The Sudanese Government launched the National SDG-6 Plan and commences its implementation to achieve and sustain universal and equitable access to basic WASH services by 2030. It is critical to ...understand the geographical heterogeneity of Sudan and patterns in the inequality of access to safe drinking water and sanitation. Through such research, the disease control strategy can be optimized, and resource allocation can be prioritized. We explored spatial heterogeneity and inequality in access to improved water and sanitation across Sudan by mapping the coverage at both the state and district levels. We decomposed the inequality across Sudan into within-state, between-state, within-district, and between-state inequalities using the Theil L and Theil T indices. We calculated the Gini coefficient to assess the inequality of access to improved water and sanitation, based on the deviation of the Lorenz curve from the line of perfect equality. The study population was 105,167 students aged 8-13 at 1,776 primary schools across the country. Geographical heterogeneity was prominent in the Central Darfur, South Darfur, East Darfur, Kassala, West Kordofan, and Blue Nile States, all of which showed severe inequality in access to an improved latrine at the household level in terms of the Theil T or Theil L index. The overall inequality in the coverage of improved sanitation went beyond the warning limit of 0.4 for the Gini coefficient. The inequality in terms of the Theil L and Theil T indices, as well as the Gini coefficient, was always higher for improved sanitation than for improved water at the household level. Within-state inequality accounted for 66% or more of national inequalities in the distribution of improved sanitation and drinking water for both the Theil L and Theil T indices. This is the first study to measure geographical heterogeneity and inequalities in improved water and sanitation coverage across Sudan. The study may help to prioritize resource allocation to areas with the greatest water and sanitation needs.
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This communication reports on the Mycetoma Research Centre of the University of Khartoum, Sudan experience on 6,792 patients seen during the period 1991-2014.The patients were predominately young ...(64% under 30 years old) males (76%). The majority (68%) were from the Sudan mycetoma belt and 28% were students. Madurella mycetomatis eumycetoma was the most common type (70%). In 66% of the patients the duration of the disease was less than five years, and 81% gave a history of sinuses discharging mostly black grains (78%). History of trauma at the mycetoma site was reported in 20%. Local pain was reported in 27% of the patients, and only 12% had a family history of mycetoma. The study showed that 57% of the patients had previous surgical excisions and recurrence, and only 4% received previous medical treatment for mycetoma. Other concomitant medical diseases were reported in 4% of the patients. The foot (76%) and hand (8%) were the most commonly affected sites. Less frequently affected sites were the leg and knee (7%), thigh (2%), buttock (2%) and arm and forearm (1%). Rare sites included the chest wall, head and neck, back, abdominal wall, perineum, oral cavity, tongue and eye. Multiple sites mycetoma was recorded in 135 (2%) of cases. At presentation, 37% of patients had massive lesions, 79% had sinuses, 8% had local hyper-hydrosis at the mycetoma lesion, 11% had regional lymphadenopathy, while 6% had dilated tortuous veins proximal to the mycetoma lesions. The diagnosis of mycetoma was established by combined imaging techniques and cytological, histopathological, serological tests and grain culture. Patients with actinomycetoma received a combination of antimicrobial agents, while eumycetoma patients received antifungal agents combined with various surgical excisions. Surgical excisions in the form of wide local excision, debridement or amputation were done in 807 patients, and of them 248 patients (30.7%) had postoperative recurrence. Different types of amputations were done in 120 patients (1.7%).
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Multi-term extended Kantorovich method (MTEKM) is used to solve the bending problem of thin skew (parallelogram) functionally graded plate resting on the Winkler elastic foundation under uniformly ...distributed transverse load. Formulations are based on the classical plate theory (CPT) with the physical neutral surface is considered as the reference plane. Various configurations of clamped, simply supported and free edges are considered. By implementing the concept of Galerkin’s weighted residual method, the fourth-order partial differential governing equation and boundary conditions are converted into two sets of ordinary differential equations (ODE), which are then solved numerically using “Chebfun” numerical computation package. Convergence and accuracy of MTEKM are investigated. Results obtained with MTEKM are compared to finite element method (FEM) solutions. FEM has been implemented using ANSYS software, in which the plate is modeled with shell elements, while the elastic foundation is modeled as a pair of contact/target elements. In addition, the effects of both the Winkler foundation stiffness and material power index have been investigated. Applying MTEKM in bending analysis of thin skew plates offered more accurate results than the single-term EKM but with the cost of more computation time but still provides simplicity and rapid convergence. It is found that MTEKM well suits the bending problem of skew FGM plates resting on elastic foundation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Defect models that are trained on class imbalanced datasets (i.e., the proportion of defective and clean modules is not equally represented) are highly susceptible to produce inaccurate prediction ...models. Prior research compares the impact of class rebalancing techniques on the performance of defect models but arrives at contradictory conclusions due to the use of different choice of datasets, classification techniques, and performance measures. Such contradictory conclusions make it hard to derive practical guidelines for whether class rebalancing techniques should be applied in the context of defect models. In this paper, we investigate the impact of class rebalancing techniques on the performance measures and interpretation of defect models. We also investigate the experimental settings in which class rebalancing techniques are beneficial for defect models. Through a case study of 101 datasets that span across proprietary and open-source systems, we conclude that the impact of class rebalancing techniques on the performance of defect prediction models depends on the used performance measure and the used classification techniques. We observe that the optimized SMOTE technique and the under-sampling technique are beneficial when quality assurance teams wish to increase AUC and Recall, respectively, but they should be avoided when deriving knowledge and understandings from defect models.
Defect prediction models—classifiers that identify defect-prone software modules—have configurable parameters that control their characteristics (e.g., the number of trees in a random forest). Recent ...studies show that these classifiers underperform when default settings are used. In this paper, we study the impact of automated parameter optimization on defect prediction models. Through a case study of 18 datasets, we find that automated parameter optimization: (1) improves AUC performance by up to 40 percentage points; (2) yields classifiers that are at least as stable as those trained using default settings; (3) substantially shifts the importance ranking of variables, with as few as 28 percent of the top-ranked variables in optimized classifiers also being top-ranked in non-optimized classifiers; (4) yields optimized settings for 17 of the 20 most sensitive parameters that transfer among datasets without a statistically significant drop in performance; and (5) adds less than 30 minutes of additional computation to 12 of the 26 studied classification techniques. While widely-used classification techniques like random forest and support vector machines are not optimization-sensitive, traditionally overlooked techniques like C5.0 and neural networks can actually outperform widely-used techniques after optimization is applied. This highlights the importance of exploring the parameter space when using parameter-sensitive classification techniques.
Defect prediction models help software quality assurance teams to allocate their limited resources to the most defect-prone modules. Model validation techniques, such as <inline-formula><tex-math ...notation="LaTeX">k</tex-math> <inline-graphic xlink:href="tantithamthavorn-ieq1-2584050.gif"/> </inline-formula>-fold cross-validation, use historical data to estimate how well a model will perform in the future. However, little is known about how accurate the estimates of model validation techniques tend to be. In this paper, we investigate the bias and variance of model validation techniques in the domain of defect prediction. Analysis of 101 public defect datasets suggests that 77 percent of them are highly susceptible to producing unstable results- - selecting an appropriate model validation technique is a critical experimental design choice. Based on an analysis of 256 studies in the defect prediction literature, we select the 12 most commonly adopted model validation techniques for evaluation. Through a case study of 18 systems, we find that single-repetition holdout validation tends to produce estimates with 46-229 percent more bias and 53-863 percent more variance than the top-ranked model validation techniques. On the other hand, out-of-sample bootstrap validation yields the best balance between the bias and variance of estimates in the context of our study. Therefore, we recommend that future defect prediction studies avoid single-repetition holdout validation, and instead, use out-of-sample bootstrap validation.
AR technology allows users to interact with virtual objects in real-world settings. Immersive AR experiences can enhance creativity and possibilities. Learners can explore real-life situations in a ...safe, controlled environment, understand abstract concepts and solve problems. This study investigates whether AR-codes affect boxing beginners' performance in some fundamental defensive techniques. An experimental and control group were created to implement a quasi-experimental design. By using the ASSURE instructional design model, AR technology was incorporated into the educational program and delivered in flipped classroom method to the experimental group. Comparatively, the control group is taught a program using a teacher's command style. A post-measurement of defensive boxing skills was conducted for both groups. Participants were 60 boxing beginners aged 12 to 14 who had enrolled in Port Fouad Sports Club's 2023/2024 training season in Port Said, Egypt. Randomly, participants were divided into control and experimental groups. They were homogenized and equivalent in terms of age, height, weight, IQ, physical fitness, and skill level. According to the study results, the experimental group performed better in post-measurements than the control group. The AR Codes technology had a large effect size on the learning of boxing defensive skills under study. Consequently, it is necessary to use AR Codes technology as an educational resource to enhance the learning process, integrate it with active learning strategies, and use it to teach defensive boxing skills and apply them to offensive and counterattack skills, thereby improving the learning process.
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The rapid spread of COVID-19 has forced schools and universities to close. Globally, education systems face unprecedented challenges, and learning management systems (LMS) are the only solution. The ...current study aimed to investigate the effectiveness of a Blackboard collaborative breakout group on the cognitive achievement of physical education teaching styles. The quasi-experimental method involved creating two groups: one experimental and one control, with the experimental group using Blackboard collaborative breakout groups and the control group relying exclusively on online lectures and continuing with the same method without breakout groups. The study sample consisted of 40 students who were randomly assigned and divided equally into the two groups. Based on the research sample, homogeneity within the group and equivalence between groups in terms of age, Grade Point Average (GPA), and high intelligence test (IQ) were evaluated. The results showed that the experimental group's cognitive achievement was superior to that of the control group. Therefore, the design of the learning process enhances student collaboration, participation, and reinforcement. Additionally, the experimental group retained the learning outcomes for a month after the cessation of all teaching and learning processes. To conclude, giving a lecture using webinar tools such as Blackboard Collaborate Ultra does not necessarily mean achieving the intended educational goals. As a result, it is necessary to look for ways to integrate active learning strategies, such as collaborative learning, to enhance student involvement in distance learning.
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