The Teachers college was established in 1995 AD. This was followed in 1999 by the Centre for Peace Studies, Computer Center and College of additional studies, which evolved into the Faculty of Community Development. The Graduate School was established in 2001.As of September 2011, the university was a member in good standing of the Association of African Universities.
The effect of poverty, parental influence and school enrolment is explored and the gender difference in school going children in Pakistan is studied. Using a quantitative cross sectional survey design, a sample of 400 public and private school students from both urban and rural areas was selected through stratified random sampling technique. A five point likert scale was used to rate economic conditions, parental attitudes and decision making and enrolment patterns by gender using structured questionnaires. The data obtained were analyzed using descriptive statistics, Pearson correlation, independent-simple t-test and multiple regression analysis in SPSS. Results show that poverty has a strong negative effect on school enrolment and parents' influence a strong positive effect. Gender disparities were significant; barriers to enrolment were greater for girls and the impact of poverty was greater for female students. The effect of economic hardships on enrolment was partially mediated by parental attitudes and decision-making, especially with respect to girls' enrolment. The study concludes that poverty reduction strategies need to be supported by specific interventions to raise awareness of parents, while taking into account gender-based disparities in education.
Overfitting and slow convergence are common problems when using the gradient descent method in neural network training. We use regularization and momentum terms, respectively, to reduce these negative phenomena. In this paper, we consider the convergence of the gradient descent method with smoothing L0 regularization and an adaptive momentum term. The objective function for normal L0 regularization is the sum of a function that is not convex, smooth, or Lipschitz. This makes the error function and the norm of the gradient oscillate. This impediment prevents neural networks from achieving optimal measurement rates for application verification. However, we can address the deficiency of the normal L0 regularization term by using the smoothing approximation techniques. This paper presents the results of weak convergence for smooth L0 regularization using the adaptive momentum method. Furthermore, we have proven the strong convergence results of the theorems. Simulations based on three learning problems—parity problems, function approximation problems, and classification tasks—illustrate the viability of the suggested approach under these circumstances. The suggested approach assumes the momentum coefficient, regularization parameter, and learning rate to be constants. We selected these problems because their unique error surfaces provide an appropriate setting for evaluating the efficacy of the suggested approach. Simulation examples demonstrate the superiority of the suggested algorithm and bolster the theoretical analysis.
The Pi-sigma neural network (PSNN) is a type of high-order feedforward neural network that has product units in the output layer. This gives it a rapid convergence speed and a lot of nonlinear mapping options. This study suggests a gradient descent algorithm with L_1 regularization and an adaptive momentum term for training PSNN. This paper mainly focuses on two challenging tasks. First, the fact that the general L_1 regularization is not differentiable at the starting point causes the error function and norm gradient to oscillate during training. This paper's key point is to modify the usual L_1 regularization term by smoothing it at the origin. This processing yields sparse and efficient neural networks while also providing a theoretical analysis of the algorithm. Second, introduce the adaptive momentum term in the iteration process to further accelerate the network learning speed. In addition, the numerical experiments show that the proposed algorithm eliminates the oscillation and increases the learning rate in computation. We also confirm the algorithm's convergence.
Background: War has far-reaching consequences for individuals, communities, governments, and even global systems. Civilians in Sudan have suffered serious and frequently targeted wounds and other suffering during the war. A collapsed health system, a lack of medical supplies, and purposeful attacks on healthcare facilities make it impossible for wounded individuals to receive care. As a result, the purpose of this study was to determine the relationship between the injurious site and the demographic characteristics of injured people during the Sudan war of 2023–2024. Methodology: This is a prospective descriptive analysis conducted at El-Obeid Teaching Hospital from May 2023 to May 2024. The study encompassed all civilian casualties resulting from the armed conflict who were admitted to El-Obeid Teaching Hospital in El-Obeid, North Kordofan State, Sudan. Results: The majority of participants were aged 18-24 years, followed by those under 18 and aged 25-34 years, accounting for 26%, 20.5%, and 17.5%, respectively. In terms of occupation, the majority of the wounded were self-employed, comprising 48.5%, followed by students at 13% and pupils at 10.5%. The most prevalent sites are the lower limbs, followed by the upper limbs, abdomen, and chest, comprising 46.5%, 25.5%, 25.5%, and 19%, respectively. Conclusion: The primary body sites impacted during armed conflict include the lower and upper extremities, as well as the abdomen. Individuals engaged in outdoor occupations and students experience the highest rates of injury.
