Search Results (3,991)

Urban wastewater treatment plants face increasing challenges in mitigating environmental impacts while achieving high treatment efficiency. This study explores the optimization of quorum-quenching (QQ) media materials for scalable membrane bioreactor (MBR) applications, focusing on their potential to reduce operational footprints and enhance sustainability. Six immobilization media were evaluated—sodium alginate (SA), polyvinyl alcohol (PVA) beads (P), magnetic beads (M), chitosan magnetic beads (CM), polymer-coated beads (PS), and flat media (SAP)—using a multi-criteria decision analysis (MCDA) framework. Key parameters, including porosity, mechanical strength, quorum-quenching activity, and durability in sludge, were quantitatively weighted according to their operational significance. SA demonstrated the most balanced performance, exhibiting superior durability and cost-effectiveness, whereas SAP showed potential in applications prioritizing high porosity and enhanced QQ activity. The incorporation of QQ media led to a significant reduction in membrane fouling, chemical consumption, and energy consumption in pilot-scale MBR systems. Ecological footprint assessment revealed a 15% reduction in indirect blue water footprints and a 20% decrease in Scope 2 carbon emissions, attributable to reduced operational energy demands. These findings highlight the efficacy of QQ media in improving MBR performance and advancing system-level sustainability. Overall, this study highlights the critical importance of material engineering and ecological footprint integration in the development of next-generation urban wastewater treatment technologies. Full article

Housing acquisition decisions play a strategic role in shaping urban habitability and long-term sustainability, as they directly influence the quality of the built environment and users’ well-being. From an architectural and urban perspective, housing selection can be understood as an assessment of urban habitat quality, in which economic, spatial, social, environmental, and risk-related dimensions interact to define the conditions of livability. This study proposes a multi-objective decision-support framework that integrates evolutionary optimization algorithms (NSGA-II and MOEA/D) with multi-criteria decision analysis (TOPSIS) to support sustainable housing decisions. The model simultaneously considers four conflicting objectives: minimizing acquisition cost, minimizing spatial accessibility and disutility from essential services, maximizing socio-spatial safety and long-term habitat value, and minimizing environmental and territorial risk. A real-world case study in the Tampico metropolitan area demonstrates how the proposed approach generates Pareto-optimal housing alternatives that explicitly reveal trade-offs between habitability dimensions. The resulting non-dominated solutions are subsequently ranked using TOPSIS to reflect user-centered preferences and facilitate transparent decision-making. The results show that the proposed framework effectively operationalizes the concept of urban habitat quality through an explainable, customizable computational tool, thereby contributing to sustainable urban development, resilience, and informed housing choices. This research supports the technological enablement of habitat assessment and aligns with the objectives of SDG 11: Sustainable Cities and Communities, offering a replicable methodology for urban and architectural decision-making contexts. Full article

This article presents methods and hardware for the multi-criteria non-destructive determination of chicken egg quality parameters, implemented using a multifunctional rotary system. Unlike traditional single-criteria sorting, which relies primarily on weight, the proposed approach utilizes a combination of physical and geometric parameters, including weight, linear dimensions, cross-sectional area and perimeter, volume, density, and shape. The experimental framework for the study was formed by measuring the parameters of 750 chicken eggs, covering the entire range of product categories and morphological variations. Geometric parameters were determined using machine vision methods, weight was determined using a strain gauge, and derived parameters were calculated using formalized models. A multi-criteria evaluation algorithm based on fuzzy set theory was used to make the classification decision, accounting for overlapping feature ranges and regulatory differences between EU and EAEU standards. The results of statistical and correlation analysis showed that egg density is identified as a relatively independent diagnostic parameter, weakly correlated with weight and geometric characteristics, justifying its inclusion in the quality model. A comparison of manual and automatic classification revealed differences in boundary categories during single-criteria sorting and indicated the potential of a multi-criteria approach. The obtained results support the feasibility of the developed methods and hardware under the conditions of the present study. Full article

This study presents research on optimizing the parameters of the passivation process for precipitation-hardening stainless steels (PHSS) to improve the corrosion resistance of AM350 and CUSTOM 450 alloys, which are extensively utilized in the aerospace and aviation sectors, since, as this is a complex process, it requires the implementation of a robust methodological approach that allows for multi-response optimization. Experiments were designed using the Taguchi method, which offered a strong framework for examining the impact of material, type of passivation solution, concentration, temperature, and passivation process time on the corrosion resistance of both PHSS alloys. To confirm the ideal PHSS passivation process parameters and measure the significance of each component, gray relational analysis (GRA) and analysis of variance (ANOVA) were also employed. The combined use of the Taguchi/GRA represents a robust and efficient methodological approach to the multi-response optimization of complex processes, overcoming the limitations inherent in the individual application of each technique. It was determined that the optimized parameters were a PHSS AM 350, a solution composed of a combination of citric acid and oxalic acid, acid concentration of 25% v/v, temperature of 50 °C, and time of 120 min. This combination of parameters resulted in significant improvements of up to 55% in corrosion resistance in the H₂SO₄ and NaCl evaluation solutions, demonstrating the effectiveness of the optimized conditions. This work emphasizes the efficacy of integrating Taguchi, GRA, and ANOVA techniques to significantly reduce the corrosion rate of PHSS undergoing the passivation process using alternatives to nitric acid. The integration of the Taguchi methodology with GRA enables the normalization and combination of responses with different scales and performance criteria into a single gray relational index, facilitating the overall evaluation of the system. Full article

Mountainous megacities face a distinctive form of pluvial waterlogging in which terrain-controlled flow convergence, accelerating imperviousness, and aging drainage interact to produce chronic, spatially clustered failures rather than stochastic events. Existing frameworks, such as hydrodynamic modeling, data-driven machine learning, and multi-criteria composite indexing, carry distinctive failure modes at the municipal scale. This study develops and externally validates a city-wide, grid-based assessment framework for Chongqing, China, through three integrated choices. First, resilience is reformulated as a stabilized adaptation-to-risk ratio and subjected to an explicit falsification test against independent waterlogging observations. Second, multi-source hydroclimatic, topographic–hydrologic, land-cover, and service-accessibility indicators are integrated on a 500 m fishnet (22,500 cells) through within-component CRITIC–Entropy weighting and TOPSIS, with robustness diagnosed by a 500-iteration Monte Carlo weight-perturbation analysis. Third, a spatially grouped LightGBM classifier with SHAP interpretation serves both as an independent validation layer and as a mechanistic lens on non-linear driver thresholds. The composite risk surface achieves ROC-AUC values of 0.834 and 0.873 against two independent waterlogging registries, is strongly spatially clustered (Moran’s I = 0.81, p < 0.001), and preserves its ranking under aggressive weight perturbation (Spearman ρ ≥ 0.95 in 95% of scenarios). A counterintuitive finding emerges from the falsification test as resilience yields ROC-AUC below 0.5 on both point sets, indicating that accessibility-based capacity proxies systematically capture urban centrality rather than drainage robustness, like a diagnosable measurement problem affecting the wider resilience-index literature. LightGBM concentrates 88.0% of waterlogging cells within the top 10% of scored grids, and SHAP-derived thresholds align with saturation-ponding, well-drained, and convergence–hotspot regimes of classical hydrology. Together, these results reframe waterlogging assessment in complex terrain from a cartographic exercise into a falsifiable, resource-aware prioritization framework, and clarify why capacity maps and risk maps should be published as complementary instruments of flood governance. Full article

Background/Objectives: Sebaceous carcinoma (SC) is a rare and aggressive malignancy most often arising in the head and neck. The reported rate of lymph node metastasis is variable and current clinical guidelines surrounding pre-treatment imaging and management of lymph nodes are not well defined. The aim of our systematic review and meta-analysis was to determine a pooled rate of clinically apparent and occult lymph node metastases for SC of the head and neck to inform clinical guidelines. Methods: Per PRISMA guidelines, systematic search of the Pubmed/MEDLINE and EMBASE databases identified studies published before October 2023 reporting regional lymph node status in adults with SC of the head and neck. Meta analysis using the random-effects model was applied to calculate the pooled proportion of subjects with lymph node metastasis. Clinical characteristics of subjects were further analyzed using chi square tests and univariate logistic regression. Results: Thirty-eight studies met inclusion criteria with a total of 2371 patients. The pooled prevalence of regional lymph node involvement, including clinically apparent and occult disease, was 16% (95% CI 13–18%, I² 65%), with increased risk with increasing T stage. The pooled rate of occult lymph node metastases was 7% (95% CI 4–9%, I² 68%). Conclusions: There is a high rate of lymph node involvement in SC of the head and neck, much of which goes undetected during initial workup and treatment. Initial workup should reflect this risk and include appropriate physical exam, imaging, consideration for sentinel lymph node biopsy, and involvement of a multi-disciplinary team. Full article

Freshwater ecosystems are increasingly threatened by eutrophication and other anthropogenic and climate-driven pressures that undermine ecological functioning and biodiversity. This study evaluates the transferability of a GIS-based multi-criteria decision analysis (GIS–MCDA) framework with Fuzzy Analytic Hierarchy Process (F-AHP), originally developed for a shallow coastal lake, to a morphologically distinct deep upland lake (Lough Tay, Ireland). Monthly in situ measurements at a single monitoring point in 2024 were analysed together with meteorological variables using Spearman rank correlations. Because spatial interpolation of in-lake water quality parameters was not feasible, eutrophication susceptibility was mapped using four external spatial drivers: distance from water resources (River Cloghoge inflows), land-based nitrogen export potential, distance from environmental pollutants represented by the transportation network, and a wind exposure index derived from a DEM and wind-rose analysis. Criteria were standardized with fuzzy membership functions, weighted using F-AHP (consistency index 0.056), and aggregated using weighted linear combination at 25 m resolution. The resulting Eutrophication Susceptibility Index (ESI) ranged from 0.18 to 0.81, indicating generally moderate to good conditions, with higher ESI values concentrated in the northern lake sector near inflow zones. The results demonstrate that GIS–MCDA can be adapted to lakes with limited monitoring by relying on external drivers, providing a spatial proxy for susceptibility rather than measured trophic status. Full article

In this study, a hybrid multi-criteria decision-making (MCDM) model was developed for analyzing consumer adoption decisions regarding generative artificial intelligence (Gen AI). By extending the technology acceptance model (TAM) into a structured decision system, the proposed framework integrates ethical and risk-related criteria, including perceived cost, perceived risk, transparency, accountability, intellectual property concerns, and data privacy, into a formal causal and evaluative structure. First, a Delphi-based consensus process is employed to identify and refine key adoption criteria. Subsequently, the decision-making trial and evaluation laboratory (DEMATEL) method is applied to quantify causal relationships among these criteria and to construct an influence network revealing prominence and directional effects. In total, 251 questionnaires were distributed in Taiwan, and 231 valid responses were collected. The results indicated the decision-making factors that underlie the adoption of Gen AI by consumers. The results highlighted transparency as a dominant causal factor that significantly influences multiple ethical and functional dimensions of Gen AI adoption. To address uncertainty and vagueness in human judgment, fuzzy importance–performance analysis was also incorporated. Best non-fuzzy performance values were obtained through defuzzification, enabling the classification and prioritization of critical adoption factors within a four-quadrant decision matrix. The proposed framework provides a mathematically grounded decision-support model for elucidating the structural interdependencies among adoption criteria and to facilitate strategic decision making for Gen AI system design and governance. This study contributes to the MCDM and operations research literature by transforming a behavioral acceptance model into a formal decision-analytic framework, thereby enhancing the analytical rigor and applicability of TAM-based adoption studies in complex socio-technical systems. Full article

This scientometric review examines the evolution and scope of invasive (blood and tissue) and non-invasive (faeces, hair, and saliva) sampling in terrestrial mammal population genetics, with particular emphasis on the comparability of observed heterozygosity (Ho), expected heterozygosity (He), and the inbreeding coefficient (Fis) between studies published from 1985 to 2026. Searches in Web of Science and Scopus, filtered under PRISMA/PRISMA-S criteria, yielded a compendium of articles analysed with Bibliometrix and VOSviewer 1.6.20 to quantify temporal production, keyword evolution, collaborative networks, and publication outlets. Searches in Web of Science and Scopus, filtered under PRISMA/PRISMA-S criteria, yielded a broad corpus of 145 articles for general scientometric analyses, of which 85 met the eligibility criteria for the focused analysis of Ho, He, and Fis. The field shows steady growth (annual rate ≈ 6.1%), substantial authorship and international collaboration, and increasing thematic diversity. Adoption of non-invasive sampling has accelerated, broadening spatial and taxonomic coverage, but also increasing exposure to DNA degradation and genotyping error when laboratory quality control is insufficient. Across the literature, reporting of quality control practices (e.g., extraction blanks, negative PCR controls, multi-tube replication, and error-rate estimation) has improved over time but remains inconsistent. Comparisons indicate that differences in Ho, He, and Fis between invasive and non-invasive sampling are generally modest once marker system and species are taken into account. These findings indicate that quality control and transparency in reporting, rather than invasiveness per se, are the main factors determining comparability among studies. The scientometric patterns also reveal a methodological transition from microsatellites to SNP-based and reduced representation approaches, with implications for synthesis across marker types. Overall, this review identifies geographic and taxonomic biases in research effort and highlights the need for standardised reporting of DNA quality indicators, inclusion thresholds, and validation protocols to strengthen genetic monitoring in mammalian conservation. Full article

The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is one of the most widely used multi-criteria decision-making (MCDM) approaches in industrial, financial, and scientific fields. However, its sequential computational cost of O(m × n), where m denotes the number of alternatives and n the number of criteria, becomes prohibitive when decision matrices have several million rows. Despite its geometric interpretability and simplicity, classical TOPSIS faces two key computational bottlenecks at scale: (i) Euclidean distance calculations O(m × n) dominating the total cost, and (ii) the O(m × log m) sorting step, both inherently sequential and memory-bound on CPUs. To overcome these limitations, we propose GPU-TOPSIS, a fully vectorized and parallel reformulation of TOPSIS based on tensor execution on graphics processing units (GPUs), whose main contributions are: (i) a formally correct reformulation of TOPSIS as a GPU tensor pipeline preserving mathematical fidelity to the original method; (ii) a two-pass fragment-processing algorithm guaranteeing exact mathematical equivalence with monolithic TOPSIS, while reducing the memory footprint from O(m × n) to O(m_t × n), where m_t < m is the size of each independently processed fragment; (iii) three independent implementations on CuPy, PyTorch, and TensorFlow, ensuring the framework’s portability and genericity. Experimental evaluations on real data from the Amazon Products 2023 dataset, using matrices of up to 200 million alternatives (via the 2-pass formulation), demonstrate speedups of up to 4.75× compared to the reference CPU implementation (NumPy), with inter-backend score differences below 5 × 10⁻⁸ and 100% ranking overlap across all tested Top-K thresholds. A perturbation sensitivity analysis of the criteria weights and cross-backend consistency tests confirms that GPU acceleration fully preserves robustness and decision reliability, making GPU-TOPSIS a practical, open, and reproducible solution for large-scale multi-criteria decision making in Big Data environments. Full article

Achieving resilient consensus in adversarial environments often requires extending the W-MSR algorithm to multi-hop communication. While the robustness guarantees of multi-hop W-MSR are now well understood, the problem of how to determine the minimal hop radius $h^{*}$ that ensures these guarantees has remained largely unaddressed. Existing work typically assumes a fixed h, leaving practitioners without a systematic way to balance robustness requirements against communication and computational cost. This paper introduces a new hop-selection framework that identifies the smallest communication horizon capable of satisfying the robustness assumptions underlying MW-MSR consensus. The framework combines exact robustness verification—when tractable—with a hierarchy of computationally efficient proxy tests based on local feasibility, normalized algebraic connectivity, and adversary-dilution criteria. These components provide a practical and scalable mechanism for establishing $h^{*}$ in both synchronous and bounded-delay asynchronous settings. Design-time and runtime procedures, complexity analysis, and validation on IEEE 14-, 30-, and 57-bus networks demonstrate that the proposed approach reliably detects resilience thresholds and substantially improves consensus behavior under stealthy and burst-type adversaries. The results show that systematic hop selection is essential for avoiding failure at small h while preventing unnecessary communication overhead at large h. The framework thus offers an implementable and deployment-oriented strategy for resilient distributed coordination in sparse and adversarial multi-agent networks. Full article

Distance measures play a crucial role in fuzzy decision-making, pattern recognition, and uncertainty modeling. However, some existing distance measures for Complex Picture Fuzzy Sets (CPiFSs) have shown limitations and may produce counterintuitive results in certain cases. Moreover, only a few studies have explored such measures. To overcome these issues, in this study, some novel measures of distance for CPiFSs are proposed to effectively handle two-dimensional uncertainty characterized by amplitude and phase components. The proposed measures are developed by integrating both magnitude and phase information in a unified mathematical framework, ensuring improved discrimination capability and structural consistency. We rigorously prove that the suggested measures fulfill the essential properties of a distance function. Additionally, the normalization characteristics and stability behavior are analytically examined to ensure robustness in practical implementations. The proposed measure of distance is then applied to a multi-criteria decision-making (MCDM) case study, where alternatives are evaluated under Complex Picture Fuzzy information to demonstrate its practical effectiveness and ranking consistency. Using a CPiFS-based TOPSIS framework, distances from the positive and negative ideal solutions are computed via the developed metric, and the relative closeness coefficient is employed to obtain a stable and discriminative ranking of alternatives. Furthermore, comparative analysis with several existing distance measures demonstrates the stability and superiority of the proposed method in distinguishing complex fuzzy information. Full article

The management of plastic packaging waste needs to be optimized to improve recycling rates. In this article, fourteen categories of non-bottle polyethylene terephthalate (PET) packages were mechanically recycled at laboratory bench scale; the generated data were assessed using a multi-criteria decision analysis (MCDA) approach to identify the categories most suited for the mechanical recycling process from social, technical and legislative viewpoints. Recycling yields varied between 75% and 92% across the 14 categories. The intrinsic viscosity (IV) values of the produced recycled PET (rPET) corresponded to molecular weights ranging from 28,000 to 35,000 g/mol. The MCDA recyclability assessment showed that 7 of the 14 categories (accounting for 72% of the sorted products by mass flow) are often composed of multiple, inseparable materials, resulting in the lowest-quality rPET. Furthermore, only 4 categories (approximately 28% of the categories) were found suitable for closed-loop mechanical recycling. The stakeholders involved in the PET packaging value chain could use these results to support decision-making and the development of a well-organized framework to valorize even the most complex types of plastic waste. Full article

During drilling in stress-sensitive fractured formations, fracture aperture dynamically evolves with wellbore pressure fluctuations. The sealing layer often undergoes repeated cycles of sealing, destabilization, and re-sealing. Formulation selection based on a single metric or empirical selection cannot simultaneously satisfy multiple objectives, including pressure-bearing capacity, loss control, and dynamic adaptability. This study proposes an entropy-weighted TOPSIS and grey relational analysis method to optimize lost circulation formulations for stress-sensitive fractured formations. A hierarchical evaluation system is established with four criteria layers and eight indicator metrics. A baseline formulation framework is determined through static fracture sealing tests. Experimental data for different elastic-material systems are obtained using a self-developed DTDL dynamic fracture plugging apparatus. Indicator weights are objectively determined using the entropy weight method. A Grey–TOPSIS model is applied to compute grey relational closeness to the positive and negative ideal solutions, enabling formulation ranking and optimal scheme identification. A case study shows that the ternary elastic formulation with Rubber:Graphite:Net = 3:2:1 achieves the highest grey relational closeness and delivers the best overall sealing performance. The ranking remains unchanged when the distinguishing coefficient ρ varies from 0.1 to 0.9, confirming the robustness and feasibility of the proposed method. Compared with entropy-weighted TOPSIS and classical TOPSIS, the proposed method provides a more integrated treatment of the multi-metric data and better aligns the evaluation with the underlying sealing behavior in stress-sensitive fractures. Therefore, it leads to more reliable and comprehensive evaluation results for formulation selection. The results demonstrate that the proposed model provides reliable support and a methodological basis for formulation optimization in dynamic fracture loss control. Full article

This study aimed to optimize the formulation of gluten-free bread (GFB) based on rice flour (RF) and Quercus rotundifolia acorn flour (AF) by evaluating the combined effects of flour substitution (0%, 50%, and 100%) and water addition (90%, 100%, and 110%) on technological, textural, colorimetric, structural, and sensory properties. A three-level full factorial design (3²) combined with response surface methodology (RSM) was used to model and optimize product quality. The developed models showed high predictive performance (R² = 0.714–0.999; non-significant lack of fit), confirming their suitability for describing complex interactions in gluten-free systems. Water addition was the dominant factor influencing moisture, crumb structure, and textural softness, while AF mainly affected color, structure, and sensory attributes. Increasing acorn content significantly decreased lightness (L*) and increased redness (a*) and darkness index (DI), reflecting higher phenolic compound content and more intense Maillard reactions. Specific volume (1.85–2.41 cm³/g) was maximized at higher hydration levels, especially when combined with intermediate to high acorn substitution, indicating a synergistic interaction between fiber-rich flour and water availability. Texture analysis showed that AF increased hardness and reduced cohesiveness, while water addition significantly improved softness, elasticity, and overall mouthfeel. Image analysis of crumb structure demonstrated that higher hydration promoted larger pore size and porosity, whereas AF increased cell density, resulting in a finer crumb structure under low hydration conditions. Sensory evaluation confirmed that breads with high acorn content were well accepted due to their characteristic nutty flavor. Multi-response desirability optimization yielded an optimal formulation with approximately 83% AF and 108% water, representing the best achievable compromise among the evaluated quality criteria. The results demonstrate that AF can serve as a key functional ingredient in GFB, provided that hydration is carefully adjusted. This study highlights the effectiveness of RSM combined with image-based analysis as a robust approach for developing high-quality gluten-free bakery products. Full article