Search Results (1,193)

Due to the increasing complexity of industrial systems, fault detection hinders the continuity of productivity. Also, many methods in industrial systems whose complexity increases over time have a mechanism based on human intervention. Therefore, the development of intelligent systems in fault detection is critical.. Avoiding false alarms in detecting real faults is one of the goals of these systems. Modern technology has the potential to improve strategies for detecting faults related to machine components. In this study, a hybrid approach was applied on two different datasets for fault detection. First, in this hybrid approach, data is given as input to the artificial neural network. Then, predictions are obtained as a result of training using the ANN mechanism with the feed forward process. In the next step, the error value calculated between the actual values and the estimated values is transmitted to the feedback layers. IWO (Invasive Weed Optimization) optimization algorithm is used to calculate the weight values in this hybrid structure. However the IWO optimization algorithm is designed to be initialized with fractal-based weighting. By this process sequence, it is planned to increase the global search power without getting stuck in local minima. Additionally, fractal-based initialization is an important part of the optimization process as it keeps the overall success and stability within a certain framework. Finally, a testing process is carried out on two separate datasets supplied by the Kaggle platform to prove the model’s success in failure detection. Test results exceed 98%. This success indicates that it is a successful model with high generalization ability. Full article

In Pakistan and most other developing nations, the residential building sector is one of the highest energy-consuming domains. The residential sector has the highest share of 50% of final electricity use of the country. Though Energy Conservation Building Codes (ECBC-2023) provide structured energy guidelines, no work has been performed to quantify the actual energy-saving potential of code-compliant retrofits in residential buildings. This study investigates the performance of ECBC-compliant retrofitting strategies for residential buildings under Pakistan’s diverse climatic conditions. The Passive House Planning Package (PHPP), a validated simulation tool, was used to assess energy performance improvements through building envelope interventions such as thermal insulation, solar shading, window glazing, and optimal orientation. Field data were collected from three representative cities, Multan (hot desert), Taxila (humid subtropical), and Quetta (cold semi-arid), to simulate both conventional and energy-efficient building scenarios. The results showed substantial seasonal energy savings in all three climates. During the heating period, energy savings were 48%, 50%, and 60% for Taxila, Multan, and Quetta, respectively. Similarly, energy savings during the cooling season were 44%, 33%, and 16%. Life cycle economic analysis revealed that these retrofits yielded Net Present Values (NPVs) of USD 752 (Taxila), USD 1226 (Multan), and USD 1670 (Quetta) over a 30-year period, with discounted payback periods ranging from 6 to 10 years. Furthermore, a life cycle assessment demonstrated that retrofitted buildings yielded up to 26% reduction in overall carbon emissions, combining both embodied and operational sources. The findings highlight that ECBC-2023 is not only a technically viable solution for energy savings but also financially attractive in residential retrofitting. By incorporating localized climate responsiveness into ECBC-compliant building design, the study provides a practical roadmap for achieving Pakistan’s energy efficiency goals. Additionally, the outcomes serve as a basis for informing policy initiatives, supporting building code adaptation, and raising public awareness of sustainable housing practices. Full article

Although significant progress has been made in the conservation of Père David’s deer (Elaphurus davidianus) populations, rapid population growth in coastal wetlands has caused severe habitat degradation. This highlights the urgent challenge of balancing ungulate population dynamics with wetland restoration efforts, particularly considering the limited data available on post-disturbance ecosystem recovery in these environments. In this study, we evaluated soil quality and bacterial community dynamics at an abandoned feeding site and a nearby control site within the Dafeng Milu National Nature Reserve during 2020–2021. The goal was to provide a theoretical basis for the ecological restoration of Père David’s deer habitat in coastal wetlands. The main findings are as follows: among the measured indicators, bulk density (BD), soil water content (SWC), sodium (Na⁺), total carbon (TC), total nitrogen (TN), total phosphorus (TP), available potassium (AK), microbial biomass nitrogen (MBN), and the Chao index were selected to form the minimum data set (MDS) for calculating the soil quality index (SQI), effectively reflecting the actual condition of soil quality. Overall, the SQI at the feeding site was lower than that of the control site. Based on the composition of bacterial communities and the functional prediction analysis of bacterial communities in the FAPROTAX database, it is shown that feeding sites are experiencing sustained soil carbon loss, which is clearly caused by the gathering of Père David’s deer. Co-occurring network analyses demonstrated the structure of the bacterial community at the feeding site was decomplexed, and with a lower intensity than the control. In RDA, Na⁺ is the main soil property that affects bacterial communities. These findings suggest that the control of soil salinity is a primary consideration in the development of Père David’s deer habitat restoration programmes, followed by addressing nitrogen supplementation and carbon sequestration. Full article

This paper is aimed at landscape managers and designers. It looks at 123 real-world coastal landscape projects and organizes them into clear design categories, i.e., wetland restoration, hybrid infrastructure, or urban green spaces. We looked at how these projects were framed (whether they focused on climate adaptation, flood protection, or other goals) and how they tracked performance. We are hoping to bring some clarity to a very scattered field, helping us to see patterns in what is actually being carried out in terms of landscape interventions and increasing sea levels. We are hoping to provide a practical reference for making better, more climate-responsive design decisions. Coastal cities face escalating climate-driven threats from increasing sea levels and storm surges to urban heat islands. These threats are driving increased interest in nature-based solutions (NbSs) as green adaptive alternatives to traditional gray infrastructure. Despite an abundance of individual case studies, there have been few systematic syntheses aimed at landscape designers and managers linking design typologies, project framing, and performance outcomes. This study addresses this gap through a meta-synthesis of 123 implemented coastal landscape interventions aimed directly at landscape-oriented research and professions. Flood risk reduction was the dominant framing strategy (30.9%), followed by climate resilience (24.4%). Critical evidence gaps emerged—only 1.6% employed integrated monitoring approaches, 30.1% provided ambiguous performance documentation, and mean monitoring quality scored 0.89 out of 5.0. While 95.9% of the projects acknowledged SLR as a driver, only 4.1% explicitly integrated climate projections into design parameters. Community monitoring approaches demonstrated significantly higher ecosystem service integration, particularly cultural services (36.4% vs. 6.9%, $p<0.001$), and enhanced monitoring quality (mean score 1.64 vs. 0.76, $p<0.001$). Implementation barriers spanned technical constraints, institutional fragmentation, and data limitations, each affecting 20.3% of projects. Geographic analysis revealed evidence generation inequities, with systematic underrepresentation of high-risk regions (Africa: 4.1%; Latin America: 2.4%) versus concentration in well-resourced areas (North America: 27.6%; Europe: 17.1%). Full article

With increasing wastewater treatment demands and decarbonization goals, synergistic reduction in pollutants and green house gas (GHG) emissions is crucial. High process emissions like N₂O pose significant challenges, yet optimized carbon reduction strategies for conventional plants are lacking. This study developed three mathematical models to quantify the impact of dissolved oxygen (DO), influent salinity, and C/N ratio on direct emissions (CH₄, N₂O) and indirect emissions. Response Surface Methodology (RSM) optimized these factors to minimize GHG emissions under three accounting scenarios: (1) plants with CH₄ reuse systems: salinity = 0.5 g L⁻¹, DO = 3.67 mg L⁻¹, C/N = 12.75; (2) plants focusing solely on direct emissions: salinity = 0.5 g L⁻¹, DO = 3.35 mg L⁻¹, C/N = 3; and (3) plants assessing total emissions: salinity = 0.5 g L⁻¹, DO = 2.5 mg L⁻¹, C/N = 7.18. Key findings indicated that increasing salinity exacerbated greenhouse gas emissions. Elevated DO levels in the aerobic stage reduced N₂O emissions but increased indirect emissions in the A²/O process. Higher C/N ratios promoted anaerobic CH₄ production, but sufficient carbon reduced N₂O by enabling complete heterotrophic denitrification. A 60−day continuous GHG emissions monitoring campaign was conducted at a WWTP to validate the actual emission reductions achievable under the identified optimal control conditions. An analysis and comparison of operational and economic costs were also performed. The findings provide practical insights into sustainable GHG emission management and offer potential solutions to advance the synergistic reduction in GHG emissions and pollutants. Full article

Winding hot-spot temperature (HST) is a key factor affecting the insulation life of transformers. This paper proposes an optimization method based on the Kriging response surface model, which minimizes HST by adjusting the key structural parameters of the number of winding zones, vertical oil channel width, and horizontal oil channel height. First, a two-dimensional axisymmetric temperature–fluid field coupling model is established, and the finite volume method is used to solve the HST under the actual structure, which is 92.59 °C. A total of 50 sample datasets are designed using Latin hypercube sampling, and the whale optimization algorithm (WOA) is used to determine the optimal kernel parameters of Kriging with the goal of minimizing the root mean square error (RMSE) under 5-fold cross-validation. Combined with the genetic algorithm (GA) global optimization of structural parameters, the Kriging model predicts that the optimized HST is 89.77 °C, which is verified by simulation to be 89.79 °C, achieving a temperature drop of 2.80 °C, proving the effectiveness of the structural optimization method. Full article

In the context of growing concerns about power disruptions, grid reliability and the need for decarbonization, this study evaluates a broad range of clean backup power systems (BPSs) to replace traditional emergency diesel generators. A scenario-based stochastic optimization framework using actual load profiles and outage probabilities is proposed to assess the most promising options from a pool of 27 technologies. This framework allows a comparison of the cost effectiveness and environmental impact of individual technologies and hybrid BPSs across various scenarios. The results highlight the trade-off between total annual system cost and emissions. Significant emission reductions can be achieved at moderate cost increases but deep decarbonization levels incur higher costs. Primary and secondary batteries are included in optimal clean fuel-based systems across all decarbonization levels, combining cost-effective power delivery and long-term storage benefits. The findings highlight the often-overlooked importance of fuel replacement on both emissions and costs. Among the assessed technologies, ammonia generators and hydrogen fuel cells combined with secondary iron–air batteries emerge as cost-effective solutions for achieving decarbonization goals. To ensure a broad range of applicability, the study outlines the impact of emergency fuel purchases, varying demand patterns and demand response options on the optimal BPS. The research findings are valuable for optimizing the design of clean BPSs to economically meet the needs of many facility types and decarbonization targets. Full article

Rural–urban student migration during the compulsory education stage is a transitional phenomenon in China’s socio-economic development and a crucial issue for achieving the goal of urban–rural integration. This paper, grounded in theoretical analysis, constructs a “willingness-capacity-behavior” framework. Based on field survey data from 916 rural households and in-depth interview materials from County D, Province X, China, this study employs a bivariate Probit model and qualitative analysis methods to explore the driving factors and formation mechanisms of rural households’ rural–urban student migration decisions. The results indicate that rural households’ decisions regarding rural–urban student migration are jointly influenced by migration willingness and migration capacity. Only households with both migration willingness and migration capacity can actualize migration behavior. Migration willingness is derived from a cost–benefit analysis and involves joint decision-making by both parents, significantly influenced by parental personal characteristics and the student’s individual characteristics. The intermediate barriers to rural–urban student migration require certain migration capacities to be overcome, which are mainly influenced by family resource endowment and parental personal characteristics. Full article

This study compares the LEED, BREEAM, and WELL certification systems using the Triple Bottom Line (TBL) framework to assess their performance across environmental, social, and economic dimensions and their alignment with sustainable development goals. A structured secondary analysis was conducted on over 50 peer-reviewed articles, case studies, and official certification manuals. Inclusion criteria required documented design targets and post-occupancy outcomes for certified buildings (2014–2024). A two-phase analytical model was applied: first, evaluating each system’s structure and priorities; then benchmarking them using the TBL framework to assess how holistically each addresses sustainability. Results show that LEED leads to energy optimization, BREEAM to lifecycle integration, and WELL to occupant health and indoor environmental quality. However, all systems exhibit post-occupancy performance gaps: LEED and BREEAM underperform by 15–30% in energy use, while WELL-certified projects may exceed 30% due to stringent indoor comfort demands. These findings highlight the need to integrate real-time post-occupancy evaluation into certification protocols. To improve overall effectiveness, the study proposes enhancements such as adaptive performance tracking, occupant feedback loops, and dynamic benchmarking aligned with actual building use. By identifying both the comparative strengths and systemic limitations of the three frameworks, this research contributes to the refinement of green building assessment tools. Practical implications include (1) integrating post-occupancy evaluation into certification renewal cycles, (2) adopting hybrid certification strategies to improve sustainability coverage, and (3) designing benchmarking tools that reflect real-world operational data. Full article

Meeting the European Green Deal’s goal of climate neutrality by 2050 calls for a 90 percent decrease in emissions from the transportation sector. Thus, there is need to accelerate the shift to more sustainable mobility for integrated and smarter multimodal and intermodal mobility. In European countries, more than 70% of the inhabitants live in metropolitan areas. Achieving low-carbon and more sustainable mobility is important to ensuring sustainable urban infrastructure. However, current mobility planning frameworks do not consider the key factors and strategies that encourage residents to choose sustainable transport modes. Hence, there is a need to identify the most efficient actions that should be employed either in the short or long term to achieve accessible, safe, cost-effective, and green transport systems specifically through the development of sustainable public transportation. Moreover, a paradigm shift is needed to explore the synergy between transportation and its relationship to the city. Accordingly, this article presents an action plan as an approach to assess key strategies needed to foster sustainable and smart mobility planning and design by deploying effective strategies and design solutions that support different green means of transportation for smart urban development. Qualitative data on sustainable mobility planning and design strategies was collected via secondary sources from the literature, and descriptive data analysis was carried out. Findings from this study identify internal and external factors required to promote sustainable multimodal and intermodal mobility based on the city’s transport policies and actions. Implications from this study provide a use case for the technological requirements required for electric mobility planning, design, and system operation for the actualization of sustainable public transportation to improve smart urban development. Full article

The Sustainable Development Goals (SDGs) have become the global framework for evaluating the effectiveness of humanitarian projects. Humanitarian mapping is considered a popular voluntary geographic information technique that provides data for disaster response. Although humanitarian mapping has contributed significantly to the SDGs, there is a lack of in-depth studies on the state of this relationship. This paper aims to assess the potential relationship between the SDGs and humanitarian mapping by (1) analyzing SDG indicators to determine their potential contribution to humanitarian mapping, and (2) identifying the actual contribution of humanitarian mapping projects to the SDGs. To achieve this, the study uses a structured methodology that combines SDG indicator analysis with project-level data filtering and text mining. Three major humanitarian mapping platforms—HOT-TM, MapSwipe, and Ushahidi—are examined in order to capture their potential and actual contributions to the SDG framework. Ultimately, the study highlights the strong alignment between humanitarian mapping activities and the need to monitor the SDGs, particularly in water, urban infrastructure, and land use, emphasizing the potential of volunteer-driven geospatial data to address critical data gaps. Full article

Despite substantial investments to curb tropical deforestation, effective conservation incentives for Indigenous peoples and local communities is not well-defined and generally under-researched. This study assessed an incentive mechanism in Peru for Indigenous communities that protect enrolled forests to explore whether the stated program goals are actualized in programmatic elements like the requirements, monitoring, and assessment of prioritized outcomes. The research team worked with Indigenous partners to develop key questions regarding how the mechanism could better support their values of conservation and development. Data were sourced from interviews with implementation experts and participants in eight Indigenous communities, a review of programmatic documents, and an assessment of nationally aggregated community data. The results revealed challenges in program capacity, a lack of cultural awareness, and a reliance on capitalistic economic indicators that exclude other aspects of well-being important for Indigenous peoples. We find that the program’s success indicators do not adequately align with conservation or social realities on the ground and that enhanced indicators are needed to ensure success and avoid negative unintended consequences. We demonstrate that enhancing the assessment of governance, economics, engagement, and social inclusion can improve the design, implementation, and monitoring in this and similar programming. We conclude with generalizable recommendations for establishing requirements and monitoring in existing and future conservation incentive programs that target Indigenous communities. Full article

Electric bikes (e-bikes) offer a low-carbon, space-efficient solution for campus mobility, yet their sustainable potential is increasingly challenged by patterns of non-compliant use, including speeding, informal parking, and unauthorized charging. This study integrates the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to examine the cognitive and contextual factors that shape such behaviors among university students. Drawing on a survey of 408 e-bike users and structural equation modeling, the results show that non-compliance is primarily driven by perceived usefulness, ease of action, and behavioral feasibility, with affective and normative factors playing indirect, reinforcing roles. Importantly, actual behavior is influenced not only by intention but also by students’ perceived capacity to act within low-enforcement environments. These findings highlight the need to align behavioral perceptions with sustainability goals. The study contributes to sustainable mobility governance by clarifying key psychological pathways and offering targeted insights for designing perception-sensitive interventions in campus transport systems. Furthermore, by promoting compliance-oriented campus mobility, this research highlights a pathway toward enhancing the resilience of transport systems through behavioral adaptation within semi-regulated environments. Full article

This study developed a model to predict ships with high deficiency risk under Port State Control (PSC) through machine learning techniques, particularly the Random Forest algorithm. The study utilized actual ship inspection data from the Port of Singapore, comprehensively considering various operational and safety indicators of ships, including but not limited to flag state, ship age, past deficiencies, and detention history. By analyzing these factors in depth, this research enhances the efficiency and accuracy of PSC inspections, provides decision support for port authorities, and offers strategic guidance for shipping companies to comply with international safety standards. During the research process, I first conducted detailed data preprocessing, including data cleaning and feature selection, to ensure the effectiveness of model training. Using the Random Forest algorithm, I identified key factors influencing the detention risk of ships and established a risk prediction model accordingly. The model validation results indicated that factors such as ship age, tonnage, company performance, and flag state significantly affect whether a ship exhibits a high deficiency rate. In addition, this study explored the potential and limitations of applying the Random Forest model in predicting high deficiency risk under PSC, and proposed future research directions, including further model optimization and the development of real-time prediction systems. By achieving these goals, I hope to provide valuable experience for other global shipping hubs, promote higher international maritime safety standards, and contribute to the sustainable development of the global shipping industry. This research not only highlights the importance of machine learning in the maritime domain but also demonstrates the potential of data-driven decision-making in improving ship safety management and port inspection efficiency. It is hoped that this study will inspire more maritime practitioners and researchers to explore advanced data analytics techniques to address the increasingly complex challenges of global shipping. Full article

Managing finances in a supply chain today is not as straightforward as it once was. The world is constantly shifting—markets fluctuate, risks emerge unexpectedly—and companies are continually trying to stay one step ahead. In all this, financial resilience has become more than just a strategy. It is a survival skill. In our research, we examined how newer technologies (such as blockchain and the Internet of Things) can make a difference. The idea was not to reinvent the wheel but to see if these tools could actually make financing more transparent, reduce some of the friction, and maybe even help companies breathe a little easier when it comes to liquidity. We employed two optimization methods (Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)) to achieve a balanced outcome. The goal was lower financing costs, better liquidity, and stronger resilience. Blockchain did not just record transactions—it seemed to build trust. Meanwhile, the Internet of Things (IoT) provided companies with a clearer picture of what is happening in real-time, making financial outcomes a bit less of a guessing game. However, it gives financial managers a better chance at planning and not getting caught off guard when the economy takes a turn. Full article