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Optimizing solar farm interconnection networks using graph theory and metaheuristic algorithms with economic and reliability analysis
Abstract As global energy demand continues to rise and the need to transition from fossil fuels becomes increasingly urgent, integrating solar farms efficiently into power grids presents a significant challenge. This study introduces a novel graph-theoretic framework for designing optimal interconnection networks among distributed solar farms. By utilizing Prim’s algorithm to construct a minimum spanning tree, the proposed method effectively reduces transmission losses and infrastructure costs. The performance of this deterministic approach is benchmarked against Particle Swarm Optimization (PSO), a widely applied metaheuristic technique. To assess network robustness under potential line failures, a new graph-based reliability metric is developed. Case studies involving a cluster of solar farms demonstrate that Prim’s algorithm outperforms PSO in minimizing both power losses and capital investment, while also offering higher topological reliability. Although PSO achieves better load balancing, the graph-based approach proves more effective for loss-sensitive and cost-driven design scenarios. The proposed framework naturally accommodates constraints such as terrain limitations and is scalable to hybrid renewable energy systems. By integrating classical graph theory with practical power system considerations, this work offers a computationally efficient and economically viable solution for the optimal physical integration of large-scale solar energy infrastructure. The proposed methodology also lays a foundation for future integration of AI and machine learning techniques to enable dynamic network optimization under uncertainty.
Comparing the performance of ChatGPT, Gemini, and Claude in English and Polish on medical examinations
Antinuclear antibodies in early multiple sclerosis reflect systemic lupus erythematosus shared risk factors
Lanthanum exposure and its metabolomic effects on Ruditapes philippinarum
Abstract The growing release of rare earth elements (REEs) into aquatic environments, driven by their extensive industrial and technological applications, has raised growing concern due to their largely unknown ecological and toxicological effects. Among these, lanthanum (La), one of the most widely used REEs, is increasingly detected in marine ecosystems. This study investigates the short-term impact of exposure on the Manila clam, Ruditapes philippinarum, a key benthic species in Mediterranean aquaculture. Clams were exposed to 10 mg/L of LaCl₃·7 H₂O, and their gill tissues were analyzed through histological examination and untargeted metabolomics. Histological analysis revealed epithelial degeneration, decreased mucus secretion, and altered glycoprotein expression following exposure. Metabolomic profiling identified 188 differentially expressed metabolites (DEMs) associated with disruptions in organic acid, fatty acid, amino acid, and nucleotide metabolism. Functional analysis of DEMs revealed that alterations in glutathione (GSH), ascorbic acid, oleic acid, and thymidine levels may serve as biomarkers of La-induced oxidative and metabolic stress in bivalves. These findings emphasize the ecological threats associated with the increasing REEs contamination in marine environments.
Achieved targeted heart rate following ivabradine therapy correlates with left ventricular reverse remodeling in non-ischemic dilated cardiomyopathy
Novel processing algorithms for efficient dam seepage surveys via improved symmetric multielectrode electrical exploration
Assessing perception and equity of cultural ecosystem services in urban parks using social media data
Seasonal biological social and cognitive mechanisms of mental health among Chinese adolescents
Abstract This study develops a “Bio-Social-Cognitive Dynamic Interaction Model” to explore seasonal mental health risks in Chinese adolescents. Using data from cross-sectional surveys (N = 6,121), longitudinal tracking (N = 1,000), and RCTs (N = 200), we assess mental health via PHQ-9, GAD-7, and SADQ scales. We analyze multidimensional indicators (melatonin, vitamin D, academic stress) via SEM and ARIMA, considering school stage and gender. Results show winter has the highest depression and anxiety scores (PHQ-9: M = 14.5 ± 4.8), linked to vitamin D deficiency (β=-0.25, p < 0.001) and social stress. Spring academic stress affects emotional stability via melatonin and sleep issues (β = 0.15, p = 0.002), while summer anxiety is tied to social overload and body dissatisfaction (β = 0.10, p = 0.04). Winter light therapy increases vitamin D (Δ = 8.2 ng/mL, p < 0.001) and reduces depression (ΔPHQ-9=-3.1, p = 0.005). We recommend seasonal interventions like winter light therapy, flexible spring exams, and summer social media literacy programs, and emphasize year-round monitoring for effective risk mitigation.
Individualized prediction tool for patients with metastatic gastric signet cell carcinoma
Synthesis of thiophene derived fluorescent sensor for Hg2+ ion detection and its applications in cell imaging, latent fingerprint, and real water analysis
Characterisation of competitive adsorption of CH4 and CO2 mixed components in a coal body based on molecular simulation
Noninvasive evaluation of renal oxygenation by blood oxygenation level-dependent magnetic resonance imaging in patients with primary aldosteronism
Abstract This study investigated renal oxygenation status in primary aldosteronism (PA) patients using blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI), with comparative analysis against healthy controls and correlation assessments with biochemical markers of renal function. A total of 48 patients and 27 healthy controls were enrolled. All participants underwent renal BOLD-MRI with a 3 T MRI scanner. The R2* values were measured in the renal cortex and medulla of the bilateral kidneys using the region of interest method. Paired-sample t tests and independent-samples t tests were used. Pearson correlation analysis examined the relationship between R2* values and clinical indicators, and ROC curve analysis evaluated the performance of R2* values in distinguishing PA patients from healthy controls. The cortical R2* values were significantly lower than the medullary R2* values for all participants. For PA patients, left kidney cortical and medullary R2* values were significantly higher than those of the right kidney. Left kidney cortical R2* values in PA patients were significantly higher than in healthy controls. Medullary R2* values positively correlated with blood urea nitrogen (r = 0.408, p = 0.005). The optimal threshold for left cortical R2* in discriminating PA patients from healthy controls was 18.955 Hz, yielding a sensitivity of 75.5% and a specificity of 63.0%, with an AUC of 0.717 (95% CI, 0.593–0.841). BOLD-MRI can detect renal hypoxia in patients with PA, suggesting its potential as a noninvasive tool for renal assessment. Specifically, the cortical R2* value in the left kidney demonstrated a moderate ability (AUC = 0.717) to distinguish PA patients from healthy controls.
Predictive nomogram for severe acute kidney injury in patients with cancer receiving anti-PD-1/PD-L1 antibodies: a multicenter retrospective study
Genome analysis of viral hemorrhagic septicemia virus isolated from Paralichthys olivaceus in China
Identification of best housekeeping gene for normalization in qRT-PCR data under different development stages and stress conditions in Vigna mungo
Dietary patterns and psoriasis severity in Thai patients: a machine learning approach for small sample data
Abstract This study investigates the relationship between dietary patterns and psoriasis severity using advanced machine learning (ML) techniques. The dataset, comprising 37 features including demographic, clinical and dietary features from 142 Thai psoriasis patients, exhibits moderately high dimensionality typical of clinical studies. To address limitations posed by the small sample size, a hybrid resampling strategy integrating bootstrapping with K-fold Cross-Validation (CV) was implemented. Using Random Forest (RF) and eXtreme Gradient Boosting (XGB), a total of 60 classification models were evaluated by varying train/test splits and applying multiple feature selection methods, including Least Absolute Shrinkage and Selection Operator (LASSO), Mean Decrease Accuracy (MDA), and Mean Decrease Impurity (MDI). Although bootstrapping alone sometimes resulted in overfitting, its combination with K-fold CV improved generalizability. In optimal configurations, both RF and XGB achieved sensitivity, specificity, and F1-scores exceeding 90%, alongside area under the curve (AUC) values above 95%. SHapley Additive exPlanations (SHAP) analysis revealed key dietary factors associated with increased psoriasis severity, including high-sodium foods, processed meats, alcohol, red meats, fermented products, and dark-colored vegetables. Clinically, prioritizing weight management is essential, as Body Mass Index (BMI) arose as the strongest feature of psoriasis severity. Dietary triggers identified in this study should inform comprehensive care plans. Popular Thai cuisines, especially Tom Yum Kung emerged as a potentially suitable option, while Som Tum, Pad Thai, Moo Kratha, and Khao Niao Mamuang were identified as potential triggers when consumed excessively. These findings highlight the importance of dietary moderation and personalized guidance, supporting health literacy, patient management, and smart healthcare innovations in Thailand.