Search Results (73,332)

The increasing interest in sustainable materials has led to the development of bio-based composites for additive manufacturing applications. This study aimed to investigate the influence of key printing parameters and new-generation infill patterns together on the maximum compressive force of PLA–wood bio-composites produced by Material Extrusion. By optimizing this material, low-cost wood-like products can be produced. New-generation 3D infill patterns (octet, cubic-subdivision, and lightning which is a biomimetic infill pattern) infill densities, printing temperatures, and layer heights were selected as variables/factors, and the Taguchi method was applied for design of the experiment. The signal-to-noise ratio and Analysis of Variance were used to evaluate the statistical significance and contribution of each parameter to the mechanical response. The signal-to-noise ratio indicated that the optimal printing settings were as follows: printing temperature, 205 °C; infill density, 80%; infill pattern, octet; and layer height, 0.2 mm (7123.4 N). ANOVA results showed that infill density was the most significant factor affecting maximum compressive force at 60%, while infill pattern also exhibited a notable effect. According to these results, infill density and infill pattern are the most important factors for achieving high compressive strength. These findings suggest that optimizing infill architecture and density can improve the mechanical performance of PLA–wood composites, also they can offer assistive design guidelines for lightweight and eco-friendly components. Full article

Topography regulates vegetation functioning by controlling water redistribution, microclimate, and solar exposure. In Mediterranean ecosystems, where water availability constitutes a fundamental limiting factor, vegetation functioning is also influenced by environmental drivers such as temperature, climatic seasonality, drought recurrence, and soil properties that interact with terrain heterogeneity. Understanding how these elements operate at the micro-scale is essential for interpreting the spatial variability of photosynthetic vigor and canopy water condition. This study evaluates the relationships between the topographic metrics Topographic Position Index (TPI), Terrain Ruggedness Index (TRI), and Diurnal Anisotropic Heat Index (DAH) and two spectral proxies of vegetation condition, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI), in Los Nogales Nature Sanctuary (central Chile). Multitemporal Sentinel-2 time series (2017–2025) were analyzed using Generalized Additive Models (GAMs) with Gaussian distribution and cubic splines to detect non-linear topographic responses. All topographic predictors were statistically significant (p < 0.001). NDVI and NDMI values were higher in concave and less rugged areas, decreasing toward convex and thermally exposed slopes. NDMI exhibited greater sensitivity to topographic position and thermal anisotropy, indicating the strong dependence of vegetation water condition on topographically driven water redistribution. These results highlight the role of terrain in modulating vegetation vigor and moisture in Mediterranean ecosystems. Full article

Wildfire smoke visualization using geostationary satellite imagery is essential for real-time monitoring and atmospheric analysis; however, inconsistencies in color tone across Geostationary Environment Monitoring Spectrometer (GEMS) images hinder reliable interpretation and model training. This study proposes a Standardized False Color Composite (SFCC) framework based on deep learning style transfer to enhance the visual consistency and interpretability of wildfire smoke scenes. Four tone-standardization methods were compared: the statistical Empirical Cumulative Distribution Function (ECDF) correction and three neural approaches—ReHistoGAN, StyTr², and Style Injection Diffusion Model (SI-DM). Each model was evaluated visually and quantitatively using six metrics (SSIM, LPIPS, FID, histogram similarity, ArtFID, and LSCI) and validated on three major wildfire events in Korea (2022–2025). Among the tested models, SI-DM achieved the most balanced performance, preserving structural features while ensuring consistent color-tone alignment (ArtFID = 1.620; LSCI mean = 0.894). Qualitative assessments further confirmed that SI-DM effectively delineated smoke boundaries and maintained natural background tones under complex atmospheric conditions. Additional analysis using GEMS UVAI, VISAI, and CHOCHO demonstrated that the styled composites partially reflect the optical and chemical characteristics distinguishing wildfire smoke from dust aerosols. The proposed SFCC framework establishes a foundation for visually standardized satellite smoke imagery and provides potential for future aerosol-type classification and automated detection applications. Full article

Background/Objectives: Accurate nuclear segmentation is a fundamental step in computational pathology, enabling reliable estimation of cellularity and nuclear morphology. However, segmentation models are typically evaluated under ideal imaging conditions, while real-world microscopy data are affected by staining variability, noise, and image degradation. This study aims to comparatively evaluate three representative convolutional architectures for nuclei segmentation, with emphasis on robustness and clinical relevance under perturbed imaging conditions. Methods: U-Net, Attention U-Net, and U-Net++ were trained and evaluated on the BBBC038 nuclei microscopy dataset using fixed train–validation–test splits. Robustness was assessed under three types of synthetic perturbations: Gaussian blur, additive noise, and color jitter. Segmentation performance was quantified using the Dice coefficient and Intersection-over-Union (IoU). Paired Wilcoxon signed-rank tests with Holm correction and Cliff’s delta were used for statistical comparison. In addition, clinically relevant nuclear descriptors—nuclear count, median nuclear area, area interquartile range (IQR), and nuclear density—were extracted from predicted masks, and descriptor stability was analyzed as relative deviation from clean conditions. Results: Under clean imaging conditions, Attention U-Net achieved the highest mean Dice score, while paired statistical analysis indicated that U-Net++ exhibited the most consistent performance across test samples. Under image perturbations, Attention U-Net demonstrated greater robustness to blur and noise, whereas U-Net++ showed superior stability under color variations. Descriptor-based analysis further indicated that U-Net++ preserved nuclear count and density most reliably under chromatic perturbations, while U-Net exhibited larger instability in nuclear count and density, particularly under noise. Conclusions: Architectural design choices strongly influence not only pixel-level segmentation accuracy but also the stability of clinically relevant nuclear morphology descriptors. Robustness evaluation under multiple perturbation types reveals important trade-offs between architectures that are not captured by clean-image benchmarks alone. These findings highlight the necessity of multi-level evaluation strategies combining overlap metrics, statistical testing, robustness analysis, and descriptor stability assessment for future benchmarking and clinically reliable deployment of nuclei segmentation systems. Full article

Background: Spain has an increasingly aging population in rural areas. These individuals often face the burden of illness and the limitations it causes in solitude, leading to greater impacts on their health and quality of life. Therefore, the aim of this study was to evaluate the effectiveness of a combined IoT-based home monitoring and Silver Caregiver support intervention on health-related quality of life and functional, cognitive, emotional, and social outcomes in older adults living alone in rural settings. Material and methods: A longitudinal study was conducted with a sample of 144 older adults from rural areas who received home support through a Silver Caregiver and IoT technology. Results: Statistically significant differences were observed in cognitive status, anxiety, depression, family functionality, social support, life satisfaction, and quality of life. Conclusions: The findings indicate that the combined intervention primarily enhances psychological well-being, social connectedness, and perceived quality of life while contributing to the maintenance of basic physical function in older adults living in rural areas. Full article

Global river water quality degradation severely impairs aquatic ecosystem stability and human health, highlighting the urgency of spatiotemporal analysis for management guidance. Based on 2014–2024 monitoring data from the Quzhou Section of Qiantang River Basin, this study adopted the Water Quality Index (WQI) and statistical methods (PCA, Mann–Kendall test) to explore the spatiotemporal characteristics of water quality across the basin. Results showed an overall mean WQI of 79.26 (classified as “Good”), with general stability, localized fluctuations, and a stable-then-declining trend, mirroring an imbalance between governance effects and emerging pollution pressures. It identifies a critical governance phase focused on securing the current good water quality and curbing the trend of further deterioration. Water quality exhibited distinct variations: upper reaches > lower reaches, tributaries > mainstreams, with priority required for the Wuxi River’s declining WQI and the Qu River’s persistently low WQI. TN, TP, and NH₃-N were identified as key factors coupled with land use patterns. A differentiated strategy prioritizing nitrogen control, synergizing phosphorus–oxygen management, and reducing organics is thus proposed. This study provides scientific references for water quality assessment and targeted aquatic ecological governance in the basin and similar river networks. Full article

Digital storytelling (DST) is an innovative pedagogical approach that integrates multimedia creation, personal narrative, and autonomy in L2 education. Yet, its influence on learner engagement remains underexplored in asynchronous delivery modes and non-conventional language learning settings, common in post-pandemic instructional practice. This study thus examines the engagement patterns of 34 student-teachers of English in an afterschool asynchronous DST task about teacher identity. The study further scrutinises their emotional engagement, given its impact on other engagement domains, and its relevance for online instructional design. Data were collected through a background information questionnaire, a validated student engagement questionnaire, and semi-structured interviews that focused on emotional engagement. Questionnaire data were analysed quantitatively using descriptive statistics and repeated measures ANOVA, and interview data were examined qualitatively using thematic analysis and specific emotional engagement-related frameworks. Results indicated participants’ higher cognitive and behavioural engagement, and lower emotional engagement. Their emotional engagement comprised positive emotions and anxiety, which emerged from specific subjective task values, autonomy, and task affordances in interaction with self-imposed personal standards and perceived digital skills. These findings challenge the common conceptualisation of emotional engagement merely as positive affect in L2 tasks and signal the importance of task- and learner-related factors in an engagement-driven online L2 pedagogy. Full article

The reproductive cycle of the European clam Ruditapes decussatus reared in suspended double-net trays in the Oualidia Lagoon was investigated from October 2017 to February 2019. This study aimed to characterize gonadal development through histological analysis, gonadal index assessment, and the biochemical composition of key macromolecules (proteins, lipids, and carbohydrates) over an annual cycle. The results revealed that R. decussatus undergoes a prolonged spawning period from April to December, with a peak in October when 100% of the population reached the maturation stage (stage IIIA). A sexual rest phase was observed between November 2017 and December 2017. An overall sex ratio of 1:0.8 was observed, indicating a slight female bias, with no significant deviations. Statistical analyses highlighted a correlation between the gonadal index and seawater temperature, suggesting that temperature plays a crucial role in regulating reproductive activity. Biochemical analyses showed that proteins were the predominant macromolecule in clam tissues, followed by lipids and carbohydrates. Seasonal variations in biochemical composition were observed; however, no direct correlation was found between biochemical compound levels and the gonadal index (p > 0.05). These findings provide valuable insights into the reproductive biology of R. decussatus under suspended aquaculture conditions, contributing to improved management and optimization of farming practices. Full article

Background and Objectives: Dysglycemia is a major determinant of adverse outcomes in COVID-19, yet the separate contributions of poor glycemic control and glycemic variability (GV) remain incompletely defined. We conducted a systematic review and meta-analysis of observational cohort studies (both prospective and retrospective) to quantify the impact of chronic hyperglycemia and glucose instability on disease severity, intensive care requirements, and mortality in patients with COVID-19. Materials and Methods: We searched PubMed, Scopus, and Web of Science from January 2020 to October 2024 for observational cohort studies reporting clinically relevant COVID-19 outcomes stratified by glycemic control or GV. Dysglycemia definitions varied across studies (HbA1c-based chronic hyperglycemia, fasting glucose, or admission/in-hospital hyperglycemia). GV was assessed using metrics including mean amplitude of glycemic excursions (MAGE), standard deviation (SD), coefficient of variation (CV), or maximum daily glucose difference. Twelve studies met inclusion criteria and were included in qualitative synthesis; five studies were eligible for quantitative synthesis of clinical outcomes. Random-effects DerSimonian–Laird models were applied due to anticipated clinical heterogeneity. Heterogeneity was evaluated using Cochran’s Q, τ², and I² statistics. Results: Overall, 12 observational studies (9 prospective and 3 retrospective cohorts; n = 1,008,310 patients) were included. In quantitative analyses of five eligible cohorts, poor glycemic control was associated with a significantly increased risk of severe or critical COVID-19 (pooled RR = 1.75, 95% CI: 1.45–2.11; I² = 29%), ICU admission (RR = 1.54, 95% CI: 1.18–2.01), and mechanical ventilation (RR = 1.72, 95% CI: 1.31–2.26). Three studies evaluating GV demonstrated a strong association with adverse outcomes (pooled RR = 2.07, 95% CI: 1.71–2.50; I² = 0%); this low heterogeneity should be interpreted cautiously given the limited number of studies. GV remained associated with mortality in multivariable models, indicating that glycemic variability is separately associated with mortality as a clinically relevant prognostic risk marker in hospitalized COVID-19 patients. Conclusions: Both chronic hyperglycemia and elevated glycemic variability are each associated with increased risk of severe COVID-19 outcomes. Glycemic variability appeared to be a consistent, low-heterogeneity prognostic marker of mortality, being separately associated with higher death risk in hospitalized COVID-19 patients, highlighting its potential utility as a dynamic metabolic biomarker. Early identification and targeted management of dysglycemia—especially glucose instability—may improve prognosis in hospitalized COVID-19 patients. PROSPERO: CRD420251250718. Full article

Objectives: To compare artificial intelligence (AI) crown design with expert or non-AI computer-aided (CAD) design for single-unit tooth and implant-supported crowns across efficiency, marginal and internal fit, morphology and occlusion, and mechanical performance. Materials and Methods: This systematic review was conducted and reported in accordance with PRISMA 2020. PubMed MEDLINE, Scopus, Web of Science, IEEE Xplore, and Dentistry and Oral Sciences Source were searched from 2016 to 2025 with citation chasing. Eligible studies directly contrasted artificial intelligence-generated or artificial intelligence-assisted crown designs with human design in clinical, ex vivo, or in silico settings. Primary outcomes were design time, marginal and internal fit, morphology and occlusion, and mechanical performance. Risk of bias was assessed with ROBINS-I for non-randomized clinical studies, QUIN for bench studies, and PROBAST + AI for computational investigations, with TRIPOD + AI items mapped descriptively. Given heterogeneity in settings and endpoints, a narrative synthesis was used. Results: A total of 14 studies met inclusion criteria, including a clinical patient study, multiple ex vivo experiments, and in silico evaluations. Artificial intelligence design reduced design time by between 40% and 90% relative to expert computer-aided design or manual workflows. Marginal and internal fit for artificial intelligence and human designs were statistically equivalent in multiple comparisons. Mechanical performance matched technician designs in load-to-fracture testing, and modeling indicated stress distributions similar to natural teeth. Overall risk of bias was judged as some concerns across tiers. Conclusions: Artificial intelligence crown design delivers efficiency gains while showing short-term technical comparability across fit, morphology, occlusion, and strength for single-unit crowns in predominantly bench and in silico evidence, with limited patient-level feasibility data. Prospective clinical trials with standardized, preregistered endpoints are needed to confirm durability, generalizability, and patient-relevant outcomes, and to establish whether short-term technical advantages translate into clinical benefit. Full article

A principled, model-agnostic framework for structural feature extraction in time series is presented, grounded in topological data analysis (TDA). The motivation stems from two gaps identified in the literature: First, compact and interpretable representations that summarise the global geometric organisation of trajectories across scales remain scarce. Second, a unified, task-agnostic protocol for evaluating structure preservation against established non-topological families is still missing. To address these gaps, time-delay embeddings are employed to reconstruct phase space, sliding windows are used to generate local point clouds, and Vietoris–Rips persistent homology (up to dimension two) is computed. The resulting persistence diagrams are summarised with three transparent descriptors—persistence entropy, maximum persistence amplitude, and feature counts—and concatenated across delays and window sizes to yield a multiscale representation designed to complement temporal and spectral features while remaining computationally tractable. A unified experimental design is specified in which heterogeneous, regularly sampled financial series are preprocessed on native calendars and contrasted with competitive baselines spanning lagged, calendar-driven, difference/change, STL-based, delay-embedding PCA, price-based statistical, signature (FRUITS), and network-derived (NetF) features. Structure preservation is assessed through complementary criteria that probe spectral similarity, variance-scaled reconstruction fidelity, and the conservation of distributional shape (location, scale, asymmetry, tails). The study is positioned as an evaluation of representations, rather than a forecasting benchmark, emphasising interpretability, comparability, and methodological transparency while outlining avenues for adaptive hyperparameter selection and alternative filtrations. Full article

Due to their complexity and nonlinearity, metaheuristic algorithms have become the standard in problem solving for problems that cannot be solved by standard computational solutions. However, the global performance of these algorithms is strongly linked to the population structuring and the mechanism of replacing the worst solutions within the population. In this paper, an Adaptive Artificial Hummingbird Algorithm (AAHA), a new version of the basic AHA, is introduced and designed to enhance performance by studying the impacts of different population initialization methods within a broad and continual migration form. For the initialization phase, four methods—the Gaussian chaotic map, the Sinus chaotic map, opposite-based learning (OBL), and diagonal uniform distribution (DUD)—are proposed as an alternative to the random population initialization method. A new strategy is proposed as a replacement for the worst solution in the migration phase. The new strategy uses the best solution as an alternative to the worst solution with simple and effective local search. The proposed strategy stimulates exploitation and exploration when using the best solution and local search, respectively. The proposed AAHA is tested through various benchmark functions with different characteristics under many statistical indices and tests. Additionally, the AAHA results are benchmarked against those of other optimization algorithms to assess their effectiveness. The proposed AAHA outperformed alternatives in terms of both speed and reliability. DUD-based initialization enabled the fastest convergence and optimal solutions. These findings underscore the significance of initialization in metaheuristics and highlight the efficacy of the AAHA for complex continuous optimization problems. Full article

Background: and objective: Globally, breast cancer (BC) raises global health concerns, being the most common cancer. Women with BC experience a significant increase in perception of stress. Therefore, this study aims to evaluate the stress levels and associated sociodemographic and clinical factors among BC women in Saudi Arabia. Methods: A cross-sectional study was conducted between January and May 2025. Women diagnosed with BC, who were at least 18 years old, were recruited conveniently from outpatient and inpatient departments in King Fahad Specialist Hospital, Dammam, Saudi Arabia. Data were collected in the Arabic language through self-reported questionnaires, including sociodemographic/clinical characteristics and the Cohen’s Perceived Stress Scale. The data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 27. Results: A total of 200 participants were included in the study. The mean stress perception score was 26.52 ± 7.34. A high proportion (71.5%) of the sample reported elevated stress. A significant association was observed between age and stress levels. Most women aged 20–40 and 41–60 reported high stress, compared to women in the 61–80 age group (p = 0.003). Among all predictors, age was the only variable significantly associated with stress scores. Increasing age was associated with lower stress levels (B = −0.179, p = 0.013), indicating that younger participants tended to report higher stress. This corresponds to an adjusted decrease of approximately 1.8 points in the PSS-10 score per 10-year increase in age. Although participants with Stage IV cancer showed higher stress scores compared to those with Stage I cancer, this association approached but did not reach statistical significance (p = 0.054). Conclusions: This study highlights the substantial psychological burden experienced by women living with BC in Saudi Arabia. The majority of participants reported high levels of perceived stress. Younger women were particularly vulnerable to elevated stress. These findings highlight the need for targeted psychosocial support within oncology care to improve emotional well-being and quality of life. Full article

Background and Objectives: To evaluate the occurrence of retinal displacement using blue-fundus autofluorescence (BFAF) imaging in eyes treated for primary rhegmatogenous retinal detachment (RRD) and its associations with clinical factors, including macular status, detachment extent, baseline visual acuity, high myopia, postoperative visual recovery, and metamorphopsia. Materials and Methods: This retrospective observational study included 98 patients who underwent surgery for primary RRD at a single center. Surgical approaches included pars plana vitrectomy (PPV), phacovitrectomy, or scleral buckling, with tamponade agents such as SF₆ gas (20%), silicone oil (≈1300 cSt), or air. Postoperative BFAF imaging assessed retinal displacement. Demographic and clinical data were recorded. Results: Macula-off detachments occurred in 56.1% of cases, while 43.9% were macula-on detachments. Phacovitrectomy was performed in 41.8%, simple vitrectomy in 33.7%, and scleral buckling in 24.5%. SF₆ gas was the most used tamponade, while silicone oil was used in 13.3%. Retinal displacement was detected in 16.3% of cases, predominantly downward (81.25%) and less commonly upward (18.75%). Macula-off detachments were significantly associated with displacement (81.2% vs. 51.2%, p = 0.027). No significant associations were found with other parameters. Metamorphopsia was reported in 12.5% of patients with displacement and 4.9% without, though the difference was not statistically significant. Conclusions: Retinal displacement can occur after primary RRD repair, irrespective of tamponade, though it tended to be less frequent with silicone oil and in macula-on detachments. It is significantly more common in macula-off cases, even with immediate postoperative prone positioning. These findings emphasize the need to refine postoperative positioning protocols to reduce displacement and its sequelae. Further studies should explore the impact of retinal displacement on visual function, particularly metamorphopsia, in patients with preserved best-corrected visual acuity. Full article

Text-to-image models such as Stable Diffusion (SD) require comprehensive, fine-grained, and high-precision methods for evaluating text–image alignment. A prior method, the text–image alignment metric (TIAM), employs a template-based approach for fine-grained, high-precision evaluation; however, it is restricted to objects and colors, limiting its comprehensiveness. This study extends the TIAM by incorporating attention maps and vision–language models to deliver a fine-grained and high-precision evaluation framework that goes beyond colors and objects to include attributes, actions, and positions. In our experiments, we analyze the evaluation scores of images generated by the proposed method and compare them with human judgments. The results demonstrate that the proposed method outperforms existing methods, exhibiting a stronger correlation with human judgments (r = 0.853, $p<{10}^{-48}$). In addition, we applied the proposed method to evaluate the generation abilities of three SD models (i.e., SD1.4, SD2, and SD3.5). Each experiment used over 900 images, totaling 9858 images across all experiments to ensure statistical significance. The results indicate that SD3.5 exhibits superior expressiveness compared with SD1.4 and SD2. Nevertheless, for more complex tasks such as multi-attribute generation or multi-action generation, limitations in text–image alignment remain evident. Full article