The possibility that researchers should be able to obtain data from all cases is questionable. There is a need; therefore, this article provides a probability and non-probability sampling. In this paper we studied the differences and similarities of the two with approach that is more of fritter away time, cost sufficient with energy required throughout the sample observed. The pair shows the differences and similarities between them, different articles were reviewed to compare the two. Quota sampling and Stratified sampling are close to each other. Both require the division into groups of the target population. The main goal of both methods is to select a representative sample and facilitate sub-group research. There are major variations, however. Stratified sampling uses simple random sampling when the categories are generated; sampling of the quota uses sampling of availability. For stratified sampling, a sampling frame is necessary, but not needed for quota sampling. More specifically, stratified sampling is a method of probability sampling which enables the calculation of the sampling error. For quota samples, this is not possible. Quota sampling is therefore primarily used by market analysts rather than stratified sampling, as it is mostly cost-effective and easy to conduct and has the appealing equity of satisfying population reach. However, it disguises potentially significant bias.

Antiretroviral therapy (ART) is the common hope for HIV/AIDS-treated patients. Total commitments from individuals and the entire community are the major challenges faced during treatment. This study investigated the progress of ART in the Federal Teaching Hospital in Gombe state, Nigeria by using various records of patients receiving treatment in the ART hospital unit. We combined artificial intelligence (AI)-based models and correspondence analysis (CA) techniques to predict and visualize the progress of ART from the beginning to the end. The AI models employed are artificial neural networks (ANNs), adaptive neuro-fuzzy inference systems (ANFISs) and support-vector machines (SVMs) and a classical linear regression model of multiple linear regression (MLR). According to the outcome of this study, ANFIS in both training and testing outperformed the remaining models given the R² (0.903 and 0.904) and MSE (7.961 and 3.751) values, revealing that any increase in the number of years of taking ART medication will provide HIV/AIDS-treated patients with safer and elongated lives. The contingency results for the CA and the chi-square test did an excellent job of capturing and visualizing the patients on medication, which gave similar results in return, revealing there is a significant association between ART drugs and the age group, while the association between ART drugs and marital status (93.7%) explained a higher percentage of variation compared with the remaining variables.

Background: Liberia is among the 30 countries with a high burden of tuberculosis worldwide. Health sciences students, who are future health professionals, have essential roles in curtailing the spread of TB. This study aims to evaluate the knowledge, attitude, and practice (KAP) of tuberculosis (TB) among health sciences students. Methods: This study used a quantitative cross-sectional design to assess Medical, Pharmacy, and Public Health students at the University of Liberia’s KAP regarding TB using a modified previously used self-administered questionnaire reviewed by subject experts from 1 April 2025 to 23 April 2025. SPSS 26 was used for analysis. Descriptive statistics, Mann–Whitney, Kruskal–Wallis, and multivariate logistic regression tests were used for analysis. Results: In total, 630 students participated, of which 51.7% were females, 83% were aged 24 or above, 81.6% were single, and 96.7% had never smoked. The KAP levels were 65.9%, 97.3%, and 94.8%, respectively. Higher TB knowledge was significantly associated with being enrolled in the Medical program (OR = 2.20, 95% CI: 1.28–3.76, p < 0.05), being in year 4 and 5 (OR = 1.79, 95% CI: 1.09–2.98, p < 0.05; OR = 2.28, 95% CI: 1.08–4.78, p < 0.05), being unemployed (OR = 1.58, 95% CI: 1.09–2.31, p < 0.05), and having personal acquaintance with individuals diagnosed with TB (OR = 1.64, 95% CI: 1.11–2.42, p < 0.05). Conclusions: The knowledge level among students was good. They had a positive attitude, and their practice levels were good. However, gaps remain in understanding latent TB and proper disinfection methods for TB-related materials. Strengthening the health curriculum to address these specific knowledge gaps is recommended to better align students’ knowledge with their attitudes and practices.

Like other nations around the world, Ethiopia has suffered negative effects from COVID-19. The objective of this study was to predict COVID-19 mortality using Artificial Intelligence (AI)-driven models. Two-year daily recorded data related to COVID-19 were trained and tested to predict mortality using machine learning algorithms. Normalization of features, sensitivity analysis for feature selection, modelling of AI-driven models, and comparing the boosting model with single AI-driven models were the main activities performed in this study. Prediction of COVID-19 mortality was conducted using a combination of four dominant feature variables, and hence, the best determination of coefficient (DC) of AdaBoost, KNN, ANN-6, and SVM in the prediction process were 0.9422, 0.8618, 0.8629, and 0.7171, respectively. The Boosting model improved the performance of the individual AI-driven models KNN, SVM, and ANN-6 by 7.94, 22.51, and 8.02 percent, respectively, at the verification stage using the testing dataset. This suggests that the boosting model has the best performance for prediction of COVID-19 mortality in Ethiopia. As a result, it suggests a promising potential performance of boosting ensemble model to be applied in predicting mortality and cases from similarly recorded daily data to predict mortality due to COVID-19 in other parts of the world.

East Africa was not exempt from the devastating effects of COVID-19, which led to the nearly complete cessation of social and economic activities worldwide. The objective of this study was to predict mortality due to COVID-19 using an artificial intelligence-driven ensemble model in East Africa. The dataset, which spans two years, was divided into training and verification datasets. To predict the mortality, three steps were conducted, which included a sensitivity analysis, the modelling of four single AI-driven models, and development of four ensemble models. Four dominant input variables were selected to conduct the single models. Hence, the coefficients of determination of ANFIS, FFNN, SVM, and MLR were 0.9273, 0.8586, 0.8490, and 0.7956, respectively. The non-linear ensemble approaches performed better than the linear approaches, and the ANFIS ensemble was the best-performing ensemble approach that boosted the predicting performance of the single AI-driven models. This fact revealed the promising capability of ensemble models for predicting the daily mortality due to COVID-19 in other parts of the globe.

The subject of hip dysplasia in dogs is still current and preoccupies both animal owners and veterinarians. Major factors affecting the development of the disorder are hip laxity and incongruent joints. Many studies on etiology, pathogenesis, and early diagnosis have been performed to reduce prevalence and select healthy dogs for breeding. The purpose of the present study was to investigate a possible relationship between dysplasia and femoral head area (FHA), femoral coverage by the acetabulum (CFH) and cranio-caudal distance of the dorsal acetabular rim (CrCdAR). Radiographs of a total of 264 skeletally mature dogs with similar physical characteristics (German wirehaired pointers (GWP), German shepherd dogs (GSD) and Labrador retrievers (LAB)) presented for routine hip dysplasia screening were recruited for the study. FHA, CFH and CrCdAR were measured and related to dysplasia status. Evaluations of FHA (p = 0.011), CFH (p < 0.001) and CrCdAR length (p = 0.003) measurements revealed significant interactions between breed, sex and FCI scores, so they had to be assessed separately. The results revealed that FHA tends to decrease as the hip dysplasia score worsens. There was no significant relationship between FHA and dysplasia assessment. FHA is breed-specific and is larger in normal and near-normal male (p = 0.001, p = 0.020) and female (p = 0.001, p = 0.013) GWP compared to GSD, respectively. FHA is greater in normal male GWP (p = 0.011) and GSD (p = 0.040) compared to females. There was a significant and strong positive correlation between FHA and CrCdAR in all breeds and sexes. Additionally, FCI scoring had a medium (GWP, GSD) to strong (LAB) negative correlation with CFH.