Reliable regional climate projections are essential for adaptation in climate-sensitive regions such as Odisha, India. This study develops an integrated framework combining Expert Team on Climate Change Detection and Indices (ETCCDI)-based extremes, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Monte Carlo sensitivity analysis to evaluate and optimise 35 bias-corrected Global Climate Models (GCMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). The optimised Multi-Model Ensemble (MME) reproduces historical temperature and precipitation patterns (1985-2014) with modest bias and strong seasonal agreement, and is applied to projections under SSP2-4.5 and SSP5-8.5 (2015-2100). Results indicate progressive warming: maximum and minimum temperatures increase by ~ 1.7°C under SSP2-4.5 and ~ 3.3-3.4°C under SSP5-8.5 by late century, with summer days (SU) rising by ~ 20-46days and tropical nights (TR) by up to ~ 112days. The extreme temperature range (ETR) widens significantly under SSP5-8.5. Precipitation increases are largest during winter and post-monsoon seasons, while monsoon-season changes remain small and slightly negative under SSP2-4.5. Very wet (R95pTOT) and extremely wet (R99pTOT) precipitation increase by ~ 3% and ~ 2% under SSP2-4.5, and by ~ 5% and ~ 4% under SSP5-8.5, indicating a growing contribution of extreme rainfall events to annual precipitation totals. Northern and coastal Odisha show higher precipitation increases, while inland regions experience stronger warming and enhanced heat stress. The framework provides a transparent and reproducible basis for regional climate assessment.

Injection molding of complex three-dimensional curved components is highly sensitive to processing conditions, particularly when dimensional accuracy and packing quality must be balanced simultaneously. This study proposes an integrated optimization and decision-making framework to improve the molding quality of a commercial curved shin guard plate (SGP) by combining Response Surface Methodology (RSM), adaptive non-dominated sorting genetic algorithm II (NSGA-II), and entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). A Box-Behnken design was employed to investigate the effects of packing pressure (PP), melt temperature (MT), and cooling time (CT) on absolute dimensional deviation (ΔD) and molded part weight (PW). The results showed that the developed quadratic response surface models exhibited good predictive capability, and analysis of variance identified PP as the most influential parameter. The surrogate models were integrated with standard and adaptive NSGA-II algorithms to perform multi-objective optimization and generate well-distributed Pareto-optimal solutions, with the adaptive NSGA-II showing improved Pareto-front exploration capability based on crowding-distance and hypervolume analyses. Entropy-weighted TOPSIS was subsequently applied to identify the most suitable compromise solution. Experimental validation was conducted at the TOPSIS-selected optimum together with two additional Pareto-optimal conditions. The validation results showed satisfactory agreement between predicted and experimental responses, with deviations remaining below 7.41% for ΔD and below 1.70% for PW. These results demonstrate the practical applicability of the proposed optimization framework for complex injection-molded components under industrial manufacturing conditions.

Backgroundβ-thalassemia poses a considerable public health burden in Bangladesh, where a high carrier frequency underlies widespread disease risk. It is necessary to distinguish β-thalassemia trait (βTT) and iron deficiency anemia (IDA) to ensure genetic counseling and enable effective prevention strategies. Despite the availability of various discriminant formulas and machine learning algorithms (MLAs), their comparative diagnostic performance within the Bangladeshi population has not been comprehensively investigated. This study aimed to assess different discriminant formulas and ML models as well as to propose novel combinations of formulas for population-specific screening of βTT.MethodsIn this cross-sectional study, we compared 47 discriminant formulas and 12 machine learning models to distinguish β-thalassemia trait from iron-deficiency anemia in 467 individuals (143 βTT, 324 anemia) drawn from a 2,514-participant cohort. DF-6 and DF-27 were two new formulas constructed by integrating high-performing formulas. Multi-criteria decision-making (MCDM) techniques, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and SECA (Simultaneous Evaluation of Criteria and Alternatives), provided the final ranking for performance. Cluster analysis was performed to identify groups with similar diagnostic performance.ResultsPopulation-specific optimal cut-off values were determined for the discriminant formulas. The newly proposed formulas, DF-6 and DF-27, ranked among the top ten performers alongside RBC, Janel (11T), Ravanbakhsh-F1, Srivastav, Alparslan, Hisham, Index 26, and Kerman I. DF-6 (AUC: 0.9707) achieved the best overall performance across the diagnostic metrics. DF-6 achieved the best overall performance (AUC: 0.98, 95% CI: 0.97–0.99, p < 0.0001). Assessment of ML models revealed that XGBoost (XGB) (AUC: 0.98, 95% CI: 0.97–0.99, p < 0.0001) and Support Vector Machine (SVM) (AUC: 0.97, 95% CI: 0.95–0.99, p < 0.0001) provided the highest diagnostic accuracy. The reliability of ensemble ML models was confirmed by MCDM and cluster analyses.ConclusionsThe combination of novel discriminant formula DF-6 and integration of XGB and SVM ML models can substantially strengthen nationwide screening programs to reduce the burden of thalassemia in Bangladesh.

Integrating Multiple Outcomes of First-Line Immunotherapy-Based Regimens for Advanced Hepatocellular Carcinoma: A Network Meta-Analysis of Phase III Trials.

Quantitative sustainability assessment and application of remediation technologies for heavy metal-contaminated sites based on multi-criteria decision-making method

Unveiling regional disparities in China's photovoltaic development capability through a multidimensional assessment framework

This study develops a hybrid multi-criteria decision-making model that incorporates the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess depositor protection outcomes in a sample of commercial banks. A total of six criteria is taken into account: Right to Information, Confidentiality and Data Protection, Deposit Insurance Effectiveness, Transparency of Banking Terms, Complaint Resolution Mechanism, and Strength of Regulatory Supervision. The results of the AHP show the greatest influence on the Deposit Insurance Effectiveness criterion (0.30), followed by Right to Information (0.24) and Transparency of Banking Terms (0.16), while the Complaint Resolution Mechanism has the lowest weight (0.07). The consistency ratio of 0.00372 strongly supports the consistency of the expert judgments, indicating high reliability. The TOPSIS analysis has determined Bank B3 as the most performing, with a closeness coefficient of 1.00, followed by B4 (0.5708), B1 (0.4413), and B2 (0.0678). Sensitivity and visualization analyses strengthen the results and underscore the prevailing importance of depositor assurance and transparency. Indeed, the protection of savings depositors is a fundamental requirement for ensuring financial stability, particularly in banking systems undergoing rapid digital and regulatory transformation. The suggested framework provides a methodological, evidence-based method of evaluating legal protection frameworks and evidence-based financial policy choices. Further studies can build upon this methodology with real-world data, intercountry comparisons, and more sophisticated hybrid models to make it more applicable in dynamic financial settings.

Driven by the dual-carbon goals, the high-proportion integration of distributed renewable energy into distribution networks poses significant challenges to operational flexibility due to the inherent intermittency and uncertainty of renewable sources. While direct control of flexible resources is possible, it often entails high costs and lacks mechanisms to incentivize proactive participation. This paper investigates the flexible optimal operation of distribution networks with the active participation of aggregated user-side flexible resources. A two-layer day-ahead optimization framework is proposed. At the lower layer, user-side flexible resource participants employ a deep learning-based intelligent decision-making model to formulate their clearing strategies rapidly, eliminating the need for detailed physical models and iterative calculations. At the upper layer, the distribution network operator (DNO) establishes a multi-objective optimization model that simultaneously minimizes comprehensive operational costs and the net load fluctuation rate to enhance flexibility. The model coordinates distributed generation, energy storage, and user-side resources via a time-of-use pricing mechanism. The fast non-dominated sorting genetic algorithm (NSGA-II) is adopted to obtain the Pareto-optimal set, from which the optimal solution is selected using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Case studies on a modified IEEE 33-bus distribution system demonstrate that the proposed method effectively guides the demand response of user-side resources. The results confirm significant improvements in the economic operation of the distribution network, along with enhanced flexibility evidenced by increased net load adequacy and a reduced net load fluctuation rate, thereby improving the system’s accommodation capability for renewable energy.

Halide double perovskite materials have been used for various applications; their bandgap (Eg) and heat of formation (ΔHf) are their key properties. They can be obtained through calculations based on high-throughput density functional theory (DFT), but such calculations are computationally expensive and time-consuming. Machine learning (ML) has proved to be an effective tool for screening potential materials. The prediction accuracy of ML models strongly depends on both input features and ML algorithms. However, there is no unified feature set with which ML models can effectively distinguish halide double perovskite materials. Although it has been proven that stacking ML models can achieve higher prediction accuracy than individual ML models, little attention has been paid to the optimization of stacking models. To solve these problems, we constructed a new feature set obtained from periodic tables for predicting the Eg and ΔHf of halide double perovskites, and we further proposed a method integrating the nondominated sorting genetic algorithm (NSGA-II) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making tool for stacking model optimization to predict the Eg and ΔHf of 540 compounds of halide double perovskites. Experimental results from 40 runs of 5-fold cross-validation demonstrate that our proposed new feature set enables ML models to achieve better performance than the original feature set. Moreover, the stacking model optimized by our proposed method yields better predicting performance than that of any individual single model and stacking regression models without optimization, with average improvements of 5.02%, 2.70%, 3.72% and 0.28% in MSE, RMSE, MAE and R2, respectively, in Eg prediction, thus providing more effective guidance for screening potential compounds for solar cells from a large quantity of materials.

In response to the challenges of handling linguistic uncertainty in multi-criteria group decision-making (MCGDM), this paper introduces a novel decision framework based on linguistic q-rung orthopair fuzzy (Lq-ROF) sets. The motivations from the need to systematically address ambiguity and inconsistency in linguistic evaluations provided by decision-makers. To this end, the study develops three main methodological contributions. Firstly, a normalized bidirectional projection measure (NBDP) and its weighted extension (WNBDP) are proposed to resolve ranking inconsistencies in linguistic environments. Secondly, an axiomatized knowledge entropy measure for Lq-ROF information is established, enabling fine-grained differentiation among linguistic assessments and facilitating dynamic expert weighting. Thirdly, a non-linear programming model is formulated to objectively derive both attribute and expert weights by integrating bidirectional projection with generalized entropy principles. The proposed framework is rigorously evaluated through comparative studies against established methods, including aggregation-based techniques, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Evaluation based on Distance from Average Solution (EDAS), and Complex Proportional Assessment (COPRAS). Results validate the robustness and theoretical advantages of the approach, confirming its effectiveness in quantifying linguistic uncertainty and delivering consistent decision support in complex MCGDM contexts.

This paper presents an integrated decision-support framework for selection of healthcare treatment based on the use of Neutrosophic Logic, Dempster-Shafer Theory (DST), and Interval-Valued Fuzzy Sets (IVFS) in an extended Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) structure. Healthcare decision-making often involves incomplete clinical data, conflicting expert opinions, and context-dependent variability, which are not adequately addressed by conventional MCDM approaches. Existing methods exhibit key limitations: classical models assume precise inputs, fuzzy models capture vagueness but not indeterminacy, and existing neutrosophic approaches lack a mechanism for resolving inter-expert conflict prior to ranking. The proposed framework addresses these gaps through a sequential uncertainty-handling process in which neutrosophic logic models truth, indeterminacy, and falsity, IVFS captures variability via interval-valued representations, and DST performs evidence-theoretic fusion to reconcile conflicting expert inputs before ranking. To overcome these problems, the proposed framework is used to transform neutrosophic evaluations into fuzzy representations by using interval-valued representations, which liberalizes the treatment of uncertainty. The DST systematically integrates expert judgment to support structured evidence fusion while avoiding premature consensus. The extended TOPSIS method is subsequently applied to generate treatment rankings using belief-weighted neutrosophic scores. The framework is evaluated using sensitivity analysis and Monte Carlo simulation, where variations in criterion weights and stochastic perturbations representing expert variability are introduced to assess ranking stability under uncertainty. The framework is applied to an illustrative numerical evaluation in a healthcare setting, where treatment options are assessed in terms of efficacy, adverse effects, cost, recovery period, and patient satisfaction. Sensitivity analysis and Monte Carlo simulations are employed to validate the approach, demonstrating its robustness and stability under varying weighting schemes and expert opinion perturbations. Results from a synthetic illustrative scenario indicate stable and interpretable rankings under weight perturbations and stochastic noise, suggesting robustness while requiring further validation with real clinical data. The proposed approach provides an uncertainty-aware decision-support tool for clinicians, administrators, and policymakers, offering interpretable treatment prioritization while remaining scalable for complex healthcare environments.

Based on the rectifying conduction principle of the Tesla valve, a self-pumping hydrodynamic mechanical seal with Tesla valve-shaped face grooves was proposed, and its corresponding computational model was established. Numerical simulations were conducted to investigate the effects of the Tesla valve diversion angle and valve clearance on the sealing performance of the proposed structure. Taking the leakage rate and liquid film stiffness as the target performance indices, a predictive model was developed by combining uniform experimental design with multiple regression analysis. Subsequently, the NSGA-II (Non-dominated Sorting Genetic Algorithm II) genetic algorithm was employed for bi-objective optimization to obtain the Pareto-optimal solution set, and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was further applied to identify the optimal combination of structural parameters under specified weighting coefficients. The results indicate that the leakage rate is not significantly affected by variations in the diversion angle or valve clearance, whereas the liquid film stiffness increases with increasing diversion angle and decreases with increasing valve clearance. Multi-objective optimization successfully identified an optimal parameter combination that improves the overall sealing performance of the proposed structure. This study provides a novel perspective and theoretical basis for innovation in face structure and for the performance optimization of self-pumping mechanical seals.

Locust outbreaks are major biological disturbances in grassland ecosystems of arid and semi-arid regions. Accurate identification of locust suitable habitats is important for regional monitoring and management. However, direct comparisons between multi-criteria analysis (MCA) and species distribution models (SDMs) under a unified framework remain limited. In this study, we compared these two approaches for dominant locust species in Xinjiang, China, including Calliptamus italicus, Gomphocerus sibiricus, and Locusta migratoria manilensis. We used the same environmental variables and occurrence records for all models. The MCA methods included the analytic hierarchy process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), and ordered weighted averaging (OWA). The SDMs included the generalized linear model (GLM), maximum entropy model (MaxEnt), extreme gradient boosting (XGBoost), and an ensemble model. The results showed that SDMs had higher area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) values than MCA under the internal point-based evaluation framework, although both approaches effectively identified locust-suitable habitats. The two approaches also showed high spatial agreement in moderately and highly suitable habitats, with Jaccard indices of 0.88-0.92, and consistently identified the northern slopes of the Tianshan Mountains, the Ili River Valley, and the margins of the Junggar Basin as core suitable areas. These results indicate that the two approaches are complementary for locust monitoring and management.

Water quality monitoring networks require strategic resource allocation to maximize effectiveness while managing budget constraints. This study presents an integrated multi-criteria decision analysis (MCDA) framework combining CRITIC (CRiteria Importance Through Intercriteria Correlation), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), and linear programming (LP) optimization for prioritizing monitoring stations and allocating resources. Using real data from 347 monitoring stations in California extracted from the United States Environmental Protection Agency (EPA) Water Quality Portal (WQP) spanning January 2023 through December 2024 (16,750 measurements across seven water quality parameters), we demonstrate a reproducible methodology for evidence-based monitoring network optimization. Prior to analysis, physical plausibility filters removed 1106 impossible values (2.4% of raw data), ensuring analytical integrity. Dissolved oxygen was treated as an optimal-range criterion (|DO - 8mg/L|) rather than a monotonic benefit, correcting a common misclassification in MCDA water quality studies. Spearman's rank correlation replaced Pearson's throughout the CRITIC computation to account for skewed parameter distributions. The CRITIC method determined objective criteria weights, with dissolved oxygen deviation (0.1813) and phosphorus (0.1804) as the most informative parameters. TOPSIS analysis identified top-performing stations with closeness coefficients ranging from 0.9813 to 0.3799, with a mean of 0.712 (MAD = 0.063). VIKOR analysis confirmed ranking consistency, yielding Spearman ( < 0.001) between TOPSIS and VIKOR rankings. LP optimization concentrated resources efficiently, achieving 36.9% improvement over uniform allocation ( vs. under uniform distribution). This integrated MCDA-optimization framework provides water resource managers with a transparent, data-driven tool for strategic planning, enabling efficient allocation of limited monitoring resources while maintaining comprehensive environmental surveillance.

Anti-nerve growth factor (NGF) monoclonal antibodies (mAbs) have emerged as a promising new class of analgesics, offering potential benefits in managing particular painful musculoskeletal (MSK) conditions. However, their long-term safety remains uncertain, leading to regulatory non-approval of these agents. This study aims to evaluate the efficacy and safety of individual anti-NGF mAbs compared to other analgesics when treating chronic MSK pain. Our literature search included PubMed, Scopus, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov through April 25th, 2025. Articles eligible for inclusion were randomized controlled trials (RCTs) comparing one of the human anti-NGF mAbs to other interventions in adults with chronic MSK pain. Primary outcomes evaluated were changes from baseline in pain, physical function, and patient global assessment (PGA) scores, as well as risks of adjudicated arthropathies (AAs) and abnormal peripheral sensation (APS). We used the Cochrane Risk of Bias 2 (RoB-2) tool to assess risk of bias. Pairwise and network meta-analyses were performed using random-effects models. Treatments were ranked using the cumulative ranking curve (SUCRA), and a multi-criteria decision analysis with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied to integrate all efficacy and safety outcomes. Statistical analyses were conducted in R (v4.3.1) using the meta, netmeta, gemtc, and Multi-Criteria Decision Aiding (MCDA) packages. A total of 29 studies, involving 27,747 patients with osteoarthritis or chronic low back pain, were included in this analysis. Compared to placebo, fasinumab showed the highest improvements in pain (standardized mean difference [SMD] - 0.40, 95% CI [- 0.52, - 0.29], p < 0.001) and physical function (SMD - 0.42, 95% CI [- 0.53, - 0.31], p < 0.001), followed by tanezumab (pain: SMD - 0.36, 95% CI [- 0.44, - 0.28], p < 0.001; function: SMD - 0.39, 95% CI [- 0.47, - 0.31], p < 0.001). For safety, both fasinumab and tanezumab demonstrated a significant risk for AAs (risk ratio [RR] 4.7, 95% CI [3.61, 6.13], p < 0.001; and RR 3.84, 95% CI [2.07, 7.14], p < 0.001, respectively) and APS (RR 1.99, 95% CI [1.49, 2.65], p < 0.001; and RR 2.46, 95% CI [1.93, 3.14], p < 0.001, respectively) relative to placebo. While fulranumab was less effective (pain: SMD - 0.25, 95% CI [- 0.42, - 0.07], p < 0.01; function: SMD - 0.25, 95% CI [- 0.43, - 0.07], p < 0.01), it showed better overall safety against placebo relative to both agents, demonstrating a significant risk only for APS events (RR 1.78, 95% CI [1.09, 2.92], p < 0.05). Anti-NGF mAbs, particularly fasinumab and tanezumab, are associated with the greatest levels of pain relief and functional improvement over placebo within this analysis. However, these benefits are counterbalanced by significant risks of joint-related adverse events. Implementation of strict safety protocols is essential when considering these agents for further evaluation. PROSPERO ID: CRD420251104612.

AI-driven co-tunneler: Automated excavation parameter selection using information entropy-assisted multi-objective optimization

This work integrates Bayesian regime detection with conditional neural processes for 24-hour electricity price forecasting in the German, French, and Norwegian markets. Regimes are inferred via a disentangled sticky hierarchical Dirichlet process hidden Markov model (DS-HDP-HMM). For each regime, an independent conditional neural process (CNP) learns localized mappings from input contexts to 24-dimensional hourly price trajectories; final forecasts are produced as regime-weighted mixtures of the regime-specific CNP outputs. Temporal robustness and cross-market generalization are evaluated on Germany (2021–2023) and on France and Norway (2023). We benchmark against deep neural networks (DNN), the Lasso estimated autoregressive (LEAR) model, extreme gradient boosting (XGBoost), Bayesian long short-term memory (BLSTM), and the temporal fusion transformer (TFT), and assess downstream value through battery storage optimization. Results indicate that the proposed regime-aware CNP often delivers higher profits or lower costs, while DNN can be exceptionally competitive in specific cost-minimization settings. Because point accuracy does not necessarily translate into operational optimality, we apply the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to aggregate forecasting and operational criteria. TOPSIS ranks the CNP as the leading model for 2023 and, overall, as the most balanced and consistently preferred solution across the considered markets.

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

The choice of appropriate nano additives for tribological purposes has many factors and requirements to be met. These include: lower friction; better wear properties; formation of protective tribofilms; good dispersibility in lubricant; and good thermal oxidation stability. In this paper, an MCDM (Multi-Criteria Decision Making) framework is presented for evaluating/ranking different types of nano additives used in the tribological field. Specifically, MoS₂, WS₂, MoO₃, TiO₂, Al₂O₃, SiO₂, and glycerol were evaluated. To select the best nano additive, a comparison was made with each other based on how close they came to the "ideal" solution. This comparison was done using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). A decision matrix was prepared from published experimental data. Then, after normalizing each criterion and assigning weights to each one, the results were ranked. Finally, to validate the rankings obtained from the TOPSIS analysis, a second decision-making technique called SAW (Simple Additive Weighting) was applied. The rankings from both decision-making techniques were consistent and confirmed the effectiveness of the multi-criteria decision-making approach. From the TOPSIS analysis, it could be concluded that MoS₂ achieved the greatest value for closeness to the "ideal" solution which indicates that MoS₂ exhibited the most favorable behavior when considering all criteria. Additionally, it appears that MoO₃ and WS₂ will serve as possible candidates for use in future studies or as substitutes in certain applications where high temperatures and/or heavy loads exist.

ABSTRACT Historical streets are the core carriers of urban cultural identity, and their long-term existence not only depends on the protection of material heritage, but also on whether they can achieve a dynamic balance between inheriting historical culture, adapting to contemporary functional needs, and promoting social and economic vitality. The current evaluation practice often falls into two misconceptions: equating “street vitality” (i.e. human activity intensity) with overall health, or using fragmented indicators, is difficult to systematically diagnose the comprehensive sustainability of the street. This study proposes a comprehensive evaluation system based on the “Time-Space-People” theoretical framework, aimed at scientifically measuring the comprehensive sustainable status of historical streets. Taking nine representative historical streets in Beijing as case studies, it integrates Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWN) for combined weighting, and uses Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to complete comprehensive evaluation. The results showed that Qianmen Street had the best overall condition, while Liulichang Cultural Street had the weakest. The overall performance of commercial streets is better. This study provides urban managers with operational and multidimensional diagnostic tools to assist in developing refined renewal strategies that balance protection and revitalization.