Search Results (908)

In this study, particles of ground grape seeds were utilized to adsorb phosphate ions from a prepared solution, aiming to reduce phosphate concentration. Through a series of adsorption experiments, the effects of the adsorbent concentration, initial phosphate ion concentration, temperature, and pH on the phosphate ion uptake were studied. The removal efficiency of the phosphate ion decreased from 77 to 61% as a 25 to 45 °C increment in temperature was observed, which indicated the exothermicity in the adsorption process. The phosphate ion movement onto the adsorbent surface that exhibited the highest uptake value favored a neutral reaction environment with a pH value of seven. The experimental results, when compared using different adsorption isotherms, showed that the best fit was exhibited by the Jovanovic isotherm, which was further confirmed owing to its high 0.974 R² value. Intraparticle diffusion and pseudo second order models describe the kinetics of phosphate adsorption onto grape seeds, with reaction constants of 8.8 × 10⁻³ (mg/g min) and 0.412 (mg/g·min^0.5), respectively. The adsorption was physiosorptive, spontaneous, exothermic, and favorable. Furthermore, the negative entropy with a value of −0.0887 kJ/mol·K revealed reduced randomness in the adsorption process system. Full article

This study investigates mesh sensitivity in the buckling analysis of composite cylindrical shells using the finite element methods. Two Carbon Fiber-Reinforced Plastic (CFRP) models with distinct layups were subjected to linear (Eigenvalue) and non-linear (Riks) analyses under axial compression. Mesh sizes ranging from 50 mm to 2.5 mm were tested using Abaqus. The results revealed that the non-linear analysis is more mesh-sensitive and computationally demanding. Model-1 showed better convergence in non-linear analysis, with <1% error, while Model-2 favored linear analysis, with <0.5% error at finer meshes. The comparison of models results with the experimental data highlights the importance of an empirical correction factor. These findings provide practical guidelines for mesh selection in composite shell analysis. Full article

This study investigated the association between temperament and retrospective growth rates in 84 Nili Ravi buffalo heifers aged 18 to 24 months. Temperament was assessed using chute score and exit velocity, measured twice at a seven-day interval, and classified as calm (≤3) or nervous (>3). Retrospective average daily weight gain data were retrieved from farm records, and blood samples were collected to measure cortisol levels. ANOVA was used to analyze data, considering temperament, age group, season, and year of birth as fixed effects, with birth weight as a covariate. Results showed that 48 heifers were calm and 36 were nervous. Calm heifers exhibited significantly higher average daily gains than nervous heifers during the post-weaning period, with an increase of 240 g/day from 4 to 6 months and 190 g/day from 6 to 12 months (p < 0.001). However, this difference was not significant at 18–24 months (p = 0.144). Calm heifers have numerically lower cortisol levels (0.96 vs. 1.27 μg/dL; p = 0.11). These findings suggest that calmer heifers grow faster in early life, emphasizing the importance of temperament in breeding programs aimed at improving growth performance and welfare. Full article

A semi-automated framework for vertebral measurement has been developed to overcome clinical limitations of subjectivity and poor reproducibility in spinal assessment. The framework integrates watershed segmentation with level-set functions and deterministic cylindrical modeling to convert pixel-based measurements to physical dimensions, achieving 2% reproducibility error. Interactive region-of-interest selection enables the effective handling of multi-vertebrae cases while preserving clinical expertise input. Validation using a lumbar spine MRI dataset on 515 patients confirms measurements fall within established anatomical parameters for L3–L5 vertebrae. This methodology provides a transparent, reproducible approach for standardized vertebral assessment that balances automation with clinical reasoning, offering immediate implementation potential without the computational demands and regulatory challenges associated with complex AI systems. Full article

The worldwide need for energy as well as environmental challenges have promoted the creation of sustainable power solutions. The combination of different working fluids is used for an organic Rankine cycle-powered vapor compression cycle (ORC-VCC) to deliver cooling applications. The selection of an appropriate working fluid significantly impacts system performance, efficiency, and environmental impact. The research evaluates possible working fluids to optimize the ORC-VCC system. Firstly, Artificial Neural Network (ANN)-derived models are used for exergy destruction ( E d t o t ) and heat exchanger total heat transfer capacity ( U A t o t ). Later on, multi-objective optimization was carried out using the acquired models for E d t o t and U A t o t using the Genetic Algorithm (GA) followed by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The optimization results showcase Decane ORC-R600a VCC as the best candidate for the ORC-VCC system; the values of E d t o t and U A t o t were found to be 24.50 kW and 6.71 kW/K, respectively. The research data show how viable it is to implement biogas-driven ORC-VCC systems when providing air conditioning capabilities. Full article

Cross-border energy trading activity via interconnection has received much attention in Southern Asia to help the South Asian Association for Regional Cooperation (SAARC) region’s energy deficit states. This research article proposed a smart metering system to reduce energy losses and increase distribution sector efficiency. The implementation of smart metering systems in utility management plays a pivotal role in advancing several Sustainable Development Goals (SDGs), i.e.; SDG (Affordable and Clean Energy), and SDG Climate Action. By enabling real-time monitoring, accurate measurement, and data-driven management of energy resources, smart meters promote efficient consumption, reduce losses, and encourage sustainable behaviors among consumers. The adoption of a smart metering system along with Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis, socio-economic analysis, current challenges, and future prospects was also investigated. Besides the economics of the electrical distribution system, one feeder with non-technical losses of about 16% was selected, and the cost–benefit analysis and cost–benefit ratio was estimated for the SAARC region. The import/export ratio is disturbing in various SAARC grids, and a solution in terms of community microgrids is presented from Pakistan’s perspective as a case study. The proposed work gives a guidelines for SAARC countries to reduce their losses and improve their system functionality. It gives a composite solution across multi-faceted evaluation for the betterment of a large region. Full article

Triple-negative breast cancer (TNBC) remains a major clinical challenge due to its aggressive behavior and lack of targeted therapies. Accurate early prediction of response to neoadjuvant chemotherapy (NACT) is essential for guiding personalized treatment strategies and improving patient outcomes. In this study, we present an attention-based multiple instance learning (MIL) framework designed to predict pathologic complete response (pCR) directly from pre-treatment hematoxylin and eosin (H&E)-stained biopsy slides. The model was trained on a retrospective in-house cohort of 174 TNBC patients and externally validated on an independent cohort (n = 30). It achieved a mean area under the curve (AUC) of 0.85 during five-fold cross-validation and 0.78 on external testing, demonstrating robust predictive performance and generalizability. To enhance model interpretability, attention maps were spatially co-registered with multiplex immunohistochemistry (mIHC) data stained for PD-L1, CD8+ T cells, and CD163+ macrophages. The attention regions exhibited moderate spatial overlap with immune-enriched areas, with mean Intersection over Union (IoU) scores of 0.47 for PD-L1, 0.45 for CD8+ T cells, and 0.46 for CD163+ macrophages. The presence of these biomarkers in high-attention regions supports their biological relevance to NACT response in TNBC. This not only improves model interpretability but may also inform future efforts to identify clinically actionable histological biomarkers directly from H&E-stained biopsy slides, further supporting the utility of this approach for accurate NACT response prediction and advancing precision oncology in TNBC. Full article

This study investigates the nonlinear aeroelastic behavior and energy harvesting performance of a two-degrees-of-freedom NACA 0012 airfoil under varying reduced velocities and electrical load resistances. The system exhibits a range of dynamic responses, including periodic and chaotic states, governed by strong fluid–structure interactions. Nonlinear oscillations first appear near the critical reduced velocity $U_r^{*}=6$, with large-amplitude limit-cycle oscillations emerging around $U_r^{*}=8$ in the absence of the electrical loading. As the load resistance increases, this transition shifts to higher $U_r^{*}$, reflecting the damping effect of the electrical load. Fourier spectra reveal the presence of odd and even superharmonics in the lift coefficient, indicating nonlinearities induced by fluid–structure coupling, which diminishes at higher resistances. Phase portraits and Poincaré maps capture transitions across dynamical regimes, from periodic to chaotic behavior, particularly at a low resistance. The voltage output correlates with variations in the lift force, reaching its maximum at an intermediate resistance before declining due to a suppressing nonlinearity. Flow visualizations identify various vortex shedding patterns, including single (S), paired (P), triplet (T), multiple-pair (mP) and pair with single (P + S) that weaken at higher resistances and reduced velocities. The results demonstrate that nonlinearity plays a critical role in efficient voltage generation but remains effective only within specific parameter ranges. Full article

This study evaluated the effect of biochars on growth performance, nutrient digestibility, carcass yield, bone mineralization, litter quality and footpad lesions in broilers. Eight hundred day-old chicks were randomly divided into four treatments, 10 replicates per treatment (20 birds/replicate) for 35 days. Treatments were basal diet (control), a control diet with corncob (CC) biochar (1%), a control diet with wheat straw (WS) biochar (1%) and a control diet with sugarcane bagasse (SCB) biochar (1%). Body weight gain (BWG), feed intake (FI) and feed conversion ratio (FCR) were recorded weekly. Nutrient digestibility, bone mineralization and carcass parameters were determined on the 21st and 35th days, while footpad lesions and litter quality were also assessed. The results revealed significant improvement (p < 0.05) in FI, BWG and FCR with supplementation. Nutrient digestibility was higher (p < 0.05) in the SCB biochar group. Tibia calcium and phosphorus levels were enhanced (p < 0.05) in the WS and SCB biochar groups, respectively. Footpad lesions were significantly lower (p < 0.05) in the CC biochar group, while litter quality was improved (p < 0.05) in the WS biochar group. Lymphoid organ relative weight results revealed that spleen weight was not affected by biochar supplementation in diet (p > 0.05), while dietary supplementation of CS and WS biochar in the diet resulted in the highest relative weights of thymus and bursa (p < 0.05). However, dietary supplementation of WS, SC and SCB biochar supplementation had affected positively the log value of the ND virus and IBV titers in birds. Overall, dietary supplementation of 1% biochars enhances growth performance, bone mineralization, footpad health immunity and litter quality in broilers. Full article

Background: Cardiac arrest remains a significant public health challenge with variable mortality trends across different demographics and regions, affecting healthcare planning and intervention strategies. We conducted this study to analyze cardiac arrest-related mortality trends from 1999 to 2023 and predict future trends up to 2035. Methods: This study analyzed data from 1999 to 2023, focusing on cardiac arrest as the primary cause of death (ICD-10: I46). Age-adjusted mortality rates (AAMRs) were standardized according to the 2000 U.S. Census. Joinpoint regression was utilized to calculate annual percentage change (APC), and an ARIMA model with Python 3.10 was used for mortality predictions. Results: A total of 365,608 cardiac arrest-related deaths were recorded in the USA from 1999 to 2023. There was a sharp decline in mortality rate until 2001 (APC: −10.35, p < 0.05), followed by a slowed decline until 2013 (APC: −2.91, p < 0.05), and then a gradual uptrend. Males exhibited a higher AAMR (5.8, 95% CI: 5.8–5.9) compared to females (4.2, 95% CI: 4.1–4.2). African Americans had the highest AAMR (8.9, 95% CI: 8.9–9), followed by Caucasians (4.8, 95% CI: 4.8–4.9) and American Indians (3.5, 95% CI: 3.3–3.7). The South region of the US had the highest AAMR, followed by the Northeast, Midwest, and West. Alabama exhibited the highest AAMR, followed by Nevada and Hawaii. Predictive analysis suggests a potential stable slow downtrend in mortality rates by 2035 (AAMR: 4.28, 95% CI: −1.8–10.4). Conclusions: The observed trends and future predictions underscore the importance of targeted public health interventions and healthcare planning to address cardiac arrest mortality. Full article

Background/Objectives: Artificial intelligence (AI) is revolutionising healthcare for people with disabilities, including those with autism spectrum disorder (ASD), in the era of advanced technology. This work explicitly addresses the challenges posed by inconsistent data from various sources by developing and evaluating a robust deep ensemble learning system for the accurate and reliable classification of autism spectrum disorder (ASD) based on facial images. Methods: We created a system that learns from two publicly accessible datasets of ASD images (Kaggle and YTUIA), each with unique demographics and image characteristics. Utilising a weighted ensemble strategy (FPPR), our innovative ASD-UANet ensemble combines the Xception and ResNet50V2 models to maximise model contributions. This methodology underwent extensive testing on a range of groups stratified by age and gender, including a critical assessment of an unseen, real-time dataset (UIFID) to determine how well it generalised to new domains. Results: The performance of the ASD-UANet ensemble was consistently better. It significantly outperformed individual transfer learning models (e.g., Xception alone on T1+T2 yielded an accuracy of 83%), achieving an impressive 96.0% accuracy and an AUC of 0.990 on the combined-domain dataset (T1+T2). Notably, the ASD-UANet ensemble demonstrated strong generalisation on the unseen real-time dataset (T3), achieving 90.6% accuracy and an AUC of 0.930. This demonstrates how well it generalises to new data distributions. Conclusions: Our findings demonstrate significant potential for widespread, equitable, and clinically beneficial ASD screening using this promising, reasonably priced, and non-invasive method. This study establishes the foundation for more precise diagnoses and greater inclusion for people with autism spectrum disorder (ASD) by integrating methods for diverse data and combining deep learning models. Full article

This study aimed to examine global hypertensive heart disease (HHD) trends (1990–2019). Methods: We extracted data from the Global Burden of Disease (GBD) 2019 Study, encompassing 204 countries and territories. We analyzed the age-adjusted mortality rates (AAMRs), crude mortality, prevalence, years lived with disability (YLD), years of life lost (YLL), and disability-adjusted life years (DALY). Joinpoint Regression Analysis was used to calculate the Annual Percentage Change (APC), with p < 0.05 indicating statistical significance. Results were stratified by region, Socio-Demographic Index (SDI), and gender. Results: Globally, the crude mortality rate for HHD rose from 12.2 (95% UI 9.9–13.6) to 14.9 (95% UI 16.5–11.1) deaths/100,000 population (1990–2019), whereas the AAMR declined from 19.3 (95% UI 5.8–21.6) to 15.1 (95% UI 11.1–16.7). A Joinpoint Analysis revealed significant APC shifts: a decrease of −1.53% (p < 0.05) from 1990 to 2006, an increase of +0.60% (p < 0.05) from 2006 to 2015, and a subsequent decrease of −1.28% (p < 0.05) from 2006 to 2019. Eastern Europe showed the highest annual rate of change in AAMR at 0.9 (95% UI: −0.1 to 1.2), whereas the high-income Asia Pacific region experienced the largest decline at −0.66 (95% UI −0.27–−0.72). Central Asian males had an AAMR of 31.1 (95% UI 35.3–22.9) in 2019, and Sub-Saharan African females reached 38.5 (95% UI 48.4–26.3). YLL trended downward in both sexes (APC: −1.94, p < 0.05 in males; −1.81, p < 0.05 in females), yet YLD rose steadily in recent years, underscoring a growing chronic burden. The AAMR was highest in 2019 among Sub-Saharan African females, which is a particularly important area. Conclusions: Targeted strategies are essential to mitigate the escalating HHD burden. Full article

Nonlinear characteristics of solar photovoltaic (PV) and nonuniform surrounding conditions, including partial shading conditions (PSCs), are the major factors responsible for lower conversion efficiency in solar panels. One major condition is the cause of the multiple peaks and oscillation around the peak point leading to power losses. Therefore, this study proposes a novel hybrid framework based on an artificial neural network (ANN) and fractional order PID (FOPID) controller, where new algorithms are employed to train the ANN model and to tune the FOPID controller. The primary aim is to maintain the computed power close to its true peak power while mitigating persistent oscillations in the face of continuously varying surrounding conditions. Firstly, a modified shuffled frog leap algorithm (MSFLA) was employed to train the feed-forward ANN model using real-world solar PV data with the aim of generating a reference solar PV peak voltage. Subsequently, the parameters of the FOPID controller were tuned through the application of the Sanitized Teacher–Learning-Based Optimization (s-TLBO) algorithm, with a specific focus on achieving maximum power point tracking (MPPT). The robustness of the proposed hybrid framework was assessed using two different types (monocrystalline and polycrystalline) of solar panels exposed to varying levels of irradiance. Additionally, the framework’s performance was rigorously tested under cloudy conditions and in the presence of various partial shading scenarios. Furthermore, the adaptability of the proposed framework to different solar panel array configurations was evaluated. This work’s findings reveal that the proposed hybrid framework consistently achieves maximum power point with minimal oscillation, surpassing the performance of recently published works across various critical performance metrics, including the $MP{P}_{efficiency}$, relative error (RE), mean squared error (MSE), and tracking speed. Full article