Search Results (18,982)

Rapid selection of appropriate antimicrobial therapy is important for improving patient outcomes and supporting antimicrobial stewardship, particularly in the context of increasing antimicrobial resistance (AMR). Conventional antimicrobial susceptibility testing (AST), although essential for clinical decision-making, may not provide actionable susceptibility information immediately. This study develops an explainable multi-label machine learning framework for patient-specific antibiotic recommendation based on susceptibility prediction and ranked decision support using routinely collected clinical microbiology data. The framework integrates leakage-controlled preprocessing, microbiological and demographic feature representation, multi-label susceptibility encoding, One-vs-Rest ensemble learning, probability-based antibiotic ranking, statistical evaluation, temporal validation, and SHapley Additive exPlanations (SHAP). The dataset comprised 234 clinical records, of which 196 bacterial/other records were retained for the primary analysis after excluding 38 fungal records. A chronological partition produced 156 development records and 40 temporally held-out test records. Thirty-three antibiotic susceptibility labels were retained based on development-set availability. XGBoost, Random Forest, and LightGBM were evaluated using five-fold out-of-fold (OOF) validation. Random Forest was selected for the final recommendation and explainability analyses based on its overall performance, achieving a Micro-F1 of 0.4204, Macro-F1 of 0.3003, AUROC of 0.7069, AUPRC of 0.3979, and Precision@5 of 0.3962. A leakage-safe local antibiogram was additionally evaluated as a population-level ranking baseline, achieving a Precision@5 of 0.3077. Feature ablation showed that the combination of organism and age produced the highest OOF Micro-F1 (0.4635) and AUROC (0.7186), while the addition of gender and specimen type improved selected ranking measures but did not consistently improve aggregate classification performance. On the temporally held-out test cohort, Random Forest achieved a Micro-F1 of 0.4267, AUROC of 0.7447, AUPRC of 0.4892, and Precision@5 of 0.4350. SHAP analysis was completed for all 33 antibiotic-specific classifiers, providing global and antibiotic-level explanations of model behavior. The framework provides an interpretable approach for ranking potentially susceptible antibiotics at the patient level and is intended as clinical decision support rather than an autonomous prescribing system. Further external and prospective multicentre validation, including dedicated evaluation of challenging cases such as pan-drug resistance, is required before clinical deployment. Full article

Developing methods for achieving safe and efficient crowd evacuation is an important research issue. In particular, controlling pedestrian flows based on predictions from a mathematical model is important. In this paper, we propose a new method for generating route opening patterns. The target area is modeled by an undirected graph, and pedestrian flows are represented by the temporal change in density at each vertex. We assume that the density at each vertex can be observed using equipment such as IoT devices. Based on the model predictive control framework, we consider the problem of finding a route opening pattern that minimizes a safety-related penalty. To solve this problem, we propose a Transformer-based solution method that combines offline and online computations. The effectiveness of the proposed method is demonstrated through a numerical simulation. In the numerical example, a route opening pattern satisfying the Signal Temporal Logic (STL) formula was obtained using the proposed method. The evacuation rate, which represents the proportion of pedestrians whose movement is complete, was $0.8526$, the penalty was 2193, and the maximum online computation time was $8.5199$ s. In the offline computation, the training dataset for the Transformer model was generated using a genetic algorithm (GA). By combining offline and online computations, we confirmed that an appropriate route opening pattern can be generated during online computation. Full article

Rapid advances in genomics and artificial intelligence require breeding courses to integrate quantitative-genetic theory with genome-scale analysis and breeding decisions. We developed a Plant Intelligent Breeding Teaching Platform using Python (version 3.12.7) and PyQt5 (version 5.15.11), integrating quality control, population-structure analysis, GWAS, genomic prediction, and cross-design simulation. The demonstration used 898 individuals of black poplar (Populus nigra L.), with nine-year diameter at breast height as the phenotype and 50,537 SNPs retained after quality control and LD pruning from approximately 30× whole-genome resequencing. The platform supports adjustable QC parameters, PCA, genomic relationship analysis, GEMMA-based linear mixed-model GWAS, and six genomic-prediction models evaluated by five-fold cross-validation. Pearson correlations ranged from 0.489 to 0.574, with RF performing best. Known female/male information and additive GBLUP were used for cross prediction. In 64 Biological Sciences students, self-reported mastery increased by 26.6–71.9 percentage points across five modules (exact McNemar tests, p < 0.001). The platform provides an integrated environment for teaching the workflow from genomic data to breeding decisions. Full article

Romania’s forest sector has undergone significant transformation over the past two decades, with certification emerging as a key instrument for improving transparency, ecological safeguards and market access. This study examines the current state of forest certification in Romania within broader European trends toward sustainable forest management. Using a documentary analysis, a four-country comparative assessment (Romania, Finland, Sweden, Germany), three illustrative Romanian case studies, and a cross-country statistical analysis (Pearson correlation and multiple linear regression) of 18 European countries, the study evaluates the ecological, economic and social benefits of certification and the structural barriers limiting its adoption, especially among private and communal owners. Results show that certification has improved biodiversity protection, monitoring and institutional accountability, while facilitating access to international markets. A comparative analysis of 18 European countries, controlling for national income and EU accession timing, shows that Romania’s certification coverage lags roughly 16 percentage points behind the level predicted by its income and accession cohort, comparable to gaps in wealthier Western European economies; this residual is not explained by income or accession timing and is consistent with, though does not on its own demonstrate, the structural barriers (ownership fragmentation, administrative capacity, infrastructure) documented qualitatively in this study. Persistent challenges, including fragmented ownership, uneven administrative capacity and infrastructure gaps, continue to restrict implementation. The study concludes that Romania is progressing toward European models of sustainable forest management, but further convergence requires coherent policies, stronger institutional capacity and targeted support for small forest owners, in line with the Agenda 2030 for Sustainable Development. Full article

Reservoir-bank landslides in canyon-type reservoirs commonly show slow creep and episodic acceleration driven by rainfall infiltration, groundwater fluctuation, reservoir regulation, and engineering disturbance. Sparse coherent targets and the high computational burden of long synthetic aperture radar image stacks limit operational time-series interferometric synthetic aperture radar monitoring (TS-InSAR). Moreover, effectively linking long-term deformation monitoring with mechanism interpretation and short-term prediction remains challenging. This study develops an integrated framework for the Xiaolangdi Reservoir, China, combining Ministack-InSAR, interpretable machine learning, and multi-source deep learning. Sentinel-1A images acquired from 2018 to 2024 were processed using Ministack-InSAR, while random forest (RF) and extreme gradient boosting (XGBoost) combined with Shapley additive explanations (SHAP) were employed to identify the dominant conditioning factors controlling deformation. Based on the identified factors and historical deformation information, multi-source deep learning models were further developed for short-term deformation prediction. Ministack-InSAR improved the spatial continuity of monitoring points (MPs) and preserved phase quality in vegetated reservoir-bank slopes. The RF/XGBoost–SHAP results identified groundwater storage, rainfall, distance to rivers, overburden thickness, and road density as the dominant controls on the spatial variability of deformation. Among the tested prediction models, the multi-source-factor convolutional neural network–long short-term memory (MSF-CNN-LSTM) model achieved the best overall performance, with a mean absolute error (MAE) of 3.0 mm, a root mean square error (RMSE) of 4.5 mm, and a coefficient of determination (R²) of 0.885. These results demonstrate that the proposed framework can effectively integrate deformation monitoring, mechanism interpretation, and short-term prediction, providing practical support for active-zone identification and early warning of reservoir-bank landslides. Full article

With growing global attention to marine resource exploitation and maritime security, unmanned surface vehicles (USVs) have come to play an increasingly important role in tasks such as ocean exploration, maritime patrol, and water-quality monitoring. In congested waters where multiple vessels interact simultaneously, the safe avoidance of clustered ships has emerged as a critical prerequisite for autonomous navigation, since the corresponding performance directly affects both navigational safety and mission efficiency. However, most existing studies are tailored to isolated targets and fall short of identifying latent fleets with coordinated kinematic patterns; meanwhile, conventional risk-domain models typically rely on symmetric geometries that lack directional adaptability, and the classical Dynamic Window Approach (DWA) suffers from weight sensitivity and a tendency to converge to local optima, rendering it inadequate for integrated decision-making in cluster scenarios. To address these issues, this paper proposes a cluster-aware USV collision-avoidance framework that integrates optimized spectral clustering, an asymmetric biased risk domain, and an improved DWA. At the perception layer, the original signed-mean neighbour representation is augmented with magnitude and dispersion descriptors to form Robust10, followed by data-dependent RBF scaling, normalized spectral clustering, and observation-based hazardous-cluster selection. At the risk-modeling layer, principal axis decomposition, four-direction asymmetric radii, and a direction-aligned centre offset define an explicit piecewise cluster domain. At the decision layer, the original three-score DWA and four-stage bypass state machine are retained, while latency-compensated vessel/domain prediction and hard 25 m separation checks are added. Spectral clustering, PCA, DWA, and finite-state control are established techniques; the contribution lies in the task-specific representation, risk-domain construction, safety modifications, and end-to-end coupling. In randomized experiments, Robust10 raises F1 from 50.73% to 73.99% over 360 perception scenes, while the original 150-realization structural planning ablation raises safe-mission success from 9.33% for PredictiveDWA to 54.0% for the complete default-weight framework. To address weight subjectivity separately from that structural ablation, a safety-first simulation-data calibration selected $[0.75,0.15,0.10]$; when frozen and evaluated on 60 previously unseen planning encounters, the calibrated vector raises safe-mission success from 41.67% for the engineering-default vector to 75.00%, with no observed 25 m separation violation. A matched non-DWA finite-control-set nonlinear model-predictive-control (FCS-NMPC) baseline achieves 81.67% safe-mission success on the same held-out encounters; the paired difference from the calibrated framework is not statistically significant ($p=0.4545$). In 60 end-to-end trials, Robust10 raises safe-mission success from 28.33% to 45.00% relative to Mean5. These results support competitive simulation-level performance under the tested conditions without implying global parameter optimality, universal superiority over MPC, or deployment-level reliability. Full article

Over the past decade, the Punjab plains of Northern India have experienced recurrent flooding driven by hydroclimatic variability, specifically shifts in western disturbances that have intensified monsoon precipitation. Following a devastating flood in 2025, the region remains highly vulnerable to hydrological extremes, a risk further exacerbated by shifting land use and agricultural patterns, geomorphic parameters, and complex fluvial systems. This study assesses flood susceptibility by integrating multi-sensor satellite observations, multi-temporal Sentinel-1 backscatter signals, and refined runoff potential estimates derived from local climate zones, accounting for land cover, soil type, and infiltration characteristics, into machine learning frameworks. The model is trained using a 2025 flood inventory generated from a synthetic aperture radar backscatter threshold ratio. The calibrated frameworks are applied to the 2023 flood events to test independent transferability. The temporal consistency and predictive performance of the models are evaluated using the precision–recall trade-offs, threshold-dependent predicted probability distribution, and Shapley Additive exPlanations (SHAP). Results indicate more balanced classification performance of Random Forest and Extreme Gradient Boosting in comparison to Artificial Neural Network performance that exhibits higher recall with lower precision. The model performance for 2023 models is considered more robust, with greater class separability of 2023 flood events than for 2025. Probability distributions for both events further demonstrate model-dependent threshold behavior, highlighting a trade-off between flood detection sensitivity. SHAP identifies rainfall, soil moisture, runoff, and elevation as the dominant contributors. The analysis further indicates that all model frameworks effectively capture the physical control of hydrological and topographical variability on the temporal flood events. The consistent contribution of hydrological and topographical factors across the two events supports model transferability, while threshold sensitivity, uncertainty, and spatial dependence are important considerations for flood susceptibility modelling. The results reflect a balanced interaction between extreme rainfall, runoff potential, and topographic control in causing periodic floods in the Punjab plains. Full article

Indentation testing is widely used to characterize microscale mechanical properties and provide input parameters for cross-scale modeling of heterogeneous rocks. However, the indentation-depth ranges over which the measured Young’s modulus and hardness represent an individual constituent or the composite matrix remain unclear. In this study, finite element modeling, dimensional analysis, and indentation experiments were used to investigate the depth-dependent response of rock particle–matrix systems and to establish depth criteria for the Oliver–Pharr (OP) and slope–energy (SE) methods. As the normalized indentation depth h_m/L increases, where h_m is the maximum indentation depth and L is the particle size, the indentation response evolves from particle-dominated, through a particle–matrix transitional regime, to matrix-dominated behavior, while the indentation-derived Young’s modulus shifts from the constituent value toward the matrix value. This transition results from matrix-induced changes in contact stiffness, projected contact area, and indentation work ratio. Particle morphology controls the initial deviation from constituent properties, whereas indenter angle and tip radius govern the convergence toward matrix properties. With a relative-deviation tolerance of approximately 10%, reliable constituent-property measurement requires h_m/L ≲ 0.01 together with a sufficiently large absolute indentation depth relative to the indenter tip radius and characteristic length scales associated with surface roughness and indentation-size effects. For the matrix side, the FEM predicts matrix-dominated depth limits of h_m/L ≳ 0.4 for the OP method and h_m/L ≳ 0.8 for the SE method under typical conditions where the particle yield strength exceeds that of the matrix. The OP-based depth limit is supported by the present indentation experiments, whereas the SE-based depth limit remains a numerical prediction requiring further direct experimental validation. Full article

The air-feeding system of a proton exchange membrane fuel cell (PEMFC) delivers oxygen for electrochemical reactions while critically influencing stack power, efficiency, and durability. Compared to hydrogen supply, air management poses greater technical challenges owing to the need for precise dynamic control, composition regulation, and impurity tolerance. Thermal management similarly governs reaction kinetics, water–thermal balance, and material longevity. To address the coupling between these two subsystems, this study proposes hybrid intelligent control architecture. For the highly nonlinear air supply system, a nonlinear enhanced sliding mode controller (ASMC) is developed that achieves finite-time convergence with improved response speed and reduced delay. For thermal management, a PID controller optimized by the RIME (Rime Ice Optimization) algorithm is designed. The coordinated strategy maintains optimal reaction conditions and meets dynamic power demands, thereby ensuring safe, efficient, and sustainable fuel cell operation. Simulation results demonstrate that the proposed ASMC reduces rise time by 85.0% compared to model predictive control and by 70.0% versus standard sliding mode control, with steady-state error 60.0% lower than that of fuzzy logic control. In thermal management, the RIME-optimized PID controller achieves rapid temperature stabilization within the optimal range, requiring 22.0% fewer iterations than the marine predator algorithm. The integrated control architecture effectively decouples air supply and thermal regulation objectives, providing a robust solution for PEMFC system operation. Full article

High-fidelity continuum models of electrostatically actuated MEMS accurately capture distributed electromechanical interactions but are computationally expensive for repeated simulation, optimization, and real-time applications. This work develops a physics-informed reduced-order modeling framework based on Lie symmetry classification for a nonlinear electrostatic MEMS microplate governed by a fourth-order integro-partial differential equation. The continuum model is recast as an extended canonical system separating local differential operators from nonlocal stretching and capacitive contributions. Lie group classification of the complete boundary value problem shows that the electrostatic singularity, constitutive coefficients, fixed geometry, and clamped boundary conditions suppress nontrivial continuous spatial symmetries in the generic bounded problem, while time translation survives only in the autonomous subclass. The discrete reflection invariances of the centered rectangular device are treated separately to identify invariant functional subspaces for Galerkin projection. A symmetry-preserving reduced-order model is then constructed in the even–even subspace, retaining bending, geometric stretching, pre-stress, capacitive feedback, dielectric inhomogeneity, and fringing field effects. Numerical verification against the high-fidelity continuum model shows close agreement with the FOM for static and transient responses while preserving reflection symmetry and remaining robust under parameter variations. For the nominal configuration, the monomodal Lie-ROM predicts a pull-in voltage of $127.73\mathrm{V}$ versus $128.21\mathrm{V}$ for the FOM, corresponding to an absolute relative error of $0.37\%$ and a signed error of $-0.37\%$. The monomodal formulation substantially reduces computational cost and consistently outperforms a classical lumped-parameter approximation, providing an interpretable and efficient basis for parametric analysis, design optimization, control-oriented modeling, and future digital twin applications. Full article

Turmeric (Curcuma longa L.) is one of the most important spice crops and a valuable medicinal plant, but it is seriously affected by various types of diseases such as fungal, bacterial, nematode and viral diseases. In this paper, a complete mathematical model of the turmeric plant disease dynamics is developed under a fractal-fractional model in this context, encompassing all four types of pathogens and associated treatment classes. The fractal-fractional Caputo derivative operator captures memory effects and, through its fractal exponent, a genuine deformation of the classical memory kernel, allowing the underlying biological dynamics to be represented more flexibly than under the classical integer-order derivative; we do not, however, claim that this kernel deformation corresponds to demonstrated self-similarity or spatial heterogeneity in the turmeric plant–pathogen system. We show the positivity and boundedness of the solutions, calculate the next-generation matrix approach-based basic reproduction number ${\mathcal{R}}_0$ and investigate the local and global stability of both disease-free and endemic equilibria by Lyapunov functionals. A sensitivity analysis of ${\mathcal{R}}_0$ is conducted to determine the most important parameters influencing disease transmission and control. The existence and uniqueness of solutions and Ulam-Hyers stability of solutions are established by fixed point theory. For the associated integer-order system, we formulate an optimal control problem is formulated with three time-dependent controls: the prevention effort ($u_1$), the enhancement of treatment ($u_2$), and the care management ($u_3$), and the optimality conditions are derived via Pontryagin’s maximum principle. Numerical simulations are conducted with three different fractal-fractional operators, namely Caputo, Caputo-Fabrizio and Atangana-Baleanu. A deep neural network is developed and trained to approximate the solution of the integer-order system. The third-layer deep neural network consists of neurons of sizes 80, 32, and 24, with activation functions of logistic sigmoid, radial basis and hyperbolic tangent, respectively, and is trained to approximate the system dynamics with the fourth-order Runge-Kutta method as a reference. The DNN is found to be very accurate in predicting the values with Nash-Sutcliffe Efficiency between 0.79 and 0.99 and Theil Inequality Coefficient around ${10}^{-2}$ in all 11 compartments, and hence proved capable of being a good surrogate modelling tool for the ODE systems. The present work contributes towards SDG 2 (Zero Hunger) and SDG 3 (Good Health and Well-being) by laying a mathematical basis for integrated disease management in turmeric cultivation for sustainable agriculture and food security. Full article

Continuous glucose monitoring enables Blood Glucose Level (BGL) forecasting, yet current deep learning models face a critical trade-off: general models lack individual accuracy, while personalized models suffer from a severe “cold-start” problem requiring extensive historical data. This study proposes an intermediate group-based forecasting paradigm utilizing time series clustering. We trained Long Short-Term Memory networks under a general, group-based, and personalized approach to forecast BGL at 60- and 120-min prediction horizons. Based on our previous research, patients were partitioned into six groups using K-means clustering applied to time series data. Results indicate that group-based models outperformed general baselines for patient groups with controlled BGL, enhancing clinical safety and reducing forecasting errors. Crucially, the group-based approach proved more robust than pure personalization, matching or exceeding personalized performance in 75% of overall cases. Most notably, personalized models failed to predict severe hypoglycemia in controlled patients due to a lack of these events in individual historical data, a vulnerability successfully bypassed by group-based models leveraging collective cluster histories. Ultimately, group-based forecasting emerges as a practical solution for Type 1 Diabetes management, providing the accuracy needed for critical event prediction while drastically reducing the data accumulation period required for rapid clinical deployment. Full article

The convergence of additive manufacturing (AM) and artificial intelligence (AI) is poised to redefine the landscape of modern production; however, the literature remains fragmented across isolated applications, lacking a unified perspective on the engineering impact and practical deployment of these technologies. This review provides a comprehensive and critical synthesis of the state of the art in AI-enhanced AM, systematically covering supervised, unsupervised, and reinforcement learning paradigms, alongside deep-learning-based computer vision, natural language processing, and robotics. In contrast to prior works that focus on singular aspects, this paper consolidates progress across four core engineering domains: (i) lightweight and manufacturable design, (ii) real-time in situ defect detection and process analysis, (iii) energy-efficient process optimization, and (iv) cost-effective build-time estimation with intelligent support minimization. Beyond cataloging these advances, this review identifies key quantitative benchmarks and recurring technical challenges, including data scarcity, poor model generalizability, and the critical gap between offline prediction and real-time closed-loop control. To transcend these isolated successes and enable industrial adoption, we propose a novel, unified closed-loop AI-AM framework that tightly integrates generative design, process planning, in situ production monitoring, and continuous model updating into a cohesive digital thread. Furthermore, a domain-stratified SWOT analysis is compiled, offering a strategic evaluation of strengths, weaknesses, opportunities, and threats across the four application pillars. By bridging the gap between laboratory prototypes and production-ready autonomous systems, this review serves as a definitive reference for researchers and practitioners aiming to navigate, deploy, and advance the rapidly evolving field of AI in additive manufacturing. Full article

Existing satellite-based radar reflectivity reconstruction methods tend to underestimate strong convective echoes and have difficulty preserving fine spatial structures. This study proposes HAF-Net, a Hierarchical Adaptive Fusion Network for generating satellite-derived proxy fields of composite radar reflectivity (CREF) from FY-4B AGRI observations and auxiliary geospatial variables. HAF-Net is designed to address three challenges in satellite-to-radar reconstruction: the scale mismatch between compact convective cores and extended cloud systems, false alarms associated with irrelevant background responses transmitted through skip connections, and the loss of echo-structure detail when predictions rely only on the final decoder output rather than complementary multi-level representations. Experiments conducted using spatiotemporally matched FY-4B and CINRAD observations show that HAF-Net achieved the best overall performance among the evaluated models across continuous, structural, and threshold-based metrics. Ablation results suggest that Multi-Receptive-Field Convective Feature Extraction block (MCFE) is associated with improved strong-echo representation, while the changes observed after removing Convective-Saliency-Guided Skip Refinement (CSGR) and Hierarchical Echo-Structure Reconstruction Fusion module (HERF) are consistent with their intended roles in false-alarm control and multi-scale structural reconstruction. The reported metrics characterize performance on a radar-covered, day-level held-out test set screened to retain echo-containing scenes within the sampled geographic domain; they should not be interpreted as geographically independent validation or as climatologically representative all-weather performance. Full article

Due to the cross-section characteristics of rectangular pipe jacking, the “soil-carrying effect” of overlying soil migration with the pipeline is prone to occur during jacking in shallow strata, resulting in a sharp increase in jacking resistance and large deformation of the strata. In this paper, a visual similarity model test of the soil-carrying effect is carried out for shallow buried large-section rectangular pipe jacking. The experiment innovatively combines VIC-3D digital image correlation technology, a 3D laser scanner and a thin-film pressure sensor to monitor the displacement of deep soil, surface heave and pipe resistance in an all-round and high-precision way. The influence of the overburden ratio and pipe–soil friction coefficient on the evolution of back soil was systematically studied. The results show that the evolution of the soil-carrying effect presents the three-stage characteristics of ‘elasticity-slip-strengthening’, and the smaller the overburden ratio, the larger the friction coefficient. And the smaller the critical displacement of the back soil, the more severe the formation disturbance. Based on the principle of mechanical balance, this paper puts forward the theoretical prediction model of the whole soil-carrying effect, deduces the critical friction coefficient and the critical jacking mileage, and compares it with the experimental results, which provides a scientific basis for the optimization of construction parameters and safety control of shallow buried rectangular pipe jacking. Full article