Search Results (5,909)

Stroke is one of the leading causes of disability worldwide. Its sequelae require early, intensive, and repetitive rehabilitation, but is often ineffective due to a lack of patient motivation. Gamification has been incorporated in recent years as a response to this issue. The aim of incorporating games is to motivate patients to perform therapeutic exercises. This study presents PACTUS, a new version of a gamified device for stroke neurorehabilitation. Using a series of colored cards, a touchscreen station, and a sensorized handle with an RGB sensor, patients can interact with three games specifically programmed to work on different areas of neurorehabilitation. In addition to presenting the technical design (including energy consumption and sensor signal processing), the results of an observational study conducted with neurological patients, healthy older adults, and caregivers (who also completed the System Usability Scale) are also presented. This usability, safety, and satisfaction study provided an assessment of the device for future iterations. The inclusion of the experiences of the three groups (patients, caregivers, and older adults) provided a more comprehensive and integrated view of the device, enriching our understanding of its strengths and limitations. Although the results were preliminarily positive, areas for improvement were identified. Full article

This study valorizes onion peel, an agro-industrial by-product rich in phenolic compounds and structural carbohydrates, for the development of cassava starch-based biodegradable films. The films were prepared using the solution casting method; a cassava starch matrix was mixed with a 2.5% glycerol solution and heated to 85 °C for 30 min. A separate solution of onion peel powder (OPP) in distilled water was prepared at 25 °C. The two solutions were then combined and stirred for an additional 2 min before 25 mL of the final mixture was cast to form the films. Onion peel powder (OPP) incorporation produced darker and more opaque films, suitable for packaging light-sensitive foods. Film thickness increased with OPP content (0.138–0.218 mm), while moisture content (19.2–32.6%) and solubility (24.0–25.2%) decreased. Conversely, water vapor permeability (WVP) significantly increased (1.69 × 10⁻⁹–2.77 × 10⁻⁹ g·m⁻¹·s⁻¹·Pa⁻¹; p < 0.0001), reflecting the hydrophilic nature of OPP. Thermal analysis (TGA/DSC) indicated stability up to 245 °C, supporting applications as food coatings. Morphological analysis (SEM) revealed OPP microparticles embedded in the starch matrix, with FTIR and XRD suggesting electrostatic and hydrogen–bond interactions. Mechanically, tensile strength improved (up to 2.71 MPa) while elongation decreased (14.1%), indicating stronger but less flexible films. Biodegradability assays showed slightly reduced degradation (29.0–31.8%) compared with the control (38.4%), likely due to antimicrobial phenolics inhibiting soil microbiota. Overall, OPP and cassava starch represent low-cost, abundant raw materials for the formulation of functional biopolymer films with potential in sustainable food packaging. Full article

High-pressure, high-volume fracturing in unconventional reservoirs often induces perforation erosion damage, endangering operational safety. This paper employs fluid–solid coupling theory to analyze the flow characteristics of fracturing fluid inside the casing during fracturing. Combined with strength theory, the stress distribution and variation law are investigated, revealing the mechanical mechanism of casing perforation erosion damage. The results indicate that the structural discontinuity at the entrance of the perforation tunnel causes an increase in fracturing-fluid velocity, and this is where the most severe erosion happens. The stress around the perforation is symmetrically distributed along the perforation axis. The casing inner wall experiences a combined tensile–compressive stress state, while non-perforated regions are under pure tensile stress, with the maximum amplitudes occurring in the 90° and 270° directions. Although the tensile and compressive stress do not exceed the material’s allowable stress, the shear stress exceeds the allowable shear stress, indicating that shear stress failure is likely to initiate at the perforation, inducing erosion. Moreover, under the impact of fracturing fluid, the contact forces at the first and second interfaces of the casing are unevenly distributed, reducing cement bonding capability and compromising casing integrity. The findings provide a theoretical basis for optimizing casing selection. Full article

Background: Bone health in youth is influenced by both modifiable factors, such as nutrition and physical activity, and non-modifiable factors, such as biological maturation and heredity. Understanding how these elements interact to predict body composition may enhance the effectiveness of early interventions. Importantly, the integration of both supervised and unsupervised machine learning models enables a data-driven exploration of complex relationships, allowing for accurate prediction and subgroup discovery. Methods: This cross-sectional study examined 94 male athletes during the developmental period. Anthropometric, performance, and nutritional data were collected, and bone parameters were assessed using dual-energy X-ray absorptiometry (DXA). Three supervised machine learning models (Random Forest, Gradient Boosting, and Support Vector Regression) were trained to predict Total Body-Less Head (TBLH) values. Nested cross-validation assessed model performance. Unsupervised clustering (K-Means) was also applied to identify dietary intake profiles (calcium, protein, vitamin D). SHAP analysis was used for model interpretability. Results: The Random Forest model yielded the best predictive performance (R² = 0.71, RMSE = 0.057). Weight, height, and handgrip strength were the most influential predictors. Clustering analysis revealed two distinct nutritional profiles; however, t-tests showed no significant differences in TBLH or regional BMD between the clusters. Conclusions: Machine learning, both supervised for accurate prediction and unsupervised for nutritional subgroup discovery, provides a robust, interpretable framework for assessing adolescent bone health. While dietary intake clusters did not align with significant differences in bone parameters, this finding underscores the multifactorial nature of skeletal development and highlights areas for further exploration. Full article

Ionic rare earths are extracted from primary sources by the in situ chemical leaching method, where the type and concentration of leaching agents significantly affect the mechanical properties and microstructure of the ore body. In this study, MgSO₄ and Al₂(SO₄)₃ solutions of varying concentrations were used as leaching agents to investigate the evolution of shear strength, the characteristics of Duncan–Chang hyperbolic model parameters, and the changes in microstructural pore characteristics of rare earth samples under different leaching conditions. The results show that the stress–strain curves of all samples consistently exhibit strain-hardening behavior under all leaching conditions, and shear strength is jointly influenced by confining pressure and the chemical interaction between the leaching solution and the soil. The samples leached with MgSO₄ exhibited higher shear strength than those treated with water. The samples leached with 3% and 6% Al₂(SO₄)₃ showed increased strength, while 9% Al₂(SO₄)₃ caused a slight decrease. With increasing leaching agent concentration, the cohesion of the samples significantly declined, whereas the internal friction angle remained relatively stable. The Duncan–Chang model accurately described the nonlinear deformation behavior of the rare earth samples, with the model parameter b markedly decreasing as confining pressure increased, indicating that confining stress plays a dominant role in governing the nonlinear response. Under the coupled effects of chemical leaching and mechanical stress, the number and size distribution of pores of the rare earth samples underwent a complex multiscale co-evolution. These results provide theoretical support for the green, efficient, and safe exploitation of ionic rare earth ores. Full article

Ultrasonication offers a safer, lower-temperature method for synthesizing zinc oxide nanoparticles (ZnO-NPs). This study details the development of a pectin-glycerol bionanocomposite film reinforced with ZnO-NPs produced using the top-down ultrasonication method. ZnO-NPs were fabricated with varying ultrasonication durations (0, 30, and 60 min) and the addition of pectin as a capping agent. Extended ultrasonication duration resulted in smaller particle size and more defined morphology. Bionanocomposite films were prepared using the solvent casting method by incorporating ZnO-NPs (0, 0.5, 1, 2.5% w/w) and glycerol (0, 10, 20% w/w) as a plasticizer to a pectin base. The inclusion of ZnO-NPs and glycerol did not affect the shear-thinning behavior of the film-forming solution. FTIR analysis indicated interactions between ZnO-NPs, glycerol, and pectin. The addition of ZnO-NPs and glycerol reduced tensile strength but increased flexibility. ZnO-NPs improved barrier and thermal properties by reducing water vapor permeability and increasing melting point, whereas glycerol lowered glass transition temperature, thus enhancing film flexibility. The best film performance was observed with a combination of 0.5% ZnO and 20% glycerol. These results highlight the effectiveness of the top-down ultrasonication method as a sustainable approach for ZnO-NPs fabrication, supporting the development of pectin/ZnO-NPs/glycerol films as a promising material for eco-friendly packaging. Full article

Twice-exceptional students, who are both gifted and present with characteristics of neurodiversity such as Autism Spectrum Disorder (ASD), possess distinctive academic and socio-emotional needs that necessitate individualized educational strategies. This qualitative case study explores the academic and socio-emotional experiences of Murat, an eighth-grade learner identified as gifted and diagnosed with ASD, from the perspectives of the student himself, his mother, and his teachers. Data were collected through semi-structured interviews and analyzed using Braun and Clarke’s six-phase reflexive thematic analysis. The findings revealed that Murat achieved success in mathematics and science, particularly within enriched, strength-oriented environments that accommodated his sensory sensitivities. Despite challenges in social skills and group participation, he benefited considerably from teacher scaffolding and interactive pedagogies. His mother’s active engagement and strong family–school collaboration emerged as pivotal factors in his developmental progress. This study extends beyond individual challenges to highlight the potential strengths that arise from by the intersection of neurodiversity and giftedness. Additionally, it contributes to the limited body of literature exploring how the notion of twice-exceptionality manifests within underrepresented educational contexts. Future research could investigate diverse socio-cultural contexts and develop strategies to enhance teacher preparation and family engagement in supporting gifted learners with ASD. Full article

The rapid development of generative artificial intelligence (GenAI) has revolutionized how individuals and organizations interact with technology. These systems, ranging from conversational agents to creative tools, are increasingly embedded in daily life. However, their effectiveness relies heavily on the usability of their graphical user interfaces (GUIs), which serve as the primary medium for user interaction. Moreover, the design of these interfaces must align with ethical principles such as transparency, fairness, and user autonomy to ensure responsible usage. This study evaluates the usability of GUIs for three widely-used GenAI applications, including ChatGPT (GPT-4), Gemini (1.5), and Claude (3.5 Sonnet) , using a heuristics-based and user-based testing approach (experimental-qualitative investigation). A total of 12 participants from a research organization in Australia, participated in structured usability evaluations, applying 14 usability heuristics to identify key issues and ethical concerns. The results indicate that Claude’s GUI is the most usable among the three, particularly due to its clean and minimalistic design. However, all applications demonstrated specific usability issues, such as insufficient error prevention, lack of shortcuts, and limited customization options, affecting the efficiency and effectiveness of user interactions. Despite these challenges, each application exhibited unique strengths, suggesting that while functional, significant enhancements are needed to fully support user satisfaction and ethical usage. The insights of this study can guide organizations in designing GenAI systems that are not only user-friendly but also ethically sound. Full article

Background/Objectives: Polybia-MP1 (MP1) exhibits antimicrobial and anticancer properties. To improve selectivity toward acidic tumor microenvironments, we designed HMP1, a histidine-substituted analog of MP1, aiming to introduce pH-responsive behavior within physiological and pathological pH ranges. Methods: HMP1 was synthesized by replacing all lysine residues in MP1 with histidines. We characterized its surfactant properties and interactions with lipid monolayers composed of DPPC under varying pH and ionic strength conditions. Langmuir monolayer experiments were used to evaluate peptide-induced morphological changes and lipid packing effects at physiologically relevant lateral pressures. Results: HMP1 displayed pH-dependent activity between pH 5.5 and 7.5, inducing significant morphological reorganization of lipid domains without reducing the condensed phase area. Ionic strength modulated these effects, with distinct behaviors observed at low and physiological saline conditions. HMP1 preferentially interacted with cholesterol-enriched membranes, while MP1 did not induce comparable effects under the same conditions, as previously reported, at physiological lateral pressures. HMP1 also exhibited non-hemolytic properties and lower cytotoxicity compared to MP1. Conclusions: The lysine-to-histidine substitution conferred pH sensitivity to HMP1, enabling selective modulation of membrane organization based on lipid composition, packing, pH, and ionic environment. These findings highlight HMP1’s potential in targeted therapeutics and pH-responsive drug delivery systems. Full article

The long-term stability of grout-reinforced fractured rock masses in acidic groundwater environments after tunnel fires is critical for the safe operation of underground engineering. In this study, grouting reinforcement tests were performed on fractured diorite specimens using a high-strength fast-anchoring agent (HSFAA), and their mechanical degradation and microstructural evolution mechanisms were investigated under coupled high-temperature (25–1000 °C) and acidic corrosion (pH = 2) conditions. Multi-scale characterization techniques, including uniaxial compression strength (UCS) tests, X-ray computed tomography (CT), scanning electron microscopy (SEM), three-dimensional (3D) topographic scanning, and X-ray diffraction (XRD), were employed systematically. The results indicated that the synergistic thermo-acid interaction accelerated mineral dissolution and induced structural reorganization, resulting in surface whitening of specimens and decomposition of HSFAA hydration products. Increasing the prefabricated fracture angles (0–60°) amplified stress concentration at the grout–rock interface, resulting in a reduction of up to 69.46% in the peak strength of the specimens subjected to acid corrosion at 1000 °C. Acidic corrosion suppressed brittle disintegration observed in the uncorroded specimens at lower temperature (25–600 °C) by promoting energy dissipation through non-uniform notch formation, thereby shifting the failure modes from shear-dominated to tensile-shear hybrid modes. Quantitative CT analysis revealed a 34.64% reduction in crack volume (V_ca) for 1000 °C acid-corroded specimens compared to the control specimens at 25 °C. This reduction was attributed to high-temperature-induced ductility, which transformed macroscale crack propagation into microscale coalescence. These findings provide critical insights for assessing the durability of grouting reinforcement in post-fire tunnel rehabilitation and predicting the long-term stability of underground structures in chemically aggressive environments. Full article

Early and accurate diagnosis of dementia is essential to improving patient outcomes and reducing societal burden. The Mini-Mental State Examination (MMSE) is widely used to assess cognitive function, yet traditional statistical and machine learning approaches often face limitations in capturing nonlinear interactions and subtle decline patterns. This study developed a novel deep learning-based dementia prediction model using MMSE data collected from domestic clinical settings and compared its performance with traditional machine learning models. A notable strength of this work lies in its use of item-level MMSE features combined with explainable AI (SHAP analysis), enabling both high predictive accuracy and clinical interpretability—an advancement over prior approaches that primarily relied on total scores or linear modeling. Data from 164 participants, classified into cognitively normal, mild cognitive impairment (MCI), and dementia groups, were analyzed. Individual MMSE items and total scores were used as input features, and the dataset was divided into training and validation sets (8:2 split). A fully connected neural network with regularization techniques was constructed and evaluated alongside Random Forest and support vector machine (SVM) classifiers. Model performance was assessed using accuracy, F1-score, confusion matrices, and receiver operating characteristic (ROC) curves. The deep learning model achieved the highest performance (accuracy 0.90, F1-score 0.90), surpassing Random Forest (0.86) and SVM (0.82). SHAP analysis identified Q11 (immediate memory), Q12 (calculation), and Q17 (drawing shapes) as the most influential variables, aligning with clinical diagnostic practices. These findings suggest that deep learning not only enhances predictive accuracy but also offers interpretable insights aligned with clinical reasoning, underscoring its potential utility as a reliable tool for early dementia diagnosis. However, the study is limited by the use of data from a single clinical site with a relatively small sample size, which may restrict generalizability. Future research should validate the model using larger, multi-institutional, and multimodal datasets to strengthen clinical applicability and robustness. Full article

Linear velocity is commonly used to estimate 1-repetition maximum (1RM) from a load–velocity profile (LVP), as well as prescribe training intensity. However, no study has assessed angular velocity, which may be more representative of joint motion. The purpose of this study was to compare the prediction of 1RM from linear velocity (1RM_linear) and angular velocity (1RM_angular) LVPs in men and women. Fourteen recreationally trained college-aged subjects (7 males, 7 females) completed 1RM testing on day 1, then a randomized submaximal (30–90% 1RM) squat protocol on day 2. Linear velocity was measured with a linear position transducer, while angular velocity was recorded using an accelerometer affixed to the thigh. 1RM_angular was not significantly different from actual 1RM (p = 0.951), with a trivial effect size (d = 0.02), and nearly perfect correlation with actual 1RM (r = 0.984). 1RM_linear had a near perfect correlation with actual 1RM (r = 0.991) but was significantly different than actual 1RM (p < 0.001) with a large effect size (d = 1.56). Additionally, 1RM_angular had a significantly (p = 0.020) lower absolute error (6.7 ± 5.3 kg) than 1RM_linear (12.9 ± 8.2 kg). Regardless of prediction method, males (12.9 ± 8.2 kg) had a greater absolute error in 1RM prediction than females (6.7 ± 5.2 kg). During submaximal loads, a significant load × gender interaction was observed for linear velocity (p < 0.001), with men showing faster velocities at 30% (p = 0.009) and 40% (p = 0.044) 1RM, with no significant interaction (p = 0.304) of main effect of gender (p = 0.116). Angular velocity may provide strength and conditioning coaches a more accurate 1RM prediction during submaximal sets of back squat than using linear velocity; however, neither meet all criteria to be considered highly valid. Lastly, the gender differences in linear velocity at submaximal exercises suggest gender-specific considerations in velocity-based training particularly at lighter loads. Full article

Elucidating amyloid formation inside biomolecular condensates requires models that resolve (i) local, chemistry specific contacts controlling β registry and (ii) mesoscale phase behavior and cluster coalescence on microsecond timescales—capabilities beyond single resolution models. We present a hybrid united atom/coarse grained (UA–CG) force field coupling a PACE UA peptide model with the MARTINI CG framework. Cross resolution nonbonded parameters are first optimized against all atom side chain potentials of mean force to balance the relative strength between different types of interactions and then refined through universal parameter scaling by matching radius of gyration distributions for specific systems using. We applied this approach to simulate a recently reported model system comprising the LVFFAR₉ peptide that can co-assemble into amyloid fibrils via liquid–liquid phase separation. Our ten-microsecond simulations reveal rapid droplet formation populated by micelle like nanostructures with its inner core composed of LVFF clusters. The nanostructures can further fuse but the fusion is reaction-limited due to an electrostatic coalescence barrier. β structures emerge once clusters exceed ~10 peptides, and the LVFFAR₉ fraction modulates amyloid polymorphism, reversing parallel versus antiparallel registry at lower LVFFAR₉. These detailed insights generated from long simulations highlight the promise of our hybrid UA–CG strategy in investigating the molecular mechanism of condensate aging. 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

Membranes were assessed on a bench scale for their performance in methylene blue dye separation. The sawdust, along with Brazilian clay and kaolin, were mixed and compacted by uniaxial pressing and sintered at 650 °C. The membranes were characterized by several techniques, including X-ray diffraction, scanning electron microscopy, porosity, mechanical strength, water uptake, and membrane hydrodynamic permeability. The results demonstrated that the incorporation of sawdust not only altered the pore morphology but also significantly improved water permeation and dye removal efficiency. The ceramic membrane had an average pore diameter of 0.346–0.622 µm and porosities ranging from 40.85 to 42.96%. The membranes were applied to the microfiltration of synthetic effluent containing methylene blue (MB) and, additionally, subjected to investigation of their adsorptive capacity. All membrane variants showed high hydrophilicity (contact angles < 60°) and achieved MB rejection efficiencies higher than 96%, demonstrating their efficiency in treating dye-contaminated effluents. Batch adsorption using ceramic membranes (M0–M3) removed 34.0–41.2% of methylene blue. Adsorption behavior fitted both Langmuir and Freundlich models, indicating mixed mono- and multilayer mechanisms. FTIR confirmed electrostatic interactions, hydrogen bonding, and possible π–π interactions in dye retention. Full article