Assessing coastal marine pollution monitoring structures using a combined AHP-TOPSIS decision model.

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

Identifying suitable habitat shifts and climate refugia under climate change is essential for the conservation of freshwater fish, particularly in mountainous river systems. However, there is a lack of comprehensive evaluation frameworks to quantitatively assess the degree to which regional fish assemblages are affected. In addition, asymmetric habitat shift patterns in fish populations of mountainous rivers remain insufficiently explored. To address these gaps, we developed a climate-responsive evaluation framework—CR-TOPSIS—based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and applied it to assess current and future habitat changes for the Top 15 climate-sensitive species and other key protected species in the Qinba mountainous region, integrating environmental DNA surveys and targeted traditional capture, cross-validated against regional checklists and expert review with MaxEnt modeling. Projections were made under two climate scenarios (SSP126 and SSP585) for the period 2070–2100. Results indicated pronounced asymmetric habitat shift patterns, with leading-edge expansion consistently exceeding trailing-edge contraction across climate scenarios. Species distribution centroids exhibited a clear northward shift, averaging 1.76 km under SSP126 and 2.64 km under SSP585, reflecting enhanced redistribution under stronger warming. Coldwater and bottom-dwelling species experienced disproportionate habitat loss under high-emission scenarios, whereas eurythermal and pelagic-spawning species showed comparatively higher adaptive potential. Core climate refugia, defined by 100% spatial overlap across scenarios, covered approximately 0.51 × 10³ km² and were primarily concentrated along the midstream Hanjiang River and its tributaries, remaining stable under both climate pathways. This study demonstrates the utility of integrating molecular monitoring and species distribution models to detect climate-sensitive shifts, evaluate species vulnerability and conservation prioritization in montane freshwater ecosystems. • Developed a CR-TOPSIS framework to evaluate climate sensitivity of freshwater fish species. • Integrated eDNA data and MaxEnt modeling to predict future habitat suitability under SSP126 and SSP585. • Revealed consistent asymmetric habitat shifts, with leading-edge expansion exceeding trailing-edge retreat. • Identified coldwater and benthic species as highly vulnerable to warming and hydrological change. • Mapped climate refugia concentrated in the midstream Hanjiang River, providing conservation targets.

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

This study assessed the perceptions and management strategy choices of maize farmers in Ghana towards fall armyworm (FAW) infestation, which poses a severe threat to their livelihoods, and examined the factors influencing farmers' adoption of FAW management strategies. All respondents demonstrated high awareness of FAW, with 98% reporting devastating effects on their farms. FAW affected an average of 30% of maize farmland, with farmers losing an estimated 536 kg ha-1, representing approximately 27% of expected maize yield. A significant knowledge gap persists in effective control measures, as indicated by a mean perception index of 0.21, reflecting farmers' limited understanding of effective FAW management strategies. Relative to biological/botanical and traditional pest management strategies, farmers largely perceive synthetic pesticides to be the ideal solution, with 42% strongly agreeing and 25% agreeing with this view. Factors significantly influencing control measure choice included age, gender, household size, labour, household head status, farm size, farm experience and non-farm income. The most pressing challenge with FAW management was difficulty in accessing synthetic control measures. Pest control practices vary significantly across agroecological zones, suggesting the need for region-specific strategies. Despite high awareness of FAW, farmers lacked adequate knowledge of effective control strategies, with many applying pesticides based on hearsay rather than expert recommendations. These findings underscore the need for targeted training programs focused on improving farmers' understanding and adoption of integrated, sustainable FAW management practices, while addressing the misguided perception that synthetic pesticides are inherently superior. © 2026 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

A quantifiable grading standard for Codonopsis Radix based on the multidimensional quality evaluation system of phenotype, chemistry, and bioactivity.

Leakages and breaks in water distribution networks (WDNs) cause significant water losses and pose health risks due to pathogen intrusion. The Water Safety Plan (WSP), developed by the World Health Organization (WHO), provides a comprehensive framework for identifying, assessing, and controlling risks within water supply systems. This study demonstrates the application of the WSP framework through a case study of a WDN in Sweden. Pipe break probabilities were estimated using three classification models: Logistic regression, random forest, and extreme gradient boosting (XGBoost), while hydraulic and health consequences were evaluated using hydraulic modelling and Quantitative Microbial Risk Assessment (QMRA) to quantify the overall health risk. A Multi-Criteria Decision Analysis (MCDA) approach, specifically the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was utilized to prioritize risk mitigation strategies through breakage and leakage control measures. The proposed approach integrates predictive modelling, consequence evaluation, and decision analysis, offering a structured method for water utilities in prioritizing interventions and improving the overall safety and reliability of WDNs.

In healthcare, protecting patient privacy is crucial due to the sensitivity of medical data and its extensive accessibility. Federated Learning (FL) offers a decentralized and privacy-preserving training paradigm, making it an ideal solution for healthcare applications. A critical challenge in healthcare is that real-world medical data often exhibits long-tailed distributions in local and global views. Existing methods addressing long-tailed FL problem typically assume that the model will be evaluated on uniform test data distribution. However, Practical test data in healthcare systems is often agnostic and unpredictable, leading to potential model failures in realworld scenarios. In this paper, we introduce a novel task termed Test-Agnostic Long-Tailed Federated Learning and propose GTAFL, a comprehensive framework to address this challenge. During the training stage, GTAFL employs adaptive re-sampling, expert classifier retraining, and selfsupervised learning to correct biased classifiers and distorted feature spaces caused by long-tailed training distributions. During the inference stage, an ensemble mechanism combines retrained expert classifiers to handle test data with unknown distributions. Extensive experiments on CIFAR10 and two medical datasets manifest that our framework outperforms other state-of-the art methods.

Model selection in data science involves evaluating multiple alternatives across conflicting criteria under uncertainty, where existing fuzzy multi criteria group decision making (MCGDM) approaches often fail to capture asymmetric uncertainty and nonlinear interactions in expert judgments. To address this limitation, this study proposes a novel MCGDM framework based on fractional orthopair fuzzy sets (FOFS). The FOFS structure enables flexible and precise modeling of uncertainty by allowing independent fractional control of membership degree (MD) and non-membership degree (NMD). Furthermore, sine trigonometric aggregation operators are introduced to capture nonlinear relationships and fluctuations in expert evaluations. An integrated FOFS-TOPSIS method is then developed to rank candidate models based on their distances from positive ideal solution (PIS) and negative ideal solution (NIS). The applicability of the framework is demonstrated through a numerical study involving fifteen predictive models, fifteen evaluation criteria, and three experts. The results indicate that Alternative [Formula: see text] achieved the highest overall ranking, followed by [Formula: see text], while mid and lower ranked models revealed trade-offs in accuracy, computational efficiency, and robustness. Comparative and sensitivity analyses confirm the framework's robustness, stability, and superior ranking performance.

Stability of nanosuspensions in drug delivery: mechanisms, characterization strategies, and advanced stabilization approaches.

Investigation and optimisation of tribological performance for AZ31 Mg hybrid composite under the effect of cryogenic treatment

Efficient and selective CO2 capture represents a crucial technological challenge for carbon emission mitigation in post-combustion processes. This study demonstrates a dual-strategy approach combining high surface area engineering with post-synthetic functionalization (sulfonation and nitration) that breaks the traditional trade-off between adsorption capacity and selectivity in porous polymers for CO2 capture, thereby simultaneously enhancing CO2 adsorption capacity and CO2/N2 selectivity. We synthesized a hyper-cross-linked polymer (HCP-TPB) using triphenylbenzene (TPB) as a rigid building block and dibromomethane as a cross-linker, achieving exceptional textural properties (BET surface area: 2738 m2 g-1) and CO2 uptake (21.3 wt% at 273 K). Through post-synthetic sulfonation and nitration, the polymer framework was deliberately engineered to deliver three notable performance improvements: (1) increased CO2 capacity to 23.7 wt% for HCP-TPB-SO3H and 23.3 wt% for HCP-TPB-NO2 at 273 K, (despite reduced surface area (1796 and 1564 m2 g-1) respectively); (2) enhanced CO2/N2 selectivity (from 16 for the pristine HCP-TPB to 32 and 42 for HCP-TPB-SO3H and HCP-TPB-NO2 at 273 K), and (3) improved Ideal Adsorption Solution Theory (IAST)-predicted selectivity (14→22→35) for 15:85 CO2/N2 mixtures at 298 K. These results establish an effective structure-property relationship between sulfonic and nitro functionalities and gas separation performance.

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.

Since the Industrial Revolution, the increasing emissions of greenhouse gases have posed unprecedented challenges to sustainable human development. As one of the most vital terrestrial ecosystems, farmland ecosystems play an irreplaceable role in balancing carbon emissions and absorption, attracting growing scholarly attention. Taking Jiangsu Province, one of China's major grain-producing regions, as the study area, this research integrates the Slacks-Based Measure (SBM) model, the entropy-weighted method, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to analyze the spatiotemporal evolution of farmland carbon effects-including carbon emissions, carbon absorption, and net carbon sequestration-during 2011-2021. Furthermore, a Grey Prediction Model was employed to forecast the carbon effects of 13 cities over the next 12 years. The results show that Jiangsu's farmland carbon emission efficiency exhibited an overall upward trend with fluctuations, with an average value of 0.76. The multi-year mean fitting degrees of resource input and agricultural output were relatively low, at 0.426 and 0.358, respectively, with substantial intercity differences. The average coupling coordination degree between resource input and agricultural output was 0.66, indicating a primary coordination state. The constructed GM (1,1) model achieved a qualification rate exceeding 73.80%, demonstrating its reliability for predicting farmland carbon effects. Forecasts suggest a potential weakening of the province's agricultural carbon sink effect, with the net carbon sequestration in 2033 expected to decline by 15.55% compared with the maximum value during the observation period. This study reveals the spatiotemporal characteristics and potential evolution patterns of farmland carbon effects, providing theoretical support for region-specific agricultural emission reduction policies and promoting the sustainable development of efficient, low-carbon agriculture.

The healing of deep wounds is severely impeded by the converging pathophysiology of persistent inflammation, oxidative stress, bacterial infection, and many other factors that necessitate an ideal solution that simultaneously targets multiple barriers to accelerate the healing of wounds. Here, we report SCLP, a multifunctional, dynamically cross-linked hierarchical hydrogel network synthesised through the strategic integration of a lab-synthesised spermine-gellan gum conjugate (S), chondroitin sulfate (C), LAPONITE®-polyethyleneimine (L-PEI) nanohybrids, and the plant-derived polyphenol protocatechualdehyde (P). The materials chemistry of SCLP relies on stable amide conjugation, polyelectrolyte complexation, and reversible Schiff-base bridging to mimic the extracellular matrix (ECM) while providing superior tissue adhesiveness and shear-thinning injectability. Physicochemical evaluations indicate robust cross-linking, an interconnected porous framework, improved mechanical reliability, and controlled biodegradation. Furthermore, the hydrogel exhibits synergistic antioxidant, anti-inflammatory, and potent antibacterial properties through the rational assembly of bioactive moieties. In vitro studies show steady exudate absorption, hemocompatibility, enhanced fibroblast viability, and the inhibition of bacterial biofilms. In vivo full-thickness wound models display accelerated wound closure and significant granulation tissue formation, without organ toxicity or adverse skin reactions. Collectively, SCLP offers a next-generation bioactive platform that simultaneously addresses the multifaceted barriers to wound healing while outperforming conventional dressings and monofunctional materials, thereby setting a new standard in effective wound care.

Diverse impacts of different green landscapes on soil biogeochemistry and multifunctionality in an urban watershed.

The rapid development of the Internet of Things (IoT) urgently demands high-performance and process-compatible integrated micro-power sources. All-solid-state thin-film batteries (ATFBs), which combine an all-solid-state architecture with on-chip integration capability, are regarded as an ideal on-chip power solution. However, their practical application is constrained by the low capacity of conventional cathode materials and the high-temperature annealing process (>500°C) required for crystallization, which is incompatible with temperature-sensitive integration processes. This study presents an annealing-free Ag2O/V2O5 composite thin-film cathode, fabricated at room temperature by magnetron co-sputtering, in which the nanoconfinement effect of the amorphous V2O5 matrix effectively suppresses Ag2O particle agglomeration to endow the electrode with satisfactory cycling stability. The composite thin-film cathode demonstrates excellent lithium storage performance, delivering an initial discharge capacity as high as 171.0 µAh cm-2 µm-1 (406.5 µWh cm-2 µm-1), which is approximately 2-3 times that of LiCoO2, while maintaining 73% capacity retention after 1000 cycles. When integrated into ATFBs, this cathode achieves 71% retention over 400 cycles and can successfully power an LED sensor and a motion sensor. This work provides a new pathway to overcome the challenges of energy density and process compatibility in microelectronic applications.

• Proposal of Analog2KG , a pipeline for turning textual analogies into knowledge graphs • Knowledge-graph version of 2 long-text analogy datasets, RattermannKG and WhartonKG • Modification of information extraction methods for maintaining analogical structure • Introduction of an LLM-free discovery methodology for higher-order relationships • Comparison to 3 LLM-enabled information extraction algorithms Analogical reasoning is an increasingly popular, lightweight solution to enable large language model (LLM)-level reasoning without computational complexity. Still, it has yet to be adopted due to its reliance on strictly hand-formatted data. Therefore, we propose Analogy2KG (“Analogy to Knowledge Graph’’), as an automatic pipeline that transforms text into a KG format via a fine-tuned version of information extraction (IE) algorithms for long-text analogies. The need to verify that the complex underlying analogical structure of the data is maintained was done via paired samples tests in the creation and validation of this pipeline. Graph density was used to evaluate the structural quality of the resulting KGs. Lastly, causal relationships were optionally detected using a novel, question-and-answer-based method. Analogy2KG was validated on the Rattermann and Wharton long-text datasets, which suggested that the proposed methodology maintains analogical structure when transforming from text to KGs. The resulting RattermannKG and WhartonKG datasets were introduced to the literature, which is the first instance of a the conversion of long-text analogy dataset into a KG format in the literature. Finally, Analogy2KG had superior performance among three LLM-enabled information extraction algorithms: ChatIE, Code4UIE, and InstructUIE for maintaining analogical structure, despite operating without the need for an LLM backend and a pre-defined relation extractor list; thus, making it an ideal lightweight solution.