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Integrating spherical fuzzy Delphi and TOPSIS technique to identify indicators for sustainable maintenance management in the oil and gas industry

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Journal of King Saud University - engineering Sciences 2024年第4期36卷 288-302页

作者： Olugu, Ezutah Udoncy Mammedov, Yslam Durdymuhammedovich Young, Jonathan Chung Ee Yeap, Pin Swee Faculty of Engineering Technology & Built Environment UCSI University Kuala Lumpur56000 Malaysia

This study offers an investigation into the degree of importance of indicators affecting the sustainable maintenance management of the oil and gas industry. Four dimensions and 12 strategic indicators for sustainable performance are identified, based on literature review and expert opinion on sustainable offshore production. The survey was conducted as a pioneer attempt to evaluate the significance of each indicator, particularly for the Malaysian and Turkmenistan oil and gas industry. In this study, the spherical fuzzy sets modified-Delphi Method was implemented to address the uncertainty and subjectivity in expert judgment. Then, the spherical fuzzy technique in order of preference by similarity to ideal solution integrated approach was proposed to prioritize strategic indicators for assistance in administrating sustainable practices within two developing countries. The study culminated with a set of conceptual indicators intended to promote the implementation of performance benchmarking of sustainable operations in the oil and gas industry offshore. © 2023 King Saud University

关键词： Fuzzy sets

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Fabrication of graphene oxide decorated with poly(dimethyl amino ethyl methacrylate) brush for efficient Cr(Ⅵ) adsorption from aqueous solution

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Chinese Journal of Chemical engineering 2024年第9期73卷 51-61页

作者： Alireza Nouri Siew Fen Chua Ebrahim Mahmoudi Abdul Wahab Mohammad Wei Lun Ang Department of Chemical and Process Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Centre for Sustainable Process Technology(CESPRO) Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Chemical and Water Desalination Engineering Program College of EngineeringUniversity of SharjahSharjah 27272United Arab Emirates

Confronting the severe health threats and environmental impacts of Cr(Ⅵ) in aquatic environments demands innovative and effective remediation approaches. In this study, Graphene oxide(GO)-decorated poly(dimethyl amino ethyl methacrylate)(PDMAEMA) brush nanocomposites(GOP1, GOP2, GOP3, and GOP4) were fabricated using atom transfer radical polymerization(ATRP) by the “graft from” *** resulting nanocomposites were utilized for removing Cr(Ⅵ) with good adsorption performance due to the electrostatic interaction of protonated nitrogen groups in the brush chains with negatively charged particles in the solution. The kinetic model of pseudo-second-order best represented the contaminants' adsorption characteristics. The Weber-Morris model further indicated that surface adsorption and intraparticle diffusion mechanisms primarily controlled the adsorption procedure. Additionally, the Langmuir and Temkin isotherm models were found to most accurately represent the adsorption characteristics of the pollutants on the nanocomposites, and GOP4 can achieve the maximum adsorption capacity of 164.4 mg·g^(-1). The adsorbents' capacity maintains above 85% after five cycles of adsorption-desorption. The nanocomposites in this study demonstrate promising potential for eliminating Cr(Ⅵ) from aqueous solutions.

关键词： Graphene oxide PDMAEMA brush Polymerization Nanoparticles Adsorption Chromium

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Circular Commons: Exploring Innate Spatial Tactics as Pathways toward a Circular built environment

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Circular Economy and Sustainability 2025年第2期5卷 1157-1199页

作者： Egger, Tamara van Dorst, Machiel Ioannou, Olga den Heijer, Alexandra Department of Architectural Engineering and Technology Faculty of Architecture and the Built Environment TU Delft Delft Netherlands Department of Urbanism Faculty of Architecture and the Built Environment TU Delft Delft Netherlands Department of Management in the Built Environment Faculty of Architecture and the Built Environment TU Delft Delft Netherlands

The built environment significantly contributes to current socioenvironmental crises, necessitating systemic change. Circularity and the commons are re-emerging as potential pathways for such transition. A circular built environment (CBE) aims to close resource loops, but its implementation is often slow and neglects social and local aspects. The commons framework emphasizes local involvement and sustainable self-management of shared resources. However, the intersection of circularity and the commons in spatial production is underexplored. This paper explores their relationship as “innate spatial tactics,” referring to the ways ordinary people interact with the built environment to meet their daily needs. Through a literature review, we developed a conceptual framework of “circular commoning,” encompassing three dimensions: resources, people, and governance. We applied this framework to analyze 16 empirical examples of circular commoning in contemporary urban settings. Our research shows that circularity and the commons are closely linked and mutually beneficial. Circular commoning involves diverse resources, changing social roles, and innovative governance. We identified three forms of circular commoning as innate spatial tactics: building circular, circular use of space, and creating spaces for circular activities. The framework developed here provides a basis for further action research. The practice review demonstrates that circular commoning is not only a distant utopian ideal but is enacted daily in diverse urban contexts. Such often-overlooked innate spatial tactics can offer valuable lessons for pathways toward a CBE involving principles of a circular society. Additionally, they can help shape new narratives and channel hope for practical progress towards circular futures. © The Author(s) 2024.

关键词： Circular built environment Circular Society Community Economies Innate Spatial Tactics Urban Commons

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Adoption of Eco-Friendly Waste Reduction Practices in the Clothing Retail Sector in Cape Town

Textile and Leather Review

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Textile and Leather Review 2024年 7卷 1360-1376页

作者： Patnaik, Sweta Patnaik, Asis Department of Clothing and Textile Technology Faculty of Engineering and the Built Environment Cape Peninsula University of Technology Cape Town South Africa

The fashion and textile industry significantly contributes to environmental degradation through unsustainable manufacturing and waste disposal practices, particularly in South Africa's retail sector. This study examines the retail merchandise cycles of two prominent clothing retailers based in Cape Town, South Africa to identify stages that generate substantial fashion and textile waste and proposes a model to enhance sustainability within the industry. Adopting a qualitative, interpretive research paradigm, data were collected through purposive sampling and in-depth interviews with key personnel, primarily buyers, within the selected companies. The research involved mapping and analyzing each retail cycle phase to identify excessive waste generation points. The findings revealed that retailers produce considerable amounts of waste (pre-and post-consumer) due to fast fashion practices and insufficient recycling systems. While both companies acknowledged the importance of sustainability, there was a notable lack of concrete policies and limited visibility into supplier processes concerning environmental impact. The study identified specific departments and operations within the retail cycles responsible for significant waste production, highlighting the need for systemic changes. Based on these insights, an improved retail cycle model was developed emphasizing waste reduction through sustainable sourcing, enhanced recycling initiatives, and stricter supplier compliance measures. The proposed model aims to minimize environmental harm by integrating sustainability into all aspects of the retail process. © 2024, idd3. All rights reserved.

关键词： Textile industry

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Predictive modeling of climate change impacts using Artificial Intelligence: a review for equitable governance and sustainable outcome

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environmental Science and Pollution Research 2025年第17期32卷 10705-10724页

作者： Ukoba, Kingsley Onisuru, Oluwatayo Racheal Jen, Tien-Chien Madyira, Daniel M. Olatunji, Kehinde O. Department of Mechanical Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa Cr Research Group Department of Physics Faculty of Sciences University of Johannesburg Johannesburg South Africa Process Energy and Environmental Technology Station Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa

The accelerating pace of climate change poses unprecedented challenges to global ecosystems and human societies. In response, this study reviews the power of Artificial Intelligence (AI) to develop advanced predictive models for assessing the multifaceted impacts of climate change. The study used the PRISMA framework to find, assess, and combine research on using AI in predicting climate change impacts. Integrating AI techniques, such as machine learning algorithms and predictive analytics, into climate modeling provides a robust framework for understanding and projecting the complex dynamics associated with global climate change. These models exhibit a high capacity for data collection, analyzing intricate patterns and integration, including their relationships within the datasets. They enable quick and accurate predictions of future climate scenarios, scenarios testing, historical eventualities, their magnitude, and adaptation. However, challenging issues like data gaps, especially in interconnected systems such as the atmosphere, are associated. Also, AI insight translation into an actionable recommendation recognizable by the policymakers, including ethical usage, is an emerging concern. Therefore, further advances to circumvent these will include the integration of AI with physical models, developing hybrid models, and generating synthetic climatic datasets to enhance data quality and gaps. Also, AI tools are being developed to aid decision-making for policy integration. AI-based predictive modeling is restructuring and bringing reformative change to the understanding of and approach toward climatic change through AI model development. AI guarantees an unfailing plan and a resilient future with sustainable approaches that empower scientists, policymakers, and communities. © The Author(s) 2025.

关键词： Prediction models

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Decentralized Flocking Algorithm of Unmanned Aerial Vehicle Swarm System with Obstacles 24

Decentralized Flocking Algorithm of Unmanned Aerial Vehicle ...

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8th International Conference on Control engineering and Artificial Intelligence, CCEAI 2024

作者： Zhang, Tian Ng, Jiunn Yea Tai, Vin Cent Faculty of Engineering Built Environment and Information Technology SEGi University Malaysia

ISBN: (纸本)9798400707971

This paper compared four flocking algorithms based on different positional constraints for Unmanned Aerial Vehicle (UAV) Swarm flight with obstacles, a leader-follower structure with one leader and five followers was adopted. The simulation results showed that both the distance and orientation of obstacles affect the smoothness of the obstacle avoidance trajectory, and weak positional constraints lead to a higher risk of collisions between drones but result in higher flight efficiency compared with strong positional constraints. © 2024 ACM.

关键词： Collision avoidance

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Identifying Risky Zones in Water Distribution Networks Using Node Burst Indices 19th

Identifying Risky Zones in Water Distribution Networks Using...

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19th International Conference on Computing in Civil and Building engineering, ICCCBE 2022

作者： Dzuwa, Christopher Nkhonjera, German Musonda, Innocent Onososen, Adetayo Faculty of Engineering the Built Environment University of Johannesburg Johannesburg South Africa Department of Civil Engineering Technology Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa

ISBN: (纸本)9783031353987

This study presents an algorithm for identifying potential points of failure (leakage) in water distribution systems by evaluating the risk of pipe burst at each node. The algorithm calculates a burst index for each node using burst factors derived from the average District Metered Area (DMA) burst pressure and relevant parameters such as pipe length, age, and thickness. A sensitivity analysis based on pressure observations at the nodes under different leakage scenarios is conducted to identify deviations from the no-leak scenario. DMAs are then ranked according to their total burst risk, and nodes are partitioned based on their average DMA burst pressure. Although this algorithm may generate a larger number of candidate nodes than necessary due to the lack of optimization, its output can be used as input for sensor location optimization algorithms, reducing the search space for these algorithms. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Risk assessment

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Neural Network-Based Ensemble Learning Model to Identify Antigenic Fragments of SARS-CoV-2

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 651-660页

作者： Bukhari, Syed Nisar Hussain Ogudo, Kingsley A. J&K Srinagar191132 India University of Johannesburg Department of Electrical and Electronics Engineering Technology Faculty of Engineering and the Built Environment Johannesburg2092 South Africa

The development of epitope-based vaccines (EBVs) necessitates the identification of antigenic fragments (AFs) of the target pathogen known as T-cell epitopes (TCEs). TCEs are recognized by immune system, specifically by T cells, B cells, and antibodies. Traditional wet lab methods for identifying TCEs are often costly, challenging, and time-consuming compared to computational approaches. In this study, we propose a neural network-based ensemble machine learning (ML) model trained on physicochemical properties of SARS-CoV-2 peptides sequences to predict TCE sequences. The performance of the model assessed using test dataset demonstrated an accuracy of >95%, surpassing the results of other ML classifiers that were employed for comparative analysis. Through fivefold cross-validation technique, a mean accuracy of approximately 95% was reported. Additionally, when compared to other existing TCE prediction methods using a blind dataset, the proposed model was found to be more accurate and effective. The predicted epitopes may have a strong probability to act as potential vaccine candidates. Nonetheless, it is imperative to subject these epitopes to further scientific examination both in vivo and in vitro, to confirm their suitability as vaccine candidates. © 2024 The Authors.

关键词： T-cells

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A Holistic Evaluation of BIM Implementation Barriers in the Indian Construction Industry: Pre- and Post-Adoption Perspectives

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International Journal of Construction Education and Research 2024年第3期20卷 358-380页

作者： Mishra, Aakash Hasan, Abid Jha, Kumar Neeraj Department of Civil Engineering Indian Institute of Technology Delhi Hauz Khas India School of Architecture and Built Environment Faculty of Science Engineering and Built Environment Deakin University Geelong Australia

The existing Building Information Modelling (BIM) research does not provide a holistic understanding of pre- and post-adoption barriers to its implementation, especially in developing countries such as India, where BIM adoption lags. First, a total of 32 barriers to BIM implementation were identified after a comprehensive literature review. Second, after discussion with experts, the barriers were classified into 23 pre- and 21 post-adoption barriers. Next, the barriers were ranked based on 218 completed survey responses from professionals working in 125 organizations in the Indian construction industry. Finally, an independent samples t-test was conducted to understand differences in the perceptions of different groups of participants. The top five pre-adoption barriers were (1) high hardware costs, (2) high software costs, (3) low adoption across the supply chain, (4) low market support, and (5) unclear benefit evaluation. In comparison, the top five post-adoption barriers were (1) high ongoing cost (license renewal, training cost, etc.), (2) shortage of skills and expertise, (3) unclear benefit evaluation, (4) client’s lack of understanding, and (5) user resistance. Moreover, clients, contractors and consultants shared different views on very few barriers, which shows that these barriers are prevalent across different types of organizations in the Indian construction industry. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： adopters Building Information Modelling (BIM) construction organization non-adopters technology adoption

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Calibration and validation of the CMADS-Driven SWAT model in the distributed hydrological simulation of the Baihe River Basin in Nanyang

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ISH Journal of Hydraulic engineering 2025年第1期31卷 97-107页

作者： BiYing, Xu Abd Rahman, Nor Faiza Tai, Vin Cent Faculty of Automation Engineering Henan Polytechnic Institute Henan Nanyang China Centre for Sustainable Design Modelling and Simulation Faculty of Engineering Built Environment & Information Technology SEGi University Petaling Jaya Selangor Malaysia

The China Meteorological Assimilation Driving Dataset for the SWAT model (CMADS) has gained widespread use for its accuracy. This study focuses on the Baihe River Basin in Nanyang, using the SWAT tool and CMADS dataset to construct two distributed hydrological models of different scales. The accuracy and effectiveness of these models were evaluated. Parameter uncertainty analysis on a monthly and annual scale was conducted usingSUFI-2 algorithm. The goal was to assess the applicability of the CMADS-driven SWAT model in theBasin by calibrating and validating flow data at both monthly and annual scales. The results form the calibration period (2009–2013) showedmonthly R2 and NSE values were 0.81 and 0.8, while the annual values were 0.91 and 0.9. During validation(2014–2016), the annual R2 and NSE values were 0.89 and 0.86, and the monthly values were 0.76 and 0.76, with reasonable values for the P-factor, R-factor, and percentage bias (PBIAS), indicating good model applicability. Seasonal variations in rainfall, surface runoff, water yield, evapotranspiration (ET), and potential evapotranspiration (PET) were analyzed. The results demonstrate that surface runoff and water yield is closely related to precipitation, while ET is largely controlled by land use types, providing insights for water resource management. © 2024 Indian Society for Hydraulics.

关键词： SWAT

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