Search Results (6,397)

Selecting a sensing architecture for knee osteoarthritis (OA) screening requires balancing biomechanical information, system complexity, and reproducibility. We audited a public Korean multimodal gait dataset and performed a leakage-resistant internal evaluation. The release contained 180 participants (90 normal, 90 knee OA) measured using a smart insole, instrumented gait mat, and inertial measurement units (IMUs); all 1080 JavaScript Object Notation (JSON) files were checked for structural, value, provenance, and duplication errors. The primary benchmark was a fixed class-balanced L2 logistic regression model using nine gait mat variables, evaluated with subject-level repeated stratified five-fold cross-validation and 10,000 outcome-stratified bootstrap resamples. The audit identified 14 source-path metadata errors and one opposing-label duplicate smart insole payload, but no parsing, schema, range, or cross-partition subject errors. The gait mat model achieved an area under the receiver operating characteristic curve (AUROC) of 0.924 (95% confidence interval [CI], 0.879–0.962), balanced accuracy 0.883 (0.833–0.928), sensitivity 0.856, specificity 0.911, and Brier score 0.102. Adding smart insole and/or IMU features did not improve AUROC. Provider-model reproduction was descriptive because the public Validation partition informed model selection. In this release, the compact gait mat feature set provided the most favorable observed balance of discrimination, interpretability, and sensing complexity; external prospective evaluation is required before clinical use. Full article

Financial media risk is jointly driven by multisource content, including news reports, corporate announcements, social media posts, short videos, and livestreams, while content authenticity, propagation velocity, asset relevance, and trading infrastructure conditions can simultaneously influence short-term market fluctuations. To address the limitations of existing methods, including their reliance on either textual information or market sequences, insufficient source verification, and inadequate alignment of asynchronous multimodal signals, FMRP-Net is proposed for artificial intelligence-driven sensing. Event semantics, risk categories, and asset association information are first extracted through a pretrained financial language model and cross-modal consistency analysis. A dual-layer hardware reliability perception module is then employed to integrate sensing evidence from cameras, microphones, terminal inertial signals, server temperature, power consumption, network traffic, and transmission latency. Heterogeneous temporal propagation graphs, cross-frequency alignment, and bidirectional propagation–market coupling are further incorporated to jointly predict market direction, volatility, risk level, and propagation trends. Experimental results demonstrate that FMRP-Net achieved an Accuracy of $0.832$, a Macro-F1 of $0.824$, a ROC-AUC of $0.891$, an MCC of $0.665$, and a PR-AUC of $0.883$ for market direction prediction over future horizons of 5, 15, 30, and 60 min, indicating a balanced performance in terms of Precision and Recall. For volatility prediction, MAE, RMSE, and MAPE values of $0.0178$, $0.0271$, and $12.46\%$ were obtained, respectively, together with an $R^2$ of $0.812$. In the ablation study, the media risk Macro-F1 and source reliability AUC reached $0.842$ and $0.929$, respectively, while the propagation-scale prediction error was reduced to $0.109$ and the average early-warning lead time reached $10.6$ min. These results demonstrate that the integration of multimedia semantics, hardware sensing evidence, and propagation structures can effectively improve the accuracy, stability, and interpretability of financial market prediction and risk early warning. Full article

Recursive evidence fusion gives an intrusion detection system temporal memory, but it also gives unreliable windows and erroneous review outcomes a path to influence later diagnoses. Existing drift-handling, open-set, conformal, continual-learning, and human-in-the-loop methods provide useful signals or update classifiers and memories; they do not, by themselves, specify when a post-classification evidential state may be written or replaced. We present RTEF-IDS, a protocol that separates current action, model-evidence admission, reviewed-feedback admission, and history replacement. The protocol retains the history-relative reliability principle from our previous work, instantiates it for singleton-plus-ignorance IDS evidence, and assigns operation-specific credentials. Reviewed windows make no base-state change, mapped-known feedback may be appended, and replacement requires persistent confirmation. On 33,384 frozen windows, 30% retrospective admission excludes 26.3% of held-out-or-misclassified mass while retaining 94.8% of known-correct evidence. Under paired imperfect feedback, retrospective replacement increases one-window future history-state agreement by 0.107 in the primary block and 0.129 in IoT-23 leave-scenario-out replay. Under a past-only rolling-budget gate within externally supplied frozen partitions, the corresponding increments are 0.001 and 0.000, indicating that the tested gate exposes few qualifying replacement opportunities; bounded external short streams show the same opportunity constraint. Independent second review reduces false authorization from 5.66 to 0.124 per 1000 first-stage reviewed windows under independent errors and from 34.27 to 0.181 under five-window correlated errors, with a corresponding increase in review demand and a reduction in admitted corrective feedback. A shared systematic label alias remains unresolved by the tested review arms. These results support explicit, auditable state-mutation control while identifying the causal-opportunity and feedback-provenance conditions under which it operates. Full article

Earth Observation (EO) analysis increasingly relies on large and heterogeneous satellite datasets, yet developing EO workflows often requires specialized expertise in data selection, geospatial programming, and cloud-based processing. Recent advances in Large Language Models (LLMs) offer new opportunities for natural-language interaction with EO systems, although challenges related to transparency, reproducibility, and domain-specific reasoning remain. This study presents SeaScope, an explainable AI framework that integrates LLMs, Retrieval-Augmented Generation (RAG), scientific knowledge retrieval, and Google Earth Engine (GEE) to transform natural-language requests into transparent and executable EO workflows. The framework combines knowledge retrieval, code generation, cloud execution, provenance tracking, and interactive visualization within a unified environment. A pilot implementation is demonstrated through maritime and coastal monitoring applications, including oil spill detection, vessel monitoring, water quality assessment, floating debris detection, and air quality analysis. Multiple state-of-the-art LLMs are evaluated under both RAG and non-RAG configurations using representative EO case studies. The results indicate substantial differences among model families and show that retrieval augmentation can significantly improve workflow generation quality and reliability for capable models, while providing more limited benefits for smaller models. The proposed framework demonstrates the potential of explainable AI agents to support transparent, reproducible, and scalable EO analysis. Full article

Preoperative immunonutritional indices are widely reported to predict survival after colorectal cancer (CRC) resection, yet their independence varies across cohorts and no data exist from the Arab Gulf. In this retrospective cohort study we analyzed 316 patients undergoing curative resection for stage I–III colorectal adenocarcinoma at a Saudi tertiary center between 2013 and 2022, of whom 48 (15.2%) presented as emergencies. The prognostic nutritional index (PNI) and a composite albumin–neutrophil-to-lymphocyte ratio (albumin–NLR) score were assessed against overall survival (OS) and disease-free survival (DFS) using Cox models adjusted for age, sex, emergency presentation, tumor site, neoadjuvant therapy, adjuvant chemotherapy and lymphovascular invasion and stratified by stage and American Society of Anesthesiologists class. Over a median follow-up of 58.1 months there were 88 deaths and 124 DFS events. The PNI independently predicted OS (adjusted hazard ratio 0.958, 95% CI 0.929–0.987) and DFS (0.964, 0.940–0.988); the albumin–NLR score did not. Albumin alone carried the signal (OS 0.938), with lymphocytes, neutrophils and the NLR all null. The PNI effect was confined to the first 36 months (0.944 versus 1.003 thereafter), and published cut-offs classified 70.8% of the cohort as high-risk. Immunonutritional prognostication in CRC is albumin-driven, time-limited and sensitive to cut-off provenance. Full article

Reliable retrieval-augmented generation requires consistency across query interpretation, evidence selection, and final answer generation. This study defines computational symmetry as bidirectional coverage among canonical query constraints, traceable evidence, and answer claims, with residual asymmetry triggering correction or abstention. The proposed framework integrates a source-linked raw text/entity/event knowledge graph, hybrid dense–sparse retrieval, cross-encoder reranking, pre-retrieval semantic alignment, and a post-retrieval verification gate. DeepSeek-V3 serves as the implementation backbone, while “ChatGPT-type” denotes the broader class of instruction-following conversational large language models. Experiments use T2Ranking for retrieval and reranking, ATIS for diagnostic intent–slot evaluation, and controlled dialogue scenarios derived from T2Ranking. The hierarchical representation improves retrieval F1 from 0.586 to 0.660, while the complete pipeline increases average answer correctness from 0.530 to 0.611 compared with direct LLM answering and from 0.559 to 0.611 compared with graph retrieval. On ATIS, the controller achieves 92.61% intent accuracy, below Joint BERT at 95.18%, and is therefore treated as a reusable orchestration module rather than a superior classifier. The results support the proposed verification correction framework within the tested settings, without claiming superiority over untested adaptive RAG systems. Full article

Modern AI often works as a “black box”: it gives an answer, but cannot show why. In high-stakes fields like law, medicine, and government—and under emerging rules such as the EU AI Act—that is a serious problem. Today’s most popular explainability tools, such as SHAP and LIME, only approximate a model’s reasoning after the fact, and their explanations can be unstable. This review explores a different, often overlooked path: logical systems. We first explain in plain terms what they are and where they come from, then compare the main families—propositional, deontic, and first-order logic paired with modern solvers—by what each can express and guarantee. Our main contribution is a comparative taxonomy organized by explanatory guarantees, which reveals that no existing class simultaneously offers natural-language input, formal verifiability, and reproducibility. We then examine neuro-symbolic systems, illustrated by a representative engine (Causal Logic Engine, CLE), where a language model reads the text but a transparent logical layer makes the decision, checked by a human; the engine is described end to end, down to a worked example traced from raw text to the final decision. The key idea: instead of opening the black box, we move the decision outside it—so the reason behind every answer becomes clear and reproducible. Full article

Automated extraction of structured threat information from unstructured cyber threat intelligence (CTI) underpins modern security operations, yet the supporting machine learning resources are almost exclusively English: no annotated Arabic CTI named entity recognition (NER) corpus has been published. We introduce AraCTI-NER, a dataset of 10,312 token-level annotated samples (275,530 tokens; 42,360 entity spans) over eight STIX-inspired entity types, built by an LLM-assisted pipeline seeded with authentic Arabic cybersecurity articles, structurally validated and rebalanced through targeted generation. We benchmark seven encoders from three families (Arabic-specialized, English cybersecurity-adapted, and multilingual) over three seeds under strict entity-level metrics, and release a 408-sentence expert-audited test subset (ATS-gold) whose reliability is quantified by a second independent expert validation (inter-annotator agreement 0.878 entity-level F1). XLM-RoBERTa Large attains the best mean F1 (0.7603; 0.7674 on ATS-gold), with AraBERTv2 close behind (0.7491), while both English-only cybersecurity encoders fall to ≈0.63, a separation that holds across every seed and survives expert correction, with the ≈3-point F1 decrease from ATS-silver to ATS-gold concentrated in Vulnerability and TTP. On 350 doubly annotated sentences from authentic Arabic cyber-incident news, a shift in both provenance and register, the strongest model reaches F1 = 0.5429 against an inter-annotator F1 of 0.616. AraCTI-NER establishes the first reproducible baseline for Arabic CTI NER and identifies domain-adaptive Arabic cybersecurity pre-training as the highest-value next step. Full article

Background: Research in behavioral genetics has demonstrated that genetic factors significantly contribute to individual differences in behavior, while environmental exposures shape gene expression through epigenetic mechanisms. This interaction is also relevant to the development of antisocial behavior and psychopathic traits. One of the first evidence of gene–environment interaction was the association between low-activity variants of the MAOA gene, childhood maltreatment, and increased risk of antisocial behavior [¹]. Similarly, our research in incarcerated populations showed that adverse paternal parenting is associated with higher levels of psychopathy and the HTR1B rs13212041 TT genotype appears to modulate the individual susceptibility to negative experiences [²]. Single genetic variants, however, exert only modest effects and current evidence supports a polygenic model in which multiple genetic factors interact with environmental adversity to influence neurodevelopment and behavioral outcomes. Using a genome-wide/endophenotype informed analysis, for example, we identified novel gene–environment interactions as risk factors for psychopathy, involving three independent genetic loci in interaction with paternal maltreatment, which were previously associated with disruptive behavior, temperament, and neuroticism [³]. More recently, we also evaluated whether machine-learning models, integrating behavioral, environmental, and genetic variables, could be helpful to predict psychopathic traits. Methods: We compared logistic regression, random forest, support vector machine, XGBoost, and multilayer perceptron. Results: Support vector machine showed the highest accuracy for predicting Psychopathy Check List-Revised (PCL-R) Factor 2 (antisocial lifestyle). Feature-importance analyses identified impulsivity (BIS-11), empathy (IRI), childhood maltreatment (MOPS), and 12 SNPs as the most informative predictors. Notably, removing genetic variables or MOPS scores substantially reduced the model accuracy, indicating that both genetic and environmental information meaningfully contributed to prediction of antisocial behavior. Conclusions: These findings confirm that genetic influences are neither deterministic nor sufficient to explain criminal behavior but may contribute to interindividual differences in vulnerability, particularly through their interaction with environmental and psychosocial factors. In forensic psychiatry, the integration of genetic and environmental information into behavioral assessment may provide additional objective correlates that complement, rather than replace, traditional clinical and psychosocial evaluations. Such an integrated approach could potentially contribute to a more comprehensive understanding of individual vulnerability and behavioral trajectories. However, the use of genetic information in assessments of criminal responsibility should be approached with caution and proven expertise, given the complex, multifactorial nature of antisocial and criminal behavior. Full article

Scientific visualization is changing from passive observation to active, AI-assisted collaboration. While Extended Reality (XR) has proven valuable for comprehending dense 3D arrays, traditional VR applications are typically deployed in rigid, single-purpose, and monolithic architectures. In this paper, we present the evolution of ASCRIBE-XR: a virtual reality platform backed by remote computation that has been re-engineered into a dynamic, service-oriented ecosystem. We introduce three core innovations that make immersive data analysis easier, faster, and more flexible when using multimodal scientific imaging. First, a lightweight Python REST interface decouples XR logic from the rendering engine, enabling real-time, programmable scene customization and on-demand data generation. Second, we present a Specimen Catalog architecture that lets the platform pivot between radically different disciplines, ranging from archaeological heterogeneous concrete and fuel-cell membranes to the root system of a bioenergy grass, by describing each dataset through portable metadata rather than hard-coded application logic. Finally, we introduce a prompt-driven layer powered by the Claude Agent SDK, allowing researchers to generate, segment, and manipulate volumetric and mesh data through natural language dialogue within the virtual space. For example, applying foundation models such as the Segment Anything Model (SAM) to perform zero-shot segmentation on demand. By bridging human intent with remote computation, ASCRIBE-XR relaxes the constraints of conventional visualization tools, offering a highly adaptable, conversational platform for scientific discovery with human auditing. Full article

Meningitis is a significant public health problem despite the availability of effective vaccination programs, especially in children and young people. The memory-dependent features of disease transmission, immunity and vaccination dynamics are not often represented in classical integer-order epidemic models. This study proposes a fractional-order model of meningitis transmission with memory using the Atangana–Baleanu–Caputo fractional derivative. The model features susceptible, vaccinated, exposed, infectious, treated, and recovered populations to assess the impact of vaccination coverage, vaccine effectiveness, loss of vaccine immunity, and treatment on the spread of meningitis. The basic mathematical characteristics of the model, such as positivity, existence, uniqueness, and stability of solution are proven. The Laplace–Adomian Decomposition Method (LADM) is used to obtain the approximate analytical solutions, and a numerical simulation is used to analyze the influence of the fractional-order memory and epidemiological parameters on the epidemic process. The most important parameters that influence the basic reproduction number are found in sensitivity analysis to be the transmission rate and the vaccination-related parameters. The results show that simply increasing the vaccination coverage and vaccine effectiveness can substantially decrease the number of disease transmissions, and vaccine coverage can produce memory effects to change the timing and duration of outbreaks. The suggested fractional-order computational framework is a framework that is vital for studying the dynamics of meningitis and can be used for the design of long-term vaccination and disease-control strategies. Full article

Large yellow croaker (Larimichthys crocea) is an economically important marine fish, yet rapid and non-destructive discrimination between sea-caught and farmed collection-source groups remains challenging. This study developed a fish-level analytical workflow integrating YOLO11n-seg instance segmentation with visible–near-infrared hyperspectral imaging to extract relative reflectance ratios from six anatomical regions. The analytical cohort comprised 258 unique fish, including 171 sea-caught and 87 farmed individuals. Source categories were assigned according to the original capture or cage-culture collection channels. Fish identity was used as the grouping unit in five-fold internal cross-validation, and classification performance was evaluated based on out-of-fold fish-level predictions. Logistic-regression models achieved AUC values ranging from 0.985 to 1.000 across the six individual anatomical regions. The combined six-region model achieved an accuracy of 0.996 (95% CI, 0.988–1.000) and an AUC of 1.000 (95% CI, 1.000–1.000) within the present cohort. For anatomical-region segmentation, the 20-image validation set contained 140 annotated instances. Bounding-box precision, recall, mAP@0.5, and mAP@0.5–0.95 were 0.976, 0.983, 0.981, and 0.700, respectively, while the corresponding mask metrics were 0.969, 0.976, 0.972, and 0.669. These results indicate that region-specific hyperspectral information can support highly accurate internal discrimination between the two collection-source groups and provide an interpretable basis for characterizing source-associated spectral differences. However, the source labels were not independently verified, potentially influential covariates were incompletely recorded, and no independent external cohort was available. Therefore, the present findings should be interpreted as internally validated collection-source discrimination rather than verified provenance authentication or evidence of external generalizability. Full article

Integrated qiaopi office–residence heritage preserves the material setting of remittance-letter operations together with domestic, educational, and ritual activities. In Lingnan’s hot–humid and disaster-prone environment, condition records need to be repeatable, spatially traceable, and continuously updatable. Taking Jingzu Jiashu and Mingde Jiashu, two former qiaopi office sites in Chaoshan, as case studies, this research develops an evidence-traceable digital conservation workflow integrating multi-source documentation; an adopted YOLOv8 surface-deterioration baseline; qualitative Grad-CAM visualization; structured deterioration records; and semi-automatic, human-confirmed Revit/HBIM association. UAV and terrestrial photography, mobile LiDAR/scanning, handheld measurement, measured drawings, point-cloud and reality-based products, and geometric models were organized into case-specific HBIM environments. The adopted deterioration dataset comprised 362 original images at 512 × 512 pixels and 2024 bounding-box annotations for five visually identifiable categories: spalling, staining, plants, saltpetering, and crack. The original images were divided into 253 training, 72 validation, and 37 independent-test images, while augmentation was restricted to the training subset, increasing the training pool to 1600 images. The previously established YOLOv8 baseline achieved a Precision of 0.85, Recall of 0.72, mAP50 of 0.83, and mAP50–95 of 0.58. Grad-CAM heatmaps were used as qualitative aids to examine model-emphasized image regions. Retained detections associated with Jingzu Jiashu and Mingde Jiashu were converted into versioned records containing source-image identifiers, deterioration classes, detector confidence, survey information, spatial references, verification states, and revision histories. Candidate spatial associations were generated through case identifiers, façade or space zones, element identifiers, and available spatial evidence, while final M1–M3 associations required human confirmation. By preserving source provenance, spatial uncertainty, and record histories, the workflow provides an auditable information basis for routine inspection, post-event review, maintenance prioritization, repair interpretation, and resilience-oriented preventive conservation. The workflow supports screening-level deterioration recognition and information management but does not provide causal diagnosis, structural assessment, exact affected-area measurement, building-independent generalization, or automatic repair recommendations. Full article

Autonomous unmanned aerial vehicle (UAV) swarms execute mission-critical tasks through distributed sensing, communication, routing, and control. Existing trust-management approaches can assign a numerical trust score to a UAV, yet they rarely explain why trust changed, which evidence chain caused the change, or how an individual UAV contributed to mission degradation. This paper introduces ProvTrust-UAV, a mission-provenance trust-management framework for explainable UAV swarm security and causal accountability. The framework formalizes a typed Mission Provenance Graph (MPG), computes bounded adaptive trust from behavior, communication, provenance integrity, and mission contribution, and estimates Mission Impact Attribution (MIA) through counterfactual interventions and approximate Shapley-style contribution. To make explainability measurable rather than decorative, the paper defines fidelity, stability, faithfulness, compactness, and operator interpretability metrics for trust-chain explanations. The proposed framework incorporates a structural causal model, a Monte Carlo convergence analysis for Shapley approximation, a probabilistic false-trust reduction analysis, and a sensitivity analysis of trust-weight parameters. Controlled synthetic mission-event simulations over 120 scenarios and 2400 event windows indicate that ProvTrust-UAV improves macro-F1, root-cause attribution accuracy, and false-trust reduction compared with Bayesian, fuzzy, blockchain, deep-learning, and graph-trust baselines. The paper explicitly treats these results as first-stage computational validation and provides an anonymized additional package with simulation summaries, a public-dataset feature-mapping template, and a reproducible scaffold for external validation. Full article

Autonomous commercial vehicles (ACVs) frequently encounter drastic variations in payload and complex road conditions during practical operations. Consequently, effectively suppressing external disturbances caused by payload and road slope uncertainties has become a critical challenge in enhancing the robustness of their low-level control systems. To address this issue, this paper proposes a sliding mode control (SMC) strategy based on Luenberger observer disturbance compensation (LOSM), aiming to simultaneously mitigate the adverse effects of these two uncertainties on the vehicle’s speed control performance. First, according to the driving characteristics of commercial vehicles, a full-condition longitudinal dynamic model encompassing uphill, downhill, and flat road scenarios is established. Second, by deeply integrating the Luenberger observer with sliding mode control theory, an active disturbance rejection LOSM speed controller is designed. Furthermore, the boundary conditions for the closed-loop system to achieve asymptotic stability are rigorously derived and proven using Lyapunov functions. Finally, to comprehensively verify the effectiveness of the proposed strategy, eight typical testing scenarios are constructed, and three benchmark algorithms—PI control, radial basis function adaptive sliding mode (RBFSM) control, and radial basis function backstepping sliding mode (RBFBSSM) control are introduced for comparative analysis. The validation results demonstrate that although all four methods can achieve speed tracking and suppress disturbances, the proposed LOSM strategy exhibits the optimal comprehensive performance across various scenarios. Specifically, its steady-state mean error is typically maintained below 2.5%, and it yields the minimum steady-state variance in the majority of scenarios. These results demonstrate that the designed LOSM method can significantly improve the precision and smoothness of ACVs’ speed control under the dual disturbances of unknown mass and road slope. Full article