Search Results (2,535)

The importance of software has increased significantly over time, and software failures have become a critical concern. As software systems have diversified in functionality, their structure has become more complex, and the types of failures that can occur in software have also diversified, stimulating the development of diverse software reliability models. In this study, we make certain assumptions regarding complex software, thereby proposing a new type of non-homogeneous Poisson process (NHPP) software reliability model that considers both dependent cases of software failure and cases that originate from the differences between the developed and actual operating environments. In addition, a new multi-criteria decision method ($MCDM$) that uses the maximum value for a comprehensive evaluation was proposed to demonstrate the effectiveness of the developed model. This improves the judgment of model excellence through multiple criteria and is suitable for multiple interpretations. To demonstrate the effectiveness of the proposed model, 15 NHPP software reliability models were compared using 13 evaluation criteria and three $MCDM$ methods across two datasets. The results showed that one dataset performed well for all the criteria, whereas the other dataset performed well for the newly proposed a multi-criteria decision method using maximum ($MCDMM$). The sensitivity analysis also showed a change in the mean value function with a change in the parameters. These results demonstrate that an extended structure for complex software can lead to improved software reliability. Full article

With the aim of reducing the environmental impact of buildings, the appropriate selection of building materials is essential, as a building is a complex system composed of various materials. With this background, a multi-criteria decision-making approach has recently gained traction. This study demonstrated the effect of building material selection on both environmental and economic parameters of a building in the context of Japan. A comparative analysis of five structural frame options was conducted utilizing a reference building model to assess the implication of material choices. The findings indicated that wooden frame options are advantageous in environmental aspects compared to non-wooden frames, provided that sustainable forestry practices and appropriate recycling scenarios are implemented. Conversely, it was found that a Cross Laminated Timber (CLT) frame is the most expensive option. This suggests that a hybrid approach, which combines various frame materials, could yield a more effective solution in terms of both environmental and economic sustainability. In addition, it was highlighted that building envelopes, such as foundation, exterior wall, and roof, should be prioritized to enhance the sustainability of a building from a material perspective. Furthermore, gypsum board, commonly used for sheathing building elements, should be selected with careful consideration of its environmental impact. Full article

Biogas and biofuels have emerged as viable alternatives to meet the targets established by the Paris Agreement. Considering the numerous variables involved in biogas production and the need to understand growth opportunities, technological improvements, and policies aimed at stabilizing the sector, a bibliographic review was conducted, analyzing 145 scientific articles. This analysis revealed a research gap related to biogas, energy generation, and the application of multicriteria decision-making methods. This study aims to contribute to filling this gap through the application of a multicriteria model designed to assist public decision-makers in selecting among three conversion pathways for biogas and biofuel production: pyrolysis, covered lagoon biodigester, and continuous stirred-tank reactor (CSTR) biodigester. These alternatives were evaluated based on environmental, social, economic, and technical criteria, applying the AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methods. The AHP method was used to rank the criteria and their respective sub-criteria, while the TOPSIS method helped select the alternative closest to the “ideal positive solution” among the conversion routes analyzed. The ranking results showed that environmental and social criteria received the highest scores compared to technical and economic criteria. Full article

Consumer-grade graphics processing units (GPUs) offer a potentially affordable and energy-efficient alternative to enterprise-class hardware for real-time image processing tasks, but systematic multi-criteria analyses of their suitability remain rare. This article fills that gap by evaluating the performance, power consumption, and cost-effectiveness of GPUs from three leading vendors, AMD, Intel, and Nvidia, in an inland water transport (ITW) context. The main objective is to assess the feasibility of using consumer GPUs for deep learning tasks involving navigational sign detection, a critical component for ensuring safe and efficient inland transportation. The evaluation includes the use of image datasets of inland water transport signs processed by widely used detector and classifier models such as YOLO (you only look once), ResNet (residual neural network l), and MobileNet. To achieve this, we propose a multi-criteria framework based on a weighted scoring method (WSM), covering 21 different characteristics such as compatibility, resting power, energy efficiency in learning and inference, and the financial threshold for technology adoption. The results confirm that consumer-grade GPUs can deliver competitive performance with lower initial costs and lower power consumption. The findings underscore the enduring value of our analysis, as its framework can be adapted for ongoing comparisons of evolving GPU technologies using the proposed methodology. Full article

Kombucha is widely recognized as a functional beverage with potential probiotic effects, yet maintaining probiotic viability remains a challenge due to the harsh conditions of fermentation. This study focuses on optimizing probiotic retention by identifying the most effective carrier for Lactobacillus rhamnosus using a multi-criteria decision-making approach. Five carrier materials—pea protein, whey protein, maltodextrin, inulin, and pectin—were assessed through three critical phases: evaluating encapsulated probiotic survival in different pH solutions, examining the impact of carriers on kombucha fermentation, and assessing probiotic stability during storage. The findings indicate that whey protein serves as the most effective carrier, offering superior bacterial protection and enhancing fermentation efficiency. Kinetic modeling further demonstrated a significant correlation between probiotic survival, pH, and titratable acidity, while artificial neural network models achieved high predictive accuracy (r² > 0.9). Functional analysis revealed that kombucha enriched with probiotic whey protein encapsulates exhibited improved bioactivity, including enhanced antidiabetic properties through α-glucosidase and α-amylase inhibition, antihypertensive effects via ACE inhibition, and antihypercholesterolemic activity through HMGCR inhibition. These findings suggest that probiotic fortification contributes to the beverage’s overall health-promoting potential. Sensory evaluation highlighted that while enriched kombucha exhibited slight modifications in texture and acidity, overall consumer acceptability remained high. The study underscores whey protein’s role as an optimal probiotic carrier, significantly enhancing kombucha’s probiotic stability and bio functional properties. The results contribute to advancements in functional beverage formulation, paving the way for the development of probiotic-enriched kombucha with improved stability, bioactivity, and consumer appeal. Full article

This study enhances coastal vulnerability assessment by introducing a Modified Fuzzy DEMATEL technique that incorporates socio-ecological and power dynamic considerations. The novelty of the study lies in integrating causal analysis with a participatory vulnerability framework tailored to coastal management. By analyzing multiple natural and socio-economic parameters, the results identify land use and land cover as the most influential factors, highlighting a shift toward socio-economic prioritization. The model also acknowledges limitations due to reliance on expert judgment and the absence of ground-truth validation. Our findings emphasize the need for location-specific vulnerability models rather than universal frameworks, offering insights for future participatory and evidence-based coastal management strategies. Full article

Accurate simulation of extreme precipitation events is crucial for managing climate-vulnerable sectors in Southern Africa, as such events directly impact agriculture, water resources, and disaster preparedness. However, global climate models frequently struggle to capture these phenomena, which limits their practical applicability. This study investigates the effectiveness of three bias correction techniques—scaled distribution mapping (SDM), quantile distribution mapping (QDM), and QDM with a focus on precipitation above and below the 95th percentile (QDM95)—and the daily precipitation outputs from 11 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset was served as a reference. The bias-corrected and native models were evaluated against three observational datasets—the CHIRPS, Multi-Source Weighted Ensemble Precipitation (MSWEP), and Global Precipitation Climatology Center (GPCC) datasets—for the period of 1982–2014, focusing on the December-January-February season. The ability of the models to generate eight extreme precipitation indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) was evaluated. The results show that the native and bias-corrected models captured similar spatial patterns of extreme precipitation, but there were significant changes in the amount of extreme precipitation episodes. While bias correction generally improved the spatial representation of extreme precipitation, its effectiveness varied depending on the reference dataset used, particularly for the maximum one-day precipitation (Rx1day), consecutive wet days (CWD), consecutive dry days (CDD), extremely wet days (R95p), and simple daily intensity index (SDII). In contrast, the total rain days (RR1), heavy precipitation days (R10mm), and extremely heavy precipitation days (R20mm) showed consistent improvement across all observations. All three bias correction techniques enhanced the accuracy of the models across all extreme indices, as demonstrated by higher pattern correlation coefficients, improved Taylor skill scores (TSSs), reduced root mean square errors, and fewer biases. The ranking of models using the comprehensive rating index (CRI) indicates that no single model consistently outperformed the others across all bias-corrected techniques relative to the CHIRPS, GPCC, and MSWEP datasets. Among the three bias correction methods, SDM and QDM95 outperformed QDM for a variety of criteria. Among the bias-corrected strategies, the best-performing models were EC-Earth3-Veg, EC-Earth3, MRI-ESM2, and the multi-model ensemble (MME). These findings demonstrate the efficiency of bias correction in improving the modeling of precipitation extremes in Southern Africa, ultimately boosting climate impact assessments. Full article

This study addresses the optimization of actuator arrangements in adaptive cable–strut tension structures to enhance structural controllability and performance. Two novel optimization criteria are proposed: (1) a weighted sensitivity criterion that integrates nodal displacements and internal force increments, and (2) a system strain energy criterion reflecting overall structural stiffness. Nonlinear optimization models are formulated for these criteria, with actuator positions as design variables, and solved using a robust multi-population genetic algorithm. The weighted sensitivity criterion prioritizes targeted control of specific nodes and members, while the strain energy criterion ensures balanced global response. Numerical validation is conducted on a Geiger cable dome and a four-layer tensegrity structure. Results demonstrate that both criteria yield actuator arrangements satisfying geometric symmetry while achieving high sensitivity in displacement and internal force control. The proposed framework offers practical insights for optimizing adaptive structures under static control requirements, and advances the field by bridging localized and global response optimization, enabling smarter, more resilient tension structures. Full article

The transition from open pit to underground mining intensifies water-related hazards such as Acid Mine Drainage (AMD), groundwater contamination, and aquifer depletion, threatening ecological and socio-economic sustainability. This study develops an Inclusive Disaster Risk Reduction (IDRR) framework using a Multi-Dimensional Risk (MDR) approach to holistically assess water hazards in China’s mining regions, integrating environmental, social, governance, economic, technical, community-based, and technological dimensions. A Multi-Criteria Decision-Making (MCDM) model combining the Fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) evaluates risks, enhanced by a Z-number Fuzzy Delphi AHP (ZFDAHP) spatiotemporal model to dynamically weight hazards across temporal (short-, medium-, long-term) and spatial (local to global) scales. Applied to the Sijiaying Iron Mine, AMD (78% severity) and groundwater depletion (72% severity) emerge as dominant hazards exacerbated by climate change impacts (36.3% dynamic weight). Real-time IoT monitoring systems and AI-driven predictive models demonstrate efficacy in mitigating contamination, while gender-inclusive governance and community-led aquifer protection address socio-environmental gaps. The study underscores the misalignment between static regulations and dynamic spatiotemporal risks, advocating for Lifecycle Assessments (LCAs) and transboundary water agreements. Policy recommendations prioritize IoT adoption, carbon–water nexus incentives, and Indigenous knowledge integration to align mining transitions with Sustainable Development Goals (SDGs) 6 (Clean Water), 13 (Climate Action), and 14 (Life Below Water). This research advances a holistic strategy to harmonize mineral extraction with water security, offering scalable solutions for global mining regions facing similar ecological and governance challenges. Full article

This paper introduces a two-phase decision support system based on information theory and financial practices to assist investors in solving cardinality-constrained portfolio optimization problems. Firstly, the approach employs a stock-picking procedure based on an interactive multi-criteria decision-making method (the so-called TODIM method). More precisely, the best-performing assets from the investable universe are identified using three financial criteria. The first criterion is based on mutual information, and it is employed to capture the microstructure of the stock market. The second one is the momentum, and the third is the upside-to-downside beta ratio. To calculate the preference weights used in the chosen multi-criteria decision-making procedure, two methods are compared, namely equal and entropy weighting. In the second stage, this work considers a portfolio optimization model where the objective function is a modified version of the Sharpe ratio, consistent with the choices of a rational agent even when faced with negative risk premiums. Additionally, the portfolio design incorporates a set of bound, budget, and cardinality constraints, together with a set of risk budgeting restrictions. To solve the resulting non-smooth programming problem with non-convex constraints, this paper proposes a variant of the distance-based parameter adaptation for success-history-based differential evolution with double crossover (DISH-XX) algorithm equipped with a hybrid constraint-handling approach. Numerical experiments on the US and European stock markets over the past ten years are conducted, and the results show that the flexibility of the proposed portfolio model allows the better control of losses, particularly during market downturns, thereby providing superior or at least comparable ex post performance with respect to several benchmark investment strategies. Full article

This study seeks to optimize the shape of a wave absorber with an inclined porous plate using an artificial neural network (ANN) model to improve the operating efficiency and experimental accuracy of a square wave basin. As our numerical tool, we employed the dual boundary element method (DBEM) to avoid the rank deficiency problem occurring at the degenerate plate boundary with zero thickness. A quadratic velocity model incorporating a CFD-based drag coefficient was employed to account for energy dissipation across the porous plate. The developed DBEM tool was validated through comparisons with self-conducted experiments in a two-dimensional wave flume. The input features such as the inclined angle and plate length affect the performance of the wave absorber. These features have been optimized to minimize the averaged reflection coefficient and the installation space (spatial footprint) with the application of a trained ANN model. The dataset used for training the ANN model was created using the DBEM model. The trained model was subsequently utilized to predict the averaged reflection coefficient using a larger dataset, aiding in the determination of the optimal wave absorber design. In the optimization process of minimizing both reflected waves and spatial footprint, the weighting factors are assigned according to their relative importance to each other, using the weighted sum model (WSM) within the multi-criteria decision-making framework. It was found that the optimal design parameters of the non-dimensional plate length ($l/h$) and inclined angle ($\theta$) are $1.46$ and $5.34°$ when performing with a weighting factor ratio (80%: 20%) between reflection and spatial footprint. Full article

A high level of urbanization, the growing role of cities, and the increasing urban population have led to a rise in the relevance of the problem of selecting investment projects in urban construction. Along with the usual factors considered in such a selection, regional peculiarities of conducting economic activity in the field of urban construction are gaining particular importance. The necessity of taking them into account requires an improvement in decision-making methods. This study develops a multi-stage adaptive method for multi-criteria project selection in urban construction. The method integrates regulatory requirements, the customer’s vision, and retrospective data on previously implemented projects in the region. It comprises the following sequential stages: the elimination of projects that do not meet the requirements; the construction of integral criteria (weighting functions) using logarithmic transformation; and an iterative reduction in the set of criteria. An experimental verification of the developed method demonstrated its application and revealed its potential for practical use. The proposed method can be effectively employed in urban planning systems and the smart management of urban spaces. Full article

Uncertainty quantification in cloud models requires simultaneous characterization of fuzziness (via Entropy, En) and randomness (via Hyper-entropy, He), yet existing similarity measures often neglect the stochastic dispersion governed by He. To address this gap, we propose HECM-Plus, an algorithm integrating Expectation (Ex), En, and He to holistically model geometric and probabilistic uncertainties in cloud models. By deriving He-adjusted standard deviations through reverse cloud transformations, HECM-Plus reformulates the Hellinger distance to resolve conflicts in multi-expert evaluations where subjective ambiguity and stochastic randomness coexist. Experimental validation demonstrates three key advances: (1) Fuzziness–Randomness discrimination: HECM-Plus achieves balanced conceptual differentiation (δC₁/C₄ = 1.76, δC₂ = 1.66, δC₃ = 1.58) with linear complexity outperforming PDCM and HCCM by 10.3% and 17.2% in differentiation scores while resolving He-induced biases in HECM/ECM (C₁–C₄ similarity: 0.94 vs. 0.99) critical for stochastic dispersion modeling; (2) Robustness in time-series classification: It reduces the mean error by 6.8% (0.190 vs. 0.204, *p* < 0.05) with lower standard deviation (0.035 vs. 0.047) on UCI datasets, validating noise immunity; (3) Design evaluation application: By reclassifying controversial cases (e.g., reclassified from a “good” design (80.3/100 average) to “moderate” via cloud model using HECM-Plus), it resolves multi-expert disagreements in scoring systems. The main contribution of this work is the proposal of HECM-Plus, which resolves the limitation of HECM in neglecting He, thereby further enhancing the precision of normal cloud similarity measurements. The algorithm provides a practical tool for uncertainty-aware decision-making in multi-expert systems, particularly in multi-criteria design evaluation under conflicting standards. Future work will extend to dynamic expert weight adaptation and higher-order cloud interactions. Full article

With increasing labor costs and rapidly dynamic changes in the market demand, as well as realizing the refined management of production, more and more attention is being given to considering workers, not just machines, in the process of flexible job shop scheduling. Hence, a new dual-resource flexible job shop scheduling problem (DRFJSP) is put forward in this paper, considering workers with flexible working time arrangements and machines with versatile functions in scheduling production, as well as a multi-objective mathematical model for formalizing the DRFJSP and tackling the complexity of scheduling in human-centric manufacturing environments. In addition, a two-stage approach based on a forensic-based investigation (TSFBI) is proposed to solve the problem. In the first stage, an improved multi-objective FBI algorithm is used to obtain the Pareto front solutions of this model, in which a hybrid real and integer encoding–decoding method is used for exploring the solution space and a fast non-dominated sorting method for improving efficiency. In the second stage, a multi-criteria decision analysis method based on an analytic hierarchy process (AHP) is used to select the optimal solution from the Pareto front solutions. Finally, experiments validated the TSFBI algorithm, showing its potential for smart manufacturing. Full article

This paper presents HERMEES—Holistic Evaluation and Ranking Model for Energy Efficient Systems. HERMEES is based on a multi-criteria decision-making (MCDM) model designed to select the optimal combination of lightweight cryptography (LWC) and topology construction protocol (TCP) algorithms for wireless sensor networks (WSNs) based on user-defined scenarios. The proposed model is evaluated using a scenario based on a medium-sized agricultural field. The Simple Additive Weighting (SAW) method is used to assign scores to the candidate algorithm pairs by weighting the scenario-specific criteria according to their significance in the decision-making process. To further refine the selection, mean shift clustering is utilized to group and identify the highest scored candidates. The resulting model is versatile and adaptable, enabling WSNs to be configured according to specific operational needs. The provided pseudocode elucidates the model workflow and aids in an effective implementation. The presented model establishes a solid foundation for the development of guided self-configuring context-aware WSNs capable of dynamically adapting to a wide range of application requirements. Full article