Search Results (7,736)

This paper presents a disaster detection framework based on aerial imagery, utilizing a Branch Convolutional Neural Network (B-CNN) to enhance feature learning efficiency. The B-CNN architecture incorporates branch training, enabling effective training and inference with reduced model parameters. To further optimize resource usage, the framework integrates DoReFa-Net for weight quantization and fixed-point parameter representation. An early exit mechanism is introduced to support low-latency, energy-efficient predictions. The proposed B-CNN hardware accelerator is implemented using TSMC 16 nm CMOS technology, incorporating power gating techniques to manage memory power consumption. Post-layout simulations demonstrate that the proposed hardware accelerator operates at 500 MHz with a power consumption of 37.56 mW. The system achieves a disaster prediction accuracy of 88.18%, highlighting its effectiveness and suitability for low-power, real-time applications in aerial disaster monitoring. Full article

Background: Maxillary sinus puncture (MSP), once a cornerstone for diagnosing and treating acute rhinosinusitis (ARS), has declined with the rise in less invasive techniques. This study explores MSP trends, its association with age, and long-term effects on quality of life using data from the Study of Health in Pomerania (SHIP). Methods: Data from SHIP-START-2 (n = 2332), SHIP-START-3 (n = 1717), and SHIP-TREND-0 (n = 4420) cohorts were analyzed to assess MSP prevalence, demographic correlations, and quality- of-life impacts using SNOT-20-D, EQ-5D-3L, and SF-12. Results: MSP prevalence was higher in older SHIP-START cohorts (11.2% in START-2) compared to SHIP-TREND-0 (9.5%), reflecting its historical decline. The procedure was more frequently reported by participants aged > 60 years (e.g., 14.0% in START-2) than by younger groups (<40 years: 3.5% in START-2). MSP was associated with increased SNOT-20-D scores across cohorts (e.g., +0.28 in START-2, p < 0.001) and minor reductions in EQ-5D-3L and SF-12 mental health scores, indicating greater symptom burden but limited general health impact. The age- and time-related decline in MSP highlights its diminishing role in modern practice. Conclusions: While MSP offers diagnostic insights and serves as an indicator for ARS, its modest impact on long-term quality-of-life underscores the need for alternative, minimally invasive techniques for sinonasal conditions. Full article

Background/Objectives: Iron deficiency without anemia (IDWA) is common among female patients with chronic kidney disease (CKD), yet the clinical implications of iron therapy in this population remain uncertain. While iron supplementation is frequently used in anemic CKD patients, evidence regarding its outcomes in non-anemic, iron-deficient individuals is limited and conflicting. Methods: This retrospective cohort study utilized the multi-institutional TriNetX database to examine the 5-year outcomes of iron therapy in adult women with stage 3 CKD, normal hemoglobin (≥12 g/dL), normal mean corpuscular volume (MCV), and low serum ferritin (<100 ng/mL). Primary outcomes included all-cause mortality, major adverse cardiovascular events (MACE), acute kidney injury (AKI), pneumonia, progression to advanced CKD (estimated glomerular filtration rate ≤30 mL/min/1.73 m²), and gastrointestinal (GI) bleeding. Results: We identified 53,769 eligible non-anemic patients with stage 3 CKD, low serum ferritin levels, and normal MCV. Propensity score matching (1:1) was conducted on demographic variables to compare iron-treated (n = 6638) and untreated (n = 6638) cohorts. Over the 5-year follow-up, iron therapy in non-anemic females with stage 3 CKD, low ferritin levels, and iron supplementation was significantly associated with increased risks of MACE, AKI, pneumonia, CKD progression, and GI bleeding (log-rank p < 0.0001). No significant difference in all-cause mortality was observed. Data on transferrin saturation and the dosage of iron supplementation were unavailable. Conclusions: In non-anemic women with stage 3 CKD and low ferritin levels, iron supplementation was linked to increased MACE, renal, and pneumonia risks without evident survival benefits. These findings suggest that iron therapy in this group of patients may not confer cardiovascular benefit and may pose risks. Full article

Background: Exercise-induced fatigue arises primarily from energy substrate depletion and the accumulation of metabolites such as lactate and ammonia, which impair performance and delay recovery. Emerging evidence implicates gut microbiota modulation—particularly via probiotics—as a means to optimize host energy metabolism and accelerate clearance of fatigue-associated by-products. Objective: This study aimed to determine whether live or heat-inactivated Lacticaseibacillus paracasei NB23 can enhance exercise endurance and attenuate fatigue biomarkers in a murine model. Methods: Forty male Institute of Cancer Research (ICR) mice were randomized into four groups (n = 10 each) receiving daily gavage for six weeks with vehicle, heat-killed NB23 (3 × 10¹⁰ cells/mouse/day), low-dose live NB23 (1 × 10¹⁰ CFU/mouse/day), or high-dose live NB23 (3 × 10¹⁰ CFU/mouse/day). Forelimb grip strength and weight-loaded swim-to-exhaustion tests assessed performance. Blood was collected post-exercise to measure serum lactate, ammonia, blood urea nitrogen (BUN), and creatine kinase (CK). Liver and muscle glycogen content was also quantified, and safety was confirmed by clinical-chemistry panels and histological examination. Results: NB23 treatment produced dose-dependent improvements in grip strength (p < 0.01) and swim endurance (p < 0.001). All NB23 groups exhibited significant reductions in post-exercise lactate (p < 0.0001), ammonia (p < 0.001), BUN (p < 0.001), and CK (p < 0.0001). Hepatic and muscle glycogen stores rose by 41–59% and 65–142%, respectively (p < 0.001). No changes in food or water intake, serum clinical-chemistry parameters, or tissue histology were observed. Conclusions: Our findings suggest that both live and heat-treated L. paracasei NB23 may contribute to improved endurance performance, increased energy reserves, and faster clearance of fatigue-related metabolites in our experimental model. However, these results should be interpreted cautiously given the exploratory nature and limitations of our study. Full article

The objective of this study is to create an integrated and sustainability-centered framework to identify optimal locations for wind energy projects in Indonesia. This research employs a novel two-phase multi-criteria decision-making (MCDM) framework that combines the strengths of Data Envelopment Analysis (DEA), Fuzzy Analytic Hierarchy Process (FAHP), and Fuzzy Combined Compromise Solution (F-CoCoSo). Initially, DEA is utilized to pinpoint the most promising sites based on a variety of quantitative factors. Subsequently, these sites are evaluated against qualitative criteria such as technical, economic, environmental, and socio-political considerations using FAHP for criteria weighting and F-CoCoSo for ranking the sites. Comprehensive sensitivity analysis of the criteria weights and a comparative assessment of methodologies substantiate the robustness of the proposed framework. The results converge on consistent rankings across methods, highlighting the effectiveness of the integrated approach. Notably, the results consistently identify Lampung, Aceh, and Riau as the top-ranked provinces, showcasing their strategic suitability for wind plant development. This framework provides a systematic approach for enhancing resource efficiency and strategic planning in Indonesia’s renewable energy sector. Full article

This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited innovation capability prevalent in existing curricula, we leverage the natural language processing (NLP) capabilities of Llama 3 through fine-tuning based on transfer learning to establish a specialized knowledge base encompassing fundamental circuit principles and fault diagnosis protocols. The implementation employs the Hugging Face Transformers library with optimized hyperparameters, including a learning rate of 5 × 10⁻⁵ across five training epochs. Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing a 12.4% improvement over the baseline model), a semantic comprehension precision of 92.3% in technical question-and-answer assessments, a mathematical computation accuracy of 78.4% (highlighting this as a current limitation), and a latency of 6.3 s under peak operational workloads (indicating a system bottleneck). Although direct trials involving students were deliberately avoided, the platform’s technical feasibility was validated through multidimensional benchmarking against established models (BERT-base and GPT-2), confirming superior domain adaptability (F1 = 0.87) and enhanced error tolerance (σ² = 1.2). Notable limitations emerged in numerical reasoning tasks (Cohen’s d = 1.15 compared to human experts) and in real-time responsiveness deterioration when exceeding 50 concurrent users. The study concludes that Llama 3 demonstrates considerable promise for automotive electronics skills development. Proposed future enhancements include integrating symbolic AI modules to improve computational reliability, implementing Kubernetes-based load balancing to ensure latency below 2 s at scale, and conducting longitudinal pedagogical validation studies with trainees. This research provides a robust technical foundation for AI-driven vocational education, especially suited to mechatronics fields that require close integration between theoretical knowledge and practical troubleshooting skills. Full article

Cutting beech plywood using abrasive water jet (AWJ) technology represents a significant area of research due to increasing demands for precision, quality, and environmental sustainability in manufacturing processes within the woodworking industry. AWJ technology enables non-contact cutting of materials without causing thermal deformation or mechanical damage, which is crucial for preserving the structural integrity and mechanical properties of the plywood. This article investigates cutting beech plywood using technical methods using an abrasive water jet (AWJ) at 400 MPa pressure, with Australian garnet (80 MESH) as the abrasive material. It examines how abrasive mass flow rate, traverse speed, and material thickness affect AWJ lag, which in turn influences both cutting quality and accuracy. Measurements were conducted with power abrasive mass flow rates of 250, 350, and 450 g/min and traverse speeds of 0.2, 0.4, and 0.6 m/min. Results show that increasing the abrasive mass flow rate from 250 g/min to 350 g/min slightly decreased the AWJ cut width by 0.05 mm, while further increasing to 450 g/min caused a slight increase of 0.1 mm. Changes in traverse speed significantly influenced cut width; increasing the traverse speed from 0.2 m/min to 0.4 m/min widened the AWJ by 0.21 mm, while increasing it to 0.6 m/min caused a slight increase of 0.18 mm. For practical applications, it is recommended to use an abrasive mass flow rate of around 350 g/min combined with a traverse speed between 0.2 and 0.4 m/min when cutting beech plywood with AWJ. This balance minimizes jet lag and maintains high surface quality comparable to conventional milling. For thicker plywood, reducing the traverse speed closer to 0.2 m/min and slightly increasing the abrasive flow should ensure clean cuts without compromising surface integrity. Full article

As medical technology develops and digital demands grow, personal health records (PHRs) are becoming more patient-centered than before based on cloud-based health information exchanges. While enhancing data accessibility and sharing, these systems present privacy and security issues, including data breaches and unauthorized access. We developed a post-quantum, group-oriented encryption scheme using the Crystal-Kyber Key encapsulation mechanism (KEM). Leveraging lattice-based post-quantum cryptography, this scheme ensures quantum resilience and chosen ciphertext attack security for layered cloud PHR environments. It supports four encryption modes: individual, group, subgroup-specific, and authorized subgroup decryption, meeting diverse data access needs. With efficient key management requiring only one private key per user, the developed scheme strengthens the privacy and security of PHRs in a future-proof, flexible, and scalable manner. Full article

The emergence of SARS-CoV-2 variants highlights the urgent need for novel therapeutic strategies, particularly entry inhibitors that could efficiently prevent viral infection. Medicinal herbs and herbal combination formulas have long been recognized for their effects in treating infectious diseases and their antiviral properties, thus providing abundant resources for the discovery of antiviral candidates. While many candidates have been suggested to have antiviral activity against SARS-CoV-2 infection, few have been validated for their mechanisms, including possible effects on viral entry. This study aimed to identify SARS-CoV-2 entry inhibitors from medicinal herbs and herbal formulas that are known for heat-clearing and detoxifying properties and/or antiviral activities. A SARS-CoV-2 pseudoparticle (SARS-CoV-2pp) system was used to assess mechanism-specific entry inhibition. Our results showed that the methanol extract of Anemarrhena asphodeloides rhizome, as well as the water extracts of Cimicifuga foetida rhizome, Xiao Chai Hu Tang (XCHT), and Sheng Ma Ge Gen Tang (SMGGT), have substantial inhibitory effects on the entry of SARS-CoV-2pps into host cells. Given the observation that Cimicifuga foetida exhibited the most potent inhibition and is a constituent of SMGGT, we further investigated the major compounds of the herb and identified caffeic acid as a bioactive component for blocking SARS-CoV-2pp entry. Entry inhibition of Cimicifuga foetida and caffeic acid was validated on both wild-type and the currently dominant JN.1 strain SARS-CoV-2pp systems. Moreover, caffeic acid was able to both inactivate the pseudoparticles and prevent their entry into pretreated host cells. The results support the traditional use of these herbal medicines and underscore their potential as valuable resources for identifying active compounds and developing therapeutic entry inhibitors for the management of COVID-19. Full article

Fat embolism is a critical medical emergency often resulting from long bone fractures or amputations, leading to acute respiratory distress syndrome (ARDS). The NOD-like receptor pyrin domain-containing 3 (NLRP3) inflammasome, a key regulator of innate immunity, is activated by reactive oxygen species and tissue damage, contributing to inflammatory responses. This study examines the role of NLRP3 in fat embolism-induced ARDS and evaluates the therapeutic potential of MCC950, a selective NLRP3 antagonist. Fat embolism was induced by fatty micelle injection into the tail vein of Sprague Dawley rats. Pulmonary injury was assessed through lung weight gain as an edema indicator, NLRP3 expression via Western blot, and IL-1β levels using ELISA. Histological damage and macrophage infiltration were evaluated with hematoxylin and eosin staining. Fat embolism significantly increased pulmonary NLRP3 expression, lipid peroxidation, IL-1β release, and macrophage infiltration within four hours, accompanied by severe pulmonary edema. NLRP3 was localized in type I alveolar cells, co-localizing with aquaporin 5. Administration of MCC950 significantly reduced inflammatory responses, lipid peroxidation, pulmonary edema, and histological damage, while attenuating MAPK cascade phosphorylation of ERK and Raf. These findings suggest that NLRP3 plays a critical role in fat embolism-induced acute respiratory distress syndrome, and its inhibition by MCC950 may offer a promising therapeutic approach. Full article

This study applies a deep neural network (DNN) to optimize the 22.5 m pontoon hull form of a small waterplane area twin hull (SWATH) vessel with fin stabilizers, aiming to reduce calm water resistance at a Froude number of 0.8 under even keel conditions. The vessel’s resistance is simplified into three components: pontoon, strut, and fin stabilizer. Four design parameters define the pontoon geometry: fore-body length, aft-body length, fore-body angle, and aft-body angle. Computational fluid dynamics (CFD) simulations using STAR-CCM+ 2302 provide 1400 resistance data points, including fin stabilizer lift and drag forces at varying angles of attack. These are used to train a DNN in MATLAB 2018a with five hidden layers containing six, eight, nine, eight, and seven neurons. K-fold cross-validation ensures model stability and aids in identifying optimal design parameters. The optimized hull has a 7.8 m fore-body, 6.8 m aft-body, 10$°$ fore-body angle, and 35$°$ aft-body angle. It achieves a 2.2% resistance reduction compared to the baseline. The improvement is mainly due to a reduced Munk moment, which lowers the angle of attack needed by the fin stabilizer, thereby reducing drag. The optimized design provides cost-efficient construction and enhanced payload capacity. This study demonstrates the effectiveness of combining CFD and deep learning for hull form optimization. Full article

With the rapid pace of urbanization, cities are increasingly facing severe challenges related to environmental pollution, ecological degradation, and climate change. Extreme climate events—such as heatwaves, droughts, heavy rainfall, and wildfires—have intensified public concern about sustainability, environmental protection, and low-carbon development. Ensuring environmental preservation while maintaining residents’ quality of life has become a central focus of urban governance. In this context, evaluating green indicators and predicting urban greenness is both necessary and urgent. This study incorporates international frameworks such as the EU Green City Index, the European Green Capital Award, and the United Nations Sustainable Development Goals to assess urban sustainability. The Extreme Gradient Boosting (XGBoost) algorithm is employed to predict the green level of cities and to develop multiple optimized models. Comparative analysis with traditional models demonstrates that XGBoost achieves superior performance, with an accuracy of 0.84 and an F1-score of 0.81. Case study findings identify “Greenhouse Gas Emissions per Person” and “Per Capita Emissions from Transport” as the most critical indicators. These results provide practical guidance for policymakers, suggesting that targeted regulations based on these key factors can effectively support emission reduction and urban sustainability goals. Full article

With the arrival of an aging society and the continuous extension of the human lifespan, the quality of life has not improved in a corresponding manner. People’s demand for happiness and health is increasing. As a result, a model emerged that integrates tourism and medical services, which is health tourism. This growing demand has prompted many service providers to see it as a business opportunity and enter the market. Tourism can help travelers release work stress and restore physical and mental balance; meanwhile, health check-ups and disease treatment can help them regain health. Consumers have long favored health and medical tourism because it helps relieve stress and promotes overall well-being. As people age, some consumers experience a gradual decline in physical functions, making it difficult for them to participate in regular travel services provided by traditional travel agencies. Therefore, this study aims to explore the service needs of health and medical tourism customers (tourists/patients) and the interrelationships among these service needs, so that health and medical tourism service providers can develop more customized and diversified services. This study identifies four key drivers of medical tourism services: medical services, medical facilities, tour planning, and hospitality facilities. This study uses the APA (attention and performance analysis) method to assess each dimension and criterion and utilizes the DEMATEL method with the NRM (network relationship map) to identify network relationships. By combining APA and NRM techniques, this study develops the APA-NRM technique to evaluate adoption strategies and identify suitable paths for health tourism services, providing tailored development strategies and recommendations for service providers to enhance the service experience. Full article

This study aimed to develop a functional powder using whey and milk matrices, leveraging the protective capacity of chia–alginate hydrogels and the advantages of electrohydrodynamic spraying (EHDA), a non-thermal technique suitable for encapsulating probiotic cells under stress conditions commonly encountered in food processing. A hydrogel matrix composed of chia seed mucilage and sodium alginate was used to form a biopolymeric network that protected probiotic cells during processing. The encapsulation efficiency reached 99.0 ± 0.01%, and bacterial viability remained above 9.9 log₁₀ CFU/mL after lyophilization, demonstrating the excellent protective capacity of the hydrogel matrix. Microstructural analysis using confocal laser scanning microscopy (CLSM) revealed well-retained cell morphology and homogeneous distribution within the hydrogel matrix while, in contrast, scanning electron microscopy (SEM) showed spherical, porous microcapsules with distinct surface characteristics influenced by the encapsulation method. Encapsulates were incorporated into beverages flavored with red fruits and pear and subsequently freeze-dried. The resulting powders were analyzed for moisture, protein, lipids, carbohydrates, fiber, and color determinations. The results were statistically analyzed using ANOVA and response surface methodology, highlighting the impact of ingredient ratios on nutritional composition. Raman spectroscopy identified molecular features associated with casein, lactose, pectins, anthocyanins, and other functional compounds, confirming the contribution of both matrix and encapsulants maintaining the structural characteristics of the product. The presence of antioxidant bands supported the functional potential of the powder formulations. Chia–alginate hydrogels effectively encapsulated L. reuteri, maintaining cell viability and enabling their incorporation into freeze-dried beverage powders. This approach offers a promising strategy for the development of next-generation functional food gels with enhanced probiotic stability, nutritional properties, and potential application in health-promoting dairy systems. Full article

Liquid calcium aluminate cement (LCAC) is an innovative material technology with significant potential for varied applications in civil engineering. However, despite its promising results, a significant gap remains in the direct application of LCAC as a concrete binder. The primary catalysts for LCAC are sodium hydroxide (NaOH) and potassium hydroxide (KOH). Therefore, it is crucial to investigate the effects of sodium and potassium ions on alkali–aggregate reactions in concrete structures. This study evaluated the durability of liquid calcium aluminate cement concrete catalyzed using four different concentrations of NaOH (0.5%, 1.0%, 1.5%, and 2.0%) as experimental variables, incorporating a control group of traditional concrete with a water–cement ratio of 0.64. The findings indicate that NaOH catalysis in the concrete significantly trigger alkali–aggregate reactions, leading to volume expansion. Furthermore, it increased chloride ion penetration and porosity in the concrete. These effects were more notable with the increase in NaOH concentration. The results suggested that NaOH catalysis can enhance certain chemical reactions within the concrete matrix; however, its concentration must be carefully controlled to mitigate adverse effects. The NaOH dosage should be limited to 0.5% to ensure optimal durability of the concrete. This study emphasizes the crucial importance of precisely balancing catalyst concentration to maintain the long-term durability and performance of liquid calcium aluminate cement concrete in structural applications. Full article