Search Results (11,402)

Selective laser melting (SLM) offers a promising route for fabricating high-performance Cu–Sn alloys; however, the extremely transient thermal behavior of the molten pool and its influence on microstructural evolution and mechanical properties remain insufficiently understood. In this study, a finite element model based on ABAQUS was developed to simulate the transient temperature field and molten pool dynamics of Cu–10Sn alloy during the SLM process. By systematically varying the volumetric energy density (VED), the interplay among molten pool geometry, thermal characteristics, microstructure, and mechanical performance was investigated through a combination of numerical simulation and experimental investigation. The results reveal that increasing VED significantly enlarges the molten pool dimensions, elevates the peak temperature, and intensifies the maximum heating and cooling rates, thereby altering solidification conditions. At a VED of 208.33 J/mm³, the molten pool reached its maximum dimensions, with a length of 230 μm, a width of 161 μm, and a depth of 85 μm, resulting in the highest relative density within the investigated range (98.33%). Under this processing condition, the Cu–10 wt.% Sn (Cu–10Sn) alloy exhibited microhardness values of 190 HV near the solidified areas of melt pool interior and 208.4 HV near the solidified areas of melt pool boundary, accompanied by an ultimate tensile strength of 494 MPa. These findings elucidate the critical role of molten pool thermal behavior in governing microstructural refinement and mechanical properties of SLM-fabricated Cu–10Sn alloys and provide a mechanistic basis for understanding the effect of process parameters. Full article

Reference-driven humanoid motion tracking aims to reproduce a source motion on a target humanoid while preserving physical executability under actuation limits and changing contact conditions. The problem becomes particularly challenging for dynamic motions involving rapid support transitions, landing impacts, mixed hand–foot contacts, and moderate topology-preserving morphology variation. Existing pipelines often rely heavily on morphology-specific world-frame targets or treat balance and contact quality only indirectly during execution, which limits their reliability under dynamic contact variation. This paper presents a task and cost formulation for reference-driven humanoid tracking within the residual-based MuJoCo model predictive control (MPC) framework. The source motion is decomposed into a pelvis-centered canonical local reference, pelvis height and tilt references, and a pelvis-derived horizontal center-of-mass (CoM) velocity intent, and is tracked online with a zero moment point (ZMP)-aware contact-conditioned residual design including slip, penetration, posture, and control regularization. The formulation is compatible with standard MuJoCo MPC planners, and the evaluation is conducted under a shared iterative linear quadratic Gaussian (iLQG) setting on nominal and morphology-varied humanoids against tracking-only and two-stage inverse-kinematics (IK)-based baselines. The proposed formulation improves success rate, support quality, slip reduction, and progression accuracy, with the clearest gains on contact-sensitive motions; for example, success rate increases from 56.7% to 76.7% on Jump–Turn and from 46.7% to 70.0% on Cartwheel relative to the tracking-only MPC baseline. These results support the use of execution-oriented reference representation and contact-conditioned residual design for physically reliable reference-driven humanoid tracking. Full article

This work presents the design, development, and proof-of-concept validation of a smart greenhouse for romaine lettuce (Lactuca sativa var. longifolia) that integrates Internet of Things (IoT) sensing/actuation with an image-based crop state assessment pipeline. The proposed pipeline combines a lightweight AI image classifier with fractal texture descriptors (box-counting fractal dimension) to support the non-destructive monitoring of leaf condition and growth stage. The system also implements resilience-oriented resource strategies, including rainwater harvesting, graywater reuse, and a hybrid power supply (photovoltaic + grid backup). Water and energy indicators are reported as estimated values derived from the prototype operating profile and literature-based baseline values (i.e., contextual comparisons rather than a contemporaneous controlled trial). Using an expanded dataset (n = 1500 images) and an independent held-out test subset (n = 350), the image classifier achieved 97.1% accuracy, with detailed precision/recall/F1 metrics reported in the Results. Overall, the proposed architecture and evaluation workflow provide an accessible and reproducible pathway toward sustainable, low-cost smart greenhouses in resource-constrained settings. Full article

We present a modular multi-attribute vehicle analysis pipeline that integrates YOLO-based models and an OCR engine into a single workflow. The system detects vehicles, classifies color, recognizes make and sub-model, detects license plates, and extracts plate characters to generate a structured vehicle record. Vehicle detection is reported with standard metrics (precision, recall, and mAP@0.5), while license plate detection is reported at IoU = 0.3 to reflect the small-object nature of plates and downstream OCR usability. Among the evaluated versions, YOLOv8 provides the most balanced overall performance across modules, while maintaining real-time-equivalent throughput of approximately 18–22 FPS for the full pipeline on recorded traffic videos, depending on scene complexity. We emphasize module-level evaluation and runtime benchmarking; instance-level end-to-end identification across unique vehicles is defined as future work once track-based ground truth becomes available. Full article

Neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) represent some of the most complex and therapeutically challenging disorders in modern medicine. Despite decades of research, the traditional one drug–one target paradigm has largely failed to deliver disease-modifying therapies. Increasing evidence suggests that these complex diseases arise from interconnected pathological networks involving protein aggregation, oxidative stress, mitochondrial dysfunction, neuroinflammation, and synaptic loss. In this context, natural products (NPs) have re-emerged as a promising pipeline for next-generation therapeutics. Unlike conventional small molecules, NPs inherently exhibit polypharmacology, targeting multiple pathways simultaneously. Recent advances (2019–2026) demonstrate a paradigm shift, from crude NPs and single-mechanism compounds toward engineered derivatives, network pharmacology, and multi-target drug design. Using AD and PD as case studies, this review critically evaluates how NPs are redefining drug discovery by highlighting key emerging NPs, translational strategies, and future directions. Full article

Colombian transitory crop production exhibits marked structural heterogeneity across department–crop combinations, yet empirical characterizations of productive scale at the subnational level remain scarce. This study presents a descriptive analysis and clustering-based productive scale segmentation of Colombian transitory crops at the departmental level for the period 2007–2024. Data from the Evaluaciones Agropecuarias Municipales(EVA) were processed through a structured CRISP-DM pipeline comprising preprocessing of 347,141 records, departmental aggregation, and engineering of five clustering features: average production, average planted area, number of active periods, and temporal and spatial Herfindahl–Hirschman indices. K-Means clustering ($k=3$)was applied to a final dataset of 490 department–crop pairs and validated based on a global silhouette coefficient of 0.888. The segmentation reveals a markedly asymmetric productive structure: 93.7% small scale (459 pairs), 5.3% medium scale (26 pairs), and 1.0% large scale (5 pairs), with natural breakpoints at approximately 35,386 t and 275,959 t. Large-scale production is concentrated in papa (Cundinamarca, Boyacá, Nariño) and arroz (Casanare, Tolima). Clustering demonstrated quantitative superiority over quartile-based classification, reducing the within-group coefficient of variation from 223.9% to 30.6% for the upper segment. The methodology is replicable across national agricultural statistics systems, and the processed dataset is publicly available under CC BY 4.0. Full article

Image stitching is a computational technique designed to align and seamlessly fuse multiple overlapping images into a single panoramic image with an extended field of view. It plays a critical role in diverse domains, including mobile photography, autonomous navigation, and visual perception systems. However, most conventional image stitching pipelines implicitly assume that the input images have been pre-corrected for geometric distortions, particularly radial distortion inherent to wide-angle and fisheye lenses. This assumption often fails in practice, as many consumer-grade cameras lack built-in correction or calibration support. Consequently, applying standard image stitching methods to the uncorrected imagery frequently degrades feature correspondence reliability and introduces visible geometric misalignments and seam discontinuities in the final panorama. To overcome these limitations, this paper introduces a task-driven joint iterative optimization framework for image stitching that unifies unsupervised radial distortion correction, distortion-aware feature registration, and seam-aware blending within a single cohesive optimization objective. Specifically, lens distortion parameters are explicitly modeled as learnable variables and embedded into both the geometric registration and seam optimization sub-problems. An efficient closed-loop optimization strategy is then employed to jointly refine distortion parameters, homography estimates, and optimal seam paths in an alternating, mutually reinforcing manner. Implementation-wise, we first propose a calibration-free initial radial distortion estimation method which leverages intrinsic image gradients and epipolar consistency to provide physically plausible initialization for subsequent optimization. During iteration, distortion parameters are progressively refined by integrating robust geometric constraints derived from current feature matches (via RANSAC-based consensus filtering) with photometric consistency cues. Extensive experiments on multiple public benchmarks featuring pronounced radial distortion demonstrate that our method achieves superior stitching fidelity using metrics including PSNR and SSIM. It also confirms enhanced feature matching stability, which outperforms both distortion-agnostic approaches and two-stage pipelines that decouple distortion correction from registration. Furthermore, comprehensive ablation studies quantitatively and qualitatively validate the functional necessity and synergistic contribution of each core module, confirming the design rationale and effectiveness of the proposed joint optimization architecture. Full article

Cardiometabolic diseases, encompassing obesity, insulin resistance, type 2 diabetes (T2D), metabolic dysfunction-associated steatotic liver disease (MASLD), hypertension, and atherosclerotic cardiovascular disease (ASCVD), represent a vast continuum driven by multi-organ network dysregulation. Clinical risk assessment remains dominated by late-stage measures (e.g., fasting glucose, HbA1c, standard lipids). While these assessments predominate the literature and clinical trial endpoints, each incompletely capture early mechanistic risk, inter-individual heterogeneity, and differential response to interventions. Multiomics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, microbiomics, and extracellular vesicle/exosome cargo profiling) expands the biomarker landscape but introduces translational barriers: high dimensionality, cohort heterogeneity, limited causal inference, and insufficient validation pipelines. AI-driven systems biology platforms can support cardiometabolic biomarker discovery and therapeutic translation by enabling systems-level biological inference across heterogeneous datasets, prioritizing mechanism and traceability over purely correlation-based models. GATC Health’s Operon™ platform is described as a proprietary, AI-driven internal scientific computing platform designed to support therapeutic discovery and development decision-making across the pharmaceutical lifecycle, including evaluation of drug efficacy, safety, off-target effects, pharmacokinetics (PK), pharmacodynamics (PD), and overall development risk. Operon evolved from earlier generations of GATC Health’s internal multiomic modeling systems (formerly referred to as the Multiomics Advanced Technology, MAT) and incorporates expanded data types, orchestration layers, validation workflows, and productization frameworks. Operon is operated by GATC scientists and generates structured, productized outputs (e.g., formal assessments, analyses, and decision frameworks) that are reviewed by experts. Operon methodologies have undergone internal validation and independent academic evaluation under blinded conditions, with reported classification performance (true positive rate 86% and true negative rate 91%) in controlled evaluation settings; these performance metrics should not be interpreted as guarantees of clinical success. This review provides a T2D-centered cardiometabolic biomarker landscape with cardiovascular extension and outlines how Operon-enabled multiomic integration and scenario-based simulation can support early screening, endotype stratification, mechanistic interpretation, and precision intervention design, including AI-guided polypharmacology strategies. Full article

EmbeddedField-Programmable Gate Array (FPGA)-Advanced RISC Machine (ARM) systems used in industrial and Internet of Things (IoT) environments increasingly operate as network-connected edge devices. While such connectivity enables distributed processing and remote monitoring, it also exposes embedded vision nodes to security threats, including command injection, frame replay, data tampering, and abnormal communication traffic. This paper presents a hardware-assisted security architecture for an FPGA-ARM embedded vision system designed for high-speed image acquisition and network streaming. The proposed solution integrates several lightweight protection mechanisms directly into the FPGA processing pipeline, including frame replay detection, cyclic redundancy check (CRC)-based frame integrity verification, frame sequence monitoring, authenticated command execution, communication anomaly monitoring, and hardware-rooted trust primitives, such as a ring-oscillator physical unclonable function (PUF) and a pseudo-random generator. Optional secure communication is provided via a lightweight ASCON-authenticated encryption core. The architecture was implemented on a Cyclone V System-on-Chip (SoC) platform using an industrial Camera Link camera and evaluated in a low-latency image-acquisition setup operating at 100 fps, with data throughput exceeding 1 Gbps. Experimental results demonstrate that the proposed security architecture introduces only about 1.6% additional FPGA logic utilization while maintaining full real-time acquisition performance. The presented approach demonstrates that practical hardware-level security mechanisms can be integrated into FPGA-based embedded vision nodes with minimal architectural modifications and negligible performance overhead. Full article

Bats are recognized reservoirs for a vast array of viral diversity, including members of the Hepadnaviridae family. Within a One Health framework, genomic surveillance of these animals is fundamental to understanding viral diversity and the potential risks of zoonotic spillover in high-density human population areas. This study describes the detection of a bat hepadnavirus through agnostic viral metagenomics in samples from passive surveillance collected in urban and peri-urban areas in Brazil. Sequencing was performed using the Oxford Nanopore Technologies (MinION) platform, and the bioinformatics pipeline involved de novo assembly and taxonomic identification against viral databases. We identified several contigs with similarity to the Tent-making bat hepatitis B virus (TBHBV) in a single liver sample. The largest contig (3182 bp) represents the complete genome, exhibiting a nucleotide identity of 80.93% with the original reference isolate. Our findings document the circulation of this viral lineage in a new epidemiological setting (the Brazilian urban interface), underscoring the importance of continuous surveillance to monitor the evolution and geographic distribution of bat orthohepadnaviruses and their relevance to public health. Full article

Background/Objectives: Prostate adenocarcinoma exhibits substantial inter-patient heterogeneity, limiting the accuracy of current prognostic tools. Prostate-specific antigen-based assessment remains insufficient for reliable survival prediction. There is a clear need for integrative, data-driven approaches that leverage multi-dimensional clinical and molecular data to improve outcome stratification. This study aimed to develop and evaluate an explicable machine learning framework for predicting overall survival in prostate adenocarcinoma. Methods: A comprehensive machine learning pipeline was constructed using clinical and laboratory data from 494 patients in the TCGA PanCancer Atlas cohort. Following data curation, 16 clinically relevant features were selected through expert-guided filtering and feature selection techniques. Missing values were addressed using imputation strategies, and class imbalance was mitigated using SMOTE. Eight machine learning models were evaluated, including a novel hybrid ensemble model combining Gradient Boosting Machine and random forest (GBM + RF). Model performance was assessed using stratified 10-fold cross-validation and quantified via accuracy, precision, recall, F1-score, and ROC-AUC. Model interpretability was examined using LIME, and prognostic relevance was validated through Cox proportional hazards regression. Results: The hybrid GBM + RF model demonstrated superior performance, achieving 97% accuracy and a ROC-AUC of 0.95 under mode imputation with SMOTE balancing. Ensemble-based models consistently outperformed single classifiers, particularly in handling missing data and class imbalance. Key predictors of survival included progression-free survival, hypoxia-related scores, genomic instability markers, and immune-associated variables. Cox regression analysis confirmed the independent prognostic significance of these features, supporting the biological plausibility of the model. Conclusions: An explainable ensemble machine learning approach enables accurate and clinically interpretable prediction of overall survival in prostate adenocarcinoma. The proposed framework provides a robust foundation for precision urology decision-support systems and warrants validation in independent cohorts. Full article

Sit-to-stand (STS) transitions are clinically informative indicators of functional independence and are sensitive to compensatory strategies associated with physical decline and pain. This study presents a non-contact, non-visual framework for quantitative STS assessment using a 60 GHz frequency-modulated continuous-wave (FMCW) radar in a residential setting. We developed a signal-processing pipeline that converts intermediate-frequency radar data into range–time intensity (RTI) maps, tracks dominant torso motion, and extracts temporal, kinematic, and spectral features. Experiments were conducted across two sensing orientations (subject-facing and side-facing), five mounting heights (45–153 cm), and three execution speeds, with approximately 30 repeated cycles per condition. For normal non-compensated STS transitions, radar-derived metrics reflected expected biomechanical scaling: mean full-cycle duration decreased from $23.90$ s (slow) to $13.95$ s (medium) and $7.98$ s (fast), while peak ascent velocity increased from $0.311$ m/s to $0.358$ m/s and dominant cadence increased from 0.0416 Hz to 0.125 Hz. Simulated abnormal transitions produced distinct and quantifiable deviations. Preparatory rocking introduced an additional oscillatory phase (mean rocking duration 2.36 s), prolonging the standing transition to 4.80 s and altering trajectory regularity. Across configurations, subject-facing mid-torso mounting provided the most continuous and separable STS signatures, whereas side-facing placement and extreme heights reduced effective radial motion or introduced clutter artifacts. These findings establish practical deployment guidelines and demonstrate that radar-derived STS metrics can serve as candidate digital biomarkers for unobtrusive, privacy-preserving detection of mobility decline, compensatory pain behaviors, and functional impairment in real-world home environments. Full article

Background: Accurate and early detection of hematological disorders from microscopic peripheral blood smear images remains a technically challenging task due to inherent imaging limitations, including noise contamination, low contrast, staining variability, and significant cellular overlap. Conventional deep learning-based object detection frameworks often exhibit limited robustness under such conditions and demonstrate reduced sensitivity to small-scale morphological structures, particularly platelets and abnormal cell variants. Methods: To address these challenges, this study proposes a hybrid detection framework that integrates a fuzzy logic-driven image preprocessing module with the YOLOv11 object detection architecture. The proposed preprocessing pipeline employs adaptive fuzzy membership functions to normalize pixel intensity distributions, suppress high-frequency noise, and enhance edge-defined cellular boundaries. This transformation produces a structurally optimized feature representation, improving downstream feature extraction and localization performance. The proposed framework was evaluated on a curated dataset of 3000 annotated microscopic blood smear images spanning five hematological classes. Results: Experimental results show that the fuzzy logic module improves mAP@0.5 by +3.4% and mAP@0.5:0.95 by +3.6%, confirming its effectiveness in enhancing both classification and localization accuracy. Conclusions: These findings demonstrate the robustness and practical applicability of the proposed hybrid approach under challenging imaging conditions. Full article

As digital payment infrastructures expand, the incidence of card-not-present fraud has become a major source of operational and financial risk for banks, payment processors, and merchants. In response, financial institutions increasingly rely on data-driven decision systems, yet fraudsters continuously adapt their strategies to evade conventional rule-based controls. A promising way to strengthen risk management is to model transactional data so as to uncover non-trivial, high-dimensional patterns characteristic of fraudulent behavior and to embed these models into real-time decision pipelines. In this work, we develop and compare a suite of learning-based fraud detectors, including a convolutional neural network and several machine learning classifiers, within a unified quantitative risk-management framework. The problem is formulated as a supervised classification task within a quantitative risk management framework, where the cost of missed fraud is particularly critical. The mathematical contribution is methodological rather than architectural: we design a leakage-safe and prevalence-faithful evaluation protocol for extremely imbalanced binary classification, combine cross-validated hyperparameter optimization with risk-aligned model selection based on metrics such as recall and Matthews correlation coefficient, and quantify uncertainty by bootstrap confidence intervals and paired McNemar tests. In addition, we connect statistical evaluation with deployment-time decisioning through a decision-theoretic, cost-sensitive threshold rule, showing how institution-specific false-positive and false-negative costs determine the operating point of the classifier. Because fraudulent transactions constitute only a small proportion of the total volume, we employ resampling strategies to mitigate severe class imbalance and systematically calibrate the models via cross-validated hyperparameter optimization. The empirical analysis on real transaction data shows that carefully tuned deep and ensemble methods can achieve strong fraud-detection performance, while the proposed framework clarifies which performance differences are statistically meaningful and which operating points are most suitable under institution-specific false-positive and false-negative costs. Full article

Cucurbit crops—including cucumber (Cucumis sativus), watermelon (Citrullus lanatus), and melon (Cucumis melo)—are of major economic and nutritional importance worldwide. Yet their productivity and quality are severely compromised by foliar fungal diseases, particularly powdery mildew (PM), downy mildew (DM), and target leaf spot (TLS). While PM and DM have been extensively studied, TLS has emerged as an increasingly prevalent and damaging disease in key production regions, yet it remains comparatively understudied—especially with respect to its molecular basis and comparative pathobiology relative to PM and DM. Current reliance on chemical fungicides is hampered by escalating pathogen resistance and concerns over residual toxicity, whereas conventional breeding approaches face inherent limitations in pyramiding durable, broad-spectrum resistance against multiple pathogens. In this context, cucumber has emerged as a pivotal model species for dissecting foliar disease resistance mechanisms in cucurbits, supported by a high-quality reference genome, extensive resequencing datasets, diverse germplasm collections, and an efficient Agrobacterium-mediated transformation system. Despite these advantages, existing reviews predominantly address PM or DM resistance in isolation; comprehensive syntheses integrating TLS resistance advances—and critically, cross-disease comparisons of genetic architecture, transcriptional reprogramming, and defense signaling—are notably scarce. Furthermore, the translational pipeline—from gene discovery and functional validation to deployment in marker-assisted or genome-edited breeding—lacks systematic evaluation. Here, we provide a focused, cucumber-centered review that (i) synthesizes recent progress in mapping QTLs and GWAS loci, and characterizing key resistance-associated gene families (such as NLRs, RLKs, PR genes) conferring resistance to PM, DM, and TLS; (ii) integrates transcriptomic, epigenomic, and proteomic evidence to delineate conserved versus pathogen-specific host responses; (iii) highlights breakthroughs and unresolved questions in TLS resistance research, including the roles of novel susceptibility factors and non-canonical immune regulators; and (iv) critically assesses bottlenecks in translating resistance genes into practical breeding outcomes—such as linkage drag, functional redundancy, and genotype-by-environment interactions—and proposes empirically grounded strategies for accelerating molecular design of multi-disease-resistant cultivars. Collectively, this review aims to bridge fundamental insights with applied breeding goals, offering a conceptual and strategic framework for integrated management of foliar fungal diseases and the development of durable, broad-spectrum resistance in cucurbits. Full article