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A critical discussion of modelling, performance assessment, and design recommendations-based case study of solar dish Stirling system
No full textSolar dish Stirling system (SDSS) has generated power in rural, urban, and isolated places. Its performance is affected by weather, irradiance, wind speed, dish diameter, receiver diameter, and type of Stirling engine (SE). The modelling and design changes enhance the SDSS performance. This study covers SDSS performance optimization, design recommendations, and case analysis. The study encompasses a review of investigations of SDSS in several nations and SDSS facilities with wide examination in several domains such as solar power plants, hybridization and storage, micro-co-generation, water desalination, and solar cookery. This paper provides detailed information on SDSS performance improvement modelling and design adjustments. Reviewing the modelling and design modification studies, important findings are presented, and optimum design parameter values at specific sites are offered. The range of output power in the reviewed literature varies from 0.103 kW to 58 kW, and the overall efficiency of the system (ηSystem) is between 10.41% and 25%. The current work has the study of SDSS modelling applied to a case study in Solan City (India), based upon which optimum design recommendations and direction of future research are suggested. The study estimates SDSS’s maximum efficiency and output power as 26.17% and 40.43 kW, respectively
POST-TRAINING MODEL QUANTIZATION APPROACHES IN COMPRESSION OF MULTITEMPORAL MULTISPECTRAL IMAGES
No full textHow Instrumental Leadership Promotes Affective Commitment: Social Skills as Mediator
No full textEmployee-organization linkages have been the subject of considerable scholarly and managerial attention. The findings of this research study provide empirical evidence that instrumental leadership attributes predict affective commitment among employees in their organizations. More specifically, based on their expertise and environmental knowledge, instrumental leaders endorse attributes that help followers adhere to the organization’s vision through providing timely feedback and facilitating tasks. Instrumental leadership thus promotes a positive workplace in which affective commitment is encouraged. The sample included 226 followers from a major telecom company in Morocco. Employees reported their levels of affective commitment, completed the Social Skills Inventory, and rated their supervisor’s instrumental leadership. Results suggest that instrumental leadership may play a role in building employees’ affective commitment and that more socially skilled employees may be more likely to develop healthy leader-follower relationships, resulting in affective commitment to the organization as a whole
EVALUATION OF THE EFFECTS OF ADVANCED HYBRID TRANMISSION CONTROL TECHNOLOGIES ON HEAVY-DUTY COMMERICAL TRUCK APPLICATIONS
No full textHybrid powertrain topologies are in the consideration for heavy-duty commercial truck applications for better energy management and emission reduction possibilities. Different hybrid powertrain architectures were already evaluated by engineers to see potential benefits, improvements on driving performance and fuel economy. Having said these, potentials of complex hybrid transmission control capabilities need to be evaluated to see where the gain is and what these can bring to the table. Aone-dimensionalvehicle model was developed in combination with an advanced hybrid transmission control unit to perform co-simulation runs on different regulatory drive cycles. The main objective of the present study is to investigate the potential benefits of P2-P3 mixed hybrid automated manual transmission with advanced transmission control techniques can provide in transient drive cycles in terms of fuel economy and engine outemission with simulation methodology. The analysis results have shown that P2-P3 mixed hybrid transmission can provide significant improvements on fuel economy (up to %15+) and reduction on engine out emissions (up to %10+) on transient drive cycles for heavy-duty commercial truck applications</p
Enhancing Bioactivity of Titanium-Based Materials Through Chitosan Based Coating and Calcitriol Functionalization.
No full textKENT KİMLİĞİNİ YANSITACAK VE İKLİM DEĞİŞİKLİĞİNE UYUM SAĞLAYABİLECEK ODUNSU BİTKİLER: ESKİŞEHİR
No full textTHE DEVELOPMENT AND OPTIMIZATION OF THE BUS DRIVING CYCLE FOR PUBLIC TRANSPORTATION AND MOBILITY SYSTEMS
No full textThe driving cycles developed for public transportation busses is crucial in terms of energy efficiency and emissions as well as enhancing fuel economy. Considering to the public bus transportation driving cycles, both international standardized like SORT, and regional driving cycles are created by researchers and authorities to evaluate the energy efficiency and also to compare the different vehicles with each other. To obtain results that are closest to real driving conditions in driving cycle tests, the driving cycles must reflect all the characteristics of the region where they were created. Otherwise, fuel consumption values obtained using driving cycles that do not include regional characteristics do not reflect real life and can lead to misleading results. This study focuses on creating driving cycles that are closest to real life for public transportation buses in Istanbul. As a different approach from the driving cycles currently used an developed, three classifications were made, and multiple driving cycles were created. Three driving cycles were obtained for specific times of the day. Four driving cycles were obtained to reflect traffic density across seasons. To create seasonal cycles, data from 15 bus lines collected over a month was correlated with the data from 500T bus lines covering a year using a new method. Additionally, to obtain results closest to real life, road profiles were designed considering gradient information. Significant differences were observed in the fuel consumption values calculated using the obtained driving cycles
A fuzzy Kano model proposal for sustainable product design: Mobile application feature analysis
No full textCompanies aim to maximize profits by effectively designing mobile applications to promote their services in a competitive market. However, identifying the design features that significantly impact mobile applications is challenging due to their subjective nature. Traditional Kano approaches face limitations, such as information loss caused by considering only the most frequent values. To address these limitations, this study proposes a novel fuzzy Kano approach to better manage the subjectivity in human judgments and the uncertainty in user preferences. This approach uncovers hidden preference levels, accounts for uncertainties, resolves dual classification issues, compares membership degrees, and emphasizes subtle details that may otherwise be overlooked. The fuzzy Kano approach was applied to survey data from 100 participants, covering 33 mobile application features. By classifying these features, the fuzzy Kano model examined their influence on user satisfaction and quality perception. The results demonstrated the feasibility and effectiveness of the proposed method, identifying key features—such as Product Details, Order Management and Returns, and Product Opinions and Reviews—that, if absent, could lead to customer dissatisfaction. Additionally, the findings revealed significant differences between the fuzzy and traditional Kano models and highlighted variations in mobile application characteristics across different demographic groups, providing valuable insights for mobile application design
On the Use of Embedding Techniques for Modeling User Navigational Behavior in Intelligent Prefetching Strategies
No full textIn today's data-intensive client-server systems, traditional caching methods often fail to meet the demands of modern applications, especially in mobile environments with unstable network conditions. This research addresses the challenge of improving data delivery by proposing an advanced prefetching framework that utilizes various embedding techniques. We explore how to model user navigation using graph-based, autoencoder-based, and sequence-to-sequence-based embedding methods and assess their impact on prefetching accuracy and efficiency. Our study shows that utilizing these embedding techniques with supervised learning models improves prefetching performance. We also present a software architecture that blends supervised and unsupervised learning approaches, along with user-specific and collective learning models, to create a robust prefetching mechanism. The contributions of this study include developing a scalable prefetching solution using machine learning/deep learning algorithms and providing an open-source prototype of the proposed architecture. This paper offers a significant improvement over previous research and provides valuable insights for enhancing the performance of data-intensive applications