Background: Malnutrition is a major public health concern among children under five years of age in low- and middle-income countries, contributing to high morbidity and mortality. Despite progress, malnutrition remains a significant issue in Bangladesh and is influenced by socioeconomic disparities and maternal education. Mid-Upper Arm Circumference (MUAC) is a practical tool for assessing nutritional status in resource-limited settings. This study aimed to determine the nutritional status of children aged 6–59 months in the outpatient department (OPD) of a tertiary hospital in Bangladesh. Methods: A retrospective observational study was conducted at Department of Pediatrics, MR Khan Shishu Hospital & Institute of Child Health, Dhaka, from January 2010 to January 2011. Data from 200 children aged 6–59 months were analyzed. Nutritional status was classified as normal, moderate or severe based on MUAC values. Associations among demographic factors, maternal education, and malnutrition were also explored. Results: Among the 200 children, 24.5% were malnourished, including 10.5% with severe malnutrition and 14% with moderate malnutrition. Children aged 36–59 months had the highest malnutrition prevalence (29%). Females (21%) were more likely to be malnourished than males (16.5%). Maternal education strongly affected outcomes, with malnutrition being the highest among children of illiterate mothers (47.5%). Conclusion: Gender and maternal education influence the nutritional status of children in Bangladesh. For nutritional assessment, the MUAC is an effective tool that underscores the need for interventions that target these disparities.

The Electronic Medical Records (EMR) system has become an essential component in the digital transformation of the healthcare sector, replacing manual record-keeping with a more efficient digital system. This study aims to analyze the quality of EMR services at the Emergency Department (IGD) of Rumah Sakit Bhayangkara TK I Pusdokkes Polri, with a focus on the challenges affecting the efficiency of the EMR system's use. The research method used is quantitative analytic with a cross-sectional design, involving 60 EMR users who provided data through interviews, observations, and questionnaires. The results show that the majority of respondents are aged 20-35 years and predominantly work as nurses. Although most respondents express high satisfaction with the quality of the EMR, there are several technical issues, such as server and network disruptions, that affect the system's efficiency. Further analysis indicates that the main factors influencing user satisfaction are data accuracy and time efficiency in record-keeping. The p-value for the relationship between data accuracy and user satisfaction is 0.03, which indicates a statistically significant relationship. The p-value for time efficiency in record-keeping is 0.02, also showing a significant relationship with user satisfaction. The study concludes that EMR can improve medical service efficiency and quality; however, improvements in technical issues, particularly server and network infrastructure, as well as user training, are essential to optimize this system.

This study proposes an artificial intelligence-driven model that can enhance productivity and reduce manufacturing costs in the brewery industry of Nigeria. The research initiated with a critical literature review on the factors of productivity in the knowledge-intensive industries, choosing thereupon the brewery sector based on expert advice. In total, three predictive models were developed, namely Artificial Neural Network, Machine Learning, and a hybrid Artificial Neural Network-Machine Learning model, for predicting productivity. The Mean Squared Error was 0.001399 for the Artificial Neural Network model, Root Mean Squared Error was 0.037407, and Mean Absolute Error was 0.037283, while the Machine Learning had Mean Squared Error of 0.040378, Root Mean Squared Error of 0.200943, and Mean Absolute Error of 0.183000, the hybrid having Mean Squared Error of 0.013982, Root Mean Squared Error of 0.118247, and Mean Absolute Error of 0.110141. It also proved the fact that the Machine Learning model is able to predict productivity based on maintenance, Mean Time Before Failure, and Mean Time to Repair indicators since the obtained values for this type of model had lower errors than all the others: Mean Absolute Error = 0.08508, Mean Squared Error = 0.19275, Root Mean Squared Error = 0.43903.

Aim: The present study was carried out to assess the level of knowledge about ionizing radiation hazards among Yemeni patients in Sana’a city, republic of Yemen. Methods: A cross-sectional study of 669 questionnaires which tested patients ’information about ionizing radiation, its’ harmful effects and protection from these effects. This study performed on participants who referred for radiological examinations to the radiology department of two academic hospitals. Results: There is a no significant difference in the level of knowledge between different age groups (p=.058) and gender (p=.179), However, there is a significant difference in the level of knowledge among different educational levels groups and between who work or study in the medical field or not (p=.000). The mean knowledge score was 7.0149 ± 2.12550, which indicate good level of knowledge between study participants. Conclusions: The study indicates a good level of knowledge between a sample of Yemeni population, and to elevated their knowledge to a higher level, the government, ministry of health, and hospitals have a responsibility to educate the population and increase their level of knowledge about ionizing radiation and its harmful effects.

This study seeks to examine the influence of family long term strategic commitment on the sustainability of family owned enterprises in the Western Highlands of Cameroon. The study made use of primary data obtained through self-administered structured questionnaires. A total of 313 questionnaires were administered to the founders and managers of the family owned enterprises in the Western Highlands of Cameroon. Data collected was entered into SPSS. The study used the Generalised Linear Model (GLM) technique to test the hypotheses. Particular focus was on organisational culture, the involvement of family members and entrepreneurial risk-taking as determinants of the sustainability of family owned enterprises. The results from the GLM regression showed a negative coefficient in the overall sample which indicated that an increase in family long-term strategic commitment predicted a decrease in sustainability of family-owned enterprises. Specifically, organisational culture has a positive coefficient of 0.0862 significant at 10%; the involvement of family members has a negative coefficient of -0.0642 and significant at 10%; and entrepreneurial risk taking has a negative coefficient of -0.00155. This meant that there was a no effect of family long term strategic commitment on the sustainability of family owned enterprises in the Western Highlands of Cameroon; since only organisational culture positively influenced sustainability in the Western Highlands of Cameroon. This study was limited to the Western Highlands of Cameroon by not considering the other ecological zones (Southern rain forests, Central savannah and Northern arid region) of Cameroon. From the findings, the study recommended that founders and managers should engage in calculative risk taking and specify the degree of involvement of family members that could not jeopardised the continuity of the business. The research questions and methods used in this research are new in the aspect of investigating the influence of family long term strategic commitment on the sustainability of family owned enterprises in the Western Highlands of Cameroon.

This article deals with the behavior of isolated piles subjected to two models used in geotechnics (Hardening Soil and Mohr Coulomb) under cyclic lateral loading and their head deformations compared to monotonic loading in sandy soil. The behavior of the cable-stayed bridge piers was predicted using PLAXIS 2D software, based on the results of in situ and laboratory geotechnical studies carried out by the control office for Building and Public Works (BCBTP). Numerical simulation is an alternative to the high cost of large-scale in situ and laboratory studies for describing the behavior of deep foundations. The results obtained show that piles subjected to lateral head loading cause a horizontal head displacement compared with the Hardening Soil and Mohr Coulomb models. Cyclic loading of a pile results in deformation of the soil mass at the surface. Lateral displacement increases with the first few cycles until it stabilizes, generating an irreversible residual displacement due to progressive soil plasticization. Lateral cyclic loading has a favorable influence on pile behavior under cyclic loading, due to the reversible effect on pile displacement.

Most nanoparticles have diameters that lie within 1 to 100 nm. A variety of metal oxide nanoparticles can be synthesised chemically as well as biologically. NiO nanoparticles, which are synthesised chemically, tend to be highly toxic in their effects. In contrast, synthesis by the route of biogenesis or biomimetics is much more favourable than chemical synthesis. The pre-existing properties of purple cabbage are remarkably enhanced by NiO nanoparticles. Nickel oxide (NiO) nanoparticles were synthesised from the leaves of Brassica oleracea var. capitata. f. rubra (purple cabbage) by reducing Nickel nitrate hexahydrate solution for the formation of NiO nanoparticles. The analysis done using a Particle size analyser reveals the spherical morphology and the size of the NiO nanoparticles. The size and shape were studied under a Scanning Electron Microscope, which shows that the nanoparticle ranges from 34.5 nm to 89.6 nm. The formation of NiO nanoparticles was confirmed by the peaks obtained in Raman spectroscopy and UV-DRS analyser. The peaks that ensure the formation of NiO nanoparticles in Raman spectroscopy are 568.40 cm-1, 1129.46 cm-1 and 1379.07 cm-1 and UV-DRS are 265.60 nm and 339.69 nm. Preliminary phytochemical analysis of the aqueous extract of purple cabbage leaves shows that there is an abundance of phytochemical content present in purple cabbage. From the preliminary phytochemical analysis of purple cabbage, it was observed that the aqueous purple cabbage leaf extract contains alkaloids, sterols, flavonoids, coumarin, tannins, cardiac glycosides, anthraquinones, saponins, quinones, and carbohydrates.