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Decoding Efficiency in Construction: A Behavioral DEA Approach to BRICS Economies

IEEE Access 2024年 12卷 113165-113177页

作者： Yih-Tzoo Chen Thi-Hien Dao Nhat-Luong Nhieu Department of Construction Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Ph.D. Program in Engineering Science and Technology College of Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Industrial Engineering and Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan

Amidst the rapidly evolving global economic landscape, the BRICS nations (Brazil, Russia, India, China, and South Africa) have emerged as pivotal players, particularly in the construction sector, which serves as a cornerstone for their continued growth and development. This sector’s performance not only reflects the economic health of these nations but also their capacity for sustainable urban and infrastructural expansion. This study aims to provide a comprehensive evaluative analysis of the construction industry’s efficiency across the BRICS nations using the Bounded Rationality Data Envelopment Analysis (BR-DEA) model, incorporating principles from Prospect Theory. Moreover, this study also investigates the relationship between behavioral economics and operational efficiency within this sector. According to well-known prospect theory, the psychological behavioral coefficients applied in the model include the loss aversion, the gain preference, the gain curvature, and the loss curvature coefficients. Key performance indicators for the model were meticulously chosen, normalized, and derived from reputable global and national databases. The findings revealed that efficiency scores generally increased with higher gain preference coefficients across different loss aversion settings, indicating that valuing gains more positively is associated with greater efficiency. Variations were observed among the BRICS countries, suggesting that efficiency is influenced by country-specific behavioral factors. This study contributes to the literature by integrating behavioral economics into efficiency analysis and offers practical insights for policymakers and industry leaders within the BRICS nations. Limitations are acknowledged, primarily relating to data availability and the inherent complexities of behavioral modeling, which provide avenues for future research to expand upon these initial findings.

关键词： Economics Construction industry Economic indicators Industries Sustainable development Decision making Socioeconomics

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Exploring Regression and Classification in Big Data with Deep Learning

Exploring Regression and Classification in Big Data with Dee...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Pandey, Ravindra Rongali, Aruna Sri Gopinath, S. Anute, Nilesh Nama, Forda Pund, Sachin Hult International Business School United States University of the Cumberlands Doctoral Information Technology Information Technology Emphasis Information Technology United States Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India ASM's Institute of Business Management and Research Pune India University of Lampung Doctoral Program of Environtmental Science Department of Informatics Bandar Lampung Indonesia Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350357769

Deep gaining knowledge is a synthetic intelligence approach used to recognize complex facts. It uses many facts to recognize the complicated relationships between various factors. With deep learning, models can be built which might be capable of understanding the complicated patterns within big datasets. In this paper, we can explore using deep mastering in regression and type tasks on large datasets. In particular, we can use deep learning strategies to build a regression model that predicts a target value primarily based on the functions in a massive dataset and a classification version that can classify an object into one of the many classes of an extensive dataset. We can then discuss the overall performance of the fashions in comparison to standard models and talk about capability areas for development. Moreover, we will analyze the scalability of deep mastering strategies used on large datasets and discuss exceptional practices for building deep mastering fashions on massive datasets. Finally, we will summarize our findings and offer our conclusions approximately the use of deep studying in regression and type obligations on large datasets. © 2023 IEEE.

关键词： approximately gaining knowledge mastering performance regression

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Deep Learning-Based Positioning Error Fault Diagnosis of Wire Bonding Equipment and an Empirical Study for IC Packaging

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IEEE Transactions on Semiconductor Manufacturing 2023年第4期36卷 619-628页

作者： Kao, Sheng-Xiang Chien, Chen-Fu International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan Intelligent Manufacturing and Circular Economy Research Center National Tsing Hua University Artificial Intelligence for Intelligent Manufacturing Systems Research Center National Science and Technology Council Department of Industrial Engineering and Engineering Management Hsinchu300044 Taiwan

Little research has been done for artificial intelligence applications of semiconductor backend. This study aims to develop a deep learning based fault diagnosis framework as prognostics and health management (PHM) solutions with a comprehensive analytics process. A linear encoder sensor is employed to measure position, while data preparation is conducted to convert position information into a signal. Then, signal processing is used to extract the features, in which high pass filter is applied to normalize the signal and emphasize the features. High-dimensional features including time domain statistical features, time-frequency domain features obtained by continuous wavelet transform (CWT), and frequency domain features converted by fast Fourier transform (FFT) are extracted to prepare the dataset. An ensemble neural network model that integrates deep neural network (DNN) and convolutional neural network (CNN) is employed to recognize the pattern and diagnose the positioning errors. The accuracy and false alarm rate are considered to support maintenance decisions. An empirical study was conducted in a world leading IC assembly and testing company for validation. The results have shown that the proposed approach can effectively detect inappropriate installation of the bond head in wire bonding equipment for predictive maintenance and cost reduction. © 1988-2012 IEEE.

关键词： Failure analysis

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A Secure Cloud-Based Infrastructure for Virtual Sensors in IoT Environments 6

A Secure Cloud-Based Infrastructure for Virtual Sensors in I...

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6th International Conference of Women in Data Science at Prince Sultan University, WiDS-PSU 2023

作者： Makda, Tahira Jehan Luiza Barros, Ana Dilek, Selma University of Doha for Science and Technology College of Business Management Doha Qatar University of Ceará Computer Science Program State Fortaleza Brazil Hacettepe University Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9781665477239

Recent work has proven the effort of researchers to integrate small sensors and a cloud environment, delivering the Internet of Things (IoT). Sensors as a service are one of the leading research concerns in this context. Nevertheless, security is becoming one of the most significant attributes of the IoT as sensors become more human-independent and are being extensively used to monitor human lives. That way, IoT brings many key security challenges that need attention, some of which we address in this position paper. We present a cloud-based infrastructure that can deliver sensors and actuators as a service, providing secure communication between them and the control nodes on which IoT applications rely while implementing Big Data algorithms. For mapping our proposal, two scenarios related to Health Assistance are discussed, considering secure communications in a sensor network. In conclusion, we propose a scope for future research in this field considering digital twin concepts. Since domains exploiting IoT technologies can benefit from adopting Digital Twin, our goal is to evolve a virtual sensor and actuator system into this technology. © 2023 IEEE.

关键词： Actuators

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Photo-Fenton treatment of wastewater from olive oil extraction industry 6th

Photo-Fenton treatment of wastewater from olive oil extracti...

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6th International Conference Wastes: Solutions, Treatments and Opportunities, 2023

作者： Dos Santos, M.V. Grabowski, T.T. Martins, R.J.E. Postgraduate Program of Environment and Sustainable Technologies Federal University of Fronteira Sul Cerro Largo Brazil Technology and Management School Polytechnic Institute of Bragança Bragança Portugal Laboratory of Separation and Reaction Engineering-Laboratory of Catalysis and Materials Faculty of Engineering University of Porto Porto Portugal FEUP Portugal

ISBN: (纸本)9781032384412

The European industry has a well-established sector in the production of olive oil. The valorization of the olive pomace by extraction highly generates pollutant effluent due to waste leaching and processing in these extractor units (OOEIW). This study aimed to treat OOEIW using the photo-Fenton process. The process efficiency for the removal of organic matter and phenolic compounds was modeled and optimized using response surface methodology with Box-Behnken design. The variables considered were iron catalyst concentration, oxidant concentration, and UV irradiation time. The optimal conditions were determined as[Fe2+] = 3 g L-1, [H2O2] = 23 g L-1 and time photo reaction 60 min. The process achieved removals of 93% (TPh) and 26% (COD) in the diluted sample (1:10);90% (TPh) and 39%(COD) in the raw sample. © 2024 The Author(s).

关键词： Olive oil

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Optimizing Distribution Network Models for a Fruit Trading Company in Thailand: A Comparative Study Using Linear programming and Optimization

Optimizing Distribution Network Models for a Fruit Trading C...

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2023 IEEE International Conference on Industrial engineering and engineering management, IEEM 2023

作者： Akkawuttiwanich, P. Yenradee, P. Horng, S. Pichpibul, T. International Academy of Aviation Industry King Mongkut's Institute of Technology Ladkrabang Department of Aviation Industry Innovation and Service Bangkok Thailand Sirindhorn International Institute of Technology Thammasat University Industrial Engineering Program PathumThani Thailand Panyapiwat Institute of Management Faculty of Logistics and Transportation Management Nonthaburi Thailand

ISBN: (纸本)9798350323153

This research investigates the distribution network model of a fruit trading company in Thailand, with a specific focus on the problem of excessive travel distances and vehicle requirements in the company's existing distribution system. Linear programming and optimization techniques are used to improve the distribution network model and to satisfy the daily demand. Results are compared between the current distribution scenario and the optimized model. The computational analysis reveals a significant 21.8% reduction (802 kilometers) in total traveling distance and a 33.33% decrease in the number of vehicles required. Additionally, a comprehensive cost analysis is proposed, incorporating fuel costs, CO2 emissions, and overtime expenses, which were previously overlooked. This research offers valuable insights into the potential benefits of optimization, including cost savings and environmental impact reduction, providing a practical template for managing distribution networks, reducing reliance on ad hoc practices, and fostering a sustainable business model to enhance competitiveness in a challenging market. © 2023 IEEE.

关键词： Distribution network model linear programming optimization

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Using Real-Time Integrated Computer Vision and Deep Learning for Advanced Factory Safety 4

Using Real-Time Integrated Computer Vision and Deep Learning...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Tsai, Shang-Te Chang, Yu-Cheng Huang, Chao-Yi Yang, Fang-Yuan Chien, Wei Wu, Zong-Rong School Art and Design Sanming University Fujian China National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan National Yunlin University of Science and Technology Department of Information Management Yunlin Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan National Yunlin University of Science and Technology Department of Technical and Vocational Program in Artificial Intelligence Yunlin Taiwan

ISBN: (纸本)9798350360721

Innovative safety in the workplace is vital as the high safety risks associated with electrical engineering construction can lead to injuries or even fatalities. Using computer vision technology, we experimented with scenarios such as 'normal operations' and 'unexpected incidents' to enhance safety measures. We integrated an Internet of Things system into the setup, enabling the system to quickly detect and alert to unexpected events in real-time, thereby improving workplace safety. © 2024 IEEE.

关键词： Computer vision

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Optimal Integration of Metaheuristic Techniques with Correction-Based Approaches for Profit Maximization in Combinatorial Production Planning: A Comparative Analysis

Optimal Integration of Metaheuristic Techniques with Correct...

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International Conference on Control, Instrumentation, Energy and Communication (CIEC)

作者： Arijit Mukherjee Surojit Mukherjee Deptartment of Electrical Engineering Future Institute of Technology Kolkata India Post-Graduate Program in Management Great Lakes Institute of Management Chennai India

To enhance production planning efficiency, it’s crucial to have well-structured models that align with metaheuristic and other optimization techniques. This research conducts a comparative analysis of three unique metaheuristic methods: sanitized-teaching-learning-based optimization (sTLBO), Particle Swarm Optimization (PSO), and Differential Evolution (DE) with a termination criterion of equal number of functional evaluations. The primary focus lies on profit-maximizing production planning within a single-level framework, encompassing intricate domain constraints, nonlinear costs, budget limitations and raw material constraints. The paper introduces three correction-based solution approaches. Upon evaluating 300 distinct instances of the problem, the study reveals that s-TLBO integrated with correction-based approach 1 and 3 consistently outperforms the other methods when it comes to devising optimal production plans for a Saudi Arabian petrochemical industry. This industry scenario involves 24 products, 54 processes, and three raw materials.

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Efficient Utilization of Metaheuristic Techniques for Profit Maximization in Combinatorial Production Planning: A Comparative Study

Efficient Utilization of Metaheuristic Techniques for Profit...

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International Conference on Control, Instrumentation, Energy and Communication (CIEC)

Efficient production planning requires well-structured models compatible with metaheuristic and other optimization techniques. This study compares three established metaheuristic methods - sanitized-teaching-learning-based optimization (s-TLBO), Particle Swarm Optimization (PSO), and Differential Evolution (DE) with a termination criterion of equal number of functional evaluations - for profit-maximizing production planning. The focus is on a single-level problem involving complex constraints, non-linear costs, budget and raw material limitations. The paper presents two solution strategies: a penalty-based and a correction-based approach. Analyzing 300 unique instances of the problem reveals that s-TLBO implemented with correction-based approach consistently outperforms the other methods in devising optimal production plans for two Saudi Arabian petrochemical industries with 24 products, 54 processes, and three raw materials for plant 1 and two raw materials for plant 2.

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Ideation of engineering Solutions for Sustainable Development Goals: A Collaborative Course Between Thai and Japanese Students 38

Ideation of Engineering Solutions for Sustainable Developmen...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Krutphong, Kodchakorn Leelawat, Natt Tang, Jing Ota, Eri Suzuki, Rie Murakami Taoka, Yuki Punyabukkana, Proadpran Environmental Engineering and Disaster Management Program Mahidol University Thailand Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Thailand Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Thailand International School of Engineering Chulalongkorn University Thailand Center for International Education Tokyo Institute of Technology Japan School of Environmet and Society Tokyo Institute of Technology Japan Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand The Learning Innovation Center Chulalongkorn University Thailand

ISBN: (纸本)9798350302196

A collaborative course called Global Awareness for technology Implementation was conducted by the Faculty of engineering, Chulalongkorn University, Thailand, and the Tokyo Institute of technology, Japan. The objective of this course was to facilitate the exchange of knowledge, perspectives, and experiences between Thai and Japanese students through project work, using an engineering design approach and the design thinking process to create innovation relevant to the Sustainable Development Goals (SDGs). During the class, experts shared their knowledge and experiences with the students. The students then worked on their respective projects to develop solution concepts using the design thinking process. The proposed ideas include the application for English class, smart water quality checker, hydrogen-based energy system for rural area, reduced wasted fashion solution, and auto waste sorting machine. Finally, this study concluded with a summary of the results and findings on the empathy process in the chosen SDGs, and of the commonalities and differences between Thailand and Japan in terms of ideation. © 2023 IEEE.

关键词： Students

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