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This research aims to comprehensively investigate the combined use of calcined red mud (RM), calcium sulfate dihydrate (CSD), and ground granulated blast furnace slag (GGBFS) in geopolymer concrete (GC), demonstrating how their optimized proportions improve GC's fresh, mechanical, durability, and microstructural properties. This study investigated the influence of partially replacing calcined red mud (RM) with GGBFS and Phosphogypsum or CSD in GC. The test program included tests on the fresh, mechanical, and durability properties of these blends, including slump value, compressive strength, ultrasonic pulse velocity, water absorption and porosity, rapid chloride penetration, electrical resistivity, mass loss due to freeze-thaw cycles, and resistance to sulfate attack. Material characterization of the GC blends was conducted using scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and differential thermogravimetric analysis (DTG). One-way ANOVA analysis was also used to examine the significance of variation between the results due to the replacement of calcined RM with GGBFS and CSD. The results showed that the mix R40-G45-C15, with 40% calcined red mud, 45% GGBFS, and 15% CSD, demonstrated the highest density at 2401 kg/m³, 15.93% greater than the R70-G15-C15 mix, and achieved a compressive strength 73.40% higher at 28 days. Additionally, R40-G45-C15 showed superior durability, with water absorption and porosity reductions of 87.42% and 83.86%, an 85.78% reduction in charge passed at 7 days, and a 40.94% lower mass loss from freeze-thaw cycles compared to R70-G15-C15. SEM analysis confirmed the formation of N–A–S–H and C–A–S–H gels in the blends, contributing to improved density and strength. XRD tests indicated the nominated peaks of calcite, quartz, and portlandite in the blends, with increased portlandite intensity due to higher RM content. These results indicate that partially substituting the calcined RM with CSD and GGBFS in GC could be a viable alternative option, offering potential contributions to sustainable construction with improved structural performances.

Bolted connections for built-up cold-formed sections are studied using different section configurations, gusset plates, and bolt arrangements. This paper presents the study of a connection between beam and column in light-weight steel frames with spans ranging between 8.0 and 12.0 m and a maximum height of 6.0 m that are used in agricultural sheds. The cross section of the beam and column is a novel built-up cold-formed cross section. A total of eight connections were tested and studied. The purpose of the study is to determine the effect of serval factors on the moment capacity and failure modes of the connection. The experimental study considered the effects of different shapes of tie plates, numbers, and spacings of bolts, as well as the effect of strengthening angles with different numbers of bolts in the connections. The experimental results showed that the failure modes in all connection types were due to a local failure in the connecting parts. The experimental results revealed that the configuration type has a great influence on the moment capacity of the connection.

BackgroundCOVID-19 poses a significant threat to global public health. As the severity of SARS-CoV-2 infection varies among individuals, elucidating risk factors for severe COVID-19 is important for predicting and preventing illness progression, as well as lowering case fatality rates. This work aimed to explore risk factors for developing severe COVID-19 to enhance the quality of care provided to patients and to prevent complications.MethodsA retrospective study was conducted in Saudi Arabia’s eastern province, including all COVID-19 patients aged 18 years or older who were hospitalized at Prince Saud Bin Jalawi Hospital in July 2020. Comparative tests as well as both univariate and multivariate logistic regression analyses were performed to identify risk factors for developing severe COVID-19 and poor outcomes.ResultsBased on the comparative statistical tests patients with severe COVID-19 were statistically significantly associated with older age and had higher respiratory rate, longer hospital stay, and higher prevalence of diabetes than non-severe cases. They also exhibited statistically significant association with high levels of potassium, urea, creatinine, lactate dehydrogenase (LDH), D-dimer, and aspartate aminotransferase (AST). The univariate analysis shows that having diabetes, having high severe acute respiratory infection chest X-ray scores, old age, prolong hospitalization, high potassium and lactate dehydrogenase, as well as using insulin, heparin, corticosteroids, favipiravir or azithromycin were all statistically significant associated with severe COVID-19. However, after adjustments in the multivariate analysis, the sole predictor was serum LDH (p = 0.002; OR 1.005; 95% CI 1.002–1.009). In addition, severe COVID-19 patients had higher odds of being prescribed azithromycin than non-severe patients (p = 0.001; OR 13.725; 95% CI 3.620–52.043). Regarding the outcomes, the median hospital stay duration was statistically significantly associated with death, intensive care unit admission (ICU), and mechanical ventilation. On the other hand, using insulin, azithromycin, beta-agonists, corticosteroids, or favipiravir were statistically significantly associated with reduced mortality, ICU admission, and need of mechanical ventilation.ConclusionThis study sheds light on numerous parameters that may be utilized to construct a prediction model for evaluating the risk of severe COVID-19. However, no protective factors were included in this prediction model.

Purpose To outline the prevalence of vitamin D and vitamin B12 deficiencies in enuretic children.Methods An analytical descriptive study was conducted on enuretic children who were followed up at the outpatient clinic for nocturnal enuresis at the Children’s Hospital, Cairo University. Sociodemographic and clinical data were recorded. The levels of vitamin D and vitamin B12 were assessed and correlated with the severity of enuresis.Results Two hundred and eighty-eight children were enrolled. Insufficiency of Vitamin D predominated (n = 139; 48.3%). Vitamin D deficiency was present in 31.3%, n = 90 and it was normal in 20.5%, n = 59). Vitamin B12 deficiency was observed in 25% of the studied children, n = 72). The one-sample Wilcoxon signed-rank test was significant for both vitamins (P value =0.001). Vitamin D showed a stronger inverse correlation with the number of enuresis episodes per day than vitamin B12 (-0.680 vs. −0.219 respectively). A cut-off of 13.7 ng/ml for vitamin D was detected, below which the child was predicted to have failed dry nights. Using multivariate logistic regression, higher vitamin D levels and behavioural treatment coexistence were significant protective factors for the absence of dry nights.Conclusion Low levels of vitamin D and B12 were detected in children with primary nocturnal enuresis, which could be considered a burden on the clinical severity of enuresis.

Introduction: Wide-awake local anesthesia and no tourniquet (WALANT) represents a revolutionary technique for hand surgeons who dismiss tourniquets and sedation. In this study, we present our experience with the WALANT technique in primary flexor tendon injuries of the hand. Patient and methods: This prospective research was carried out on 30 patients undergoing hand primary, flexor tendon repair surgery. Flexor tendon injury zones 2, 3, 4, and 5 were included. WALANT was prepared and injected. The tendons were surgically managed by a cruciate single cross-stitched locked 4-strand technique. The pain was assessed using a visual analog scale (VAS) score. The range of motion (ROM) of affected fingers was assessed according to the Strickland evaluation system. Results: There was a highly significant relationship between the patient's compliance with physiotherapy and obtained ROM of the affected finger with a P value

To effectively tackle the challenges posed by climate change, it is crucial to enhance the accuracy of rainfall-runoff models to ensure reliability amidst changing climatic conditions. Neural networks, renowned for their ability to capture complex patterns and relationships within uncertain input and output data, offer valuable tools in this pursuit. This study aims to evaluate the efficacy of two neural network (NN) models: the Radial Basis Function Neural Network (RBFNN) and the Model Tree M5 Neural Network (MTM5NN). These models are assessed both individually and in combination with the Wavelet (WT) data processing technique for rainfall-runoff modeling in the Kolar River watershed located in Madhya Pradesh, India. Fifteen models were developed employing four algorithms: RBFNN models, WRBFNN (RBF model integrating wavelet components of rainfall as inputs), MTM5NN, and WMTM5NN (MT model incorporating wavelet components of rainfall as inputs). Initially, rainfall and runoff data underwent normalization and were applied to RBFNN and MTM5NN networks. Subsequently, time series data for both rainfall and runoff were decomposed using wavelet transforms, resulting in various sub-time series signals such as approximations and decompositions. These derived signals were then utilized as input data for RBFNN and MTM5NN, specifically designated as WRBFNN and WMTM5NN. The most effective model identified was Model 8 of WMTM5NN, which demonstrated R2 values close to 0.97, outperforming the other models. These results underscore the superior performance of the WMTM5NN model, highlighting its effectiveness in achieving heightened accuracy in predicting runoff for this specific watershed.