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Diabetes mellitus is a chronic disease affecting over 38.4 million adults worldwide. Unfortunately, 8.7 million were undiagnosed. Early detection and diagnosis of diabetes can save millions of people’s lives. Significant benefits can be achieved if we have the means and tools for the early diagnosis and treatment of diabetes since it can reduce the ratio of cardiovascular disease and mortality rate. It is urgently necessary to explore computational methods and machine learning for possible assistance in the diagnosis of diabetes to support physician decisions. This research utilizes machine learning to diagnose diabetes based on several selected features collected from patients. This research provides a complete process for data handling and pre-processing, feature selection, model development, and evaluation. Among the models tested, our results reveal that Random Forest performs best in accuracy (i.e., 0.945%). This emphasizes Random Forest’s efficiency in precisely helping diagnose and reduce the risk of diabetes.

Background The COVID-19 pandemic accelerated the adoption of telemedicine across healthcare specialties, with digital health technologies becoming essential tools for maintaining patient care while ensuring safety. Objective This cross-sectional study compared patient satisfaction and clinical outcomes between two widely used telemedicine modalities in urology: traditional phone calls versus WhatsApp messaging consultations. Methods Conducted in Madinah, Saudi Arabia, from March 2021 to December 2022, the study included 77 participants who chose either phone consultations (n = 32, 41.6%) or WhatsApp consultations (n = 45, 58.4%). Patient satisfaction was measured using validated 10-point scales, and urologist confidence in diagnosis was similarly assessed. Statistical analysis employed t-tests and Fisher's exact tests. Results Phone call users reported significantly higher initial satisfaction than WhatsApp users (8.7 vs. 8.0; p = 0.030), with no differences in urologist diagnostic confidence (p = 0.935) or follow-up satisfaction (p = 0.570). Phone calls were more frequently used for urgent cases (59.4% vs. 28.9%; p = 0.010), whereas WhatsApp was preferred for elective cases (71.1% vs. 40.6). Conclusions Both telemedicine modalities are clinically effective. Phone consultations provide superior immediate satisfaction, ideal for urgent needs, while WhatsApp offers a practical alternative for elective, asynchronous care. Healthcare systems can integrate either platform based on clinical urgency and patient preference.

This study included the study of the efficiency of peppermint oil inhibition of aluminum corrosion in acidic medium HCl at concentrations (0.5, 1, 1.5, 2 M) on the corrosion rate in the presence and absence of the inhibitor peppermint oil at different concentrations (1000, 2000, 3000, 4000 ppm).In order to study the effect of changing the temperature (303, 313, 323, 333 K) on the corrosion rate in the absence and presence of the inhibitor, different concentrations of the inhibitor were chosen in addition to the acid concentration (1M). The experiments were carried out using the weight loss method to calculate the erosion rate. The expected heat of adsorption enthalpy was (-29.913 KJ/mol), with a negative sign indicates that the process is exothermic. The value of the free energy of adsorption ranged from (11.086-15.067 KJ/mol. (The results showed that Freundlich adsorption isotherm equation is the best fit to examine the adsorption process, also a scanning electron microscope. Showed the surface of aluminum before and after corrosion according to the results of the study, it was found that the used inhibitor reduces corrosion in acidic media. Its anti-corrosion efficacy also improves with increasing inhibitor concentration and with decreasing temperature.

The quality of service (QoS) in 5G/6G communication enormously depends upon the mobility and agility of the network architecture. An increase in the possible uses of 5G vehicular network simultaneously expands the scope of the network’s quality of service (QoS). To this end, a safety-critical real-time system has become one of the most demanding criteria for the vehicular network. Although different mathematical and computation methods have traditionally been used to optimize the allocation of resources, but the nonconvexity of optimization issues creates unique type of challenges. In recent years, machine learning (ML) has emerged as a valuable tool for dealing with computational complexity that involves large amounts of data in heterogeneous vehicular networks. By using optimization and cutting-edge machine learning techniques, this article gives readers an insight about how 5G vehicular network resources can be allocated to reinforce network communication. Furthermore, a new federated deep reinforcement learning- (FDRL-) based vehicle communication method is presented as a new insight. Finally, a UAV-aided vehicular communication system based on FDRL-based UAVs is proposed as a novel resource management technique to optimize 5G and 6G quality of services.

To improve the structural stiffness, strength and reduce the weight of nanoplate structure, functionally graded (FG) graphene-reinforced nanocomposite (GRNC) laminated plates are exploited in this paper. The bending and buckling behaviors of FG-GRNC laminated nanoplates are investigated by using novel quasi-3D hyperbolic higher order shear deformation plate theory in conjunction with modified continuum nonlocal strain gradient theory, which considered both length and material scale parameters. The modified model of Halpin–Tsai is employed to calculate the effective Young’s modulus of the GRNC plate along the thickness direction, and Poisson’s ratio and mass density are computed by using the rule of mixture. An analytical approach of the Galerkin method is developed to solve governing equilibrium equations of the GRNC nanoplate and obtain closed-form solutions for bending deflection, stress distributions and critical buckling loads. A detailed parametric analysis is carried out to highlight influences of length scale parameter (nonlocal), material scale parameter (gradient), distribution pattern, the GPL weight fraction, thickness stretching, geometry and size of GPLs, geometry of the plate and the total number of layers on the stresses, deformation and critical buckling loads. Some details are studied exclusively for the first time, such as stresses and nonlocality effect.

Temperature variations have a significant impact on the performance and durability of rigid (concrete) pavement. As concrete is subjected to daily and seasonal temperature changes, it experiences thermal expansion and contraction. These movements, if not properly managed, can lead to cracking, joint deterioration, and loss of structural integrity. The pavement system is adversely affected by intense heat and significant flooding. This study aims to analyze the impact of several parameters on the performance of rigid pavement under typical, thermal, and flooding situations. This study investigates the properties of concrete and the dimensional design of rigid pavement with FEACONS IV software to assess their impact on the performance of concrete pavement during thermal and flooding conditions. The main conclusions of this study derived from the FEACONS IV analysis are as follows. Rigid pavement can enhance load-carrying capacity due to a lower elastic modulus, adequate flexural strength, and aggregates with a lower coefficient of thermal expansion. Increased thickness of concrete slabs and shorter slab lengths assist in minimizing load- and temperature-induced stresses. The increase in the subgrade modulus reaction value during flooding conditions improves pavement strength. However, in higher thermal conditions, a higher subgrade reaction modulus can increase the stress induced by temperature and load. Rigid pavement using porous limestone aggregate exhibits a reduced elastic modulus and coefficient of thermal expansion, suggesting higher resilience compared to rigid pavement composed of river gravel or granite. The findings suggest that higher thermal conditions will cause pavement damage. Agencies need to account for higher temperatures while designing and maintaining pavement. Flooding saturates the concrete pavement and subgrade layer, adversely affecting its performance over time.

This study aims to synthesis new chemical compounds via the combination of some amino acids glutamine, asparagine, histidine, arginine and glycine by Schiff base reactions using phthaldehyde. The principle aims to synthesis compounds of some Schiff base compounds which prepared by the reactions between phthalaldehyde and various amino acids. Also to study some of biological applications anti-Cancer, anti-Fungi and anti-inflammatory of the study Schiff base compound. The biological investigation of the resulting compounds including determination of sample cytotoxicity on cells (MTT protocol), effect of complexes on HRBC hemolytic and membrane stabilization & In-vitro antimicrobial activities on 5 species of Fungi.