Search

AbstractThis paper describes deep neural network (DNN) models based on hyperparameter optimization for the prediction of the compressive strength of concrete. The novelty of this research lies in the implementation of optimized hyperparameters to train the DNN models with the aim to enhance their predictive accuracy. Utilizing the Keras Tuner library, the most effective hyperparameters for the DNN models were identified. These models are then trained and evaluated using an experimental dataset of 1030 instances, encompassing nine quantitative variables that include various concrete mix ingredients and age. The target variable for all models is the compressive strength of concrete. The assessment of model performance, based on metrics like mean squared error (MSE) and R2 values, was conducted on previously unseen test data. The optimal configuration for the hidden layers in the model was identified as five layers, containing 12, 16, 16, 40, and 26 neurons, respectively. The optimal learning rate for the model was 0.001. With this set of optimal hyperparameters, the best DNN model achieved an MSE of 28.76 and an R2 value of 0.89 on the testing data. The results demonstrate a significant improvement in the DNN models' performance when trained with the optimized hyperparameters. In comparison to the regression model, the performance of the DNN models was significantly better. Adopting the predictive models developed in this research offers the potential for substantial cost and time savings by circumventing the need for labour-intensive and time-consuming laboratory tests.

تهدف الدراسة إلى تطوير طرق تدريس التربية البدنية وتحسين تجربة تعلم الطلاب في مجال التربية البدنية، وتهتم ان تساهم في تصميم مناهج تعليمية مبتكرة ومتكاملة لمادة التربية البدنية، وفي تحديد الأهداف التعليمية وتنظيم المحتوى وتحديد الطرق والوسائل المناسبة، توفير بيئة تعليمية تفاعلية ومبتكرة للطلاب، تتبع وقياس أداء الطلاب وتقديم ملاحظات فورية لتحسين أدائهم. منهج الدراسة: استعمل الباحثون المنهج التجريبي لمناسبته لطبيعة هذا البحث، وذلك بإتباع التصميم التجريبي لمجموعة تجريبية واحدة باستعمال القياسات القبلية والبعدية مجتمع وعينة الدراسة قام الباحثون باختيار عينة البحث بالطريقة العمدية من طلاب المرحلة الثالثة للعام الدراسي (2022/2023م). وعددهم (200) طالب واستنتج الباحثون -أن هندسة المناهج شهدت تحسنًا بنسبة 88.35٪ -أن استراتيجيات الذكاء الاصطناعي شهدت تحسنًا بنسبة67.5%- أن المنهجية الرقمية شهدت تحسنًا بنسبة 70.28 %، ويوصي الباحثون: السعي دائماً في استخدام تلك المعايير في بناء المنهجية الرقمية في الالعاب المختلفة في مادة التربية البدنية وعلوم الرياضة.

Design of bridge piers and abutments is significantly impacted by hydrodynamic processes that cause scouring of the foundation. Although, many empirical formulae are available in the literature to estimate the depth of scouring, but they suffer from several limitations. A major limitation of empirical formulae is that they are largely applicable to the hydraulic conditions for which they have been derived. In this research, a deep neural network (DNN) has been developed and applied to predict the depth of scour around bridge piers and abutments. The practicality of the proposed model has been demonstrated using the experimental data sets consisting of 211 data points. The novelty of the DNN model applied herein lies in the use of Adam Optimizer for optimizing the parameters of the DNN model. The performance of the DNN model was evaluated for each parameter set using statistical indicators such as the coefficient of determination, root mean square error, and mean absolute error. A regression equation based upon the available data set has also been proposed. Based upon the values of the statistical parameters, the DNN model has been found to be significantly better than the regression model. The model proposed herein performs better than the regression model. A distinct practical advantage of the model proposed herein is that it eliminates the need of hit and trial procedure to determine the optimal parameter set for the model.

Introduction: We aimed to analyze the functional outcomes based on the admission characteristics in individuals with moderate-to-severe traumatic brain injury (TBI) over a 5-year period. Methods: A retrospective cohort study was conducted to assess the cognitive, physical, and functional outcomes based on traditional and novel metrics used in potential outcome prediction. Results: A total of 201 participants were enrolled with a mean age of 31.9 ± 11.9 years. Glasgow Coma Score (GCS) at emergency department did not correlate with the functional independence measure (FIM) score or Ranchos Los Amigos (RLA) scores at discharge. The absolute functional gain was significantly higher in individuals who sustained TBI with RLA 4–5 (34.7 ± 18.8 vs. 26.5 ± 15.9, P = 0.006). Participants with RLA 4–5 on admission to rehabilitation showed good correlation with the absolute FIM gain. On multivariate regression analysis, only age (odds ratio 0.96; 95% confidence interval: 0.93–0.98; P = 0.005) was found to be the independent predictor of good functional outcome. Conclusions: Initial GCS is not a predictor of functional outcome in individuals who sustained TBI. Consideration of age and development of novel functional measures might be promising to predict the outcomes in individuals with moderate-to-severe TBI.

The growth of renewable energy in the last two decades has led to the development of new power electronic converters. The DC microgrid can operate in standalone mode, or it can be grid-connected. A DC microgrid consists of various distributed generation (DG) units like solar PV arrays, fuel cells, ultracapacitors, and microturbines. The DC-DC converter plays an important role in boosting the output voltage in DC microgrids. DC-DC converters are needed to boost the output voltage so that a common voltage from different sources is available at the DC link. A conventional boost converter (CBC) suffers from the problem of limited voltage gain, and the stress across the switch is usually equal to the output voltage. The output from DG sources is low and requires high-gain boost converters to enhance the output voltage. In this paper, a new high-gain DC-DC converter with quadratic voltage gain and reduced voltage stress across switching devices was proposed. The proposed converter was an improvement over the CBC and quadratic boost converter (QBC). The converter utilized only two switched inductors, two capacitors, and two switches to achieve the gain. The converter was compared with other recently developed topologies in terms of stress, the number of passive components, and voltage stress across switching devices. The loss analysis also was done using the Piecewise Linear Electrical Circuit Simulation (PLCES). The experimental and theoretical analyses closely agreed with each other.

The study was carried out in Dohok in the north of Iraq, exactly in Zakho region, Where Populus nigra stands are widely spread within irrigated stands that are depending on feshkhabour river. Study area involved (45) samples which are randomly selected regions that represents state of the stands. Each sample was (0.1) hectare area, from these plots eighty six reading were recorded for the diameter over and under the bark taken from different height level for 13 standard trees that represent all ages, densities, and diameters for these stands. Data were inserted in linear and non linear regression program, to determine bark factor (K), which found to be (0.95). The volume of the bark was calculated by taking (30) standard trees that represent all ages and densities. Tree volume over and under the bark and difference between them were calculated. Several mathematical equations were found that represent the relation between bark volume as a depended variable, and the diameter at d.b.h. and total height as an in depended variables. From these equations, bark volume was estimated depending on these easy in depended variables. Also we be able to found a mathematical equation to estimate bark thickness from diameter.

Objective: There are limited data examining the relationship between transplant center volume and their use of Status 2 exceptions for heart transplant (OHT). Methods: A retrospective review of the Organ Procurement and Transplantation Network (OPTN) database identified all patients undergoing OHT under Status 2 exception between late 2018 and early 2023. Demographics were collected and transplant centers were categorized based on the number of OHT performed annually (very low volume = < 5 OHT per year; low volume = 5–24 OHT per year; medium volume = 25–50 OHT per year, high volume = > 50 OHT per year). Results: Across all centers, 6348 OHT were included, with n = 68 performed at very low volume centers, n = 1001 performed at low volume centers, n = 1834 performed at medium volume centers, and n = 3445 performed at high volume centers. Medium and high volume centers applied for at least one Status 2 exception about 30%–35% of the time, compared to 50%–60% of the time observed at very low and low volume centers. Compared to very low volume centers, medium volume centers applied for half the amount of Status 2 exceptions (IRR = 0.52 [0.35–0.76]; p < 0.001) while high volume centers applied for less than half the amount (IRR = 0.42 [0.29–0.62]; p < 0.001). High‐volume centers were also 18% less likely to apply for exceptions than medium‐volume centers (IRR = 0.82 [0.74–0.91]; p < 0.001). Conclusions: Lower volume transplant centers apply for Status 2 exceptions at a significantly higher rate, with a stepwise decrease in exception use with increasing transplant center volume. [ABSTRACT FROM AUTHOR]

The potential for increased rates of morbidity of SARS-CoV-2 within immunocompromised populations has been of concern since the pandemic's onset. Transplant providers and patients can face particularly challenging situations, in the current settings as data continues to emerge for the prevention and treatment of the immunocompromised subpopulation. This case report details a patient 9-months post orthotopic heart transplant that developed SARS-CoV-2 infection despite two prior doses of the Pfizer-BioNtech COVID-19 vaccine, and had successful rescue from refractory hypoxemia with veno-venous extracorporeal membrane oxygenation (VV ECLS). [ABSTRACT FROM AUTHOR]

The study highlights the importance of "green education" in facing the times' challenges and its role in achieving sustainable development. The study discusses the concept of green education" and its various dimensions and presents a modern, renewed vision for its application in educational institutions. The study highlights the role of the teacher, student, and society in the success of the green education process. The conclusions were that "Green education is imperative to meet today's challenges and achieve sustainable development. Green education" can be achieved by integrating its principles into all curricula and educational activities. The teacher plays a pivotal role in the success of the "green education" process by making students aware of the importance of environmental issues and motivating them to take sustainable behaviors. The student actively participates in the "green education" process. Through its participation in ecological and research activities, the community is an essential partner in the success of the "green education" process by providing the necessary support to educational institutions and encouraging sustainable behaviors. The recommendations include Adopting "green education" as a basic curriculum in all educational institutions, training teachers on the principles of "green education" and how to apply it in the classroom, providing the necessary resources to implement "green education" programs in schools, and engaging students in environmental and research activities and raising community awareness of the importance of "green education" and its role in achieving sustainable development. [ABSTRACT FROM AUTHOR]

Research significance: Digital transformation comes as a global phenomenon affecting various aspects of life, including Education. The study of its impact on the educational environment of private universities is of great research importance for several reasons, including The rapid development of technology increasing demand for digital Education increasing Competition - the challenges of traditional Education, the study of the impact of digital transformation on the educational environment of private universities aims to achieve many goals, including: Understanding what digital transformation is and its different components, analyzing the impact of digital transformation on various aspects of private universities' educational environment, including curricula, Teaching methods, assessment methods, infrastructure and management, identifying challenges and opportunities facing private universities in the digital transformation process, and providing practical recommendations to help private universities effectively embrace digital transformation. Education, contributing to the development of knowledge about the role of technology in higher Education, the curriculum used in the study, the experimental and descriptive curriculum and the analytical curriculum, conclusions easy access to information and educational resources (0.72). Effective use of technology in the learning process (0.68). Quality of technical support available to students (0.74). Satisfaction with the services provided to them (0.76), faculty and Administrative staff regularly use learning management systems. Of participants believed that learning management systems have improved academic management efficiency78%, recommendations to conduct studies on larger samples of students at different universities. Conducting experimental studies to confirm the relationship between providing advanced technical infrastructure and improving the quality of services provided to students. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the impact of metaverse techniques on the development of sport skills through simulation-based training. The study focused on exploring the effect of metaverse techniques, including augmented reality, spatial computing, data science, virtual reality, running skill, bicycle, and swimming, on the improvement of exercise skills. The research utilized a pre-test and post-test design, with measurements taken before and after the implementation of metaverse techniques, The results revealed significant improvements in exercise skills across various variables. Augmented reality demonstrated a large effect size (Cohen's d = 10) and a percentage of improvement of 76.92%, indicating a substantial enhancement in exercise skills. Spatial computing showed a moderate effect size (Cohen's d = 3.6) and a 72% improvement rate, suggesting noticeable progress in exercise skills through the use of metaverse techniques. Data science exhibited a moderate effect size (Cohen's d = 4.38) with a percentage of improvement of 45.45%, indicating a moderate impact on exercise skill development, Virtual reality's effect size could not be calculated due to the absence of the standard deviation for the tribal measurement. However, the percentage of improvement was found to be 161.8%, suggesting a substantial enhancement in exercise skills. The effect size and percentage of improvement for running skill and bicycle could not be accurately determined without the standard deviation for the tribal measurement,Swimming demonstrated a large effect size (Cohen's d = 14.57) and a percentage of improvement of 171.21%, indicating significant progress in exercise skills,Overall, the findings indicate that the use of metaverse techniques has a positive impact on the development of exercise skills, with varying levels of effectiveness across different variables. This study contributes to the understanding of the potential benefits of metaverse techniques in enhancing exercise skills through simulation-based training. [ABSTRACT FROM AUTHOR]