Search Results (1,591)

Power system models of electric systems are crucial in system planning for operations, economics, and expansion analyses. However, as these models contain critical infrastructure data, they are not publicly available. This poses challenges in future expansion scenarios and evaluating technological advancements in an electric grid. Synthetic models are realistic power system models, both topologically and operationally. However, since the electrical network is typically produced using statistical data and often uses machine learning, it does not contain propriety information. This allows researchers to evaluate system behavior under various operating conditions and as test cases for emerging technologies. These test cases are particularly important in highly evolving electric grids and areas of high renewable energy integration such as Alaska. Currently, no publicly available benchmark power system models of rural Alaskan island microgrids exist. This paper presents a rural Alaskan microgrid synthetic power system model and the methodology adopted to develop the model. The performance of the developed synthetic grid was assessed through steady state and positive-sequence dynamic simulations under various operating conditions. Full article

With the rising urgency of global environmental challenges, understanding the mechanisms behind green consumption has become increasingly vital. This study investigates how green product information disclosure influences consumers’ green consumption intention, focusing on new energy vehicle (NEV) consumers in China. Grounded in the Theory of Planned Behavior (TPB), the study introduces environmental concern as a mediator and brand reputation as a moderator to enhance the explanatory power of the model. A total of 527 valid questionnaires were collected on-site from NEV exhibitions in Beijing. Structural equation modeling and PROCESS macro analysis were employed to test the research hypotheses. The results indicate that environmental information disclosure significantly promotes green consumption intention, both directly and indirectly, through the mediating effects of green consumption attitude, subjective norms, and environmental concern. However, the direct effect of information communication channels was not statistically significant. Moreover, brand reputation positively moderates the relationship between environmental information disclosure and green consumption intention. These findings provide new theoretical insights by extending TPB with contextual and psychological variables and offer practical implications for NEV manufacturers and marketers. Specifically, companies are encouraged to prioritize transparent and credible environmental information disclosure, strengthen brand reputation, and consider consumers’ attitudes and social norms when designing green marketing strategies. Full article

Background: Splenic arterial embolization (SAE) is a well-established technique in the non-operative management of splenic trauma and aneurysms. While its short-term safety and efficacy have been widely documented, medium- and long-term impacts on splenic volume and function remain under-investigated. This study aimed to evaluate volumetric changes and hematological parameters following SAE, with emphasis on its role in preserving splenic integrity and potential integration with AI-enhanced imaging technologies. Methods: We retrospectively analyzed 17 patients treated with SAE between January 2014 and December 2023. Volumetric measurements were performed using computed tomography (CT) with 3D reconstructions before and after SAE. Patients were divided into two groups based on indication: polytrauma (n = 8) and splenic artery aneurysm (n = 9). Hematological parameters including white blood cells (WBCs), red blood cells (RBCs), and hemoglobin (Hb) were evaluated in correlation with clinical outcomes. Statistical significance was assessed using Student’s t-test, and power analysis was conducted. Results: Among the trauma group, mean splenic volume decreased from 190.5 ± 51.2 cm³ to 147.8 ± 77.8 cm³ (p = 0.2158), while in the aneurysm group, volume decreased from 195.4 ± 78.9 cm³ to 143.7 ± 81.4 cm³ (p = 0.184). Though not statistically significant, these changes suggest post-procedural splenic remodeling. The technical success of SAE was 100%, with no cases of late follow-up infarction, abscess, immunological impairment, or secondary splenectomy required. Hematologic parameters remained within normal limits in follow-up assessments. Conclusions: SAE represents a safe and effective intervention for spleen preservation in both traumatic and aneurysmal conditions. Although a reduction in splenic volume has been observed, white blood cell counts, a reliable indicator of splenic function, have remained stable over time. This finding supports the preservation of splenic function following SAE. Full article

Background/Objectives: Dementia is a leading cause of cognitive decline, with significant challenges for early detection and timely intervention. The lack of effective, user-centred technologies further limits clinical response, particularly in underserved areas. This study aimed to develop and describe a co-design process for creating a Diagnostic and Statistical Manual of Mental Disorders (DSM-5)-compliant, AI-powered Smart Assistant (SmartApp) to monitor neurocognitive decline, while ensuring accessibility, clinical relevance, and responsible AI integration. Methods: A co-design framework was applied using a novel combination of Agile principles and the Double Diamond Model (DDM). More than twenty iterative Scrum sprints were conducted, involving key stakeholders such as clinicians (psychiatrist, psychologist, physician), designers, students, and academic researchers. Prototype testing and design workshops were organised to gather structured feedback. Feedback was systematically incorporated into subsequent iterations to refine functionality, usability, and clinical applicability. Results: The iterative process resulted in a SmartApp that integrates a DSM-5-based screening tool with 24 items across key cognitive domains. Key features include longitudinal tracking of cognitive performance, comparative visual graphs, predictive analytics using a regression-based machine learning module, and adaptive user interfaces. Workshop participants reported high satisfaction with features such as simplified navigation, notification reminders, and clinician-focused reporting modules. Conclusions: The findings suggest that combining co-design methods with Agile/DDM frameworks provides an effective pathway for developing AI-powered clinical tools as per responsible AI standards. The SmartApp offers a clinically relevant, user-friendly platform for dementia screening and monitoring, with potential to support vulnerable populations through scalable, responsible digital health solutions. Full article

Background/Objectives: Composite resins are widely used in restorative dentistry due to their aesthetic properties and ease of handling. Preheating prior to light polymerization has been proposed to improve flowability, degree of conversion, and mechanical properties. This in vitro study aimed to evaluate the effect of preheating on the microhardness of three nanoparticulate composite resins—IPS Empress Direct (Ivoclar), Filtek Z350 XT (3M-ESPE), and Forma (Ultradent)—when cured with a high-power LED light. Methods: Sixty disc-shaped samples (n = 20 per material) were fabricated and divided into preheated and non-preheated groups. After polishing and 24 h storage in distilled water at 37 °C, samples were subjected to Knoop microhardness testing under a 300 g load for 15 s. Statistical analysis was conducted using R software. Results: Preheating produced a significant increase in surface microhardness for IPS Empress Direct (32.8%) and Filtek Z350 XT (5.8%) (p < 0.05 for both), whereas Forma showed no significant change. Conclusions: Under the conditions of this in vitro study, preheating can enhance the mechanical performance of specific composite resins by increasing microhardness; however, the effect is material-dependent. Full article

The growing application of artificial intelligence in education has created new avenues for second language learning. The following research explores the impact of learning with the help of chatbots on English conversation among secondary students in the Northern Borders Region in Saudi Arabia. The quasi-experimental design involved 30 students divided into two groups: an experimental group that interacted with an intervention using a GPT-powered chatbot for three weeks, and a control group that underwent traditional teaching. Pre- and post-tests were given to assess conversation competence. At the same time, students’ attitudes toward the chatbot-assisted learning experience were measured through questionnaires, teacher observation, and usage logs in the chatbot. Results showed statistically significant improvement in the experimental group’s speaking competence (mean gain = 5.24, p < 0.001). Students showed high motivation, elevated confidence, and high satisfaction with the learning experience provided through the chatbot (overall attitude mean = 4.35/5). Teacher observations testified that the students were much more engaged and spontaneous, and using the chatbot was positively correlated with score gain (r = 0.61). The outcomes indicate that chatbot-based learning is a practical approach for facilitating the development of spoken English, particularly in low-resource learning environments. The research provides empirical proof in favour of the incorporation of interactive AI into EFL teaching in all the secondary schools in Saudi Arabia. Full article

Objective: This study aims to develop and validate an interpretable deep learning framework that leverages self-supervised time reversal (TR) pretraining to identify consistent, biologically plausible functional network biomarkers across multiple neurological and psychiatric disorders. Methods: We pretrained a hierarchical LSTM model using a TR pretext task on the Human Connectome Project (HCP) dataset. The pretrained weights were transferred to downstream classification tasks on five clinical datasets (FBIRN, BSNIP, ADNI, OASIS, and ABIDE) spanning schizophrenia, Alzheimer’s disease, and autism spectrum disorder. After fine-tuning, we extracted latent features and employed a logistic regression probing analysis to decode class-specific functional network contributions. Models trained from scratch without pretraining served as a baseline. Statistical tests (one-sample and two-sample t-tests) were performed on the latent features to assess their discriminative power and consistency. Results: TR pretraining consistently improved classification performance in four out of five datasets, with AUC gains of up to 5.3%, particularly in data-scarce settings. Probing analyses revealed biologically meaningful and consistent patterns: schizophrenia was associated with reduced auditory network activity, Alzheimer’s with disrupted default mode and cerebellar networks, and autism with sensorimotor anomalies. TR-pretrained models produced more statistically significant latent features and demonstrated higher consistency across datasets (e.g., Pearson correlation = 0.9003 for schizophrenia probing vs. −0.67 for non-pretrained). In contrast, non-pretrained models showed unstable performance and inconsistent feature importance. Conclusions: Time Reversal pretraining enhances both the performance and interpretability of deep learning models for fMRI classification. By enabling more stable and biologically plausible representations, TR pretraining supports clinically relevant insights into disorder-specific network disruptions. This study demonstrates the utility of interpretable self-supervised models in neuroimaging, offering a promising step toward transparent and trustworthy AI applications in psychiatry. Full article

Trail running involves constant changes in terrain and slope, complicating the accurate assessment of energy expenditure during performance. This study aimed to examine the relationship between running power output (RPO), oxygen consumption (VO₂), carbon dioxide production (VCO₂), and energy expenditure per minute (EE_min) across positive and negative slopes in trained trail runners under standardized laboratory conditions. Fifteen male trail runners performed five randomized 5 min treadmill runs at 70% of VO₂ maximal speed on −7%, −5%, 0%, +5%, and +7% slopes. VO₂, VCO₂, EE_min, respiratory exchange ratio (RQ), heart rate (HR), and RPO were recorded. Statistical analysis included Shapiro–Wilk tests for normality, repeated-measures ANOVA to compare variables across slopes, and Spearman or Pearson correlations between RPO and physiological variables. Moderate to strong positive correlations were found between RPO and VO₂ (Rho = 0.80–0.84, p < 0.001) and between RPO and EE_min (Rho= 0.74–0.87, p < 0.01) across all conditions. These findings suggest that RPO measured via a wearable device may reflect changes in energy expenditure and supports the integration of wearable power metrics into training and nutritional strategies for trail running. However, further studies in female athletes, outdoor settings, extreme slopes, and altitude conditions are needed to confirm the generalizability of these results. Full article

Background/Objectives: This meta-analytical review assesses the relationship between effect size and replication success in genetic studies of athletic performance, focusing on the ACTN3 and ACE polymorphisms across power- and endurance-based sports. The analysis revealed substantial heterogeneity in reported effect sizes (overall I² = 72.3%), indicating considerable variability between studies, likely influenced by differences in population genetics, study design, and sample size. Methods: For ACTN3, the pooled effect sizes were 1.40 (95% CI: 1.18–1.65) for power sports and 1.35 (95% CI: 1.12–1.58) for endurance sports. Although the difference between these estimates is small, it reached statistical significance (p = 0.0237), reflecting the large sample size, but it remains of limited practical and clinical significance. For the ACE polymorphism, effect sizes were similar in both endurance (ES = 1.22, 95% CI: 1.05–1.41) and power sports (ES = 1.20, 95% CI: 1.03–1.43), with overlapping confidence intervals, indicating no meaningful difference in association strength between sport types. Effect sizes were calculated as odds ratios (OR) with 95% confidence intervals for case–control designs, with standardized conversion protocols applied for alternative study designs reporting standardized mean differences or regression coefficients. Results: Publication bias was detected, particularly in smaller studies on ACTN3 and power sports (Egger’s test p = 0.007). The pooled effect of ACTN3 in power sports (OR 1.40, 95% CI: 1.18–1.65, 95% PI: 0.89–2.20) was adjusted to OR 1.32 (95% CI: 1.15–1.51) following trim-and-fill publication bias correction. The high degree of heterogeneity (I² = 72.3%) cautions against overgeneralization of the pooled results and highlights the need for careful interpretation, robust replication studies, and standardized methodologies. Conclusions: The findings emphasize that, while genetic markers such as ACTN3 and ACE are statistically associated with athletic performance, the magnitude of these associations is modest and should be interpreted conservatively. Methodological differences and publication bias continue to limit the reliability of the evidence. Future research should prioritize large, well-powered, and methodologically consistent studies—ideally genome-wide approaches—to better account for the polygenic and multifactorial nature of elite athletic ability. Full article

Background: Several studies have established correlations between low serum levels of vitamin D and various pathologies, such as diabetes mellitus and its complications. However, few studies analyze its levels in matrices other than blood plasma, such as tears and saliva. In this study, we aimed to demonstrate the feasibility of using tears and saliva as alternative biological matrices for laboratory assessment of vitamin D and glucose concentration in individuals with type II diabetes mellitus and healthy individuals, using the electrochemiluminescence method. Methods: This study included volunteers with type II diabetes and healthy controls, excluding those with certain comorbidities or a BMI ≥ 40. Blood, tear, and saliva samples were taken after 3 h of fasting for biochemical analysis of fasting glucose and vitamin D. Statistical analysis was conducted using GraphPad Prism^® 8.0—with Pearson and other tests to evaluate correlations—at a significance level of 5% and test power > 95%. Results: A negative correlation between serum vitamin D values and those found in saliva (p = 0.041) was found, as well as a positive correlation between serum glucose values and those found in tears (p = 0.0254). Conclusions: Tears and saliva samples can be used as proxies for venous blood samples in specific situations, such as studying blood glucose levels and vitamin D levels. However, expanding the sample size is essential to confirm the correlation and develop an accurate equation for estimating serum levels of these markers using these alternative matrices. Full article

Background/Objectives: Alzheimer’s disease (AD) is a devastating neurodegenerative disorder that progressively impairs cognitive, neurological, and behavioral functions, severely affecting quality of life. The current diagnostic process relies on expert interpretation of extensive clinical assessments, often leading to delays that reduce the effectiveness of early interventions. Given the lack of a definitive cure, accelerating and improving diagnosis is critical to slowing disease progression. Electroencephalography (EEG), a widely used non-invasive technique, captures AD-related brain activity alterations, yet extracting meaningful features from EEG signals remains a significant challenge. This study introduces a machine learning (ML)-driven approach to enhance AD diagnosis using EEG data. Methods: EEG recordings from 36 AD patients, 23 Frontotemporal Dementia (FTD) patients, and 29 healthy individuals (HC) were analyzed. EEG signals were processed within the 0.5–45 Hz frequency range using the Welch method to compute the Power Spectral Density (PSD). From both the time-domain signals and the corresponding PSD, a total of 342 statistical and spectral features were extracted. The resulting feature set was then partitioned into training and test datasets while preserving the distribution of class labels. Feature selection was performed on the training set using Spearman and Pearson correlation analyses to identify the most informative features. To enhance classification performance, hyperparameter tuning was conducted using Bayesian optimization. Subsequently, classification was carried out using Support Vector Machines (SVMs) and k-Nearest Neighbors (k-NN) the optimized hyperparameters. Results: The SVM classifier achieved a notable accuracy of 96.01%, outperforming previously reported methods. Conclusions: These results demonstrate the potential of machine learning-based EEG analysis as an effective approach for the early diagnosis of Alzheimer’s Disease, enabling timely clinical intervention and ultimately contributing to improved patient outcomes. Full article

This study investigates the use of artificial neural networks (ANNs) and deep neural networks (DNNs) for estimating photovoltaic (PV) energy output, with a particular focus on hyperparameter tuning. Supervised regression for photovoltaic (PV) direct current power prediction was conducted using only sensor-based inputs (PanelTemp, Irradiance, AmbientTemp, Humidity), together with physically motivated-derived features (ΔT, IrradianceEff, IrradianceSq, Irradiance × ΔT). Samples acquired under very low irradiance (<50 W m⁻²) were excluded. Predictors were standardized with training-set statistics (z-score), and the target variable was modeled in log space to stabilize variance. A shallow artificial neural network (ANN; single hidden layer, widths {4–32}) was compared with deeper multilayer perceptrons (DNN; stacks {16 8}, {32 16}, {64 32}, {128 64}, {128 64 32}). Hyperparameters were selected with a grid search using validation mean squared error in log space with early stopping; Bayesian optimization was additionally applied to the ANN. Final models were retrained and evaluated on a held-out test set after inverse transformation to watts. Test performance was obtained as MSE, RMSE, MAE, R², and MAPE for the ANN and DNN. Hence, superiority in absolute/squared error and explained variance was exhibited by the ANN, whereas lower relative error was achieved by the DNN with a marginal MAE advantage. Ablation studies showed that moderate depth can be beneficial (e.g., two-layer variants), and a simple bootstrap ensemble improved robustness. In summary, the ANN demonstrated superior performance in terms of absolute-error accuracy, whereas the DNN exhibited better consistency with relative-error accuracy. Full article

Understanding rock behavior under cutting tools is critical for enhancing cutting processes and forecasting rock behavior in engineering contexts. This study examines the link between mechanical properties and indentation crater morphology of six rocks using a conical indenter until initial fracture. Through indentation testing, mechanical properties (indentation stiffness index k and hardness index HI) were assessed, and crater morphology was analyzed using a 3D laser profilometer. The rocks were categorized into three groups based on specific energy: Class I (slate, shale), Class II (sandstone, marble), and Class III (granite, gneiss). The morphological features of their indentation craters were analyzed both quantitatively and qualitatively. The linear model was used to establish the relationship between crater morphology indices and mechanical properties, with model parameters determined by linear regression. Key findings include: (1) Fracture depth, cross-sectional area, and contour roundness are independent morphological indicators, serving as characteristic parameters for crater morphology, with qualitative and quantitative analyses showing consistency; (2) Post-classification linear fitting revealed statistically significant morphological prediction models, though patterns varied across rock categories due to inherent properties like structure and grain homogeneity; (3) Classification by specific energy revealed distinct mechanical and morphological differences, with significant linear relationships established for all three indicators in Classes II and III, but only roundness showing significance in Class I (non-significant for cross-sectional area and depth). However, all significant models exhibited limited explanatory power (R² = 0.220–0.635), likely due to constrained sample sizes. Future studies should expand sample sizes to refine these findings. Full article

Background/Objectives: Emerging evidence suggests a role of immune–inflammatory mechanisms in the pathophysiology of schizophrenia, particularly in the early stages of the illness. Cytokines, as key mediators of inflammation, may affect brain function and clinical presentation. Drug-naive patients with first-episode psychosis (FEDN) offer a unique opportunity to investigate these associations free from confounding pharmacological effects. Methods: This study included 38 patients with drug-naive first episode psychosis and 22 age- and sex-matched healthy controls. Serum concentrations of IL-1β, IL-2, IL-6, and IL-10 were measured using ELISA. Clinical symptoms were assessed using the PANSS scale. Statistical analyses included Mann–Whitney U tests, Spearman’s correlations, and ROC curve analysis. Results: Significantly elevated serum levels of IL-1β, IL-2, and IL-10 were observed in the FEDN group compared to the controls (p < 0.01), while IL-6 levels did not differ significantly. IL-2 exhibited the highest discriminatory power in differentiating the patients from the controls (AUC = 0.917; 95% CI: 0.759–1000.0; p < 0.001). IL-1β levels positively correlated with negative and general psychopathology symptoms, including hostility and grandiosity. IL-10 was associated with volitional disturbance and overall PANSS severity. Conclusions: Our findings underscore the relevance of immune dysregulation in the early stages of psychosis and highlight the potential of specific cytokines, particularly IL-2 and IL-1β, as peripheral biomarkers. Their diagnostic utility and correlation with symptom dimensions suggest a promising role in the development of precision psychiatry approaches, including early detection strategies and individualised therapeutic targeting. Longitudinal studies are needed to validate these findings and to assess their prognostic significance. Full article

This study proposes a novel, scalable framework for the automated classification and synthesis of survey literature by integrating state-of-the-art Large Language Models (LLMs) with robust ensemble voting techniques. The framework consolidates predictions from three independent models—GPT-4, LLaMA 3.3, and Claude 3—to generate consensus-based classifications, thereby enhancing reliability and mitigating individual model biases. We demonstrate the generalizability of our approach through comprehensive evaluation on two distinct domains: Question Answering (QA) systems and Computer Vision (CV) survey literature, using a dataset of 1154 real papers extracted from arXiv. Comprehensive visual evaluation tools, including distribution charts, heatmaps, confusion matrices, and statistical validation metrics, are employed to rigorously assess model performance and inter-model agreement. The framework incorporates advanced statistical measures, including k-fold cross-validation, Fleiss’ kappa for inter-rater reliability, and chi-square tests for independence to validate classification robustness. Extensive experimental evaluations demonstrate that this ensemble approach achieves superior performance compared to individual models, with accuracy improvements of 10.0% over the best single model on QA literature and 10.9% on CV literature. Furthermore, comprehensive cost–benefit analysis reveals that our automated approach reduces manual literature synthesis time by 95% while maintaining high classification accuracy (F1-score: 0.89 for QA, 0.87 for CV), making it a practical solution for large-scale literature analysis. The methodology effectively uncovers emerging research trends and persistent challenges across domains, providing researchers with powerful tools for continuous literature monitoring and informed decision-making in rapidly evolving scientific fields. Full article