample, which used descriptive survey research design. Information from the respondents via standardised questionnaire were regressed using the Statistical Package for Social Sciences. Result shows transportation (.03+1.82), purchasing (.03+0.73), and storage (.03+1.11), had a favourable and significant impact on project delivery. While planning (03-0.15) had a negative but minor impact on project delivery. The study advises Dantata & Sawoe Construction Company (Nigeria) Limited and Habitat and Building Concepts Limited accordingly. Managers should manage materials adequately and decide on efficient means of transportation, procurement, and storage.

16

Authors: Tryson Yangailo, Chanda Sichinsambwe

This study assessed the quality of service delivery by councils in rural districts of Zambia, particularly Senanga Town Council. The study respondents were randomly selected from the residents of Senanga District in twelve wards and two hundred and fifty residents representing households from different wards were randomly selected to participate. Data were collected using an FM-SERVQUAL model questionnaire and analyzed using Social Statistics Package for Social Sciences (SPSS) and Mega Stat software. The results of the study show that out of four components, only one component of the working process met the minimum required level of quality service standards. The Human Capital, Technology and ICT, and Property Management components were all below the required service standards. On average, the Council's performance was below the required service quality standards. The study results also showed that there was no relationship between Senanga Town Council's service quality and customer satisfaction. There was no relationship between service quality components and customer satisfaction, although the results further showed that there was a significant relationship between technology and ICT and customer satisfaction. This means that technology and ICT have an impact on customer satisfaction in Senanga District.

18

Authors: Mohammad Asif, Ashish Saxena, Shashank Bhardwaj, Sadhana Tiwari, Sharad Chaturvedi

This study aimed to predict the bankruptcy risk of companies listed on the National Stock Exchange (NSE) in India using the Altman Z-score model. By examining the impact of variable factors such as market value equity/book value of total liabilities, retained earnings/total assets, working capital/total assets, earnings before interest and taxes/total assets, and sales/total assets, the research sought to assess their influence on the financial condition of listed companies. The Altman Z-score model is a widely accepted tool for predicting bankruptcy risk and has been utilized in various industries across different countries. The model comprises five financial ratios that capture a company's liquidity, profitability, leverage, solvency, and activity. By analyzing these ratios, the model calculates a composite Z-score, which can be used to classify companies into different risk categories. Using a sample of NSE-listed companies, this study employed the Altman Z-score model to predict bankruptcy risk and examined the impact of the five variable factors on the firms' financial health. The findings provided insights into the relationship between these financial ratios and the companies' bankruptcy risk, offering valuable information for investors, policymakers, and other stakeholders in the Indian financial market. The study demonstrated the applicability of the Altman Z-score model in predicting bankruptcy risk for NSE-listed companies in India and highlighted the importance of each variable factor in assessing a firm's financial condition. These findings can help stakeholders make informed decisions regarding investment, risk management, and policy formulation in the Indian financial market.

6

Authors: Olesya P. Kazachenok

The article is devoted to the study of the prospects for increasing the financing of technologies and innovations through the development of remote digital investment transactions in Russia. Based on the best international experience of countries from the IMD Digital Competitiveness Ranking for 2022, using the method of regression analysis, econometric modelling of the significance of the risk of loss of an investor and the legal protection of remote digital investment transactions for financing technology and innovation was carried out. As a result, it was proved that the risk of investor losses and the legal protection of remote digital investment transactions determine the amount of technology and innovation financing. The development of remote-digital investment transactions in Russia opens up broad prospects for increasing funding for technology and innovation. For this purpose, author's recommendations are proposed. The originality of the article is that it revealed a new look at the financing of technologies and innovations - from the standpoint of remote digital investment transactions.

6

Authors: K. R. Priya Dharshini, R. Kalaivani

Wireless communication must be designed with the inbuilt characteristics inclusive of high data rates, transmission efficiency, high quality of data transmission and multimedia transmission in communication framework. In order to improve the spectral efficiency, higher bandwidth, demand in performance and efficiency MIMO system is used. MIMO system with multiple array of antennas utilize maximum spectrum and increase the spectral bandwidth, improve performance, efficiency and bandwidth. This paper aims to design the channel multiplexing based on MIMO PSK modulation and demodulation scheme. MATLAB tool is used to obtain design specifications. Xilinx FPGA is used to implement obtained design specifications using RTL coding.

8

Authors: Faisal Alharthi

The economic development of countries depends upon their technological competitiveness. Computer technology is a major component of technology. More the people are competent in computer technology, the greater the chance of economic development. Saudi Arabia is no exception to this common view. Although computer education was started in Saudi Arabia in 1984, the progress had been slow for a long time. However, now, with the implementation of Vision 2030, there is a high focus on elevating the skills of the Saudi workforce to the global levels. Computer technology has a big role in this regard. The full implementation and achievement of developing computer skills among the Saudi workforce are affected by many issues and challenges. This paper reviews these challenges and issues and the possible solutions offered for some of them, using published literature. A systematic review methodology was utilised for this research. A total of 29 papers were shortlisted and reviewed. Challenges were noted both in learning and in teaching sides. Despite cultural segregation of gender, women are increasingly enrolling in computer programming courses due to government support, family influence, and a stable workplace. Needs and demands on computer skills are rapidly changing with time. This necessitates frequent updating of computer curricula. Many problems are faced by freshers and their seniors. Lack of knowledge in problem-solving affects their ability to construct algorithms and codes and variables for their programming. This is aggravated by a lack of feedback on errors. Other challenges include inadequate practice, inadequacies of time allocated for labs and tutorials, poor quantity and quality of homework, poor selection of instructors, discouragement by universities on computer use, lack of competency of instructors to motivate students, and absence of quality assurance and monitoring. Solutions have been suggested for some of these like 3D animation software like ALICE and automatic assessment. Teaching challenges include workloads due to large classes affecting individual attention to weak students and the need to address students’ problems listed above. Common problems both students and teachers need to be solved through further research. Specific computer programming needs of healthcare professionals, hard of hearing students are to be addressed by implementing specific solutions. Some research in the future will also be helpful. The above challenges can be addressed by adapting solutions from research to the Saudi Arabia context.

8

Authors: Imaduddin, Komal Prasad Kanojia, Bharti Chourasia

he research paper proposes a novel approach for signal detection in cognitive radio networks, aiming to improve spectrum utilization and overall performance. The approach combines matched filter-based detection and the Salp Swarm Algorithm (SSA)-optimized energy detection.Matched filtering is a technique used to detect the presence of a known signal. It correlates the received signal with a reference waveform to determine if the signal is present. In the proposed approach, matched filtering is utilized to detect known signals in the cognitive radio network.On the other hand, energy detection is employed to identify unknown signals. Energy detection measures the energy level of the received signal and compares it to a predetermined threshold. If the energy exceeds the threshold, it is considered as a signal. In this approach, energy detection is optimized using the Salp Swarm Algorithm (SSA). SSA is a metaheuristic algorithm inspired by the behavior of salps in nature, and it is used to find an optimal energy threshold for energy detection in order to improve detection accuracy. The proposed approach is evaluated through simulations, and the results demonstrate its superiority over existing methods in terms of probability of detection, probability of false alarm, and receiver operating characteristics. This indicates that the proposed hybrid approach offers better performance in detecting both known and unknown signals, leading to more efficient spectrum utilization.

8

Authors: Naser Abed Hasen, Jawdat K. Abbas

This research studies geogrid reinforcement of sandy soils under the influence of the three earthquakes (Halabjah, Bolunun, and Ali Al-Gharbi) due to the recent increase in seismic activity and the need to reduce damage to foundations due to settlement. Sand samples prepared with a relative density of 70%, with and without geogrid reinforcement, were tested on a shaking (vibrating table) table. Five geogrid layers were used, respectively. Settlement occurs due to dynamic force, and the plastic foundation was used with dimensions of 100 * 100 * 30 mm. Withstands a static load of 5.294 kN/m2. Increasing the acceleration of the earthquake leads to an increase in the settlement of the foundation in the sandy soil. The greater the number of geogrids reinforcing layers, the leads to a decrease in the base settlement, where the rate of settlement decrease is greater, the lower the earthquake acceleration and the greater the number of geogrid reinforcement layers. The time of onset of settlement and the general pattern of settlement are similar in all cases studied. The rate of settlement is approximately twice as high when the soil is saturated than when the soil is dry, and the effect of geogrids is less for the soil when it is saturated than when it is dry.

8

Authors: Pankaj Kumar, Rajinder Singh Sodhi

This literature review explores of Analytical Hierarchical Process (AHP) application in conjunction with Intuitionistic Fuzzy Sets (IFS) as a powerful decision-making framework within the context of the food manufacturing industry. The review synthesizes a wide array of research contributions and findings to provide insights into the dynamic and uncertain nature of this industry. The study reveals that the AHP-IFS methodology offers a structured approach to address the complexities inherent in the food manufacturing sector, which undergoes distinct phases from inception to maturity, and potentially, decline or renewal. Decision criteria and factors, including market dynamics, technological advancements, regulatory compliance, sustainability concerns, and financial considerations, are analyzed and prioritized using AHP-IFS, considering their interdependencies and uncertainties. Strategic planning, resource allocation, and competitive analysis within the industry have been significantly enhanced through the application of this methodology. Moreover, as sustainability and environmental issues become increasingly important, AHP-IFS is also employed to evaluate the environmental impact of business decisions, aligning with global efforts to promote sustainability. Challenges associated with data availability and computational complexities have been acknowledged, but the potential for refinement and expansion of the methodology, including the incorporation of machine learning techniques, offers promising avenues for future research. Ultimately, AHP-IFS stands as a valuable tool to guide informed decision-making processes throughout the ever-evolving business life cycle of the food manufacturing industry, ensuring adaptability and competitiveness in a complex and uncertain landscape.

10

Authors: Amal Alowaimir

This paper aimed at a systematic review of the role of FinTech in enhancing the digital transformation of Saudi Arabian banks. Google Scholar was searched with related search terms to finally select 28 papers using the PRISMA process of screening and selection. There were very few papers directly dealing with the use of FinTech as a tool for digital transformation in banks, not specifically in traditional banks. There were a few papers on digital tools used in FinTech. Overall, the digital transformation of any kind of bank using FinTech tools had been slow and a small percentage of the total. The situation was worse in the case of Saudi banks despite the large amounts of budgetary allocations for technologies in banking as a part of its Vision 2030. This was because of the late entry of Saudi Arabia into these concepts. The need for Islamic banks to follow Sharia rules was not found to be a great problem. All these points indicate to the need for greater acceleration of using FinTech tools for the digital transformation of Saudi traditional banks. Some limitations of this review are the low rigour of the available papers due to methodological limitations, discussion papers without empirical data dominating and a wide range of aims provided in the papers. The need for substantially more work on Saudi banks has been highlighted. The validity of the conclusions from this review needs to be verified using empirical research.

10

Authors: Chanun Chanhom, Khwanta Benchakhan, Sunhanat Jakkapattarawong

This research paper scrutinizes the connection between demographic aspects and the perception of executive leadership at Suan Sunandha Rajabhat University (SSRU), as perceived by academic and support staff. Emphasizing demographic factors such as gender, academic qualifications, and job type, the study explores staff perspectives and their influence on the perception of executive leadership. Data was collected from 253 participants using stratified random sampling and a questionnaire, with findings analyzed via t-Test and One-way ANOVA. The results revealed no significant correlation between gender or academic qualifications and goal-directed leadership. However, a statistically significant difference was found between job classification and goal-directed leadership. These findings offer valuable implications for tailoring leadership approaches at SSRU, suggesting that job type may play a more significant role in shaping leadership perceptions than previously assumed. Consequently, this could inform future leadership strategies, contributing to improved staff engagement and organizational success at SSRU.

10

Authors: Yakov Kryukov, Dmitriy Pokamestov, Andrey Brovkin, Artem Shinkevich, Georgiy Shalin

Power Domain Non-Orthogonal Multiple Access (PD-NOMA) is a promising technique for future wireless networks. Currently, the growing number of researchers are addressing PD-NOMA issues. The great number of known results are based on the Shannon capacity theorem, assuming infinite code block length. However, they cannot be achieved in practice when modulation and coding schemes (MCS) with finite code block length are used. To solve this problem, we propose an approach which allows utilizing LTE MCSs in the link-level simulation of PD-NOMA. In this paper, we present an adaptive MCS map for a PD-NOMA system with two users, considering non-perfect Serial Interference Cancelation (SIC). In addition, we have developed a map for power allocation coefficients (PAC). The max-sum rate criterion and 10%-Block Error Rate constraint are utilized. The MCS and PAC maps allow access point to jointly and adaptively select MCS parameters for the users depending on their SNR values. The proposed MCS maps can be used in a link-level simulation of the PD-NOMA system to near-practical performance.

10

Authors: Rustam R. Gaynutdinov, Sergey F. Chermoshentsev

The paper proposes an approach to predicting the electromagnetic resistance of an unmanned aerial vehicle (UAV) when exposed to an electrostatic discharge. The approach is based on the study of the electromagnetic environment and electromagnetic interference in the interface communication lines of the UAV onboard equipment. The effect of a discharge of static electricity from an engine blade on a UAV fuselage made of a composite material is considered. From the results of the study of the electromagnetic environment in the intra-fuselage space, it can be seen that the strength of the electromagnetic field at the calculation points reaches sufficiently large values, therefore, the electromagnetic compatibility of the UAV onboard equipment may deteriorate. The predicted levels of electromagnetic interference in the interface communication lines will also lead to a malfunction of the onboard equipment of the UAV. Technical measures are proposed that will ensure the electromagnetic stability of the UAV when exposed to a discharge of static electricity.

10

Authors: S. B. Vinay Kumar, B. P. Singh, Raman Batra, Akash Kumar Bhagat

The network connection within the car, the "Controller Area Network" (CAN) bus serves as an alternative protocol for electric automobiles. Tragically, the lack of a data authentication technique in the CAN bus protocol makes it susceptible to several types of assaults, making it easier for attackers to infiltrate the network. The CAN dataset is collected and the collected datasets attains for preprocessing stage using z-score normalization. For feature extraction, a restricted boltzmann machine (RBM) is used to extract the data. Next, our proposed method (MCFO-DANN) is used to identify and mitigate CAN Bus attacks in electric vehicles. Evaluation against other CAN bus anomaly detection methods demonstrates the superiority of MCFO-DANN, exhibiting higher accuracy. This proactive security solution fortifies electric vehicles against cyber-threats, ensuring real-time monitoring and response, thereby preserving the integrity and safety of the CAN Bus network in electric automobiles.

10

Authors: Ranganathaswamy Madihalli Kenchappa, Rakesh Kumar Yadav, Alka Singh, Arvind Kumar Pandey

Industrial automation systems (IASs) are utilized in vital facilities to sustain society's fundamental services. As a consequence, protecting them against terrorist operations, natural catastrophes and cyber-threats is essential. The research on techniques for identifying cyber-attacks in IAS environments is lacking. The study proposed the Stochastic Turbulent water flow optimization based restricted Boltzmann machine (STWFO-RBM) to overcome the challenges. The proposed STWFO-RBM integrates anomaly detection into the fabric of industrial automation, enhancing system resilience and responsiveness. We collected datasets from the water industry and preprocessed them through min-max normalization, and then principal component analysis was used for feature extraction. The results show that the suggested technique applies to a real-world IAS situation, with state-of-the-art accuracy of 97%, F1 score of 96%, precision of 98%, recall of 95% and 6.1s of computational time. Our proposed method is better than the average of earlier endeavors.

10

Authors: Syed Rashid Anwar, Narmadha Thangarasu, Girija Shankar Sahoo, Kumud Saxena

The growing demand for superior metallic components in several industries has emphasized the necessity for effective and dependable inspection techniques. Conventional manual inspection procedures are lengthy, based on personal judgment and susceptible to human mistakes. In this study, we introduce a dynamic multi-layered auto-encoder with a robust deep neural network (DMAE+DNN) system for inspecting flaws in metallic surfaces.We acquired images of the metal surface defects. The neural network design is improved by incorporating a dynamic multi-layered auto-encoder, enabling the system to obtain highly detailed representations of surface data. The results demonstrate the improved performance of the suggested system, showcasing higher levels of recall, precision and F1-score in comparison to conventional defect detection methods. This technological development has the possibility to completely transform quality control procedures by minimizing the need for manual inspections and improving the overall quality of products that heavily rely on metal elements.

10

Authors: Sneha Kashyap, P. S. Raghavendra Rao, Pavan Chaudhary, Savita Yadav

The rising advancements in Industry 4.0 technologies have made more usual to acquire significant volumes of machine operating data in real time. In response to inconsistent data distribution and label scarcity in target domains, this work suggests a machine learning (ML) approach for rolling element bearing failure identification under a variety of circumstances. This study presents, a new method called Composite coyote optimized resilient linear regression (CCO-RLR) for defect recognition and classification in rolling element bearings. Early rolling bearing failure diagnosis is a crucial and time-sensitive operation that guarantees the dependability and security of mechanical fault systems. Initially, the rolling element bearings dataset is collected and preprocessed using Min-max normalization. For extracting the feature, Fourier transform (FT) is employed. The result shows that the CCO-RLR accuracy is 97.8% when compared with those existing methods. Our suggested method offers an effective means of quantifying flaws and significantly improving classification effectiveness.

10

Authors: Vinod Mansiram Kapse, Arun Kumar Marandi, Beemkumar Nagappan, Ankita Agarwal

The increased durability and performance features of polymer concrete panels have led to their widespread application in construction. The manufacturing of precise and effective techniques for identifying forms and flaws is vital to guarantee the high quality of these panels. To increase the accuracy of structure and defect-recognition in polymer concrete panels, this study presents a new Stochastic raven roosting optimization enhanced artificial neural network (SRRO-EANN) forecasting technique. The data sample used to assess the completed model fits the training dataset is referred to the test dataset. The Gaussian filter (GF) is a tool used in the pre-processing and Principal Component Analysis (PCA) feature extraction, leading to more effective utilization and understanding the defect capturing. The findings of the research indicate the effectiveness for the future development of forecasting technologies in the realm of quality control and building material inspection.

10

Authors: Amita Shukla, Amit Prakash Sen, D. Ganesh, B.P. Singh

The Hybrid Galactic Gravitational Optimization (HGGO) method of Three-area power system (3APS) control of frequency is the main subject of this work. The suggested method has advantages of easy implementation, computing efficiency and consistent convergence. The goal is to use the HGGO algorithm to fine-tune the Proportional-integral-derivative (PID) controller and create stable, trustworthy system. A thorough simulation of the three-area Load Frequency Control (LFC) system is carried out in MATLAB-SIMLINK environment. By optimizing PID control settings. The previous subject moves to the gravitational search algorithm (GSA), which is known for its optimization ability but is hampered by problems with local optima. This is addressed by the clustering-based learning used by the Hybrid Galactic Gravitational Optimization, which divides the programmed into clusters and uses a variety of techniques. To validate the proposed HGGO controller, load disturbances are applied to the power system and simulated outcomes for several HGGO-based load configurations are obtained.

10

Authors: Akhilendra Pratap Singh, Mohammad Shahid, Anupam Kumari, M. P. Karthikeyan

In the dynamic realm of manufacturing, it is essential to optimize production processes to attain efficiency and competitiveness. This study presents an innovative enhanced dragonfly optimization (EDFO) method to improve production by utilizing a diverse strategy that combines swarm intelligence and virtual cell development. The suggested methodology includes the parallel EDFO algorithm, which is a cutting-edge variety of swarm intelligence, to address the intricate optimization difficulties related to virtual cell creation. The virtual cell construction process entails the consolidation of machines into cells to optimize output and reduce manufacturing lead times. The benchmark test results offer valuable insights into the algorithm's capabilities and effectively demonstrate its effectiveness in optimizing virtual cell generation for various manufacturing conditions. The proposed approach, which simultaneously takes numerous essential characteristics, is a comprehensive solution for improving production efficiency in virtual cellular manufacturing systems due to its multifunctional nature.

10

Authors: Vijay Prasad, Pranab Das, Y. Jayanta Singh

Software quality assurance has grown in importance in the fast-paced world of software development. One of trickiest parts of creating and maintaining software is predicting how well it will perform. The term "computer evaluation" refers to use of advanced AI techniques in software quality assurance, replacing human evaluations and paving the way for a new era in software evaluation. We proposed Hybrid Elephant herding optimized Conditional Long short-term memory (HEHO-CLSTM) to estimate Software Quality Prediction. Software quality prediction and assurance has grown in importance in ever-changing world of software development. Software quality prediction encompasses a wide range of activities aimed at improving the quality of software systems via the use of data-driven approaches for prediction, evaluation and enhancement. We have collected Software Defects data and we feature extracted the attributes using linear discriminant Analysis (LDA). The suggested system improves the accuracy, AUC and Buggy instance compared with the current methods.

12

Authors: Nivedita Tiwari, Lalji Prasad

Traffic flow prediction is a research topic signified by several researchers in a league span of disciplines. Traffic flow prediction is an important aspect in Intelligent Transport Management System (ITMS). In this context, one of the most in-demand techniques of Machine Learning, especially Time series based techniques, helps in predicting traffic flow forecasting and increases the accuracy of the prediction model. In order to deliver extremely precise traffic forecasts, it is crucial that we put the prediction system into practice in the actual world. Our aim is to perform computations related to traffic on the traffic datasets and find out the accuracy for each model. For this purpose we are using three distinct time series models: Long Short Term Memory (LSTM), the Autoregressive Integrated Moving Average (ARIMA), and the Seasonal Autoregressive Integrated Moving Average (SARIMA). From the results obtained, it is concluded that the proposed model achieves highest prediction accuracy with the lowest root mean squared error.

12

Authors: Matthew Barsalou, Robert Perkin

There are many possible quality tools available for RCA and this research seeks to identify which quality tools are used by quality professionals specifically when performing an RCA. An anonymous survey was emailed to an organization’s quality leaders at locations around the world. The survey asked the respondents to list the quality tools that they use when performing an RCA as well as the quality tools that they think everybody should know for RCA. The results were then statistically analyzed. The Ishikawa diagram is both the most commonly used quality tool and the most recommended quality tool for RCA. Five whys and is/is-not were also commonly used quality tools. This research identifies which quality tools are actually used by quality professionals in industry when performing an RCA. This information can be useful for organizations when selecting a limited tool set for RCA.

12

Authors: Shweta Singh, Vivek Ranjan, Padmaja Tripathy, M. N. Nachappa

Finding a balance between the loads and generating demands in power systems is a significant challenge since power producers require important inputs such as security, reliability and quality. In this study, a unique method for balanced load frequency control (BLFC) is proposed in two-area power systems, integrating a customized World Cup Algorithm (CWCA)-Driven PID optimization. The primary objective is to enhance the stability and performance of power systems. For this purpose, the integral time-multiplied absolute error (ITAE) is minimized by evaluating the objective function using time-domain simulation. The proposed methodology aims to improve the overall response of the control system, ensuring a reliable and stable power supply. Within the scope of two-area power systems with BLFC, simulation results show the effectiveness and resilience of the CWCA-Driven PID Optimization. The outcomes of the proposed.

12

Authors: Ashuvendra Singh, Sandeep Singh Rawat, Navneet Kumar

The planning of tall buildings is an important part of urban growth, and more and more attention is being paid to sustainability, safety, and efficiency in the building process. Traditional design and manufacturing processes frequently have trouble maximizing these factors, which results in inefficiencies and higher prices. This research introduces a revolutionary strategy that combines dynamic firefly tuned-AdaBoost (DFT-AdaBoost) in order to address the drawbacks of conventional tall building design methodologies. The goal is to increase manufacturing efficiency while also enhancing the tall building design's sustainability and safety features. The DFO is employed in an iterative manner to modify various design parameters, including material types, sizes, and shapes. On the other hand, AdaBoosting is utilized to improve the predicted accuracy of the model. The iterative nature of this methodology enables the ongoing improvement of design solutions in order to get the necessary level of manufacturing efficiency. The findings of this study indicate notable enhancements in manufacturing efficacy pertaining to the construction of tall buildings. The utilization of the DFT-AdaBoost method enables the identification of optimized design parameters, resulting in the reduction of material waste and a decrease in production costs. This study highlights the DFT-AdaBoost approach's potential as a potent tool for improving manufacturing effectiveness in tall building designs. This method contributes to the construction of tall buildings that are more cost-effective, safe, and environmentally responsible by integrating real-time structural optimization with manufacturing process prediction.

14

Authors: J. Nancy Namratha, P. Venkata Subramanian, Rama Koteswara Rao Alla

A modern power system demands an open communication channel to support the vast number of real-time data exchanges, which may introduce time delays and communication failures thus creates new challenges in power systems. To cope up with these issues, the paper proposed an Internal Model-Based Robust Controller (IMBRC) and IMBRC-PID controller designs for the decentralized LFC (Load Frequency Control) of the modern power system. Initially, a finite-ordered linear model of the power system integrated with RES (Renewable Energy Sources) and aggregated Electrical Vehicles (EV) has been developed. Later the full-order model was employed in the proposed design to achieve complete decentralized, robust, more reliable, and effortless control performances. The Internal Model Compensator (IMC) filter time constant is tuned using Artificial Bee Colony (ABC) optimization algorithm. The objective function considered was the scalarized integral of squared and absolute errors with various weighting factors. The Least-Square Model (LSM) approximation of the IMBRC transfer function determines the PID controller gains. The controller's robustness is verified for the power system components affected by structured and unstructured uncertainties. The error performance indices and simulation results convey that the suggested design keeps the system robustly stable even when subject to varying time delays and uncertainties.

8

Authors: Olga V. Afanaseva, Oleg K. Bezyukov, Anastasiia A. Ignatenko

Protection of the crew and passengers from vibration and noise of machines, auxiliary mechanisms and propeller, as well as reduction of sound emission into the surrounding space, are paramount tasks in solving the problem of vibration and noise in shipbuilding. The magnitude of the level of vibration and noise affects the performance, well-being and health of people, the operation of equipment and mechanisms, etc. It should be noted that vibration not only has a negative physiological effect on the human body, but also serves as a source of airborne noise. Noise values are determined mainly by the acoustic and vibrational power generated by the source. The determination of defects by vibration parameters is one of the most intellectually intensive sections of diagnosing reciprocating machines, which include marine engines. The studies presented in this paper are aimed at eliminating these gaps, primarily on the basis of a more complete description of vibrational processes. The article presents the results of a study related to the control, diagnostics and evaluation of the technical condition of an internal combustion engine running on diesel fuel. The possibility of establishing a connection between the vibrational parameters of the engine and the change in its design characteristics during operation is shown. The results of the work of the analytical and experimental information system for monitoring the vibration activity of diesel engines are presented.

8

Authors: INFLUENCE OF TAX REVENUES ON THE ECONOMIC GROWTH OF THE COUNTRY

In the conditions of the modern global economy, tax policy is a direct tool of management that influences the economic development of the country. This paper dwells on the assessment of the interconnection between tax policy and economic development of the Kyrgyz Republic. The paper considers the issue of adapting the country’s tax policy to the conditions of the world economy, as well as to domestic needs and possibilities. This research focuses on the specific features of Kyrgyzstan's tax system, including the structure and recent changes in the legislation and their influence on fiscal policy and economic indicators. The authors emphasize that to achieve sustainable economic growth and raise the population’s well-being, it is important to create a tax system that would adequately react to the changes in the world economy. This paper contains a detailed analysis of macroeconomic factors and key economic indicators that is aimed at determining the current trends and potential opportunities for the optimisation of the tax burden. Special attention is paid to the development of tax tools that facilitate the formation of a favourable environment for sustainable economic development and prosperity of the national economy. This research is a complete overview of the tax system of the Kyrgyz Republic, with an analysis of the recent changes in the context of their impact on fiscal policy and economic indicators. Summing up, this paper distinguishes the strategic significance of effective tax policy for the stimulation of sustainable economic growth.

10

Authors: Maria V. Petrovskaya, Marina M. Shailieva, Yulia N. Nesterenko, Gadzhimirze I. Gadzhimirzoev

Currently, at various levels, there is an awareness of the need to include people with disabilities in the social and labour spheres. Increasing attention is being paid to creating a comfortable, convenient and accessible environment with equal opportunities for everyone. However, despite the positive changes in this direction, in our country at the moment the inclusive approach in various spheres of economic and socio-cultural activity is in the initial stage of development that can serve as an example of progress and successful development. In particular, this concerns such an important aspect of the socialization of disabled people as the labour sphere, where, as it is worth noting, there is practically no involvement of disabled people in economic processes. The labour integration of citizens with disabilities is one of the effective ways of their social integration and allows us to destroy stereotypes about them as dependent people. We believe that self-employment is the most effective way to fully integrate people with disabilities into society since self-employed people are less limited by work schedules and can perform the necessary medical and rehabilitation procedures without compromising the work process. Psychologically, self-employment is also suitable for people with disabilities, as they can influence their social environment, which is not always possible in the traditional employment model. The importance of self-employment of people with disabilities is determined not only by economic, but also by social aspects. This helps them socialize, increases their self-esteem and allows them to realize their creative potential. However, to expand opportunities for labour inclusion of people with disabilities, it is necessary to create appropriate conditions, which require support from the state and public organizations. The current research is devoted to finding the most optimal solutions in this direction.

14

Authors: Ulyana V. Ashmarina, Zhanna V. Gornostaeva, Anastasia A. Sozinova, Nadezhda K. Savelyeva

The purpose of this article is to determine the place of responsible HRM in the quality management system at enterprises of industry 4.0 in Russia, as well as to identify prospects and develop recommendations for improving the Russian model of this management. The study of Russia’s experience for 2021-2023 based on the statistics of the stock indices of the Moscow Stock Exchange using the regression analysis method has revealed that corporate social responsibility stimulates to the development of enterprises in industry 4.0 (proved by the example of telecommunications). Based on statistical data for 2018-2021, a significant contribution of responsible personnel management to improving the efficiency of quality management of Russian enterprises in industry 4.0 was substantiated using correlation analysis. As a result, an improved quality management model of enterprises in industry 4.0 based on responsible HRM has been developed, which has identified key management practices and advantages of responsible HRM at each stage of the innovation process. The main conclusion based on the results of the study is that responsible HRM occupies a central place in the quality management system at enterprises of industry 4.0 in Russia. The contribution of the article to the literature is that it opens up a new perspective on the prospects for improving the quality management at enterprises in industry 4.0 – with the help of responsible HRM. The article describes these prospects in detail on the example of Russia and offers applied recommendations taking into account the specifics of the Russian HR model of quality management at enterprises of industry 4.0. The practical significance of the authors’ conclusions and developments is that they make it possible to realize the potential of improving product quality of industry 4.0 in Russia in the “Decade of Action”.

14

Authors: Aziza B. Karbekova, Saida G. Мakhkamova, Nadezhda A. Inkova, Olesya K. Pakhomova

The article aims to improve the approach to product quality management in industry 4.0 by substantiating the feasibility and developing recommendations for automation based on datasets and AI of corporate accounting and sustainability reporting. Based on the international empirical experience for 2022 and official statistics, the article has revealed the importance of corporate accounting and sustainability reporting for product quality in industry 4.0 through regression analysis. The significant role of AI-driven automation in the development of corporate accounting and sustainability reporting practices has also been substantiated. The theoretical significance lies in the fact that the article has clarified the structure of the product quality of industry 4.0In addition to Quality 4.0, a new quality component has been highlighted: product sustainability, the achievement of which depends not only on the implementation of the SDGs by the business, but also on the effectiveness of its management information systems. The central role of the effectiveness of management information systems in quality management of products in industry 4.0 to achieve their sustainability has also been substantiated. The practical significance of the article is related to the fact that the article has revealed the prospects (Pareto-optima) and proposed the authors’ recommendations for improving quality in industry 4.0 in countries with emerging practices of corporate accounting and sustainbility reporting – Estonia, Israel, Russia, Saudi Arabia and Turkey – through the automation of this practice based on AI. The obtained and mathematically described models can be used to reveal prospects and develop recommendations in other countries of the world. The managerial significance is expressed in the fact that the developed authors’ mechanism will improve the practice of quality management in industry 4.0 through automation based on datasets and AI of corporate accounting and sustainbility reporting. The advantage of the improved mechanism is the higher effectiveness of management information systems of enterprises in industry 4.0.

14

Authors: Anastasia A. Sozinova, Nadezhda K. Savelyeva, Dmitriy V. Chuprakov, Victoria A. Saidakova

The purpose of the article is to develop a scientific and practical approach to quality management in industry 4.0 through the movement of labor resources towards the “core” of the digital economy. For the most complete coverage of the digital era, the study was conducted in 2017-2022 based on the experience of 72 countries covered by digital competitiveness statistics that is, representing the global “core” of the digital economy. The article defines the impact of the movement of labor resources towards the “core” of the digital economy on quality in industry 4.0 using the regression analysis method. The authors present forecasts for the “Decade of Action” that reveal the prospects for improving quality in industry 4.0 through optimizing the movement of labor resources towards the “core” of the digital economy. As a result, the article proposes a promising mechanism for quality management in industry 4.0 through the movement of labor resources towards the “core” of the digital economy. This mechanism contains framework recommendations for improving quality management through the regulation of migration flow and HRM in Industry 4.0. The originality of the article lies in the fact that it forms a new vision of the process of labor movement to the global “core”, taking into account the specifics of industry 4.0, which for the first time makes it possible to study it from the point of view of its impact on quality (and not quantity – as it was during the first three industrial revolutions) in industrial production, which defines the uniqueness of the Fourth Industrial Revolution. The theoretical significance is that the article has clarified the specifics of the movement of labor resources towards the global “core” in the digital economy from the standpoint of quality in industry 4.0. Migration policy implication is related to the fact that the author’s recommendations will ensure an increase in the effectiveness of this policy by optimizing the flow of labor resources towards the global “core.

14

Authors: Nodirjon M. Yuldashev, Raxim R. Dusbayev, Anna L. Shevyakova, Yelena S. Petrenko

The research focuses on the scientific search for a solution to the problem associated with the imperfection of the existing approach to the management information systems in the business of Industry 4.0, expressed in the fact that this approach does not fully ensure the agility of the business and does not make it possible to fully disclose its potential in terms of improving product quality. The research aims to identify a perspective on improving the management information systems to support the quality and agility of Industry 4.0 business. Using international statistics for 2022, the authors developed an econometric model using regression analysis to explain the important role of management information systems in ensuring the quality and agility of Industry 4.0 business. Based on this model, the authors conclude that the use of big data and analytics and knowledge transfer has a strong positive impact on the agility of companies and cyber security, more than the impact of management professionalism. The authors also prove that the use of big data and analytics and knowledge transfer is determined not only by the professionalism of management but also by digital competencies. The theoretical significance of this research is that it revealed the prospect of improving the management information systems to support the quality and agility of Industry 4.0 business through the transition to a new dataset approach to this management. The practical significance of this research is expressed in the fact that the developed dataset approach to the management information systems in the business of Industry 4.0 will make it possible to overcome the gap in the technological provision of the management information systems and in the provision of quality and agility management of Industry 4.0 business.

16

Authors: Elena G. Popkova, Abdula M. Chililov, Elena V. Makarova, Daniil E. Ryabyshev

The article is devoted to determining the role of climate innovation for the practice of quality management of enterprises in industry 4.0. To do this, it conducts an empirical study of climate innovation in the international practice of quality management of enterprises of industry 4.0 in 2023, based on statistics by the end of 2022, using methods of regression, correlation and trend analysis, as well as variation analysis. As a result, the impact of innovation on product quality 4.0 from the standpoint of climate sustainbility has been identified and quantified. It has been established that ICT and infrastructure, staffing, organization of production and innovation funding for industry 4.0 determine the quality of 4.0 from the standpoint of climate sustainability to a much greater extent than the climate-related features of innovation in industry 4.0. The article reveals a pattern of changes in the impact of innovation on quality during neoindustrialization 4.0. This pattern is that innovation increasingly reduces quality 4.0 from the point of view of climate sustainbility in the course of neoindustrialization 4.0. The theoretical significance of the article lies in the fact that it has formed a new climate dimension of quality 4.0 in the "Decade of Action", embodied in the authors’ concept of climate TQM 4.0 and taking into account the environmental properties of products 4.0 in determining and managing its quality. The practical significance of the article is related to the fact that the proposed authors' recommendations will improve the quality of 4.0 through climate optimization of innovation. The managerial significance of the article is that the developed new approach to quality management of enterprises in industry 4.0 with the help of climate innovation has rethought this management practice through the prism of the stages of the innovation process. The approach includes management measures of climate TQM 4.0 at each stage of the innovation process.

16

Authors: Jasur T. Nosirov, Ludmila I. Kukaeva, Bashar A. Abdul Hussein, Victoria S. Ustenko

The motivation for this research was the desire to strengthen the scientific and methodological base to reduce the global digital divide. The purpose of the article is to develop a new approach to the disclosure of the export potential of high-tech enterprises. For this purpose, the article examines the dynamics of high-tech exports in 2007-2021 in a complete sample of countries for which the World Bank keeps statistical records, and substantiates a weak relationship of high-tech exports with economic growth, but a strong relationship with quality 4.0 using the method of correlation analysis. Based on data for 2021 from a sample of the top 20 digital economies that most actively use a set of technologies for quality management 4.0, it has been proved that industrial and manufacturing engineering 4.0 makes a greater contribution to the disclosure of the export potential of high-tech enterprises than digital management information systems. The theoretical significance lies in the fact that the scientific concept of neomercantilism 4.0 has been developed to explain the essence of international trade in the context of industry 4.0; the economic cycle of industry 4.0 has been proposed and its difference from the usual economic cycle has been substantiated to explain the dynamics of change and high-precision forecasting of high-tech exports. The practical significance is due to the fact that the developed new approach to the disclosure of the export potential of high-tech enterprises describes more objectively and makes it possible to predict the dynamics of changes in high-tech exports with greater accuracy, and also provides wider use of the advanced capabilities of industry 4.0 to modernize the development of high-tech industries and strengthen their global digital competitiveness.

16

Authors: Svetlana I. Ershova, Nadezhda A. Dmitrienko, Natalia V. Serbinovskaya, Elvira L. Kutsova

This paper solves the problem of quality management in national research universities in Russia. The goal of this paper was to study the empirical experience and to substantiate the prospects for improving quality management in national research universities in Russia. Based on statistics on 30 national research universities of the Russian Federation in 2030, an econometric model of quality management in these universities was compiled. Based on the model, the important role of research competencies in quality management in Russian universities was substantiated. The Pareto optimum was found, which demonstrated the possibility for the maximisation of quality in Russian universities through the development of research competencies. For its achievement, a map of competencies in quality management in national research universities of Russia was offered. The main conclusion is that the quality of activities in national research universities is determined by the level of the development and the level of disclosure of research competencies of scholars working in them: citations of their publications, number of concluded licensing agreements, and science-making activity of young scientists. The theoretical significance consists in the development of a new – competence-based – approach to quality management in national research universities of Russia, the originality of which lies in the transition from norming to the development of research competencies in universities. The practical significance is that the developed competence-based approach will allow improving the practice of quality management in national research universities of Russia. Thus, the paper supports the project "Decade of Science and Technologies in Russia”.

18

Authors: Guzalkhon O. Akhmedova, Mirislom M. Mirolimov, Vitaly V. Vanin, Nikolai G. Sinyavsky

The purpose of the article is to propose and substantiate the preference of a new vector of entrepreneurship development in industry 4.0 – the vector of its sustainable development based on environmental certification of product quality. The methodological apparatus of the study is based on the use of the regression analysis method for modeling patterns based on international experience for 2022. The authors' main conclusion is that environmental quality certification accelerate the development of industry 4.0 and has almost as much impact on the environment as the development of industry 4.0. The article contributes to the literature through the development of the TQM concept, justifying the expediency of choosing environmental certification of product quality as a new vector of sustainable entrepreneurship development in industry 4.0. The originality of the article is due to the study of industry 4.0 and its products from an ecological perspective, but from a new angle, in which, instead of calculating environmental damage, the possibilities of improving the environmental characteristics of industry 4.0 and its products are revealed. The theoretical significance of the results of this article is due to the fact that it has proposed a new dimension of the quality of the products of business entities in industry 4.0: "Green 4.0", and also has developed a new approach to TQM of entrepreneurship in industry 4.0 to achieve the quality of "Green 4.0" and the implementation of the vector of sustainable development of industry 4.0. The practical significance of the authors' conclusions and recommendations is that they will allow the fullest disclosure of the potential of entrepreneurship development in industry 4.0 due to the systemic improvement of its product quality in unity and consistency of its technical and environmental properties.

6

Authors: Neha Agarwal, Rajendra Pandey P, Smitha Rajagopal

The Industrial Internet of Things (IIoT) explosive expansion has raised questions regarding the safety of industrial systems. Networks like these are crucially protected from a variety of cyber threats by intrusion detection systems (IDSs). In order to detect intrusions in the IIoT environment utilizing TCP/IP packets, this work introduces a novel Hybrid Deep Convolutional Autoencoder and Splinted Decision Tree (HDCA-SDT) technique. High-level features are extracted from the unprocessed TCP/IP packet data using the DCA. The retrieved features are then classified using the SDT algorithm into various intrusion categories. In order to enable quicker decision-making yet preserve accurate results, the SDT technique effectively divides the feature space. The NSL-KDD dataset is used to train and assess the model. The efficiency of the suggested hybrid strategy is shown by experimental findings. Comparing the proposed hybrid approach to conventional intrusion detection methods, it acquired higher detection accuracy. The model also demonstrates robustness to fluctuations in traffic on the network and possesses the ability to identify known and unidentified intrusions with high recall rates.

8

Authors: Komagal Anupama K., C. V. Subramanian

Housing evolution has been driven by cultural, economic, and technological advancements. Architecture has a significant role in shaping this evolution, as architects continuously respond to changing needs by using innovative designs and materials. This results in functional, creative, optimizable, and customizable housing solutions. Research and this manuscript are focused on adapting and incorporating elements of vernacular architecture into modern housing designs. This aims to create sustainable and culturally-sensitive homes that balance tradition and modernity, while still retaining emotional and visual connections. The courtyard and surrounding spaces induce a strong positive effect on the occupants. Studies demonstrate that activeness and excitement are positive feelings that are induced by a courtyard and surrounding spaces respectively. By analyzing the data collected from diverse houses and people strongly judge that certain people are triggered to induce these spaces in housing. It confirms the hypothesis that a physical setting has a significant role in inducing Positive affective among the occupants of the vernacular settings.

8

Authors: Shubhendra Pratap Singh, Beemkumar N., Ganesh Kumar Kantak

In this research, a new approach based on a dynamic-stepped maximum power point tracking (MPPT) technology and a neural adaptive water cycle (NA-WC) method is proposed to improve the power gathering effectiveness of fuel cells. Although fuel cells have drawn a lot of interest as a clean and effective energy source, maximizing their power harvesting effectiveness is still a major problem. To solve this problem, we present a brand-new dynamic-stepped MPPT method that constantly alters the fuel cell's functioning position to achieve optimum output of power under various environmental circumstances. A 7 kW proton-exchange membrane fuel cell (PEM-FC) that supplied a resistive load through a boost converter created utilizing the suggested MPPT controller was effectively used to study the effectiveness of the suggested NA-WC MPPT. The suggested NA-WC outperforms the traditional MPPT approaches in terms of convergence rate, overshoot, and steady state fluctuations, according to simulation findings obtained by utilizing the Matlab tool. Additionally, the fuel cell's lifespan and effectiveness can both be increased by the suggested controller's current ripple minimization.

8

Authors: Manish Shrivastava, Priyank Singhal, Bhuvana J.

The availability of manufacturing machinery is crucial for having a productive production line. So, for industrialists, being successful in the field of maintenance is crucial if they want to make sure that key equipment is performing as it should and that unscheduled downtime is kept to a minimum. Predictive maintenance skills are viewed as being essential with the rise of complex industrial processes. The assistance that contemporary value chains may provide for a company's maintenance role is another area of focus. The development of sensors and Industry 4.0 technologies has greatly improved access to data from equipment, processes, and products. Electric motor condition monitoring and predictive maintenance help the industry avoid significant financial losses brought on by unforeseen motor breakdowns and significantly increase system dependability. This research offers Enhanced Nave Bayes Artificial Neural Network-based machine learning architecture for Predictive Maintenance. The system was tested in an industrial setting by building a data collection and analysis system using sensors, analyzing the data with a machine learning approach, and comparing the results to those generated by a simulation tool. With the help of the Azure Cloud, the Data Analysis Tool may access information collected by a wide variety of sensors, machine PLCs, and communication protocols. Preliminary results show that the method correctly predicts a wide range of machine states.

8

Authors: Akash Kumar Bhagat, Prashant Kumar, Pawan Bhambu, Pandey V. K., Om Prakash, Raghu N.

Wireless sensor networks (WSNs) are made up of a large number of sensor nodes which collect data and send it to a centralized location. Nevertheless, the WSN has several security difficulties because of resource-constrained nodes, deployment methodologies, and communication channels. So, it is very necessary to identify illegal access in order to strengthen the safety measures of WSN. The use of network intrusion detection systems (IDS) to safeguard the network is now standard procedure for any communication system. While deep learning (DL) methods are often utilized in IDS, their efficacy falls short when faced with imbalanced attacks. An IDS based on a novel transfer deep multicolumn convolution neural network (TDMCNN) technique was presented in this study to address this problem and boost performance. The most significant features of the dataset are chosen using a cross-correlation procedure and then included into the suggested methods for detecting intrusions. The accuracy, precision, sensitivity, and specificity are used to conduct the analysis and comparison. The experimental findings verified the effectiveness of the suggested method over the status quo of deep learning models for attack detection.

8

Authors: Prabhat Kumar Srivastava, Ezhilarasan G., Divya Paikaray, Sakshi Singh, Manish Srivastava, Anshu Kumar

Multi-Terminal-High Voltage Direct Current (MT-HVDC) transmission lines are used to transmit electricity over long distances with lower losses compared to traditional Alternating Current (AC) transmission lines. However, HVDC transmission lines have limitations in terms of their ability to handle AC system faults and the need for expensive AC/DC converters at each end of the line. To address these limitations, a hybrid AC/MT-HVDC transmission line can be used. This type of transmission line combines the benefits of both AC and DC transmission, allowing for more efficient and reliable operation. In this article, an Enhanced-Fish Swarm Optimizer (E-FSO) is presented for the efficient maintenance of hybrid AC/MT-HVDC transmission lines. An external repository is included in the proposed E-FSO to preserve that is not dominant. Additionally, fuzzy decision-making is used to choose the hybrid AC/HVDC transmission lines' optimal compromise operating point. In these systems, in addition to the complete control of AC line through parameters for the dedicated generators and transformers connections, and Volt-Ampere Reactive (VAR) compensations, the Voltage Source Converters (VSCs) action and response controllable voltage is engaged. The outcomes of the experiment show the efficacy and dominance of the suggested algorithm, which has high stability indices compared to several competitive algorithms. Nevertheless, the suggested E-FSO is effective in producing a compromise operating point that satisfies the operator's needs while also obtaining well-diversified solutions.

8

Authors: Mohhammad Rashid Ansari, Mona Devi, Sunny Verma, Sashilal Singh, Joshi Manohar V, Shubhendra Pratap Singh

Based on the continuous growth of the economy, widespread adoption of intermittent renewable energy sources, and extensive use of information and communication technologies, conventional electric power systems are no longer able to meet the enormous demands of the information age. Diverse renewable energy technologies have been quickly developed to address the energy issue and environmental damage. However, since renewable energy sources are unpredictable and erratic, the widespread use of different renewable energy technologies has consequently put significant strain on the security and dependability of conventional power networks. The Smart Grid (SG) is a modernized electrical network that makes use of cutting-edge communication, control, and information technology to facilitate the integration of renewable energy sources, increase energy efficiency, and improve dependability and security. The invention of computational modeling and evaluation methodologies for SG energy transmission and production networks is the main topic of the research. The Internet of Energy (IoE), which will eventually replace the conventional power production and distribution networks, increases the need to be familiar with the proper computing tools in order to conduct any future SG investigation. The software for simulation that is significant to the modeling and analysis of electrical power production, transmission, distribution, and related systems is examined in this research. The study's conclusions are anticipated to aid in the creation of power generation and transmission systems that are more effective, dependable, and sustainable.

10

Authors: Solomon Jebaraj, Davendra Kumar Doda, Vineet Saxena

The effectiveness and dependability of these vital energy infrastructures depend heavily on the early detection of anomalies in nuclear power plants (NPPs). Anomalies in a plant's operations might be signs of the impending equipment failure, a danger to workers' safety, or departure from ideal performance, all of which call for quick attention and preventative actions. Traditional NPP monitoring methods depend on the human inspections and predetermined thresholds, which are only sometimes successful in picking up the complicated irregularities. This Study introduces a new, Improved Bat and Grey Wolf Optimized Recurrent Neural Network (IBGWO-RNN) approach to detect the anomalies in NPPs. In this case, the RNN classification effectiveness is increased by using the IBGWO method. The American Nuclear Society ANSI / ANS-3.5 Nuclear Simulator Standard dataset has been used to assess the success of the suggested approach. Each input feature vector will be normalized by using the Z-score Normalization. A Kernel Principal Component Analysis (KPCA) is performed to extract the properties from segmented data. The results of the research show that the recommended methodology beats earlier approaches in terms of the Accuracy, Precision, Recall, and F1-score. Our suggested approach advances anomaly identification, resulting in safer and more effective operations for NPPs.

10

Authors: Harsh Shrivastava, Sreekanth Reddy, Umesh Kumar Singh, Usha Chauhan, Pradumn Kr. Gupta, Sunny Verma

A variety of Electric Vehicle (EV) charging algorithms provide various EV charging load profiles, when utilized together, has an impact on the electrical grid functions. Present-day charging an EV Models of demand are either based on level of charging when an EV arrives or smart charging algorithms strengthened with specific charging levels and/or procedures. In this work, a brand-new data-driven technique for calculating EV charging load is suggested. They start by introducing a mathematical model that describes an adaptability of demand for EV charging. The characteristics of several EV load models are then identified, and advanced simulation techniques are suggested to simulate EV charging demand under various power market realizations. The suggested EV load modeling technique may act as a benchmark system by simulating various EV operating schedules, charging levels, and consumer engagement. The suggested framework would also give EV charging infrastructure advice from transmission system operators development in contemporary power networks.

10

Authors: Mayur Agarwal, Shagufta Khan, Amit Kumar, Preeti Verma, Sunil Dubey, Bishakh Paul

The increasing desire to wind power production systems is a result of rising worries about the energy problem and safeguarding the environment. Researchers and engineers urgently need to create new electrical equipment and drives for the production of wind energy since they are essential parts of wind turbines. An in-depth analysis of contemporary electric drives and machines used in the production of wind energy is provided in this study, with a focus on machine topologies, operating theories, performance traits, and control methods. The major characteristics associated with electrical drives and machines are contrasted and summarized, along with their benefits and drawbacks, such as efficiency, torque/power weight, and cost. The trade-offs inherent in the different methodologies and solutions given are emphasized. The main obstacles and problems that electric drives and equipment for the production of wind energy face are highlighted. Additionally, new opportunities and trends are exposed, and the most recent developments are also covered.