Search Results (232,119)

To improve the predictive accuracy of wind-field distributions during fires in high-rise buildings, this study targets the shortcomings of traditional prediction methods, including insufficient information fusion and dispersed feature representations under high-rise fire conditions. An efficient attention mechanism, termed Adaptive Channel and Multi-Scale Spatial Fusion Attention Mechanism (CSFAM), is proposed, which endows the model with enhanced adaptive focusing and multi-scale integration capabilities. CSFAM can account for environmental features across multiple dimensions to enable high-spatial-resolution wind-field reconstruction, thereby improving robustness and prediction accuracy in complex environments. To validate the effectiveness of CSFAM for predicting wind fields under high-rise-fire conditions, CFD-based scenario modeling was employed to generate a dataset of 1050 CFD-derived wind-field distributions across diverse inflow-wind and fire-source scenarios, partitioned into training, testing, and validation sets according to the fire-source size. When applying the CSFAM-enhanced multi-layer perceptron (MLP), the wind-field predictions achieved a mean squared error (MSE) of 0.0004, a mean absolute error (MAE) of 0.0141, and an R² of 0.9766, outperforming state-of-the-art methods. The results demonstrate that CSFAM plays a significant role in markedly improving wind-speed prediction accuracy during high-rise-building fires, and enhances the model’s ability to identify and express vortex-like and other key aerodynamic features generated by the fire, thereby improving the capture of the complex nonlinear aerodynamic structures induced by fire. Full article

Estimating forest aboveground biomass (AGB) in mountainous forest ecosystems remains a significant challenge due to complex terrain, the high cost and limited applicability of traditional field-based methods. To address this issue, a remote sensing-based AGB estimation framework integrating intelligent optimization and machine learning was developed for Mount Tai in eastern China. Sentinel-2 multispectral data were selected to derive 105 candidate variables, including spectral bands, vegetation indices, texture features, and topographic factors, from which 17 key variables were selected using Pearson correlation analysis for model construction. A Support Vector Machine (SVM) optimized by the Pigeon-inspired optimization (PIO) algorithm was developed to adaptively determine optimal hyperparameters, and its performance was compared with that of Random Forest (RF) and standard SVM models. Among the three models, PIO-SVM produced the highest numerical accuracy. For the training dataset, it obtained an R² of 0.85 and an RMSE of 46.12 t/hm². For the testing dataset, it achieved an R² of 0.73 and an RMSE of 62.19 t/hm², compared with 0.72 and 66.25 t/hm² for the standard SVM model and 0.70 and 65.19 t/hm² for the RF model. The spatial distribution of AGB derived from the optimal model shows higher AGB values in the central and northern regions characterized by dense forest cover, in close agreement with field observations. Overall, the results suggest that PIO-based parameter optimization can improve SVM performance for AGB estimation in mountainous forests. This study provides a reliable and efficient framework for regional-scale monitoring of forest biomass and carbon sink dynamics. Full article

Pharmaceutical care provides the conceptual foundation for integrating pharmacogenetics into everyday pharmacy practice. Defined by Hepler and Strand as “the responsible provision of drug therapy for the purpose of achieving specific outcomes that improve a patient’s quality of life”, pharmaceutical care emphasizes a patient-centered approach in which the pharmacist collaborates with the patient, physician, and other healthcare professionals to design, implement, and monitor individualized therapeutic plans. In this context, pharmacogenetics can be regarded as an extension of pharmaceutical care: while the traditional model relies on monitoring patient outcomes and adherence, PGx adds a genetic dimension that allows treatment to be optimized from the very beginning. The pharmacist’s role therefore evolves from not only ensuring safe and effective use of medicines, but also interpreting genetic test results, supporting adherence to genetically guided therapy, and educating patients about the implications of their personal genetic profile. The introduction of pharmacogenetics testing as one of the potential services offered by community pharmacies is a promising proposition that may revolutionize the approach to drug therapy. Pharmacogenetics, a subset of pharmacogenomics, focuses on the study of DNA sequence variations that influence response to drugs. Thanks to advances in the field of genomics, it has become possible to study the genetic basis of variability in drug response. The identification of alleles responsible for the rapid or slow metabolism of xenobiotics has ushered in a new era in pharmacology. The aim of this interdisciplinary field, combining genetics and pharmacology, is to adapt treatment to a specific patient based on the analysis of their genome and gene polymorphism. Throughout the world, pharmacogenetics is gaining importance as a tool for personalizing medicine. In countries such as the United States, Canada, and the United Kingdom, programs integrating pharmacogenetics with healthcare are being developed. Clinical trials and the implementation of genetic tests into medical practice allow for better matching of medications and reducing the risk of side effects. Pharmacists will play a key role in integrating pharmacogenetics into healthcare. As specialists in the field of pharmacotherapy, they will support physicians in interpreting the results of genetic tests and adapting drug therapy to the individual needs of the patient. Additionally, pharmacists can educate patients and healthcare professionals about the benefits of pharmacogenetics and monitor the effects and safety of medications. Their involvement in the process of personalization of treatment may contribute to improving the effectiveness and safety of pharmacological therapies. Full article

This study aimed to enhance the accuracy and interpretability of erosion gully classification within black soil regions by focusing on Changxing Township, Xinxing District, Qitaihe City, Heilongjiang Province as the research site. Utilizing RTK (Real-Time Kinematic) surveying technology, three-dimensional topographic data were collected for 139 actively developing erosion gullies. Key morphological parameters—including gully length, depth, gradient, average top width, average bottom width, and slope gradients on both sides—were extracted to construct interactive features. The variable set was refined through correlation analysis and variance inflation factor (VIF) diagnostics to mitigate multicollinearity. A random forest model was employed as the primary classification approach and benchmarked against logistic regression, support vector machines (SVM), decision trees, and backpropagation neural networks. To address class imbalance, a combination of class weighting, Synthetic Minority Over-sampling Technique (SMOTE), and undersampling methods was implemented. Model tuning and interpretability assessments were performed using cross-validation, grid search optimization, and SHapley Additive exPlanations (SHAP) analysis. The findings demonstrate that the random forest model achieved superior overall performance, with test set accuracy, macro-averaged F1 score, and balanced accuracy values of 0.9143, 0.8087, and 0.8427, respectively. Among imbalance handling techniques, class weighting yielded better results compared to oversampling and undersampling. Feature importance and SHAP analyses identified gully length, average crest width, and their interaction with gully depth as the principal determinants influencing gully grade classification. These results elucidate the synergistic developmental dynamics of gully longitudinal extension, vertical deepening, and lateral widening. The proposed methodology offers valuable technical support for the rapid surveying, classification, and management decision-making processes related to black soil erosion gullies. Full article

Background/Objectives: Trauma management in emergency departments (EDs) requires rapid and reliable diagnostic tools. The Focused Assessment with Sonography in Trauma (FAST) is a bedside ultrasound examination used for the early detection of free fluid in the intraperitoneal cavity, pericardium, and pleural spaces. Expanding FAST use to trained emergency nurses may support timely bedside evaluation in high-demand settings. However, data on agreement with physicians remains limited. This study aimed to evaluate the inter-rater agreement between a trained emergency nurse and physicians in performing FAST and to explore the diagnostic accuracy of nurse-performed FAST compared with computed tomography (CT). Methods: A prospective pilot observational agreement study was conducted between October and December 2023 in the ED of a general hospital in Cyprus. FAST examinations were independently performed by a nurse trained in FAST and by physicians from the radiology department. Four anatomical areas were assessed: right upper quadrant (RUQ), left upper quadrant (LUQ), subxiphoid-pericardial area (SUPH), and suprapubic area (BLADDER). Findings were recorded independently to promote blinding. Diagnostic performance of nurse-performed FAST was explored in a subset of patients undergoing CT. Results: The sample included 68 trauma patients, of whom 58 underwent FAST by both the nurse and the radiologists and were included in the inter-rater agreement analysis. Fluid was detected in four patients (6.9%) in the RUQ area and in one patient (1.7%) in both the LUQ and SUPH regions, while no positive findings were recorded in the BLADDER area. Agreement in the RUQ area was 98.3% (Cohen’s kappa = 0.85, p < 0.001) while agreement was observed in all cases in the SUPH region (100%, Cohen’s kappa = 1.00, p < 0.001), although this finding was based on a single positive case. High observed agreement was also noted in LUQ (98.3%) and BLADDER regions; however, Cohen’s kappa could not be reliably estimated in these regions due to limited variability and the very small number of positive cases. In a subgroup of patients who underwent CT (n = 23), as well as in an additional Trauma Team subgroup (n = 10), diagnostic accuracy estimates were 100% for sensitivity and specificity; however, these estimates were based on a very small number of positive cases (only two positive cases in each subgroup) and were associated with wide confidence intervals. Conclusions: This pilot study suggests that, under specific training conditions, a trained emergency nurse may achieve a high level of agreement with physician assessments when performing FAST. The findings regarding diagnostic accuracy are preliminary and should be interpreted with caution due to the small sample size and low number of positive cases. Further studies with larger samples and multiple operators are required to confirm these findings and to evaluate their clinical implications. Future research is also needed to determine whether nurse-performed FAST may contribute to improved patient safety and emergency department workflow. Full article

Transparent clinical decision-making remains a critical barrier to deploying deep learning in medical diagnosis. Post hoc explanation methods approximate model behaviour after training but cannot guarantee that explanations faithfully reflect the underlying reasoning. This study proposes a Self-Explaining Neural Network (SENN) for Parkinson’s Disease (PD) screening via Ground Reaction Force (GRF) gait analysis, enforcing intrinsic interpretability through learnable basis concepts and input-dependent relevance scores computed jointly with the prediction. The architecture combines a four-block residual CNN backbone with stochastic depth regularisation, a 16-concept encoder with diversity and stability constraints, and temperature-scaled probability calibration for reliable clinical operating points. Evaluated on the PhysioNet Gait in Parkinson’s Disease dataset (306 subjects, 16 GRF sensors per foot), SENN achieves a subject-level ROC-AUC of 0.916 [95% CI: 0.867–0.964], sensitivity of 0.913 [0.862–0.963], specificity of 0.671 [0.485–0.858], and Average Precision of 0.942 [0.918–0.967], reported across five independent random seeds. Comparative evaluation against four deep learning baselines—CNN-Residual, BiLSTM, CNN-LSTM, and CNN-Attention—confirms that the interpretability constraints impose no statistically significant reduction in discriminative performance, with all pairwise ROC-AUC confidence intervals overlapping. Concept-level analysis reveals that the three most discriminative concepts correspond to disrupted midfoot loading patterns, increased step-length variability, and bilateral cadence asymmetry—all established biomechanical hallmarks of parkinsonian gait—providing clinically grounded, patient-specific explanations without post hoc approximation. These findings demonstrate that rigorous intrinsic interpretability and competitive predictive accuracy are simultaneously achievable in deep gait analysis, supporting the clinical adoption of transparent diagnostic AI. Full article

Myelofibrosis (MF) is a chronic myeloproliferative neoplasm characterized by progressive bone marrow fibrosis, extramedullary hematopoiesis (particularly symptomatic splenomegaly), constitutional symptoms, progressive cytopenias, and, in a subset of patients, leukemic transformation. The advent of the JAK1/2 inhibitor ruxolitinib has revolutionized the management of MF, substantially improving splenomegaly, symptom burden, and, in some settings, overall survival. However, a substantial percentage of patients fail to achieve sustained benefit, are intolerant, or become refractory; real-world and clinical trial data indicate that approximately half of treated patients discontinue ruxolitinib treatment within 3 years and up to approximately 75% within 5 years, with poor outcomes after discontinuation (median survival in several series is approximately 12–14 months). In recent years, several new small molecules that act beyond the JAK-STAT axis have emerged in clinical development. These include agents targeting telomerase (imetelstat), epigenetic regulation via BET inhibition (pelabresib/CPI-0610), the MDM2-p53 axis (navtemadlin/KRT-232), erythroid maturation and the bone marrow microenvironment (luspatercept), PI3K signaling (parsaclisib), and PIM inhibitors (nuvisertib). Early clinical data show promising results for symptom and splenic control in specific settings and, importantly, suggest potential disease-modifying activity (improvements in marrow fibrosis and molecular responses) for some compounds. This review summarizes the biological rationale, key clinical data (efficacy and safety), ongoing randomized trials, and remaining knowledge gaps for these non-JAK small molecules in MF and offers practical considerations for integrating them into contemporary treatment algorithms. Full article

This paper presents a lightweight semantic retrieval framework driven by an in-memory knowledge graph (IMKG) to overcome the limitations of traditional keyword matching and the prohibitive hardware costs of deep learning models in digitizing ancient Chinese literature. By extracting structured metadata from canonical texts, we construct a dense, bidirectional graph schema. Diverging from resource-intensive neural architectures, our system abandons heavyweight vector embeddings in favor of a highly optimized, template-based heuristic matching engine natively implemented in Java. This purely symbolic approach ensures deterministic execution, zero-dependency deployment, and seamless operation on standard CPU-only servers. To handle complex historical inquiries, the framework integrates a context-aware dialogue manager for multi-turn anaphora and ellipsis resolution, alongside a synergistic tiered caching mechanism. Extensive evaluations on a benchmark of 13,652 annotated queries demonstrate that the system achieves an exceptional intent recognition accuracy of 97.14%, robust context retention, and ultra-low response latency (≤17 ms). Ultimately, this architecture provides a sustainable, highly reproducible, and cost-effective paradigm for the semantic exploration of classical textual heritage, exceptionally suited for small-to-medium cultural institutions. Full article

This study addresses the analysis of velocity–force duality in redundantly actuated systems under actuation constraints. While velocity and force capabilities have typically been evaluated separately, their coupled relationship has not been systematically investigated. To this end, an optimization-based framework is developed to characterize achievable velocity and force along specified directions by incorporating system kinematics and actuator limits. A distributed actuation mechanism is considered as a representative system to demonstrate the proposed formulation. The resulting feasible velocity and force boundaries are interpreted as directional performance limits and are used to examine their intrinsic trade-off. The analysis reveals that velocity and force exhibit complementary characteristics depending on the actuation configuration, providing a basis for performance-oriented selection under task requirements. The proposed framework is further applied to a gait-inspired motion generation problem, where force-oriented characteristics are assigned to the stance phase and velocity-oriented characteristics to the swing phase. The generated motion reflects phase-dependent features consistent with human gait. These results demonstrate that the proposed framework provides a systematic approach for analyzing and utilizing velocity–force duality in redundantly actuated systems. Full article

Robust pose estimation and high-fidelity scene reconstruction in dynamic environments represent core challenges in the field of Visual Simultaneous Localization and Mapping (SLAM). Although 3D Gaussian Splatting (3DGS)-based techniques have demonstrated significant potential, existing methods typically assume static scenes and struggle to address the inconsistency between photometric and geometric observations in dynamic settings, leading to a notable degradation in pose estimation and map accuracy. To address these issues, this paper presents a novel dynamic SLAM method: Loop Closure with 3D Gaussian Splatting for Dynamic SLAM (LCD-Splat). Taking RGB-D images as input, LCD-Splat integrates Mask R-CNN with an improved multi-view geometry approach to detect dynamic objects, generating static scene maps and filling in occluded backgrounds. By leveraging 3DGS submaps and a frame to model tracking strategy, LCD-Splat achieves dense map construction. The method initiates online loop closure detection and employs a novel coarse to fine 3DGS registration algorithm to compute loop closure constraints between submaps. Global consistency is ultimately ensured through robust pose graph optimization. Experimental results on real-world datasets such as TUM RGB-D and Bonn demonstrate that LCD-Splat outperforms existing state-of-the-art SLAM methods in terms of tracking, scene reconstruction, and rendering performance. This approach provides novel insights for high-precision SLAM in dynamic environments and holds significant implications for scene understanding in complex settings. Full article

Background/Objectives: B-cell maturation antigen (BCMA)-directed immunotherapies, including chimeric antigen receptor T-cell (CAR-T) therapies and bispecific antibodies (BsAbs), have improved clinical outcomes in multiple myeloma. However, cytokine release syndrome (CRS) remains a major safety concern, and comparative real-world evidence across BCMA-directed agents remains limited. This study aimed to evaluate and compare CRS reporting patterns associated with BCMA-targeted CAR-T and BsAb therapies using the FDA Adverse Event Reporting System (FAERS) data and to identify predictors of CRS reporting using machine learning-based approaches. Methods: A pharmacovigilance analysis was conducted using FAERS reports from 2021 Q1 to 2025 Q3. Disproportionality analyses were performed using the reporting odds ratio (ROR), proportional reporting ratio (PRR), and information component (IC), and signals were considered present when predefined thresholds were met. Multivariable logistic regression was applied to estimate adjusted odds ratios (aORs) for CRS reporting while adjusting for demographic and reporting characteristics. Machine learning models, including XGBoost, LightGBM, and random forest were developed to predict CRS reporting. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: Among 4046 reports included in the final dataset, CAR-T therapies showed higher CRS reporting odds than BsAbs (aOR: 2.55, 95% CI: 2.16–3.01). Disproportionality analyses identified significant CRS signals for CAR-T therapies across all indices, whereas BsAbs did not meet signal detection thresholds. At the agent level, idecabtagene vicleucel was the only agent meeting all predefined signal detection criteria and exhibited the strongest reporting pattern in multivariable analysis (aOR: 6.96, 95% CI: 5.53–8.75). Among the evaluated models, LightGBM achieved the highest predictive test AUROC (0.762). SHAP analysis identified idecabtagene vicleucel, United States region, and reporting year as the most influential predictors of CRS reporting. Conclusions: CAR-T therapies, particularly idecabtagene vicleucel, exhibited higher CRS reporting odds than BsAbs, with substantial agent-level heterogeneity observed across BCMA-directed immunotherapies. Integrating pharmacovigilance and machine learning approaches may facilitate more individualized safety monitoring by identifying agent-specific differences in CRS risk among BCMA-targeted therapies. Full article

This paper presents the results of tests on concrete modified with waste powder from the production of epoxy powder coating, planned using design of experiment’s (DOE) experimental design methods. The scope of the investigation included detailed identification of the waste itself (TG/DTA, FTIR, SEM + EDS, laser diffraction), as well as evaluation of selected properties of concretes containing this waste, including compressive strength, density, and durability parameters such as frost resistance and chemical resistance. The scope of the experiment was defined by varying modifier content in the range of 4 to 11% of the cement mass and a water-cement ratio between 0.44 and 0.56. The concrete mixes obtained were characterized by good workability, fluidity, and consistency stability over time, despite the use of the modifier as an additional component in the concrete mix. No adverse effect of the waste used on the durability of the concrete was observed. Concretes modified with waste from the production of epoxy powder coating achieved a frost resistance class of F150 and showed good resistance to chemically aggressive environments (sulfates and chlorides). No products indicating adverse reactions between waste powder and reagents were found. The use of the DOE approach made it possible to determine, in the form of functional relationships, the influence of the modifier content depending on the water-cement ratio (w/c) of the concrete on its compressive strength and density. In general, a decrease in the compressive strength of concrete containing a waste powder modifier was observed, ranging from approximately 11% to 26% compared to unmodified concrete. However, the trend of decreasing compressive strength was reduced as the water-cement ratio of concrete decreased. At a water-cement ratio (w/c) of 0.443, no further decrease in compressive strength was observed. Concrete with 11% waste powder and a w/c ratio of 0.443 achieved 4.7% higher compressive strength than unmodified concrete with the same water-cement ratio. A beneficial interaction was found between a carboxylate-based plasticizer and the waste powder from the production of epoxy powder coatings. The proposed method of using waste as a concrete component is promising and may contribute to reducing the problem of waste management, as well as greenhouse gas emissions. Full article

Environmental, Social, and Governance (ESG) text classification is important for applications in sustainable finance. However, it remains a challenging task due to domain terminology and regulatory constraints. While transformer-based models achieve strong predictive performance, they often lead to high energy costs and provide limited interpretability. To address these limitations, we introduce ESG-Graph, a lightweight and interpretable graph-based framework for modeling ESG disclosures. In our approach, each sentence is represented as a token-level dependency graph augmented with virtual nodes initialized from a European Sustainability Reporting Standards (ESRS)-based taxonomy, enabling the addition of new ESG concepts without retraining. A multi-layer Graph Attention Network is used instead of transformer encoders, allowing grammatical structure and domain semantics to be modeled jointly. Experiments on three ESG benchmark datasets show that ESG-Graph achieves performance comparable to efficient transformer baselines while consuming up to 60× less energy and using 10× fewer parameters. Additional attribution and ablation studies suggest the method’s policy alignment, interpretability, and robustness. Full article

Proton exchange membrane fuel cell vehicles (PEMFCVs) have attracted widespread attention in recent years. However, there are many challenges existing in the development, such as the durability and economy of the fuel cell system (FCS). In this investigation, a fuzzy energy management strategy based on Grey Bernoulli Prediction (FEMS-GBP) is proposed to mitigate these two issues. Grey Bernoulli Prediction (GBP) is used to predict the FCS short-term future power demand with a low calculation amount, which is suitable for real-time on-board applications in PEMFCVs. Therefore, FEMS-GBP can proactively adjust FCS output power to reduce large load change times during PEMFCV operation, thereby improving FCS durability. Fuzzy control is employed to accomplish the energy management task between the FCS and the battery for better fuel economy. Numerical simulations and experiments under different vehicle driving cycles are carried out to evaluate the performance of FEMS-GBP. By comparing it with two other conventional energy management strategies, FEMS-GBP is demonstrated to be feasible and effective, as it achieves favorable performance in balancing durability and economy, especially under practical driving conditions. Full article

Mine ventilation network (MVN) regulation faces severe challenges: strong variable coupling, high search dimensionality, and the inherent conflict between energy conservation and safety constraints. To address these issues, we propose a novel airflow optimization framework integrating a Minimum Influence Spanning Tree (MIST), sensitivity attenuation boundaries, and an Improved Dung Beetle Optimizer (IDBO). Initially, high-influence co-tree chords are strategically extracted via MIST to compress the mathematical optimization dimensionality. Subsequently, effective ventilation resistance search intervals are bounded using sensitivity attenuation, preventing the algorithm from performing invalid searches in high-resistance regions. Furthermore, the standard DBO is enhanced via Fuchs chaotic initialization, Golden Sine and Lens Imaging collaborative learning, and differential mutation to minimize system power consumption. A 46-branch MVN case study validates the approach, identifying an 8-dimensional control combination as the absolute minimum requirement for full compliance. Compared to state-of-the-art baselines (DBO, SSA, WOA, DE), IDBO achieved the lowest power consumption. Post-optimization, the airflow constraint satisfaction rate improved from 89.13% to 100%, and total system power decreased by 11.87% (from 185.03 kW to 163.08 kW). Ultimately, this method robustly achieves Ventilation on Demand (VoD), providing a reliable computational tool for intelligent underground mining. Full article