Search Results (131)

Underwater imaging suffers from significant degradation due to scattering by suspended particles, selective absorption by the medium, and depth-dependent noise, leading to issues such as contrast reduction, color distortion, and blurring. Existing enhancement methods typically address only one aspect of these problems, relying on unrealistic assumptions of uniform noise, and fail to jointly handle the spatially heterogeneous noise and spectral channel attenuation. To address these challenges, we propose the variational-based spatial–spectral joint enhancement method (VSJE). This method is based on the physical principles of underwater optical imaging and constructs a depth-aware noise heterogeneity model to accurately capture the differences in noise intensity between near and far regions. Additionally, we propose a channel-sensitive adaptive regularization mechanism based on multidimensional statistics to accommodate the spectral attenuation characteristics of the red, green, and blue channels. A unified variational energy function is then formulated to integrate noise suppression, data fidelity, and color consistency constraints within a collaborative optimization framework, where the depth-aware noise model and channel-sensitive regularization serve as the core adaptive components of the variational formulation. This design enables the joint restoration of multidimensional degradation in underwater images by leveraging the variational framework’s capability to balance multiple enhancement objectives in a mathematically rigorous manner. Experimental results using the UIEBD-VAL dataset demonstrate that VSJE achieves a URanker score of 2.4651 and a UICM score of 9.0740, representing a 30.9% improvement over the state-of-the-art method GDCP in the URanker metric—a key indicator for evaluating the overall visual quality of underwater images. VSJE exhibits superior performance in metrics related to color uniformity (UICM), perceptual quality (CNNIQA, PAQ2PIQ), and overall visual ranking (URanker). Full article

The primary objective of this study is to bridge the gap between descriptive geometry and mechanistic design by establishing a dual-domain fractal framework to analyze the internal architecture of asphalt mixtures. This research quantitatively assesses the sensitivity of volumetric indicators—namely air voids (VV), voids in mineral aggregate (VMA), and voids filled with asphalt (VFA)—by employing the coarse aggregate fractal dimension (D_c), the fine aggregate fractal dimension (D_f), and the coarse-to-fine ratio (k) through Grey Relational Analysis (GRA). The findings demonstrate that whereas D_f and k substantially influence macro-volumetric parameters, the mesoscopic void fractal dimension (D_V) remains structurally unchanged, indicating that gradation predominantly dictates void volume rather than geometric intricacy. Sensitivity rankings create a prevailing hierarchy: Process Control (Compaction) > Skeleton Regulation (D_c) > Phase Filling (Pb) > Gradation Adjustment (k, D_f). D_c is recognized as the principal regulator of VMA, while binder content (Pb) governs VFA. A “Robust Design” methodology is suggested, emphasizing D_c to stabilize the mineral framework and reduce sensitivity to construction variations. A comparative investigation reveals that the optimized gradation (OG) achieves a more stable volumetric condition and enhanced mechanical performance relative to conventional empirical gradations. Specifically, the OG group demonstrated a substantial 112% enhancement in dynamic stability (2617 times/mm compared to 1230 times/mm) and a 75% increase in average film thickness (AFT), while ensuring consistent moisture and low-temperature resistance. In conclusion, this study transforms asphalt mixture design from empirical trial-and-error to a precision-engineered methodology, providing a robust instrument for optimizing the long-term durability of pavements in extreme cold and arid environments. Full article

In complex decision-making scenarios, such as smart city renewable energy project selection, decision-makers must contend with multi-dimensional uncertainty, conflicting expert opinions, and evolving temporal dynamics. This study introduces a novel Fractional q-Rung Orthopair Fuzzy Tensor (Fq-ROFT)-based group decision-making methodology that integrates the flexibility of q-rung orthopair fuzzy sets with tensorial representation and fractional-order dynamics. The proposed framework allows for the modeling of positive and negative membership degrees in a multi-dimensional, time-dependent structure while capturing memory effects inherent in expert evaluations. A detailed case study involving six renewable energy alternatives and six criteria demonstrates the method’s ability to aggregate expert opinions, compute fractional dynamic scores, and provide robust, reliable rankings. Comparative analysis with existing approaches, including classical q-ROFSs, intuitionistic fuzzy sets, and weighted sum methods, highlights the superior discriminative power, consistency, and dynamic sensitivity of the Fq-ROFT approach. Sensitivity analysis confirms the robustness of the top-ranked alternatives under variations in expert weights and fractional orders and membership perturbations. The study concludes by discussing the advantages, limitations, and future research directions of the proposed methodology, establishing Fq-ROFT as a powerful tool for dynamic, high-dimensional, and uncertain group decision-making applications. Full article

Biaxially stretched polytetrafluoroethylene (PTFE) membranes are promising media for high-efficiency air filtration because of their stable node–fiber microstructure and environmental durability. To clarify how resin properties and microstructure govern filtration behavior, ten PTFE resins with different average molecular weights (Mn) and particle size characteristics were processed into membranes under essentially identical biaxial stretching and sintering conditions. Resin particle size, fiber diameter and pore size distributions were quantified, and coefficients of variation (CVs), together with Spearman rank correlations, were used to analyze material–structure–performance links. Filtration efficiency, pressure drop and quality factor (QF) were measured according to ISO 29463-3 using 0.1–0.3 μm aerosols. Higher Mn combined with lower particle-size dispersion favored finer fibers and narrower pores, yielding efficiencies close to 100%, but increased pressure drop and slightly reduced QF, indicating a trade-off between efficiency and flow resistance. The sample with the lowest Mn in its group and a high machine-direction draw ratio (12×), showed pronounced fibril breakage, node coalescence, broadened pore-size distribution and degraded QF, illustrating the sensitivity of structure and performance to resin-process mismatch. Overall, the study establishes a hierarchical material–fiber–pore–performance relationship that can guide resin selection, structural tuning and process optimization of biaxially stretched PTFE membranes. Full article

As global efforts to achieve the Sustainable Development Goals (SDGs) enter a critical phase, there is a growing need for analytical tools that reflect the complexity and heterogeneity of development pathways. This study introduces a probabilistic classification framework designed to uncover latent typologies of national performance across the seventeen Sustainable Development Goals. Unlike traditional ranking systems or composite indices, the proposed method uses raw, standardised goal-level indicators and accounts for both structural variation and classification uncertainty. The model integrates a Bayesian decision tree with penalised spline regressions and includes regional covariates to capture context-sensitive dynamics. Based on publicly available global datasets covering more than 150 countries, the analysis identifies three distinct development profiles: structurally vulnerable systems, transitional configurations, and consolidated performers. Posterior probabilities enable soft classification, highlighting ambiguous or hybrid country profiles that do not fit neatly into a single category. Results reveal both monotonic and non-monotonic indicator behaviours, including saturation effects in infrastructure-related goals and paradoxical patterns in climate performance. This typology-sensitive approach provides a transparent and interpretable alternative to aggregated indices, supporting more differentiated and evidence-based sustainability assessments. The findings provide a practical basis for tailoring national strategies to structural conditions and the multidimensional nature of sustainable development. Full article

University rankings based on scientometric indicators frequently rely on compensatory aggregation models that allow extreme values to dominate the evaluation, while also remaining sensitive to outliers and unstable weighting procedures. These issues reduce the reliability and interpretability of the resulting rankings. This study proposes a hybrid BWM–GRA–PROMETHEE (BGP) framework that combines judgement-based weighting Best-Worst Method (BWM), outlier-resistant normalization Grey Relational Analysis (GRA), and a non-compensatory outranking method Preference Ranking Organization Methods for Enrichment Evaluation (PROMETHEE II). The framework is applied to an expert-validated set of scientometric indicators to generate more stable and behaviorally grounded rankings. The results show that the proposed method maintains stability under weight and threshold variations and preserves ranking consistency even under outlier-contaminated scenarios. Comparative experiments further demonstrate that BGP is more robust than Additive Ratio Assesment (ARAS), Multi-Attributive Border Approximation Area Comparison (MABAC), and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), achieving the highest Spearman. This study contributes a unified evaluation framework that jointly addresses three major methodological challenges in scientometric ranking, outlier sensitivity, compensatory effects, and instability from data-dependent weighting. By resolving these issues within a single integrated model, the proposed BGP approach offers a more reliable and methodologically rigorous foundation for researchers and policymakers seeking to evaluate and enhance research performance. Full article

This paper presents new aggregation operators for $p,q,r$-fractional fuzzy sets based on the Frank t-norm and t-conorm. We introduce the $p,q,r$-fractional fuzzy Frank weighted average and $p,q,r$-fractional fuzzy Frank weighted geometric operators and discuss their algebraic properties, including closure, boundedness, idempotency, and monotonicity. Based on new operations, we develop a multi-criteria group decision-making framework that integrates the evaluations of multiple experts via the proposed Frank operators and ranks the alternatives under $p,q,r$-fractional fuzzy information. The model is applied to a cryptocurrency stability assessment problem, where four coins are evaluated with respect to six criteria. The results show that both aggregation operators yield consistent rankings with good discriminatory power among the alternatives. A sensitivity analysis is conducted to check the stability of the model under parameter variations. A comparative study further demonstrates the compatibility and advantages of the proposed method over several existing decision-making approaches. The proposed framework is well suited to decision-making scenarios in which multiple experts’ opinions must be integrated within a complex fuzzy information environment. Full article

Modern smart cities face increasing pressure to invest in sustainable and reliable energy systems while navigating uncertainties arising from fluctuating market conditions, evolving technology landscapes, and diverse expert opinions. Traditional multi-criteria decision-making (MCDM) approaches often fail to fully represent these uncertainties as they typically rely on crisp inputs, lack temporal memory, and do not explicitly account for stochastic variability. To address these limitations, this study introduces a novel Stochastic Fractional Fuzzy Tensor (SFFT)-based Group Decision-Making framework. The proposed approach integrates three dimensions of uncertainty within a unified mathematical structure: fuzzy representation of subjective expert assessments, fractional temporal operators (Caputo derivative, $\alpha =0.85$) to model the influence of historical evaluations, and stochastic diffusion terms ($\sigma =0.05$) to capture real-world volatility. A complete decision algorithm is developed and applied to a realistic smart city renewable energy selection problem involving six alternatives and six criteria evaluated by three experts. The SFFT-based evaluation identified Geothermal Energy as the optimal choice with a score of 0.798, followed by Offshore Wind (0.722) and Waste-to-Hydrogen (0.713). Comparative evaluation against benchmark MCDM methods—TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VIšekriterijumsko KOmpromisno Rangiranje), and WSM (Weighted Sum Model)—demonstrates that the SFFT approach yields more robust and stable rankings, particularly under uncertainty and model perturbations. Extensive sensitivity analysis confirms high resilience of the top-ranked alternative, with Geothermal retaining the first position in 82.4% of 5000 Monte Carlo simulations under simultaneous variations in weights, memory parameter ($\alpha \in [0.25,0.95]$), and noise intensity ($\sigma \in [0.01,0.10]$). This research provides a realistic, mathematically grounded, and decision-maker-friendly tool for strategic planning in uncertain, dynamic urban environments, with strong potential for deployment in wider engineering, management, and policy applications. Full article

Sugarcane production in China is severely constrained by frequent seasonal droughts, especially in the major planting region of Guangxi. Identifying drought-resistant varieties is crucial for ensuring yield stability. This study aimed to comprehensively evaluate the drought resistance of 15 sugarcane varieties and screen key identification indicators. A pot experiment was conducted with both well-watered (control) and drought-stress treatments. Fifteen agronomic and physiological traits were measured, and drought resistance was assessed using the comprehensive drought resistance evaluation value (D value), the comprehensive drought resistance coefficient (CDC), and the weighted drought resistance coefficient (WDC). Results showed significant variations in trait responses to drought: green leaf number (NGL) decreased the most (66.06%), while proline (Pro) increased the most (88.09%). PCA reduced 15 traits to 5 principal components, with a cumulative variance contribution rate of 82.26%. Comprehensive evaluation using D values, comprehensive drought resistance coefficients (CDCs), and weighted drought resistance coefficients (WDCs) showed consistent overall drought resistance rankings, with slight differences in individual varieties. Cluster analysis based on D values classified the 15 varieties into three groups: 10 drought-resistant (66.67%, e.g., YZ08-1609, LT5), 3 moderately drought-resistant (e.g., GT08-56), and 2 drought-sensitive (GT10-612, ZT13-012). Grey relational analysis identified single stalk weight (SSW), number of leaves (NL), and number of green leaves (NGL) as key indicators closely associated with drought resistance. This study provides a scientific basis for establishing a drought-resistant sugarcane variety evaluation system and lays the foundation for breeding drought-resistant varieties. Full article

This study aims to establish a method-integrative framework for emotion-oriented architectural image generation. The framework combines Stable Diffusion with targeted LoRA (Low-Rank Adaptation), a lightweight and parameter-efficient fine-tuning approach, together with ControlNet-based structural constraints, to examine how controllable design-element manipulations influence emotional responses. The methodology follows a closed-loop “generation–evaluation” workflow, with each LoRA module independently targeting a single design element. Guided by the relaxation–arousal emotional dimension, the framework is evaluated using subjective ratings and electroencephalogram (EEG) measures. Twenty-seven participants viewed six architectural space categories, each comprising four conditions (baseline, color, material, and form modification). EEG α/β power ratio (RAB) served as the primary neurophysiological marker of arousal. Statistical analysis indicated that LoRA-based modifications of design elements produced distinct emotional responses: color and material changes induced lower arousal, whereas changes in form elicited a bidirectional pattern involving relaxation and arousal. The right parietal P4 electrode site showed the most sensitive emotional response to design element changes, with consistent statistical significance. P4 is a human scalp EEG location associated with cortical activity related to visuospatial processing. Descriptive results suggested opposite directional effects with similar intensity trends; however, linear mixed-effects model (LMM) inference did not support significant group-level linear coupling, indicating individual variation. This study demonstrates the feasibility of emotion-guided architectural image generation, showing that controlled manipulation of color, material, and form can elicit measurable emotional responses in human brain activity. The findings provide a methodological basis for future multimodal, adaptive generative systems and offer a quantitative pathway for investigating the relationship between emotional states and architectural design elements. Full article

Singlet oxygen is a type of reactive oxygen species that is typically generated via type II photosensitization reactions. Since 1,3-diphenylisobenzofuran (DPBF), a commonly used chromogenic probe, exhibits peak absorbance at 410 nm for singlet oxygen detection, it severely interferes with blue light irradiation and compounds that absorb in this wavelength region. This study investigated developing and validating a fluorescence-based method using DPBF to quantitatively analyze the type II photosensitizing property of riboflavin (RF) and its heterocyclic flavin derivatives. DPBF fluorescence-based analysis provided more sensitive and practical results than traditional colorimetric methods. It effectively overcomes spectral interference from colored photosensitizers, such as RF and its derivatives, under blue light irradiation (λ peak 447 nm). This method permitted more effective measurement of their activity without interference from their intrinsic color and maintained high linearity and low variation across different sample concentrations, even with short irradiation times. The type II photosensitizing potency of the tested compounds under blue light was consistently ranked as follows: RF > flavin mononucleotide > flavin adenine dinucleotide > lumiflavin > lumichrome. The results suggest that the DPBF fluorescence-based assay is a more effective approach than colorimetric analysis, making it a practical and reproducible tool for assessing the type II photosensitizing properties of diverse compounds. This study also provides a refinement of an existing probe-based assay for relative comparisons under visible light conditions. Full article

This study selects typical existing office buildings in Zhengzhou, a region with a cold climate, as the research object and conducts a comparative analysis of the influencing factors of cooling energy consumption in west-facing and south-facing office spaces. A multi-stage sensitivity analysis methodology integrating global and local sensitivity methods is systematically applied to evaluate 13 key parameters across four categories: building morphology, envelope structure, shading measures, and active design strategies. Five parameters are consistently ranked among the top seven most sensitive parameters for both west- and south-facing orientations: the infiltration rate, the window-to-wall ratio, the cooling setpoint temperature, the number of shading boards, and building width. Two parameters exhibit orientation-specific differences, namely lighting power density and the external wall heat transfer coefficient in west-facing spaces, whereas shading board width and the external window heat transfer coefficient play a greater role in south-facing spaces. Local sensitivity analysis further reveals that within the parameter variation range, the five parameters with higher energy-saving rates for both orientations are air tightness, the window-to-wall ratio, the cooling setpoint temperature, the number of horizontal shading boards, and horizontal shading board width. By increasing the cooling setpoint temperature, south-facing spaces can achieve an energy-saving rate of 25.32%, which is significantly higher than the 21.77% achieved by west-facing spaces. Horizontal shading board width exhibits the most pronounced orientation difference, with south-facing spaces achieving an energy-saving rate of 16.69%, while west-facing spaces only reach 2.97%. The research findings offer quantitative scientific evidence for formulating targeted energy-saving retrofit strategies for existing office buildings in cold climate regions, thereby contributing to the meticulous development of building energy efficiency technologies. Full article

Retinal blood vessel tortuosity is a promising early biomarker for diseases such as diabetic retinopathy. However, the lack of a standardized evaluation method hinders its clinical application. This study presents a framework with 50 features, including 32 developed and refined from our prior unpublished work. All features were tested for sensitivity and scaling to ensure robust performance. To address the influence of blood vessels’ representation on tortuosity estimation, we tested several resampling approaches and proposed the 1-Equidistant Pixel Sampling method (1EPS), which demonstrated accuracy and approximate scale invariance. The framework was evaluated on a public retinal tortuosity dataset, RET-TORT, consisting of 30 arteries and 30 veins ranked in increased tortuosity. Data augmentation expanded the dataset to 330 arteries and veins for improved reliability. Spearman’s rank correlation coefficient analysis revealed resampling variations in tortuosity estimation, with our method outperforming literature and most features favoring arteries. Using the augmented dataset, Gaussian Process Regression achieved near-perfect performance ($R^2$ = 1.0 for arteries; 0.999 for veins). Feature selection analysis identified artery- and vein-specific features. This work highlights the importance of accurate vessel preprocessing and feature sensitivity to scaling on tortuosity estimation and introduces a scalable, robust framework of 50 hand-crafted features for clinical tortuosity assessment. Full article

Environmentally friendly (green) stock investment has evolved into a global trend over the past few decades, including in the Indonesian capital market. However, the process of selecting sustainability-oriented stocks involves various complex criteria that are often qualitative, subjective, and uncertain. Therefore, an analytical tool is needed to support the decision-making process more adaptively and objectively. This study proposes the Criteria Importance Through Inter-criteria Correlation–Weighted Fuzzy Soft Set (CRITIC-WFSS) integration model, a decision-making method that combines WFSS with the objective, data-driven weighting mechanism of the CRITIC method. In the proposed model, parameter weights are determined by considering data variation (standard deviation) and inter-criteria correlation, ensuring that more discriminative and informative parameters receive higher weights. The model was applied to data on environmentally friendly stocks in the SRI-KEHATI Index, obtained from the Indonesia Stock Exchange (IDX) official website, to evaluate and identify stocks with optimal performance. The model’s performance is evaluated through a comparative study with the AHP-WFSS and Entropy–WFSS methods, complemented by a sensitivity analysis. The results show that UNVR ranked highest with a perfect score of 1, indicating an optimal balance between financial performance and sustainability. Furthermore, a comparative study demonstrated that CRITIC-WFSS can generate rankings that are more reliable, appropriate, and logical than those generated by two comparison methods. Meanwhile, the results of the sensitivity analysis indicate that the CRITIC-WFSS model demonstrates strong robustness to variations in input parameters, ensuring stable rankings. The model shows significant potential to support more accurate and transparent investment decision-making by generating consistent stock rankings based on a balanced integration of financial, and sustainability (environmental, social, and governance (ESG)) aspects. This research was conducted in order to support the achievement of various goals through SDG 8 (Decent Work and Economic Growth). Full article

Sustainable urban water governance in rapidly transforming cities requires integrative decision-making frameworks capable of balancing social equity, economic efficiency, and environmental resilience. This study develops an Integrative Decision-Making Framework (IDMF) for optimizing urban water policy in Yerevan, Armenia, built upon AI- and GIS-assisted diagnostics and incorporating a Governance Readiness Index (GRI) together with spatial hotspot overlay analysis. The framework employs an AHP–TOPSIS multi-criteria structure to evaluate five policy alternatives—leakage reduction, demand-side management, decentralized reuse, green–blue infrastructure, and data-driven governance—based on normalized quantitative indicators across social, economic, and ecological domains. Results show that Leakage Reduction (A1) and Data-Driven Governance (A5) consistently rank as the top-performing strategies across both baseline and sensitivity scenarios, while equity-prioritized weightings enhance social outcomes without significantly compromising economic performance. The approach also demonstrates robustness under ±10–20% weight variations. Acknowledging limitations related to data availability and expert-based judgments, the study outlines the minimum governance and data-readiness conditions required for transferability. The IDMF thus advances decision-support science in urban water management by integrating governance feasibility with spatial diagnostics and provides adaptable guidance for mid-income cities facing institutional and environmental constraints. Full article