Search Results (1,543)

Pharmaceutical cold chains operate under tightly coupled compliance, operational, and sustainability requirements, yet managers still face a practical challenge when deciding which improvement initiatives should be implemented first under limited resources and uncertain expert judgment. This study develops an integrated multi-criteria decision framework to prioritize pharmaceutical cold chain improvement initiatives by combining fuzzy DEMATEL and fuzzy TOPSIS. Thirteen evaluation criteria were derived from the literature and organized into three clusters covering risk, operational performance, and sustainability, while nine implementable initiatives were evaluated by a cross-national panel of pharmaceutical cold chain experts. Fuzzy DEMATEL was used to model causal interdependencies among the criteria and to derive structurally informed weights, and fuzzy TOPSIS was then applied to rank the initiatives. The results show that monitoring reliability, handling and process compliance, deviation management capability, and traceability event quality act as the main upstream drivers in the system. In the resulting prioritization, handling procedure redesign and targeted training, followed by formal excursion management, emerged as the highest priority initiatives. Packaging qualification, monitoring governance, and interoperable event capture formed the next tier. Sensitivity analysis showed that the leading priorities remained stable under plausible weight changes, supporting the robustness of the proposed framework. This study moves beyond method combination by linking expert perceived interdependencies among pharmaceutical cold chain risk, performance, and sustainability criteria to a sequenced portfolio of implementable initiatives. It contributes a theory-informed and operationally interpretable prioritization framework while recognizing that the inferred influence structure reflects structured expert judgement rather than externally validated operational causality. Full article

Navigation lock manifolds are key components of filling-and-emptying systems, and port-flow distribution affects chamber flow stability and filling efficiency. Under unsteady filling conditions, port-flow distribution is governed by discharge variation and manifold geometry, making rapid prediction and engineering-constrained screening challenging. This study develops a surrogate-assisted prediction and Pareto-screening framework for a large-scale navigation lock manifold. Three-dimensional computational fluid dynamics (CFD) simulations were used to examine unsteady port-flow evolution. The peak-flow condition was selected as a representative control condition, and the flow non-uniformity coefficient α and system resistance coefficient ξ were used as performance indicators. Based on 243 parametric CFD samples and 144 independent external test samples, artificial neural network (ANN), Gaussian process regression (GPR), and support vector regression (SVR) models were evaluated. ANN performed best, with independent-test R² values of 0.9999 and 0.9928 for α and ξ. Feature-attribution analysis identified port width, culvert height, and port number as dominant variables. Pareto screening within a predefined engineering design space identified representative candidates with CFD verification errors below 1.1%. The TOPSIS-based candidate reduced ξ by 32.2% while maintaining α nearly unchanged. Full article

With the continuous expansion of government investment in transportation infrastructure, transportation investment has increased, and bridge engineering has flourished. However, safety accidents frequently occur during the construction stage, so the safety situation of bridge construction units remains unfavorable. Bridge construction is a high-risk occupational activity because operations are performed under complex site conditions, variable geological and hydrological environments, long construction periods, and frequent interaction among workers, machinery, materials, technologies, and surrounding environments. Existing bridge construction safety assessment methods have improved hazard identification and risk prioritization; however, many still have difficulty representing uncertainty, hesitation, and subjective judgment in expert-based occupational safety evaluation. To address this problem, this study proposes an integrated Pythagorean fuzzy TOPSIS decision-support framework for occupational safety risk prioritization in bridge construction projects. A safety risk indicator system is established from five dimensions: human, management, material, technical, and environmental factors. Expert judgments are expressed using linguistic variables and converted into Pythagorean fuzzy numbers. The Pythagorean fuzzy weighted average operator is used to aggregate multi-expert evaluation information, and criterion importance is determined from expert-based Pythagorean fuzzy assessments. The model is applied to a bridge reconstruction project, followed by sensitivity analysis and comparison with several existing methods. The results indicate that low work quality, non-standard construction, construction site environment, lack of safety awareness, and insufficient construction technology are the most critical occupational safety risk indicators. The proposed method provides a practical decision-support tool for identifying priority safety risks under fuzzy and uncertain evaluation conditions. Full article

Regulated deficit irrigation (RDI) is an important water-saving strategy in arid regions. To quantify the effects of RDI on foxtail millet yield and water use efficiency and determine an optimal RDI strategy, a three-year field trial was carried out over dry, normal, and wet rainfall years in the Loess Plateau. Full irrigation throughout the whole growth period served as the control, whereas mild, moderate, and severe deficit irrigation treatments were conducted at the jointing–booting stage, heading–flowering stage, and across the whole growing period, respectively. The results indicate that the effects of RDI on foxtail millet yield varied with crop growth stage and deficit severity. During the heading–flowering stage, mild RDI showed statistically similar grain yield and WUE relative to those under full irrigation. In normal and wet years, moderate and severe RDI had no statistically significant effects on grain yield and WUE. Additionally, moderate and severe RDI significantly improved irrigation water use efficiency by 19.94–28.50% and 34.35–47.72%, respectively. The primary reason is that RDI at this stage maintained root development and led to only limited suppression of plant growth. In contrast, moderate and severe RDI at the jointing–booting stage or throughout the whole growth period significantly inhibited root establishment and plant development, reduced dry matter accumulation, and consequently led to substantial yield losses. The inhibitory effect became more pronounced with increasing deficit severity. Specifically, severe RDI at the jointing–booting stage and throughout the entire growth period significantly reduced yield by 19.35–54.98% and 31.47–100%, respectively. Furthermore, to identify the optimal RDI regime adaptable to variable rainfall years, a multi-model comprehensive evaluation system based on yield and WUE was established by integrating three individual evaluation models, including the membership function method, TOPSIS, and grey relational analysis, with the Fuzzy–Borda combined evaluation model. The result showed that the heading–flowering stage is the critical period for implementing RDI in foxtail millet in the Loess Plateau. Mild RDI during this stage is preferred because it maintains stable yield and WUE while substantially reducing irrigation amount over various rainfall years. Additionally, moderate and severe RDI can also maintain stable yield while significantly improving irrigation water use efficiency in normal and wet years. Full article

The increasing complexity of marine spatial management and the rapid growth of nautical tourism require the use of formal and transparent decision-making models. Identifying optimal locations for nautical anchorages is a multi-criteria decision problem (MCDP) in which navigation safety, spatial constraints, and environmental protection often conflict. This study presents an integrated framework combining Geographic Information Systems (GIS) and multi-criteria decision-making (MCDM) methods for the systematic evaluation and ranking of nautical anchorages. As a case study, 86 potential locations in Split-Dalmatia County, Croatia, were analysed based on 18 criteria encompassing hydrological, meteorological, and spatial factors, as well as risk factors relevant to navigation safety. The methodological approach applies six MCDM methods implemented in the R programming language: Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), ViseKriterijska Optimizacija I Kompromisno Rjesenje (VIKOR), Multi-Objective Optimisation on the Basis of Ratio Analysis (MOORA), Complex Proportional Assessment (COPRAS), Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), and Evaluation Based on Distance from Average Solution (EDAS). To reduce methodological bias, a final Consensus rank was calculated to synthesise the results of all applied methods. The stability of the obtained ranking was examined through an analysis of rank agreement between methods, using a diagonal matrix of rank overlaps and the corresponding heatmap visualisation. The results indicate a high level of consistency among individual MCDM methods and strong stability of the final consensus ranking. The proposed model ranks locations from best to worst based on how well they meet the established criteria, while ensuring strict navigational safety and compliance with environmental constraints. These findings confirm that the integrated GIS–MCDM approach is a reliable, repeatable, and scientifically grounded tool for supporting spatial planning and concession allocation in the development of nautical infrastructure. Full article

Online travel website reviews contain abundant hotel attribute information. Nevertheless, evaluations of such hotel attributes are not completely objective, as they are affected by subjective perceptions from different types of travelers. This study explores the correlations of attribute perceptions across different traveler groups and optimizes hotel recommendation models from the perspective of attribute correlation. Hotel attributes are analyzed based on online reviews collected from TripAdvisor, and the TOPSIS method is used to identify the core attributes. The SentiWordNet sentiment lexicon and PolarityRank algorithm are utilized to calculate attribute sentiment values, which are then applied to OLS regression analysis to explore the relationships between attributes. Finally, a hotel recommendation model based on the collaborative filtering algorithm is constructed, incorporating traveler types and attribute correlation. The research results show core attributes for different types of travelers, verify the existence of attribute perception correlation, and demonstrate that a model considering attribute correlation enhances recommendation accuracy, with an accuracy improvement of 3.16 percentage points. Full article

In the leveling process of ultra-high-strength steel plates, sample scarcity—driven by high prototyping costs and small-batch production—coupled with a narrow and unevenly distributed feasible region due to high yield-to-tensile ratios and limited ductility, impedes the balanced optimization of plate shape quality and energy consumption. To address this issue, this paper develops an optimization framework for the balanced trade-off between these two objectives. First, a high-precision response surface model based on Box–Behnken experimental design and finite element simulation was constructed using initial wave height, entry roll reduction, exit roll reduction, and leveling speed as key process parameters; peak residual stress difference (characterizing potential sheet quality) and leveling energy consumption as co-optimization objectives; and post-leveling flatness as a constraint. Next, by introducing the NSGA-II multi-objective genetic algorithm, the Pareto optimal solution set for the quality and energy efficiency objectives was obtained, clearly revealing the trade-off relationship between the two; furthermore, the TOPSIS decision-making method was employed to select the comprehensive optimal process scheme that achieves a balance between quality and energy efficiency from the Pareto solution set. An adaptive recommendation curve for the leveling process parameters of MS1500 ultra-high-strength steel plates was established, covering an initial wave height range of 10.5–14.6 mm, thereby enabling intelligent parameter matching based on different incoming material conditions. Finally, industrial validation demonstrated that this optimized scheme significantly reduced leveling energy consumption while ensuring that post-leveling flatness meets the high-quality requirement of less than 3.5 mm·m⁻¹. This achieves a balanced optimization of quality and energy efficiency. This study provides a reliable theoretical basis and practical engineering solution for the efficient and environmentally friendly leveling production of ultra-high-strength steel. Full article

Industrial energy transition has moved from pilot deployment to system integration, where renewable supply must be assessed together with process fit, organisational maturity, and uncertainty. This study proposes a risk-aware assessment framework integrating ISO 50001 energy-management maturity, ISO 31000 risk-management logic, a digital-twin-ready operational architecture, scenario simulation, Conditional Value-at-Risk (CVaR), and multi-criteria decision analysis. The study does not report a live plant-level digital twin or empirical survey validation. Instead, it specifies a five-layer implementation architecture, uses a synthetic survey-like dataset solely to demonstrate parameter recovery, and applies 350 Monte Carlo replications to an industrial energy hub comprising photovoltaic and wind generation, battery storage, and optional Power-to-H₂-to-Power storage. The quantitative workflow is reported with explicit equations, input assumptions, random seed, CVaR estimator, TOPSIS weights, and weight-sensitivity analysis. Under the adopted assumptions, the PV–wind–battery configuration achieved the lowest mean cost and CVaR, whereas hydrogen storage substantially reduced curtailment but increased mean cost and tail risk without materially reducing grid purchases. These results are conditional on the stated model assumptions and should not be generalised as empirical evidence. The framework supports structured investment and operational assessment by linking technical performance, organisational readiness, and cost–risk–decarbonisation trade-offs. Full article

Existing residential buildings constructed under earlier thermal-design standards often lack effective external solar control systems. Building envelope retrofits must extend beyond mere cooling load reductions; instead, they require a holistic evaluation of summer heat rejection, winter solar gain preservation, transmitted solar exposure, and retrofit-induced embodied carbon. This study develops a screening-level method for climate-adaptive passive shading retrofits. The workflow integrates hourly solar-position reconstruction, facade irradiance mapping, shading geometry interception, and a reduced-order 2R2C thermal network. NSGA-II is used to generate Pareto-optimal alternatives, CV-TOPSIS is applied to identify representative trade-off solutions, and a life-cycle-informed carbon payback check within an A1–A4 + B6 boundary is used to test whether operational carbon savings can offset the upfront carbon of shading components and glazing replacement. Five Chinese cities—Haikou, Shanghai, Beijing, Lhasa, and Urumqi—are selected to represent the transition from cooling- to heating-dominated climates. For comparative screening, the reduced-order model shows acceptable agreement with an EnergyPlus benchmark, with NMBE, CV(RMSE), and $R^2$ values of +2.11%, 28.25%, and 0.804, respectively. The selected solutions reveal strong climate dependence in both shading morphology and carbon performance. For instance, Haikou exhibits the largest annual electricity savings (2030.3 kWh/yr) and the shortest Carbon Payback Period (1.8 years). In Lhasa, by contrast, the CV-TOPSIS-selected compromise scheme reduces the transmitted solar exposure proxy but increases annual energy use by 706.1 kWh/yr, indicating that this selected compromise, rather than fixed shading in general, is not carbon-effective within the defined boundary. The proposed method supports climate-specific retrofit screening by jointly considering heating–cooling balance, solar radiation conditions, and regional grid carbon intensity. Full article

Establishing an equitable, evidence-based mechanism for allocating flood prevention funding is critical to mitigating the risk of flash floods. However, existing research seldom accounts for the multi-dimensional nature of vulnerability or achieves an appropriate balance between efficiency and equity. To address this gap, we propose the Multi-dimensional Vulnerability-based Flood Disaster Fund Allocation Optimization Model (MD-FAOM), which integrates the coupling effects of exposure, sensitivity, adaptive capacity, and equity into allocation strategies using the NSGA-II algorithm, TOPSIS method, and geographical detectors. The model prioritizes funding for ecologically targeted flood prevention. We apply this framework to southern Tibet to derive optimal fund allocations and quantitatively assess the resulting benefits. Our results show that areas characterized by negative vulnerability account for 25.22% of the study region, mainly concentrated in Lhasa and Shannan. Under equivalent conditions, MD-FAOM delivers benefits across an area of 85,915 km², achieving an improvement rate of 30.11%. These findings demonstrate that integrating vulnerability science with distributive equity can optimize the allocation of limited resources, thereby enhancing both flood resilience and ecosystem conservation. This approach advances ecohydrological disaster management and supports the achievement of Sustainable Development Goals (SDGs) 13 (Climate Action) and 15 (Life on Land). Full article

Forest ecological product value realization (FEPVR) seeks to convert forest ecosystem services into identifiable, accountable, compensable, tradable, and financeable value returns through institutional and market arrangements. Existing studies have mainly emphasized aggregate evaluation or conversion efficiency, with less attention to the structural relationship between ecological supply capacity and value-capture capacity. This study develops a dual-dimensional framework of use-value realization (UVR) and exchange-value realization (EVR), and constructs a city-level panel of 147 policy-practice sample cities (prefecture-level and above) in China over 2019–2023. An entropy-weighted composite index and an entropy-weighted TOPSIS model are applied to measure FEPVR mechanism development, structural configurations, and relative closeness to the sample-defined ideal state. The results show that the mean composite score increased from 0.194 in 2019 to 0.337 in 2023, while the coefficient of variation declined from 0.561 to 0.398, indicating overall improvement and narrowing intercity disparities. Global Moran’s I remains positive and significant throughout the study period, indicating significant and positive spatial autocorrelation in FEPVR mechanism development. The UVR–EVR decomposition reveals substantial structural divergence: the HH, HL, LH, and LL configurations include 29, 33, 25, and 60 cities, respectively. TOPSIS results further show that EVR relative closeness increased markedly, whereas UVR relative closeness declined slightly, indicating that institutional and market-based value-capture capacity expanded faster than the ecological supply base. Robustness checks suggest that the rise in EVR is strongly associated with institutional-entry indicators, and should therefore be interpreted as the expansion of value-capture instruments rather than direct evidence of realized market performance or ecological improvement. The findings provide a descriptive evaluation of FEPVR mechanism development in cities with documented policy or practice foundations, and should not be generalized as the average condition of all Chinese cities. Full article

The dust extraction impeller is a core component of the vehicle engine auxiliary system that filters dust from the intake air to ensure stable engine operation; its reliability directly affects the performance and operational safety of the vehicle. Critically, the dust extraction impeller can exhibit severe erosion wear in extreme environments, but conventional degradation testing methods are costly and require considerable time to complete. Therefore, this study conducted accelerated degradation testing using the change in impeller blade thickness as the degradation indicator and the dust concentration and impeller rotational speed as dual elevated stress factors to obtain time-series degradation data from 48 blade samples. Linear, exponential, power-law, natural logarithmic, and Gompertz models were subsequently fit to the data for a single sample, and then the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was employed to select the optimal degradation trajectory model. The accuracy of the selected linear model was verified using the data from all samples, confirming that it can be applied to predict the degradation of the dust extraction impeller over time. The contribution of this study comprises the establishment of a degradation assessment framework combining accelerated degradation testing with TOPSIS-based model selection to provide a practical basis for the reliability design and maintenance planning of vehicle dust extraction impellers operating in extreme environments. Full article

This study develops an interval-valued q-spherical fuzzy rough set TOPSIS framework (IVq-SFRS-TOPSIS) for multi-criteria group decision-making when expert judgments contain interval uncertainty, neutrality, and granular indiscernibility. The revised framework clarifies the relationship between interval-valued q-spherical and interval-valued T-spherical fuzzy models, defines admissible approximation operators over compatible domains, and introduces a radial projection step that guarantees closure under the IVq-SFN constraint whenever component-wise extrema would otherwise violate it. The proposed framework provides a mathematically balanced representation of interval-valued q-spherical fuzzy information, reflecting the concept of symmetry and supporting reliable group decision-making under uncertainty. The TOPSIS procedure is then formulated through expert aggregation, benefit–cost normalization, entropy-based criteria weighting, ideal-solution distance calculation, and closeness-coefficient ranking. The method is illustrated through a sustainable smart city development case using four AI-based alternatives and six criteria. Rather than claiming unconditional superiority, the revised comparative and sensitivity analyses examine how the ranking changes under alternative fuzzy decision models, different q values, perturbations to criteria weights, and perturbations to the decision matrix. The results indicate that the proposed framework provides an interpretable rough-boundary representation and a reproducible ranking mechanism for complex MCDM problems under interval-valued q-spherical uncertainty. Full article

Deep-sea transportation is faced with complex navigation environments, long voyages, limited emergency response resources, and interacting safety risks. Existing studies have mainly focused on individual risk factors, while the correlations and coupling effects among different factors have received insufficient attention. To identify the key risk factors affecting deep-sea transportation safety, this paper proposes a novel key factor identification model based on complex network theory. Firstly, 34 risk factors affecting deep-sea transportation safety are selected from five aspects using a literature analysis method. Secondly, a weighted directed network of risk factors is constructed based on complex network theory. Then, to evaluate the node importance, six node importance evaluation indicators are established, and a node importance evaluation method is proposed by integrating the analytic hierarchy process (AHP), technique for order preference by similarity to an ideal solution (TOPSIS), and gray relational analysis (GRA). Key risk factors are then determined according to the node importance evaluation results. Finally, the effectiveness of the proposed model is verified through a case study. The results show that the top five most critical risk factors are risk of leakage, emergency speed, physical and chemical properties of the cargoes, sense of personnel safety duty, and seasonal route. The findings can provide practical support for maritime authorities, shipping companies, and safety managers in formulating targeted prevention, control, and emergency response measures for deep-sea transportation safety. Full article

Underground coal gasification (UCG) requires rigorous geological suitability evaluation to reduce project risks, and scientific site selection is critical for success. Taking the No. 15 coal seam in the Yushe–Wuxiang Block (Qinshui Basin) as the focus, this study evaluates the feasibility of deep UCG using a multi-criteria decision-making framework. A hierarchical evaluation model comprising four primary and 10 secondary geological indicators (e.g., coal thickness, parting coefficient, fault fractal dimension, roof lithology) was constructed. Subjective weights were derived from the Analytic Hierarchy Process (AHP) and combined with objective weights from the coefficient of variation method. The TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method was then applied to rank seven development units. Results indicate that the No. 15 coal seam has reasonable potential for UCG implementation. The most favorable areas (Blocks II and VII) are characterized by thick coal seams (>5 m), low parting coefficients (<8%), simple fault networks (fractal dimension ≤0.5–1.05), and competent mudstone roofs. Blocks III, V, and VI are moderately favorable, while Blocks I and IV are marginally favorable. These findings provide a prioritized roadmap for pilot-scale UCG testing in the Yushe–Wuxiang Block. Full article