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Integrating environmental sustainability in construction Time-Cost trade-off for decision-making using hybrid NSGA-III and MOPSO approach

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Asian Journal of Civil engineering 2025年第4期26卷 1527-1542页

作者： Nagayo, Analene Montesines Singh, Rekha Dhawan, Amit Manjunath, T.C. Qasem, Altayeb Sethi, Krushna Chandra Sharma, Kamal Department of Engineering and Technology University of Technology and Applied Sciences-Al Musanna Al Muladdah Oman Department of Civil Engineering Raj Kumar Goel Institute of Technology Ghaziabad India Department of Civil Engineering Dr. Bhim Rao Ambedkar Polytechnic College Gwalior India Computer Science Engineering Department Rajarajeswari College of Engineering Bangalore India Department of Building Engineering College of Architecture and Planning Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Civil Engineering Centurion University of Technology and Management Gajapati India Department of Civil Engineering Madhav Institute of Technology and Science Gwalior India

Construction project management often involves optimizing time and cost while ensuring minimal environmental impact. This study presents an innovative hybrid approach combining non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective particle swarm optimization (MOPSO) to address the time-cost-environmental sustainability trade-off (TCEST) in construction projects. The proposed model aims to minimize project completion time and cost while maximizing environmental sustainability. A case study is conducted to validate the model, incorporating diverse construction activities and their respective time, cost, and environmental sustainability metrics. The results reveal Pareto-optimal solutions demonstrating significant trade-offs among the three objectives. The hybrid approach outperforms standalone algorithms in terms of solution diversity, convergence, and hypervolume indicators. Weighted sum methods are employed to select the most suitable solution from the Pareto front based on project priorities. Correlation analysis further explores interdependencies among objectives, emphasizing the feasibility of balancing these critical factors. The study contributes a robust decision-support tool for sustainable project planning, facilitating informed decision-making in modern construction management. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Hybrid Optimization MOPSO NSGA-III Sustainable Construction Time–Cost-Environmental Trade-off

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Interdisciplinary perspectives on privacy awareness in lifelogging technology development

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2291-2312页

作者： Wilkowska, Wiktoria Offermann, Julia Colonna, Liane Florez-Revuelta, Francisco Climent-Pérez, Pau Mihailidis, Alex Poli, Angelica Spinsante, Susanna Ziefle, Martina Human-Computer Interaction Center RWTH Aachen University Aachen Germany Swedish Law and Informatics Research Institute Stockholm University Stockholm Sweden Department of Computer Technology University of Alicante Alicante Spain Department of Occupational Science and Occupational Therapy University of Toronto Toronto Canada Department of Information Engineering Marche Polytechnic University Ancona Italy

Population aging resulting from demographic changes requires some challenging decisions and necessary steps to be taken by different stakeholders to manage current and future demand for assistance and support. The consequences of population aging can be mitigated to some extent by assisting technologies that can support the autonomous living of older individuals and persons in need of care in their private environments as long as possible. A variety of technical solutions are already available on the market, but privacy protection is a serious, often neglected, issue when using such (assisting) technology. Thus, privacy needs to be thoroughly taken under consideration in this context. In a three-year project PAAL (‘Privacy-Aware and Acceptable Lifelogging Services for Older and Frail People’), researchers from different disciplines, such as law, rehabilitation, human-computer interaction, and computer science, investigated the phenomenon of privacy when using assistive lifelogging technologies. In concrete terms, the concept of Privacy by Design was realized using two exemplary lifelogging applications in private and professional environments. A user-centered empirical approach was applied to the lifelogging technologies, investigating the perceptions and attitudes of (older) users with different health-related and biographical profiles. The knowledge gained through the interdisciplinary collaboration can improve the implementation and optimization of assistive applications. In this paper, partners of the PAAL project present insights gained from their cross-national, interdisciplinary work regarding privacy-aware and acceptable lifelogging technologies. © 2022, The Author(s).

关键词： Human computer interaction

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Exploring the Potential of Transfer Learning for Accurate Segmentation of Pathological Structures in X-Ray Images 2

Exploring the Potential of Transfer Learning for Accurate Se...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kishore, Lalit Shaikh, M.Z. Goel, Pawan Kumar Badhoutiya, Arti SRMIST Delhi Ncr Department of Computer Application UPGhaziabad Modinagar India Shree Bhagubhai Mafatlal Polytechnic College Department of Electronics & Telecommunication Maharastra Mumbai India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Gla University Department of Electrical Engineering UP Mathura India

ISBN: (纸本)9798350371055

This paper explores the capability of transfer learning for the correct segmentation of pathological systems in X-ray pictures. Switch mastering is a way in gadget mastering wherein understanding received from a source challenge is transferred to an extraordinary goal task to enhance performance. This paper specializes in how transfer getting to know may be applied to segmenting pathological systems in X-ray photos. The paper discusses the proposed technique's overall performance on datasets from public medical resources gathered from healthy and diseased lungs and stomach organs. The experiments used in this paper display that with the resource of pre-educated models in transfer getting to know, the performance of the segmentation of the pathological regions in X-ray photos may be stepped forward. Eventually, the paper outlines the ability of transfer mastering for clinical picture segmentation and discusses how it can be further progressed in the future. © 2024 IEEE.

关键词： Healthy Pathological Potential And Medical Transfer

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Drivers' Heart Rate Monitoring System Based on Dual Fiber Optic Sensors

Drivers' Heart Rate Monitoring System Based on Dual Fiber Op...

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2023 Photonics Global Conference, PGC 2023

作者： Lyu, Weimin Yu, Jianxun Wang, Qing Chen, Shuyang Li, Yujian Yu, Changyuan The Hong Kong Polytechnic Univ. Shenzhen Research Institute Shenzhen China The Hong Kong Polytechnic University Photonics Research Institute Department of Electronic and Information Engineering Hong Kong Hong Kong Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong The Hong Kong Polytechnic University School of Nursing Hong Kong Hong Kong

This study investigates the application of dual fiber optic sensors-based ballistocardiography (BCG) system to monitor drivers' heart rate (HR) under simulated driving vibration conditions. © 2023 IEEE.

ISBN: (纸本)9798350318371

关键词： Fiber optic sensors

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Machine Learning Framework for Loss Range Prediction of Hollow-core Anti-resonant Fibers

Machine Learning Framework for Loss Range Prediction of Holl...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Jewani, Yordanos Petry, Michael Sanchez-Arias, Rei Habib, Md Selim Department of Data Science and Business Analytics Florida Polytechnic University LakelandFL33805 United States Department of Aerospace and Geodesy Technical University of Munich Munich80333 Germany Department of Electrical Engineering and Computer Science Florida Institute of Technology FL32901 United States

A supervised machine learning framework is implemented to predict the propagation loss of randomly structured nested hollow-core anti-resonant fiber for the first time. The random forest classifier outperforms other m... 详细信息

ISBN: (纸本)9781957171395

关键词： Prediction models

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Machine Learning Framework for Loss Range Prediction of Hollow-core Anti-resonant Fibers

Machine Learning Framework for Loss Range Prediction of Holl...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Adversarial machine learning

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Effective Density-Based Concept Drift Detection for Evolving Data Streams 24th

Effective Density-Based Concept Drift Detection for Evolving...

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24th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2023

作者： Cui, Zelin Tian, Hui Shen, Hong Institute of Information Security Engineering Chinese Academy of Sciences Beijing China School of Information and Communication Technology Griffith University Brisbane Australia Faculty of Applied Sciences Macao Polytechnic University China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9789819982103

Concept drift is a common phenomenon appearing in evolving data streams of a wide range of applications including credit card fraud protection, weather forecast, network monitoring, etc. For online data streams it is difficult to determine a proper size of the sliding window for detection of concept drift, making the existing dataset-distance based algorithms not effective in application. In this paper, we propose a novel framework of Density-based Concept Drift Detection (DCDD) for detecting concept drifts in data streams using density-based clustering on a variable-size sliding window through dynamically adjusting the size of the sliding window. Our DCDD uses XGBoost (eXtreme Gradient Boosting) to predict the amount of data in the same concept and adjusts the size of the sliding window dynamically based on the collected information about concept drifting. To detect concept drift between two datasets, DCDD calculates the distance between the datasets using a new detection formula that considers the attribute of time as the weight for old data and calculates the distance between the data in the current sliding window and all data in the current concept rather than between two adjacent windows as used in the exiting work DCDA [2]. This yields an observable improvement on the detection accuracy and a significant improvement on the detection efficiency. Experimental results have shown that our framework detects the concept drift more accurately and efficiently than the existing work. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Exploring Convolutional Neural Networks for Fingerprint Damage Detection and Classification

Exploring Convolutional Neural Networks for Fingerprint Dama...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Swathi, R. Kansal, Vipashi Shyamala Valsalan, Prajoona Vekariya, Vipul Taqui, Syed Noeman Prasad v Potluri Siddhartha Institute of Technology Department of Electrical and Electronics Engineering Andhra Pradesh Vijayawada521108 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Government Polytechnic for Women Department of Electronics and Communication Engineering Karnataka Ramannagara562159 India Dhofar University Department of Electrical and Computer Engineering Dhofar Salalah Oman Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India Saveetha School of Engineering Simts Saveetha University Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350375237

This study proposes a novel approach to fingerprint classification using Deep Convolutional Neural Networks (CNNs), leveraging datasets from prominent sources like the National institute of Standards and Technology (NIST), Fingerprint Verification Competition (FVC), and the SHUFFLE database. The research involves a systematic pipeline, beginning with data collection and preprocessing to create a comprehensive dataset that represents real-world variations in fingerprint patterns. A tailored CNN architecture is designed to capture hierarchical features essential for accurate classification, trained on the amalgamated dataset using state-of-the-art optimization algorithms and loss functions. The model's performance is rigorously evaluated across diverse fingerprint patterns using separate datasets from NIST, FVC, and SHUFFLE, showcasing its superiority and adaptability compared to existing benchmark methods. Additionally, the study addresses privacy and ethical considerations associated with biometric data usage, ensuring the model's reliability and applicability in various fingerprint recognition applications. © 2024 IEEE.

关键词： Database systems

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An Experimental Artificial Neural Network Model:Investigating and Predicting Effects of Quenching Process on Residual Stresses of AISI 1035 Steel Alloy

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Journal of Harbin institute of Technology(New Series) 2024年第5期31卷 78-92页

作者： Salman Khayoon Aldriasawi Nihayat Hussein Ameen Kareem Idan Fadheel Ashham Muhammed Anead Hakeem Emad Mhabes Barhm Mohamad Mechanical Engineering Department Kut Technical InstituteMiddle Technical UniversityBaghdad 10074Iraq College of Agriculture University of KirkukKirkuk 36001Iraq Computer Science Department Kut Technical InstituteMiddle Technical UniversityBaghdad 10074Iraq Department of Petroleum Technology Koya Technical InstituteErbil Polytechnic UniversityErbil 44001Iraq

The present study establishes a new estimation model using an artificial neural network(ANN) to predict the mechanical properties of the AISI 1035 *** experiments were designed based on the L16 orthogonal array of the Taguchi method.A proposed numerical model for predicting the correlation of mechanical properties was supplemented with experimental *** quenching process was conducted using a cooling medium called “nanofluids”.Nanoparticles were dissolved in a liquid phase at various concentrations(0.5%,1%,2.5%,and 5% vf) to prepare the *** investigations were done to assess the impact of temperature,base fluid,volume fraction,and soaking time on the mechanical *** outcomes showed that all conditions led to a noticeable improvement in the alloy's hardness which reached 100%,the grain size was refined about 80%,and unwanted residual stresses were removed from 50 to 150 *** 5% of CuO nanoparticles to oil led to the best grain size refinement,while adding 2.5% of Al_(2)O_(3) nanoparticles to engine oil resulted in the greatest compressive residual *** experimental variables were used as the input data for the established numerical ANN model,and the mechanical properties were the *** of 99% of the training network's correlations seemed to be *** estimated result,nevertheless,matched the experimental dataset ***,the ANN model is an effective tool for reflecting the effects of quenching conditions on the mechanical properties of AISI 1035.

关键词： Quenching nanofluids residual stresses steel alloy artificial neural network MANOVA

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Analysis of a Transformerless High Step-Down DC-DC Converter

Analysis of a Transformerless High Step-Down DC-DC Converter

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Truong, Phan Nhat Anh Dung, Nguyen Liu, Yu-Chen Chiu, Huang-Jen National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei Taiwan Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering Blacksburg United States National Taipei University of Technology Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798350316445

The analysis and performance of a proposed high step-down DC-DC converter without employing transformers are discussed in this article. The operating principle of the control signals in this proposed converter is similar to the traditional two-phase interleaved buck converter, resulting in a simple controller. Moreover, the proposed converter has lower switches' voltage stress than the traditional buck converter. Therefore, the low voltage rating switches can be considered to achieve low losses, high efficiency, and low cost. Besides, the comparisons between the proposed converter and the previous high step-down topologies are presented to show the benefits of the proposed topology such as lower passive component count and higher effective duty cycle range. The experimental results of the 48W prototype have been provided to validate the theoretical analysis. © 2023 IEEE.

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