Search Results (102,359)

Photovoltaic (PV) power forecasting plays a central role in power system operation, electricity markets, and the integration of high shares of renewable energy. Over the past decade, forecasting approaches have evolved from classical statistical time-series models to advanced machine learning and deep learning architectures. This review analyses 119 studies published between 2016 and 2026, providing a structured assessment of PV forecasting methodologies, including model types, data requirements, validation strategies, and performance evaluation practices. Beyond summarizing existing approaches, the paper identifies three major methodological gaps in the literature: (i) fragmentation of evaluation metrics, which limits cross-study comparability; (ii) insufficient reporting of data preprocessing procedures and temporal leakage prevention; and (iii) limited integration of forecasting accuracy with economic and operational performance metrics. A systematic comparison of representative studies is conducted to highlight dominant modelling trends and persistent limitations. Beyond a descriptive summary, this review highlights significant limitations in methodological reporting across the 119 studies analysed, particularly regarding temporal leakage prevention in Deep Learning-based forecasting. To address these issues, we introduce a reproducibility checklist and propose a strategic roadmap aimed at strengthening the link between statistical accuracy (e.g., RMSE/MAE) and operational relevance in electricity markets. Full article

Mountain photovoltaic (PV) stations are increasingly deployed in complex terrain, where generation is jointly controlled by solar-resource variability, near-surface meteorology, and local topography. However, the quantitative contribution of topographic factors to regional-scale PV generation remains insufficiently evaluated, and many prediction studies rely on single-station or short-term records. In this study, monthly measured generation from 118 standardized village-level mountain PV stations in Badong County, western Hubei Province, China (2019–2021), was integrated with Solargis Global Horizontal Irradiance (GHI)-related solar-resource data, high-resolution gridded meteorological data, a 25 m digital elevation model, seasonal-cycle variables, and historical-generation features. After seasonally grouped median-absolute-deviation (MAD) outlier screening, GIS-based spatial matching, terrain extraction, and viewshed-derived shading analysis, regression models and climatology baselines were compared under both chronological validation and station-exclusion spatial cross-validation. Under the strict chronological validation, CatBoost achieved the best temporal performance among the tested models (R² = 0.3119, MAE = 2719.7 kWh, RMSE = 3245.6 kWh), slightly outperforming the monthly climatology baseline. In the station-exclusion spatial cross-validation, XGBoost achieved the highest mean R² (0.8659), indicating good spatial transferability to unseen stations. Correlation and partial-correlation analyses showed that the temperature-related variable group and monthly radiation were the dominant meteorological controls, whereas elevation, slope, and terrain shading showed weak direct correlations with monthly generation for already-sited stations. Annual 90% prediction intervals were further estimated using residual bootstrapping, with an empirical coverage of 94.9%. The proposed framework provides a practical basis for monthly generation forecasting and operational assessment of already-built distributed PV stations in mountainous regions, while its application to greenfield site selection requires additional site engineering and near-field obstruction information. Full article

Background/Objectives: While previous research has explored the effects of moral distress and the work environment separately, there is limited evidence on how these two factors are associated with nurses’ turnover intention. Therefore, in this study, we assessed the mediating role of moral distress in the correlation between nurses’ work environments and turnover intention. Methods: This study employed a multicenter cross-sectional design of emergency nurses from April to June 2025. The Measure of Moral Distress—Healthcare Professionals, Practice Environment Scale of the Nursing Work Index (PES-NWI), and Turnover Intention Scale were used to collect data. The mediating effect was analyzed using Hayes’ PROCESS macro (Model 4, Version 4.2) software with the bootstrap technique (5000 repetitions, 95% bias-corrected confidence interval). Statistical significance was set at a threshold of p < 0.05. Results: Mediation analysis revealed that work environment had a significant negative effect on moral distress (β = −0.251, B = −45.293, 95% CI [−70.376, −20.210], p < 0.001). Moral distress significantly increased nurse turnover (β = 0.202, B = 0.008, 95% CI [0.003, 0.012], p = 0.003), while the work environment had a significant negative direct effect on turnover (β = −0.391, B = −2.629, 95% CI [−3.507, −1.751], p < 0.001). The total effect of work environment on nurse turnover was also significant (β = −0.442, B = −2.970, 95% CI [−3.837, −2.102], p < 0.001). Bootstrapping confirmed a significant indirect effect of moral distress (β = −0.051, 95% CI [−0.092, −0.016]), indicating partial mediation. Conclusions: This study revealed that nurses’ work environment was significantly associated with turnover intention, both directly and indirectly, through moral distress. Moral distress acted as a statistically significant but modest partial mediator of the association between the work environment and turnover intention, suggesting that it may partially explain this relationship. Strategies by healthcare organizations should be organized to optimize proactive work environments and mitigate moral distress among nurses. Full article

Monitoring genetic gain is critical for evaluating breeding program performance. This study assessed genetic trends in the Zambia national maize breeding program using historical data (2001–2017) from 2225 hybrids tested across years and locations. Best linear unbiased estimates (BLUEs) were calculated, and genetic trends were determined by regressing entry means on first-year testing data. Mean heritability was moderate for grain yield, plant height, and ear height, and high for anthesis and silking dates, indicating strong reliability for flowering traits. Significant positive genetic gains were observed for most traits except days to silking. Grain yield (GY) increased at 0.021 t ha⁻¹ per year (0.85% annually), reflecting progress but remaining below levels required to meet regional future production demands. Plant and ear height increased by more than 1.3 cm annually, suggesting directional selection for taller plant architecture. Grain texture declined by 1.28% per year, indicating a shift toward flint-type kernels. Anthesis date and ears per plant showed minimal genetic variation. Regression models explained more than 15% of the total variation in plant height, ear height, ear number, and grain texture, confirming consistent genetic progress. Although measurable gains were achieved, the study’s baseline indicates that accelerating yield improvement will require rapid-cycle breeding, enhanced trait heritability, modern breeding tools, and a strategic reallocation of resources to sustain long-term impact. Full article

Introduction: Aquatic chemical pollution is among the most worrying threats to ecosystem health. There is an ever-increasing variety of pollutant substances detected across the source-to-sea continuum, causing loss of biodiversity and ecological disequilibrium. Achieving cleaner and healthier systems relies on carrying out sustained, cost-effective, diagnosis and aquatic effects monitoring, within the adaptive management cycle. The available methods are, however, cumbersome, which creates a clear need for innovative expeditious approaches for low-cost surveillance monitoring. In the last decade, Raman Spectroscopy (RS) has gained wide recognition for application to biological questions, for its ability to uncover the complexity of molecules and their interactions. Various fields, from pharmacology to disease diagnosis and prognosis, have suffered an innovation revolution through the application of RS. In this technique inelastic light scattering of a small part of photons of an incident electromagnetic monochromatic light beam (ranging from near-infrared to visible or ultraviolet) is caused by the molecular vibration of chemical bonds. This results in shifts in energy, which indicate discrete vibrational modes of polarisable molecules, providing qualitative and quantitative assessments of the chemical composition and molecular structure of the sample. The technique shows high sensitivity, no need for sample preparation and the possibility of use in non-invasive and label-free analysis. Objective: The aim of this work is to present and discuss evidence about the application of Raman Spectroscopy (RS) to environmental diagnosis and aquatic effect monitoring of pollution. Methodology: The technique was applied to different biological models, i.e., diatoms, zebrafish embryos and larvae and freshwater snails. Quality assessments with diatoms were tested in environmental monitoring, while assessments with other models were done upon exposure to metals and organic contaminants. Results and conclusions: The Raman spectra obtained from the samples analysed comprised bands detected within the 800 to 2000 cm⁻¹ wavenumber range. These were related to bond vibrations of carbohydrates, DNA phosphate groups, proteins or CH, NH and OH stretching in lipids and proteins. Data analysis using chemometric methods clearly distinguished pollutant exposure from control sites or treatments, pointing out the potential for surveyance monitoring. The next steps include the comparison with other sensitive methods (e.g., locomotion and avoidance behaviours, omics methods) to assess efficiency and bring further mechanistic understanding. Full article

Background/Objectives: This prospective observational cohort study aimed to evaluate the influence of heated tobacco (HT) and electronic cigarettes (ECs) on bone remodeling markers such as receptor activator of nuclear factor kappa-B ligand (RANKL) and osteoprotegerin (OPG), and periodontal status, at baseline and at 3 months after initial periodontal therapy. Methods: The sample comprised 236 participants (130 women, 106 men; mean age 38.96 ± 7.69 years), distributed across non-smokers (n = 72), heated tobacco/HT product users (n = 83), and electronic cigarette/EC users (n = 81). For each patient, the periodontal charting included periodontal probing depth (PPD), bleeding on probing (BOP), and clinical attachment loss (CAL). Unstimulated saliva samples were analyzed for RANKL and OPG levels. All patients underwent nonsurgical periodontal therapy (scaling and root planing). Between-group comparisons were performed using the Kruskal–Wallis test followed by Bonferroni-adjusted pairwise comparisons, while within-group changes over time were assessed using the Wilcoxon signed-rank test. To complement the primary nonparametric analyses, two-way mixed-design ANOVA and ANCOVA models adjusted for baseline values and periodontitis stage were performed as sensitivity analyses. Statistical significance was set at p < 0.05. Results: At baseline, both product user groups exhibited significantly higher PPD (p = 0.005) and CAL (p = 0.001) compared with non-smokers, with no differences between HT and EC users. Salivary RANKL levels were significantly higher in HT and EC users than in non-smokers, and OPG levels did not differ significantly. Following non-surgical periodontal therapy, all parameters improved significantly across groups (p < 0.001). At the 3-month follow-up, both product user groups maintained higher PPD (p = 0.008), CAL (p = 0.001), and salivary RANKL levels, compared with non-smoking individuals (p < 0.001). The RANKL/OPG ratio remained significantly different only for EC users compared with non-smokers (p < 0.001). Conclusions: HT and EC use were associated with differences in periodontal parameters and higher RANKL levels, while differences in the RANKL/OPG ratio were observed in EC users compared with non-smokers. Non-surgical periodontal therapy improved clinical parameters and reduced the RANKL/OPG ratio, highlighting the importance of biofilm control. Full article

This study explores the multifaceted attitudes of high school students toward the use of artificial intelligence (AI) and large language models (LLMs) like ChatGPT in educational contexts. Drawing upon a tripartite model of attitudes, our research evaluates affective, cognitive, and behavioral dimensions to offer a nuanced understanding of students’ perceptions. The affective dimension assesses emotional responses to AI tools, the cognitive dimension examines beliefs about the utility and ethical considerations of AI, and the behavioral dimension evaluates actual usage patterns of AI technologies. Utilizing a newly developed survey instrument tailored for the educational context, data was collected from 93 high school students across different regions of Italy in the period that ranged from February 2024–March 2024. Exploratory factor analysis (EFA) was employed to explore the underlying structure of the survey instrument and identify underlying factors influencing AI acceptance. The analysis reveals three distinct factors—Mindful AI Learning, Embracing AI Effects, and LLM as Learning Companion, highlighting the complexity of students’ attitudes toward AI. Results indicate a cautious but optimistic reception of AI in education, offering crucial insights into Information Intelligence for enhanced learning and the design of personalized learning pathways. The study contributes to the literature by offering a novel scale to measure attitudes toward artificial intelligence, specifically focusing on both general AI and Generative AI large language models, such as ChatGPT. Moreover, it highlights the critical need for AI literacy, ethical digital learning frameworks, and robust institutional policies to bridge the digital divide. Consequently, this work is framed as a preliminary exploratory investigation. Ultimately, these findings advance our knowledge of transformative digital learning processes and inform future strategies for human–machine integration in educational systems. Full article

In the task planning of heterogeneous unmanned aerial vehicle formations, problems such as dynamic topological instability and sparse Pareto front exist, which affect the robustness of the planning. To address this, this paper proposes a cooperative task planning method based on multi-objective dolphin echolocation optimization driving. Firstly, a differentiated dynamic model of heterogeneous unmanned aerial vehicles covering different configurations such as rotors and fixed wings is constructed, and a dynamic communication topology model is established based on time-varying graph theory to quantify transmission delay and link stability. Then, a multi-objective optimization model is designed with task completion, energy balance, and time cost as the core, Bayesian networks are introduced to construct a dynamic threat field, and risk assessment and real-time response are achieved in complex environments. Based on this, a multi-objective dolphin echo optimization algorithm is adopted to solve the model, and its echo beam focusing search and adaptive weight allocation mechanism are utilized to effectively improve the convergence and distribution of the Pareto solution set. Finally, a “decision execution” hierarchical collaborative control architecture is constructed, utilizing the decision layer to output a global planning scheme and the execution layer to achieve rolling optimization and precise tracking of instructions through distributed model predictive control. The simulation test results show that this method can maintain high task completion, energy balance, and communication stability in different formation sizes and complex environments significantly better than traditional algorithms. When the formation size is between 20 and 60 sorties, the hypervolume (HV) index of this method is superior to that of the comparison method. In cases of sudden obstacles and complex electromagnetic interference scenarios, the average energy consumption of a single unmanned aerial vehicle after applying this method is maintained at 150–250 Wh, and the transmission delay is stable at 50–200 ms. The experimental results verify that this method has good planning robustness and collaborative real-time performance. Full article

An earthquake lethality model was employed to assess the casualty distribution in Yunnan, Guizhou, and Sichuan provinces, taking into account the ground motion acceleration with different 50-year exceedance probabilities. When the probability is 63%, fatalities are predominantly concentrated in central and south-western Yunnan, as well as central, southern, and western Sichuan. At a 10% probability, the peaks of the casualties are observed in southern, eastern, and central Sichuan. In Yunnan (excluding the northwest and southeast regions), the casualty density exhibits unevenness, whereas Guizhou experiences relatively low casualties (except in the eastern and western mountainous areas). Xichang incurs the most substantial losses, followed by Lancang. Xundian, Songming, and Dongchuan demonstrate a high propensity for fatalities, and the risk is relatively high in the vicinity of the Longjiang and Nujiang faults. If a destructive earthquake occurs near these areas within the next 50 years, the probability of a Level-I emergency response exceeds 10%. When the ground motion acceleration doubles (especially when the exceedance probability drops to 2% in 50 year and 0.1% in a year), the predicted number of casualties remains relatively stable. However, the grid of the casualty population exhibits a higher degree of spatial concentration of casualties, and the disaster-affected area expands. There exists no linear correlation between earthquake-induced fatalities and the ground motion level. When the 50-year exceedance probability decreases from 63% to 10%, the casualty rate may increase by several dozen times. Full article

This paper addresses probabilistic and statistical methods for calculating technical energy losses in direct current (DC) networks. A DC network model is adopted as the basis for the analysis, and several approaches are compared in terms of qualitative features and computational efficiency. The load profile is described using probabilistic indicators, emphasizing the importance of accounting for correlation moments (CMs) between node powers and CMs between voltages to reduce calculation errors. A correction procedure for the mathematical expectation of node voltages is proposed, which significantly improves the accuracy of loss estimation. Simulation studies on representative four-node DC test networks show that the proposed method reduces the root mean square error in loss estimation by up to 15–20% compared with traditional approaches based solely on mean load values. The results confirm that the correction of node voltage expectations provides a good balance between accuracy and computational cost and can be recommended as an independent procedure within existing probabilistic frameworks for loss assessment. Full article

Aboveground biomass (AGB) mapping is fundamental to tropical forest carbon monitoring, yet national-scale estimation remains challenging because field plots are sparse and model performance is often sensitive to predictor choice and validation design. This study assessed whether geospatial embeddings improve national AGB mapping in Guyana when combined with environmental and topographic predictors. Predictor selection was undertaken using repeated grouped resampling at the plot-cluster level, and model performance was evaluated across 100 independent train–test repeats. Three final random forest models were compared. The environmental baseline model (Env + SRTM-derived elevation; 8 predictors) achieved a mean R² of 0.179, an RMSE of 148.5 Mg/ha and a relative RMSE of 36.1%. A retained 8-predictor model combining environmental variables with a selected embedding subset (Env + Emb*) improved performance slightly, with a mean R² of 0.189, an RMSE of 147.6 Mg/ha and a relative RMSE of 35.9%. The best performance was obtained with a 22-variable full-stack model combining environmental, topographic and embedding predictors, after all Sentinel-2 predictors had been eliminated during feature selection; this model achieved a mean R² of 0.203, an RMSE of 146.3 Mg/ha and a relative RMSE of 35.5%. Across models, isothermality, a measure of how day-to-night temperature variation compares to annual temperature variation, and precipitation of the coldest quarter were consistently the most influential predictors. Mean ensemble coefficient of variation, representing relative model disagreement, ranged from 0.336 to 0.361. These results indicate that geospatial embeddings provide useful complementary information, but predictive performance remained modest overall, with the best model explaining only about one-fifth of plot-level AGB variance. The resulting maps are therefore best interpreted as broad-scale decision-support products rather than high-precision local estimates of AGB. Full article

The clinical relevance of food-specific IgG antibodies in pediatric gastrointestinal disorders remains controversial. Although current international guidelines discourage their use as standalone diagnostic tools, their significance within a broader immune–gut inflammatory framework has not been sufficiently explored. This study aimed to investigate associations between food-specific IgG reactivity, inflammatory and permeability biomarkers, microbiological findings, and abdominal ultrasound abnormalities in children with chronic gastrointestinal symptoms. Methods: (1) Children presenting chronic gastrointestinal symptoms associated with food-specific IgG polysensitization, elevated inflammatory and permeability biomarkers, and abdominal ultrasound abnormalities (number (n) = 196); (2) a symptomatic gastrointestinal group without the complete multimodal profile (n = 146); and (3) a control group with normal abdominal ultrasound findings and biomarkers within reference ranges (n = 210). All participants underwent food-specific IgG testing using a 216-antigen ELISA panel, abdominal ultrasound examination, and assessment of intestinal inflammatory and permeability biomarkers. Food-specific IgG antibodies were not interpreted as diagnostic markers of food allergy or food intolerance. Comparative analyses, correlation analyses, multivariable logistic regression, and receiver operating characteristic (ROC) analyses were performed. Results: Food-specific IgG polysensitization was significantly more frequent among children presenting the multimodal inflammatory profile compared with symptomatic and control groups (all p < 0.001). Reactivity predominantly involved gluten-containing cereals, dairy proteins, and mixed gluten–dairy patterns. Elevated fecal calprotectin, zonulin, and fecal histamine concentrations were more frequently observed in this subgroup, together with a higher prevalence of ultrasound abnormalities, including bowel wall thickening and mesenteric lymphadenopathy. Correlation analyses demonstrated significant associations between cumulative IgG burden and bowel wall thickness (r = 0.48, p < 0.001), while fecal calprotectin showed the strongest association with ultrasound abnormalities (r = 0.62, p < 0.0001). Multivariable logistic regression identified elevated calprotectin, increased zonulin, IgG polysensitization, and mixed gluten–dairy reactivity as independent predictors of pathological ultrasound findings. The integrated multimodal model demonstrated higher classification performance than isolated biomarkers. Conclusions: Children presenting chronic gastrointestinal symptoms, food-specific IgG polysensitization, inflammatory biomarker abnormalities, and ultrasound changes represented a multimodal clinical subgroup within the study population. These findings support evaluating food-specific IgG reactivity within a broader immune–gut assessment framework rather than as a standalone diagnostic biomarker. The observed associations should be considered exploratory and hypothesis-generating, requiring prospective validation and mechanistic investigation. Full article

Background: Hyperglycemia after aneurysmal subarachnoid hemorrhage (aSAH) is associated with poor outcome, but admission glucose may not reflect dynamic metabolic stress during neurocritical care. Unlike previous studies focused primarily on admission measurements, we evaluated longitudinal glycemic trajectories and repeated glucose–potassium ratio (GPR) assessment across multiple observation windows in relation to clinical outcomes after aSAH. Methods: This retrospective single-center cohort study included 199 consecutive adults with aSAH treated between 2014 and 2025. Serial glucose and potassium measurements obtained during intensive care unit (ICU) stay were used to calculate admission values, longitudinal means across predefined observation windows, glycemic variability, hyperglycemia burden, and GPR. Primary outcomes were 30-day mortality and poor functional outcome at discharge (modified Rankin Scale ≥ 3). Secondary outcomes included delayed cerebral ischemia (DCI), delayed neurological deterioration (DND), transcranial Doppler (TCD) vasospasm, neurological deficit at ICU discharge, and length of stay. Results: Thirty-day mortality occurred in 35 patients (17.6%). Longitudinal metabolic markers demonstrated stronger associations with outcomes than admission values. Mean 30-day GPR was independently associated with mortality (OR 2.56, 95% CI 1.66–4.16; p < 0.001) and poor functional outcome (OR 2.90, 95% CI 1.80–5.03; p < 0.001). Hyperglycemia burden was associated with mortality (OR 1.10 per additional hyperglycemic day, 95% CI 1.02–1.20; p = 0.020) and poor functional outcome (OR 1.39, 95% CI 1.19–1.71; p < 0.001). Early GPR during the early brain injury period was associated with DCI (OR 1.40, 95% CI 1.01–1.93; p = 0.043), whereas 30-day GPR was associated with DND (OR 1.47, 95% CI 1.08–2.07; p = 0.019). ICU-specific GPR was associated with neurological deficit at ICU discharge (OR 2.06, 95% CI 1.29–3.50; p = 0.004), but not with TCD-defined vasospasm. Addition of GPR improved mortality prediction compared with the clinical model alone (AUC 0.86 vs. 0.77; p = 0.002). Conclusions: Longitudinal metabolic dysregulation after aSAH is strongly associated with mortality and neurological outcomes. Persistent hyperglycemia and repeated GPR assessment provide prognostic information beyond admission glucose, with early abnormalities associated with DCI and sustained disturbances linked to mortality and disability. Full article

Flexible casing piles can form locally enlarged sections by expanding flexible casings during concrete casting, thereby filling karst cavities and improving the adaptability and bearing capacity of pile foundations in karst areas. However, the damage evolution and failure mechanism of the enlarged section under vertical loading remain insufficiently understood. In this study, a three-dimensional finite element model of a flexible casing pile was established using the Concrete Damaged Plasticity (CDP) model. The stress transfer, plastic strain development, and tensile–compressive damage evolution of the enlarged section under vertical static loading were investigated. The effects of karst cavity spacing, cavity number, and cavity diameter on the vertical bearing behavior were further analyzed. The results show that damage localization is governed by the transition zone between the pile shaft and the enlarged section, where plastic strain, tensile damage localization, and compressive damage accumulation develop in a coupled manner. Increasing the number and diameter of enlarged sections improves the ultimate bearing capacity, whereas cavity spacing mainly controls the interaction and synchronization of damage zones between adjacent enlarged sections. These findings establish a damage-based interpretation for identifying the failure-control region of flexible casing piles in karst cavities and provide a basis for bearing-capacity assessment and structural optimization. Full article

This study integrated Item Response Theory (IRT) models with ordinal survey instruments to establish a baseline psychometric framework and identify multidimensional factors associated with academic achievement among first-semester leveling students (N = 1558 pre-test; N = 1676 post-test) at the Escuela Politécnica Nacional, Ecuador. A dual-component methodology was employed in this study. Initially, an 80-item ordinal survey was utilized to assess eight latent constructs, yielding substantial validation metrics through Confirmatory Factor Analysis (CFA). Secondly, structured diagnostic assessments in core STEM and language subjects were calibrated using three-parameter logistic (3PL) IRT models via Expected A Posteriori (EAP) estimation. Results demonstrated high internal consistency ($r = 0.93$ between IRT and raw scores), with mean IRT-scaled ability $\overline{\theta } = 10.45$ ($SD = 3.51$) on a 1–20 scale. Estimated item parameters yielded a mean discrimination of $\overline{\mathrm{a}} = 1.92$ and a centered mean difficulty of $\overline{b} = 0.05$. The Orlando–Thissen $S-X^2$ goodness-of-fit test, applied at a significance threshold of $p < 0.01$, identified 19 items (23.75%) whose observed response patterns deviated significantly from model predictions, with the majority concentrated in the physics and chemistry content domains. Factor scores and performance outcomes were statistically contrasted against 24 categorical demographic variables, revealing differential performance patterns across student subgroups. This research provides validated psychometric instruments, reproducible IRT-LMS integration protocols, and empirical evidence supporting targeted interventions to strengthen university transition. Full article