Search Results (28,442)

The exponential growth of genomic and proteomic data has made computational protein–protein interaction (PPI) prediction indispensable, driving the need for a comprehensive and method-aware evaluation of supervised learning approaches. PPIs are fundamental to understanding cellular processes and disease mechanisms, yet experimental identification remains slow, costly, and difficult to scale. This survey systematically investigates ten supervised learning models—Extreme Learning Machine (ELM), Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Deep Neural Networks (DNNs), Naïve Bayes, Probabilistic Decision Tree, Support Vector Machine (SVM), Least Squares SVM (LS-SVM), K-Nearest Neighbor (KNN), and Weighted K-Nearest Neighbor (WKNN)—through a tri-layered framework that integrates Comparative Quantitative Analysis, Comparative Observational Analysis, and Experimental Evaluations. Beyond conventional accuracy summaries, this work provides critical commentary tied to real-world use, analyzing where techniques succeed or fail in practice—for instance, when instance-based methods bottleneck during inference, when kernel choices influence SVM variance, or when deep architectures trade accuracy for computational cost. The survey also offers concrete deployment guidance, such as calibration insights for WKNN versus KNN under varying feature noise or dataset curation quality, delivering operational perspectives that typical surveys omit. Comparative Quantitative Analysis consolidates metrics such as accuracy, F1-score, and computational time from the existing literature, while Comparative Observational Analysis evaluates interpretability, scalability, dataset suitability, and efficiency. Complementing these, Experimental Evaluations conducted by the authors empirically validate model performance on benchmark datasets. Together, these layers provide a unified and evidence-backed perspective on algorithmic strengths, weaknesses, and practical applicability. Findings show that GNNs and DNNs achieve the highest predictive accuracy due to their ability to capture structural and topological relationships, whereas ELM and Naïve Bayes offer superior efficiency. SVM and LS-SVM maintain robust stability under noisy conditions, and CNNs are well-suited for sequence-based prediction tasks. By combining empirical validation, critical insights, and deployment-focused recommendations, this survey delivers decision-grade guidance that bridges theoretical understanding with real-world implementation, thus clarifying the trade-offs among accuracy, efficiency, and scalability in PPI detection research. Full article

Background. Artificial intelligence (AI) is increasingly recognized as a transformative technology in healthcare, with growing interest in its applications within pediatric dentistry. Given the unique clinical, developmental, and behavioral characteristics of pediatric patients, AI-based systems may offer valuable support in improving diagnosis, prevention, and treatment planning. Methods. A narrative review was conducted to synthesize current evidence on AI applications in pediatric dentistry. A comprehensive search strategy, including predefined keywords and free terms, was applied across multiple databases (Embase, Scopus, PubMed, and Web of Science) up to 1 January 2026. Reviews addressing AI-based technologies in pediatric dental care were selected and analyzed. Results. The available literature indicates that AI is being progressively applied across multiple domains of pediatric dentistry, although with varying levels of evidence. More extensively investigated areas include diagnostic imaging, caries detection, orthodontic assessment, and growth evaluation, where AI systems—particularly those based on machine learning and deep learning—have demonstrated high accuracy and reproducibility. Other emerging fields, such as remote monitoring, behavioral management, preventive strategies, and patient education, show promising potential but remain less explored. Overall, AI-based tools appear to enhance diagnostic support, enable early detection of oral conditions, and contribute to more personalized and efficient clinical workflows. Conclusions. AI represents a rapidly evolving adjunct in pediatric dentistry with the potential to improve clinical decision-making, preventive care, and patient management. Despite encouraging results, further validation in real-world settings, along with careful consideration of ethical, legal, and data-related challenges, is required to support its responsible integration into routine clinical practice. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. Early and accurate diagnosis is critical to delaying disease progression, alleviating clinical symptoms, and improving the long-term quality of life for the affected patients. The deep integration of artificial intelligence (AI) and medical imaging enables efficient early AD screening, overcoming traditional limitations. This study presents a systematic review of AI-driven applications in the early diagnosis of AD with a dual focus on single-modal and multimodal analytical frameworks, comprehensively analyzing core technical components across existing research including data preprocessing pipelines, mainstream deep learning and machine learning diagnostic models, standard performance evaluation metrics, and widely adopted public research datasets, while further qualitatively comparing the diagnostic efficacy and applicability of diverse methodologies across distinct imaging and non-imaging modalities. In addition, this review systematically delineates and compares the application merits, technical bottlenecks, and clinical suitability of AI-enabled diagnostic methods across diverse modalities, providing robust methodological guidance and clear directional references for future research on the early diagnosis of AD and facilitating the advancement of the field toward higher diagnostic precision, broader population applicability, and tighter integration with real-world clinical practice. Full article

This prospective observational study aimed to estimate the annual service-based incidence of individuals with First Episode Psychosis (FEP) and high-risk states for psychosis presenting to a public Community Mental Health Center within a defined urban catchment area in Northwestern Greece. It offers novel real-world insights into early intervention in psychosis within a resource-constrained, post-crisis health care setting. All individuals aged ≥16 years who presented to the Community Mental Health Center of the University of Ioannina between January 2023 and December 2024 were assessed. Those diagnosed with FEP or identified as being at a high risk for psychosis using the Comprehensive Assessment of At-Risk Mental States were included, while duration of untreated psychosis (DUP) was estimated with the Symptom Onset in Schizophrenia inventory. Among 1115 service users, 51 (4.6%) met criteria for FEP (N = 33) or high-risk states (N = 18), rising to 7.5% among those aged 16–36 years. The annual service-based incidence of FEP was 10.26 per 100,000 in the general population, increasing to 51.62 in individuals aged 16–36 and 63.17 in those aged 16–26. Including high-risk cases, service-based incidence reached 109.71 per 100,000 in the 16–26 age group. Mean DUP was 39.4 weeks but was 7.0 weeks among 80% with DUP < 1 year. Most FEP patients (63.6%) required brief hospitalization, and over half reported family history of mental illness. These findings highlight substantial community caseloads and the need to strengthen early intervention services. Full article

Accurate fruit ripeness assessment is essential for optimizing harvest timing and ensuring postharvest quality, particularly in climacteric fruits such as peaches, which exhibit rapid and heterogeneous ripening. This study proposes a deep learning-based approach for multistage peach ripeness classification across five maturity levels (green, green-blushed, blushed, yellow-blushed, and fully yellow). Four datasets were constructed using controlled image acquisition, segmentation, data augmentation, and perceptual hashing to mitigate data leakage. The performance of AlexNet, EfficientNet-B0, and three YOLO (You Only Look Once) architectures (YOLOv8, YOLOv11, and YOLOv12) was evaluated using standard metrics, including accuracy, precision, recall, F1 score, mAP, and inference speed. Results show that YOLO-based models significantly outperform classical networks, achieving accuracies between 95.25% and 98.3% and mAP@0.5 above 98.25%, while also reducing inference time to 8.1–12.7 ms compared with 722.23 ms for AlexNet and 171.87 ms for EfficientNet-B0. In a practical sorting experiment with 214 peaches, YOLOv12 achieved 92.06% accuracy, demonstrating robust real-world performance. Misclassifications were primarily observed between adjacent ripeness stages. These findings indicate that YOLO-based models provide an effective and scalable solution for real-time fruit sorting, while the use of perceptual hashing enhances dataset reliability and model generalization for deployment in agricultural quality control systems. Full article

Background: Anti-MRSA agents are essential for treating severe infections, yet their use is constrained by distinct toxicity profiles. However, comparative real-world data remain scarce. Methods: This nationwide pharmacovigilance study used the Japanese Adverse Drug Event Report (JADER) database (2004–2025). Disproportionality analyses (proportional reporting ratio [PRR]) were performed at the Standardized MedDRA Query and Preferred Term levels, complemented by Weibull-based time-to-onset modeling, to characterize AE patterns associated with vancomycin (VCM), teicoplanin (TEIC), arbekacin (ABK), daptomycin (DAP), linezolid (LZD), and tedizolid (TZD). Results: Distinct agent-specific AE profiles were observed. VCM showed disproportionate reporting of acute renal failure (PRR 6.66) and severe cutaneous reactions. TEIC displayed fewer renal signals but relatively higher reporting of hematologic events (PRR 3.51). ABK demonstrated high disproportionality in acute and chronic renal failure, reflecting aminoglycoside nephrotoxicity. DAP showed a high reporting signal for eosinophilic pneumonia (PRR 23.30), interstitial lung disease, and creatine kinase elevation/rhabdomyolysis, with wear-out hazard patterns suggesting a possible time-dependent reporting tendency. LZD exhibited hematopoietic signals (PRR 6.13) and additional associations with hyponatremia, lactic acidosis, and optic neuropathy, consistent with marrow suppression and mitochondrial toxicity. Weibull analysis indicated cumulative “wear-out” risks for renal, hepatic, and hematologic events, whereas hypersensitivity and many pulmonary events followed random-failure patterns. Conclusions: This large-scale JADER analysis delineated the distinct safety profiles of the six anti-MRSA agents. The key findings included DAP pulmonary and muscle toxicities, LZD hematological events, and VCM nephrotoxicity. Time-to-onset modeling indicates potential cumulative versus random risk patterns, suggesting the need for individualized monitoring and cross-validation. Full article

Accident reports provide a detailed account of environmental causes, unsafe human behaviors, and subsequent chain reactions. These records serve as essential resources for analyzing accident mechanisms and exploring potential risk patterns within production safety processes. Currently, Graph based Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with Knowledge Graphs (KGs), has emerged as a leading approach for complex causal question answering over extensive unstructured accident documentation. However, the application of this technology in the production safety domain still encounters two primary challenges. First, knowledge graph construction using a single granularity fails to capture fine-grained case details and macro-level standard systems. Second, traditional one-step retrieval paradigms lack the capacity to track deep causal chains or interpret the complex logic of multi-factor coupling. To address these limitations, we propose CausalAgent, a hierarchical graph-enhanced multi-agent framework for causal question answering in production safety accident reports. This framework innovatively combines a Hierarchical Causal Graph (HC-Graph) and a Multi-Agent Collaborative Reasoning (MACR) mechanism. Specifically, the HC-Graph employs a two-layer architecture that links a fine-grained instance layer with a national standard causation layer to resolve conflicts in semantic granularity. The MACR mechanism converts complex natural language queries into executable structured queries and logic verification steps through the sequential cooperation of four specialized agents, namely the Graph Parsing Agent, the Problem Analysis Agent, the Query Generation Agent, and the Reasoning Insight Agent. CausalAgent enables in-depth mining of accident causation mechanisms and provides scientific, robust and interpretable intelligent support for data-driven risk assessment and emergency decision-making. Experiments on real-world accident datasets demonstrate that CausalAgent achieves a $100.0\%$ query execution rate and an $87.3\%$ reasoning accuracy, outperforming the SOTA baseline by $45.2\%$ in terms of absolute accuracy. Full article

Action chunking strategies in robot imitation learning struggle to dynamically balance between long-range motion efficiency and short-range operational precision due to their fixed planning horizon. This paper presents an Adaptive Action Chunking framework that enables robots to dynamically predict the optimal action chunk length based on real-time visual context. We design an end-to-end dual-branch network comprising a shared visual encoder, a parallel action prediction head, and a chunk-size prediction head. Experiments on two real-world bimanual robot manipulation tasks (transport-and-place and flip-and-handover) demonstrate that the method autonomously derives two distinct intelligent strategy patterns—phase-aware switching and sustained high-frequency adjustment—in response to task uncertainty. It significantly outperforms fixed-chunk baselines in both success rate and efficiency. Ablation studies confirm that the performance gain stems from the adaptive decision-making mechanism itself. Full article

Microcredentials have become a valuable educational pathway for individuals seeking to build relevant, in-demand skills. These concise, stackable courses are intended to demonstrate real-world skills to potential employers. A literature review was conducted to examine existing microcredential programs, including their types, benefits, and challenges. This review focused on the potential of various microcredential programs to enhance educational and employment opportunities, especially for individuals from Racial Groups with Small Populations (RGSP). This study explored the possibility of microcredentials in geospatial and data science to advance careers and bridge skill gaps. A brief survey was also conducted among Tuskegee University students to understand preliminary perceptions, needs, preferences, and benefits associated with microcredential programs. The responses indicate a varying level of familiarity with geospatial and data science disciplines. Among the students surveyed, affordability, course content, career advancement opportunities, flexible schedules, and online delivery were identified as key factors influencing enrollment decisions in microcredential programs. This review showed that most microcredential programs found are likely to be offered by large institutions. Given the persistent disparities and relatively low employment rate in geospatial and data science fields for RGSP learners, this report explores how microcredentials may provide opportunities for skill development and enhance economic mobility. Full article

The extensive use of 2,4-dichlorophenoxyacetic acid (2,4-D) in agriculture has led to water contamination and associated health risks, highlighting the need for eco-friendly detection strategies. Herein, Fe₃O₄ nanoparticles were green-synthesized for the first time using an aqueous extract of Rumex nervosus (R. nervosus) as a natural reducing and stabilizing agent and successfully employed for the electrochemical sensing of 2,4-D, representing the first reported application of R. nervosus-mediated Fe₃O₄ nanoparticles for this purpose. The phytochemical composition of the extract and synthesized R-Fe₃O₄ nanoparticles were systematically characterized. The R-Fe₃O₄-modified glassy carbon electrode (GCE) was evaluated for charge transfer properties using electrochemical impedance spectroscopy (EIS). Cyclic voltammetry (CV) showed no redox peak for 2,4-D at the bare GCE, whereas R-Fe₃O₄/GCE exhibited a distinct reduction peak at ~−1.5 V in 0.1 M phosphate buffer (pH 7), attributed to reductive dechlorination. Square-wave voltammetry (SWV) exhibited a linear response over the concentration range of 50–325 µM with a detection limit of 3.35 µM for 2,4-D. Although this performance is slightly above the guideline limits recommended by the World Health Organization (~0.14 µM) and the United States Environmental Protection Agency (~0.32 µM), it is suitable for the routine monitoring of elevated 2,4-D levels in environmental samples. The sensor demonstrated high selectivity with negligible interference and satisfactory recoveries of 96.6–98.3% in real water samples. Full article

Financial fraud has become a critical challenge for modern financial systems due to the rapid growth of digital transactions, online banking services, and electronic payment platforms. Traditional rule-based fraud detection systems are increasingly inadequate in addressing the evolving and adaptive strategies employed by fraudsters. Consequently, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as powerful tools for detecting fraudulent activities in large-scale financial datasets. This paper presents a comprehensive survey of ML/DL approaches for financial fraud detection. The survey systematically reviews existing research across multiple methodological paradigms, including classical supervised learning, anomaly detection, graph-based methods, deep neural networks, multimodal architectures, and cost-sensitive learning frameworks. Particular emphasis is placed on emerging techniques such as graph neural networks, transformer-based architectures, and federated learning approaches designed to address privacy and scalability challenges. In addition to reviewing model architectures, this work analyzes key challenges inherent to fraud detection systems, including extreme class imbalance, concept drift, adversarial behavior, data privacy constraints, and real-time deployment requirements. Furthermore, the survey examines evaluation methodologies, highlighting the limitations of commonly used metrics and discussing more realistic evaluation strategies that incorporate operational costs and risk management considerations. This paper also provides a structured taxonomy of fraud detection methods, comparative analyses of commonly used datasets, and a synthesis of current research trends. Finally, open challenges and promising research directions are identified, including adaptive learning systems, interpretable Artificial Intelligence models, graph-based behavioral modeling, and privacy-preserving collaborative fraud detection frameworks. Full article

Indoor localization for unmanned aerial vehicles (UAVs) remains challenging in GNSS-denied environments due to the difficulty of position calibration of multiple ultra-wideband (UWB) anchors and the asynchronous fusion of heterogeneous sensors. This paper proposes a multi-sensor fusion localization framework that integrates multi-anchor one-shot calibration with factor graph optimization (FGO). First, Landmark Multidimensional Scaling (LMDS) is used to reconstruct the relative geometry of the anchors and the onboard tag from ranging measurements. Then, rigid Procrustes alignment is performed using a small number of anchors with known coordinates in the East–North–Up (ENU) frame to recover the transformation to the ENU frame, thereby enabling efficient position calibration of multiple UWB anchors and UAV pose initialization. Subsequently, a tightly coupled factor graph is constructed by incorporating inertial measurement unit (IMU) pre-integration, UWB ranging, laser rangefinder height measurements, and visual–inertial odometry (VIO) pose constraints. The resulting nonlinear optimization problem is solved using incremental smoothing, which improves robustness against non-line-of-sight (NLOS) errors and long-term drift. Experimental results on anchor calibration, public datasets, and real-world indoor UAV flights demonstrate that the proposed method improves the accuracy and robustness of indoor UAV localization. In particular, on the real-world rectangle trajectory, FGO-TC reduces the RMSE by approximately 38.8% compared with FGO-LC. Full article

The problem of optimizing a causal objective function emerged in recent work, where the behavior of objects needs to be expressed in terms of interventional or counterfactual probabilities. A key example is the unit selection problem introduced by Li and Pearl, where the goal is to find the individuals who maximize a benefit function that scores their characteristics (called units) using counterfactual probabilities. Previous work on unit selection focused mainly on this specific objective function and on identifying its value using bounds. We complement this line of work by developing a theory that treats unit selection as a computational problem, assuming a fully specified causal model is available and a more general class of objective functions. At the core of our treatment is a novel reduction that transforms the computation of a broad class of causal objective functions into a classical associational probability on a meta-model called the objective model. Based on this reduction, we propose the first exact algorithm for finding the optimal units by applying Variable Elimination (VE) on the objective model. We then characterize the complexity of causal unit selection, showing that it is ${\mathrm{NP}}^{\mathrm{PP}}$-complete, and that the runtime of VE must be exponential in the constrained treewidth of the objective model, which is larger and denser than the original input model. To address this challenge, we compile the objective model into a special class of tractable arithmetic circuits, allowing the optimal units to be computed in time linear in the circuit size. Finally, we present experiments demonstrating the substantial speedup from the circuit-based method over the VE-based method, and the speedup from the VE-based method over a baseline search method, together with a case study on a real-world ecology problem. Full article

Autonomous odor source localization remains a challenging problem for aerial robots due to turbulent airflow, sparse and delayed sensory signals, and strict payload and computation constraints. While prior unmanned aerial vehicle (UAV)-based olfaction systems have demonstrated gas distribution mapping or reactive plume tracing, they rely on predefined coverage patterns, external infrastructure, or extensive sensing and coordination. In this work, we present a complete, open-source UAV system for online odor source localization using a minimal sensor suite. The system integrates custom olfaction hardware, onboard sensing, and a learning-based navigation policy that we train in simulation and deploy on a real quadrotor. Through our minimal framework, the UAV is able to navigate directly toward an odor source without constructing an explicit gas distribution map or relying on external positioning systems. We incorporate vision as an optional complementary modality to accelerate navigation under certain conditions. We validate the proposed system through real-world flight experiments in a large indoor environment using an ethanol source, demonstrating consistent source-finding behavior under realistic airflow conditions. The primary contribution of this work is a reproducible system and methodological framework for UAV-based olfactory navigation and source finding under minimal sensing assumptions. We elaborate on our hardware design and open-source our UAV firmware, simulation code, olfaction–vision dataset, and circuit board to the community. Full article

Hypertension is a major risk factor for cardiovascular disease and requires effective long-term monitoring. Photoplethysmography (PPG), acquired from wearable optical sensors, offers a convenient and non-invasive signal source for cuffless blood pressure (BP) estimation, but existing studies have mainly emphasized model architecture optimization, with limited systematic investigation of signal representation. This study systematically compares seven one-dimensional-to-two-dimensional signal transformation methods and evaluates multiple architectural variants for PPG-based cuffless BP estimation under a unified framework. Experiments were conducted using PPG and arterial BP signals from the UCI Open Blood Pressure Database. The best-performing configuration, based on continuous wavelet transform (CWT), achieved estimation errors of 3.80 ± 5.02 mmHg for systolic BP and 1.65 ± 2.70 mmHg for diastolic BP. Further real-world validation on 26 participants using an Omron cuff-based monitor as the reference showed good consistency, with correlation coefficients of R = 0.96 for SBP and R = 0.74 for DBP. The results demonstrate that appropriate signal representation, particularly CWT, plays a critical role in improving estimation accuracy and robustness, and may facilitate the development of wearable cuffless BP monitoring systems. Full article