Search Results (22,447)

Background/Objectives: This study evaluates students’ awareness and perceptions of interdisciplinary education. It focuses specifically on digital dentistry among students in the Department of Dental Technology. The findings aim to support the development of interdisciplinary courses and programs to enhance students’ skills in response to the growing digitalization of dental healthcare. Methods: A cross-sectional survey was conducted using a 23-item online questionnaire administered to a total of 203 students to collect data on general characteristics, perceptions of interdisciplinary education, the perceived necessity of such education, and the demand for interdisciplinary training, including topics related to CAD/CAM and 3D printing technologies. A t-test was performed to analyze grade-level differences in perceptions. Correlation analysis was conducted between perceptions and digital dental laboratory technology skills. Results: Despite the relatively low level of awareness regarding interdisciplinary education, students expressed a strong perceived need for it. A total of 76.6% of respondents preferred to collaborate with the Department of Dental Hygiene. No clear link exists between students’ perceptions of interdisciplinary education and their digital dental competencies. Practical training is more important than awareness. A significant difference in competencies was seen between lower- and higher-year students, indicating that advanced programs for higher-year students may be effective. Conclusions: Clear guidance on interdisciplinary education can enhance student understanding and acceptance. Interdisciplinary education with the dental hygiene department may increase engagement. Full article

The construction sector is a major contributor to climate change, with embodied carbon emissions from building materials representing a critical share of its environmental footprint. Selecting zero-carbon materials is therefore essential for reducing life-cycle emissions while advancing global climate goals. This study investigates six decision-making factors, including cost-effectiveness, durability, buildability, embodied carbon, availability, and aesthetics, and evaluates four alternative materials (wood, hemp, rammed earth, and straw bale) in the New Zealand context. A survey of 203 industry professionals was analysed using descriptive statistics, one-sample t-tests, and structural equation modelling (SEM). Using a 5-point Likert scale, the survey assessed six factors affecting material choice: cost-effectiveness, durability, buildability, embodied carbon, aesthetics, and material availability. Descriptive and inferential analyses were performed using SEM via Partial Least Squares analysis. The results revealed that embodied carbon and material availability were the most influential factors shaping zero-carbon material selection. Among the available alternatives, hemp emerged as the most preferred material, while cost-effectiveness and wood showed moderate impacts, and aesthetic considerations had the least influence. These findings highlight that environmental performance and practical accessibility are central drivers of decision-making when adopting zero-carbon materials. This study contributes to developing effective strategies for promoting the widespread adoption of zero-carbon materials, thereby supporting New Zealand’s progress toward achieving the Sustainable Development Goals and the 2030 Agenda for reducing greenhouse gas emissions. Full article

Surge capacity is the ability to manage sudden patient influxes beyond routine levels and can be evaluated using the 4S Framework: staff, stuff, system, and space. While low-resource settings like Rwanda face frequent mass casualty incidents (MCIs), most surge capacity research comes from high-resource settings and lacks generalisability. This study assessed Rwanda’s hospital surge capacity using a cross-sectional survey of emergency and surgical departments in all referral hospitals. Descriptive statistics, t-tests, Fisher’s exact test, ANOVA, and linear mixed-model regression were used to analyze responses. Of the 39 invited participants, 32 (82%) responded. On average, respondents believed that they could manage 13 MCI patients (95% CI: 10–16) while maintaining routine care, with significant differences between tertiary and secondary hospitals (11 vs. 22; p = 0.016). The intra-class correlation was poor for most variables except for CT availability and ICU beds. Surge capacity perception did not vary significantly by professional category, though less senior staff reported higher capacity. Significantly higher capacity was reported by those with continuous access to imaging (p < 0.01). Despite limited resources, Rwandan hospitals appear able to manage small to moderate MCIs. For larger incidents, patient distribution across facilities is recommended, with critical cases prioritized for tertiary hospitals. Full article

Although previous studies have examined various factors that influence biceps brachii activation, such as grip position, load, and exercise variation, to our knowledge, no prior studies have compared muscle activation during a traditional biceps curl and a Bayesian cable curl. Therefore, this study aimed to examine the differences in biceps brachii muscle activation between these two training modalities. Data from eleven volunteers (age: 25 ± 6 y; weight: 86 ± 13 kg; height: 177 ± 8 cm) were included in the analysis. Muscle activity was assessed using the normalized root mean square (RMS) values obtained from surface electromyography (sEMG). A within-subjects, counterbalanced design was utilized where all participants completed both testing conditions in a randomized order to control for potential order effects. Participants visited the laboratory and fitness center on two occasions. On the first day, anthropometric measurements were obtained, along with one repetition maximum (1-RM) for both the dumbbell biceps curl and the Bayesian curl. On the second day, participants performed an isometric maximal voluntary contraction (MVC), followed by electromyographic assessment of muscle activity during the dumbbell biceps curl and the Bayesian curl, each performed at 80% of their respective 1-RM. When the normal distribution was confirmed via the Shapiro–Wilk test (p > 0.05), a paired t-test was used for statistical analysis. On the other hand, when normality was not confirmed, the Wilcoxon test was utilized. Statistically significant differences (p = 0.003) were observed in the EMG amplitude (%) between the biceps curl (111.46 ± 26.80) and the Bayesian curl (93.39 ± 15.65) with a large effect size (d = 0.82). Based on the EMG analysis, the dumbbell biceps curl elicited significantly greater muscle activation compared to the Bayesian curl, suggesting that the conventional movement places a higher mechanical and neuromuscular demand on the biceps brachii. Full article

Background/Objectives: Several predictive models have been proposed to estimate the probability of cardiac transthyretin amyloidosis (ATTR-CA). The aim of our study was to evaluate the usefulness of electrocardiographic parameters, as well as parameters consisting of a combination of myocardial thickness and QRS voltage, as potential predictors of ATTR-CA. Methods: In 2018–2025, 285 consecutive patients with suspected cardiac amyloidosis were evaluated, including blood tests, standard 12-lead electrocardiography, transthoracic echocardiography, and [^99mTc]Tc-DPD scintigraphy. Results: The ratio of posterior wall thickness to minimum QRS voltage in limb leads (PWT/minQRS ratio) as well as several ECG-derived parameters were independent predictors of ATTR-CA. In a comparison of ROC curves, PWT/minQRS ratio exceeded both the minimum and maximum voltage of QRS complexes in limb leads, demonstrated similar predictive value to TCAS and T-amylo scores, and had similar or superior predictive characteristics to posterior wall thickness. A cut-off of >3.3 for PWT/minQRS ratio was as accurate as the published cut-offs for TCAS score ≥6, T-amylo score ≥7, and posterior wall thickness ≥14 mm. In the subgroup of patients with myocardial thickness of at least 15 mm, PWT/minQRS ratio >3.3 was superior to posterior wall thickness ≥14 mm and T-amylo score ≥7 and had similar predictive value for ATTR-CA as TCAS score ≥6. Conclusions: In a cohort of undifferentiated patients referred for [99mTc]Tc-DPD scintigraphy due to suspected cardiac amyloidosis, PWT/minQRS ratio showed strong predictive value for ATTR-CA, which was even more pronounced in the subgroup of patients with increased myocardial thickness. Full article

The ultrasound-assisted antisolvent precipitation method was used to prepare lignin nanoparticles from Thlaspi arvense L. rhizomes. The influence of each experimental variable on the average particle size (APS) of the lignin nanoparticles was determined via single-factor experiments. The optimal conditions for the preparation of the lignin nanoparticles were investigated in detail, and the APS of the lignin nanoparticles was 118 ± 3 nm. Compared with those of untreated lignin, the lignin nanoparticles prepared via this method were spherical and evenly distributed, and the structure was not damaged. Ultrasound generated local extreme physical conditions through its cavitation effect to promote nucleation, triggered high-speed turbulence to refine the particle size and improve uniformity, and applied mechanical disturbance to inhibit particle agglomeration, which promoted the preparation of lignin nanoparticles with a small size and good dispersion. A solubility test revealed that the lignin nanoparticles had greater solubility, which was improved 9-fold. The determination of antioxidant capacity revealed that the lignin nanoparticles had high free radical scavenging activity, which provided a broader space for the multifaceted utilization of a kind of grass lignin with the structural characteristics of T. arvense lignin (p-hydroxyphenyl lignin). Full article

Accurate segmentation of multiple sclerosis (MS) lesions from 3D MRI scans is essential for diagnosis, disease monitoring, and treatment planning. However, this task remains challenging due to the sparsity, heterogeneity, and subtle appearance of lesions, as well as the difficulty in obtaining high-quality annotations. In this study, we propose Efficient-Net3D-UNet, a deep learning framework that combines compound-scaled MBConv3D blocks with a lesion-aware patch sampling strategy to improve volumetric segmentation performance across multi-modal MRI sequences (FLAIR, T1, and T2). The model was evaluated against a conventional 3D U-Net baseline using standard metrics including Dice similarity coefficient, precision, recall, accuracy, and specificity. On a held-out test set, EfficientNet3D-UNet achieved a Dice score of 48.39%, precision of 49.76%, and recall of 55.41%, outperforming the baseline 3D U-Net, which obtained a Dice score of 31.28%, precision of 32.48%, and recall of 43.04%. Both models reached an overall accuracy of 99.14%. Notably, EfficientNet3D-UNet also demonstrated faster convergence and reduced overfitting during training. These results highlight the potential of EfficientNet3D-UNet as a robust and computationally efficient solution for automated MS lesion segmentation, offering promising applicability in real-world clinical settings. Full article

This study introduces a real-time processing framework for decoding motor imagery EEG signals by integrating manifold learning techniques with shallow classifiers. EEG recordings were obtained from six healthy participants performing five distinct wrist and hand motor imagery tasks. To address the challenges of high dimensionality and inherent nonlinearity in EEG data, five nonlinear dimensionality reduction methods, t-SNE, ISOMAP, LLE, Spectral Embedding, and MDS, were comparatively evaluated. Each method was combined with three shallow classifiers (k-NN, Naive Bayes, and SVM) to investigate performance across binary, ternary, and five-class classification settings. Among all tested configurations, the t-SNE + k-NN pairing achieved the highest accuracies, reaching 99.7% (two-class), 99.3% (three-class), and 89.0% (five-class). ISOMAP and MDS also delivered competitive results, particularly in multi-class scenarios. The presented approach builds upon our previous work involving EEG datasets from individuals with spinal cord injury (SCI), where the same manifold techniques were examined extensively. Comparative findings between healthy and SCI groups reveal consistent advantages of t-SNE and ISOMAP in preserving class separability, despite higher overall accuracies in healthy subjects due to improved signal quality. The proposed pipeline demonstrates low-latency performance, completing signal processing and classification in approximately 150 ms per trial, thereby meeting real-time requirements for responsive BCI applications. These results highlight the potential of nonlinear dimensionality reduction to enhance real-time EEG decoding, offering a low-complexity yet high-accuracy solution applicable to both healthy users and neurologically impaired individuals in neurorehabilitation and assistive technology contexts. Full article

Background/Objectives: Obesity increases reactive oxygen species (ROS), thereby triggering oxidative stress. Coriander seeds contain polyphenolic compounds that act as natural antioxidants to reduce oxidative stress. Coriander seed ethanolic extract has been proven to decrease malondialdehyde and increase catalase activity in the liver of high-fat-diet-fed rats. Thus, coriander seeds are thought to protect against obesity-induced oxidative liver damage; however, their molecular mechanism has not been revealed. Nuclear factor erythroid 2-related factor 2 (Nrf2) and Forkhead Box O3 (FOXO3) are transcription factors involved in cellular antioxidant regulation (e.g., superoxide dismutase/SOD, glutathione peroxidase/GPx expression, and reduced glutathione/GSH) that are negatively regulated by Kelch-like ECH-associated Protein 1 (Keap1) and 14-3-3 protein to maintain cellular homeostasis. This study aimed to analyze the regulation of antioxidant expression through in silico and in vivo experiments. Methods: The in silico study assessed the potential of coriander seed ethanolic extract to inhibit Keap1 and 14-3-3 using molecular docking. Then, the drug-likeness, pharmacokinetics, and toxicity of the top three compounds were analyzed. Meanwhile, the in vivo study investigated how the coriander seed ethanolic extract impacted the level of Nrf2, FOXO3, and their downstream effectors (T-SOD, MnSOD, GPx, and GSH). The in vivo study involved five groups of rats with obesity induced by a high-fat diet that were fed with 100 mg/kgBW coriander seed ethanolic extract for 12 weeks. Results: The in silico tests revealed that shionoside b had the highest potential to inhibit Keap1 (ΔG = −8.90 kcal/mol; Ki = 298.01 nM) and 14-3-3 protein (ΔG = −6.85 kcal/mol; Ki = 9.46 µM). The in vivo tests showed that the Nrf2, FOXO3, MnSOD, and GPx mRNA expression was significantly different between the groups (p < 0.05). Meanwhile, T-SOD, MnSOD, GPx, and GSH activity were not significantly different between the groups (p > 0.05). Nrf2 was significantly correlated with FOXO3 as well as the T-SOD, MnSOD, and GPx activity, and FOXO3 was significantly correlated with the T-SOD, MnSOD, GPx, and GSH activity. Conclusions: In obese rats, coriander seeds tend to increase Nrf2 and FOXO3 expression, which is positively correlated with their downstream enzymatic and nonenzymatic antioxidant activity. This is possibly due to the interaction between the coriander seed phytoconstituents and protein inhibitors (Keap1 and 14-3-3), which contribute to the stability and nuclear mobilization of Nrf2 and FOXO3. Full article

The Yellow River Delta is one of China’s most ecologically fragile regions, experiencing prolonged pressures from rapid urbanization and ecological degradation. Existing research, however, has predominantly focused on constructing ecological security patterns under single scenarios, with limited systematic multi-scenario comparisons and insufficient statistical support. To address this gap, this study proposes an integrated framework of “land use simulation—multi-scenario ecological security pattern construction—statistical comparative analysis.” Using the PLUS model, three scenarios were constructed—Business-as-Usual (BAU), Priority Urban Development (PUD), and Priority Ecological Protection (PEP)—to simulate land use changes by 2040. Habitat quality assessment, Multi-Scale Pattern Analysis (MSPA), landscape connectivity, and circuit theory were integrated to identify ecological source areas, corridors, and nodes, incorporating a novel hexagonal grid partitioning method. Statistical significance was evaluated using parametric tests (ANOVA, t-test) and non-parametric tests (permutation test, PERMANOVA). Analysis indicated significant differences in ecological security patterns across scenarios. Under the PEP scenario, ecological source areas reached 3580.42 km² (12.39% of the total Yellow River Delta), corresponding to a 14.85% increase relative to the BAU scenario and a 32.79% increase relative to the PUD scenario. These gains are primarily attributable to stringent wetland and forestland protection policies, which successfully limited the encroachment of construction land into ecological space. Habitat quality and connectivity markedly improved, resulting in the highest ecosystem stability. By contrast, the PUD scenario experienced an 851.46 km² expansion of construction land, resulting in the shrinkage of ecological source areas and intensified fragmentation, consequently increasing ecological security risks. The BAU scenario demonstrated moderate outcomes, with a moderately balanced spatial configuration. In conclusion, this study introduces an ecological restoration strategy of “five zones, one belt, one center, and multiple corridors” based on multi-scenario ecological security patterns. This provides a scientific foundation for ecological restoration and territorial spatial planning in the Yellow River Delta, while the proposed multi-scenario statistical comparison method provides a replicable methodological framework for ecological security pattern research in other delta regions. Full article

The primary aim of this study was to evaluate the feasibility, safety, and preliminary outcomes of the Equine Assisted Growth and Learning Association (Eagala) model of equine-assisted psychotherapy for active-duty military and veteran trauma survivors. This was a retrospective multi-site observational study. Study participants completed four psychological instruments pre- and post-intervention. These were the PTSD Checklist for DSM-5, the Patient Health Questionnaire, the Satisfaction with Life Scale and the Sheehan Disability Scale. The Client Satisfaction Questionnaire-8 was also completed post-intervention. Paired-sample t-tests were conducted to assess for changes in the primary outcome variables pre- and post-intervention. The RAPID qualitative approach was used to analyze the qualitative data and develop subthemes. Subjects were 107 participants at 12 sites. Participants ranged in age from 22 to 78 and were predominately male. Findings revealed that the Eagala model intervention can be implemented for this population across multiple sites. Further, treatment engagement may be better than found with conventional psychotherapy interventions for this population. Pre-to-post-intervention changes in scores on the psychological instruments revealed significant decreases in PTSD symptoms, depression, and disability as well as increases in satisfaction with life. Future randomized controlled trails of this intervention are warranted. Full article

Content Delivery Networks (CDNs) have evolved to meet surging data demands and stringent low-latency requirements driven by emerging applications like high-definition video streaming, virtual reality, and IoT. This paper proposes a hybrid CDN architecture that synergistically combines edge caching, Multi-access Edge Computing (MEC) offloading, and reinforcement learning (Q-learning) for adaptive routing. In the proposed system, popular content is cached at radio access network edges (e.g., base stations) and computation-intensive tasks are offloaded to MEC servers, while a Q-learning agent dynamically routes user requests to the optimal service node (cache, MEC server, or origin) based on the network state. The study presented detailed system design and provided comprehensive simulation-based evaluation. The results demonstrate that the proposed hybrid approach significantly improves cache hit ratios and reduces end-to-end latency compared to traditional CDNs and simpler edge architectures. The Q-learning-enabled routing adapts to changing load and content popularity, converging to efficient policies that outperform static baselines. The proposed hybrid model has been tested against variants lacking MEC, edge caching, or the RL-based controller to isolate each component’s contributions. The paper concludes with a discussion on practical considerations, limitations, and future directions for intelligent CDN networking at the edge. Full article

Objective: This study explores the prevalence of violence and forced displacement as indicators of inequity among Bogotá’s elderly population, with a particular focus on how these factors affect their health and access to healthcare services. Methods: This is a subsidiary analysis of the SABE-Bogotá survey. The design was a probabilistic cluster sample of 2000 people aged 60 and over. The study was carried out by the Pontificia Universidad Javeriana’s Institute on Aging and cosponsored by Colciencias. The variables of interest were displacement and experiences of violence, assessed through self-reporting. A descriptive analysis of all variables was performed, calculating simple frequency distributions. Subsequently, dependency and association analyses were performed using Chi-square, T-tests, and multivariate logistic regressions, depending on each case. Results: 43.32% of the subjects were victims of some type of violence in the last year, among which offensive language was one of the most frequent. Individuals with severe depression (OR 2.10 [1.21–3.65]) and those who had been victims of displacement (OR 2.55, CI 95% [1.65–3.95]) had the highest risk of violence. The results reveal a direct correlation between these experiences and pre-existing health conditions. For instance, severe depression and a history of displacement were associated with a higher risk of experiencing violence, while the risk of displacement was higher among individuals with diabetes, severe depression, and, crucially, those who lacked access to health insurance. Conclusion: A high percentage of the elderly population in the city of Bogotá has been victims of different types of violence, including ones related to armed conflict and forced displacement, which is a particular and exclusive form of violence suffered by this group of people. These findings suggest that violence and displacement are social determinants of health that exacerbate inequities, underscoring the need for more inclusive health policies and improved access to medical care for this vulnerable population. Full article

Background: Urban freight transport is essential for the functioning of cities. The COVID-19 pandemic accelerated the growth of e-commerce, creating new challenges for courier services. While consumer satisfaction has been extensively studied, little attention has been paid to courier drivers’ own perceptions and preferences. This study aims to fill that gap. Methods: A questionnaire survey was conducted among 139 drivers employed in eight courier companies in Serbia. Data were analyzed using parametric statistical methods (Independent Samples T-Test, Paired-Samples T-Test, and One-way ANOVA), with additional post hoc tests to explore group differences. Results: Statistically significant differences were observed across demographic, operational, and safety-related factors (gender, age, residence, occupation, license ownership, delivery area, and type of goods). A strong preference emerged for passenger vehicles as the safest mode of delivery, highlighting a misalignment between current operational practices and drivers’ safety perceptions. Conclusions: The findings emphasize the importance of tailoring delivery strategies to demographic and operational contexts. Practical recommendations include improving transport safety, optimizing delivery zones, and addressing driver satisfaction as a determinant of service quality. The study contributes new insights into last-mile delivery by focusing on the perspectives of courier drivers rather than consumers. Full article

Under the context of “Energy Conservation and Emission Reduction,” low-sulfur fuel has become widely adopted in maritime operations, posing significant challenges to cylinder lubrication systems. Traditional oil injection strategies, heavily reliant on manual experience, suffer from instability and high costs. To address this, a lightweight image retrieval model for cylinder lubrication is proposed, leveraging deep learning and computer vision to support oiling decisions based on visual features. The model comprises three components: a backbone network, a feature enhancement module, and a similarity retrieval module. Specifically, EfficientNetB0 serves as the backbone for efficient feature extraction under low computational overhead. MobileViT Blocks are integrated to combine local feature perception of Convolutional Neural Networks (CNNs) with the global modeling capacity of Transformers. To further improve receptive field and multi-scale representation, Receptive Field Blocks (RFB) are introduced between the components. Additionally, the Convolutional Block Attention Module (CBAM) attention mechanism enhances focus on salient regions, improving feature discrimination. A high-quality image dataset was constructed using WINNING’s large bulk carriers under various sea conditions. The experimental results demonstrate that the EfficientNetB0 + RFB + MobileViT + CBAM model achieves excellent performance with minimal computational cost: 99.71% Precision, 99.69% Recall, and 99.70% F1-score—improvements of 11.81%, 15.36%, and 13.62%, respectively, over the baseline EfficientNetB0. With only a 0.3 GFLOP and 8.3 MB increase in model size, the approach balances accuracy and inference efficiency. The model also demonstrates good robustness and application stability in real-world ship testing, with potential for further adoption in the field of intelligent ship maintenance. Full article