Search Results (15,371)

Plastic waste, particularly polyethylene terephthalate (PET), poses a growing environmental challenge. This study investigates the feasibility of incorporating recycled PET into clay bricks as a sustainable alternative in construction. Bricks were fabricated with 0%, 5%, 10%, and 15% PET content. Clay characterization included particle size distribution, Atterberg limits, and moisture content. Physical and mechanical tests evaluated dimensional variability, void percentage, warping, water absorption, suction, unit compressive strength ($f_b^{\prime }$), and prism compressive strength ($f_m^{\prime }$). Statistical analysis (Shapiro–Wilk, p < 0.05) validated the results. PET addition improved physical properties—reducing water absorption, suction, and voids—while slightly compromising mechanical strength. The 15% PET mix showed the best overall performance ($f_b^{\prime }$ = 24.00 kg/cm²; $f_m^{\prime }$ = 20.40 kg/cm²), with uniform deformation and lower absorption (18.7%). Recycled PET enhances key physical attributes of clay bricks, supporting its use in eco-friendly construction. However, reduced compressive strength limits its structural applications. Optimizing PET particle size, clay type, and firing conditions is essential to improve load-bearing capacity. Current formulations are promising for non-structural uses, contributing to circular material strategies. Full article

Background: Nowadays, there is a lack of reliable and minimally invasive diagnosis methods for the early detection of Alzheimer’s disease. The development and validation of such tools could significantly reduce the dependence on more invasive and costly confirmatory procedures, such as cerebrospinal fluid biomarkers analysis and neuroimaging techniques. Objectives: The main objective of this study is to validate the clinical performance of a previously developed diagnosis model based on plasma biomarkers from patients in a cognitive disorder unit. Methods: A new cohort of patients was recruited from the same cognitive disorder unit (n = 93). Specifically, demographic data (gender, age, and educational level), plasma biomarkers levels, and genotype (glial fibrillary acidic protein, phosphorylated Tau 181, amyloid-beta42/amyloid-beta40, apolipoprotein E) were collected to evaluate both approaches of the previous diagnosis model (one-cut-off, two-cut-off). Results: The one-cut-off approach showed a sensitivity of 74.3%, a specificity of 89.5%, and an area under the curve of 0.888, while the values for the two-cut-off approach were sensitivity of 66.7%, specificity of 99.9%, and area under the curve of 0.867. Conclusions: A multivariate diagnostic tool was temporally validated for implementation in a clinical unit. In fact, satisfactory results were obtained from both approaches (one-cut-off, two-cut-offs), but the two cut-offs approach was more consistent in correctly identifying non-Alzheimer’s disease cases, allowing us to identify a large number of cases with high specificity. Full article

Primary school teaching buildings represent a typical category of densely populated public architecture, where the safe evacuation of occupants is essential to ensuring their safety. The spatial organizational structure plays a pivotal role in determining overall evacuation efficiency. However, systematic research linking spatial organization with evacuation performance remains limited. This study addresses this gap by analyzing 102 real-world cases of primary school teaching buildings, identifying common spatial organizational patterns, and developing a spatial structural framework based on fundamental units and their organizational relationships. A hybrid methodology integrating weighted network analysis and evacuation simulation is employed to quantitatively evaluate the relationship between spatial organization types and evacuation performance, ultimately proposing three design principles—Integrity, Balance, and Stability—to guide evacuation efficiency optimization. The findings provide a methodological reference for evacuation research in public buildings and offer practical design guidance for optimizing primary school facility layouts. Full article

Background: Basketball is characterized as a high-intensity, intermittent sport that places considerable demands on the cardiorespiratory, neuromuscular, and mechanical systems. These physiological requirements are modulated by contextual variables and the athlete’s stage of biological maturation, both of which significantly influence physical fitness outcomes. Consequently, it is imperative to employ age- and development-specific assessment protocols. Objectives: This study aimed to evaluate the differences in physical fitness across competitive categories and to explore the interrelationships among the various physical assessment tests. Twenty-four male players (U14 = 12; U16 = 12) participated in this research. Methods: Athletes were monitored using WIMUPRO inertial measurement units and completed the SBAFIT test battery to evaluate physical fitness parameters. Statistical analyses included both inferential and correlational approaches, with effect sizes calculated for all relevant variables. The independent variable was the competitive age category of the players. Results: The results indicated notable differences in physical performance between developmental groups, primarily attributed to biological maturation. Significant disparities were observed in measures of aerobic capacity, linear speed, agility, and centripetal force. Conclusions: The comparative nature of this study across developmental categories offers novel insights and practical implications for talent development and training optimization. Full article

This study presents the development and evaluation of an automated ultraviolet-C irradiation system for maize seed treatment, emphasizing disinfection performance, environmental control, and vision-based monitoring. The system features dual 8-watt ultraviolet-C lamps, sensors for temperature and humidity, and an air extraction unit to regulate the microclimate of the chamber. Without air extraction, radiation stabilized within one minute, with internal temperatures increasing by 5.1 °C and humidity decreasing by 13.26% over 10 min. When activated, the extractor reduced heat build-up by 1.4 °C, minimized humidity fluctuations (4.6%), and removed odors, although it also attenuated the intensity of ultraviolet-C by up to 19.59%. A 10 min ultraviolet-C treatment significantly reduced the fungal infestation in maize seeds by 23.5–26.25% under both extraction conditions. Thermal imaging confirmed localized heating on seed surfaces, which stressed the importance of temperature regulation during exposure. Notable color changes ($\Delta E>2.3$) in treated seeds suggested radiation-induced pigment degradation. Ultraviolet-C intensity mapping revealed spatial non-uniformity, with measurements limited to a central axis, indicating the need for comprehensive spatial analysis. The integrated computer vision system successfully detected seed contours and color changes under high-contrast conditions, but underperformed under low-light or uneven illumination. These limitations highlight the need for improved image processing and consistent lighting to ensure accurate monitoring. Overall, the chamber shows strong potential as a non-chemical seed disinfection tool. Future research will focus on improving radiation uniformity, assessing effects on germination and plant growth, and advancing system calibration, safety mechanisms, and remote control capabilities. Full article

Background: Clinical pharmacy services (CPSs) play a key role in ensuring medication safety, optimizing pharmacotherapy, and improving patient outcomes. While their benefits are well-documented globally, their specific impact within the Saudi healthcare system remains underexplored. Objective: This study aimed to evaluate the impact of pharmacist-led interventions in a tertiary medical center in Saudi Arabia. Methods: A retrospective chart review was conducted at a 1200-bed academic hospital in western Saudi Arabia. Pharmacist interventions documented between 1 January 2023 and 31 December 2023 were analyzed. Interventions were categorized into 13 types, including dosage errors, unavailable medications, and drug–drug interactions. Descriptive statistics were used to summarize the data. Results: A total of 38,143 pharmacist interventions were recorded. Dosage errors accounted for 77.2% (n = 29,584) of interventions, followed by issues with medication availability (6.57%, n = 2519) and incorrect medication orders (4.59%, n = 1761). The most frequently implicated medications were acetylsalicylic acid, enoxaparin, and paracetamol, collectively representing 43.55% of interventions. The highest intervention rates were in the Emergency Department (25.3%, n = 11,050), Oncology Clinics (9.81%, n = 4285), and Male Medical Units (9.43%, n = 4119). Conclusions: Clinical pharmacists play a significant role in reducing medication errors and improving patient safety across various specialties. Their targeted interventions optimize therapeutic outcomes, highlighting the need for integrating advanced tools and expanding CPSs to meet evolving healthcare demands in Saudi Arabia. Full article

Respiratory syncytial virus (RSV) remains a leading cause of pneumonia in young children in Mexico and worldwide. To investigate RSV dynamics in Mexico, we conducted a multicenter study from August 2021 to July 2023 in six hospitals across five States, analyzing respiratory samples from children under five years with pneumonia. Multiplex RT-PCR identified 203 RSV-positive cases, of which 123 were RSV-B and 80 RSV-A. Interestingly, 77% of the collected samples showed evidence of coinfection with other respiratory pathogens, with rhinovirus, Haemophilus influenzae, and Streptococcus pneumoniae being the most common. Also, RSV-B dominated in 2021–2022, whereas RSV-A prevailed in 2022–2023, mirroring trends observed in the United States. Sequences of the genes encoding G and F proteins showed that RSV-A lineages were more diverse, with A.D.1, A.D.1.8, and A.D.5.2 being frequently detected. In contrast, nearly all RSV-B sequences belonged to lineage B.D.E.1. Finally, ancestral state inference suggests repeated introductions from the USA and other North American countries, with limited evidence of sustained local circulation. These findings show different trends in RSV circulation between two consecutive seasons and the importance of genomic surveillance to monitor RSV diversity, evaluate vaccine impact, and inform public health strategies in Mexico’s evolving post-pandemic respiratory virus landscape. Full article

Background: This study aimed to evaluate the clinical effectiveness of dupilumab and its impact on CRSwNP and type 2 inflammatory biomarkers in patients with severe uncontrolled asthma, with or without comorbidities, within a real-life cohort. Methods: This was a single-center, prospective, and observational real-life study conducted at the Severe Asthma Unit of Germans Trias i Pujol University Hospital. The objective of this study was to assess the real-world response to dupilumab treatment in patients with severe asthma, with or without nasal polyposis, bronchiectasis, obesity, or switching from another biologic drug for their asthma. Results: The ACT score significantly increased (13.7 vs. 20.6; p = 0.001), while the number of exacerbations decreased (3.1 vs. 0.7; p = 0.005). Patients with CRSwNP showed an increase in the ACT score (13.1 vs. 19.8; p = 0.011) and a decrease in the number of exacerbations (3.0 vs. 1.3; p = 0.217). Patients with nasal polyps showed an increase in both SNOT22 (78.3 vs. 38.3; p = 0.013) and global VAS (8 vs. 4.2; p = 0.028). Patients with bronchiectasis receiving dupilumab showed an increase in the ACT score (12.7 vs. 21.3; p = 0.039) and a marked decrease in the number of exacerbations (2.8 vs. 0; p = 0.025). Obese patients treated with dupilumab showed an improvement in the ACT score (14.1 vs. 21.3; p = 0.044) and a decrease in the rate of exacerbations (3.2 vs. 1.3; p = 0.030). Patients with rhinoconjunctivitis receiving dupilumab showed an increase in the ACT score (13.4 vs. 19.1; p = 0.017) and a decrease in the number of exacerbations (3.3 vs. 0.8; p = 0.024). Conclusions: Dupilumab is a highly effective treatment for severe asthma, showing significant improvements in lung function, reductions in exacerbations, and enhanced quality of life for patients with and without nasal polyps. The results of this real-life study support dupilumab as a valuable addition to the therapeutic armamentarium for asthma, particularly for those with type 2 inflammation despite the presence of comorbidities such as bronchiectasis or obesity, or for patients in whom a previous biologic treatment failed. Full article

As governments increasingly explore digital financial instruments to diversify funding channels and expand citizen participation, Thailand’s G-Token represents an early attempt to integrate blockchain technology into sovereign debt issuance. This study examines its potential implications through a multi-dimensional risk and governance framework, situating the analysis within both domestic regulatory structures and international benchmarks. The evaluation considers macroeconomic effects—such as potential shifts in monetary policy transmission, bank disintermediation risks, and systemic liquidity impacts—alongside micro-level concerns involving investor protection, market integrity, and financial literacy. Using comparative analysis with the European Union, Singapore, and United States regulatory approaches, the paper identifies critical gaps in legal classification, oversight maturity, and structural safeguards. Findings indicate that while Thailand’s design—particularly its separation from payment systems—supports monetary coherence, its ad hoc legal integration, reliance on administrative investor protections, and early-stage market infrastructure pose vulnerabilities if adoption scales. The study concludes that achieving long-term viability will require explicit statutory authorization, enhanced disclosure and governance standards, strengthened interagency oversight, and inclusive market access strategies. These insights provide a structured basis for emerging economies seeking to adopt government-backed tokenized instruments without undermining financial stability or public trust. Full article

The accelerating pace of urbanization has significantly complicated traffic management systems, leading to mounting challenges, such as persistent congestion, increased travel delays, and heightened environmental impacts. In response to these challenges, this study presents a novel deep learning framework designed to enhance short-term traffic flow prediction and support intelligent transportation systems within the context of smart cities. The proposed model integrates Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) networks, augmented by an attention mechanism that dynamically emphasizes relevant temporal patterns. The model was rigorously evaluated using the publicly available datasets and demonstrated substantial improvements over current state-of-the-art methods. Specifically, the proposed framework achieves a 3.75% reduction in the Mean Absolute Error (MAE), a 2.00% reduction in the Root Mean Squared Error (RMSE), and a 4.17% reduction in the Mean Absolute Percentage Error (MAPE) compared to the baseline models. The enhanced predictive accuracy and computational efficiency offer significant benefits for intelligent traffic control, dynamic route planning, and proactive congestion management, thereby contributing to the development of more sustainable and efficient urban mobility systems. Full article

Background/Objectives: Type 2 diabetes (T2D) is a pressing public health challenge in the United States (U.S.). Community-based diabetes education programs equip individuals with T2D with the knowledge and skills to improve dietary behaviors, build confidence, and better manage their condition to reduce complications. However, few studies evaluate self-care adherence and self-efficacy together. This study examined participant adherence to diabetes self-care practices and confidence in managing diabetes through a community-based education program. Methods: Do Well, Be Well with Diabetes (DWBWD) is a 5-week program focused on reinforcing the best practices in diabetes management through dietary practices, physical activity, and gaining self-confidence in managing T2D. The program was evaluated among 137 participants across 14 Texas counties using pre- and post-surveys that assessed the number of days per week participants engaged in diabetes self-care practices, as well as their confidence in performing these behaviors (rated on a scale from 1 = not at all confident to 5 = extremely confident). Results: Participants were mostly male (75.9%), White (60.6%), and over 65 years (58.4%). Most participants had T2D (57.7%) or prediabetes (27.0%). Compared to the program entry, participants reported improvements (p < 0.001) in self-care practices, as reflected by their mean differences (MD), including following a healthful eating plan (MD −1.46), consuming five servings of fruit and vegetables (MD −0.87), spacing carbohydrate intake evenly throughout the day (MD −1.64), engaging in at least 30 min of daily physical activity (MD −0.74), testing blood glucose (MD −1.08), and checking their feet (MD −1.09). Confidence in performing all self-care behaviors significantly improved (p < 0.001), with MDs between −0.53 and −1.13, indicating higher post-program scores. Conclusions: Participation in the DWBWD program increased confidence in diabetes management and enhanced engagement in key health behaviors associated with reducing diabetes complications. Full article

Shell-like housing structures for motors and compressors can be found in everyday products. Consumers significantly evaluate acoustic emissions during the first usage of products. Unpleasant sounds may raise concerns and cause complaints to be issued. A prevention strategy is a holistic acoustic design, which includes predicting the emitted sound power as part of end-of-line testing. The hybrid experimental-simulative sound power prediction based on laser scanning vibrometry (LSV) is ideal in acoustically harsh production environments. However, conducting vibroacoustic testing with laser scanning vibrometry is time-consuming, making it difficult to fit into the production cycle time. This contribution discusses how the time-consuming sampling process can be accelerated to estimate the radiated sound power, utilizing adaptive sampling. The goal is to predict the acoustic signature and its uncertainty from surface velocity data in seconds. Fulfilling this goal will enable integration into a product assembly unit and final acoustic quality control without the need for an acoustic chamber. The Gaussian process regression based on PyTorch 2.6.0 performed 60 times faster than the preliminary reference implementation, resulting in a regression estimation time of approximately one second for each frequency bin. In combination with the Equivalent Radiated Power prediction of the sound power, a statistical measure is available, indicating how the uncertainty of a limited number of surface velocity measurement points leads to predictions of the uncertainty inside the acoustical signal. An adaptive sampling algorithm reduces the prediction uncertainty in real-time during measurement. The method enables on-the-fly error analysis in production, assessing the risk of violating agreed-upon acoustic sound power thresholds, and thus provides valuable feedback to the product design units. Full article

The purpose of the current study is to determine the effect of the differentiated instruction approach on 3rd-grade primary school students’ academic achievement (N = 45) in the “Electrical Devices and Tools” unit and to explore their opinions regarding the differentiated instruction process. In this context, the content of the lessons prepared using student-centred approaches on students’ science achievement was examined, and students’ opinions on the differentiated instruction approach were also evaluated. The study was conducted in the spring term of the 2024–2025 school year in a major city located in the central region of Türkiye, and a mixed research design combining both quantitative and qualitative approaches was employed. In the current study, during the instructional process of the experimental group, differentiated instruction lesson plans available on the Education Information Network (EIN) portal provided by the Ministry of National Education (MoNE) were used. In the control group, the process outlined by the current curriculum was followed. When the findings were evaluated, statistically significant differences were found in favour of the experimental group, in which activities were implemented based on the differentiated instruction plan, compared to the control group that received instruction within the framework of the current curriculum. In addition, students’ opinions regarding the process indicated that the implementation contributed positively to their learning. In light of the findings obtained, recommendations were made for future research. Full article

Motion capture (MoCap) data derived from wearable Inertial Measurement Units is essential to applications in sports science and healthcare robotics. However, a significant amount of the potential of this data is limited due to missing data derived from sensor limitations, network issues, and environmental interference. Such limitations can introduce bias, prevent the fusion of critical data streams, and ultimately compromise the integrity of human activity analysis. Despite the plethora of data imputation techniques available, there have been few systematic performance evaluations of these techniques explicitly for the time series data of IMU-derived MoCap data. We address this by evaluating the imputation performance across three distinct contexts: univariate time series, multivariate across players, and multivariate across kinematic angles. To address this limitation, we propose a systematic comparative analysis of imputation techniques, including statistical, machine learning, and deep learning techniques, in this paper. We also introduce the first publicly available MoCap dataset specifically for the purpose of benchmarking missing value imputation, with three missingness mechanisms: missing completely at random, block missingness, and a simulated value-dependent missingness pattern simulated at the signal transition points. Using data from 53 karate practitioners performing standardized movements, we artificially generated missing values to create controlled experimental conditions. We performed experiments across the 53 subjects with 39 kinematic variables, which showed that discriminating between univariate and multivariate imputation frameworks demonstrates that multivariate imputation frameworks surpassunivariate approaches when working with more complex missingness mechanisms. Specifically, multivariate approaches achieved up to a 50% error reduction (with the MAE improving from 10.8 ± 6.9 to 5.8 ± 5.5) compared to univariate methods for transition point missingness. Specialized time series deep learning models (i.e., SAITS, BRITS, GRU-D) demonstrated a superior performance with MAE values consistently below 8.0 for univariate contexts and below 3.2 for multivariate contexts across all missing data percentages, significantly surpassing traditional machine learning and statistical methods. Notable traditional methods such as Generative Adversarial Imputation Networks and Iterative Imputers exhibited a competitive performance but remained less stable than the specialized temporal models. This work offers an important baseline for future studies, in addition to recommendations for researchers looking to increase the accuracy and robustness of MoCap data analysis, as well as integrity and trustworthiness. Full article

This study evaluates media-level filtration behaviour and short-term fuel consumption outcomes for five spin-on lubricating oil filters operated under real driving conditions at high altitude. To improve interpretability, filters are reported using parameter-based identifiers (media descriptors and equivalent circular diameter, ECD) rather than internal codes. Pore-scale morphology was quantified by microscopy and expressed as ECD, and bulk fluid cleanliness was summarised using ISO 4406 codes. Trials were conducted over representative urban and extra-urban routes at altitude; fuel consumption was analysed using ANCOVA. The results indicated clear media-level differences (tighter pore envelopes and cleaner ISO codes, particularly for two OEM units). However, fuel-consumption differences were not statistically significant (ANCOVA, p = 0.29). Accordingly, findings are reported as short-term cleanliness and media characterisation under high-altitude duty rather than durability or efficiency claims. The parameter-based framing clarifies trade-offs across metrics and avoids over-generalisation from brand or part numbers. The work highlights the value of ECD as a comparative pore metric and underscores limitations of microscopy/cleanliness data for inferring engine wear or long-term consumption. Future work will incorporate formal multi-pass testing (ISO 4548-12), direct differential-pressure instrumentation, used-oil viscosity tracking, and wear-metal spectrometry to enable cross-vendor benchmarking and causal interpretation. Findings are presented as short-term cleanliness and media characterisation; no durability claims are made in the absence of direct wear measurements. Full article