The continuous development of flexible probability distributions has become essential for accurately describing complex stochastic phenomena. In this study, a new theoretical based on the New Sine Type II Generalized Topp-Leone distribution is investigated. The model is constructed by integrating the sine trigonometric transformation with the Type II Topp-Leone generator applied to an arbitrary baseline distribution. This combination introduces additional shape parameters that enhance the adaptability of the resulting family of distributions. Several mathematical properties of the proposed model are derived. These include explicit expressions for the cumulative distribution function (cdf), probability density function (pdf), survival function, and hazard rate function. In addition, analytical forms of the quantile function, moments, moment generating function, and order statistics are established. The author’s adopted two estimation techniques in this work: the maximum likelihood estimation (MLE) and the maximum product of spacing (MPLS) estimation methods. The structural characteristics of the distribution reveal that it is capable of producing a wide range of density and hazard rate shapes depending on the selected baseline model and parameter values. The theoretical results presented in this work provide a general framework for constructing new members of the family and offer a foundation for further statistical developments involving estimation techniques and practical implementations. Plots of the pdf, cdf as well as the validity check on the pdf of the new model indicate that the model is adequate capable of handling all the intricate associated with real-life datasets.

Neonatal mortality poses a major hurdle in global health efforts. As per the 2022 Kenya Demographic and Health Survey (KDHS), the neonatal mortality rate was 21 deaths per 1000 live births. Although the rates have been declining over the years, the rate remains significantly higher than the target set by Sustainable Development Goal 3.2 of 12 deaths per 1,000 live births by 2030 for preventable deaths endorsed by the United Nations meeting in 2015. To achieve this target, we need a predictive model that could provide actionable insights and timely interventions to predict and avert neonatal deaths. Machine learning has great potential to improve health outcomes for newborns, especially in areas with limited resources. The objective of this study was to develop a predictive model based on ensemble machine learning algorithms to predict neonatal mortality in Kenya using the 2022 KDHS dataset and identify the critical features contributing to the prediction of neonatal mortality. The Synthetic Minority Over-Sampling Technique was used to address the imbalance in the dataset. The Categorical gradient boosting model outperformed others in terms of accuracy (98.00%), precision (97.46%), recall (98.00%), F1 score (97.57%), and the Receiver Operating Characteristic (ROC) area under the curve (92.24%). The major factors identified included the number of children below five in the household, the total births recorded over the past five years, region, religion, sex of the newborn, source of drinking water, occupation, sex of the household head, use of mosquito nets, place of residence, ever-terminated pregnancy and duration of pregnancy. The research findings support the integration of machine learning into healthcare approaches, such Kenya Health Information System, to facilitate the accurate prediction of neonatal deaths and enhance survival for our newborns. The government should also promote family planning in the country to lower the number of children below five years in households and reduce the number of births recorded per woman in the last five years. The model's generalizability might be constrained by the exclusion of crucial clinical variables from missing data despite high performance.

Youth physical violence remains a major public health and social problem, particularly in developing countries such as Nigeria. This study examined the demographic, family, psychological, and economic factors associated with youth physical violence in Makurdi Metropolis using a Multinomial Logistic Regression (MLR) approach. Data were obtained through a structured questionnaire administered to youths aged 15–35 years, with responses collected via Google Forms using a random sampling technique. Youth physical violence was measured as a categorical outcome based on the frequency of physical fights, classified into multiple non-ordinal categories. Descriptive statistics were employed to summarize respondents’ characteristics after which multinomial logistic regression analysis was applied using R statistical software. The findings revealed that age and sex were significant demographic predictors of youth violence, with youths aged 21–30 years and males having a higher likelihood of engaging in violent behavior. Family support was found to be a significant protective factor, as youths with strong family support were less likely to engage in physical violence. Psychological factors showed mixed effects, with levels of stress and anxiety demonstrating significant associations with youth violence. Economic factors, particularly employment status, were also significant, as unemployed youths were more likely to engage in violent behavior compared to their employed counterparts, while tertiary education showed no significant effect. Model diagnostics confirmed good model fit, absence of multicollinearity and satisfaction of key regression assumptions. The study concludes that youth physical violence in Makurdi is influenced by a complex interaction of demographic, family, psychological, and economic factors. It recommends targeted interventions focusing on employment creation, family-based support systems, and youth-centered social programs to reduce violent behavior. The use of multinomial logistic regression provides a robust analytical framework for understanding the multifaceted nature of youth physical violence and offers valuable insights for policymakers and stakeholders.

Background: Unpaid domestic and caregiving work constitutes a critical yet invisible component of household functioning and national economies. In India, such work is predominantly undertaken by women, reflecting entrenched gender inequalities. Understanding the extent and patterns of unpaid work is essential to inform policy frameworks aimed at achieving Sustainable Development Goal 5 (SDG-5) on gender equality. Methods: This study utilized secondary data from the Ministry of Statistics and Programme Implementation (MOSPI), India for the three years 2020, 2021 and 2022. Statistical analyses included the Chi-square test for associations, Mann-Whitney U test for gender-based comparisons, and one-way ANOVA for age-group differences. Categories examined were unpaid domestic services, unpaid caregiving services, and their rural-urban variations, along with educational level as a covariate. Results: The Chi-square test revealed a significant association between gender and unpaid domestic as well as caregiving services across both rural and urban areas (p < 0.001). The Mann-Whitney U test confirmed that females had significantly higher mean ranks for all categories of unpaid work compared to males (p < 0.001), while no significant difference was observed with respect to education (p = 0.724). ΑNOVA results indicated no significant variation in unpaid domestic and caregiving services across age groups, except for rural caregiving services, which was statistically significant (p = 0.050). However, education level showed a highly significant difference across groups (p < 0.001). Conclusion: The findings highlight stark gender disparities in unpaid work, with women consistently bearing a disproportionate share of domestic and caregiving responsibilities across rural and urban settings. Age did not substantially influence unpaid work distribution, whereas educational attainment showed significant associations. These results underscore the urgent need for policy interventions that recognize, redistribute, and reduce unpaid work burdens to advance gender equality and align with India's commitments under SDG-5.

Automation in agriculture can improve product quality and consistency while reducing labor and time costs. Manual mango grading is often slow, subjective, and error-prone; therefore, an automated grading approach is desirable. In this study, we propose an image-based mango grading framework that classifies mangoes into four grades (green, semi-ripe, ripe, and rotten) using five models: Artificial Neural Network (ANN), Decision Tree (DT), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). We collected 4,207 mango images from multiple markets and locations and used a 70%/30% train–test split. Performance was evaluated using Accuracy, Precision, Recall, and F1-score across five repeated runs. CNN achieved the best overall performance (mean accuracy ≈ 0.99) and consistently outperformed the classical machine-learning baselines, indicating that deep learning can provide a reliable solution for automated mango grading and can be extended to other agricultural products.

This paper presents a comprehensive methodology for assessing the measure of rotatability for response surface design in second-order through the use of suitably chosen pairs of dissimilar incomplete block designs. The proposed framework incorporates multiple combinations of block structures, including symmetrically unequal block arrangements (SUBA) with two unequal block sizes combined with balanced incomplete block designs (BIBD), pairwise balanced designs (PBD) combined with BIBD, and PBD combined with SUBA with two unequal block sizes. By integrating these complementary block designs, the study aims to generate second-order designs that achieve improved precision and enhanced rotatability properties. To demonstrate the applicability of the method, several worked examples are constructed, highlighting the steps involved in deriving the design points and computing the corresponding measures of rotatability. Furthermore, three-dimensional response surface plots are provided to visually examine the geometric and variance characteristics of the resulting designs. These graphical representations support the interpretation of the theoretical results and illustrate the advantages of the proposed approach in constructing efficient rotatable second-order designs.

This study uses a simulated macroeconomic dataset with 100 observations to compare Ordinary Least Squares (OLS) regression versus Bayesian regression for GDP modelling. Investment, consumption, and government spending are included in the model definition as important explanatory variables. Under stringent distributional assumptions, OLS, which is based on the traditional frequentist paradigm, yields parameter estimates that are solely obtained from the observed sample. In contrast, Bayesian regression produces posterior estimates by integrating prior distributions with the likelihood, providing a probabilistic description of parameter uncertainty. The methodological significance of prior specification was highlighted by the unstable inferences obtained from initial Bayesian estimation using weakly informative priors. However, posterior convergence and predictive alignment with OLS findings were significantly enhanced by the addition of sophisticated, commercially viable priors. While Bayesian regression provided wider credible intervals reflecting uncertainty, OLS produced more accurate (narrower) predicted intervals. The results confirm that Bayesian regression is a rigorous and reliable substitute for OLS when backed by well-informed priors, especially in situations with sparse data or ambiguous model assumptions.

This study uses a factor analysis approach to examine the fundamental aspects of academic accomplishment among students at the University of Calabar's Faculty of Computing. Performance was evaluated using a dataset that included 500 students' exam results from six fundamental computing courses: System Analysis, Structured Programming, Object Oriented Programming, Numerical Analysis, Distributed Computing, and Algorithm & Complexity Analysis. Finding latent factors that explain performance differences and provide insights beyond conventional grading schemes was the main goal. The Kaiser-Meyer-Olkin (KMO) measure produced a value of 0.85, and Bartlett's Test of Sphericity was very significant (p <.001), confirming the applicability of the dataset. Two components that accounted for 75.7% of the total variation were identified using principal component analysis. The choice to keep two factors was supported by the scree plot, which showed a distinct "elbow" at Component 2. By grouping the courses into two significant dimensions; programming and analytical skill and systems and computational complexity, varimax rotation improved interpretability. These observations show several cognitive domains in computer education. The results emphasize the importance of data-driven curriculum design and recommend that teaching methods be in line with the structure of student performance. Teachers can improve learning outcomes and optimize instructional routes by classifying courses based on prevailing skill sets. By showing how factor analysis can reveal underlying structures in educational results and direct more successful academic interventions, this study advances academic analytics.

The stress-strength reliability measure, defined as R = P(Y < X), is a widely used index in reliability analysis, representing the probability that a system’s strength exceeds the applied stress. This paper investigates the Exponential-Gamma stress-strength model, assuming the stress variable follows an exponential distribution while the strength variable follows a gamma distribution. Analytical expressions for the reliability function are derived and generalized to the case of standby redundant systems. Estimation of reliability is developed using maximum likelihood and uniformly minimum variance unbiased approaches, and both exact and asymptotic confidence intervals are obtained. A detailed Monte Carlo simulation study evaluates the finite-sample properties of the proposed estimators, highlighting the superior small-sample performance of the UMVUE and the asymptotic efficiency of the MLE. The practical usefulness of the model is demonstrated through real data applications, showing that the Exponential-Gamma framework provides an effective and tractable tool for modeling system reliability in applied settings.

This research article presents an inventory production model incorporating time-dependent production rate, deterioration rate and demand rate, specifically tailored for industries like sugar manufacturing. The model assumes a deterministic framework without shortages, making it highly relevant for perishable goods industries where maintaining continuous supply is crucial. The primary objective is to derive an optimal production strategy considering the dynamic nature of these factors. The mathematical formulation is developed using differential equations to model inventory behavior over time. A numerical example is provided to illustrate the model’s practical applicability, followed by a sensitivity analysis highlighting the collision of parameter variations. The findings suggest managerial insights for optimizing inventory levels, minimizing wastage, and ensuring a smooth production flow.