Search Results (57,178)

This study aimed to evaluate bone regeneration in critical-size rat calvarial defects treated with a xenogeneic bone graft alone or combined with autologous platelet concentrates prepared using two distinct centrifugation protocols, one based on horizontal centrifugation to produce horizontal platelet-rich fibrin (H-PRF) and the other based on vertical fixed-angle centrifugation to produce leukocyte–platelet-rich fibrin (L-PRF). Calvarial defects were created in 24 rats and allocated to four groups: blood clot (C), xenograft (XEN), xenograft plus L-PRF (XEN+L-PRF), and xenograft plus H-PRF (XEN+H-PRF) (n = 6/group). Calcein and alizarin were administered at 14 and 30 days, respectively. After 35 days, specimens were analyzed by micro-computed tomography, confocal laser scanning microscopy, histomorphometry, and histopathology. Data were analyzed using ANOVA and Tukey’s post hoc test (p < 0.05). All treated groups showed higher bone volume and lower trabecular separation than group C. The XEN+L-PRF group showed significantly higher bone volume than the XEN group. The XEN+H-PRF group showed higher bone volume, connectivity density, alizarin labeling, mineral apposition rate, and newly formed bone area, as well as lower trabecular separation, than the other groups (p < 0.05). Under the experimental conditions evaluated, both PRF protocols improved bone regeneration when combined with a xenogeneic bone graft, whereas the H-PRF protocol was associated with superior structural and histomorphometric bone regeneration outcomes and with more favorable spatiotemporal mineralization kinetics. Full article

Ruminants harbor a ruminal microbiome that converts their host-indigestible diet into nutrients. This microbiome contains three distinct communities: liquid, solid, and epithelial (or epimural). Currently, there is limited research examining the diet-dependent responses of all three microbiomes within the same animal. Here, we used next-generation 16S rRNA sequencing to characterize the ruminal solid (RS), liquid (RL), and epimural (RE) microbiotas of 13 lactating and cannulated Holstein dairy cows fed either a high- or low-starch diet in a crossover experimental design. Independent of diet, we found that all sample types were dominated by the phyla Firmicutes and Bacteroidetes. Proteobacteria and Epsilonbacteraeota were also highly abundant but only in the RE. Although the total VFA molar abundance did not differ between diets, propionate and valerate were found to increase with the high-starch diet, while acetate was increased with the low-starch diet. Overall, we found that the RE microbiota was more diverse than the RS and RL communities, with diet impacting community diversity in the RS. We found that sample type, diet treatment, and their interaction significantly impacted community structure and composition. Notably, Prevotella was most abundant in the RL and RS, particularly on the low-starch diet. We also found that Lachnospiraceae were significantly enriched in the RS and RL with a high-starch diet, whereas Succiniclasticum was highly abundant in the RE with a low-starch diet. These data suggest that diet influences all three ruminal microbiomes and provides a useful framework for understanding the role of these microbiomes in mediating host production. Full article

Background/Objectives: Artificial intelligence (AI) is transforming prosthodontic procedures into data-driven support systems. This scoping review systematically maps the literature using an evidence maturity framework, distinguishing it from recent applications-focused compilations by Aljulayfi, Schwendicke, and others. Methods: A comprehensive search of PubMed, Scopus, and Web of Science (January 2023–March 2026) was conducted in accordance with PRISMA-ScR guidelines, supplemented by targeted manual citation tracking. Fifty studies were included in the review. Results: The evidence base comprises approximately half primary research (computational, in vitro, clinical) and half review articles. Foundational diagnostic CNNs report high technical performance metrics (i.e., sensitivity 98.67%, precision 78.12%), while generative CAD/CAM approaches reduce design time by up to 52% in vitro. However, the evidence synthesis reveals a significant gap between algorithmic efficacy and clinical utility. Most applications currently reside at the “technical validation” stage, while emerging domains such as large language models remain largely experimental. Conclusions: Impressive computational metrics do not automatically equate to patient-centered clinical benefits. Current AI systems must be regarded as adjunctive decision-support instruments rather than autonomous tools. Future research must prioritize prospective multicenter validation, implementation science, explainability, and robust regulatory oversight. Full article

Background/Objectives: This scoping review aimed to map the existing evidence on the aims, characteristics, outcomes and implementation facilitators and barriers of peer-support interventions designed to promote self-management in individuals living with an intestinal ostomy. Methods: A scoping review was conducted following the Joanna Briggs Institute methodology and reported according to the PRISMA Extension for Scoping Reviews (PRISMA-ScR). A systematic literature search was conducted in December 2025 in the MEDLINE (PubMed), CINAHL (EBSCOhost) and APA PsycINFO (ProQuest) databases. Quantitative, qualitative, and mixed-methods design studies written in any language were eligible for inclusion, with no restrictions on publication date, language or geographical location. Rayyan was used for duplicate removal and reference management. A total of 944 titles and abstracts were screened independently by two reviewers. Fifty-three full-text studies were assessed for eligibility. Data were extracted and synthesised descriptively and narratively. Results: Fourteen eligible publications were included, representing a range of study designs, including randomised controlled trials (n = 6), observational (n = 2) and quasi-experimental (n = 1) designs, feasibility, and economic evaluation studies. Studies were conducted primarily in the USA (n = 7), followed by Turkey (n = 3). Most peer-support interventions aimed to improve quality of life (n = 6). Peer-support interventions varied considerably in delivery mode, duration, facilitation, and intervention content. Interventions were delivered by peers alone (n = 2) or involved peers in collaboration with healthcare professionals or as part of multicomponent interventions and included face-to-face, remote, and other delivery formats, ranging from a single visit to a four-month duration. Several publications reported improvements in quality of life and self-management. Structured intervention content was reported to facilitate implementation, whereas accessibility and technological barriers were reported to limit intervention delivery. Conclusions: Peer-support interventions may represent a promising approach for supporting self-management among individuals living with an ostomy, particularly with several publications reporting improvements in self-management related outcomes. However, these findings should be interpreted cautiously given the heterogeneity of intervention characteristics and study designs, together with the absence of critical appraisal of included publications. Future research should identify core components of peer-support interventions and evaluate their longer-term outcomes across diverse healthcare and cultural contexts. Full article

In the context of educational digital transformation, automated grading of paper-based examinations faces two challenges: data privacy concerns and data heterogeneity across educational institutions. Existing Optical Mark Recognition systems are predominantly designed under centralized learning paradigms, requiring examination data to be collected and processed at a central server, which may expose sensitive student information. To address this limitation, this paper proposes a federated OMR framework for automated examination paper digitization and assessment in distributed environments. The proposed system consists of five stages, employing YOLO26 for answer-region localization, ORB-based image registration for geometric alignment, and EfficientNet-B0 for answer-state classification, automated grading, and result aggregation. Federated Averaging is utilized to train a global model across five clients representing heterogeneous data domains with different answer-sheet layouts, ink characteristics, and marking styles, without sharing raw data. Experimental results demonstrate that the federated model achieves an ROI-level accuracy of $99.42\%$ and a Macro F1-score of $95.82\%$. Furthermore, the proposed system attains a Sheet-level accuracy of $84.62\%$, outperforming local training approaches while achieving performance comparable to centralized learning. These findings suggest the feasibility of Federated Learning for developing privacy-preserving and highly generalizable OMR systems for large-scale educational assessment. Full article

Probabilistic power flow quantifies voltage and phase angle uncertainty under variable photovoltaic generation, but repeated AC Monte Carlo simulation is costly. A local topology change also modifies the electrical operator and state dimension when only a small target data set is available. We propose a Basis Constrained Graph Convolutional Network (BCGCN) for few-shot adaptation after local bus additions in small-scale grids. BCGCN predicts nonlinear residuals around a first-order solution using graph Laplacian and Proper Orthogonal Decomposition modes. It transfers source coordinates; adapts only the new bus rows, rotation, readouts, and correction gate; and freezes the backbone. The experimental results indicate that BCGCN leads all four reported errors on the IEEE 14 and IEEE 57 expansions. IEEE 118 and Polish 2746 establish the scale boundary. BCGCN wins only 9 of 64 IEEE 118 error cells and none on Polish 2746, while retaining compact updates. The paired IEEE 118 PV study shows that target pilots reduce zero-shot error and residual correction removes most high variability linearization error. BCGCN is therefore effective for local few-shot adaptation in small grids but not an accuracy-preserving adapter for large networks. Full article

Conventional inverse synthetic aperture radar (ISAR) imaging techniques can produce high-resolution imagery from complete observation data. However, in practical scenarios, incomplete data caused by undersampling or missing data often leads to defocused results with traditional methods. While compressive sensing or low-rank reconstruction approaches have been proposed to address this challenge, existing techniques frequently fail to fully exploit both the sparsity and low-rank properties inherent in ISAR scenes. Moreover, they typically rely on convex approximations that introduce estimation bias, weaken sparsity promotion, and increase computational complexity, ultimately degrading imaging performance. To overcome these limitations, this work presents an enhanced sparse ISAR imaging method that jointly enforces non-convex sparsity and low-rank constraints for incomplete data recovery. The imaging model incorporates both inherent sparsity priors and a low-rank constraint. The resulting non-convex optimization problem is solved via an efficient iterative algorithm based on the alternating direction method of multipliers, where the sparse component is reconstructed using an iterative reweighted scheme with a regularizer and the low-rank component is recovered through truncated singular value decomposition. Experimental results on both simulated and measured data demonstrate the efficacy and superior performance of the proposed method. Full article

Time series anomaly detection is one of the core tasks in time series data analysis, aiming to identify abnormal events or behaviors from normal temporal data, and plays a critically important role across numerous domains. In seismic data analysis, the identification of anomaly intervals preceding earthquake occurrences holds significant research value for subsequent earthquake prediction. This study evaluates the effectiveness of a large time series model named Timer in detecting anomalies within seismic time series data collected by tiltmeters. As a multi-task time series model, Timer has achieved state-of-the-art performance on forecasting, imputation, and anomaly detection tasks across multiple benchmark datasets, and is therefore selected as the core model in this study. This study employs the Timer model combined with the Constrained Gaussian-Noise Optimization and Smoothing (COGNOS) method for anomaly detection in seismic time series data, and compares the results with those of the original Timer model. Additionally, several mainstream time series anomaly detection models are selected as baselines for comparison. To comprehensively validate the model’s performance, comparative experiments were conducted on both the publicly available SIRGAS GNSS dataset and a real-world seismic dataset collected by tiltmeters. The experimental results demonstrate that the Timer model exhibits outstanding performance in anomaly detection on seismic time series data, and achieves further improvement when integrated with the COGNOS method. Full article

Reliable fatigue life prediction for corroded bridge steel remains challenging. Severe surface damage, sparse tests, and heterogeneous literature data obscure the link between corrosion morphology, stress state, and fatigue resistance. In this study, electrochemical accelerated corrosion, three-dimensional laser scanning, tensile testing, axial fatigue testing, and scanning electron microscopy were combined with an interpretable machine learning framework for pre-corroded Q345C steel. A dimensionless feature-aligned support vector regression model was developed by normalizing stress amplitude with material strength, adding Gaussian noise regularization, and integrating multi-source corrosion fatigue data. For 27 experimental validation samples, the model achieved a coefficient of determination of 0.795 and a root mean square error of 0.146, with 26 samples falling within the predefined twofold error band. Shapley additive explanations identified mass loss ratio as the dominant predictor and showed a physically consistent negative effect of normalized stress amplitude on fatigue life. These results suggest that physically informed dimensionless feature alignment can improve small-sample corrosion fatigue prediction while retaining interpretable links to damage mechanisms. Full article

Background: Unilateral complex training has been proposed as an effective strategy to improve lower-limb neuromuscular function; however, its effects on isokinetic strength, inter-limb strength asymmetry, and athletic performance in competitive basketball players remain insufficiently investigated. This study examined the effects of an eight-week unilateral complex training program on lower-limb isokinetic strength, strength asymmetry, hamstring-to-quadriceps (H/Q) ratio, and athletic performance in young male basketball players. Methods: Twenty-nine competitive male basketball players (age: 19.14 ± 0.99 years) competing in the Turkish Basketball Youth League and U18 League were randomly assigned to an experimental group (n = 15) or a control group (n = 14). The experimental group performed unilateral complex training twice weekly for eight weeks in addition to regular basketball practice, whereas the control group continued routine basketball training only. Before and after the intervention, lower-limb isokinetic strength (60°·s⁻¹), bilateral strength asymmetry, H/Q ratio, countermovement jump (CMJ), Abalakov jump, 20-m sprint performance, and force-platform-derived jump variables were assessed. Data were analyzed using two-way mixed-design analysis of variance. Results: Significant Group × Time interactions were observed for right and left knee flexor strength and left knee extensor strength (all p < 0.05). Bilateral quadriceps and hamstring strength asymmetries were significantly reduced by approximately 47% and 63%, respectively, accompanied by an improvement in the left H/Q ratio (p < 0.05). The intervention also produced significant improvements in CMJ height, Abalakov jump performance, flight time, time to takeoff, and 20-m sprint performance (all p < 0.05), whereas no significant interaction was found for peak force (p > 0.05). Conclusions: Adding an eight-week unilateral complex training program to regular basketball training was associated with improvements in selected lower-limb isokinetic strength and athletic performance outcomes, reductions in measured inter-limb strength asymmetry indices, and a side-specific improvement in the left H/Q ratio in competitive young male basketball players. However, because the control group did not receive an additional time- and volume-matched training stimulus, these findings should be interpreted as the effects of adding the overall training program rather than as evidence of the specific effects of its unilateral or complex components. Full article

As information technology evolves rapidly, image data is exposed to growing risks of security breaches and privacy leaks during transmission and storage. Therefore, image encryption has attracted significant attention as an effective protection measure. Nevertheless, most chaos-driven image encryption schemes suffer from inferior chaotic randomness, making them prone to cryptanalytic cracking in practice. To solve this problem, a new image encryption scheme is proposed by integrating the fireworks algorithm with a convolution operation. First, the original image is permuted via the Arnold transform and an improved permutation strategy. Then, the classical Logistic map is iterated to generate an initial pseudo-random sequence, which is further optimized by the fireworks algorithm. Finally, a reversible convolution operation is integrated with a bit-level diffusion mechanism to achieve image encryption. Experimental results confirm that the proposed scheme exhibits superior performance in terms of statistical analysis, robustness analysis, and image-quality assessment, and it possesses remarkable security against various cryptanalytic attacks. Full article

This exploratory study examines the implementation of a GenAI-supported curriculum redesign in a project-based multimedia computing course by comparing students’ experiences in Python-based and GenAI-supported production pathways in relation to creativity, applied digital competence, and perceived professional readiness. A mixed-methods, quasi-experimental design was employed to compare two naturally occurring undergraduate cohorts at a Gulf-region university. Data was collected from 29 students through post-course surveys, open-ended reflections, project reports, student-generated artefacts, and instructional observations. Quantitative data were analyzed using nonparametric statistical tests and effect size reporting, while qualitative evidence was examined through inductive thematic analysis supported by human-in-the-loop AI-assisted coding. The findings indicate that the GenAI-supported cohort reported significantly higher project satisfaction, perceived ease of use, and job relevance, with additional positive trends in creativity and professional output. Qualitative evidence and illustrative project examples further suggest that students experienced fewer technical barriers and greater opportunities for multimodal production, particularly in community-facing projects. Based on these findings, the study presents the INSPIRE framework, a seven-stage, practice-informed conceptual model for integrating GenAI into project-based computing education. Although limited by a small sample and single-institution context, the study provides exploratory comparative evidence and a practice-informed framework that offers actionable guidance for responsible and structured GenAI integration in higher education. Full article

Cancer remains a major global health challenge, requiring innovative diagnostic and therapeutic strategies. Cell culture models are essential tools in cancer research. While traditional two-dimensional (2D) cultures are widely used because of their accessibility and simplicity, they inadequately represent the tumor microenvironment. This review provides a molecular perspective on the biological differences between 2D and 3D cancer models, emphasizing extracellular matrix signaling, mechanotransduction, metabolic adaptation, tumor heterogeneity, and therapeutic resistance rather than a general comparison of culture systems. It discusses the limitations of 2D models and highlights the advantages of 3D systems, including spheroids and organoids, for more accurately recapitulating the tumor microenvironment and improving the predictive value of anticancer drug testing. This narrative review synthesizes current evidence on the roles of 2D and 3D cell culture methods in oncology. The literature was searched in PubMed, Google Scholar, and Web of Science from 2015 to 2026 using keywords including “2D cell culture,” “3D cell culture,” “spheroids,” “organoids,” and “cancer.” Studies were eligible for inclusion if they addressed cancer research using 2D or 3D cell culture models and provided relevant experimental, preclinical, or translational evidence, particularly regarding tumor microenvironment, extracellular matrix signaling, cellular interactions, therapeutic response, or molecular mechanisms. Studies unrelated to cancer or 2D/3D culture models, duplicate records, and publications lacking relevant primary data were excluded. Study selection was performed by screening titles and abstracts followed by full-text assessment according to the predefined eligibility criteria. Seminal studies were also included when considered relevant to the conceptual framework of the review. Findings were synthesized narratively without quantitative analysis. 2D cultures limit cell–cell and cell–extracellular matrix interactions, reducing the applicability of findings to in vivo conditions. In contrast, 3D cultures better reproduce oxygen and nutrient gradients and cellular interactions, providing a more realistic representation of the tumor microenvironment. Recent advances in 3D systems have improved predictive accuracy for drug screening and personalized medicine. Both 2D and 3D culture methods possess unique strengths and limitations. Used in complementary ways, they can improve the translation of in vitro findings to in vivo and clinical applications, accelerating progress in cancer research and therapeutic development. Full article

Online social networks generate large volumes of textual data that reflect users’ opinions, affective expressions, and broader patterns of engagement and social behavior. However, natural language processing approaches frequently examine sentiment, trust-related signals, and behavioral indicators independently, limiting their ability to represent the multidimensional nature of online interaction. This study conducts a systematic comparative evaluation of lexicon-enhanced fine-grained sentiment classification using linguistic, message-level statistical, and lexicon-derived affective information across a common experimental framework. The empirical analysis combines TF–IDF features, word-count information, and sentiment indicators derived from TextBlob, SentiStrength, and VADER, while the broader multi-level organization is used to relate the resulting affective evidence to online social-behavior analysis. Fifteen classical machine learning algorithms and seven deep learning architectures are evaluated on a real-world Twitter dataset containing 41,157 COVID-19-related tweets labeled across five sentiment-intensity classes. The experimental evaluation considers four feature configurations and seven performance metrics, complemented by Friedman and post hoc Wilcoxon signed-rank tests. The results show that TextBlob provides modest improvements, SentiStrength produces broader and more consistent gains, and VADER yields the strongest overall performance. AdaBoost combined with VADER achieves the best results, with 93.16% accuracy, 93.20% macro F1, 93.27% balanced accuracy, and an MCC of 0.913, while the Dense Neural Network is the strongest deep learning model. These results demonstrate that lexicon-derived affective features can substantially strengthen fine-grained sentiment classification, although their effectiveness depends strongly on the learning algorithm used to exploit them. The empirical contribution of this study is confined to fine-grained sentiment classification, while trust-related and attachment-related dimensions are retained as higher-order interpretive constructs rather than directly predicted or empirically validated outcomes. Full article

Recently developed polyphenol-rich extra virgin olive oils (EVOOs) are well-known for their health-promoting properties and provide the opportunity to extract polyphenolic content for innovative nutraceutical development. The extraction of phenolics from three polyphenol-rich EVOOs was optimized using response surface methodology (RSM) and ethanol/water mixtures. The effects of water content in ethanol and the solvent-to-oil ratio were evaluated based on results obtained using the Folin–Ciocalteu method. The extracts produced under the optimum conditions were also subjected to HPLC-DAD analysis and their phenolic profiles were obtained. The models derived showed high statistical significance (p ≤ 0.001) and a good fit to the experimental TPC data, as reflected by their R² values of 94.41%, 96.25%, and 93.85%, for EVOOs -A, -B, and -C, respectively. Optimum extraction conditions (100% ethanol, ratio of 7.4–10:1) maximized phenolic yield of 1365.80, 1045.56, and 1005.67 mg Tyr. Eq./kg, for EVOOs -A, -B, and -C, respectively. The optimized extracts were validated in terms of their phenolic profiles, revealing high amounts of hydroxytyrosol, tyrosol, oleocanthal, oleuropein, ligstroside aglycone, and oleacein. These findings contribute to the efficient recovery of polyphenols from polyphenol-rich EVOOs for potential use in the development of innovative nutraceutical supplements, medicinal formulations, and/or functional foods. Full article