Search Results (139)

Diabetes mellitus (DM) is a chronic metabolic disorder that can induce systemic and ocular complications. Among the latter, an increase in central corneal thickness (CCT) has been reported, potentially affecting endothelial function and increasing the risk of ocular disease. This study aimed to determine the impact of DM on CCT and to assess its correlation with diabetes duration and glycosylated hemoglobin (HbA1c) levels. A systematic literature search was conducted in Web of Science (1980–2025) following a PICO-based strategy. Observational studies evaluating CCT in diabetic patients were included. Data were analyzed using a random-effects model. Statistical heterogeneity was assessed with χ² test, p values, and I² index. Publication bias was evaluated using Begg’s funnel plot and Egger’s regression test. Twenty-nine studies were included in the meta-analysis. Diabetic patients showed significantly higher CCT values compared to controls, particularly in those with long-standing DM (p < 0.001) and poor glycemic control (HbA1c, p < 0.001). Egger’s regression suggested an association between increasing CCT, disease duration, and HbA1c levels, while funnel plot asymmetry indicated potential publication bias. CCT appears to increase in patients with long-term DM and inadequate glycemic control. These findings highlight the relevance of CCT assessment as a potential indicator of corneal changes in diabetic patients. Full article

The integration of renewable energy and power-to-gas (P2G) technology into park-level integrated energy systems (PIES) offers a sustainable pathway for low-carbon development. This paper presents a low-carbon economic dispatch model for PIES that incorporates uncertainties in renewable energy generation and load demand. A novel two-stage P2G, replacing traditional devices with electrolysers (EL), methane reactors (MR), and hydrogen fuel cells (HFC), enhances energy efficiency and facilitates the utilisation of captured carbon. Furthermore, adjustable thermoelectric ratios in combined heat and power (CHP) and HFC improve both economic and environmental performance. A ladder-type carbon trading and green certificate trading mechanism is introduced to effectively manage carbon emissions. To address the uncertainties in supply and demand, the study applies information gap decision theory (IGDT) and develops a robust risk-averse model. The results from various operating scenarios reveal the following key findings: (1) the integration of CCT with the two-stage P2G system increases renewable energy consumption and reduces carbon emissions by 5.8%; (2) adjustable thermoelectric ratios in CHP and HFC allow for flexible adjustment of output power in response to load requirements, thereby reducing costs while simultaneously lowering carbon emissions; (3) the incorporation of ladder-type carbon trading and green certificate trading reduces the total cost by 7.8%; (4) in the IGDT-based robust model, there is a positive correlation between total cost, uncertainty degree, and the cost deviation coefficient. The appropriate selection of the cost deviation coefficient is crucial for balancing system economics with the associated risk of uncertainty. Full article

To address the issue of uncertainty in renewable energy and its impact on the safe and stable operation of power systems, this paper proposes a transient stability constrained optimal power flow (TSCOPF) calculation method that takes into account the uncertainty of wind power and load. First, a non-parametric kernel density estimation method is used to construct the probability density function of wind power, while the load uncertainty model is based on a normal distribution. Second, a TSCOPF model incorporating the critical clearing time (CCT) evaluation metric is constructed, and corresponding probabilistic constraints are established using opportunity constraint theory, thereby establishing a TSCOPF model that accounts for wind power and load uncertainties; then, a semi-invariant probabilistic flow calculation method based on de-randomized Halton sequences is used to convert opportunity constraints into deterministic constraints, and the improved sooty tern optimization algorithm (ISTOA) is employed for solution. Finally, the superiority and effectiveness of the proposed method are validated through simulation analysis of case studies. Full article

Visual field (VF) testing is crucial for the management of glaucoma. However, the process is often hindered by technician shortages and reliability issues. In this study, we leveraged machine learning to predict pattern standard deviation (PSD) using clinical inputs. This machine learning retrospective study used publicly accessible data from 743 eyes (541 glaucoma and 202 non-glaucoma controls). An automated neural network (ANN) model was trained using seven clinical input features: mean retinal nerve fiber layer (RNFL), IOP, patient age, CCT, glaucoma diagnosis, study protocol, and laterality. The ANN demonstrated efficient training across 1000 epochs, with consistent error reduction in training and test sets. Mean RMSEs were 1.67 ± 0.05 for training, and 2.27 ± 0.27 for testing. The r was 0.89 ± 0.01 for training, and 0.81 ± 0.04 for testing, indicating strong predictive accuracy with minimal overfitting. The LOFO analysis revealed that the primary contributors to PSD prediction were RNFL, CCT, IOP, glaucoma status, study protocol, and age, listed in order of significance. Our neural network successfully predicted PSD from RNFL and clinical data with strong performance metrics, in addition to demonstrating construct validity. This work demonstrates that neural networks hold the potential to predict or even generate VF estimations based solely on RNFL and clinical inputs. Full article

Contemporary society demands the development of creative critical thinking (CCT) as a fundamental objective in science education. However, there appears to be a dissonance between this recognised need and its actual implementation in educational practices. This study explores the potential of modelling practices to intentionally, explicitly, and reflectively integrate the development of CCT in educational settings. To examine this possibility while laying the foundations for future research, the theoretical frameworks of CCT and modelling are synthesised, and their possible points of convergence are analysed in order to test the proposed hypothesis. Two elements that may strengthen their synergy are identified: first, the resources for modelling, such as analogies, which activate analytical, evaluative, creative, and argumentative skills, thereby fostering critical dispositions and a deeper understanding of the nature of science. Second, argumentation, closely connected with communication and information management, is considered a relevant component, especially when addressing socioscientific issues. While further empirical research is needed, the analysis indicates that modelling practices could contribute to the development of CCT. Full article

Implementing the provisions related to energy transition, decarbonization, and, thus, the implementation of the Green Deal in the European Union requires increasing the share of renewable energy sources in the energy generation mix. On the one hand, this approach enables the acquisition of clean energy, but, on the other hand, it can affect the stability of energy supply to consumers in terms of the time and quantity required. Therefore, in the presented research, the authors proposed and verified the following thesis: Innovative coal technologies can play a temporary but crucial role in building the stability of the energy system by developing an operational stability index for the energy system in Poland. To this end, they determined the energy system stability index (ESSI) level, verified its variability over time, and simulated changes in the index when clean coal technology was used. The proposed method is highly universal and can be applied to any country, and the program written specifically for this research fully automates the ESSI calculation process. It is an excellent tool for facilitating decision making and enables the creation of simulations and scenarios of the impact of potential energy development strategies on its operational stability. The set of indicators developed by the authors characterizes the operational stability of the energy system according to the four-dimensional energy security paradigm. This allows for the consideration of the entire spectrum of operational and structural indicators when analysing the stability of the energy system. The developed ESSI allows for the assessment of the system’s stability in a technical sense, but also its adaptability, power and energy balancing, and, ultimately, its independence. Full article

Background: Basal-like breast cancer (BLBC) is a highly aggressive molecular subtype characterized by the strong expression of a gene cluster found in the basal or outer epithelial layer of the adult mammary gland. Patients with BLBC typically face a poor prognosis, with a shorter disease-free period and overall survival. Methods: In this study, we explored the proteomic profiles of BLBC patients using publicly available data from two large cohorts of breast cancer patients. By integrating cluster analysis, predictive modeling, protein differential abundance expression, and network analysis, we identified and validated the presence of two distinct subgroups, characterized by 256 upregulated and 99 downregulated proteins. Results: We report the upregulation of spliceosome components, especially SNRPG and its partners (BUD13, CWC15, SNRNP70, ZMAT12), indicating altered splicing activity between TNBC subgroups. Collagen proteins (COL1A1, COL1A2, COL3A1, COL11A1) were associated with tumor progression and metastasis. Proteins in the CCT complex and microtubule-associated proteins (TUBA1C, TUBB) were linked to cytoskeletal structure and chemotherapy resistance. Aminoacyl-tRNA synthetases (DARS1, IARS1, KARS1) may also play a role in TNBC development. Conclusions: These findings suggest the existence of novel molecular signatures that could improve TNBC classification, prognosis, and potential therapeutic targeting. Full article

With the increasing uncertainty introduced by the large-scale integration of renewable energy sources, traditional power dispatching methods face significant challenges, including severe frequency fluctuations, substantial forecasting deviations, and the difficulty of balancing economic efficiency with system stability. To address these issues, a deep learning-based dispatching framework is proposed, which integrates spatiotemporal feature extraction with a stability-aware mechanism. A joint forecasting model is constructed using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to handle multi-source inputs, while a reinforcement learning-based stability-aware scheduler is developed to manage dynamic system responses. In addition, an uncertainty modeling mechanism combining Dropout and Bayesian networks is incorporated to enhance dispatch robustness. Experiments conducted on real-world power grid and renewable generation datasets demonstrate that the proposed forecasting module achieves approximately a 2.1% improvement in accuracy compared with Autoformer and reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 18.1% and 14.1%, respectively, compared with traditional LSTM models. The achieved Mean Absolute Percentage Error (MAPE) of 5.82% outperforms all baseline models. In terms of scheduling performance, the proposed method reduces the total operating cost by 5.8% relative to Autoformer, decreases the frequency deviation from 0.158 Hz to 0.129 Hz, and increases the Critical Clearing Time (CCT) to 2.74 s, significantly enhancing dynamic system stability. Ablation studies reveal that removing the uncertainty modeling module increases the frequency deviation to 0.153 Hz and raises operational costs by approximately 6.9%, confirming the critical role of this module in maintaining robustness. Furthermore, under diverse load profiles and meteorological disturbances, the proposed method maintains stable forecasting accuracy and scheduling policy outputs, demonstrating strong generalization capabilities. Overall, the proposed approach achieves a well-balanced performance in terms of forecasting precision, system stability, and economic efficiency in power grids with high renewable energy penetration, indicating substantial potential for practical deployment and further research. Full article

The human circadian rhythm is connected to the body’s endogenous clock and can influence people’s natural sleeping habits as well as a variety of other biological functions. According to research, various electric light sources in interior locations can disrupt the human circadian rhythm. Many psychological studies, on the other hand, reveal that different colors can have varied connections with and a variety of effects on people’s emotions. In this study, the effects of light source attributes and interior space paint color on human circadian rhythm were studied using 24 distinct computer simulations. Simulations were performed using the ALFA plugin for Rhinoceros 6 on an unfurnished bedroom 3D model at night. Results suggest that cooler hues, such as blue, appear to have an unfavorable effect on human circadian rhythm at night, especially when utilized in spaces that are used in the evening, which contradicts what psychologists and interior designers advocate in terms of the soothing mood and nature of the color. Furthermore, the effects of Correlated Color Temperature (CCT) and the intensity of a light source might be significant in minimizing melanopic lux to prevent melatonin suppression at night. These insights are significant for interior designers, architects, and lighting professionals aiming to create healthier living environments by carefully selecting lighting and color schemes that support circadian health. Incorporating these considerations into design practices can help mitigate adverse effects on sleep and overall well-being, ultimately contributing to improved occupant comfort and health. Full article

Objective: To test for in-hospital mortality and complication rates in a population-based group of patients with vs. without a history of heart valve replacement undergoing radical prostatectomy (RP). Methods: Relying on the National Inpatient Sample (2000–2019), prostate cancer patients undergoing RP were stratified according to the presence or absence of heart-valve replacement. Multivariable logistics and Poisson regression models addressed adverse hospital outcomes. Results: Within the NIS, 220,358 patients underwent RP. Of those, 694 (0.3%) had a history of heart valve replacement. The patients undergoing heart valve replacement were older (median age 66 vs. 62 years). The proportion of patients with a history of heart valve replacement increases with the Charlson Comorbidity Index (CCI): CCI 0–0.3%, CCI 1–0.4%, and CCI ≥ 2–0.7%. Patients with a history of heart valve replacement exhibited higher rates of postoperative bleeding (<1.5% vs. <0.1%; odds ratio (OR) 16.2; p < 0.001), cardiac complications (7.5% vs. 1.2%; OR 3.9; p < 0.001), infections (<1.5% vs. 0.1%; OR 3.7; p = 0.01), critical care therapy (CCT) use (<1.5% vs. 0.4%; OR 2.5; p = 0.003), intraoperative complications (8.8% vs. 4.1%; OR 1.9; p < 0.001), transfusions (11% vs. 7.2%; OR 1.5; p < 0.001), longer hospital stay (mean 3.39 vs. 2.37 days; rates ratio [RR] 1.4; p < 0.001), and higher estimated hospital cost (median 33,539 vs. 30,716 $USD; RR 1.1; p < 0.001). Conversely, no statistically significant differences were observed in vascular complications (p = 0.3) or concerning in-hospital mortality (p = 0.1). Conclusions: After RP, patients with a history of heart valve replacement exhibited a higher rate of eight out of nine adverse in-hospital outcomes. However, these differences did not translate into higher in-hospital mortality. Full article

Flash floods are natural disasters that have significant impacts on human life and economic damage. The detection of flash floods using remote-sensing techniques provides essential data for subsequent flood-risk assessment through the preparation of flood inventory samples. In this research, a new deep-learning approach for bi-temporal flash-flood detection in Synthetic Aperture Radar (SAR) is proposed. It combines a U-Net convolutional network with a Transformer model using a compact Convolutional Tokenizer (CCT) to improve the efficiency of long-range dependency learning. The hybrid model, namely CCT-U-ViT, naturally combines the spatial feature extraction of U-Net and the global context capability of Transformer. The model significantly reduces the number of basic blocks as it uses the CCT tokenizer instead of conventional Vision Transformer tokenization, which makes it the right fit for small flood detection datasets. This model improves flood boundary delineation by involving local spatial patterns and global contextual relations. However, the method is based on Sentinel-1 SAR images and focuses on Erbil, Iraq, which experienced an extreme flash flood in December 2021. The experimental comparison results show that the proposed CCT-U-ViT outperforms multiple baseline models, such as conventional CNNs, U-Net, and Vision Transformer, obtaining an impressive overall accuracy of 91.24%. Furthermore, the model obtains better precision and recall with an F1-score of 91.21% and mIoU of 83.83%. Qualitative results demonstrate that CCT-U-ViT can effectively preserve the flood boundaries with higher precision and less salt-and-pepper noise compared with the state-of-the-art approaches. This study underscores the significance of hybrid deep-learning models in enhancing the precision of flood detection with SAR data, providing valuable insights for the advancement of real-time flood monitoring and risk management systems. Full article

Background/objectives: Racial and ethnic minority patients are underrepresented in cancer clinical trials (CCTs) and multilevel strategies are required to increase participation. This study describes barriers and facilitators to minority CCT participation alongside feedback on a multilevel intervention (MLI) designed to reduce participation barriers, as posited by the socioecological model (SEM). Methods: Interviews with Moffitt Cancer Center (MCC) physicians, community physicians, patients with cancer, community residents, and clinical research coordinators (CRCs) were conducted from June 2023–February 2024. Verbal responses were analyzed using thematic analysis and categorized into SEM levels. Mean helpfulness scores rating interventions (from 1 (not helpful) to 5 (very helpful)) were summarized. Results: Approximately 50 interviews were completed. Thematic findings confirmed CCT referral and enrollment barriers across all SEM levels. At the community level, MCC patients and community residents felt that community health educators can improve patient experiences and suggested they connect patients to social/financial resources, assist with patient registration, and provide CCT education. While physicians and CRCs reacted positively to all institutional-level tools, the highest scored tool simultaneously addressed CCT referral and enrollment at the institution (e.g., trial identification/referrals) and interpersonal level (communication platform for community and MCC physicians) (mean = 4.27). At the intrapersonal level, patients were enthusiastic about a digital CCT decision aid (mean = 4.53) and suggested its integration into MCC’s patient portal. Conclusions: Results underscore the value of conducting formative research to tailor interventions to target population needs. Our approach can be leveraged by future researchers seeking to evaluate MLIs addressing additional CCT challenges or broader health topics. Full article

Cryptochromes (CRYs), as essential blue-light photoreceptors, play pivotal roles in modulating plant growth, development, and stress responses. Although CRY-mediated light signaling has been extensively studied in model species, their functions remain limited in wheat. In this work, a comprehensive analysis of the TaCRY gene family was performed in wheat, identifying 12 TaCRY members localized to distinct chromosomes 2, 6, and 7. TaCRYs contain the conserved PHR and CCT domains and diverse gene structures. Collinearity relationships indicated their dynamic evolution patterns during polyploidization. Cis-acting elements of TaCRY members associated with light responsiveness, phytohormone signaling, and abiotic stress were also identified. Transcriptome analysis revealed that the differential expression patterns of TaCRY members under seed vigor process. This study expands our understanding of TaCRY diversity and provides valuable molecular information for marker-assisted selection in wheat improvement. Full article

Background/Objectives: The aim of this study was to evaluate the associations between optical coherence tomography angiography (OCTA)-based retinal vessel diameter (RVD) measurements, with retinal ganglion cell (RGC) function assessed by means of steady-state pattern electroretinography (ssPERG) using ganglion cell layer-inner plexiform layer thickness (GCL-IPLT) measurements and with Humphrey field analyzer (HFA) global indices in glaucoma suspects (GSs). Methods: Thirty-one eyes (20 participants) underwent a comprehensive ophthalmologic examination, ssPERG measurements utilizing the PERGLA paradigm, HFA, optical coherence tomography (OCT), and OCTA. The OCTA scans were processed using ImageJ software, Version 1.53, allowing for measurement of the RVD. Multiple linear regression models were used. Results: After controlling for age, race, central corneal thickness (CCT), and spherical equivalent (SE), a linear regression analysis found that the RVD explained the 4.7% variance in magnitude (Mag) (p = 0.169), 9.2% variance in magnitudeD (MagD) (p = 0.021), and 16.9% variance in magnitudeD/magnitude (p = 0.009). After controlling for age, CCT, and signal strength (SS), a linear regression analysis found that the RVD was significantly associated with the GCL-IPLT measurements (average GCL-IPL, minimum GCL-IPL, superior, superonasal, inferior, and inferonasal sectors) (p ≤ 0.023). An identical regression analysis where the RVD was replaced with the PERG parameters showed a significant association between the MagD and almost all GCI-IPLT measurements. RVD measurements were significantly associated with HFA VFI 24-2 (p = 0.004), MD 24-2 (p < 0.001), and PSD 24-2 (p = 0.009). Conclusions: Decreased RVD measurements were significantly associated with RGC dysfunction, decreased GCL-IPLT, and all HFA global indices in the GSs. Full article

Coffee berry disease (CBD), caused by Colletotrichum kahawae, significantly threatens global Coffee arabica production, leading to major yield losses. Traditional detection methods are often subjective and inefficient, particularly in resource-limited settings. While deep learning has advanced plant disease detection, most existing research targets leaf diseases, with limited focus on berry-specific infections like CBD. This study proposes a lightweight and accurate solution using a Compact Convolutional Transformer (CCT) for classifying healthy and CBD-affected coffee berries. The CCT model combines parallel convolutional branches for hierarchical feature extraction with a transformer encoder to capture long-range dependencies, enabling high performance on limited data. A dataset of 1737 coffee berry images was enhanced using bilateral filtering and color segmentation. The CCT model, integrated with a Multilayer Perceptron (MLP) classifier and optimized through early stopping and regularization, achieved a validation accuracy of 97.70% and a sensitivity of 100% for CBD detection. Additionally, CCT-extracted features performed well with traditional classifiers, including Support Vector Machine (SVM) (82.47% accuracy; AUC 0.91) and Decision Tree (82.76% accuracy; AUC 0.86). Compared to pretrained models, the proposed system delivered superior accuracy (97.5%) with only 0.408 million parameters and faster training (2.3 s/epoch), highlighting its potential for real-time, low-resource deployment in sustainable coffee production systems. Full article