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Background and Aims: Blood, vital for transporting nutrients and maintaining balance, comprises red blood cells, white blood cells, and platelets, each pivotal. Imbalances lead to issues—low red cells cause fatigue (anemia), high white cells hint at infection, low counts raise infection risks. Using trendy statistical approaches, investigating the complex link between platelet counts and numerous blood components. Our investigation, leveraging count regression approaches, revealed deep insights into the interaction between platelet counts and other important hematological markers. Methods: A cross‐sectional study utilized data from 3120 individuals, including both male and female participants, who visited these hospitals between June 16, 2022 and December 17, 2022, to assess their blood samples through testing by using convenience non‐parametric sampling framework. Platelet count was taken into account as a measure of outcome in this research. This specific study region was chosen for its easy accessibility, which helped the seamless execution of the data‐gathering technique. Count regression, negative binomial regression, and quasi‐Poisson regression techniques have been employed for examining relationship of the data sets. Results: Three different count regression models were utilized to assess the proper association between the response and the relevant covariates and we found negative binomial count regression model (Akaike information criterion = 76.55, Bayesian information criterion = 76.59, and deviance = 3.14) was providing comparatively better performance than others. Based on the chosen model we found white blood cell, erythrocyte sedimentation rate, and eosinophils are significant but neutrophil, monocyte, and lymphocyte are not significant. We have also gone through proper model adequacy checking for our selected model and we found enough evidence to justify our model. Conclusion: From the result, we found insightful remarks into the mechanisms involved in platelet production and regulation, which can aid in developing increased effective treatments and interventions to maintain optimal platelet levels and prevent health problems related to abnormal platelet counts. [ABSTRACT FROM AUTHOR]

Background: Epidemiological and nutritional modifications are causing an increase in stunting in many low‐ and middle‐income countries (LMIC), which will eventually result in juvenile diseases and mortality. Therefore, this study aimed to identify the influential factors contributing to stunting among under‐five children in Cambodia. Methods: A secondary dataset consisting of 3268 under‐five children was extracted from the latest Cambodian Demographic and Health Survey (CDHS)‐2021/2022 dataset. The Chi‐square test and Boruta algorithm were used for covariate selection, and logistic regression approaches were used to determine the influence of demographic, socioeconomic and other factors on the presence of stunting. Results: Findings revealed that about 21% of under‐five children were stunted, and the prevalence of stunting was higher in rural areas than in urban areas. The prevalence of child stunting was lower in families with highly educated parents. A child whose father had a secondary education had 0.71 times lower (adjusted odds ratio [AOR]: 0.71, 95% CI: 0.520–0.969) chance of stunting than a child whose father had no education. Findings revealed that Ratnak Kiri, Mondul Kiri, Stung Treng, Pursat and Kampot had a greater prevalence of stunting than other places, ranging from 27.11% to 35.70%, whereas Banteay Meanchey, Phnom Penh and Kandal had the lowest rates, ranging from 12.80% to 16.00%. Results of the Boruta algorithm and logistic regression suggested that under‐five stunting is significantly influenced by factors such as the child's age, size at birth, mother's age at first birth, mother's body mass index (BMI), father's educational status, cooking fuel, and wealth index. Conclusions: It is necessary to take initiatives for reducing the prevalence of stunted children prioritising the identified factors that ultimately help to reduce the burden of child health. The authors believed that the findings of this study will be helpful for policymakers in designing the appropriate policies and actions to achieve the Sustainable Development Goals (SDGs) by reducing stunting among under‐five children in Cambodia. [ABSTRACT FROM AUTHOR]

As the world copes with increasing geopolitical tensions, energy crises, and environmental challenges, the imperative for affordable and clean energy transitions is clearer than ever. Lower-middle-income economies, home to a significant portion of the global population, are particularly impacted. Addressing their unique energy challenges requires an understanding of their specific contexts, barriers, and potential solutions. This study brings a novel perspective to this critical issue, introducing a robust ranking system for these economies based on their accessibility to affordable and clean energy. Utilizing the Entropy method for criteria weighting and the technique for order of preference by similarity to ideal solution (TOPSIS) method for final ranking, the study unravels the complex interplay of factors influencing energy transition in these countries. International financial flows and renewable capacity per capita emerge as crucial determinants, accentuating the need for global policy focus in these areas. This research is driven by the urgency to bridge the gap in energy access among lower-middle-income countries, recognizing that their progress is vital for global sustainability. By providing a clear ranking, our study not only highlights the disparities but also aims to catalyze immediate international intervention and policy adaptations, making the pursuit of clean and affordable energy a tangible and urgent goal. The ranking reveals an Asian predominance in the top tier, with Bhutan leading, followed by India and Pakistan. In contrast, nations from geographically distant islands and Africa find themselves at the lower end of the spectrum. The study underscores the urgent need for concerted global action towards achieving sustainable development goal- 7 (SDG-7) and accelerating the transition towards affordable and clean energy across lower-middle-income economies. [ABSTRACT FROM AUTHOR]

Background: C-section prevalence has increased drastically over the past few decades across the globe. This growth has been caused by an array of factors, including maternal, socio-demographic, and institutional factors, and it is a global concern in both developed and developing countries. Therefore, the objective of this study is to identify relevant risk factors for the delivery type, and find a more accurate ML-based model for identifying cesarean women. Methods: The study is based on 5139 delivery cases from the Bangladesh Demographic Health Survey (BDHS) 2017-18. The number of C-sections performed in the nation has increased to at least 45 percent in the two years prior to 2022. Because of this, we have used multiple logistic regression and machine learning algorithms to determine cesarean delivery and identify the socio-demographic risk factor among mothers in Bangladesh. Results: An independent X² test was performed before we considered six popular machine learning (ML) algorithms to predict C-section among women in Bangladesh, including logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), naive Bayes (NB), and decision tree (DT). Model evaluation criteria included accuracy, mean absolute error (MAE), Cohen's kappa, precision, specificity, area under the curve (AUC), and F1 score value. Bivariate analysis results revealed that higher educated mothers and fathers, the richest family, overweight mothers, and hospital delivery had a higher percentage of cesarean babies. With an accuracy of 83.74%, NB (naive Bayes) outperforms the other five classifiers. We can get more precise information than accuracy from the ROC curve and the AUC. Depending on the AUC value, we can see that among all classifiers, Logistic Regression (LR) and Random Forest (RF) provide the most accurate classification for determining c-section among Bangladeshi women. Conclusion: Our findings contribute to a better understanding of how to categorize C-section intentions among Bangladeshi women. The technique will be useful in identifying the women who are most likely to undergo a C-section in the healthcare system. As a result, the government can launch an effective public awareness campaign. [ABSTRACT FROM AUTHOR]

In the present study, we have demonstrated the Indonesian natural zeolite and modified zeolite was used to remove the acid blue 25 (AB25) from wastewater. The adsorption capacity of AB25 on zeolite and modified zeolite (zeolite-CTAB) were investigated by various batch adsorption experiments. The modification effect on the surface of zeolite was analyzed using Fourier transforms infrared spectra, scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray fluorescence, and X-ray diffraction, respectively. The maximum removal of AB25 was obtained under acidic conditions at pH 2. The kinetic experimental results imply that the adsorption of AB25 onto these adsorbents well followed the second-order kinetic model. The maximum adsorption capacity of 64.2 mg/g was found in Zeolite at 30°C and 112.44 mg/g for zeolite-CTAB at 60°C. The results revealed that the adsorption of AB25 onto zeolite-CTAB fitted better to Langmuir model and Zeolite fitted better with Freundlich model. The AB25 adsorption on zeolite-CTAB increases with an increasing temperature indicates that the preferential adsorption may occur at a higher temperature. The positive value of ΔH° in zeolite-CTAB material thermodynamic parameters indicates that the process was an endothermic process. These results indicate that zeolite-CTAB has high adsorbent efficiency and it is promising adsorbents for removing the dye AB25. [ABSTRACT FROM AUTHOR]