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Given their political incentives, governments in more autocratic polities can strategically channel unearned government and household income in the form of foreign aid and remittances to finance patronage, which extends their tenure in political office. I substantiate this claim with duration models of government turnover for a sample of 97 countries between 1975 and 2004. Unearned foreign income received in more autocratic countries reduces the likelihood of government turnover, regime collapse, and outbreaks of major political discontent. To allay potential concerns with endogeneity, I harness a natural experiment of oil price–driven aid and remittance flows to poor, non–oil producing Muslim autocracies. The instrumental variables results confirm the baseline finding that authoritarian governments can harness unearned foreign income to prolong their rule. Finally, I provide evidence of the underlying causal mechanisms that governments in autocracies use aid and remittances inflows to reduce their expenditures on welfare goods to fund patronage.

Sign language is an essential means of communication for individuals with hearing disabilities. However, there is a significant shortage of sign language interpreters in some languages, especially in Saudi Arabia. This shortage results in a large proportion of the hearing-impaired population being deprived of services, especially in public places. This paper aims to address this gap in accessibility by leveraging technology to develop systems capable of recognizing Arabic Sign Language (ArSL) using deep learning techniques. In this paper, we propose a hybrid model to capture the spatio-temporal aspects of sign language (i.e., letters and words). The hybrid model consists of a Convolutional Neural Network (CNN) classifier to extract spatial features from sign language data and a Long Short-Term Memory (LSTM) classifier to extract spatial and temporal characteristics to handle sequential data (i.e., hand movements). To demonstrate the feasibility of our proposed hybrid model, we created a dataset of 20 different words, resulting in 4000 images for ArSL: 10 static gesture words and 500 videos for 10 dynamic gesture words. Our proposed hybrid model demonstrates promising performance, with the CNN and LSTM classifiers achieving accuracy rates of 94.40% and 82.70%, respectively. These results indicate that our approach can significantly enhance communication accessibility for the hearing-impaired community in Saudi Arabia. Thus, this paper represents a major step toward promoting inclusivity and improving the quality of life for the hearing impaired.

Background Diabetes mellitus (DM) is a growing public health crisis in Bangladesh, fueled by urbanization and lifestyle changes. This study aimed to determine the prevalence of diabetes and prediabetes among Bangladeshi adults and identify key demographic and clinical risk factors. Methods A cross-sectional study was conducted across Bangladesh, enrolling 5738 participants (mean age 43.96 ± 14.14 years). Data were obtained through structured questionnaires, clinical assessments, and medical record reviews. Diagnosis of diabetes and prediabetes was based on fasting blood glucose, following the ADA 2010 criteria. Descriptive statistics, bivariate analysis, and multinomial logistic regression were performed using SPSS version 27.0 to identify risk factors. Results The prevalence of diabetes was 47.5%, with 37.1% classified as prediabetic and only 15.4% as non-diabetic. Diabetes was significantly associated with older age, hypertension (38.8%), and family history of diabetes (4.5%). Diabetic individuals had higher systolic (134.93 ± 21.92 mmHg) and diastolic blood pressure (82.59 ± 12.76 mmHg), glucose levels (9.32 ± 3.23 mmol/L), and BMI (22.60 ± 3.88) compared to prediabetic and non-diabetic individuals ( p  < 0.05). Multinomial logistic regression confirmed age, gender, BMI, hypertension, and family history as significant predictors ( p  < 0.05). Conclusions The high prevalence of diabetes and prediabetes in Bangladesh highlights an urgent need for public health interventions. Early screening, lifestyle modifications, and integrated management of comorbidities should be prioritized. Strengthening primary healthcare, increasing public awareness, and improving access to diabetes care, particularly for high-risk groups, are critical. Future research should investigate longitudinal trends and socio-economic determinants to inform evidence-based diabetes prevention strategies.

The development of the advanced metering infrastructure (AMI) and the application of artificial intelligence (AI) enable electrical systems to actively engage in smart grid systems. Smart homes with energy storage systems (ESS) and renewable energy sources (RES)-known as home microgrids-have become a critical enabling technology for the smart grid. This article proposes a new model for the energy management system of a home microgrid integrated with a battery ESS (BESS). The proposed dynamic model integrates a deep learning (DL)-based predictive model, bidirectional long short-term memory (Bi-LSTM), with an optimization algorithm for optimal energy distribution and scheduling of a BESS-by determining the characteristics of distributed resources, BESS properties, and the user’s lifestyle. The aim is to minimize the per-day electricity cost charged by time-of-use (TOU) pricing while considering the day-basis peak demand penalty. The proposed system also considers the operational constraints of renewable resources, the BESS, and electrical appliances. The simulation results from realistic case studies demonstrate the validation and responsibility of the proposed system in reducing a household’s daily electricity cost.

Concrete serves as the foundation of modern infrastructure and is commonly used in construction for its strength, durability, and versatility. However, traditional concrete has its limitations, especially when it comes to handling tensile stresses. To address this issue, different types of fiber reinforcement have been developed, with steel fiber proving to be particularly effective due to its high tensile strength and compatibility with concrete. This study investigated the effect of using waste tire steel fiber (WTSF) on the mechanical properties of concrete, where different percentages of WTSF were used namely, 0.25%, 0.5% and 1% by volume replacement of concrete. Compared to the control mix, the incorporation of steel fibers, whether commercially manufactured (MSF) or recycled (WTSF), negatively affected workability. Slump values in MSF mixes decreased between 37.5% and 62.5%, while WTSF mixes showed higher reductions, ranging from 87.5 to 100%, indicating that WTSF had a more adverse impact on workability. The compressive strength results showed that using WTSF at volume ratios of 0.25%, 0.5%, and 1% led to reductions of 6.4%, 30%, and 46%, respectively. In contrast, incorporating steel fiber enhanced mechanical performance, with tensile strength increasing by up to 67% for MSF and 38% for WTSF, while flexural strength improved by up to 40% and 19%, respectively.

Abstract Congenital adrenal hyperplasia (CAH) is a group of autosomal recessive disorders affecting cortisol biosynthesis. Reduced activity of an enzyme required for cortisol production leads to chronic overstimulation of the adrenal cortex and accumulation of precursors proximal to the blocked enzymatic step. The most common form of CAH is caused by steroid 21-hydroxylase deficiency due to mutations in CYP21A2. Since the last publication summarizing CAH in Endocrine Reviews in 2000, there have been numerous new developments. These include more detailed understanding of steroidogenic pathways, refinements in neonatal screening, improved diagnostic measurements utilizing chromatography and mass spectrometry coupled with steroid profiling, and improved genotyping methods. Clinical trials of alternative medications and modes of delivery have been recently completed or are under way. Genetic and cell-based treatments are being explored. A large body of data concerning long-term outcomes in patients affected by CAH, including psychosexual well-being, has been enhanced by the establishment of disease registries. This review provides the reader with current insights in CAH with special attention to these new developments. Graphical Abstract Graphical Abstract

Brain tumor, a leading cause of uncontrolled cell growth in the central nervous system, presents substantial challenges in medical diagnosis and treatment. Early and accurate detection is essential for effective intervention. This study aims to enhance the detection and classification of brain tumors in Magnetic Resonance Imaging (MRI) scans using an innovative framework combining Vision Transformer (ViT) and Gated Recurrent Unit (GRU) models. We utilized primary MRI data from Bangabandhu Sheikh Mujib Medical College Hospital (BSMMCH) in Faridpur, Bangladesh. Our hybrid ViT-GRU model extracts essential features via ViT and identifies relationships between these features using GRU, addressing class imbalance and outperforming existing diagnostic methods. We extensively processed the dataset, and then trained the model using various optimizers (SGD, Adam, AdamW) and evaluated through rigorous 10-fold cross-validation. Additionally, we incorporated Explainable Artificial Intelligence (XAI) techniques-Attention Map, SHAP, and LIME-to enhance the interpretability of the model’s predictions. For the primary dataset BrTMHD-2023, the ViT-GRU model achieved precision, recall, and F1-score metrics of 97%. The highest accuracies obtained with SGD, Adam, and AdamW optimizers were 81.66%, 96.56%, and 98.97%, respectively. Our model outperformed existing Transfer Learning models by 1.26%, as validated through comparative analysis and cross-validation. The proposed model also shows excellent performances with another Brain Tumor Kaggle Dataset outperforming the existing research done on the same dataset with 96.08% accuracy. The proposed ViT-GRU framework significantly improves the detection and classification of brain tumors in MRI scans. The integration of XAI techniques enhances the model’s transparency and reliability, fostering trust among clinicians and facilitating clinical application. Future work will expand the dataset and apply findings to real-time diagnostic devices, advancing the field.

Hypertension (HTN), a major global health concern, is a leading cause of cardiovascular disease, premature death and disability, worldwide. It is important to develop an automated system to diagnose HTN at an early stage. Therefore, this study devised a machine learning (ML) system for predicting patients with the risk of developing HTN in Ethiopia. The HTN data was taken from Ethiopia, which included 612 respondents with 27 factors. We employed Boruta-based feature selection method to identify the important risk factors of HTN. The four well-known models [logistics regression, artificial neural network, random forest, and extreme gradient boosting (XGB)] were developed to predict HTN patients on the training set using the selected risk factors. The performances of the models were evaluated by accuracy, precision, recall, F1-score, and area under the curve (AUC) on the testing set. Additionally, the SHapley Additive exPlanations (SHAP) method is one of the explainable artificial intelligences (XAI) methods, was used to investigate the associated predictive risk factors of HTN. The overall prevalence of HTN patients is 21.2%. This study showed that XGB-based model was the most appropriate model for predicting patients with the risk of HTN and achieved the accuracy of 88.81%, precision of 89.62%, recall of 97.04%, F1-score of 93.18%, and AUC of 0. 894. The XBG with SHAP analysis reveal that age, weight, fat, income, body mass index, diabetes mulitas, salt, history of HTN, drinking, and smoking were the associated risk factors of developing HTN. The proposed framework provides an effective tool for accurately predicting individuals in Ethiopia who are at risk for developing HTN at an early stage and may help with early prevention and individualized treatment.

I use a natural experiment of oil-price-driven remittance flows to poor, non-oil-producing Muslim countries to demonstrate that remittances deteriorate the quality of governance, especially in countries with weak democratic institutions. The results indicate that a 1 standard deviation increase in remittances raises corruption by 1.5 index points (on a 6-point scale), which is equivalent to a $ 600 decrease in per capita GDP. Concomitantly, remittances may enable governments to reduce their delivery of public services (for example, health care, school enrollment). The results suggest that political institutions may mediate the potentially beneficial socioeconomic effects of remittance inflows.

Objective We aimed to evaluate the efficacy and safety of infliximab biosimilar, CT-P13, for patients with inflammatory bowel disease. Methods We searched PubMed, Scopus, Ovid, and Web of Science for relevant clinical trials discussing CT-P31 administration for IBD patients either naïve to biological therapy or switched from IFX therapy. Data of the rates of clinical response, clinical remission, and adverse events were extracted and pooled in a random effect model meta-analysis using CMA version 2. Results Thirty-two studies with a total of 3464 IBD patients treated with CT-P13 were identified. The pooled rates of clinical response among Crohn’s disease (CD) and ulcerative colitis (UC) at 8–14 weeks were 0.81 (95% CI = 0.72 to 0.87) and 0.68 (95% CI = 0.63 to 0.72), respectively, and at 48–63 weeks were 0.69 (95% CI = 0.48 to 0.85) and 0.54 (95% CI = 0.45 to 0.63) respectively. After switching from IFX to CT-P13, the pooled rates of sustained clinical response among CD and UC at 30–32 weeks were 0.84 (95% CI = 0.57 to 0.96) and 0.96 (95% CI = 0.58 to 0.99), respectively, and at 48–63 weeks were 0.51 (95% CI = 0.22 to 0.79) and 0.83 (95% CI = 0.19 to 0.99) respectively. Moreover, adverse events were reported (CD = 0.10, 95% CI 0.04 to 0.22; UC = 0.18, 95% CI 0.05 to 0.15). Conclusion CT-P13 is effective and well tolerated in short and long-term periods. Switching to CT-P13 is recommended for the management of IBD.