Search results

To prevent the coil from burning or getting damaged, it is necessary to estimate the duration of its operation as long as its temperature does not exceed the permissible limit. This paper aims to investigate the effect of switching on the thermal behavior of impregnated and nonimpregnated windings. Also, the safe operating time for each winding is determined.

The power loss of the winding is expressed as a function of the winding specifications. Using homogenization techniques, the equivalent thermal properties for the homogenized winding are calculated and used in a proposed thermal equivalent circuit for winding modeling and analysis. The validity and accuracy of the proposed model are determined by comparing its analysis results and simulation and measurement results.

The results show that copper windings have better thermal behavior and lower temperature compared to aluminum windings. On the other hand, by impregnating or increasing the packing factor of the winding, the thermal behavior is improved. Also, by choosing the right duty cycle for the winding current source, it is possible to prevent the burning or damage of the winding and increase its lifespan. Comparing the measurement results with the analysis results shows that the proposed equivalent circuit has an error of less than 4% in the calculation of the winding center temperature.

In this paper, the effect of temperature on the electrical resistance of the coil is ignored. Also, rectangular wires were not investigated. Research in these topics are considered as future work.

By calculating the thermal time constant of the winding, its safe operation time can be calculated so that its temperature does not exceed the tolerable value (150 °C). The proposed method analyzes both impregnated and nonimpregnated windings with various schemes. It investigates the effects of switching on their thermal behavior. Additionally, it determines the safe operating time for each type of winding.

Machine learning (ML) technologies are increasingly being applied in building cost estimation as an advanced method to overcome the challenge of insufficient data and subjective effects of experts. To address the gap of lacking a review of ML applications in building cost estimation, this research aimed to conduct a systematic literature review to provide a robust reference and suggest development pathways for creating novel ML-based building cost prediction models, ultimately enhancing construction project management capabilities.

A systematic literature review according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) was adopted using quantitative bibliographic analysis and qualitative narrative synthesis based on the 70 screened publications from Web of Science (WOS) and Scopus databases. The VOSviewer software was used to prepare the thematic focus from the bibliographic data garnered.

Based on the results of a bibliographic analysis, current research hotspots and future trends in the application of ML to building cost estimation have been identified. Additionally, the mechanisms behind existing ML models and other key points were analyzed using narrative synthesis. Importantly, the weaknesses of current applications were highlighted and recommendations for future development were made. These recommendations included defining the availability of building attributes, increasing the application of emerging ML algorithms and models to various aspects of building cost estimation and addressing the lack of public databases.

The findings are instrumental in aiding project management professionals in grasping current trends in ML for cost estimation and in promoting its adoption in real-world industries. The insights and recommendations can be utilized by researchers to refine ML-based cost estimation models, thereby enhancing construction project management. Additionally, policymakers can leverage the findings to advocate for industry standards, which will elevate technical proficiency and ensure consistency.

Compared to previous research, the findings revealed research hotspots and future trends in the application of ML cost estimation models in only building projects. Additionally, the analysis of the establishment mechanisms of existing ML models and other key points, along with the developed recommendations, were more beneficial for developing improved ML-based cost estimation models, thereby enhancing project management capabilities.

The aim of the study is to investigate the effects of perceived healthcare service quality (human aspects, technical aspects and tangible aspects) on satisfaction and guest loyalty in the hotel industry in the COVID-19 pandemic era.

A total of 130 guests in the hotel were selected purposively in Iraq. Data from self-administered questionnaires were analyzed through the VB-SEM statistical technique using Smart-PLS software towards testing the hypotheses.

The findings indicated that perceived service quality influences satisfaction and guest loyalty of guests in the hotel. This study reveals that human aspects, technical aspects and tangible aspects directly positively affect satisfaction and guest loyalty in the hotel industry.

This study highlights that perceived service quality (human aspects, technical aspects and tangible aspects) are vital and practical strategic tools that could be positioned to accelerate guest loyalty in the hotel industry. Furthermore, satisfaction mediates the relationship between human aspects, technical aspects, tangible aspects and guest loyalty.

This study clarifies the history and significance of cryptocurrencies. It explores the underlying decentralisation and trustlessness concepts that set these digital assets apart from conventional fiat currencies. It clarifies how blockchain technology functions as the core component of decentralised money. The mechanics of mining, its function in creating and validating Bitcoin transactions, and the emergence of substitute consensus mechanisms to solve environmental issues are all covered in this study. An in-depth analysis of blockchain technology covers its advantages, such as immutability and transparency, as well as its architecture and consensus processes. This study continues with a focus on the future by examining the development of decentralised finance (DeFi) and showcasing numerous DeFi applications, including yield farming, lending protocols, and decentralised exchanges (DEXs). As a result of the development of cryptocurrencies and blockchain technology, DeFi has become possible, ushering in a new era of financial independence and inclusivity. This study emphasises the significance of striking a balance between innovation and suitable regulatory measures as the globe embraces this revolution in order to enable the proper integration of DeFi into the global financial environment. The revolutionary potential of DeFi, particularly in increasing financial inclusion and empowerment for marginalised groups globally, is one of the major themes discussed. To negotiate legal frameworks while maintaining DeFi's decentralised nature, this study looks at the regulatory problems that come with this potential.

Material selection, driven by wide and often conflicting objectives, is an important, sometimes difficult problem in material engineering. In this context, multi-criteria decision-making (MCDM) methodologies are effective. An approach of MCDM is needed to cater to criteria of material assortment simultaneously. More firms are now concerned about increasing their productivity using mathematical tools. To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material. In addition, by using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the inherent ambiguities of decision-makers in paired evaluations are considered in this research. It goes on to construct a mathematical bi-objective model for determining the best item to purchase.

The entropy perspective is implemented in this paper to evaluate the weight parameters, while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system. The intermediate pipes are used to join the components of the exhaust systems. The materials usually used to manufacture intermediate pipe are SUS 436LM, SUS 430, SUS 304, SUS 436L, SUH 409 L, SUS 441 L and SUS 439L. These seven materials are evaluated based on tensile strength (TS), hardness (H), elongation (E), yield strength (YS) and cost (C). A hybrid methodology combining entropy-based criteria weighting, with the TOPSIS for alternative ranking, is pursued to identify the optimal design material for an engineered application in this paper. This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes. After that, the authors searched for and considered eight materials and evaluated them on the following five criteria: (1) TS, (2) YS, (3) H, (4) E and (5) C. The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes, on their performance and on the cost. In this structure, the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment. This essentially measures the quantity of information each criterion contribution, indicating the relative importance of these criteria better. Subsequently, the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative. The results show that SUS 309, SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.

The material matrix of the decision presented in Table 3 was normalized through Equation 5, as shown in Table 5, and the matrix was multiplied with weighting criteria ß_j. The obtained weighted normalized matrix V_ij is presented in Table 6. However, the ideal, worst and best value was ascertained by employing Equation 7. This study is based on the selection of material for the development of intermediate pipe using MCDM, and it involves four basic stages, i.e. method of translation criteria, screening process, method of ranking and search for methods. The selection was done through the TOPSIS method, and the criteria weight was obtained by the entropy method. The result showed that the top three materials are SUS 309, SUS 432L and SUS 436 LM, respectively. For the future work, it is suggested to select more alternatives and criteria. The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality (ELECTRE), Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE).

The results provide important conclusions for material selection in this targeted application, verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.

The study aimed to determine the static return connectedness between Brazil, Russia, India, China and South Africa (BRICS) equity markets and crypto assets.

The study employs the time-varying parameter vector autoregression (TVP-VAR) method to examine the static and dynamic connectedness between crypto assets and the BRICS stock market. The study sample size was segmented into full sample, pre-COVID-19 and post-COVID-19 for in-depth analysis.

Empirical findings pointed out the significant rise in the total connectedness between both markets in the pre-COVID-19 period. Our result also exhibits a lower level of connectedness during the post-COVID-19 period. During the full sample period, it was found that cryptocurrencies and Indian, Chinese and South African stock markets remained key return transmitters, while Russian and Brazilian stock markets were seen as recipients. Moreover, during the pre-COVID period, cryptocurrencies played the role of return transmitter while the stock markets in BRICS remained recipients of return spillover.

This study contains practical insights for investors and portfolio managers in diversifying their portfolios considering the aforementioned connectivity of both markets, especially during periods of instability.

The study highlighted the importance of the TVP-VAR method in analyzing the static and dynamic connectedness of returns between cryptocurrencies and BRICS stock markets in different periods, including pre- and post-COVID-19. It further pragmatized the dynamic roles of cryptocurrencies as transmitters of returns and the BRICS stock markets as receivers where investors and policymakers can navigate market uncertainties.

The purpose of this study is to examine the mediator role of blockchain technology (BT) in developing the trust among the users of digital payment (DP).

The research is of conclusive in nature. Purposive convenience sampling method was used to collect the data through a structured questionnaire. Statistical software Smart PLS 4 and SPSS 26 is used for the analysis of 540 valid responses.

This study concludes that resulting model is capable to predict the mediating role of BT in framing the trust. It also identified that all the factors (perceived ease of use, perceived usefulness, social norms and facilitating conditions) examined were found to have significant influence on the adoption of DP system.

This study uses variables that influences human behaviour in DP adoption. In future the work can be extended to check the moderating effect of BT on trust towards DP system.

This study demonstrates a key driver of towards DP. In addition, it highlights the pivotal role of BT in developing trust in both current and future adoption of DPs.

The goal of this research is to look at how urban microfinance affects livelihood transformation in terms of poverty reduction, living standards, social well-being, empowerment and entrepreneurship.

This paper analyses the role of urban microfinance towards livelihood with special reference to Western Uttar Pradesh. Primary data were collected from 321 respondents who are users of a microfinance programme using a standardised questionnaire. The data were collected using a stratified random sampling technique, and the data were analysed using structural equation modelling.

Urban microfinance has a considerable impact on poverty reduction, the standard of living, social well-being, empowerment and entrepreneurship in the urban poor, according to the findings.

The fact that the majority of the borrowers were uneducated was the most significant barrier to them filling out the questionnaire. Their anxiety was the most significant psychological obstacle to successfully answering the questions, and it took time. As a result, it is urged that proper counselling be conducted before the poor borrowers fill out the questionnaire.

The current study highlights the factors that lead to the utilisation of microfinance services. This research will aid MFIs in selecting the appropriate products and services for the urban poor. The results of this study will aid them in understanding and meeting the expectations of microfinance CEOs.

This is a first study conducted in Northern zone of India measuring the roles urban microfinance institutions (MFIs) in uplifting the livelihood of urban poor.

Study on knowledge-based systems for scientific publications is growing very broadly. However, most of these studies do not explicitly discuss the knowledge management (KM) component as knowledge management system (KMS) implementation. This background causes academic institutions to face challenges in developing KMS to support scholarly publication cycle (SPC). Therefore, this study aims to develop a new KMS conceptual model, Identify critical components and provide research gap opportunities for future KM studies on SPC.

This study used a systematic literature review (SLR) method with the procedure from Kitchenham et al. Then, the SLR results are compiled into a conceptual model design based on a framework on KM foundations and KM solutions. Finally, the model design was validated through interviews with related field experts.

The KMS for SPC focuses on the discovery, sharing and application of knowledge. The majority of KMS use recommendation systems technology with content-based filtering and collaborative filtering personalization approaches. The characteristics data used in KMS for SPC are structured and unstructured. Metadata and article abstracts are considered sufficiently representative of the entire article content to be used as a search tool and can provide recommendations. The KMS model for SPC has layers of KM infrastructure, processes, systems, strategies, outputs and outcomes.

This study has limitations in discussing tacit knowledge. In contrast, tacit knowledge for SPC is essential for scientific publication performance. The tacit knowledge includes experience in searching, writing, submitting, publishing and disseminating scientific publications. Tacit knowledge plays a vital role in the development of knowledge sharing system (KSS) and KCS. Therefore, KSS and KCS for SPC are still very challenging to be researched in the future. KMS opportunities that might be developed further are lessons learned databases and interactive forums that capture tacit knowledge about SPC. Future work potential could identify other types of KMS in academia and focus more on SPC.

This study proposes a novel comprehensive KMS model to support scientific publication performance. This model has a critical path as a KMS implementation solution for SPC. This model proposes and recommends appropriate components for SPC requirements (KM processes, technology, methods/techniques and data). This study also proposes novel research gaps as KMS research opportunities for SPC in the future.

This study investigates the influence of social media marketing (SMM) functionalities on perceived destination image (DI) and its dimensions, which in turn impact residents’ support for sustainable tourism development (SSTD). The study also examines the moderated mediation effect of the residency length and resident’s income in the indirect relationship between SMM and SSTD through the DI.

The best technique found appropriate for this study was the survey approach. To gather empirical data, people who currently reside in Alexandria completed 351 questionnaires via an online survey. The questionnaire was translated into Arabic while adhering to back-translation guidelines. SPSS and AMOS software were used to analyze the collected data.

It is found that there is a direct effect of SMM and its functionalities on overall DI and its dimensions. In addition, DI mediates the relationship between SMM and SSTD. These results hold valuable insights for academicians and practitioners in the tourism industry, potentially informing SMM strategies to support tourism activities.

This research introduces a novel framework emphasizing the functionalities of the SMM as a tool for enhancing both affective and cognitive DI and supporting sustainable tourism development. It contributes to the SMM and SSTD literature by examining the links between SMM functionalities and sustainable tourism development in emerging countries such as Egypt.