Search Results (21,742)

Traditional leaf area index (LAI) measurement methods are destructive, time-consuming, and labor-intensive. In this study, 282 four-year-old central-leader apple trees were used as research subjects. Canopy reflectance spectra in the range of 4000−10,000 cm⁻¹ were collected, and the corresponding true LAI values were measured destructively by harvesting all leaves from a representative branch of each tree using a leaf area meter. The dataset was randomly divided into training (70%) and testing (30%) sets. Eight spectral pretreatment methods were compared. The Competitive Adaptive Reweighted Sampling (CARS) algorithm was employed to extract characteristic wavelengths. Subsequently, both a BP neural network and a Support Vector Machine (SVM) model for LAI prediction were constructed. The optimal model was selected based on evaluation metrics including the coefficient of determination (R²), mean absolute error (MAE), mean bias error (MBE), and mean absolute percentage error (MAPE). The combined preprocessing of MSC and SD yielded the optimal results, screening out 26 characteristic wavelengths. The SVM linear kernel model (c = 5, g = 0.3) constructed based on MSC + SD preprocessing performed best, achieving a validation set R² of 0.90, MAE of 0.2117, MBE of −0.1214, and MAPE of 16.09%. The performance on the training set and validation set was comparable, with no overfitting observed. The MSC + SD preprocessing combined with CARS feature screening and SVM linear kernel modeling enables rapid, non-destructive estimation of apple canopy LAI, providing an effective technical tool for precision orchard management. Full article

Plant-derived sweet proteins are promising low-calorie natural sweeteners that may reduce dietary sugar intake and prevent non-communicable diseases. Although seven have been identified—thaumatin, miraculin, monellin, mabinlin, brazzein, pentadin, and curculin (neoculin)—only thaumatin is currently approved as a food additive. The development of others requires comprehensive safety assessments, particularly regarding allergenicity. This systematic review aims to investigate and synthesize allergenicity assessment methods (in silico, in vitro, in vivo, and clinical) applied to these seven sweet proteins. The literature searches were conducted following PRISMA guidelines across Scopus, PubMed, and Wiley Online Library databases, up to 30 November 2025, with no time restrictions. The risk of bias in selected studies was evaluated using GRADE. After the selection process, 14 out of 2634 studies met the inclusion criteria. Thaumatin, miraculin, monellin, and brazzein emerged as the most extensively studied proteins. In silico approaches (sequence and structural homology) and in vitro assays (digestibility and cell-based methods) were the most commonly employed methods. In contrast, in vivo studies (animal models) and clinical evaluations (skin prick tests, oral food challenges) were rarely reported. Allergenicity studies on pentadin, mabinlin, and curculin (neoculin) are limited, indicating a research gap that requires further study to support regulatory approval and consumer acceptance. Full article

Introduction: Distress during pediatric magnetic resonance imaging (MRI) neuroimaging can compromise scan quality and negatively impact children’s experiences. This review aimed to systematically synthesize biomarkers and psychological factors associated with distress in children, adolescents, and young adults undergoing neuroimaging. Methods: This systematic review was conducted according to PRISMA and AMSTAR-2 guidelines and preregistered in OSF. A systematic search was performed in six electronic databases, including observational articles published between 2000 and 2025 that assessed distress during MRI and functional MRI (fMRI). Data extraction and risk of bias assessment (QUIPS tool) were performed independently by two reviewers. Results: Ten studies (n = 558) examining distress during neuroimaging were included in this review. Distress was assessed through subjective self- and parent-reports, objective physiological measures, and qualitative interviews. Overall, distress levels were low to moderate; most participants tolerated scans well, though younger age, male sex, parental anxiety, procedure length, and chronic illness were associated with greater discomfort. Noise, immobility, and boredom emerged as the most frequent triggers, while strategies such as distraction, age-appropriate information, and reducing waiting times were perceived as helpful. Among participants with cancer, scan-related anxiety was closely linked to fear of recurrence and perceived stress. Risk of bias across studies was moderate to high, particularly in domains of attrition and statistical reporting. Conclusions: Distress during scanning is driven by anticipatory and parental anxiety, procedure length, and chronic illness. Biomarkers (e.g., cortisol, blood pressure) showed inconsistent links with subjective distress, highlighting the need for integrated measures. Full article

The third-quadrant operation of silicon carbide (SiC) MOSFETs is investigated from the perspective of carrier transport, focusing on the interaction between two parallel conduction paths. Through experimental characterization and TCAD simulation, the conduction behavior of the PiN body diode and MOS channel under various gate-source bias conditions is examined. Results reveal that body-effect-induced threshold voltage (Vth) reduction enables channel conduction even under negative gate bias. Based on this mechanism, a transfer-characteristic-based method is developed to identify gate-voltage boundaries between conduction modes. The impact of negative gate bias on reverse recovery parameters, peak current (I_rr), charge (Q_rr), and time (t_rr), is quantitatively evaluated. At the unit-cell level, current sharing between the two paths is analyzed, clarifying the physical mechanism governing their redistribution. Full article

Artificial intelligence (AI) has become increasingly embedded in technology-enhanced learning environments, where peer assessment now serves both instructional and analytic purposes. Beyond allocating feedback and grades, it also produces data that is later interpreted through learning analytics systems. In practice, visible indicators such as students’ fairness perceptions and the degree of agreement among peer raters are often treated as signs that the assessment process is functioning effectively. However, these indicators do not necessarily correspond to grading validity. Students may regard a peer assessment process as fair even when peer-generated ratings remain weakly aligned with expert judgement. This study, therefore, examines whether the socio-technical configurations associated with high perceived fairness in a digitally mediated peer assessment environment also correspond to criterion-referenced grading validity. Data were collected from 215 undergraduate students enrolled in an Artificial Intelligence Foundations course over two consecutive semesters at a university in Taiwan, with instructor ratings serving as an external expert reference within the course context, rather than as a universal ground truth. Because anonymity conditions and semester were fully confounded in the study design, differences linked to anonymity should not be interpreted as isolated causal effects. A two-stage fuzzy-set Qualitative Comparative Analysis (fsQCA) was used. In the first stage, three equifinal configurations associated with high perceived fairness were identified. In the second stage, these configurations were examined against four grading objectivity outcomes: peer–instructor alignment, peer convergence, familiarity bias, and leniency bias. The findings show that fairness perception and grading validity are only partially aligned. Configurations anchored in explicit criterion transparency consistently supported both experiential legitimacy and evaluative accuracy. By contrast, one configuration was associated with high peer convergence while remaining weakly aligned with instructor standards, a pattern described here as false objectivity; this context-dependent configurational finding warrants further investigation across other settings. The study contributes to research on digitally enhanced assessment and learning analytics by showing that fairness perception, peer convergence, and grading validity should be treated as analytically distinct dimensions of assessment quality. Full article

Introduction: This systematic review examines the association between ultraprocessed food (UPF) intake and cognitive and executive function in adults. Given the global rise in overweight and obesity and the increasing consumption of UPFs, understanding their potential impact on brain health is of growing importance. Method: A comprehensive literature search was conducted in PubMed, EBSCO, and Scopus databases following PRISMA guidelines. Fourteen studies met inclusion criteria, encompassing cross-sectional, longitudinal, and experimental designs. Risk of bias was assessed using the National Institutes of Health Quality Assessment Tool. Results: The majority of studies (78.5%) reported a significant association between higher UPF consumption and poorer cognitive outcomes, including deficits in memory, executive function, and global cognition. Longitudinal studies consistently demonstrated that increased UPF intake is linked to accelerated cognitive decline and a higher risk of mild cognitive impairment and dementia, particularly in middle-aged and older adults. In contrast, cross-sectional findings were more heterogeneous, and evidence in younger populations remains limited and inconclusive. Conclusions: Overall, the findings suggest that high UPF consumption may be a modifiable risk factor for cognitive decline. However, methodological variability and the predominance of observational studies highlight the need for further longitudinal and experimental research to clarify causal mechanisms. Full article

Blood pressure is a central marker of cardiovascular risk, but continuous monitoring remains difficult because cuff-based measurements are intermittent and uncomfortable. Photoplethysmography (PPG) is already ubiquitous in wearables and can, in principle, enable cuffless blood pressure estimation from a single optical signal. However, many deep learning approaches that perform well in floating-point are impractical for microcontroller-class devices, where memory budgets, latency, and integer-only arithmetic constrain what can be deployed. A key open question is which neural architectures retain accuracy after full-integer quantization, rather than only under desktop inference. Here, we show an end-to-end, microcontroller-oriented evaluation framework that benchmarks multiple 1D convolutional models for cuffless systolic and diastolic pressure estimation from single-channel PPG, jointly optimizing estimation error, model footprint, and quantization robustness. We find that floating-point accuracy alone is a poor predictor of deployability: some lightweight CNNs exhibit substantial performance drift after INT8 conversion, whereas a compact residual 1D CNN preserves its predictions with near-identical error statistics after integer quantization. We then deploy the selected integer-only model on an STM32N6 microcontroller using an industrial toolchain and confirm that on-device inference maintains low bias and limited error dispersion while meeting real-time constraints for continuous operation. These results highlight architecture-dependent quantization stability as a critical design dimension for sensor-edge intelligence and support the feasibility of fully on-device cuffless blood pressure monitoring without multimodal sensing or cloud processing. Full article

Background/Objectives: Dental caries is one of the most prevalent chronic diseases in childhood, disproportionately affecting socially vulnerable populations. This scoping review aimed to analyze the clinical effects of selected minimally invasive materials and approaches, specifically mouthrinses, fluoride varnishes, silver diamine fluoride, and glass ionomer-based interventions, for the prevention and management of dental caries in pediatric patients, with emphasis on public health and community-based settings. Methods: This scoping review followed the Population, Concept, and Context (PCC) framework. Electronic searches were conducted up to 23 January 2026, using tailored strategies for mouthrinses, fluoride varnishes, silver diamine fluoride (SDF), and glass ionomer cements (GICs). Randomized clinical trials (RCTs) were included. Data extraction and qualitative synthesis focused on clinical outcomes and applicability in public health contexts. Results: Fifty-five RCTs were included. Fluoride- or chlorhexidine-based mouthrinses showed potential in controlling cariogenic biofilm, with evidence primarily based on microbiological outcomes. Fluoride varnishes were associated with enamel remineralization and control of early white spot lesions, particularly in supervised programs. SDF was reported to achieve high caries’ arrest rates in cavitated dentin lesions of primary teeth, while its preventive effect on sound surfaces appeared comparable to other fluoride-based interventions. GICs were associated with acceptable clinical performance as pit-and-fissure sealants and in atraumatic restorative treatment. Conclusions: Minimally invasive dentistry (MID) approaches show promise for the prevention and management of childhood dental caries in public health and community-based settings. However, these findings should be interpreted with caution due to the heterogeneity of interventions and outcome measures, the predominance of short-term and surrogate (microbiological) outcomes, and the absence of a formal risk-of-bias assessment. As a scoping review, the synthesis is narrative in nature, which limits the ability to draw definitive conclusions. Further studies with standardized clinical outcomes and longer follow-up are needed to strengthen the evidence. Full article

Hurricane Florence (2018) proved to be a damaging tropical cyclone that formed off the coast of the Cabo Verde Islands. On 12 UTC 14 September 2018, Florence made landfall as a weakened category 1 Hurricane in Wrightsville Beach, NC. In the midst of landfall, Florence’s ground speed stalled considerably to near zero. Because of this stall, Florence continued to accumulate feet of rain along the coastline, and the inundation of seawater became extreme. Due to the impacts of Florence, the Weather Research and Forecasting Model (WRF-ARW) was used to simulate the tropical cyclone and provide insight into the thermodynamics and dynamics that played a significant role at the time of landfall. After the control case, several sensitivity experiments were conducted. The historical sensitivity experiments utilize the thermodynamic and moisture fields of ERA5 reanalysis data from 1968 and 1998, respectively, to modify the thermodynamic and moisture fields in the initial conditions of the WRF–ARW control case. In addition, to study the potential future climate impacts of Florence, the NCAR CESM Global Bias-Corrected CMIP5 Output to Support WRF/MPAS Research dataset was utilized. The same approach was taken as the historical versions of Florence for sensitivity experiments for future climate, i.e., thermodynamic and moisture fields for both 2038 and 2068 under the RCP6.0 and RCP8.5 climate scenarios, respectively. Results suggest a corresponding intensity shift with minor track deflections. Based on these modifications, synoptic and mesoscale dynamics will be studied to provide insight into how Florence-like hurricanes may change based on certain climate scenarios. Full article

Disruptions caused by disasters, pandemics, and systemic crises have increased the complexity and vulnerability of global supply chains, highlighting the need for advanced analytical approaches to risk and resilience management. In this context, artificial intelligence (AI) has emerged as a promising analytical capability for improving risk assessment and decision-making in disrupted supply chains. The study follows PRISMA 2020 reporting guidelines adapted for bibliometric research and presents a bibliometric and knowledge-mapping analysis of artificial intelligence applications in disaster supply chain risk and resilience management. Using the Web of Science Core Collection, a dataset of 288 peer-reviewed publications was analyzed through keyword co-occurrence, bibliographic coupling, citation analysis, and collaboration network mapping. The findings indicate a rapidly expanding research field in which AI supports predictive risk assessment, real-time monitoring, and resilience-oriented decision-making in disaster-prone supply networks. The analysis identifies dominant thematic clusters, emerging research directions, and opportunities for integrating AI-enabled analytics into supply chain risk management frameworks. The mapped literature also suggests secondary interpretive implications for financial risk exposure and supply chain finance, rather than indicating a separately operationalized finance-specific bibliometric subfield. To enhance interpretive depth, an AI-assisted analytical layer was applied to refine thematic clusters and detect emerging trends. However, this layer operates as a complementary interpretive tool and is subject to methodological limitations, including sensitivity to keyword semantics, dependence on bibliometric outputs, and potential interpretive bias in AI-assisted thematic labeling. Consequently, the AI-assisted analysis is used to support, rather than replace, bibliometric findings. Overall, this study contributes to the emerging literature on artificial intelligence in disaster supply chain risk management and highlights future research opportunities, including improved methodological integration and enhanced analytical transparency in AI-assisted bibliometric research. Full article

Background: Magnesium sulfate (MgSO₄) added to local anesthetics has been investigated as an adjuvant in regional anesthesia, but its role in ultrasound-guided erector spinae plane block (ESPB) remains uncertain. Methods: We conducted a PRISMA 2020-compliant systematic review of randomized controlled trials evaluating MgSO₄ added to the local anesthetic solution in ESPB. In the predefined core comparison (MgSO₄ added to local anesthetic vs. local anesthetic alone in adult postoperative surgery), four trials (225 participants enrolled; 160 contributing to the comparison) informed the qualitative synthesis. Results: Eight randomized controlled trials were included. In the predefined core comparison, 24 h pain intensity was reported heterogeneously and was frequently not extractable as continuous data, precluding pooling. Opioid consumption or rescue analgesia more often favored MgSO₄; however, outcome metrics, analgesic drugs, and assessment windows were not harmonized, and these effects were not consistently accompanied by reductions in pain intensity at 24 h, limiting their interpretation as true analgesic benefit. Safety reporting was frequently incomplete and often lacked structured adverse event tabulation. Risk of bias varied across domains, and GRADE certainty for all core outcomes was very low. Conclusions: Current randomized evidence does not support routine use of MgSO₄ as an adjuvant in ESPB. Future trials using standardized ESPB techniques, harmonized magnesium dosing strategies, and core outcome sets are required to determine whether magnesium provides clinically meaningful incremental analgesic benefit. Full article

Accurately forecasting the weighted mean temperature (Tm) is critical for converting the zenith wet delay (ZWD) into global navigation satellite system (GNSS)-based precipitable water vapor (PWV) for real-time sensing and forecasting applications. The forecast Vienna Mapping Function 1 (VMF1-FC) is a global forecast product developed by TU Wien based on numerical weather prediction models and can provide grid-wise Tm one day ahead. In this study, we evaluate the accuracy of VMF1-FC-forecasted Tm using observations from 319 global radiosonde (RS) sites during 2019–2021. The results indicate that VMF1-FC-forecasted Tm shows a relatively low RMSE but a relatively large bias (0.75 K) relative to the widely used Global Pressure and Temperature 3 (GPT3) model. To improve the accuracy of VMF1-FC-forecasted Tm, three refined models, XTm, LTm, and CTm, are developed using Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost), respectively, based on observations from 319 RS sites. The models use longitude, latitude, ellipsoidal height, floating day of year (fdoy), and VMF1-FC Tm as input features, and RS Tm as the target variable. Validation using RS data from 2022 that are not involved in model development shows that the refined models significantly reduce bias, with biases of 0 K, 0 K, and −0.03 K for XTm, LTm, and CTm, respectively. Benefiting from the effective reduction in bias, the root mean square error (RMSE) is correspondingly reduced. The RMSEs of XTm, LTm, and CTm are 1.45 K, 1.45 K, and 1.46 K, respectively, achieving improvements of 18.50%/64.93%, 18.44%/64.91%, and 18.11%/64.76% compared with the VMF1-FC and GPT3 models. In addition, three refined models demonstrate higher accuracy and improve stability across different latitude bands, ellipsoidal height ranges, and temporal scales. The refined models provide more accurate global-scale Tm and offer strong potential for GNSS meteorological applications, particularly real-time GNSS-based PWV sensing and weather forecasting. Full article

Background: Quantitative genetic analysis of image- or video-derived phenotypes is increasingly being performed for a wide range of traits. Pig body weight values estimated by a conventional approach or a computer vision system can be considered two different measurements of the same trait but with different sources of phenotyping error. Previous studies have shown that trait measurement error, defined as the difference between manually collected phenotypes and image-derived phenotypes, can be influenced by genetics, suggesting that the error is systematic rather than random and is more likely to lead to misleading quantitative genetic analysis results. Therefore, we investigated the effect of trait measurement error on the genetic analysis of pig body weight (BW). Results: Calibrated scale-based and image-based BW showed high coefficients of determination and goodness of fit. Genomic heritability estimates for scale-based and image-based BW were mostly identical across growth periods. Genomic heritability estimates for trait measurement error were consistently negligible, regardless of the choice of computer vision algorithm. In addition, genome-wide association analysis revealed no overlap between the top markers identified for scale-based BW and those associated with trait measurement error. Overall, the deep learning-based regressions outperformed the adaptive thresholding segmentation methods. Conclusion: This study showed that manually measured scale-based and image-based BW phenotypes yielded the same quantitative genetic results. We found no evidence that BW trait measurement error could be influenced, at least in part, by genetic factors. This suggests that trait measurement error in pig BW does not contain systematic errors that could bias downstream genetic analysis. Full article

Background: Continuous positive airway pressure (CPAP) is the most effective treatment for obstructive sleep apnoea (OSA). However, CPAP adherence in randomized controlled trials (RCTs) is frequently inadequate, potentially leading to an underestimation of the therapy’s true effect on relevant outcomes. The aim was to identify patient and study characteristics that predict adherence to CPAP therapy in RCTs. Methods: PubMed and the existing meta-analyses were searched (1984 to 31 December 2024). A study-level meta-analysis of RCTs comparing CPAP with inactive control in patients with OSA was conducted. Meta-regressions and subgroup analyses (<4 h vs. ≥5 h usage) were undertaken to identify the predictors of CPAP adherence. Risk-of-bias was assessed using the Cochrane RoB-2 tool. Results: In 136 RCTs reporting on CPAP use, including 8827 patients with OSA (55 [49.5–59.8] years, 77.4 [61.2–89.2]% male, BMI 31 [28.9–33.2] kg/m², Epworth Sleepiness Scale (ESS) 10.0 ± 2.8, apnoea–hypopnoea-index (AHI) 35.7 ± 13.4/h), mean nocturnal CPAP use was 4.5 ± 1 h. CPAP use of ≥4 h, ≥5 h, and ≥6 h per night was observed in 71.3%, 34.1%, and 7.8% of RCTs, respectively. Higher baseline AHI was the strongest predictor of longer CPAP use in meta-regressions (p < 0.001, β = 0.02, 95% CI 0.01–0.04). Baseline AHI was also significantly higher (40.3 ± 12.8 vs. 29.9 ± 12.6) in the ≥5 h vs. <4 h subgroup (p < 0.01, large effect size d = 0.84). A higher nightly CPAP usage was more likely in smaller (p < 0.05, d = 0.45) and single-centre trials (p < 0.05, h = 0.52). Sex distribution, age, BMI, ESS, and follow-up had no significant effect on nightly CPAP use. Conclusions: Higher baseline AHI independently predicted longer CPAP use in RCTs, while sleepiness and demographics did not. This study was registered at PROSPERO (CRD420250653394) and received no external funding. Full article

Large language models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. We categorize biases as intrinsic and extrinsic, analyzing their manifestations in various natural language processing (NLP) tasks. The review critically assesses a range of bias evaluation methods, including data-level, model-level, and output-level approaches, providing researchers with a robust toolkit for bias detection. We further explore mitigation strategies, categorizing them into pre-model, intra-model, and post-model techniques, highlighting their effectiveness and limitations. Ethical and legal implications of biased LLMs are discussed, emphasizing potential harms in real-world applications such as healthcare and criminal justice. By synthesizing current knowledge on bias in LLMs, this review contributes to the ongoing effort to develop fair and responsible artificial intelligence (AI) systems. Our work serves as a comprehensive resource for researchers and practitioners working towards understanding, evaluating, and mitigating bias in LLMs, fostering the development of more equitable AI technologies. Full article