Search Results (31,203)

Background: Portal vein tumor thrombus (PVTT) marks advanced hepatocellular carcinoma (HCC) and carries a dismal prognosis. Survival varies widely even within this stage, yet simple tools for individualized risk stratification remain scarce. Methods: We retrospectively enrolled 134 HCC patients with PVTT and randomly divided them into a training set (n = 94) and a validation set (n = 40). Clinical predictors were selected by variance inflation factor screening and backward elimination Cox regression. A radiomics score (Rad-score) was constructed from portal-venous phase computed tomography (CT) images using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression with 10-fold cross-validation. Three Cox models were built: a clinical model, an imaging model based solely on the Rad-score, and a combined model integrating both. Discrimination was assessed by C-index and time-dependent area under the curve (AUC). Calibration was examined with bootstrap-based calibration curves. Decision curve analysis evaluated net benefit. A nomogram was developed from the combined model. Results: Four clinical variables (alpha-fetoprotein (AFP), body mass index (BMI), high-density lipoprotein cholesterol (HDL-C), and alkaline phosphatase (ALP)) and two CT texture features (GLRLM_SRHGE and GLZLM_SZHGE) were retained as independent predictors. The combined model gave the highest C-index in both the training set (0.843) and the internal validation set (0.815). Its 1-year AUC reached 0.953 and 0.947 in the two sets. Calibration slopes ranged from 1.044 to 1.291 across time points, indicating a tendency toward mild overdispersion; nevertheless, decision curve analysis confirmed net benefit across clinically relevant thresholds. The combined model offered greater net benefit than either single-domain model across a 0–50% threshold range. A nomogram incorporating all five predictors was generated for individualized 12- and 24-month survival prediction. Conclusions: A combined model integrating routine laboratory variables and a CT-based radiomics score improved survival prediction over clinical or imaging models alone. The corresponding nomogram uses inputs from a basic blood panel and a single portal-venous phase CT, suggesting its potential as a low-cost prognostic stratification tool for HCC patients with PVTT, although external validation in prospective multicenter cohorts is required before clinical implementation. Full article

Achieving integrated water resources management at all levels, as called for by Sustainable Development Goal (SDG) target 6.5, requires assessment tools that operate at the local administrative scale. However, watershed sustainability assessments are mostly conducted at the whole-basin or provincial scale, which masks the spatial disparities that matter for local water management. This study develops a sub-district-scale Watershed Sustainability Index (WSI) for the Nong Han Basin, Thailand, by integrating the HELP framework (Hydrology, Environment, Life, and Policy) with the Pressure–State–Response structure, a calibrated QSWAT hydrological model, and spatial analysis in a geographic information system, covering 25 sub-districts. The results show that the basin has a moderate-to-high level of sustainability, with a mean WSI of 0.620: 18 sub-districts are classified as high and 7 as moderate, and none fall into the low category. The Life and Hydrology dimensions are the strongest, whereas the Policy dimension is the limiting factor in most sub-districts. This limitation arises from a low Response component (0.19) rather than from a lack of institutional capacity, as confirmed by the finding that sub-districts with low and high policy scores differ only in the Policy dimension. The apparently uniform aggregate index, combined with the high disparity among dimensional scores, confirms the value of diagnosis at the sub-district scale. The proposed framework translates the assessment results into spatial prioritization, an agency-linked decision matrix, and an intervention typology, thereby supporting evidence-based water management by local administrative organizations. Full article

Accurate channel state information (CSI) is essential for reliable orthogonal frequency division multiplexing (OFDM) transmissions, especially when training resources are limited and iterative receiver processing is employed. This paper revisits least squares (LS) and minimum mean square error (MMSE) channel estimation based on training sequences and analyzes their impact on an iterative turbo receiver framework. The initial channel estimate is obtained from an OFDM training transmission, while subsequent refinement is performed using soft information generated by a soft-input soft-output (SISO) equalizer and decoder. Unlike conventional approaches that keep the channel estimate fixed after the training phase, the proposed architecture enables decision-directed channel refinement using reconstructed transmit symbols. The OFDM stage is employed for channel estimation, whereas BER performance is evaluated using independently generated turbo-coded BPSK sequences transmitted through the analyzed channel. The performance analysis investigates the influence of training sequence length and signal-to-noise ratio (SNR) on iterative estimation gains. Simulation results show that, for short training sequences, the proposed iterative strategies can significantly improve BER performance compared with conventional non-iterative LS and MMSE estimators. These results provide practical insights for the design of training-efficient OFDM-based communication systems. Full article

Spontaneous combustion in fire-affected coal seams can degrade coal quality, alter rock mechanical parameters and reduce mining profitability. Traditional pit limit optimization methods ignore coal fire-induced quality degradation, ignore the coupling effect of economic fluctuation and slope stability, and lack quantitative optimization for fire-affected open-pit mines. Here, we optimize loss-reducing mining boundaries for the southern fire-affected highwall in the first mining district of the Dananhu No. 2 Mine, Hami, Xinjiang. The aim is to move beyond binary decisions that either sterilize or fully extract fire-affected reserves. We instead integrate economic return, slope stability and coal-price uncertainty into a single boundary-optimization framework. First, we established a three-dimensional Cartesian coordinate system for the study area. We then modeled and fitted the coal-seam roof and floor using MATLAB-based multiple integration, reducing edge errors in solid surfaces. Laboratory analyses of borehole coal samples defined how calorific value varied with advance distance. These data were used to derive the coal-quality curve. Net mining profit was then formulated as the objective function, replacing the conventional stripping-ratio criterion. Profit was calculated across advance distances to identify the economically optimal boundary. Mechanical parameters of thermally altered rocks were obtained from laboratory deformation tests. Rhino and FLAC3D 6.0 were then used to evaluate three-dimensional slope stability at critical locations. Coal-price perturbation scenarios were finally introduced to test the sensitivity of net profit and optimal advance distance. Under the baseline coal price, the slope remained stable at an advance distance of 193 m. At this boundary, net profit reached a maximum of RMB 676.608 million. The southern surface boundary contracted by 47 m relative to the initial boundary, reducing unnecessary land disturbance. Sensitivity analysis showed that lower coal prices sharply reduced both the optimal advance distance and maximum net profit. When coal price decreased by 30%, the optimal advance distance contracted to approximately 116.9 m. Maximum net profit fell to approximately RMB 248.239 million. Higher coal prices expanded the optimal boundary outward. Once coal price reached approximately 128.7 yuan/t, or 18.5% above baseline, the optimum reached the upper constraint of 240 m. Net profit then increased substantially with further price growth. These results provide a quantitative basis for dynamic boundary optimization and disturbance-reducing extraction in fire-affected open-pit coal mines. Full article

Temperature forecasting from distributed temperature sensing supports thermal characterization and ventilation decisions in grain storage. However, existing studies mainly focus on individual sensor points and are insufficient to describe spatial thermal evolution. This study proposes a thermal state field forecasting framework integrating a Temporal Convolutional Network and Mamba. Sensor observations are aggregated into spatial blocks to construct average temperature, temperature variance, and three-dimensional temperature gradients. Spatial attention captures local correlations, while the hierarchical TCN and Mamba encoder and Cross-Scale Trend Attention extract multi-scale temporal features and long-range evolution patterns. Across all 30 spatial blocks, the proposed model achieved RMSE values of 0.2988 ± 0.0374, 0.5573 ± 0.0112, 0.0380 ± 0.0009, 0.0442 ± 0.0031, and 0.0788 ± 0.0050, and MAE values of 0.2591 ± 0.0330, 0.4270 ± 0.0093, 0.0344 ± 0.0008, 0.0379 ± 0.0031, and 0.0684 ± 0.0053 for average temperature, temperature variance, and gradients in the X, Y, and Z directions, respectively. Based on the predicted thermal state field, an illustrative forecast-driven decision-support framework provides event-triggered ventilation decision support and regional priority recommendations. The decision analysis further illustrates the relationship between predicted thermal evolution and ventilation timing and regional prioritization under predefined rules. Full article

Farm animal welfare research increasingly recognizes that farmers’ welfare decisions are shaped by ethical orientations. This study examines associations between livestock farmers’ contextual anthropocentrism, personality traits, farm structural characteristics, and links to animal welfare attitudes. Survey data from 619 German livestock farmers were analyzed using descriptive statistics, reliability analysis, correlations, and an ordinary least squares regression model. An anthropocentric orientation index (AOI) showed acceptable internal consistency (Cronbach’s alpha = 0.72; mean = 3.68 on a 1–5 scale). Higher emotionality, agreeableness, and openness were negatively associated with anthropocentric orientation. Organic farming, keeping of suckling cows, dairy cows, and laying hens were also negatively associated with anthropocentric orientations. The model explained a modest share of variance (R² = 0.147; adjusted R² = 0.119). Correlations indicated that higher anthropocentric orientations were more closely aligned with appreciation of biological functioning indicators and less aligned with positive welfare indicators, including species-typical behavior and natural outdoor access. The findings indicate tentatively that contextual anthropocentrism among livestock farmers might be operationalized empirically and might be linked to personality, farm structure, and welfare interpretation. Future research should show if contextual anthropocentrism may inform more differentiated animal welfare communication that accounts for farmers’ distinct ethical and practical animal welfare orientations. Full article

Foam concrete is a lightweight material characterized by low density and moderate mechanical strength, which can be combined with aluminum alloys to form a novel type of column. Such composite members enable rapid assembly, disassembly, and functional reconfiguration in prefabricated structures. However, research on the axial compressive performance of this new column system remains limited. This study investigates the axial behavior of aluminum alloy-foam concrete short columns through a combination of numerical simulation, theoretical analysis, and machine learning prediction enhanced by the SHAP (SHapley Additive exPlanations) interpretability method. A three-dimensional finite element model was developed in ABAQUS to examine the effects of frame thickness, foam concrete strength, and section dimension on load-bearing capacity. The results indicate that the column sectional dimensions have a significant influence on the axial compressive capacity. The foam concrete strength and frame thickness have relatively smaller effects. In addition, the frame thickness can effectively restrain lateral deformation and delay buckling. Based on the confinement mechanism, polynomial fitting, Mander’s model, and a composite column formulation were proposed for axial capacity prediction. Furthermore, eleven machine learning models were trained on 64 simulation datasets 64 independent computational experiments, among which the Gradient Boosting Decision Tree (GBDT) demonstrated the best performance (R² = 0.9984, MAE = 5.97, RMSE = 7.51). SHAP analysis further revealed the relative contributions of key features, showing that section dimension is the most influential parameter, followed by foam concrete strength, while frame thickness contributes the least. These findings not only enhance the theoretical understanding of the load-transfer mechanism of columns but also provide reliable predictive models and analytical formulations for their application in lightweight prefabricated structures. Full article

The growing adoption of renewable energy and small-scale power producers has increased the need for reliable and transparent peer-to-peer (P2P) energy trading. Traditional centralized markets often struggle with high transaction fees, limited transparency, and a greater risk of manipulation, which restrict efficient energy distribution. To overcome these issues, this study presents a decentralized P2P trading framework that implements a fully functional blockchain-based trading system with smart grid simulation and demonstrates a prototype machine learning forecasting module (Random Forest, 84% accuracy) designed for future integration. The trading mechanism is developed using Ethereum smart contracts and a custom ERC-20 token, the TUM Energy Coin (TEC), enabling secure and traceable energy exchange. System security is strengthened through dual confirmation steps, role-based access control, and consensus-driven market clearing. A double-sided auction model is used to match buyers and sellers fairly. Real-time grid behavior such as fluctuating loads, prosumer generation, and consumer demand is modeled using MATLAB Simulink to reflect realistic operating conditions. To enhance decision-making, a Random Forest model is integrated for load forecasting and dynamic pricing, achieving an accuracy of 84%. The simulation results show improved transaction throughput, more stable pricing, and strong resilience against false-data injection attacks. The primary novelty of this work lies in (1) an entirely operational and validated blockchain-trading system simulation with synchronized time using Simulink, (2) a working Random Forest forecasting tool demonstrating feasibility for incorporation in the future, and (3) an analysis of the system’s robustness in the case of FDIA attacks. The authors point out that the ML component used is a prototype and not yet integrated into the functioning block chain. Full article

Earthquakes remain a critical threat to global infrastructure. Recent catastrophic events, such as the 2023 Kahramanmaraş earthquakes in Türkiye and Syria, underscore the vital necessity of rapid, accurate post-disaster building damage evaluations. Structural collapse under seismic loading leads to substantial loss of life and severe economic disruption, particularly in regions dominated by aging building stocks that predate modern seismic design codes. To address the limitations of conventional manual inspections, this study introduces a comprehensive artificial intelligence (AI) framework designed to automate and enhance post-earthquake structural assessments. Leveraging a heterogeneous dataset from the 2021 Haiti earthquake, which includes both categorical building attributes and post-disaster imagery, the proposed approach employs rigorous data preprocessing and exploratory analysis to identify key vulnerability indicators and resolve data inconsistencies. Independent predictive pipelines were developed utilizing state-of-the-art machine learning algorithms for tabular data and deep learning architectures for image analysis. Subsequently, a novel hybrid meta-classifier was implemented to fuse these distinct modalities. By integrating spatial and structural context with direct visual evidence of damage, the hybrid model is successful in estimating structural damage severity. Among all evaluated approaches, this multimodal framework significantly improved predictive reliability. The hybrid model achieved a classification accuracy of 89%, consistently outperforming isolated tabular and image-based models. These findings highlight the efficacy of multimodal data fusion in disaster analytics and suggest that AI-driven hybrid architectures can serve as robust, scalable decision support tools for structural engineers and emergency response agencies. Full article

Ornamental aquaculture represents a high-value segment of global aquaculture and plays an important role in income diversification for small-scale producers. Despite its economic relevance, investment decisions in ornamental fish farming are often supported by limited economic information and rarely incorporate risk and uncertainty into economic assessments. This study developed and applied a stochastic framework to evaluate the economic performance and investment risk of intensive ornamental aquaculture using Betta splendens production as a representative case study. A representative production system was developed from technical and economic surveys conducted on 20 commercial family operated farms located in one of Brazil’s major ornamental fish production clusters. The production system comprised three greenhouse units with an estimated annual output of 162,288 marketable fish. Production costs were estimated using conventional cost-accounting procedures, whereas economic risk was assessed through Monte Carlo simulation incorporating uncertainty in biological, productive, and market variables. The estimated total production cost was US$0.14 fish⁻¹, while the weighted average selling price reached US$0.18 fish⁻¹, resulting in a benefit–cost ratio of 1.33, a profitability of 25.09%, and an annual return on invested capital (ROIC) of 23.90% under the deterministic baseline scenario. Monte Carlo simulation estimated a mean annual ROIC of 22.46%, with a 98.53% probability of positive economic returns. Sensitivity analysis identified labor demand, the selling price of premium males, and reproductive productivity as the principal drivers of economic risk. The results indicate that economic returns in B. splendens farming are primarily influenced by managerial efficiency, labor requirements, and market conditions rather than production volume alone. The proposed framework provides a robust tool for evaluating economic viability and investment risk in ornamental fish farming under uncertainty and may support decision-making in small-scale aquaculture systems. Full article

Large Language Models (LLMs) have been rapidly evolving lately, resulting in the need for strong, explainable models to detect the difference between human-generated and machine-generated articles. Existing approaches which are mostly based on fine-tuned transformers suffer from several drawbacks such as rapid obsolescence, paraphrasing attacks, and lack of interpretability. To improve their ability to detect, this paper proposes a novel paradigm called Human vs. LLM Identification (HLI) which introduces a Retrieval-Augmented Generation (RAG)-inspired evidence-based detection strategy alongside a fine-tuned transformer classifier. Our core model, DeBERTa-Sentinel, is built on top of a fine-tuned Microsoft DeBERTa-v3-small model, which uses a disentangled attention mechanism to better capture subtle syntactic and stylistic deviations characteristic of AI-generated text. We evaluate our framework on a balanced dataset of 43,456 text samples, curated from the OpenGPTText corpus and covering AI-generated and human-authored content across diverse domains including news, education, and creative text. The experimental results show improved performance over the selected baselines, with our framework achieving an accuracy of 97.53%, precision of 95.89%, recall of 99.34%, and ROC-AUC of 99.53%. In addition, explainability is integrated into our framework through Local Interpretable Model-agnostic Explanations (LIME) analysis, providing token-level insight into classification decisions. This study establishes a benchmark for scalable, explainable AI text detection, with implications for academic integrity, content moderation, and combating misinformation. Full article

This study proposes the Expanding-Window Tensor TOPSIS method, a novel dynamic multi-criteria decision analysis framework that integrates temporal feature extraction with an expanding-window estimation scheme. The method structures panel data as a third-order tensor and extracts three time-series features—linear trend, mean, and standard deviation—from rolling windows for each alternative-criterion pair. These features are retained as distinct dimensions of the decision matrix. At each target year, normalization bounds, entropy weights, and ideal solutions are calculated based on the information accumulated up to that year, thereby avoiding look-ahead bias. A burn-in threshold prevents unreliable early estimates, while complementary diagnostics assess the convergence of weights and sensitivity to the window width. The method yields a dynamic score matrix representing the evolution of the relative position of each alternative over time. The proposed framework is applied to the ND-GAIN Country Index, which includes data for 152 countries from 2000 to 2024. The dynamic rankings have high face validity, almost perfect robustness to the choice of window width and burn-in threshold, fast convergence of the entropy weights, and robustness to changes in the alternative set. Extensive robustness checks—including Monte Carlo weight-uncertainty analysis, comparisons with alternative weighting (CRITIC, standard-deviation-based, equal weighting) and aggregation (VIKOR) methods, and benchmarks against Dynamic TOPSIS-Entropy and a rolling-window variant—confirm the insensitivity of the findings to the choice of weighting and aggregation strategy. The methodology is not domain-specific and offers a transparent and diagnostically rich tool for dynamic performance evaluation. Full article

Background/Objectives: To inform policy making for the German breast cancer (BC) screening program, we systematically evaluated the long-term benefits and harms of extended age limits compared to the current standard of biennial mammography at ages 50–69 years using a decision-analytic approach. Methods: We developed and applied a Markov state-transition model for mammography screening in Germany to systematically assess the benefit–harm trade-offs of various screening strategies varying in age at start and end of screening as well as in screening frequency. The model was populated with international data for sensitivity and specificity of mammography along with German epidemiological, clinical and age-specific quality-of-life data. In deterministic analyses, the following outcomes were projected: detected ductal carcinoma in situ (DCIS) and invasive BC, BC-related deaths, life years (LY), and quality-adjusted life years (QALY), number of positive, false-positive, and total mammograms, overdiagnosis, and the incremental harm–benefit ratio (IHBR). Results: In the base-case analysis, mammography at ages 45–79 (annual, age 45–49; biennial, 50–79) achieved the highest gain in LY (10.0 life years gained [LYG] per 100 women) compared with current screening. Biennial mammography at ages 45–74 resulted in the highest benefits considering both life expectancy and quality of life (3.5 QALYs gained/100 women). Compared to current biennial mammography screening at ages 50–69, lowering the start age from 50 to 45 years resulted in an IHBR of 47 additional mammograms/LYG. Compared to biennial mammography at ages 45–69, biennial mammography at age 45–74 results in 96 additional mammograms/LYG. Further extended screening results in substantially less favorable IHBRs. Conclusions: Based on our results, extending biennial mammography screening to women aged 45 to 74 years may prevent additional BC deaths and increase remaining life expectancy at an acceptable benefit–harm ratio, and improve quality-adjusted life expectancy. Full article

Using data from Chinese listed non-financial firms from 2015 to 2023, this study examines how environmental, social, and governance (ESG) rating divergence affects banks’ loan limit decisions and the underlying decision-making mechanisms within the sustainable finance system. Drawing on cognitive psychology, we conceptualize bank lending as an organizational decision-making process in which conflicting ESG signals create information ambiguity and influence banks’ risk assessments. Greater ESG rating divergence is associated with significantly lower total annual bank loan limits. Mechanism analyses provide evidence consistent with heightened bank concerns about firm default risk, whereas we find no supporting evidence for the information-quality channel captured by discretionary accruals. Further analysis reveals an asymmetric response to conflicting ESG information: banks appear to place greater weight on relatively unfavorable ESG ratings, while favorable ratings do not exert a comparable moderating effect. The negative association between ESG rating divergence and bank loan limits is more evident among non-state-owned enterprises, firms with weaker repayment capacity, firms with lower financial information disclosure quality, and firms without third-party ESG assurance. These findings extend the literature on ESG rating divergence and sustainable finance by showing how conflicting ESG information is associated with contractual credit allocation through banks’ risk assessment and asymmetric information-processing behavior. They also highlight the importance of improving ESG information governance and banks’ capacity to evaluate inconsistent sustainability signals. Full article

Fast charging of lithium-ion batteries requires balancing charging efficiency with electrochemical safety to minimize degradation and lithium plating. Conventional charging strategies and existing reinforcement learning approaches often lack physical consistency and model interpretability, limiting their applicability in safety-critical battery management systems. This paper proposes a physics-informed Attention-Proximal Policy Optimization (Attention-PPO) framework for intelligent battery fast charging by integrating the Single Particle Model with Electrolyte (SPMe) with a transformer-based attention mechanism. SPMe provides physically meaningful battery state transitions, while the attention-enhanced PPO dynamically learns informative electrochemical representations for charging control. To improve transparency, a monotonic XGBoost surrogate model is employed for lithium-plating risk estimation, and the learned policy is further distilled into an interpretable decision tree. Experimental results demonstrate that the proposed Attention-PPO achieves substantially faster and more stable policy convergence than the baseline PPO, reaching convergence at episode 37 compared with episode 82 for PPO, corresponding to a 54.9% reduction in training episodes. The reward standard deviation is also reduced from 0.132 to 0.041, indicating 68.9% lower reward variability. In terms of electrochemical safety, Attention-PPO achieves a mean plating overpotential of +0.023 V compared with −0.015 V for PPO, providing a 38 mV improvement and a positive safety margin against lithium plating. Compared with conventional CC-CV and CC-COP controllers, the proposed framework requires a longer charging duration because it prioritizes electrochemical safety; however, it consistently maintains positive plating overpotential while achieving reliable charging performance. Compared with conventional CC-CV, CC-COP, and standard PPO controllers, the proposed framework provides a larger electrochemical safety margin while preserving reliable charging performance. Transformer attention analysis and policy distillation provide interpretable representations of the learned charging policy, while SHAP analysis characterizes feature contributions within the auxiliary plating-risk estimator. The proposed framework provides an effective and explainable physics-informed reinforcement learning solution for safe lithium-ion battery fast charging, offering a promising approach for next-generation intelligent battery management systems. Full article