Search Results (437)

This paper shows that artificial intelligence is fundamentally transforming college curricula by enabling data-driven personalization, which enhances student outcomes and better aligns educational programs with evolving workforce demands. Specifically, predictive analytics, machine learning algorithms, and natural language processing were applied here, grounded in constructivist learning theory and Human–Computer Interaction principles, to evaluate student performance and identify at-risk students to propose personalized learning pathways. Results indicated that the AI-based curriculum achieved much higher course completion rates (89.72%) as well as retention (91.44%) and dropout rates (4.98%) compared to the traditional model. Sentiment analysis of learner feedback showed a more positive learning experience, while regression and ANOVA analyses proved the impact of AI on enhancing academic performance to be real. Therefore, the learning content delivery for each student was continuously improved based on individual learner characteristics and industry trends by AI-enabled recommender systems and adaptive learning models. Its advantages notwithstanding, the study emphasizes the need to address ethical concerns, ensure data privacy safeguards, and mitigate algorithmic bias before an equitable outcome can be claimed. These findings can inform institutions aspiring to adopt AI-driven models for curriculum innovation to build a more dynamic, responsive, and learner-centered educational ecosystem. Full article

The global coffee supply chain is a complex multi-stakeholder ecosystem plagued by fragmented records, unverifiable origin claims, and limited real-time visibility. These limitations pose risks to ethical sourcing, product quality, and consumer trust. To address these issues, this paper proposes a blockchain and IoT-enabled framework for secure and transparent coffee supply chain management. The system integrates simulated IoT sensor data such as Radio-Frequency Identification (RFID) identity tags, Global Positioning System (GPS) logs, weight measurements, environmental readings, and mobile validations with Ethereum smart contracts to establish traceability and automate supply chain logic. A Solidity-based Ethereum smart contract is developed and deployed on the Sepolia testnet to register users and log batches and to handle ownership transfers. The Internet of Things (IoT) data stream is simulated using structured datasets to mimic real-world device behavior, ensuring that the system is tested under realistic conditions. Our performance evaluation on 1000 transactions shows that the model incurs low transaction costs and demonstrates predictable efficiency behavior of the smart contract in decentralized conditions. Over 95% of the 1000 simulated transactions incurred a gas fee of less than ETH 0.001. The proposed architecture is also scalable and modular, providing a foundation for future deployment with live IoT integrations and off-chain data storage. Overall, the results highlight the system’s ability to improve transparency and auditability, automate enforcement, and enhance consumer confidence in the origin and handling of coffee products. Full article

We propose Observer-Linked Branching (OLB), a mathematically rigorous extension of quantum theory in which an observer’s cognitive commitment actively modulates collapse dynamics at macroscopic scales. The OLB framework rests on four axioms, employing a norm-preserving nonlinear Schrödinger evolution and Lüders-type projection triggered by crossing a cognitive commitment threshold. Our expanded formalism provides five main contributions: (1) deriving Lie symmetries of the observer–environment interaction Hamiltonian; (2) embedding OLB into the Consistent Histories and path-integral formalisms; (3) multi-agent network simulations demonstrating intentional synchronisation toward shared macroscopic outcomes; (4) detailed statistical power analyses predicting measurable biases (up to ~5%) in practical experiments involving traffic delays, quantum random number generators, and financial market sentiment; and (5) examining the conceptual, ethical, and neuromorphic implications of intent-driven reality selection. Full reproducibility is ensured via the provided code notebooks and raw data tables in the appendices. While the theoretical predictions are precisely formulated, empirical validation is ongoing, and no definitive field results are claimed at this stage. OLB thus offers a rigorous, norm-preserving and falsifiable framework to empirically test whether cognitive engagement modulates macroscopic quantum outcomes in ways consistent with—but extending—standard quantum predictions. Full article

Workplace contextual emotions are structured ways of emotionally thinking about specific cues in the context that employees share within their organization. These dynamics reflect how employees emotionally interpret and respond to organizational environments. Contextual emotions may shape working relationships into different types of toxic emotional dynamics (e.g., claiming, controlling, distrusting, provoking) or, conversely, positive emotional dynamics (i.e., exchanging), thus setting the emotional tone that affects employees’ actions and their level of comfort/discomfort. The present study uses latent profile analysis (LPA) to identify subpopulations of employees who may experience differing levels of both positive and negative emotional dynamics (i.e., different configurations of emotional patterns of workplace behavior). Moreover, it examines whether the emergent profiles predict work-related (i.e., job satisfaction, burnout) and health-related outcomes (i.e., sleep disturbances, physical and mental health). Using data from 801 Italian employees, we identified four latent profiles: “functional dynamics” (low toxic emotions and high exchange), “dialectical dynamics” (co-existence of medium toxic emotions and medium exchange), “mild dysfunctional dynamics” (moderately high toxic emotions and low exchange), and “highly dysfunctional dynamics” (extremely high toxic emotions and extremely low exchange). Moreover, employees in the dialectical, mild dysfunctional, and highly dysfunctional groups reported progressively higher levels of poor health outcomes and progressively lower levels of satisfaction, whereas the functional group was at low risk of stress and was the most satisfied group. The theoretical and practical implications of the LPA-classified emotional patterns of workplace behavior are discussed in light of the relevance of identifying vulnerable subpopulations of employees diversely exposed to toxic configurations of emotional/relational ambience. Full article

LK-99, with chemical formula Pb_10−xCu_x(PO₄)₆O, was recently reported to be a room-temperature superconductor. While this claim has met with little support in a flurry of ensuing work, a variety of calculations (mostly based on density-functional theory) have demonstrated that the system possesses some unusual characteristics in the electronic structure, in particular flat bands. We have established previously that within DFT, the system is insulating with many characteristics resembling the classic cuprates, provided the structure is not constrained to the P3(143) symmetry nominally assigned to it. Here we describe the basic electronic structure of LK-99 within self-consistent many-body perturbative approach, quasiparticle self-consistent GW (QSGW) approximation and their diagrammatic extensions. QSGW predicts that pristine LK-99 is indeed a Mott/charge transfer insulator, with a bandgap gap in excess of 3 eV, whether or not constrained to the P3(143) symmetry. When Pb₉Cu(PO₄)₆O is hole-doped, the valence bands modify only slightly, and a hole pocket appears. However, two solutions emerge: a high-moment solution with the Cu local moment aligned parallel to neighbors, and a low-moment solution with Cu aligned antiparallel to its environment. In the electron-doped case the conduction band structure changes significantly: states of mostly Pb character merge with the formerly dispersionless Cu d state, and high-spin and low spin solutions once again appear. Thus we conclude that with suitable doping, the ground state of the system is not adequately described by a band picture, and that strong correlations are likely. Irrespective of whether this system class hosts superconductivity or not, the transition of Pb₁₀(PO₄)₆O from being a band insulator to Pb₉Cu(PO₄)₆O, a Mott insulator, and multi-determinantal nature of doped Mott physics make this an extremely interesting case-study for strongly correlated many-body physics. Full article

Insurance is essential for financial risk protection, but claim management is complex and requires accurate classification and forecasting strategies. This study aimed to empirically evaluate the performance of classification algorithms, including Logistic Regression, Decision Tree, Random Forest, XGBoost, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes to predict high insurance claims. The research analyses the variables of claims, vehicles, and insured parties that influence the classification of high-cost claims. This investigation utilizes a dataset comprising 802 observations of bodily injury claims from the motor liability portfolio of a private insurance company in Albania, covering the period from 2018 to 2024. In order to evaluate and compare the performance of the models, we employed evaluation criteria, including classification accuracy (CA), area under the curve (AUC), confusion matrix, and error rates. We found that Random Forest performs better, achieving the highest classification accuracy (CA = 0.8867, AUC = 0.9437) with the lowest error rates, followed by the XGBoost model. At the same time, logistic regression demonstrated the weakest performance. Key predictive factors in high claim classification include claim type, deferred period, vehicle brand and age of driver. These findings highlight the potential of machine learning models in improving claim classification and risk assessment and refine underwriting policy. Full article

In the news media, there are regular reports of family caregivers killing the people for whom they care, but scholarly research on this phenomenon is fragmented, and there has been little effort to predict or prevent future deaths. The aim of this study was to develop a typology of caregiver-perpetrated homicides that could provide a framework for more rigorous research and targeted responses in policy and practice. Ideal Type Analysis was applied to sixty-four homicides and homicide–suicides perpetrated by family caregivers in England and Wales between January 2015 and December 2019. The cases clustered into seven clear types: Ending Suffering; Genuine Burden of Care; Pre-existing Mental Illness; Neglect; Exploitation; Caregiver as Victim of Domestic Violence, Abuse or Coercive Control; and Caregiver as Perpetrator of Domestic Violence, Abuse, or Coercive Control. Each type was characterised by a distinct motive, context, or course of events leading to the homicide. This is the first typology of homicides and homicide–suicides perpetrated by caregivers. The Caregiver-Perpetrated Homicide Typology challenges previous claims that caregiver-perpetrated homicides are isolated events and provides a framework for the development of evidence-based prediction and prevention initiatives. Full article

Advances in the field of asteroid dynamics continue to yield new knowledge regarding the behavior and characteristics of asteroids, allowing unprecedented levels of accuracy for predicting trajectories and contributing to impact avoidance strategies. Meanwhile, more detailed information regarding the physical composition of asteroids has reignited interest in asteroid mining as a potential new resource sector. This article considers some of the technical, ethical, legal and social issues facing global planetary defense efforts and off-world mining proposals. It considers issues such as claim jumping, weaponization of the space environment and ownership issues for resources extracted from space. Full article

Objectives: This study aimed to assess the concordance between depression and anxiety case definitions derived from algorithms based on medico-administrative data and structured interviews aligned with the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria in older adults. Methods: We analyzed data from 1405 primary care older adults (≥65 years) from the Étude sur la Santé des Aînés (ESA)-Services cohort (2011–2013) in Quebec, Canada, who had available survey and medico-administrative data. Cases of depression and anxiety were identified using algorithms incorporating combinations of hospitalization records, physician-visit claims, and medication claims for antidepressants or anxiolytics. The agreement was assessed with the kappa statistics (κ), and the algorithms’ sensitivity, specificity, and positive and negative predictive values were calculated using the case definitions derived from the DSM-IV-aligned ESA-Services interviews as the gold standard. Results: Agreements between the algorithms and the interviews were fair (κ: 0.06–0.22) for depression gooand slight (κ: 0.02–0.09) for anxiety. The algorithms had low sensitivity (2–39.7% for depression and 1.4–39.9% for anxiety) but high specificity (84.5–99.6% for depression and 73–99.2% for anxiety), depending on the algorithm. Conclusions: The agreement between algorithms based on administrative data and DSM-IV-aligned interviews for anxiety or depressive disorders was low. The two methods identified older adults with different characteristics. Despite these discrepancies, algorithms with high specificity provide valuable insights into healthcare utilization patterns associated with these disorders. Full article

Background and Objectives: Oral cancer remains a critical global health burden. Oral potentially malignant disorders (OMPDs) such as leukoplakia and oral lichen planus can precede oral squamous cell carcinoma (OSCC). Inflammation, tissue remodeling, and dysregulated signaling pathways are central to malignant transformation. This observational study aimed to evaluate the expression patterns of Maspin, β-catenin, and MMP-14 by immunohistochemistry (IHC) in oral leukoplakia, oral lichen planus, OSCC, and normal mucosa, exploring associations with lesion type, with no prognostic inferences drawn from a single timepoint. Materials and Methods: Biopsy specimens from 67 patients presenting with oral lesions (27 leukoplakia, 22 lichen planus, 18 OSCC), and 10 healthy controls were collected between January 2015 and January 2023. Inclusion criteria were age over 18 years and no other chronic illness, and a histopathologic diagnosis of oral leukoplakia, oral lichen planus or OSCC. Exclusion criteria were smokers, alcohol abuse, and prior head and neck radiotherapy, prior immunosuppressive therapy, systemic inflammatory diseases, absence of histopathological confirmation of the clinical diagnosis, and squamous cell carcinoma of the vermilion. Two pathologists independently scored staining in 10 high-power fields. Normal mucosa served as baseline. Immunohistochemical analysis was conducted using specific antibodies targeting Maspin, β-catenin, and MMP-14. Marker expression was assessed using a semi-quantitative scoring system based on staining intensity and classified into four categories: negative (−), weakly positive (+) for 1–10%, moderately positive (++) for 11–50%, and highly positive (+++) for more than 50%. Results: Maspin showed moderate (++) cytoplasmic/nuclear staining in leukoplakia and lichen planus in 78% of cases and high (+++) in OSCC and stroma in all cases. β-catenin shifted from membranous moderate positivity in 100% of OPMD cases to cytoplasmic/nuclear high positivity in all cases of OSCC. MMP-14 showed positivity (+) in 89% of OPMDs and high positivity (+++) in 100% of OSCC. Conclusions: Maspin, β-catenin, and MMP-14 exhibit distinct expression patterns across lesion types. While Maspin may reflect early tissue remodeling, β-catenin and MMP-14 changes suggest Wnt signaling activation and matrix remodeling in OSCC. Longitudinal studies are needed to establish their predictive value. This observational study refrains from prognostic claims and instead highlights biomarkers for future validation. Full article

Technological advancements in algorithmic personalization are widely believed to influence user behavior on online gambling platforms. This study explores how such developments, potentially including AI-driven mechanisms, may affect cognitive and motivational processes, especially in relation to risk perception, decision-making, and betting persistence. Using ordinary least squares (OLS) and panel regression models applied to behavioral data from a gambling platform, we examine patterns that are consistent with increased personalization between two distinct time periods, 2016 and 2021. The datasets do not contain any direct metadata regarding AI interventions. However, we interpret changes in user behavior over time as indicative of evolving personalization dynamics within a broader technological and contextual landscape. Accordingly, our conclusions about algorithmic personalization are inferential and exploratory, drawn from temporal comparisons between 2016 and 2021. Our findings show that users receiving personalized bonuses or making early cash-out decisions tend to adjust their stake sizes and betting frequency in systematic ways, which may reflect indirect effects of technological reinforcement strategies. These behavioral patterns raise important ethical and regulatory questions, particularly regarding user autonomy, algorithmic transparency, and the protection of at-risk users. This research contributes to the literature on digital behavior influencing gambling by framing the analysis as observational and quasi-experimental and suggests that further studies use experimental and log-level data to more specifically analyze the algorithmic effects. However, no causal claims can be made about AI influence as the temporal contradictions are interpreted as broad phenomena of technological developments, since they are not measured as algorithmic interventions. Further studies should also investigate the development of predictive models aimed at countering gambling addiction; evaluate the long-term ethical implications of algorithmic personalization; and discuss potential solutions codeveloped to foster a responsible gambling climate. Full article

Fishing vessels are essential for the activities of catching, moving, and storing fish. However, fishing vessel accidents claim thousands of deaths every year. This study presents a novel integrated safety monitoring and early warning system designed for fishing vessels, offering significant advancements in maritime safety through real-time alerts based on vessel attitude motion and environmental conditions. The innovation of the system lies in its dual-subsystem architecture: a sensing terminal equipped with a nine-axis sensor, temperature and humidity sensors, a GPS module, and a surveillance camera collects critical data, while a decision support subsystem processes this information via a fuzzy logic-based algorithm to generate a “danger score”. This score quantifies the vessel’s safety status, enabling the system to trigger alerts through SMS and web notifications when predefined thresholds are exceeded. Field trials in the Zhoushan Sea area confirmed the system’s effectiveness in accurately predicting safety hazards and providing timely alerts. The results highlight its potential to enhance operational safety and contribute to the digitization of fisheries management by offering reliable real-time data on vessel conditions. The system’s modular and cost-efficient design ensures it is scalable and adaptable for widespread use across the fishing industry. Our study addresses the limitations of existing technologies by providing a balanced solution that combines comprehensive sensing capabilities with real-time responsiveness and cost-effectiveness, offering a practical and innovative approach to improve fishing vessel safety. Full article

Fire, whether wild or urban, depends on the triad of oxygen, fuel, and heat. Urban fires, although smaller in scale, have devastating impacts, as evidenced by the 2018 wildfire in Mati, Attica (Greece), which claimed 104 lives. The elderly and children are the most vulnerable due to mobility and cognitive limitations. This study applies Grammatical Evolution (GE), a machine learning method that generates interpretable classification rules to predict the consequences of urban fires. Using historical data (casualties, containment time, and meteorological/demographic parameters), GE produces classification rules in human-readable form. The rules achieve over 85% accuracy, revealing critical correlations. For example, high temperatures (>35 °C) combined with irregular building layouts exponentially increase fatality risks, while firefighter response time proves more critical than fire intensity itself. Applications include dynamic evacuation strategies (real-time adaptation), preventive urban planning (fire-resistant materials and green buffer zones), and targeted awareness campaigns for at-risk groups. Unlike “black-box” machine learning techniques, GE offers transparent human-readable rules, enabling firefighters and authorities to make rapid informed decisions. Future advancements could integrate real-time data (IoT sensors and satellites) and extend the methodology to other natural disasters. Protecting urban centers from fires is not only a technological challenge but also a moral imperative to safeguard human lives and societal cohesion. Full article

Background: Late-onset pre-eclampsia (LO-PE) remains difficult to predict because placental angiogenic markers perform poorly once maternal cardiometabolic factors dominate. Methods: We reanalyzed a publicly available cell-free RNA (cfRNA) cohort (12 EO-PE, 12 LO-PE, and 24 matched controls). After RNA-seq normalization, we derived LO-PE candidate genes using (i) differential expression and (ii) elastic-net feature selection. Predictive accuracy was assessed with nested Monte-Carlo cross-validation (10 × 70/30 outer splits; 5-fold inner grid-search for λ). Results: The best LO-PE elastic-net model achieved a mean ± SD AUROC of 0.88 ± 0.08 and F1 of 0.73 ± 0.17—substantially higher than an EO-derived baseline applied to the same samples (AUROC ≈ 0.69). Enrichment analysis highlighted immune-tolerance and metabolic pathways; three genes (HLA-G, IL17RB, and KLRC4) recurred across >50% of cross-validation repeats. Conclusions: Plasma cfRNA signatures can outperform existing EO-based screens for LO-PE and nominate biologically plausible markers of immune and metabolic dysregulation. Because the present dataset is small (n = 48) and underpowered for single-gene claims, external validation in larger, multicenter cohorts is essential before clinical translation. Full article