Search Results (523)

The molecular structure and mechanical resilience of the binder are crucial for mitigating volume expansion, maintaining electrode structural integrity, and enhancing the cycling stability of silicon-based anode materials in lithium-ion batteries. In this study, from the perspective of binder molecular structural design, commercial carboxymethyl cellulose (CMC) was modified with silk protein (SF), which has good mechanical properties and abundant surface functional groups, to address issues such as high brittleness, poor compliance and easy cracking of the electrode structure during charge and discharge cycles, and to enhance the mechanical properties of the CMC-based binder and its interaction with silicon particles, so as to improve the cycle stability of silicon-based materials. The mechanical properties of the CMC binder were significantly improved and the interaction between the binder and the surface of the silicon particles was enhanced by the addition of SF. When the SF content was optimized at 6 wt%, the electrode exhibited the best electrochemical performance, delivering a specific capacity of 1182 mAh/g at a high current density of 5000 mA/g, and retaining a capacity of 1138 mAh/g after 50 cycles at 1000 mA/g, demonstrating excellent electrochemical durability. Full article

Land Surface Temperature (LST) is a critical parameter for analyzing urban heat islands, surface–atmosphere interactions, and environmental management. This study enhances the temporal resolution of LST data by leveraging machine learning and deep learning models. A novel methodology was developed using Landsat 8 thermal data and Sentinel-2 multispectral imagery to predict LST at finer temporal intervals in an urban setting. Although Sentinel-2 lacks a thermal band, its high-resolution multispectral data, when integrated with Landsat 8 thermal observations, provide valuable complementary information for LST estimation. Several models were employed for LST prediction, including Random Forest Regression (RFR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Gated Recurrent Unit (GRU). Model performance was assessed using the coefficient of determination (R²) and Mean Absolute Error (MAE). The CNN model demonstrated the highest predictive capability, achieving an R² of 74.81% and an MAE of 1.588 °C. Feature importance analysis highlighted the role of spectral bands, spectral indices, topographic parameters, and land cover data in capturing the dynamic complexity of LST variations and directional patterns. A refined CNN model, trained with the features exhibiting the highest correlation with the reference LST, achieved an improved R² of 84.48% and an MAE of 1.19 °C. These results underscore the importance of a comprehensive analysis of the factors influencing LST, as well as the need to consider the specific characteristics of the study area. Additionally, a modified TsHARP approach was applied to enhance spatial resolution, though its accuracy remained lower than that of the CNN model. The study was conducted in Tehran, a rapidly urbanizing metropolis facing rising temperatures, heavy traffic congestion, rapid horizontal expansion, and low energy efficiency. The findings contribute to urban environmental management by providing high-temporal-resolution LST data, essential for mitigating urban heat islands and improving climate resilience. Full article

In higher education, institutional quality is traditionally assessed through metrics such as academic programs, research output, educational resources, and community services. However, it is important that their activities align with student expectations, particularly in relation to interactive learning environments, learning management system interaction, curricular and co-curricular activities, accessibility, support services and other learning resources that ensure academic success and, jointly, career readiness. The growing popularity of student engagement metrics as one of the key measures to evaluate institutional efficacy is now a feature across higher education. By monitoring student engagement, institutions assess the impact of existing resources and make necessary improvements or interventions to ensure student success. This study presents a comprehensive analysis of student feedback from the StudentSurvey.ie dataset (2016–2022), which consists of approximately 275,000 student responses, focusing on student self-perception of engagement in the learning process. By using classical topic modelling techniques such as Latent Dirichlet Allocation (LDA) and Bi-term Topic Modelling (BTM), along with the advanced transformer-based BERTopic model, we identify key themes in student responses that can impact institutional strength performance metrics. BTM proved more effective than LDA for short text analysis, whereas BERTopic offered greater semantic coherence and uncovered hidden themes using deep learning embeddings. Moreover, a custom Named Entity Recognition (NER) model successfully extracted entities such as university personnel, digital tools, and educational resources, with improved performance as the training data size increased. To enable students to offer actionable feedback, suggesting areas of improvement, an n-gram and bigram network analysis was used to focus on common modifiers such as “more” and “better” and trends across student groups. This study introduces a fully automated, scalable pipeline that integrates topic modelling, NER, and n-gram analysis to interpret student feedback, offering reportable insights and supporting structured enhancements to the student learning experience. Full article

Current ringing in dual-active bridge (DAB) converters significantly degrades efficiency and reliability, particularly due to resonant interactions in the magnetic tank impedance network. We propose a novel impedance resonant channel shaping technique to suppress the ringing by systematically modifying the converter’s equivalent impedance model. The method begins with establishing a high-fidelity network representation of the magnetic tank, incorporating transformer parasitics, external inductors, and distributed capacitances, where secondary-side components are referred to the primary via the turns ratio squared. Critical damping is achieved through a rank-one modification of the coupling denominator, which is analytically normalized to a second-order form with explicit expressions for resonant frequency and damping ratio. The optimal series–RC damping network parameters are derived as functions of leakage inductance and winding capacitance, enabling precise control over the effective damping factor while accounting for core loss effects. Furthermore, the integrated network with the damping network dynamically shapes the impedance response, thereby attenuating ringing currents without compromising converter dynamics. Experimental validation confirms that the proposed approach reduces peak ringing amplitude by over 60% compared to the conventional snubber-based methods, while maintaining full soft-switching capability. Full article

Background/Objectives: Diabetes Mellitus (DM) remains a critical public health issue in Saudi Arabia, shaped by complex interactions among genetic, lifestyle, and sociodemographic factors. This study explores interplay of modifiable and non-modifiable determinants of DM among Saudi adults. Methods: An analytical cross-sectional study was conducted among 3411 adults aged 18 years and above in the Jazan region, southwest of Saudi Arabia, in May–June 2024. Data was collected via a structured, pretested questionnaire assessing sociodemographic, dietary patterns, physical activity, smoking habits, and family history of DM. Bivariate analysis and logistic regression were used to identify associations with self-reported diabetes. Results: Out of 3411 participants (1735 males and 1676 females), 424 (12.4%) reported DM. Diabetics were older (48 vs. 32 years), more often male, married, had lower education, had larger families, had higher BMIs, and exhibited more tobacco use (p < 0.05), and a family history of diabetes was strongly associated with diagnosis of DM (p < 0.001). Diabetics were more likely to choose low-fat meats, avoid sugary foods, and select low-fat products (p < 0.05). In multivariate analysis, predictors were age (OR = 1.07, 95% CI: 1.06–1.09), male sex (OR = 1.65, 95% CI: 1.26–2.16), family history (OR = 7.68, 95% CI: 5.67–10.57), traditional housing (OR = 1.82, 95% CI: 1.11–3.05), and whole grain intake (OR = 0.67, 95% CI: 0.52–0.85). Conclusions: DM in Saudi Arabia is driven by both inherited and behavioral risks. These findings support the urgent need for integrated, culturally tailored prevention strategies that combine early screening for individuals with higher risk. Targeted actions such as relevant lifestyle interventions can help reduce disease burden and align with Saudi Vision 2030 health priorities. Full article

The Niland Moving Mud Spring, located near the southeastern margin of the Salton Sea, represents a rare and evolving geotechnical hazard. Unlike the typically stationary mud pots of the Salton Trough, this spring is a CO₂-driven mud spring that has migrated southwestward since 2016, at times exceeding 3 m per month, posing threats to critical infrastructure including rail lines, highways, and pipelines. Emergency mitigation efforts initiated in 2018, including decompression wells, containment berms, and route realignments, have since slowed and recently almost halted its movement and growth. This study integrates hydrochemical, temperature, stable isotope, and tritium data to propose a refined conceptual model of the Moving Mud Spring’s origin and migration. Temperature data from the Moving Mud Spring (26.5 °C to 28.3 °C) and elevated but non-geothermal total dissolved solids (~18,000 mg/L) suggest a shallow, thermally buffered groundwater source influenced by interaction with saline lacustrine sediments. Stable water isotope data follow an evaporative trajectory consistent with imported Colorado River water, while tritium concentrations (~5 TU) confirm a modern recharge source. These findings rule out deep geothermal or residual floodwater origins from the great “1906 flood”, and instead implicate more recent irrigation seepage or canal leakage as the primary water source. A key external forcing may be the 4.1 m drop in Salton Sea water level between 2003 and 2025, which has modified regional groundwater hydraulic head gradients. This recession likely enhanced lateral groundwater flow from the Moving Mud Spring area, potentially facilitating the migration of upwelling geothermal gases and contributing to spring movement. No faults or structural features reportedly align with the spring’s trajectory, and most major fault systems trend perpendicular to its movement. The hydrologically driven model proposed in this paper, linked to Salton Sea water level decline and correlated with the direction, rate, and timing of the spring’s migration, offers a new empirical explanation for the observed movement of the Niland Moving Mud Spring. Full article

The LiiNK Project^® is a well-researched recess intervention that integrates four 15 min recess breaks and a 15 min character development lesson daily. Previous literature has demonstrated this intervention is effective at 60 min daily to improve muscular strength (MusS) and neuromuscular control (NC) in elementary-aged children; however, it remains unclear whether similar benefits can be achieved when the intervention is modified to 30 min daily when the children reach fourth and fifth grade. Additionally, the LiiNK intervention has not examined psychological variables with physical assessments. Thus, the purpose of this study was to examine MusS, NC, and resilience at two time points in children who engaged in a modified LiiNK recess intervention. Fourth- and fifth-grade children (N = 164) from one school district participated in MusS, NC, and resilience assessments at two time points (September 2024 and January 2025). A two-way MANOVA was used to determine the assessment change score differences by grade and sex. No statistically significant main effects or interactions by grade, F (3, 160) = 1.95, p = 0.077, or sex, F (3, 160) = 1.13, p = 0.347, were found. These findings suggest 30 min of recess daily may be insufficient to produce developmental benefits observed in previous 60 min daily recess interventions. Full article

This study introduces a novel numerical approach to analyze the axisymmetric bending behavior of functionally graded (FG) graphene platelet (GPL)-reinforced annular sandwich nanoplates featuring a porous core. The nanostructures are exposed to coupled magnetic and hygrothermal environments. The porosity distribution and GPL weight fraction are modeled as nonlinear functions through the thickness, capturing realistic gradation effects. The governing equations are derived using the virtual displacement principle, taking into account the Lorentz force and the interaction with an elastic foundation. To address the size-dependent behavior and thickness-stretching effects, the model employs the nonlocal strain gradient theory (NSGT) integrated with a modified version of Shimpi’s quasi-3D higher-order shear deformation theory (Q3HSDT). The differential quadrature method (DQM) is applied to obtain numerical solutions for the displacement and stress fields. A detailed parametric study is conducted to investigate the influence of various physical and geometric parameters, including the nonlocal parameter, strain gradient length scale, magnetic field strength, thermal effects, foundation stiffness, core thickness, and radius-to-thickness ratio. The findings support the development of smart, lightweight, and thermally adaptive nano-electromechanical systems (NEMS) and provide valuable insights into the mechanical performance of FG-GPL sandwich nanoplates. These findings have potential applications in transducers, nanosensors, and stealth technologies designed for ultrasound and radar detection. Full article

This study aims to investigate the spatiotemporal evolution of cavitating flow and the associated acoustic responses around a NACA0015 hydrofoil. A coupled fluid–acoustic interaction model is developed by integrating a nonlinear cavitation model with vortex–sound coupling theory. Numerical simulations are conducted within a computational domain established for the hydrofoil to capture the interactions between cavitation dynamics and acoustic radiation. The results indicate that the temporal variations in cavity evolution and pressure fluctuations agree well with experimental observations. The simulations predict a dominant pressure fluctuation frequency of 30.15 Hz, consistent with the cavitation shedding frequency, revealing that the evolution of leading-edge vortex structures governs the periodic variations in the lift-to-drag ratio. Cavitation significantly modifies the development of vortex structures, with vortex stretching effects mainly concentrated near cavitation regions. The dilation–contraction term is closely associated with cavity formation, while the pressure–torque tilting term predominantly affects cloud cavitation collapse. Dynamic mode decomposition (DMD) shows that the coherent structures of the leading modes exhibit morphological similarity to multiscale cavitation and vortex structures. Furthermore, hydrofoil cavitation noise consists mainly of loading noise and cavitation-induced pulsating radiation noise, with surface acoustic sources concentrated in cloud cavitation shedding regions. The dominant frequency of cavitation-induced radiation noise is highly consistent with experimental measurements. Full article

Existing force-measuring devices lack versatility in studying the dynamic coupling process between the seedling-picking device and the plug seedling pot during automatic transplanting. This research developed a universal force-measuring device featuring a centrally symmetrical clamping needle layout and a simultaneous insertion and clamping mechanism. The force-measuring device enables the flexible adjustment of the number of clamping needles (2/3/4 needles) via a modular structure. It can also modify the insertion depth and angle of the clamping needles to accommodate three specifications of plug seedlings, namely 50-hole, 72-hole, and 128-hole plug seedlings. A real-time monitoring system with dual pull-pressure sensors is integrated to precisely acquire the dynamic response curves of the clamping force (F_J) and the disengaging force (F_N) of the plug seedling pot during the seedling-picking process. Taking water spinach plug seedlings as the research object and combining with EDEM-RecurDyn coupling simulation, the interaction mechanism between the clamping needle and the plug seedling pot was elucidated. The performance of the force-measuring device was verified through systematic force-measuring experiments. The main research findings are as follows: The force-measuring device designed in this study can successfully obtain the mechanical characteristic curve of the relevant seedling plug pot throughout the automatic seedling-picking process. The simulation results show high consistency with the experimental results, indicating that the force-measuring device can effectively reveal the dynamic coupling process between the seedling-picking device and the plug seedling pot. The verification experiment demonstrates that the force-measuring device can effectively quantify the mechanical properties of the of plug seedling pots under different plug seedlings specifications and different clamping needles configurations. Reducing the hole size and increasing the number of clamping needles can effectively decrease the peak value of the disengaging force (F_Nmax). The peak clamping force (F_Jmax) is approximately inversely proportional to the needle number, with the four-needle layout providing the most uniform force distribution. The force-measuring device developed in this study is functional, applicable, and versatile, offering a general force-measuring tool and a theoretical foundation for optimal seedling-picking device design. Full article

Comprehensive evaluation of suburban landscape perception is essential for improving environmental quality and fostering integrated urban–rural development. Despite its importance, limited research has systematically extracted local visual features and analyzed influencing factors in suburban landscapes using multi-source data and machine learning. This study investigated Chongming District, a suburban area of Shanghai. Using Baidu Street View 360° panoramic images, local visual features were extracted through semantic segmentation of street view imagery, spatial multi-clustering, and random forest classification. A geographic detector model was employed to explore the relationships between landscape characteristics and their driving factors. The findings of the study indicate (1) significant spatial variations in the green visibility, sky openness, building density, road width, facility diversity, and enclosure integrity; (2) an intertwined spatial pattern of blue, green, and gray spaces; (3) the emergence of natural environment dimension factors as the primary drivers influencing the spatial configuration. In the suburban industrial dimension, the interaction between the GDP and commercial vitality exhibits the highest level of synergy. Based on these findings, targeted strategies are proposed to enhance the distinctive landscape features of Chongming Island. This research framework and methodology are specifically applied to Chongming District as a case study. Future studies should consider modifying the algorithms and index systems to better reflect other study areas, thereby ensuring the validity and precision of the results. Full article

Background: Rheumatoid arthritis is the most prevalent systemic inflammatory disease, mainly affecting the synovial tissue of small and large joints, also associated with significant extra-articular manifestations. Throughout the progression of the disease, cardiac structures may be affected, including the conducting system, myocardium, endocardium, coronary arteries, and valves, potentially resulting in a higher incidence of cardiac arrhythmias. Methods: We performed a narrative review of the most recent studies that highlight the epidemiology, pathophysiology, diagnosis, and management of arrhythmias occurring in patients with rheumatoid arthritis. Furthermore, we examined the impact of disease-modifying antirheumatic drugs (DMARDs)—including conventional synthetic (csDMARDs), biologic (bDMARDs), and targeted synthetic agents (tsDMARDs)—on cardiac electrophysiology. Results: Cardiac immune cells may influence arrhythmogenesis through non-canonical and inflammatory mechanisms by modifying myocardial tissue architecture or by interacting with cardiomyocytes, potentially altering their electrical function. Conclusions: This review emphasizes the essential role of a multidisciplinary approach integrating rheumatology and cardiology expertise in the screening and management of arrhythmias in patients with rheumatoid arthritis. Full article

Neurodegenerative diseases such as Alzheimer’s disease (AD), frontotemporal lobar degeneration (FTLD), and α-synucleinopathies—including Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA)—are characterized by the accumulation of misfolded protein aggregates. Advances in positron emission tomography (PET) imaging have enabled in vivo visualization of these pathologies, particularly tau and α-synuclein fibrils, facilitating early diagnosis and differential classification. Tau PET tracers such as ¹⁸F-florzolotau have demonstrated robust imaging of both AD-type and 4-repeat tauopathies, including atypical parkinsonian syndromes in FTLD such as progressive supranuclear palsy and corticobasal degeneration. Cryo-electron microscopy has elucidated the molecular interactions underlying tracer binding, highlighting hydrophobic grooves in cross-βstructures as binding components commonly present in multiple tau fibril types. For α-synucleinopathies, new tracers with a modified cross-β-binding scaffold, including ¹⁸F-SPAL-T-06 and ¹⁸F-C05-05, have shown promise in detecting MSA-related pathology and, more recently, midbrain pathology in PD and DLB. However, sensitive detection of pathologies in early PD/DLB stages remains a challenge. The integration of high-resolution PET technologies and structurally optimized ligands may enable earlier and more accurate detection of protein aggregates, supporting both clinical decision-making and the development of targeted disease-modifying therapies. Full article

Microbial growth and inhibition are complex biological processes influenced by diverse environmental and chemical factors, posing challenges for accurate modelling and prediction. Traditional mechanistic models often struggle to capture the nonlinear and multidimensional interactions inherent in real-world food systems, especially when multiple environmental variables and inhibitors are involved. This study presents the development of a novel, dynamic software platform that integrates classical predictive microbiology models—including both one-step and two-step frameworks—with advanced machine learning (ML) methods such as Support Vector Regression, Random Forest Regression, and Gaussian Process Regression. Uniquely, this platform enables direct comparisons between two-step and one-step modelling approaches across four widely used growth models (modified Gompertz, Logistic, Baranyi, and Huang) and three inhibition models (Log-Linear, Log-Linear + Tail, and Weibull), offering unprecedented flexibility for model evaluation and selection. Furthermore, the platform incorporates ML-based modelling for both microbial growth and inhibition, expanding predictive capabilities beyond traditional parametric frameworks. Validation against experimental and literature datasets demonstrated the platform’s high predictive accuracy and robustness, with machine learning models, particularly Gaussian Process Regression and Random Forest Regression, outperforming classical models. This versatile platform provides a powerful, data-driven decision-support tool for researchers, industry professionals, and regulatory bodies in areas such as food safety management, shelf-life estimation, antimicrobial testing, and environmental monitoring. Future work will focus on further optimization, integration with large public microbial databases, and expanding applications in emerging fields of predictive microbiology. Full article

Augmented reality features, such as overlaying information in real time, modifying the projected scene, or dynamically adjusting parameters like contrast, zoom, and brightness, show promise in addressing the specific challenges faced by people with low vision. These tailored solutions enhance their visual experiences. When combined with mobile technology, these features significantly improve the personalization of visual aids and the monitoring of patients with low vision. Retiplus emerges as a personalized visual aid and rehabilitation system, utilizing smart glasses and augmented reality technology for visual aid functions, along with a mobile app for visual assessment, aid customization, and usage monitoring. This wearable system quickly assesses visual conditions, providing deep insights into the visual perception of patients with low vision. Designed to enhance autonomy and quality of life, Retiplus seamlessly integrates into indoor and outdoor environments, enabling the programming of rehabilitation exercises for both static and ambulatory activities at home. In collaboration with specialists, the system meticulously records patient interaction data for subsequent evaluation and feedback. A clinical study involving 30 patients with low vision assessed the effect of Retiplus, analyzing its impact on visual acuity, contrast sensitivity, visual field, and ambulation. The most notable finding was an average increase of 61% in visual field without compromising ambulation safety. Retiplus introduces a new user-centered approach that emphasizes collaboration among a multidisciplinary team for the customization of visual aids, thereby minimizing the gap between the perceptions of low vision specialists and technologists regarding user needs and the actual requirements of users. Full article