Search Results (17,427)

Traditional academic monitoring practices rely on retrospective data analysis, generally identifying at-risk students too late to take meaningful action. To address this, this paper proposes a real-time, rule-based decision support system designed to increase student achievement by early detection of disengagement, meeting the growing demand for prompt academic intervention in online and blended learning contexts. The study uses the Open University Learning Analytics Dataset (OULAD), comprising over 32,000 students and millions of virtual learning environment (VLE) interaction records, to simulate weekly assessments of engagement through clickstream activity. Students were flagged as “at risk” if their participation dropped below defined thresholds, and these flags were associated with assessment performance and final course results. The system demonstrated 72% precision and 86% recall in identifying failing and withdrawn students as major alert contributors. This lightweight, replicable framework requires minimal computing power and can be integrated into existing LMS platforms. Its visual and statistical validation supports its role as a scalable, real-time early warning tool. The paper recommends integrating real-time engagement dashboards into institutional LMS and suggests future research explore hybrid models combining rule-based and machine learning approaches to personalize interventions across diverse learner profiles and educational contexts. Full article

A simple Lagrangian travelling slice model has been successfully used to predict the relations between the process parameters and the strand temperatures in the continuous casting of steel. The present paper aims to include a simple macrosegregation, grain structure and mechanical stress and deformation model on top of the thermal slice framework. The basis of all the mentioned models is the slice heat-conduction model that considers the complex heat extraction mechanisms in the mould, with the sprays, rolls, and through radiation. Its main advantage is the fast calculation time, which is suitable for the online control of the caster. The macroscopic thermal and species transfer models are based on the continuum mixture theory. The macrosegregation model is based on the lever rule microsegregation model. The thermal conductivity and species diffusivity of the liquid phase are artificially enhanced to consider the convection of the melt. The grain structure model is based on cellular automata and phase-field concepts. The calculated thermal field is used to estimate the thermal contraction of the solid shell, which, in combination with the metallostatic pressure, drives the elastic-viscoplastic solid-mechanics models. The solution procedure of all the models is based on the meshless radial basis function generated finite difference method on the macroscopic scale and the meshless point automata concept on the grain structure scale. Simulation results point out the areas susceptible to hot tearing. Full article

In human cells, the biogenesis of membrane proteins, which account for one quarter of polypeptides and sixty percent of human drug targets, is initiated at the membrane of the endoplasmic reticulum (ER). This process involves N-terminal signal peptides or transmembrane helices in the membrane protein precursors. Over one hundred proteins enable membrane-targeting and -insertion of the precursors as well as their folding and covalent modifications. Four targeting pathways to the Sec61 channel in the ER membrane with their effectors and three cooperating or independent membrane protein–insertases have been identified. We combined knock-down of individual components of these pathways and insertases in HeLa cells with label-free quantitative mass spectrometric analysis of the proteomes. Differential protein abundance analysis in comparison to control cells was employed to identify clients of components involved in the targeting or membrane insertion of precursors. Alternatively, knock-out cells or relevant patient fibroblasts were employed. The features of the client polypeptides were characterized to identify the client types of the different components and, ideally, their rules of engagement. In this review/article-hybrid, the focus is on global lessons from and limitations of the proteomic approach in answering the cell biological question, as well as on new aspects, such as N-terminal acetylation of membrane protein precursors. Full article

This study focuses on the numerical analysis of heat transfer in biological tissue. The proposed model is formulated using the Pennes equation for a two-dimensional cylindrical domain. The tissue undergoes laser irradiation, where internal heat sources are determined based on the Beer–Lambert law. Moreover, key parameters—such as the perfusion rate and effective scattering coefficient—are modeled as functions dependent on tissue damage. In addition, a fuzzy heat source associated with magnetic nanoparticles is also incorporated into the model to account for magnetothermal effects. A novel aspect of this work is the introduction of uncertainty in selected model parameters by representing them as triangular fuzzy numbers. Consequently, the entire Finite Pointset Method (FPM) framework is extended to operate with fuzzy-valued quantities, which—to the best of our knowledge—has not been previously applied in two-dimensional thermal modeling of biological tissues. The numerical computations are carried out using the fuzzy-adapted FPM approach. All calculations are performed due to the fuzzy arithmetic rules with the application of α-cuts. This fuzzy formulation inherently captures the variability of uncertain parameters, effectively replacing the need for a traditional sensitivity analysis. As a result, the need for multiple simulations over a wide range of input values is eliminated. The findings, discussed in the final Section, demonstrate that this extended FPM formulation is a viable and effective tool for analyzing heat transfer processes under uncertainty, with an evaluation of α-cut widths and the influence of the degree of fuzziness on the results also carried out. Full article

Background: Road traffic accidents continue to be a leading cause of mortality and morbidity worldwide. Psychological and behavioural factors play a crucial role in traffic safety and are not yet fully understood. Among these, the relationship between individuals and road rules plays a key role in driving behaviour and risk perception. We introduce and validate the MORDE (Moral Disengagement in Road Driving Evaluation) scale, a novel instrument designed to assess the specific cognitive mechanisms through which drivers morally justify risky or rule-violating behaviours. Methods: The scale was developed and validated through a three-step process involving 1336 licensed drivers. Exploratory and confirmatory factor analyses were conducted to test its factorial structure, and internal consistency was evaluated using Cronbach’s alpha. Convergent and predictive validity were assessed using self-reported measures of traffic violations and road safety attitudes. Results: The final 14-item version of the MORDE scale shows a robust two-factor structure: (1) Normative Justification of Transgressive Driving and (2) Attribution of Blame and Displacement of Responsibility. The instrument demonstrates strong internal reliability and significant predictive power for driving behaviours and road safety attitudes, beyond what is explained by general moral disengagement. The MORDE scale thus shows good psychometric properties and incremental validity. Conclusions: By identifying psychological risk factors that contribute to unsafe and unsustainable driving, the MORDE scale provides a validated tool that can support educational interventions, traffic safety campaigns, and behaviour change programs. Its use may contribute to the promotion of a safer, more responsible, and environmentally sustainable road culture. Full article

Background/Objectives: Dizziness is a frequent medical complaint with neurological, otolaryngological, and psychological origins. Imaging studies such as CT (Computer Tomography), cervical X-rays, and ultrasound aid diagnosis, while MRI (Magnetic Resonance Imaging) is crucial for detecting brain abnormalities. Our purpose is to identify structural brain changes associated with vertigo, assess pre-MRI diagnostic approaches, and evaluate treatment strategies. Methods: A case-control study of 232 vertigo patients and 232 controls analyzed MRI findings, pre-MRI examinations, symptoms, and treatments. Statistical comparisons were performed using chi-square and t-tests (p < 0.05). Results: White matter lesions, lacunar infarcts, Circle of Willis variations, and sinusitis were significantly more frequent in vertigo patients (p < 0.05). Pre-MRI diagnostics frequently identified atherosclerosis (ultrasound) and spondylosis (X-ray). Common symptoms included headache, imbalance, and visual disturbances. The most frequent post-MRI diagnosis was Benign Paroxysmal Positional Vertigo (BPPV). Treatments included lifestyle modifications, physical therapy (e.g., Epley maneuver), and pharmacological therapies such as betahistine. Conclusions: MRI revealed structural brain changes linked to vertigo. Pre-MRI assessments are essential for ruling out vascular and musculoskeletal causes. A multidisciplinary treatment approach is recommended. Trial Registration: This study was registered in ClinicalTrials.gov with the trial registration number NCT06848712 on 22 February 2025. Full article

Mine water inrush is a significant environmental catastrophe during the coal mining process, and the timely discrimination of the source of water inrush is the key to ensuring safe production in coal mines. This work suggests a mine water inrush—belief rule base (MWI-BRB) source discrimination model to overcome the interpretability and performance issues with conventional models. MWI-BRB firstly automatically constructs the reference values of prerequisite attributes using the Sum of Squared Errors—K-means++ algorithm, which effectively combines expert knowledge and data-driven methods, and solves the limitation of the traditional belief rule base model relying on specialist knowledge. Secondly, the hierarchical incremental structure solves the rule explosion problem caused by complex features while using XGBoost to select features. Finally, in the inference process, the model adopts an evidential reasoning algorithm to realize transparent causal inference, guaranteeing the model’s interpretability and transparency. The Penalized Covariance Matrix Adaptation Evolution Strategy algorithm optimizes the model parameters to increase the discriminative accuracy of the model even more. Experimental results on a real coal mine dataset (a total of 67 samples from Hebei, China, covering four water inrush sources) demonstrate that the proposed MWI-BRB achieves 95.23% accuracy, 95.23% recall, and 95.36% F1-score under a 7:3 training–testing split with parameter tuning performed via leave-one-out cross-validation. The near-identical values across accuracy, recall, and F1-score reflect the balanced nature of the dataset and the robustness of the model across different evaluation metrics. Compared with baseline models, MWI-BRB’s accuracy and recall are 4.78% higher than BPNN and 9.52% higher than KNN, RF, and XGBoost; its F1-score is 4.85% higher than BPNN, 10.64% higher than KNN, 10.19% higher than RF, and 9.65% higher than XGBoost. Moreover, the model maintains high interpretability. In conclusion, the MWI-BRB model can realize efficient and accurate water inrush source discrimination in complex environments, which provides a feasible technical solution for the prevention and control of mine water damage. Full article

Due to their zoonotic potential and close interaction with humans, feline parasitic infections are an important public health concern. This study investigated 2758 domiciled and feral cats sampled across Mexico to assess the occurrence of parasites, coinfections, and associated risk factors. Twelve genera of parasites were identified, with Ancylostoma and Ctenocephalides being the most frequent. Coinfections were common, often involving both intestinal and ectoparasites. Multivariable logistic regression revealed that feral lifestyle, absence of recent antiparasitic treatment, female sex, and climatic conditions were significant predictors of infection. Cats with unrestricted outdoor access and direct contact with other cats, where hunting behavior and the ingestion of prey cannot be ruled out (ESCCAP risk group B), were more than five times as likely to be infected as those cats that live indoors (ESCCAP risk group A). Although antiparasitic use was reported in some cats, inappropriate drug choice and long treatment intervals reduced effectiveness, while nearly seven out of ten cats had never received treatment. These findings highlight major gaps between current practices in Mexico and international guidelines. Strengthening surveillance, promoting owner education, and implementing risk-based strategies are critical to reducing feline parasitism and associated zoonotic risks within a One Health framework. Full article

To address the challenges in multimodal information integration and the inefficiency of knowledge transfer in the construction technology disclosure of deep foundation pit projects, an intelligent generation method based on graph rule reasoning and template mapping was proposed. First, a multi-level domain knowledge structure model was constructed by designing domain concepts and relationship types using the Work Breakdown Structure (WBS). Second, entity and attribute extraction was performed using regular expressions and the BERT-BiLSTM-CRF model, while relationship extraction was conducted based on text structure combined with the BERT-CNN model. For image and video data, cross-modal data chains were built by adding keyword tags and generating URLs, utilizing semantic association rules to form a multimodal knowledge graph of the domain. Finally, based on graph reasoning and template mapping technology, the intelligent generation of construction disclosure schemes was realized. The case verification results showed that the proposed method significantly improved the structural integrity, procedural logical consistency, parameter traceability, knowledge reuse rate, environmental compliance, and parameter compliance of the schemes. This method not only promoted the standardization and efficiency of construction technology disclosure activities for deep foundation pit projects but also enhanced the visualization and intelligence level of the schemes. Full article

Background/Objectives: An ASD diagnosis from EEG is challenging due to non-stationary, low-SNR signals and small cohorts. We propose a compact, interpretable pipeline that pairs a shift-invariant Stationary Wavelet Transform (SWT) with Fisher’s Linear Discriminant (FLDA) as a supervised projection method, delivering band-level insight and subject-wise evaluation suitable for resource-constrained clinics. Methods: EEG from the KAU dataset (eight ASD, eight controls; 256 Hz) was decomposed with SWT (db4). We retained levels 3, 4, and 6 (γ/β/θ) as features. FLDA learned a low-dimensional discriminant subspace, followed by a linear decision rule. Evaluation was conducted using a subject-wise 70/30 split (no subject overlap) with accuracy, precision, recall, F1, and confusion matrices. Results: The β band (Level 4) achieved the best performance (accuracy/precision/recall/F1 = 0.95), followed by γ (0.92) and θ (0.85). Despite partial overlap in FLDA scores, the projection maximized between-class separation relative to within-class variance, yielding robust linear decisions. Conclusions: Unlike earlier FLDA-only pipelines and wavelet–entropy–ANN approaches, our study (1) employs SWT (undecimated, shift-invariant) rather than DWT to stabilize sub-band features on short resting segments, (2) uses FLDA as a supervised projection to mitigate small-sample covariance pathologies before classification, (3) provides band-specific discriminative insight (β > γ/θ) under a subject-wise protocol, and (4) targets low-compute deployment. These choices yield a reproducible baseline with competitive accuracy and clear clinical interpretability. Future work will benchmark kernel/regularized discriminants and lightweight deep models as cohort size and compute permit. Full article

Speaker recognition has become a critical component of human–robot interaction (HRI), enabling personalized services based on user identity, as the demand for home service robots increases. In contrast to conventional speech recognition tasks, recognition in home service robot environments is affected by varying speaker–robot distances and background noises, which can significantly reduce accuracy. Traditional approaches rely on hand-crafted features, which may lose essential speaker-specific information during extraction like mel-frequency cepstral coefficients (MFCCs). To address this, we propose a novel speaker recognition technique for intelligent robots that combines SincNet-based raw waveform processing with an adaptive neuro-fuzzy inference system (ANFIS). SincNet extracts relevant frequency features by learning low- and high-cutoff frequencies in its convolutional filters, reducing parameter complexity while retaining discriminative power. To improve interpretability and handle non-linearity, ANFIS is used as the classifier, leveraging fuzzy rules generated by fuzzy c-means (FCM) clustering. The model is evaluated on a custom dataset collected in a realistic home environment with background noise, including TV sounds and mechanical noise from robot motion. Our results show that the proposed model outperforms existing CNN, CNN-ANFIS, and SincNet models in terms of accuracy. This approach offers robust performance and enhanced model transparency, making it well-suited for intelligent home robot systems. Full article

As hybrid microgrids become increasingly widespread in real-world applications, the need for intelligent energy management strategies that ensure reliability, economic efficiency, and robustness to uncertainties is growing. This study presents a real-time capable model predictive control (MPC)-based energy management for a medium-sized hybrid microgrid at the Karabuk University Demir Çelik campus. The system comprises 100 kW photovoltaic (PV) panels, a 500 Ah battery energy storage system (BESS), a 440 kW diesel generator, and a 75 MVA utility connection. The proposed MPC approach is evaluated under ten realistic operating scenarios, incorporating dynamic pricing and fault conditions. Simulation results show up to 43% reduction in operational costs and 35% decrease in grid dependency, while keeping unserved critical loads below 3%. Compared to conventional rule-based methods, the proposed strategy offers improved scalability, adaptability, and resilience, highlighting its practical potential for deployment in smart energy systems. Full article

This study estimates student loan borrowers’ repayment burdens (RBs) in South Korea. Using data from the Survey Report on Labor Conditions by Employment Type and novel administrative records, we estimate life-cycle earnings profiles by income quantile through RIF regression. These estimates are then used to derive RBs for hypothetical borrowers under income-contingent loans (ICLs) and mortgage-type loans, and to evaluate RBs for actual ICL borrowers by matching them with estimated income profiles. The findings suggest that Korea’s student loan system plays a positive role in expanding access to higher education, particularly through ICLs. Many low-income students who benefited from ICLs are later found in the top income deciles. However, raising the repayment threshold irrespective of borrower income may delay repayment and reduce system efficiency. These results underscore the importance of aligning repayment rules with borrowers’ earnings trajectories to ensure both equity and the long-term sustainability of the loan system. Full article

The move toward environmentally friendly methods in the global manufacturing sector has led to the use of minimum quantity lubrication (MQL) as an eco-friendly alternative to traditional flood cooling. However, the natural limits of MQL in high-performance settings have led to the use of nanotechnology, which has resulted in the creation of nanofluids, engineered colloidal suspensions that significantly improve the thermophysical and tribological properties of base fluids. This paper gives a complete overview of the latest developments in nanofluid technology for use in machining. It starts with the basics of MQL and the rules for making, describing, and keeping nanofluids stable. The review examines the application and effectiveness of single and hybrid nanofluids in various machining processes. It goes into detail about how they improve tool life, surface integrity, and overall efficiency. It also examines the benefits of integrating nanofluid-assisted MQL (NMQL) with more advanced and hybrid systems, including cryogenic cooling (cryo-NMQL), ultrasonic atomization, electrostatic–magnetic assistance, and multi-nozzle delivery systems. The paper also gives a critical look at the main problems that these technologies face, such as the long-term stability of nanoparticle suspensions, their environmental and economic viability as measured by life cycle assessment (LCA), and the important issues of safety, toxicology, and disposal. This review gives a full picture of the current state and future potential of nanofluid-assisted sustainable manufacturing by pointing out important research gaps, like the need for real-time LCA data, cost-effective scalability, and the use of artificial intelligence (AI) to improve processes, and by outlining future research directions. Full article

With the rapid development of urban air mobility, achieving safe and segregated flight for unmanned aerial vehicles amid the surging demand for low-altitude logistics has become a critical issue. This paper proposes a method for planning the public route network of urban low-altitude terminal logistics while considering environmental risks in the digital airspace. First, based on parallel system theory, we develop a digital airspace environment model that supports public route network planning by mapping physical and social elements to an artificial system. Furthermore, we establish a digital airspace grid partitioning system, develop grid access rules, and create a quantification model for urban low-altitude airspace environmental risks. Utilizing a layered airspace approach, this paper configures approach–departure grids, develops methods for initial public route network planning, and facilitates orderly re-planning of routes, ultimately establishing a hub-and-spoke public route network with segregation. This study conducts detailed case simulation studies based on realistic constraints, focusing on environmental risk, accurate grid configuration, comprehensive cost, algorithm complexity, and network scale. Simulation results demonstrate that the proposed method effectively constructs conflict-free networks, while maintaining low risks and inflection points. The findings align with the current development stage of urban air mobility characterized by the principle of ‘isolation first, then integration.’ This approach enables a gradual transition from route isolation to future integrated flight, thereby providing technical support for advancing low-altitude logistics operations. Full article