Results in Engineering最新文献

{"title":"Strategic engineering and functional mechanism elucidation of advanced materials in detoxification of contaminated water matrices","authors":"Sathish Sundararaman ,&nbsp;Sugapriya Dhanasekaran ,&nbsp;Vickram A S ,&nbsp;Aravind kumar J ,&nbsp;Madarapu Yamini Priya ,&nbsp;Sahana ,&nbsp;Michael Rahul Soosai ,&nbsp;Anu Santhanakrishnana ,&nbsp;Pradeep Jangir ,&nbsp;Mohammad Khishe ,&nbsp;Gulothungan G","doi":"10.1016/j.rineng.2025.104851","DOIUrl":"10.1016/j.rineng.2025.104851","url":null,"abstract":"<div><h3>Background</h3><div>The 2030 Agenda contained 17 Sustainable Development Goals (SDGs), some of which support circular and sustainable production and consumption such as SDGs 11 and 12. One of the primary goals is also waste reduction and management. Agricultural waste is a significant obstacle with high potential for new value products under the circular bioeconomy approach. Reuse and recycling are essential to the circular economy, potentially enhancing waste value and reducing environmental harm. Using bio-waste, including pulp, stubble, seeds, leaves, and bagasse, to synthesise nanoparticles is an economical, low-energy, and ecofriendly method.</div></div><div><h3>Methods</h3><div>In order to solve wastewater treatment issues, recent research has concentrated on developing efficient and environmentally friendly biosorbents from agricultural waste. Finding locally accessible agricultural byproducts to remove dyes, and heavy metals has therefore become more crucial. An innovative and dependable way to enhance wastewater treatment and remediation is using nanotechnology. This includes making nanoparticles, hybrid nanocomposites in degrading or getting rid of contaminants from wastewater because of their improved surface characteristics and chemical reactivity.</div></div><div><h3>Significant Findings</h3><div>Research on agricultural waste management has had a significant increase recently, with 4688 publications published over the previous four years—comprising 77 % research articles and 23 % review papers. This review focus towards the methods to increase the effectiveness of biosorbents, recent progress made in the modification of adsorbents for the maximum removal of remove contaminants. The maximum adsorption capacities for nanobiosorbents at room temperature were found to be greater than 400 mg/g. According to the data, the BET/N2 specific surface varies from 1.311 m2/g to 23.9 m2/g. It was found that percentage removal of pollutants ranges from 85 % to 99.0 %. This study will contribute to developing more effective pollutant removal systems by bridging the gap between laboratory results and industrial applications and also the challenges with their mitigation measures.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104851"},"PeriodicalIF":6.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bibliometric analysis of trends in rainfall-runoff modeling techniques for urban flood mitigation (2005–2024)","authors":"Abd Rakhim Nanda ,&nbsp;Nurnawaty ,&nbsp;Amrullah Mansida ,&nbsp;Hartono Bancong","doi":"10.1016/j.rineng.2025.104927","DOIUrl":"10.1016/j.rineng.2025.104927","url":null,"abstract":"<div><div>Urban flooding poses significant challenges globally, driven by climate change and rapid urbanization. This bibliometric study reviewed 618 documents published between 2005 and 2024, focusing on rainfall-runoff modelling for urban flood mitigation. Key findings reveal that China (100 publications), the United States (81), and the United Kingdom (55) dominate research output, with emerging contributions from Southeast Asia and the Middle East. Traditional models such as the Storm Water Management Model (SWMM) and the Hydrologic Modelling System (HEC<img>HMS) remain widely used, while machine learning (ML), Geographic Information Systems (GIS), and Low-Impact Development (LID) approaches drive innovation in model precision and adaptability. However, gaps persist in evaluating long-term LID effectiveness and incorporating real-time data to address extreme climate variability. By offering quantitative insights into current research efforts, this analysis highlights the critical need for integrating advanced technologies and sustainable strategies to further enhance resilience in urban flood management frameworks.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104927"},"PeriodicalIF":6.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kinematic and dynamic modeling of mechanical systems towards Digital Twins","authors":"Chiara Nezzi ,&nbsp;Veit Gufler ,&nbsp;Renato Vidoni ,&nbsp;Erwin Rauch","doi":"10.1016/j.rineng.2025.104874","DOIUrl":"10.1016/j.rineng.2025.104874","url":null,"abstract":"<div><div>The development of Digital Twins has become a central topic in digital transformation, offering new possibilities for the prediction, control, and optimization of physical systems. In a general sense, a Digital Twin features real-time data exchange between a precise replica of a physical object in the virtual world, and vice versa. The virtual replica of the physical entity is usually referred to as Digital Model. A faithful representation can however affect the computational effort required by the model and consequently the data exchange in real-time. This is why, in recent years, the evolution of kinematic and dynamic models of mechanical systems in Digital Twins is essential but challenging, particularly due to the trade-off between model fidelity and computational feasibility required for real-time integration. This paper presents a systematic literature review of the methods and practices employed in modeling such systems for Digital Twin applications, focusing on tools and simulation environments used, as well as communication and computational challenges. As a final contribution, a theoretical framework is proposed as a guidance in the development of fully integrated Digital Twins for mechanical systems.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104874"},"PeriodicalIF":6.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing sustainable cities and communities with internet of things: Global insights, trends, and research priorities for SDG 11","authors":"Shaher Zyoud ,&nbsp;Ahed H. Zyoud","doi":"10.1016/j.rineng.2025.104917","DOIUrl":"10.1016/j.rineng.2025.104917","url":null,"abstract":"<div><div>The quest for sustainable development is confronted with many challenges. Technological innovation, especially the Internet of Things (IoT), has come up as a key enabler in reaching sustainability. This research analyzes research landscapes on IoT supporting Sustainable Development Goal (SDG) 11. A targeted search combining SDG 11 with IoT-related terminology was conducted via the Scopus database, covering publications from 2010 to 2024. VOSviewer software was employed to map collaborative networks, co-citation patterns, and key themes. Furthermore, the SciMAT software was employed was employed to analyze themes' evolution and intellectual structures, hence showing emerging trends and assessing thematic consistency over time. A thorough analysis of 6,334 publications revealed the top contributors as China (1,356; 21.4 %), India (1,301; 20.5 %), the United States (736; 11.6 %), and Saudi Arabia (569; 9.0 %). Developing nations in Asia and the Middle East made significant contributions to IoT-SDG 11 research, driven by urbanization challenges. Key research themes include intelligent transportation systems (ITSs), cyber-physical systems, smart buildings, and urbanization. Specialized subjects explore the use of IoT-enabled drones for augmenting SDG 11 goals, particularly in disaster management. Findings emphasize the need to design green IoT models and close technology gaps. Harnessing the potential of IoT requires the incorporation of ethical, social, and environmental factors into its design for guaranteeing convergence with sustainability goals. Data security issues, market fragmentation, and lack of infrastructure, though, necessitate regulatory intervention and investment. Additionally, citizen engagement and the evolution of 5G, artificial intelligence (AI), and edge computing will further enhance IoT's role in sustainability.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104917"},"PeriodicalIF":6.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of artificial intelligence on research efficiency","authors":"Mitra Madanchian ,&nbsp;Hamed Taherdoost","doi":"10.1016/j.rineng.2025.104743","DOIUrl":"10.1016/j.rineng.2025.104743","url":null,"abstract":"<div><div>Artificial intelligence (AI) is changing the research landscape through automation, data analysis, and better decision-making in various ways that are of immense help to researchers in conquering obstacles and accelerating their discoveries. From literature search to data analysis, to design experiments and manuscript writing, AI-powered tools using robotics, machine learning (ML), and natural language processing (NLP) go a long way in facilitating easy research. Technology enhances efficiency by summarizing articles, recommending publications, and pointing researchers in the right path. However, challenges such as bias in algorithms, concerns about data privacy, and deficiencies in the infrastructure impede wide-scale application. Training and supporting policies are needed for skill shortages and to surmount resistance to change in order for full utilization of AI in research. The present review has sought to explore how AI has influenced the efficiency of research through an analysis of its uses, advantages, disadvantages, and consequences across many fields. By examining the current tools and making projections on future trends, this study aims at educating academics, policymakers, and institutions on how AI might influence research in a fair and sustainable way.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104743"},"PeriodicalIF":6.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Green hydrogen revolution: Advancing electrolysis, market integration, and sustainable energy transitions towards a net-zero future","authors":"Veeraraghavan sakthimurugan ,&nbsp;G Lakshmikanth ,&nbsp;N Balaji ,&nbsp;R Roopashree ,&nbsp;Dhruv Kumar ,&nbsp;Yuvarajan Devarajan","doi":"10.1016/j.rineng.2025.104849","DOIUrl":"10.1016/j.rineng.2025.104849","url":null,"abstract":"<div><div>Green hydrogen is emerging as a key driver in global decarbonization efforts, particularly in hard-to-abate sectors such as steel manufacturing, ammonia production, and long-distance transportation. This study evaluates the techno-economic and environmental aspects of green hydrogen production, storage, and integration with renewable energy systems. Electrolysis remains the dominant production method, with efficiency rates ranging from 70 to 80 % for Alkaline Electrolyzers (AEL), 75–85 % for Proton Exchange Membrane Electrolyzers (PEMEL), and up to 90 % for Solid Oxide Electrolyzers (SOEL). Capital costs are steadily decreasing, with AEL costs falling from $1200/kW in 2018 to $800/kW in 2024, while PEMEL costs are projected to decline to $600/kW by 2030. Green hydrogen significantly reduces carbon emissions, with a footprint of 0.5–1 kg CO₂ per kg of H₂, compared to 10–12 kg for gray hydrogen and 1–3 kg for blue hydrogen. Its potential to cut global CO₂ emissions by 6 gigatons annually by 2050 underscores its role in climate action. However, its high water demand—approximately 9 liters per kilogram of hydrogen—necessitates efficient management strategies such as desalination and recycling. Economically, green hydrogen is becoming more competitive, with its levelized cost decreasing from $6/kg in 2018 to $3–4/kg in 2024, and projections indicating a further drop to $1.50/kg by 2030. Global investments exceeding $500 billion in 2024, along with major projects like Saudi Arabia's NEOM Green Hydrogen Project and Australia's Asian Renewable Energy Hub, are accelerating adoption. Policy frameworks such as the EU Hydrogen Strategy and the U.S. Inflation Reduction Act further support deployment. Despite progress, challenges remain in infrastructure, storage, and regulatory frameworks, necessitating continued innovation and international collaboration. Green hydrogen aligns with key Sustainable Development Goals (SDGs), including SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). As the world transitions to a low-carbon economy, green hydrogen presents a transformative opportunity, contingent on sustained technological advancements, investment, and policy support.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104849"},"PeriodicalIF":6.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of multimodal fake news detection on social media using deep learning models","authors":"Maged Nasser ,&nbsp;Noreen Izza Arshad ,&nbsp;Abdulalem Ali ,&nbsp;Hitham Alhussian ,&nbsp;Faisal Saeed ,&nbsp;Aminu Da'u ,&nbsp;Ibtehal Nafea","doi":"10.1016/j.rineng.2025.104752","DOIUrl":"10.1016/j.rineng.2025.104752","url":null,"abstract":"<div><div>The volume of data circulating from online sources is growing rapidly and comprises both reliable and unreliable information published through many different sources. Researchers are making plausible efforts to develop reliable methods for detecting and eliminating fake web news. Deep learning (DL) methods play a vital role in addressing various fake news detection problems and are found to perform better compared to conventional approaches, making them state-of-the-art in this field. This paper provides a comprehensive review and analysis of existent DL-based models for multimodal fake news detection, focusing on diverse aspects, including user profiles, news content, images, videos, and audio data. This study considered the latest articles within the last seven years, starting from 2018 to 2025, and about 963 quality articles were obtained from the journals and conferences selected for this study. Subsequently, 121 studies were chosen for our SLR after careful screening of the abstract and the full-text eligibility analysis. The findings showed that the Transformer models and Recurrent Neural Networks (RNNs) are the most popular deep learning techniques for detecting multimodal fake news, followed by the Convolutional Neural Networks (CNNs) techniques. The Twitter and Weibo datasets are the two most frequently used standard datasets, and the most frequently used metrics to evaluate the performance of these models are the accuracy, precision, recall, and F-scores. In conclusion, the limitations of the current methods were summarized and some exciting possibilities for future research were highlighted, including designing robust multilingual fake news detection systems, hybridization of deep learning models to enhance detection accuracy, integration of explainable AI (XAI), and facilitating real-time fake news detection models.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104752"},"PeriodicalIF":6.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soiling estimation methods in solar photovoltaic systems: Review, challenges and future directions","authors":"J. Agbogla ,&nbsp;C.K.K. Sekyere ,&nbsp;F.K. Forson ,&nbsp;R. Opoku ,&nbsp;B. Baah","doi":"10.1016/j.rineng.2025.104810","DOIUrl":"10.1016/j.rineng.2025.104810","url":null,"abstract":"<div><div>Soiling, the accumulation of dust and particulate matter on solar photovoltaic (PV) panels, reduces their efficiency, energy yield, and increases operational costs, particularly in dust-prone regions. This review critically examines methods for estimating soiling losses, focusing on approaches to accurately quantify dust accumulation. It explores direct methods such as gravimetric, optical, and imaging techniques, which offer high accuracy but face challenges in scalability, cost, and environmental sensitivity<strong>.</strong> Indirect methods, including Performance Ratio (PR) analysis and meteorological models, provide scalable, cost-effective solutions but often lack precision due to confounding factors like shading and system degradation<strong>.</strong> Hybrid models that integrate both direct and indirect techniques improve accuracy but require substantial data and computational resources. A major challenge identified in the review is th<strong>e</strong> lack of standardized protocols for soiling measurement, making comparisons across studies and regions difficult. The review emphasizes the importance of real-time monitoring<strong>,</strong> machine learning integration for predictive maintenance<strong>,</strong> and the development of anti-soiling coatings and self-cleaning technologies. Long-term studies across diverse climates are needed to create universally applicable soiling estimation models. By addressing these challenges and advancing existing technologies, the solar industry can more effectively estimate soiling losses, enhance PV system efficiency, and contribute to achieving global sustainability goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action)<strong>.</strong></div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104810"},"PeriodicalIF":6.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-wire additive manufacturing: A comprehensive review on materials, microstructure, methodological advances, and applications","authors":"Rupendra S. Tanwar,&nbsp;Suyog Jhavar","doi":"10.1016/j.rineng.2025.104814","DOIUrl":"10.1016/j.rineng.2025.104814","url":null,"abstract":"<div><div>The industrial demand for high-performance multi-material components is rapidly growing due to their superior functionality in advanced applications. Traditional Wire Arc Additive Manufacturing (WAAM) is typically limited to the use of a single wire, which possesses a specific chemical composition and results in fixed properties for the fabricated component. Multi-Wire Arc Additive Manufacturing (MWAAM) has emerged as a promising alternative due to its ability to achieve property variations at specific locations, along with its cost-effectiveness and flexibility. This review examines the advancements in MWAAM for multi-material fabrication. It aims to evaluate the microstructural and mechanical properties of components produced, identify research gaps, and propose strategies for future advancements. The review synthesises findings from studies published between 2015 and 2024, specifically focusing on MWAAM applications in fabricating functionally graded materials (FGMs), bimetallic structures (BMS), high-entropy alloys (HEAs), shape memory alloys (SMAs), and intermetallic compounds (IMCs). Factors such as wire feeding mechanisms, deposition strategies, microstructural evolution, and mechanical performance have been analysed. MWAAM demonstrates significant potential in fabricating advanced multi-material components with enhanced strength, thermal stability, and corrosion resistance. Its capabilities include precise composition control and the creation of gradient structures using advanced wire feeding systems, such as dual-wire and twin-wire configurations. However, MWAAM is challenged by microstructural heterogeneity, phase segregation, and prevalence of porosity and cracking. Addressing these challenges through computational modelling and real-time monitoring systems can lead to broadening its industrial adoption and impact.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104814"},"PeriodicalIF":6.0,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in energy harvesting techniques for sustainable IoT devices","authors":"Ahsan Ali ,&nbsp;Hamna Shaukat ,&nbsp;Hassan Elahi ,&nbsp;Shaista Taimur ,&nbsp;Muhammad Qasim Manan ,&nbsp;Wael A. Altabey ,&nbsp;Sallam A. Kouritem ,&nbsp;Mohammad Noori","doi":"10.1016/j.rineng.2025.104820","DOIUrl":"10.1016/j.rineng.2025.104820","url":null,"abstract":"<div><div>This paper reviews the energy harvesting techniques for sustainable Internet of Things (IoT) devices. With the passage of time, more things and objects are connected to the internet, paving the way for the development of IoT. The term IoT describes the network of everyday devices that are interconnected together and exchange data via the Internet. Wireless sensors are included in IoT devices to collect data in an accurate and useful way for process monitoring and activity control. The batteries that run these wireless sensors have a limited lifespan, rapidly drain, and need replacement. The replacement and maintenance process is costly; thus, smart energy management is essential for IoT devices to be energy-efficient. Therefore, energy harvesting is a promising method to supply such low-powered IoT devices by utilizing various energy resources. This energy can be harvested from different environmental, mechanical, chemical, and bioenergy sources, eliminating battery dependence. First, this review explains the importance of energy harvesting techniques for powering IoT devices, followed by IoT energy harvesting. Then it explains different energy harvesting techniques, followed by a table showing the analysis of these techniques. After that, this review explains the cost of these harvesters. Finally, there are future recommendations and conclusion, which shows some challenges that IoT energy harvesting faces that need to be addressed to grow sustainable IoT devices.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104820"},"PeriodicalIF":6.0,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}