Removal of N-Nitrosodiphenylamine from contaminated water: A novel modeling framework using metaheuristic-based ensemble models.

Investigating the complex interactions among physicochemical variables that influence the adsorptive removal of pollutants is a challenge for conventional one-variable-at-a-time (OVAT) batch methods. The adoption of machine learning-based chemometric prediction models is expected to be more accurate than the conventional method. This study proposed a novel modeling framework for predicting and optimizing the adsorptive removal of N-Nitrosodiphenylamine (NDPhA). Initially, models were trained by using OVAT data, with their hyperparameters subsequently fine-tuned through Bayesian optimization. In the second phase, the particle swarm optimization (PSO) technique was adopted to identify optimal parameters, specifically time, concentration, temperature, pH, and dose, to ensure the highest removal. The adopted analytical method enhances both prediction accuracy and removal efficiency. Utilizing OVAT data for NDPhA removal, the XGBoost regressor significantly outperformed other models. With a correlation coefficient of 0.9667 in the testing dataset, the XGBoost model exhibited its accuracy, emphasized by its low mean squared errors of 28.45 and mean absolute errors of 0.0982. Feature importance analysis consistently identified time and concentration as the most critical factors across all models.

Physics-informed NN-based adaptive backstepping terminal sliding mode control of buck converter for PEM electrolyzer.

This paper proposes an advanced control approach to controlling a DC-DC buck converter for a proton exchange membrane (PEM) electrolyzer within the framework of a direct current (DC) microgrid. The proposed adaptive backstepping terminal sliding mode control (ABTSMC) leverages a physics-informed neural network (PINN) to accurately estimate and compensate for system uncertainty. The composite controller achieves finite-time convergence of the tracking error by combining backstepping control and terminal sliding mode control (TSMC). The proposed PINN aims to optimize the unconstrained parameters by utilizing observed training points from the solution, ensuring the network accurately interpolates a limited portion of the solution. The efficacy of the proposed hybrid control method is validated using a hardware-in-the-loop (HIL) implementation under various test settings, ensuring the preservation of the actual performance of the PEM electrolyzer during testing. The experimental verification results demonstrate that the proposed control method exhibits greater benefits, such as a faster dynamic response and greater robustness against parameter uncertainties than improved sliding mode-based controllers. In situations where operational conditions change, a rapid response is achieved within a mere 0.025s of settling time, exhibiting a minimal percentage overshoot of about 17.5% and presenting minimal fluctuations.

Simultaneous COVID-19 Pandemic and Dengue Epidemic: A Double Challenge to Geriatric Health Security in Bangladesh.

Bangladesh faces distinct challenges as a resource-poor country due to the combined effects of the COVID-19 pandemic and simultaneous dengue outbreaks. Older adults are particularly vulnerable to infection and death from COVID-19. While overall health and life expectancy in the general population have improved substantially in Bangladesh, health services for older adults are still lacking. No specialized geriatric units have been established in hospitals, and no home care programs have been established for older adults. COVID-19 mortality rates were highest among older adults ages 61 to 70 years (35%), and 71 to 80 years (20%) in 2022. Although the country's average COVID-19 mortality rate was low at 1.76%, in older adults, it was much higher (55%), accounting for 14,797 deaths, despite that most cases (55%) were recorded in young adults. During the COVID-19 pandemic, Bangladesh also experienced a dengue epidemic. Around 21,193 dengue patients were admitted to hospitals between January 1 and October 8, 2022. Without a well-established and all-encompassing social care program, the indirect socioeconomic burden of COVID-19 continues to fall on older adults. There is an immediate need for robust healthcare and support services, especially for older adults in Bangladesh, which are particularly susceptible to the dual threats posed by the COVID-19 pandemic and the dengue epidemic. Recommendations are made to protect older adults from the devastating effects of the 2 simultaneous epidemics.

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects.

Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes.

Synthesis and Characterization of Eco-Friendly Bio-Composite from Fenugreek as a Natural Resource.

The present study show the usability of starch (tamarind) based-bio-composite film reinforced by fenugreek by various percentages to replace the traditional petrochemical plastics. The prepared bio-composite films were systematically characterized using the universal testing machine (UTM), soil degradation, scanning electron microscope (SEM), X-ray diffraction (XRD), thermogravimetric analyzer (TGA), and antibacterial tests. The experiments showed that a lower percentage of fenugreek improves biodegradation and mechanical strength. More than 60% of biodegradation occurred in only 30 days. Almost 3 N/mm2 tensile strength and 6.5% tensile strain were obtained. The presence of micropores confirmed by SEM images may accelerate the biodegradation process. Antibacterial activity was observed with two samples of synthesized bio-composite, due to photoactive compounds confirmed by FTIR spectra. The glass transition temperature was shown to be higher than the room temperature, with the help of thermal analysis. The prepared bio-composite containing 5% and 10% fenugreek showed antibacterial activities.

Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach.

Autism spectrum disorder (ASD) is a neurological illness characterized by deficits in cognition, physical activities, and social skills. There is no specific medication to treat this illness; only early intervention can improve brain functionality. Since there is no medical test to identify ASD, a diagnosis might be challenging. In order to determine a diagnosis, doctors consider the child's behavior and developmental history. The human face can be used as a biomarker as it is one of the potential reflections of the brain and thus can be used as a simple and handy tool for early diagnosis. This study uses several deep convolutional neural network (CNN)-based transfer learning approaches to detect autistic children using the facial image. An empirical study is conducted to select the best optimizer and set of hyperparameters to achieve better prediction accuracy using the CNN model. After training and validating with the optimized setting, the modified Xception model demonstrates the best performance by achieving an accuracy of 95% on the test set, whereas the VGG19, ResNet50V2, MobileNetV2, and EfficientNetB0 achieved 86.5%, 94%, 92%, and 85.8%, accuracy, respectively. Our preliminary computational results demonstrate that our transfer learning approaches outperformed existing methods. Our modified model can be employed to assist doctors and practitioners in validating their initial screening to detect children with ASD disease.

Exploring spatial variations in level and predictors of unskilled birth attendant delivery in Bangladesh using spatial analysis techniques: Findings from nationally representative survey data.

BACKGROUND: Bangladesh has failed to meet the United Nations goal for reducing maternal mortality in the last decade. The high prevalence of unskilled birth attendant (UBA) delivery (47%) has resulted in negative consequences for the health of mothers and newborn babies in the country. Spatial variations in UBA delivery and its predictors are yet to be explored in Bangladesh, which could be very helpful in formulating cost-effective policies for reducing that. This study examines the spatial variations in UBA delivery and its predictors in Bangladesh. METHODS: This study analyzed the characteristics of 672 clusters extracted from the 2017/18 Bangladesh Demographic and Health Survey, and healthcare facility data from the 2017 Bangladesh Health Facility Survey. These data were analyzed using descriptive and spatial analyses (hot spot analysis, Ordinary Least Squares Regression, and Geographically Weighted Regression) techniques. RESULTS: Statistically significant hot spots of UBA delivery were concentrated in parts of the Mymensingh, Sylhet, Barishal, and Rangpur regions, while Khulna was the safest region. Predictive strengths of the statistically significant predictors of spatial variation in UBA delivery were observed to vary considerably across the regions. Poorest household wealth status and less than four antenatal care contacts emerged as strong predictors of UBA delivery in all the aforementioned hot spot-stricken regions, except Barisal. Additionally, primiparity and all secondary education or higher were strong predictors of lower UBA delivery rates in Mymensingh and Sylhet, while poorer household wealth status was also a strong predictor of UBA delivery in Sylhet. Multiparity was an additional strong predictor of UBA delivery in Rangpur. In Barisal, only poorer household wealth status exerted a strong positive influence on UBA delivery. CONCLUSIONS: The remarkable spatial variations in UBA delivery and its predictors' strengths indicate that geographically-targeted interventions could be a cost-effective method for reducing the UBA delivery prevalence in Bangladesh, thereby improve maternal and child health.

Vulnerabilities of older adults and mitigation measures to address COVID-19 outbreak in Bangladesh: A review.

The infection with coronavirus disease (COVID-19) had an extremely negative influence on public health and the global economy. Covid-19 infection is more likely to affect the elderly than younger people, and pre-existing medical conditions, such as cardiovascular disease, diabetes, high blood pressure, and respiratory diseases, might lead to death due to COVID-19 infection. In low-income, developing, and highly dense countries like Bangladesh, the aging population is particularly vulnerable to the pandemic due to inadequate health services, socio-economic circumstances, environmental settings, religious and cultural beliefs, personal cleanliness habits, and a contemplative approach to infectious disease. Besides, recent cyclones and floods have combined effects on older people's increasing vulnerabilities. In this study, we reviewed and examined the vulnerabilities of older adults to the COVID-19 outbreak in Bangladesh. Different mitigation measures are discussed to protect the elderly from the adverse effect of the pandemic. This study proposes several steps to reinforce the commitment to social care and health care services to guarantee well-being, encourage preventive measures, and increase access to older people's health services in Bangladesh. The core findings will provide a valuable guideline for older adults, scientists, and policymakers to take effective long-term measures to mitigate the pandemic's risk.

Functional constipation in Bangladeshi school aged children: A hidden misty at community.

BACKGROUND: Constipation is a common problem in children and a frequent cause of hospital visit in both primary & specialized care, which needs proper evaluation & management. Presentation of constipation is variable among children. In Bangladesh there has been no published data regarding constipation in community among school aged children. AIM: To determine the magnitude of functional constipation and its risk factors in community among Bangladeshi school children. METHODS: This descriptive cross sectional study was conducted in different schools of Dhaka division, Bangladesh. All school aged children between 5-16 years of age who attended school were included in this study. Samples were collected randomly. Proper clinical history & physical examinations (without digital rectal examination) & available investigations (if done previously) were recorded. Diagnosis of functional constipation was done by Rome IV criteria and was compared with children without constipation. Children with any red flag sign, known chronic disease or any findings suggestive of organic disease and on treatment of constipation were excluded. Statistical analysis of the results was done by using Windows based software device with Statistical Packages for Social Science 20. For all statistical tests, P value of less than 0.05 was considered as statistically significant. RESULTS: Total study populations were 707 and male was 443 and female 264. Among them, 134 (19%) children had constipation. In constipated children, 78 children fulfilled the Rome IV criteria for functional constipation and it was 11% of total population. Mean age of children having functional constipation was 11.24 ± 3.54 years and Male female ratio was 1:1.78. Anorexia, nausea, abdominal pain, hard stool, blood with hard stool, alternate hard and loose stool and fecal mass in left iliac fossa were analyzed between two group and all were significantly higher in children with functional constipation group. Children of school, where toilet numbers were inadequate had 2.5 times more constipation risk in comparison to children of school with adequate toilet number (OR = 2.493, 95%CI: 1.214-5.120). Children who feel embarrassed to use toilet at school, had 3.6 times higher risk of constipation (OR = 3.552, 95%CI: 1.435-8.794). Here children with H/O affected sibs and parents/grandparents had 4 and 2.6 times more chance of constipation respectively in comparison to children without H/O affected sibs (OR = 3.977, 95%CI: 1.884-8.397) and parents/grandparents (OR = 2.569, 95%CI: 1.172-5.629). Children with inadequate fluid intake had 2 times more risk of constipation in comparison to children with adequate fluid intake (OR = 1.972, 95%CI: 1.135-3.426). Children who passed electronic screen time of > 2 h/d had 2 times more chance of constipation in comparison to children who passed electronic screen time < 2 h (OR = 2.138, 95%CI: 1.063-4.301). CONCLUSION: Constipation is not uncommon in Bangladeshi school aged children. Inadequate toilet number, family history of constipation, inadequate fluid intake, feeling embarrassed to use toilet at school, and electronic screen time for > 2 h/d were found as risk factors in the present study for functional constipation.

Influences of climatic and non-climatic factors on COVID-19 outbreak: A review of existing literature.

Coronavirus disease 2019 (COVID-19) has become a significant global public health issue resulting from SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). COVID-19 outbreak approaches an unprecedented challenge for human health, the economy, and societies. The transmission of the COVID-19 is influenced by many factors, including climatic, environmental, socioeconomic, and demographic. This study aimed to investigate the influences of climatic and sociodemographic determinants on COVID-19 transmission. The climatic variables considered herein were air temperature, relative humidity, wind speed, air pollution, and cumulative precipitation. Sociodemographic variables included population density, socioeconomic conditions, misinformation, and personal hygiene practices towards the pandemic. Review results indicated that lower temperatures and greater incidence of COVID-19 are reported in a more significant number of studies. Another factor linked to COVID-19 occurrence was the humidity. However, the results were varied; some research reported positive, and others reported negative relationships. In addition, poor air quality, along with strong winds, makes the virus more vulnerable to spreading, leading to a spike in COVID-19 cases. PM2.5, O3, and NO2 also showed a strong correlation with the recent epidemic. The findings on rainfall were inconsistent between studies. Among the non-climatic factors, population density, education, and income were credited as potential determinants for the coronavirus outbreak. Climatic and sociodemographic factors showed a significant correlation on the COVID-19 outbreak. Thus, our review emphasizes the critical importance of considering climatic and non-climatic factors while developing intervention measures. This study's core findings will support the decision-makers in identifying climatic and socioeconomic elements that influence the risks of future pandemics.