Search Results (729)

Interpretability in corporate financial risk profiling must support not only predictive performance but also governance-oriented decision-making. This study proposes a three-class financial risk assessment workflow for B2B settings and introduces Risk-Weighted Uncertainty-Conditioned Feature Importance (RW-UCFI) as a post-explanation prioritization framework. RW-UCFI is not a new attribution method; rather, it reorganizes existing explanation outputs according to class sensitivity, predictive uncertainty, and asymmetric risk relevance. The empirical analysis uses a single cross-sectional dataset of 954 Indonesia Stock Exchange-listed firms with organizationally provided Low Risk, Medium Risk, and High Risk labels. A stacked ensemble model is used as the explanatory substrate, followed by calibration analysis, uncertainty analysis, and governance-oriented explainability aggregation. On the held-out validation set, the model achieved an accuracy of 0.7487 and a macro ROC-AUC of 0.8630. Repeated stratified validation indicated moderately stable aggregate performance, although class-level reliability remained uneven, with High Risk recall emerging as the weakest and most variable component. The original model showed the most favorable probability reliability among the evaluated variants, whereas temperature scaling and one-vs-rest isotonic regression did not improve calibration. Uncertainty analysis further showed that the most uncertain cases concentrated substantially more misclassifications and High Risk misses; the top 30% most uncertain cases captured 52.1% of all errors and 43.8% of High Risk misses. RW-UCFI produced a materially different feature-priority structure from standard global SHAP ranking, suggesting that explanation outputs may become more decision-relevant for governance-oriented review when contextualized by uncertainty and asymmetric risk conditions in the present setting. Full article

High-alkali coal can cause slagging and fouling and impact the operational lifespan of the boilers. Traditional single-indicator methods often yield inconsistent results when evaluating the slagging risk of high-alkali coal. In this study, six coal samples were selected and systematically analyzed for their slagging characteristics using scanning electron microscopy (SEM), X-ray fluorescence (XRF), X-ray diffraction (XRD), and ash morphology analysis. Furthermore, a comprehensive evaluation model was constructed by integrating the technique for order preference by similarity to ideal solution (TOPSIS) with the entropy weight method. Additionally, based on images of ash morphology, the fractal dimension (D) was introduced as a quantitative indicator to predict slagging tendency through crack characteristics. The results show that TF, ZD, and KB samples, which are rich in alkaline oxides (CaO, Fe₂O₃, Na₂O, K₂O), form low-melting-point eutectic silicates during combustion, resulting in significant melting and agglomeration with wide cracks between aggregates, indicating a strong slagging tendency. Their fractal dimensions (D) range from 1.81 to 1.92. In contrast, HM and WQ samples, dominated by SiO₂ and Al₂O₃, form high-melting-point mullite and quartz, showing loose ash morphology with uniformly distributed cracks and a weak slagging tendency, with D values of 1.68 and 1.75, respectively. A significant negative correlation was observed between D and the E-TOPSIS model (y = 3.54 − 1.72x). Therefore, fractal analysis allows for rapid assessment of slagging risk without the need for complex chemical testing. This study provides valuable insights for predicting the slagging tendency of high-alkali coal during combustion. Full article

Flocs, visible particles formed in sugar-sweetened beverages, reduce clarity and consumer acceptance of products. Their presence can be caused not only by different types of trace impurities in the sugar but also by interactions among beverage components. In this review, scientific reports on acid beverage flocs (ABFs) and alcohol flocs are summarized, the main pathways for their formation are described, and practical options for detecting them and preventing their formation in beverages are compiled. Using Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 and related guidance, literature searches of Scopus, Web of Science (WoS), PubMed, Food Science and Technology Abstracts (FSTA), CAB Abstracts, and International Commission for Uniform Methods of Sugar Analysis (ICUMSA) resulted in the inclusion of 56 studies. In various types of beverages, complexes formed between proteins (Ps) and polyphenols (PPs) often initiate haze and floc formation, while polysaccharides (dextran, pectin, and starch), silica or silicates, and inorganic ions influence charge balance, particle bridging, and floc growth rate. Ethanol in alcohol beverages can further destabilize colloids and promote aggregation. For beet sugars, saponin–protein interactions are a likely pathway for the formation of ABF, but the available evidence is not consistent. In cane sugars, the reported roles of proteins, polysaccharides, silica, and starch in floc formation vary considerably between studies. For quality assurance, ICUMSA floc tests (GS2-40 and GS2-44) should be complemented by turbidity or haze measurement and colloid characterization such as light scattering, ζ–potential, and infrared IR-based analytical methods supported by chemometrics. Risk mitigation works best as a two-level strategy that combines impurity removal during sugar production and stabilization steps in beverage formulation and storage, including the use of clarification agents and control of pH, temperature, ionic strength, and oxygen exposure. Standardized reporting and validation of rapid predictors against ICUMSA benchmarks remain essential. Full article

This short communication synthesizes evidence on how the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) altimetry data are used by private sector actors and the implications for economic value creation. Using secondary research that collected and summarized information from existing data from reports, journals, websites, and databases, the work identifies 54 companies across 9 sectors leveraging ICESat-2-derived elevation, canopy height, bathymetry, and surface measurements to inform decision-making, risk assessment, and new business models. The analysis situates ICESat-2 within a broader context where freely available Earth observation data can generate substantial private- and public-sector value, potentially exceeding hundreds of billions in aggregate when scaled across industries such as geospatial services, climate management, real estate, and insurance. The paper uses a four-pillar conceptual model to guide valuation of data-driven impacts: Data Utility (intrinsic information value of altimetry and related metrics), Decision Impact (tangible economic benefits from improved models and operations), Strategic Integration (emergence of new business models and market opportunities), and Data Ecosystem Exclusivity (development of proprietary datasets and workflows that enable competitive differentiation). Empirical findings illustrate how these pillars manifest in practice. The paper seeks to connect private-sector uptake to NASA’s Earth Science to Action framework and related capacity-building efforts, highlighting pathways for broader utilization through training, tutorials, and accessible interfaces. Limitations of the study include partial sector coverage and reliance on publicly reported use cases. Future work should quantify economic returns with standardized metrics and extend the dataset to capture dynamic shifts in data products, governance, and IP development within the evolving data ecosystem. Full article

In the context of the rapid development of smart horticulture, a deep remote sensing-based dual detection method for horticultural crop growth anomalies and safety risks was proposed to address the limitations of existing remote sensing monitoring approaches. These conventional methods, which predominantly focused on growth vigor assessment or single-task anomaly detection, had difficulty distinguishing anomalies from actual production risks and exhibited insufficient sensitivity to weak anomalies and complex temporal disturbances. Within a unified framework, a growth state modeling branch and an anomaly perception branch were constructed, enabling the joint modeling of normal growth trajectories and anomalous deviation features. By further introducing a risk joint discrimination mechanism, an integrated analysis pipeline from anomaly identification to risk assessment was achieved. Multi-temporal remote sensing features were used as inputs, through which normal crop growth patterns were characterized via trend perception, texture modeling, and temporal aggregation, while sensitivity to local disturbances and weak anomaly signals was enhanced by anomaly embeddings and energy representations. Systematic experiments conducted on multi-regional and multi-crop horticultural remote sensing datasets demonstrated that the proposed method significantly outperformed comparative approaches, including traditional threshold-based methods, support vector machines, random forests, autoencoders, ConvLSTM, and temporal transformer models. In the dual task of horticultural crop growth anomaly detection and safety risk identification, an accuracy of approximately 0.91 and an F1 score of 0.88 were achieved, indicating higher anomaly recognition accuracy and more stable risk discrimination capability. Further anomaly-type awareness experiments showed that consistent performance was maintained across diverse real-world production scenarios, including climate stress, disease-induced anomalies, and management errors. Full article

Refined urban flood risk assessment serves as a fundamental safeguard for urban sustainability. However, most studies based on scenario analysis method tend to rely on a single risk evaluation criterion, with limited consideration of applicability differences arising from underlying computational principles. Furthermore, as flood events are inherently dynamic spatial–temporal processes, most studies often overlook the three-dimensional characteristics of flood risk, particularly the connectivity of risk in physically adjacent spaces. To address these issues, this paper proposes a comprehensive flood risk assessment framework that integrates the spatial–temporal characteristics of disaster-causing factors. An improved analysis method for grid-scale flood assessment is proposed based on the comprehensive mechanical analysis method and the drowning factor. In addition, a quantitative approach for characterizing the spatial aggregation of urban flood risk is established using risk thresholds and aggregation area thresholds. These methods are then integrated through a combination weighting–cluster analysis framework for comprehensive flood risk assessment. The results show that the improved analysis method can better reflect the change in risk of flow velocity and water depth combined. Spatiotemporally, the Yinshan Road and western section of the Dongzhong Road, exhibiting high localized risk, moderate overall risk, high risk on the time scale and high spatial agglomeration status, are comprehensively assessed as extremely high-risk flooded zones. The proposed framework effectively characterizes the spatial–temporal distribution of disaster-causing factors, providing a scientific basis for disaster prevention and contributing to urban sustainability. Full article

Microplastic pollution has emerged as a major environmental concern due to its persistence, widespread distribution and potential risks to ecosystems and human health. Among the various types of microplastics, fibrous microplastics (FMPs) account for 60% to 90% of all detected microplastic particles in surface waters, primarily originating from synthetic textile production, laundering, and wastewater discharge. Their elongated morphology, high aspect ratio, and complex surface chemistry differentiate them significantly from microplastic fragments or beads, creating unique challenges for effective removal in water treatment systems. In recent years, engineered biochar has attracted increasing attention as a promising and sustainable material for microplastic removal due to tunable pore structure, surface chemistry, and adsorption capacity. However, existing reviews largely discuss microplastic removal in general terms, with limited attention to the distinctive properties of textile FMPs and their implications for biochar design and performance. This review provides a comprehensive and focused analysis of the functional characteristics of biochar that enable the effective removal of textile FMPs in water systems. First, the environmental significance and physicochemical characteristics of textile-derived FMPs are summarized. Next, the major mechanisms governing biochar–microplastic interactions, including physical interception, adsorption, and aggregation processes, are discussed. The review then examines key functional characteristics of engineered biochar, such as pore structure, surface functional groups, hydrophobicity, and composite modifications, that enhance the sequestration of FMPs. Finally, current technological challenges, research gaps, and future directions for developing scalable biochar-based solutions for textile microplastic mitigation are discussed. By linking the unique properties of textile FMPs with the functional design of biochar, this review provides a framework to guide the development of more effective and sustainable treatment strategies for reducing microplastic contamination in aquatic environments. Full article

Predicting loan defaults is a significant challenge for financial institutions; however, current machine learning techniques often encounter issues in areas such as data privacy, cross-institutional cooperation, and model transparency. The restrictions on the practical implementation of advanced predictive models are centralized training paradigms, which limit the application of advanced models because of regulatory and confidentiality issues, and black-box decision making, which diminishes confidence in automated credit risk tools. This study mitigates these problems by adopting a federated-inspired decentralized ensemble learning model combined with explainable artificial intelligence (XAI) in predicting loan defaults. Various machine learning classifiers are trained on partitioned institutional data without the need to share any data; they include K-Nearest Neighbors, support vector machine, random forest, and XGBoost. They use a prediction-level aggregation strategy to simulate the collaborative decision-making process without losing locality of data. SHAP and LIME are used to promote model interpretability by giving both global and local explanations of the consequences of prediction. The proposed framework was tested on a large public dataset of loans that contains more than 116,000 records, including various financial and borrower-related features. The experimental findings show that XGBoost has high and reliable predictive accuracy in both centralized and decentralized scenarios, achieving 99.7% accuracy under federated-inspired evaluation. The explanation analysis shows interest rate spread and upfront charges as the most significant predictors of loan default risk. The main contributions of this research are as follows: (i) a privacy-preserving decentralized ensemble learning framework that is applicable in multi-institutional financial contexts, (ii) a detailed analysis of centralized and decentralized predictive performances, and (iii) the pipeline of the XAI, which can be used to increase its transparency and regulatory confidence in automated credit risk evaluation. These results prove that decentralized learning combined with explainable AI can provide high-performing, transparent and privacy-sensitive loan default prediction systems in practice in real-world banking systems. Full article

Background/Objectives: High-grade serous ovarian cancer (HGSOC) represents the most aggressive subtype of epithelial ovarian cancer and is frequently diagnosed at advanced stages. Increasing evidence suggests that systemic inflammation plays an important role in tumor progression and clinical outcomes. This study aimed to evaluate the association between preoperative systemic inflammatory indices and tumor burden, perioperative outcomes, and recurrence risk in patients with HGSOC undergoing primary debulking surgery. Methods: We conducted a retrospective study including 125 patients with histopathologically confirmed HGSOC who underwent primary debulking surgery between January 2020 and December 2025. Preoperative hematological parameters obtained within 24 h before surgery were used to calculate inflammatory indices including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI). Associations between inflammatory markers, clinicopathological characteristics, perioperative outcomes, and recurrence were analyzed using non-parametric tests and logistic regression models. Results: The mean patient age was 53.66 ± 9.14 years, and most patients presented with advanced disease (FIGO III–IV: 70.4%). Patients with T3 tumors showed significantly higher monocyte (0.66 vs. 0.50 × 10⁹/L, p = 0.003), neutrophil (5.43 vs. 4.99 × 10⁹/L, p = 0.042), and platelet counts (325 vs. 280 × 10⁹/L, p = 0.006) and lower lymphocyte counts (1.79 vs. 1.96 × 10⁹/L, p = 0.009). Composite inflammatory indices were also increased in advanced disease, including PLR (177 vs. 153, p = 0.009), AISI (492 vs. 341, p = 0.002), and SIRI (1.65 vs. 1.18, p = 0.018). Patients requiring postoperative blood transfusion had higher neutrophil counts (7.65 vs. 4.97 × 10⁹/L, p < 0.001) and elevated SIRI (2.56 vs. 1.55, p < 0.001). Patients with recurrence had significantly higher platelet counts (339 vs. 293 × 10⁹/L, p = 0.001) and SII values (2849 vs. 2586, p = 0.012). In multivariate analysis, SII remained independently associated with recurrence (OR 1.022 per 100-unit increase; 95% CI 1.002–1.043; p = 0.033) together with advanced FIGO stages (OR 2.863; 95% CI 1.011–8.104; p = 0.048). Conclusions: Preoperative systemic inflammatory markers are significantly associated with tumor burden, surgical outcomes, and recurrence risk in HGSOC. An elevated SII appears to be an independent predictor of recurrence and may represent a practical biomarker for improving preoperative risk stratification and postoperative surveillance. Full article

Background: This study examines long-term trends in intra-EU trade among the Visegrad Group (V4) countries from 2004 to 2023, focusing on changes in export specialisation and comparative advantages in the context of trade resilience and sustainability. Methods: Trade performance is analysed at both the aggregate level and across SITC product groups, using Eurostat data. The analysis applies export and import dynamics, trade balance, export–import coverage ratio, trade balance index, and the symmetric revealed comparative advantage index. Results: The findings show significant heterogeneity in specialisation and competitiveness across the V4 countries. Poland reveals competitive advantages and trade stability in agri-food products. After European Union (EU) accession, comparative advantages and export specialisation emerged mainly in manufacturing and selected medium- and high-processed goods (SITC6–8), especially in Czechia and Hungary, and increasingly in Poland. Poland and Czechia shifted most clearly towards higher value-added products, Hungary followed a mixed pattern, while Slovakia remained narrowly focused on the automotive sector. Export competitiveness is closely linked to the business cycle, with upturns strengthening advantages and downturns causing only temporary weakening. Conclusions: The V4 intra-EU trade exhibits structural resilience, as key competitive positions persist and recover after economic shocks. Only Slovakia’s highly concentrated specialisation may entail risks for sustainable growth. Full article

This study investigates the impact of geopolitical risk measures (aggregate geopolitical risk, geopolitical acts, and geopolitical threats) on 40 global stock market indexes from developed and emerging markets for a sample of 20 years. By employing simultaneous quantile regression and a Two-Stage Quantile-on-Quantile Regression (QQR) framework, we analyze the risk transmission mechanisms across the conditional distribution of stock returns. The empirical results reveal a notable regime-dependent reversal: a negative influence is exerted by geopolitical risk during a bullish market regime, while a counterintuitive positive association is present for the bearish market conditions. This effect is more pronounced for emerging and commodity-rich markets, which may provide a potential hedge during supply-side shocks. Moreover, the QQR analysis focused on the United States of America stock market provides an examination of the different potential transmission mechanisms of geopolitical variants. The results suggest that geopolitical threats (GPRT) represent a persistent factor that negatively affects the market for normal and bullish market regimes, while geopolitical acts (GPRA) represent a tail-risk catalyst that exacerbates losses during severe market crashes. The results remain robust to an alternative specification of returns and indicate the necessity of distinguishing between geopolitical acts and threats from a risk management standpoint, as well as correctly identifying the market regime. Full article

Social media platforms contain rich but noisy narratives of psychological distress, creating opportunities for early mental health risk detection. However, existing datasets capture heterogeneous constructs such as suicide risk severity, depression diagnosis, and DSM-5 symptom presence, and most prior models are trained and evaluated on a single corpus, limiting their clinical alignment and cross-dataset generalizability. In this study, we fine-tune a domain-specific long-document transformer, AIMH/Mental-Longformer-base-4096, for binary mental health risk detection (risk vs. no risk) using two clinically aligned Reddit datasets: the C-SSRS Reddit corpus and the eRisk 2025 depression dataset. To handle long user histories, we introduce an LLM-based summarization pipeline that compresses posts exceeding 2000 tokens while preserving mental health-relevant information. We also conduct a seven-configuration ablation study across combinations of three corpora (C-SSRS, eRisk, and ReDSM5) to examine how dataset semantics influence model performance. On a held-out C-SSRS + eRisk test set (n = 279), the proposed model achieves a mean balanced accuracy of 0.89 ± 0.01 across five random seeds, with a best run of 0.90 and a 5.74 percentage point improvement over the strongest baseline (TF-IDF + Random Forest). The model also shows strong cross-platform generalization, achieving BA = 0.78 on the depression-reddit-cleaned dataset (n = 7731) and BA = 0.85 (ROC-AUC = 0.92) on a Twitter suicidal-intention dataset (n = 9119) without additional fine-tuning. The ablation analysis shows that although a three-dataset configuration (C-SSRS + eRisk + ReDSM5) maximizes aggregate performance, the ReDSM5 labels encode symptom presence rather than clinical risk, creating a semantic mismatch. This finding highlights the importance of label compatibility when combining heterogeneous mental health corpora. Explainability analysis using Integrated Gradients and attention visualization shows that the model focuses on clinically meaningful expressions such as therapy references, diagnosis, and hopelessness rather than isolated keywords. These results demonstrate that clinically aligned long-context transformers can provide accurate and interpretable mental health risk detection from social media while emphasizing the critical role of dataset semantics in multi-corpus training. Full article

Excessive accumulation of copper ions (Cu²⁺) in the environment and biological systems poses severe risks to ecological balance and human health, necessitating accurate detection and monitoring of Cu²⁺. Schiff base derivatives with favorable optical properties provide an efficient strategy for copper ion recognition. In this paper, fluorescent probe L (5-methyl-2-hydroxybenzaldehyde-(7-diethylaminocoumarin-3-formyl) hydrazone) was synthesized through a three-step reaction using 4-diethylaminosalicylaldehyde and diethyl malonate as starting materials. The structure of probe L was confirmed by melting point analysis, infrared spectroscopy, and nuclear magnetic resonance. Single-crystal X-ray analysis revealed that probe L crystallized into a triclinic lattice with space group $P_1^{-}$. Optical investigations, including UV–Vis spectroscopy, fluorescence spectroscopy, and aggregation-induced emission studies, demonstrated highly sensitive and selective fluorescence “turn-off” behavior of probe L towards Cu²⁺ ions in DMSO, with negligible interference from other metal ions. Job’s plot and crystallographic analysis revealed a 1:1 binding stoichiometry between probe L and Cu²⁺, forming the complex [Cu(L)]. Fluorescence titration experiments revealed a binding constant (K_b) of 5.2 × 10⁶ L/mol and a detection limit of 7.8 × 10⁻⁷ mol/L, indicating excellent sensitivity. These results suggest that probe L has considerable promise for Cu²⁺ detection in aqueous environments, with potential applications in environmental monitoring and public health protection. Full article

Rice sheath blight caused by Rhizoctonia solani is one of the most destructive diseases of rice. Bixafen has been proposed as a promising control agent with moderate resistance risk; however, its cellular mode of action remains unclear. Therefore, this study investigated the antifungal mechanism of bixafen from the perspective of programmed cell death (PCD). Bioassays showed that bixafen strongly inhibited R. solani, with a median effective concentration (EC₅₀) of 1.16 μg/mL. Morphologically, bixafen induced hyphae collapse, vacuolization, chromatin aggregation, and mitochondrial disruption. Transcriptome analysis further revealed that bixafen significantly altered the expression of genes involved in the tricarboxylic acid cycle and PCD pathways. In addition, bixafen, at the concentration of EC₅₀, triggered ROS accumulation accompanied by increased malondialdehyde (MDA) levels. These oxidative effects led to mitochondrial damage, characterized by loss of membrane potential, reduced Tomm20 expression, and decreased Aco-2 activity. Subsequently, bixafen activated apoptosis, as evidenced by induction of the mitochondria-associated inducer of death (AMID), down-regulation of Bcl-2, and DNA fragmentation. Moreover, bixafen also induced autophagy by reducing p62 and increasing Beclin-1 expression, which suggests the clearance of damaged mitochondria. Collectively, these results demonstrated that bixafen induced mitochondrial-dependent apoptosis and autophagy in R. solani, which provided novel insights into its cellular antifungal mechanism and supported its potential as a PCD-targeted fungicide. Full article

This study develops and applies a Climate–Water–Health (CWH) Nexus Index to compare multi-dimensional risk trajectories across six African Least Developed Countries, namely, Chad, Democratic Republic of Congo, Lesotho, Madagascar, Niger, and Togo, each representing major climatic regions. Using decadal averages for 2000–2009 and 2010–2020, the study constructs three sub-indices—Climate Risk Index, Water Insecurity Index, and Health Burden Index—and then aggregates them into a composite CWH index. Indicators are harmonized via min–max normalization, and water and health measures are expressed per 100,000 population to ensure cross-country comparability under differing population sizes. The results of the study indicate substantial heterogeneity in both levels and drivers of nexus risk. The CWH risk decreased in most countries from the 2000s to the 2010s, while relative positions shifted as improvements occurred unevenly across dimensions. Sensitivity analysis with equal and dimension-focused weights confirms that core country groupings and extremes are robust to plausible weighting schemes. External consistency checks show a strong negative Pearson correlation between the standard CWH and the Human Development Index in both decades, indicating that higher human development is associated with lower Nexus risk. The proposed framework is transparent, scalable, and suitable for extension to broader African coverage and subnational mapping. Full article