Search Results (79,191)

With recent advancements in digital environments, user authentication is becoming increasingly important. Traditional authentication methods such as passwords and PINs suffer from inherent limitations, including vulnerability to theft, guessing, and replay attacks. Consequently, there has been a growing body of research on more accurate and efficient user authentication methods. One such approach involves the use of biometric signals to enhance security. However, biometric methods face significant challenges in ensuring stable authentication accuracy, primarily due to variations in the user’s environment, physical activity, and health conditions. To address these issues, this paper proposes a biometric-signal-based user authentication system using graph neural networks (GNNs). The feasibility of the proposed system was evaluated using an electromyogram (EMG) dataset specifically constructed by Chosun University for user authentication research. GNNs have demonstrated exceptional performance in modeling the relationships among complex data and attracted attention in various fields. Specifically, GNNs are well-suited for modeling user behavioral patterns while considering temporal and spatial relationships, making them an ideal method for adapting to dynamic and evolving user patterns. Unlike traditional neural networks, GNNs can dynamically learn and adapt to changes or evolutions in user behavioral patterns over time. This paper describes the design and implementation of a user authentication system using GNNs with an EMG dataset and discusses how the system can adapt to dynamic and changing user patterns. Full article

Background: This study investigated the biomechanical and physiological demands of the hiking position in Laser sailing, a posture requiring sailors to extend their upper bodies outside the boat to counter wind-induced heeling. This study utilized a mixed-methods approach. Methods: Twenty-two experienced Laser sailors participated in both on-land and offshore assessments. The study combined subjective discomfort ratings, biomechanical measurements, digital human modeling, and muscle activation analysis to evaluate the effects of hiking during and after exertion. Results: A two-way ANOVA showed significant effects by body region and time. The quadriceps, abdominals, and lower back reported the highest discomfort. Key postural angles were identified, including knee and hip flexion, trunk inclination, and ankle dorsiflexion. Muscle activation analysis revealed the highest engagement in the rectus abdominis (46.1% MVC), brachialis (~45%), and psoas major (~41%), with notable bilateral asymmetries. The trunk region had the highest overall activation (28.7% MVC), followed by the upper limbs (~18.7%), while the lower limbs were minimally engaged during static hiking. Conclusions: On-water conditions resulted in greater variability in joint angles, likely reflecting wind fluctuations and wave-induced boat motion. Findings highlight the quadriceps, abdominals, and lower back as primary contributors to sustained hiking, while also emphasizing the importance of targeted endurance training and ergonomic equipment design. These insights can guide training, recovery, and ergonomic strategies to optimize performance and reduce injury risk in Laser sailors. Full article

Beef production is an important component of the world’s food supply, with production being near 59 million tons in 2023 (USDA, 2023). Enhancing our understanding of the factors influencing metabolism will lead to improvements in production efficiency. Using RNA-seq and WGCNA of longissimus muscle samples, gene expression and metabolic pathway analyses were performed to examine relationships with ultrasound and body mass variables. In this study, body weight (BW), ultrasound back fat (BF), ultrasound muscle depth (MD), and body condition score (BCS) were traits recorded for 18 cull beef cows. As expected, all production-related traits monitored (WT, BF, MD, and BCS) in this study exhibited a positive correlation with each other. Large-scale transcriptome analyses were performed using RNA extracted from longissimus dorsi muscles. Weighted correlation network analysis (WGCNA) was employed to associate changes in traits with gene expression. In WGCNA, the dark-green module demonstrated a positive correlation (cor) with all traits, with the highest observed for BF (cor = 0.45, p = 0.07) and MD (cor = 0.45, p = 0.07). Functional analysis of the dark-green module highlighted olfactory transduction (p = 0.03) and RNA processing as significantly correlated (p = 0.08) with production traits. Additionally, the hematopoietic cell lineage pathway was reported as the most significant negative correlation with muscle depth (cor = −0.71, p = 0.001). We identified four hub genes (i.e., SEPTIN9, NONO, CCDC88C, and CACNA2D3) showing relationships with the traits measured. These findings provide further understanding of the molecular mechanisms influencing muscle and fat accretion in cull beef cows. Full article

The anodic oxidation technique was used for surface modification, resulting in the creation of a titanium-based nanotube oxide layer on a coarse-grained and ultrafine-grained Ti-13Nb-13Zr alloy. The modified surface morphology was analyzed using scanning electron microscopy (SEM), atomic force microscopy (AFM), and X-ray diffraction (XRD). The electrochemical impedance spectroscopy (EIS) method was employed to evaluate the corrosion stability of the Ti-13Nb-13Zr alloy before and after anodic oxidation. Corrosion stability was determined by exposing the examined alloy to a solution that simulates environment in the human organism (Ringer’s solution). To examine the titanium-based nanotube oxide layer adhesion on the Ti-13Nb-13Zr alloy’s surface, a scratch test was performed. The hydrophilicity of the modified surface was measured using the contact angle between a drop of Ringer’s solution and the modified surface. The anodic oxidation led to the creation of a nanotube oxide layer on the surface of the Ti-13Nb-13Zr (wt.%) alloy. The impact of the ultrafine-grained structure on the homogeneity of the nanotube oxide layer obtained using anodic oxidation was observed. The ultrafine-grained structure contributed to the increased diameter of the nanotubes, while the combined effect of anodic oxidation and high-pressure torsion significantly increased the roughness of the Ti-13Nb-13Zr alloy’s surface, which is expected to enhance biomechanical compatibility by reducing cytotoxicity, providing a more adaptable modulus of elasticity for human body conditions and ensuring adequate corrosion resistance and hydrophilicity. In this study, it was established that the examined alloy had suitable corrosion resistance for utilization in medicine as a metallic implant in the human body. The scratch test showed acceptable adhesion from the titanium-based nanotube oxide layer created using anodic oxidation. Also, the determination of the surface contact angle showed that the surface after anodic oxidation was more hydrophilic than the surface before anodic oxidation. Full article

Micro-accelerometers are in high demand across many due to their compact size, low energy consumption, and excellent precision. Since gravity causes a large movement when the device is positioned vertically, measuring low gravitational acceleration is challenging. This study examines the intrinsic relationship between applied voltage levels and displacement in micro-accelerometers. The study introduces a novel design that integrates hybrid flexures, comprising both linear and angular configurations, with an out-of-plane overlap varying (OPOV) electrostatic actuation mechanism. This design aims to measure the micro-accelerometer’s movement and low frequency response. The proposed device with silicon material is designed and simulated using the IntelliSuite^® software, considering its small dimensions and 25 µm thickness. The norm value of 28.0916 μN from gravity’s reaction forces on the body, a resonant frequency of 179.668 Hz at the first desired mode, and a maximum stress of 24.7 MPa were obtained through the electro-mechanical analysis. A comparison of the proposed design was conducted with other configurations, measuring a frequency of 179.668 Hz at a minimum downward displacement of 7.69916 µm under the influence of gravity without electrostatic mechanisms. Following this, an electrostatic actuation mechanism was introduced to minimize displacement by applying different voltage levels, including 1 V, 1.5 V, and 3 V. At 3 V, a significant improvement in displacement reduction was observed compared to the other applied voltages. Additionally, dynamic and sensitivity analyses were carried out to validate the performance of the proposed design further. Full article

The transport sector significantly contributes to global greenhouse gas emissions, making electromobility crucial in the race toward the United Nations Sustainable Development Goals. In recent years, the increasing competition among manufacturers, the development of cheaper batteries, the ongoing policy support, and people’s greater environmental awareness have consistently increased electric vehicles (EVs) adoption. Nevertheless, EVs charging needs—highly influenced by EV drivers’ behavior uncertainty—challenge their integration into the power grid on a massive scale, leading to potential issues, such as overloading and grid instability. Smart charging strategies can mitigate these adverse effects by using information and communication technologies to optimize EV charging schedules in terms of power systems’ constraints, electricity prices, and users’ preferences, benefiting stakeholders by minimizing network losses, maximizing aggregators’ profit, and reducing users’ driving range anxiety. To this end, accurately forecasting EV charging demand is paramount. Traditionally used forecasting methods, such as model-driven and statistical ones, often rely on complex mathematical models, simulated data, or simplifying assumptions, failing to accurately represent current real-world EV charging profiles. Machine learning (ML) methods, which leverage real-life historical data to model complex, nonlinear, high-dimensional problems, have demonstrated superiority in this domain, becoming a hot research topic. In a scenario where EV technologies, charging infrastructure, data acquisition, and ML techniques constantly evolve, this paper conducts a systematized literature review (SLR) to understand the current landscape of ML-based EV charging demand forecasting, its emerging trends, and its future perspectives. The proposed SLR provides a well-structured synthesis of a large body of literature, categorizing approaches not only based on their ML-based approach, but also on the EV charging application. In addition, we focus on the most recent technological advances, exploring deep-learning architectures, spatial-temporal challenges, and cross-domain learning strategies. This offers an integrative perspective. On the one hand, it maps the state of the art, identifying a notable shift toward deep-learning approaches and an increasing interest in public EV charging stations. On the other hand, it uncovers underexplored methodological intersections that can be further exploited and research gaps that remain underaddressed, such as real-time data integration, long-term forecasting, and the development of adaptable models to different charging behaviors and locations. In this line, emerging trends combining recurrent and convolutional neural networks, and using relatively new ML techniques, especially transformers, and ML paradigms, such as transfer-, federated-, and meta-learning, have shown promising results for addressing spatial-temporality, time-scalability, and geographical-generalizability issues, paving the path for future research directions. Full article

To achieve miniaturization and lightweight design of a flapping-wing aircraft, a high-performance biomimetic butterfly flyer was designed based on an analysis of the butterfly’s body structure and flight principles. The aircraft has a mass of 20.6 g and a wingspan of 0.295 m. To validate the rationality of the design, sensitivity analysis of the flapping-wing drive mechanism was first conducted using MATLAB 2022B software, and the length of the driving rod was optimized. Subsequently, a dynamic model was established to calculate the aerodynamic performance of the flapping wing. Then, the aerodynamic performance of the aircraft was verified using simulation software (XFLOW 2022). Finally, the flight stability of the aircraft was validated using the SIMULINK toolbox. Flight test results show that the biomimetic butterfly flyer achieves a maximum flight speed of 0.9 m/s, a climb rate of 0.12 m/s, and a flight endurance of up to 3 min, with good flight stability. This design provides a new approach for the development of small and lightweight flapping-wing aircraft. Full article

A 2-year-old male neutered domestic shorthair cat was presented with a 12-month history of anorexia, weight loss, vomiting, polyuria, and polydipsia. Physical examination revealed a responsive but debilitated cat with icteric mucous membranes and a low body-condition score. Laboratory tests showed mild macrocytic anemia, elevated liver enzymes, hyperproteinemia with hypoalbuminemia, hyperbilirubinaemia, and a decreased vitamin B12 level. Abdominal ultrasound revealed marked dilation of the common bile duct (CBD) and gallbladder, consistent with suspected extrahepatic biliary tract obstruction, as well as secondary pancreatic and intrahepatic duct distension. Cytological and bacteriological analyses confirmed bacterial cholangiohepatitis due to a mixed infection with Escherichia coli and Peptostreptococcus canis. Despite initial conservative management, the cat’s clinical condition remained unchanged, prompting exploratory laparotomy. Intraoperative findings confirmed the ultrasonographic suspicions and revealed severe pancreatitis. Based these findings, a cholecystectomy, choledochoenterostomy, and placement of a cholecystostomy tube as well as an abdominal drain and jejunal feeding tube were performed. Postoperatively, the cat initially showed improvement but subsequently developed signs of sepsis requiring intensive care. Although temporary stabilization was achieved, the cat died suddenly ten days after surgery due to unknown causes. This case report highlights the diagnostic and therapeutic challenges associated with bacterial cholangiohepatitis secondary to biliary obstruction in cats and documents the first clinical application of choledochoenterostomy—a surgical procedure previously only described hypothetically in this species. Full article

Background: Exercise is known to help with the effects of the menopause, but women from low socioeconomic status (SES) tend to exercise less at this stage of life. Therefore, this study aimed to investigate the physical and psychological effects of a Zumba Gold^® community-based intervention in postmenopausal women from low SES. Methods: Forty-three women were divided into a Zumba Gold^® (ZG) and control (C) group, and participated in pre- and post-testing sessions, separated by a 12-week intervention period. The outcomes measured were: body composition, sit-to-stand (STS), six-minute walk test (6MWT), balance, Short Form Health Survey (SF-12), Multidimensional Fatigue Index (MFI-20), Self-Efficacy for Exercise (SEE), Behavioral Regulations in Exercise (BREQ-2), Hospital Anxiety and Depression Scale (HADS), and Social Connectedness questionnaires. Semi-structured interviews were also performed on 15 ZG participants. Results: The results showed significant improvements in the ZG group only for STS, 6MWT, mental health, fatigue (general, physical, and mental), social connectedness score, and a significantly lower level of amotivation. Zumba Gold^® had positive effects on four main themes: belonging, non-judgmental place, psychological motivational factors, and mind–body connection. Conclusions:This is the first study to show that ZG is beneficial for this population and encourages similar studies on other aspects of the menopause. Full article

The green landscapes of oasis cities play an important role in maintaining ecological security. However, these ecosystems face increasing threats from desertification and fragmentation, driven by intensifying climate change and rapid urbanization. Understanding the characteristics and driving mechanisms behind changes in green landscape patterns is crucial for advancing sustainable urban green space management. This study explores the spatio-temporal changes in the green landscape pattern in Urumqi during 1990–2020 using a random forest classifier. This study also applies geographical detectors and geographically weighted regression to comprehensively determine the driving mechanism and spatio-temporal nonstationarity. The results are as follows: (1) The landscape types are primarily dominated by unused land, urban green spaces, and construction land, accounting for more than 80%. The areas of urban green spaces, water bodies, cropland, and unused land decreased by 0.38%, 37.41%, 0.57%, and 4.58%, respectively, from 1990 to 2020. With rapid urbanization, construction land exhibited a significant expansion trend, and the degree of fragmentation of urban green spaces increased spatially over these 30 years. (2) From 1990 to 2020, each landscape index exhibited fluctuating characteristics. Overall, the Shannon’s diversity and evenness indices of the urban green landscapes exhibited an increasing trend. The contagion and connectivity indices exhibited a decreasing trend, decreasing from 50.894 and 99.311 in 1990 to 46.584 and 99.048 in 2020, respectively. (3) During these 30 years, the dynamics of urban greenery were affected by a combination of natural and social factors, with elevation determining the overall urban green distribution pattern. Precipitation and temperature dominate the urban green space changes in the north and south of Urumqi. Socioeconomic factors such as GDP, population, river distance, and town distance regulate the urban green space changes in the central built-up area. Full article

Background: Previous studies have shown a relationship between Visceral Adipose Tissue (VAT) and Hepatic Fat Content (HFC), and increases in HFC are linked to metabolic abnormalities similar to those associated with elevated VAT. Several short-term and long-term studies have supported these findings. Lifestyle interventions remain the cornerstone of treatment for Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), although the ideal dietary regimen is still under debate. Methods: Data on 2040 patients were extracted from the Clinical Nutrition Unit database between 2017 and 2019. Of these, 474 subjects with MASLD and Body Mass Index (BMI) ≥ 35 kg/m² were treated with a four-month low-carbohydrate dietary intervention called the “Strong Diet” (StD). VAT and liver stiffness were measured at baseline and after four months of treatment using ultrasound. Results: Our study demonstrates the significant efficacy of StD in reducing VAT in MASLD patients with moderate hepatic steatosis. In subjects with severe steatosis, there is no statistically significant response to dietary intervention. This may be attributed to several irreversible molecular mechanisms that fundamentally alter the hepatic microenvironment and limit the liver’s capacity for regeneration and metabolic recovery. Conclusions: Improvements were largely confined to patients with moderate MASLD, with limited benefit in severe disease. Although dietary intervention remains the cornerstone of MASLD management, patients with severe steatosis should be informed about the potential limited resolution of steatosis, even with optimal metabolic control. Full article

Background: Despite more than 25 years of intensive effort following the landmark “To Err Is Human” report, conventional top-down medical error prevention strategies, grounded in the Safety-I paradigm, have largely failed to reduce patient harm. This persistent shortcoming underscores the need for a new prevention model. The medical literature contains an extensive yet systematically underutilized body of physician-generated experiential knowledge on “clinical pitfalls”—specific high-risk scenarios in which errors are likely to occur. This resource presents an opportunity for a novel, physician-driven approach to medical error prevention. The present paper proposes and evaluates such a model, grounded in the principles of Safety-II and translational medicine. Methods: The methodology involved a three-part conceptual analysis: (1) a critical review of the literature assessing the effectiveness of established error prevention strategies, (2) a quantitative bibliometric analysis of the PubMed database to determine the volume and temporal trends of publications on “clinical pitfalls”, and (3) a conceptual synthesis to design a novel physician-driven error prevention model. Each method is described in detail at the beginning of its respective section. Results: The literature review confirms the limited effectiveness of existing top-down safety initiatives, particularly in complex domains such as diagnosis and treatment. The bibliometric analysis identified more than 43,000 publications containing the keyword “pitfall,” with a sustained and significant upward trend in annual publications over the past three decades. The conceptual synthesis demonstrates that a physician-driven system—centered on a “Pitfall Bank”—addresses core weaknesses of current strategies, including unreliable data, heterogeneous knowledge, and cognitive biases. Structured as a circular translational mechanism, the proposed system facilitates a continuous cycle of practice-based problem identification and science-informed solution implementation. Conclusions: A physician-driven prevention system, architected as a translational engine, offers a promising and sustainable strategy to overcome the current impasse in medical error reduction and create a more resilient and adaptive healthcare system. Full article

Inflammation-induced preterm birth (PTB) significantly impacts neonatal development, particularly due to fetal lung immaturity. The lungs undergo critical development both in utero and postnatally, and PTB disrupts this process, leading to impaired pulmonary function. Current treatments for promoting lung maturation in preterm infants have limited efficacy and safety. Melatonin, known for its potent antioxidant and anti-inflammatory properties, has shown promise in preventing PTB, but its effects on fetal and postnatal lung maturation remain unclear. This study evaluated the therapeutic efficacy of melatonin in a mouse model of intrauterine inflammation-induced PTB (IPTB). Pregnant mice (Pregnancy Day 17, [PD17]) were assigned to control, lipopolysaccharide (LPS), and LPS + melatonin groups. LPS (25 µg) was injected into the right uterine horn, with melatonin (10 mg/kg) administered intraperitoneally 30 min prior. Uterine tissues were collected at 6 and 24 h post-LPS administration for molecular and histological analyses. PTB occurred in seven out of eleven (63.6%) IPTB mice within 24 h of LPS injection, whereas melatonin significantly reduced this rate to 25% (2/8). In melatonin-treated mice, the downregulation of pro-inflammatory genes in uterine tissues, restoration of placental blood flow, increased lamellar body counts, and prevention of LPS-induced vacuolation in PD18 fetal lungs were observed. Furthermore, melatonin administration enhanced surfactant protein B expression and improved lung structure. In the offspring of IPTB mice that survived, melatonin further suppressed pro-inflammatory markers and promoted lung septal thickening at postnatal day 3. In conclusion, melatonin prevents PTB, mitigates inflammation, and supports fetal lung maturation in IPTB mice, highlighting its therapeutic potential for improving neonatal pulmonary outcomes. Full article

Fungi are a prolific source of diverse bioactive metabolites, yet many remain unexplored. Among these, depsidones are a rare class of compounds with significant biological potential, but they are seldom reported in mushrooms. This study investigated the medicinal fungus Ganoderma lucidum, known for its extensive therapeutic use in traditional medicine. Fruiting bodies were extracted using petroleum ether, ethyl acetate, n-butanol, and methanol. Extracts were screened phytochemically and assessed for total phenolic content and antioxidant activity using the DPPH assay. Ethyl acetate extract exhibited the highest phenolic yield and antioxidant potential and was subsequently evaluated for cytotoxicity against HepG2, HCT116, MCF7, and A549 cancer cell lines. It showed notable anticancer activity with minimal toxicity to normal Vero cells. UHPLC/Q-TOF-MS/MS analysis of G. lucidum ethyl acetate extract tentatively identified nine minor depsidones including mollicellin G, simplicildone I, mollicellin B, talaromyone B, simplicildone A, purpactin C, emeguisin B, mollicellin E, and simplicildone D on the basis of high-resolution negative-mode detection and characteristic MS/MS fragmentation patterns. Molecular docking revealed strong binding affinities between these compounds and cancer-related targets (AKT1, CDK2, ERK1, TNFα), with simplicildone D and mollicellin G demonstrating particularly high interactions. These findings provide mechanistic insights into the observed bioactivity and highlight G. lucidum as a promising source of therapeutic depsidones for future anticancer drug development. Full article

To evaluate the analytical performance and clinical utility of the automated fluorescence-based POC immunoassay system (AFIAS), compared with established enzyme-linked immunosorbent assay (ELISA) methods for measuring adalimumab and anti-adalimumab antibodies (AAAs) in patients with rheumatoid arthritis and ankylosing spondylitis. 96 patients receiving adalimumab for rheumatoid arthritis (RA) or ankylosing spondylitis (AS) were consecutively recruited. Measurements of adalimumab trough levels and AAAs were taken before the patients’ scheduled adalimumab injection. Three ELISA techniques (RIDASCREEN^®, IDKmonitor^®, and LISA TRACKER) were compared with the AFIAS method. Statistical analyses included Bland–Altman, Passing–Bablok regression, kappa values, and intraclass correlation coefficients. Clinical and demographic characteristics were examined to determine the association between adalimumab concentration and AAA detection. The diagnoses included 58 RA diagnoses and 38 AS diagnoses. The median concentrations were 9.33, 7.4, 7.4, and 9.38 µg/mL for RIDASCREEN, IDKmonitor, LISA TRACKER, and AFIAS, respectively. Strong correlations were observed between the techniques. Bland–Altman analysis revealed bias differences of 0.85, 2.03, and 2.76 µg/mL, and the Passing–Bablok regression slopes were 1.046, 1.391, and 1.274 for RIDASCREEN, IDKmonitor, and LISA TRACKER, respectively, compared with AFIAS. Agreement in AAA detection showed kappa values of 0.81 and 0.75 for AFIAS versus IDKmonitor and LISA TRACKER, respectively. A high body mass index, extended injection interval, and RA diagnosis were associated with low adalimumab concentrations in the multivariate analysis. Antinuclear antibody positivity, a higher rheumatoid factor, and disease activity were associated with AAA positivity in univariate analysis. The AFIAS POC measurement method demonstrated time-efficient and highly agreeable results for adalimumab and AAA measurements compared with the results of commercial ELISA methods. Full article