Toward improved siting of wind–solar hybrid farms: A novel framework integrating multi-criteria decision making, machine learning–driven feature selection, and spatial clustering

Mapping groundwater-dependent ecosystems in a groundwater flow system: A case in central Mexico

An IFC-based framework for semantic integration of BIM and mobile crowd sensing in real-time evacuation routing

Thermal generation of acrylamide, 5-HMF, and AGEs in roasted cashews: Correlation with aroma-active volatiles and quality evaluation.

The laminated structure and lithological alternation characteristics of continental shale restrict the height propagation of hydraulic fractures, thereby increasing the challenges in fracturing design. Based on scratch tests, indentation experiments and seepage–stress coupling model, this study systematically characterizes fracture height propagation patterns in monolithologic reservoir formations. The results demonstrate that argillaceous shale tends to develop short and wide fractures, whereas felsic shale predominantly generates long and narrow fractures. Within the multilithology fracture propagation framework, we establish a fracture height growth coefficient through analytic hierarchy process by integrating three key mechanical parameters: brittleness index, tensile strength, and fracture toughness. The cross-layer propagation capability of fractures is characterized using a cross-lithology gradient cumulative value. A negative correlation is observed between the fracture height growth coefficient and the achieved fracture height. As the cross-lithology gradient cumulative value increases, fracture height exhibits a decreasing trend. Consequently, the fracturing initiation point should preferentially be selected at locations minimizing the vertical cross-lithology gradient cumulative value. This study provides a theoretical basis for optimizing fracturing locations. By enhancing fracture height growth, it improves vertical reservoir utilization efficiency and contributes to the efficient development of shale oil resources.

Trajectory planning for traffic safety with dynamic ethical risk adjustment.

Floods, intensified by climate change and anthropogenic activities, pose significant global threats to human life and infrastructure. Although several studies have demonstrated the efficacy of the multicriteria decision-making (MCDM) methods involving human expertise and machine learning (ML) methods utilizing machine intelligence for developing flood hazard maps, there is a need for an integrated approach that combines the advantages of both methods and addresses their respective shortcomings. This study proposes an integrated MCDM and ML framework using the analytical hierarchy process and the random forest algorithm (AHP-RF). The upper Assam region of India, highly susceptible to floods, was selected as a case study. Factors influencing flooding, such as topography, land cover, embankment breach, river confluence points, normalized difference moisture, vegetation and water index, and rainfall-runoff characteristics, were utilized for developing a high-resolution flood hazard map using the proposed AHP-RF approach. The predicted maps were compared with historical flood inventory utilizing the area under the curve (AUC) and accuracy metrics for validation. Results indicate that the AHP-RF approach provides more accurate representations of flood hazard zones, with an improved AUC score of 0.74 and accuracy of 0.792 compared to the standalone AHP (AUC score of 0.654 and accuracy of 0.595). The derived high-resolution flood hazard map highlights that the built-up and agricultural lands of the Dhemaji and Lakhimpur districts are highly susceptible, while Dibrugarh and Tinsukia are moderate and low susceptible, respectively. Overall, the proposed AHP-RF approach can offer a reliable flood hazard map, aiding authorities and stakeholders in flood mitigation.

Industry 4.0 technologies, such as artificial intelligence, machine learning, and the Industrial Internet of Things (IIoT), are reshaping industries and hold considerable promise for advancing productivity and sustainable economic development. However, their adoption is fraught with risks, particularly for small and medium-sized enterprises (SMEs) that play a pivotal role in driving entrepreneurship, innovation, and the circular economy. Based on survey data collected between May and July 2020 from 307 professionals in mining, automotive manufacturing, and construction industries, this study examines critical risks across three stages of technology adoption: identification and selection; pilot testing, and full-scale implementation. Using the fuzzy analytical hierarchy process, the analysis reveals that technical risks dominate across stages and industries, while key social and environmental risks for achieving sustainability goals remain consistently deprioritized. However, risk profiles diverge by firm size and industry: SMEs are disproportionately affected by organizational and financial risks, whereas larger firms demonstrate consistent approaches through established risk management and economic policy frameworks. These findings demonstrate the necessity of a staged, context-sensitive, and sustainability-oriented adoption strategy. This study contributes to understanding how SMEs can balance economic efficiency and sustainable technology adoption in the transition toward Industry 4.0 by offering industry-specific propositions and managerial implications.

Stakeholder-driven assessment of optimal waste-to-energy plant selection in the Tamale metropolitan area using the analytical hierarchy process

As social infrastructure intensively developed during the high economic growth period of the 1970s faces simultaneous aging, there is an urgent need to transition from conventional reactive maintenance to preventive maintenance utilizing various data (data-driven asset management. However, the greatest barrier in practice is that inspection data is unevenly distributed in analog formats such as paper and unstructured files, and heavily relies on the subjective visual evaluation of expert engineers (e.g., discrete graded evaluations from A to D). The intervention of this “Assessor Bias” makes it difficult to ensure the robustness required for direct statistical analysis. This paper serves as a bridge between this analog expert knowledge and quantitative data science. It formulates human cognitive conflicts (true state, peer pressure, avoidance of cognitive load) using the distance-decay model of the Analytic Hierarchy Process (AHP) and the Softmax function, constructing a micro-macro link model accompanied by stochastic variations. Through large-scale multi-agent simulations (N=107) validating the model’s convergence, it was demonstrated that in long-tail distributions formed under peer pressure, macroscopic statistical distance metrics such as the Kullback-Leibler (KL) divergence ignore the fact that a small number of true signals are non-linearly suppressed, causing a statistical misinterpretation that “the error is within an acceptable range”. This implies that as long as macroscopic statistical indicators are over-trusted, signs of critical deterioration (minorities) will be structurally marginalized. Returning to the debate on “Homogeneity (Homogenität)” in German social statistics, this paper advocates that in order to realize objective “Micro-segmentation of Homogeneous Statistical Populations,” a paradigm shift from qualitative methods relying on human intuition to quantitative methods incorporating multi-criteria decision making is essential, rather than simply expanding the sample size.

Towards sustainable waste management: A systematic PRISMA review of environmentally responsible landfill siting.

ABSTRACT Knowing the status of emergency capacity for disaster risk reduction helps the government and stakeholders to minimize vulnerabilities and disaster risk. However, there is no widely applied methodology for emergency capacity assessment. This study develops a multidimensional framework integrating vulnerability, susceptibility, and adaptability assessments to evaluate regional flood emergency capacity. The information quantity method, the Maxent model, and the entropy‐weighted TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) are used to assess the regional vulnerability, susceptibility, and adaptability of disaster emergency capacity, respectively. These capacity features are then integrated into the overall disaster capacity using the analytic hierarchy process (AHP)‐entropy method. The exemplary application in Zengcheng District (ZCD), China shows the effectiveness of the proposed method. The identified vulnerability hotspots of flood over ZCD are located in Xintang and southern Yongning, driven by their high road density, GDP per capita, residential disposable income, and high population density. Susceptibility modeling revealed land use, Vegetation Index, and elevation as the dominant drivers, with Xintang exhibiting the highest risk. Adaptability assessments highlighted superior resilience in Paitan, Zhongxin, and Shitan, driven by enhanced disaster‐preparedness investments. Integrated analysis prioritized emergency resource storage capacity, drainage capacity, and emergency rescue capacity as critical determinants of flood response capacity. Lower emergency capacity is concentrated in Zhongxin, Xiaolou, Zhenguo, and Zhucun. This paper constructs a multidimensional assessment framework covering the entire chain of pre‐disaster prevention, in‐disaster response, and post‐disaster recovery, to systematically evaluate a region's comprehensive emergency capacity, providing potentially feasible insights for urban areas vulnerable to flood disasters.

Selecting the appropriate roof tile based on house specifications and user preferences is often a challenge, especially when facing a wide variety of available products. This study aims to design and develop a web-based Decision Support System (DSS) to assist in selecting roof tiles at the Genteng Bedingin Industry Center in Ponorogo. The method used is the Analytic Hierarchy Process (AHP), which allows for pairwise comparison among criteria and subcriteria and evaluates consistency in decision-making. The system considers six main criteria: tile type, price, batten spacing, kluntung size, thickness, and ease of installation, with weights that can be adjusted directly by users. Usability testing using the System Usability Scale (SUS) method involving 15 respondents resulted in a score of 92.48%, indicating that the system is highly user-friendly and delivers logical, needs-based recommendations. This system has proven to be effective in providing accurate and relevant tile selection recommendations and has potential for broader industrial implementation.

Groundwater is an important resource for domestic consumption, industrial and agricultural use. Overexploitation of groundwater, unpredictable rainfall and severe climate change have imposed a pressure on global groundwater resources. As demand for potable water is increasing, there is a need for evaluating and mapping groundwater potential. In Shimla district, people are dependent on the groundwater for household/agriculture purpose. Geospatial-based studies have gained importance in mapping of groundwater potential zones. This study has been undertaken to create the groundwater potential zone map of Shimla district. The analytical hierarchy process (AHP) was employed to delineate groundwater potential zones. Seven thematic layers were analysed using ArcGIS. Pairwise comparison matrix is formed for these layers and are analysed using weighted overlay analysis tool in ArcGIS. The results are presented as a groundwater potential map with five classes: very low, low, moderate, high and very high. Validation was performed using water yield data through the area under curve method. The AHP is widely recognised as an effective method for groundwater potential mapping and monitoring. The results show that 39% of the district area falls under high groundwater potential, while 47% has moderate potential. These findings can support effective groundwater planning and policy formulation.

Groundwater in semi-arid regions is increasingly stressed by intensive abstraction and contamination, while aquifer productivity and intrinsic vulnerability are commonly evaluated separately. This study investigates whether structured integration of groundwater potential and intrinsic vulnerability can provide a more reliable basis for sustainable groundwater management in the Halabja-Khwrmal area, northeast Iraq. Groundwater potential was delineated using the Analytical Hierarchy Process (AHP) applied to seven hydrogeological and environmental factors, whereas intrinsic vulnerability was assessed using the standard DRASTIC model. Both indices were independently validated prior to integration. Receiver Operating Characteristic (ROC) analysis based on 430 well discharge records yielded an Area Under the Curve (AUC) of 0.751, indicating acceptable discrimination of productive zones. Regression between DRASTIC index values and measured nitrate concentrations showed a strong positive relationship (R² = 0.797), supporting vulnerability reliability. Cross-classification of the validated ordinal groundwater potential and vulnerability indices generated nine Potential-Vulnerability zones, where highly productive areas largely coincide with low intrinsic vulnerability. High groundwater potential occupies 49.24% of the basin, while high intrinsic vulnerability covers 20.65%, with limited spatial overlap between highly productive and highly vulnerable conditions. The class-preserving integration prevents compensatory masking between productivity and susceptibility and provides a transparent spatial framework for regulated abstraction and priority protection. The proposed Potential-Vulnerability framework offers a transferable spatial basis for groundwater management in hydrogeologically variable semi-arid aquifer systems.

Flooding is a persistent problem in Lokoja, located at the confluence of the Niger and Benue rivers. This study integrated geospatial and geophysical data to map flood susceptibility in the Sub-Niger River Basin. Using remote sensing, GIS, and aeromagnetic data, seven factors—elevation, slope, drainage density, distance from rivers, lineament density, land use/land cover, and precipitation—were analyzed with the Analytical Hierarchy Process. Results show 36.85% of the basin is highly flood-prone, especially around Ganaja and Gadumo. Model validation (AUC = 0.81) confirms strong accuracy. The map supports floodplain zoning, land-use planning, and improved flood risk management in Lokoja.

An integrated spatial approach using graph theory and analytic hierarchy process for assessing transportation network efficiency and connectivity in Ranchi city, India

Optimal selection of railway alignment schemes is a critical phase in railway line design. However, existing studies primarily adopt an engineering perspective, often overlooking economic and social factors. Current decision-making models also face limitations in quantifying qualitative indicators and balancing subjective and objective weights, thereby undermining the scientific rigor and effectiveness of the evaluation process. To address these issues, this study proposes a multi-attribute optimization model for railway alignment selection based on combined weighting and the interval number model. First, the key factors influencing railway alignment are systematically identified through expert consultations and an extensive literature review. On this basis, a multi-dimensional evaluation index system comprising 20 indicators is established, encompassing four dimensions: technical feasibility, economic rationality, environmental impact, and regional coordinated development. Subjective weights are derived using the Analytic Hierarchy Process (AHP), while objective weights are calculated through an improved CRITIC method. Game theory is then employed to integrate them. To further enhance the objectivity and robustness of the evaluation, the study develops a decision-making model based on interval number distance. This model quantifies qualitative indicators using interval numbers and ranks alternative schemes by computing their distances to an ideal solution. A case study on the Jieshipu-Pingliang section of the Dingxi-Pingliang railway confirms the method's validity. The results show that the comprehensive weighting method reduces the influence of weight factor sensitivity on route selection. Furthermore, the scheme ranking derived from the interval number model closely aligns with the scheme recommended by the design institute and demonstrates stability. The research findings can provide a scientific reference for optimizing railway alignment in economically underdeveloped regions.

Purpose Choosing the right contractor is very important for the successful management of construction projects, especially when it comes to road maintenance. In order to improve the contractor for road maintenance, this study aims to create a thorough and efficient multiple-criteria decision-making (MCDM) model that goes beyond price-only assessments to include a wider range of criteria. Design/methodology/approach The study used both the Fuzzy Analytic Hierarchy Process (F-AHP) and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). There were six main groups of 26 sub-criteria: Contract Bid Price and Financial Capacity, Technical Capacity, Experience, Management Capacity, Safety and Environment. F-AHP was used to figure out how important each of these groups was compared to the others. TOPSIS was then used to rank possible contractors based on these weighted criteria. Findings The analysis showed that some criteria were especially important when choosing a contractor. Bid Price, Financial Performance, History of Non-Performance of Contracts, and Timely Completion of Projects were the most important factors in deciding whether a contractor was right for the job. These results show how important it is to use both financial and performance-related indicators when evaluating the appropriate contractor. Originality/value By addressing significant flaws in the conventional selection model, this study offers a fresh methodical approach to contractor evaluation. It provides a standard for enhancing the Roads Administration's procurement procedures and establishes a strong basis for upcoming studies and policy creation targeted at boosting accountability, efficacy and transparency in the execution of road maintenance.

Purpose This study aims to construct a quality indicator system for artificial intelligence-generated content (AIGC), enhance users’ ability to identify high-quality AIGC and provide a reference for service providers to optimize their content. Design/methodology/approach This study collected primary data through literature review and in-depth user interviews, and applied grounded theory to conduct three-level coding to identify and extract indicators for evaluating AIGC quality. Based on these indicators, a questionnaire survey and the analytic hierarchy process were used to determine indicator weights and to construct a comprehensive AIGC quality evaluation system. Findings This study identifies four first-level indicators and 21 second-level indicators for evaluating AIGC quality, along with their respective weights. To further highlight the uniqueness of the constructed indicator system, this study also reveals the commonalities and differences in quality assessment dimensions among AIGC, professionally generated content, and user-generated content through comparative analysis. Originality/value This research aims to enrich the academic discussion on AIGC quality indicator system, help users identify high-quality AI-generated content and provide guidance for relevant stakeholders to formulate policies.