Search Results (1,815)

Distinguishing mood from emotion has long posed challenges for psychology, with persistent definitional ambiguity limiting both theoretical precision and applied effectiveness. Our early work, identified duration and cause attribution as the most reliable markers differentiating short-lived, event-linked emotions from more diffuse, enduring moods. Researchers further advanced understanding by conceptualising emotions as feedback signals that support learning and adaptation, while the 4Rs model translated these insights into applied practice by embedding cause attribution within affect regulation. This paper integrates these conceptual, functional, and applied perspectives to demonstrate why accurate classification of affective states is a functional necessity in high-performance contexts. I propose that misclassifying moods and emotions may contribute to inefficient deployment of self-regulatory resources, whereas distinguishing states based on cause attribution may support more targeted and efficient regulation. Drawing on examples from sport, healthcare, performing arts, military operations, and corporate leadership, this paper synthesizes existing work to highlight the practical implications of the mood–emotion distinction for applied psychology. Full article

Financial fraud has expanded rapidly with the growth of the digital economy, evolving from conventional transactional misconduct to more complex and data-intensive forms. Traditional rule-based detection methods are increasingly inadequate for addressing the scale, heterogeneity, and dynamic behavior of modern fraud. In this context, artificial intelligence (AI) has become a core tool in financial fraud detection research. This review systematically surveys AI-based financial fraud detection studies published between 2015 and 2025. It summarizes representative machine learning and deep learning approaches, including tree-based models, neural networks, and graph-based methods, and examines their applications in major fraud scenarios such as credit card fraud, loan fraud, and anti-money laundering. In addition, emerging research on cryptocurrency- and blockchain-related fraud is reviewed, highlighting the distinct challenges posed by decentralized transaction environments. Through a comparative analysis of methods, datasets, and evaluation practices, this review identifies persistent issues in the literature, including severe class imbalance, concept drift, limited access to labeled data, and trade-offs between detection performance and interpretability. Based on these findings, the paper discusses practical considerations for applied fraud detection systems and outlines future research directions from a data-centric and application-oriented perspective. This review aims to provide a structured reference for researchers and practitioners working on real-world financial fraud detection problems. Full article

Artificial Intelligence (AI) holds transformative potential to revolutionize teaching and learning, yet its rapid integration poses significant challenges for teacher preparation. While AI competencies—encompassing knowledge, skills, and attitudes—are critical for effective integration, limited research has holistically addressed these three interconnected domains. To bridge this gap, this quasi-experimental study (N = 259) evaluated a triadic instructional design synergizing the intelligent technological, pedagogical, and content knowledge (Intelligent-TPACK) framework, Synthesis of Qualitative Data model, and curated AI tools. Pre-service English as a foreign language (EFL) teachers were assigned to an experimental group (n = 137) receiving the structured intervention or a control group (n = 122) engaging in self-directed AI exploration. Results reveal that the experimental group achieved greater gains across all Intelligent-TPACK dimensions and demonstrated higher-order AI applications in lesson planning. Furthermore, the experimental group experienced a significant reduction in perceived pressure and reported higher perceived usefulness regarding AI integration. Qualitative data revealed that hands-on AI tasks enhanced participants’ confidence, yet challenges with prompts and critical adaptation persisted. The findings demonstrate that systematic training is essential for transforming pre-service teachers’ passive awareness into competent AI integration. Finally, this paper proposes practical implications for integrating this triadic framework into teacher education curricula to facilitate sustainable AI adoption. Full article

Persistent fiscal stress in South African municipalities undermines service delivery, yet practical tools for early detection remain limited. This study predicts one-year-ahead municipal financial distress to support risk-based prioritisation. We develop machine learning models using a 2018/19–2022/23 municipality panel, combining 13 financial health indicators from State of Local Government (SoLG) reports with selected socio-economic variables. Penalised logistic regression is benchmarked against random forest and XGBoost under a leakage-aware, time-ordered split into training, validation, and an out-of-time test year; class imbalance is handled through class weighting. Performance is evaluated using PR-AUC, ROC-AUC, calibration, and a capacity-constrained Top-30 rule. All models outperform a naïve last-year baseline on the out-of-time test (PR-AUC 0.934–0.954; ROC-AUC 0.886–0.923), with bootstrap intervals supporting robustness. Random forest performs best overall, while penalised logistic regression remains competitive. Under the Top-30 rule (12.3% workload), precision is high (precision@30 0.967–1.000) while recall is modest (recall@30 0.186–0.192). SHAP values and logistic odds ratios identify liquidity, solvency, cash coverage, and employment deprivation as key drivers. The Top-30 rule corresponds to an annual intensive monitoring portfolio that is reasonable under constrained staffing and budget capacity in national and provincial oversight units, while probability thresholds are reported as conventional benchmarks rather than as policy triggers. Full article

Hosts generating unsolicited network traffic increasingly operate in a coordinated manner rather than in isolation. Scanning and exploitation activities are often distributed across multiple hosts that share common infrastructure, toolchains, and behavioral patterns, forming loosely coupled yet persistently aligned sender groups. Accurately attributing such groups is critical for understanding organized activities and strengthening network defense capabilities. However, existing attribution approaches face notable limitations. Methods that rely on threat intelligence suffer from delayed updates and limited coverage. Static feature-based approaches ignore temporal ordering and therefore fail to capture multi-stage behavioral evolution. Although dynamic sequence models incorporate temporal patterns, they typically overlook the collaborative structural relationships among coordinated senders. In this paper, we propose SentinelGraph, a temporal graph reasoning framework for sender group attribution from honeypot traffic. SentinelGraph constructs a temporal knowledge graph and integrates a recurrent graph evolution module to jointly model coordination structures and their temporal dynamics. A structure enhancement module further exploits contextual information available at the target time, while an auxiliary relation loss encourages the learning of enriched entity representations. This design enables accurate attribution even for previously unseen senders by leveraging information from their observed neighbors. Experiments on real-world honeypot data demonstrate that SentinelGraph substantially outperforms state-of-the-art methods in modeling coordinated network behaviors. Full article

A persistent interdisciplinary gap continues to hinder the development of Health 4.0 educational initiatives. Biomedical Engineering programs typically emphasize physiology and instrumentation while providing limited exposure to modern software ecosystems, whereas Informatics curricula often overlook the physical and physiological foundations of bio-instrumentation. To address this dual deficiency, this paper presents a low-cost and modular educational intervention aligned with the “Computing, Electronics, and Health for Everybody” philosophy. The proposed approach is a hands-on technical workshop that translates core biomedical signal-processing concepts into an accessible learning experience using the Arduino platform and the AD8232 ECG sensor. The intervention was implemented simultaneously across universities in Chile, Peru, and Ecuador, involving a total of $n=92$ undergraduate engineering students. Learning outcomes were evaluated using a pre–post assessment design. The results demonstrate a statistically significant improvement in participants’ conceptual understanding of ECG signal components ($p<0.001$), with mean scores increasing across all evaluated dimensions. In addition, students reported higher confidence in interpreting physiological signals and applying interdisciplinary reasoning. These findings indicate that the proposed intervention effectively supports interdisciplinary learning for software-oriented engineering students by introducing core biomedical acquisition and signal-processing concepts through an accessible and scalable educational framework. Full article

This study revisits the Efficient Markets Hypothesis by employing a GRU-D neural network to predict stock return distributions across global equity markets, accounting for missing and irregular data. It examines whether stock returns exhibit statistically significant departures from purely random behavior. By combining price, technical and fundamental inputs, it tests both weak and semi-strong market efficiency. We implement the GRU-D model on a global dataset of stock returns, where daily returns are classified into quartiles. Model performance is assessed using Micro-Average Area Under the Curve (AUC) and Relative Classifier Information (RCI). Robustness checks include sub-sample tests across countries and sectors, an examination of the COVID-19 sub-period, and a price-memory persistence analysis. The results reveal that the GRU-D model achieves a ranking accuracy of approximately 75% when classifying returns, with statistical significance at the 99.99% confidence level, and exhibits modest but robust deviations from strict market efficiency. These deviations persist for up to 200 trading days. Notably, the findings indicate that the GRU-D model is more robust during the COVID-19 period. These findings are consistent with the Adaptive Markets Hypothesis and underscore the relevance of machine-learning frameworks, particularly those designed for imperfect data environments, for identifying time-varying departures from strict market efficiency in global equity markets. Full article

This paper introduces SmartHouseOperator, a multi-agent intelligent control framework for adaptive and energy-efficient smart-home management. Modern smart homes integrate heterogeneous devices and sensors, yet most existing solutions rely on static rules or manual coordination, limiting their ability to adapt to dynamic environmental conditions and evolving user preferences. SmartHouseOperator addresses these limitations through an agentic architecture that coordinates device-specific agents for air conditioning, lighting, refrigeration, and shutters under a central orchestrator. The system combines contextual inputs (e.g., weather, occupancy, power load), persistent knowledge, reinforcement-learning-based preference modeling, and LLM-powered reasoning to enable coordinated and personalized control decisions. Experimental results show that the framework achieves consistent reasoning performance across multiple agent orchestration engines and reduces air-conditioning power consumption by up to 16% under critical load conditions. These findings demonstrate the potential of multi-agent, learning-enabled control systems to deliver intelligent, energy-aware, and user-centric smart-home operation. Full article

Maritime refrigerated containers operate under harsh and highly variable conditions, where gradual equipment degradation can lead to temperature excursions, cargo losses, and operational disruptions. In current practice, monitoring relies largely on threshold-based temperature alarms, which are reactive and provide limited insight into early abnormal behaviour. This study proposes a hybrid framework for early anomaly detection in maritime refrigerated containers that combines a lightweight physics-based digital twin with a deep learning anomaly detector trained exclusively on fault-free operation. The approach is designed for shipboard constraints and uses only controller-level signals augmented by locally derived features, enabling low-complexity edge execution. The digital twin produces physically interpretable temperature residuals, while a convolutional autoencoder learns normal multivariate operating patterns and flags deviations via reconstruction error. Both indicators are integrated using conservative persistence gating to suppress short-lived transients typical of maritime operation. The framework is evaluated in a simulation environment calibrated to representative reefer thermal dynamics under variable ambient conditions and progressive fault injection across gradual and abrupt fault categories. Results indicate earlier and operationally credible detection compared to conventional alarms, supporting practical predictive maintenance in maritime cold-chain logistics. Full article

Satellite swarm operations require robust methodologies to distinguish between leader-induced reference frame biases (global errors) and individual follower anomalies (local deviations). This is the challenge of distributed fault diagnosis. In leader–follower topologies, distinguishing between a global reference error (leader satellite broadcasting incorrect navigation data) and a local node error (follower satellite drifting) is mathematically ambiguous when we use standard methods. Even recent unsupervised frameworks, such as Model-Guided Online Transfer Learning (MGOTL), that excel at single-satellite component diagnosis, suffer from adaptation and signal bleed when they are applied directly to distributed topologies. Therefore, we propose the Isolation-First Consensus Anomaly Detection (IF-CAD) framework for Decoupling Global and Local Faults in Satellite Swarms. We introduce a Smart Freeze mechanism to prevent the learning of persistent faults and a hierarchical logic that prioritizes local isolation over global agreement. The IF-CAD framework successfully decouples global leader faults from local follower faults. Fault detection remains stable even during long-duration anomalies. Full article

Background and Objectives: Minimally invasive surgery (MIS) has become a cornerstone approach in cardiac surgery. A debate persists regarding the optimal aortic clamp occlusion strategy, with limited comparative data. The two principal strategies, which are transthoracic cross-clamping (TTCC) and endo-aortic balloon occlusion (EABO), offer distinct advantages, but comparative clinical data remain limited. This study compares the two techniques in terms of procedural safety and early outcome. Materials and Methods: This single-center retrospective study included consecutive adult patients undergoing elective MIS via video-assisted right mini-thoracotomy between 2012 and 2018 for mitral valve surgery. Tricuspid repair, atrial fibrillation and redo surgery were included in the final cohort. Aortic occlusion was performed with transthoracic cross-clamping (TTCC) or endo-aortic balloon occlusion (EABO). Primary endpoints were intra-operative complications and the rate of conversion to full sternotomy; secondary outcomes were overall mortality and Society of Thoracic Surgeons (STS)-defined comorbidities. Results: A total of 163 patients were analyzed (TTCC: n = 99, 60%; EABO: n = 64, 40%). While both techniques demonstrated equivalent safety profiles (overall mortality: 0%), EABO was associated with higher conversion to full sternotomy [(n = 7, 10.9%) vs. TTCC (n = 1, 1.3%), p = 0.016]. In a generalized estimation equations (GEE) model, no patient-level covariate predicted conversion, suggesting technical or procedural factors as the primary contributors. In addition, EABO was associated with longer cross-clamp time [median: 87 min (IQR: 73, 100) vs. TTCC median: 77 min (IQR: 65.5, 87.5), p = 0.03]. Stroke, acute kidney injury, respiratory failure, reoperation and wound infection did not differ significantly; also, hospital stay was similar between groups. Conclusions: In this single-center series, EABO showed longer operative times and a higher conversion rate to sternotomy, but without excess mortality or major complications. This may be correlated with the initial learning phase and redo cases; further comparison is needed to assess the benefits of EABO. Full article

All-optical memristors possess light-sensing and storage capabilities while simultaneously simulating human synaptic functions, demonstrating immense potential in the field of brain-inspired computing for realizing bionic synapses and brain-like intelligence. In this work, we successfully produced ε-Ga₂O₃ films, ε/β-Ga₂O₃ mixed-phase films, and β-Ga₂O₃ films via chemical vapor deposition (CVD). The optical output and optical response characteristics of the thin films are investigated under 254 nm and 365 nm lasers. The CVD-grown ε-Ga₂O₃ is found to process a small amount of defects and insignificant memristive properties and the β-Ga₂O₃ obtained from the annealing of ε-Ga₂O₃ exhibits superior crystal quality but lacks memristive properties, while the ε/β-Ga₂O₃ mixed-phase films grown directly by CVD contain a fair amount of defects and demonstrate persistent resistance retention exceeding 104 s. Based on the excellent memristive properties of ε/β-Ga₂O₃ mixed-phase films, we conducted experiments simulating optical synapses. By adjusting optical pulse parameters (intensity, repetition rate, and duration), we successfully modeled the short-term plasticity (STP) and long-term plasticity (LTP) observed in biological synapses. Experiments confirm that light stimulation can effectively induce synaptic behaviors, such as the progressive conversion of short-term memory (STM) into long-term memory (LTM), and further fully reproduce the neuroplasticity process of “learning-forgetting-relearning.” This study demonstrates a photoconductive synapse memristor based on the wide-bandgap material gallium oxide, exhibiting exceptional air stability with sustained photoconductivity maintained for over a year. This study provides new insights into the practical application feasibility of all-optical artificial synapses based on gallium oxide. Full article

Coastal erosion is a growing concern along many Mediterranean sandy coasts, particularly where reduced fluvial sediment supply, relative sea-level rise and coastal development coincide. This study uses multi-mission Landsat 5/7/8/9 and Sentinel-2 data in Google Earth Engine to extract long-term shoreline series (1984–2025) from MNDWI-based composites. DSAS-style metrics quantify multi-decadal change, while a supervised linear regression forecasting model—validated against a 2013 orthophoto and an independent 2017–2025 test set using an RMSE-based acceptance criterion—is employed to forecast shoreline positions up to 2050. Using this framework, we reconstruct and forecast shoreline evolution along the ~38 km Ionian coast of Basilicata (southern Italy), a microtidal, sediment-starved littoral that has been affected by significant erosion over the past few decades, threatening natural habitats, infrastructure and economic activities. Results show pervasive erosion over the last four decades, with an average shoreline retreat of ≈47 m along the entire coast, and localized retreats exceeding 400 m, particularly at the mouths of the Agri and Sinni rivers and near the Metaponto sector. Forecasts, under linearity and trend-persistence assumptions, indicate further substantial retreat by 2050 in already critical sectors. Methodologically, this work provides a reproducible framework to inform scenario-based coastal planning in similar Mediterranean environments and the first multi-decadal, spatially continuous satellite-based analysis and machine learning-supported forecast for the Basilicata coast, offering a robust basis for regional coastal management. Full article

The Hypernetic Law of Experience (HLE) generalizes Ashby’s neglected Law of Experience from determinate machines to stochastic, gradient-driven adaptive systems. The HLE characterizes a persistent tendency of adaptive systems exposed to sustained directional experience: internal variety is progressively consumed, and system trajectories converge toward increasingly narrow regions of state space, even when local transitions remain probabilistic. We formalize this contraction pressure using the Rebis equation, a discrete-time variance-contraction dynamic that relates optimization pressure and novelty injection to the evolution of internal diversity. Through cross-domain comparative analysis, we show that HLE-consistent geometry appears in biological evolution, recursive model collapse in machine learning, economic cycles, neural plasticity and habituation, linguistic convergence, and institutional lock-in. In these domains, excessive variety consumption is associated with brittle attractors and heightened vulnerability under distributional shift. We further show that biological systems employ countervailing mechanisms—such as sexual recombination, mutational plasticity, sleep-driven renormalization, and variance-preserving neuromodulation—that mitigate, but do not eliminate, the contraction pressure described by the HLE. We conclude that the HLE and the Rebis equation provide a systems-level diagnostic for identifying and explaining optimization-induced fragility and for informing the design of regulators, AI architectures, and institutions that remain viable under drift. Full article

Background: Clozapine (CLZ) is an atypical antipsychotic mainly prescribed for treatment-resistant schizophrenia. Beyond psychotic symptoms, patients often exhibit persistent cognitive impairments across domains such as attention, learning, and memory. The mechanisms by which CLZ may influence cognition and provide neuroprotection are not fully elucidated. Accordingly, this study examined how CLZ modulates lipopolysaccharide (LPS)-induced neurotoxicity in rats. Method: Rats were administered LPS to induce cognitive impairment and subsequently treated with CLZ. Behavioral assessments were performed using maze tests (elevated plus-maze (EPM), novel object recognition (NOR), and Y-maze). Biochemical analyses included cholinergic function (acetylcholine (ACh)), neurodegeneration-associated enzymes (glycogen synthase kinase-3 beta (GSK-3β), β-site amyloid precursor protein cleaving enzyme-1 (BACE-1), and dipeptidyl peptidase-4 (DPP-4)), oxidative stress markers (lipid Peroxidation (LPO), catalase, and reduced glutathione (GSH)), and apoptotic proteins (B-cell lymphoma-2 (Bcl-2), Bcl-2-associated X protein (Bax), and cleaved Caspase-3 (c-Caspase-3)). Results: CLZ treatment markedly improved performance in EPM, NOR, and Y-maze tasks, indicating recovery of cognitive function in LPS-exposed rats. At the molecular level, CLZ enhanced ACh levels, upregulated the anti-apoptotic protein Bcl-2, and restored antioxidant defenses (catalase and GSH). Conversely, CLZ reduced LPS-induced neurotoxicity by lowering GSK-3β activity, LPO, and pro-apoptotic markers (Bax and c-Caspase-3). Conclusion: The findings demonstrate that CLZ exerts neuroprotective effects in an LPS-induced rat model, improving cognition through modulation of cholinergic transmission, oxidative stress, and apoptosis pathways. These results clarify key mechanistic pathways through which CLZ may exert cognitive benefits and highlight its potential relevance for improving schizophrenia-related cognitive dysfunction. Further molecular studies are warranted to confirm and extend these observations toward clinical translation. Full article