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Abstract The ongoing global warming is likely to cause changes in the growth and species composition of aquatic epilithic communities. To be able to predict such changes, a field study was conducted in the cooling water discharge area of a nuclear power plant that releases brackish cooling water to the Baltic Sea at Forsmark. The power plant creates a temperature gradient in the field from 0 to ca. 8 °C above normal temperature. The project was concentrated on the growth and distribution of four dominant taxa in the upper littoral zone: the colonial cyanobacterium Rivularia atra and the three filamentous macroalgae Cladophora glomerata (green), Ulva spp. (green) and Ceramium tenuicorne (red). Overall, Cladophora increased strongly, Ulva spp. increased slightly and Ceramium decreased strongly in abundance with higher water temperature. For Rivularia these results were corroborated in a laboratory experiment and studied in more detail. The colonies collected at 18 °C in the field were experimentally exposed to 18 °C, 22 °C and 26 °C in climate chambers for 50 days. Growth rates were highest in 18 °C and lowest in 22 and 26 °C. Addition of extra stress in the form of a heavy metal (copper) decreased growth in all temperatures, but highest growth rates still occurred in 18 °C. Rivularia was less stressed in 18 °C than in 22 °C and 26 °C, which was shown by, higher chlorophyll a (chla) concentration, higher photosynthetic performance (measured as electron transport rate by pulse amplitude modulated fluorescence), lower zeaxanthin concentration and a lower carotenoids:chla ratio.

It is quite evident that some of the experimental research works have been carried out by researchers in the area of spiral polishing using abrasive for surface finishing improvement but most of the experiment has been employed on Steel or Alloy Steel as a material. Very few number of research works have been performed by researchers across the globe on the Spiral Polishing Method of Titanium holes made by Electrical Discharge Machining (EDM). Therefore, experimental investigations were carried out in the area of Spiral Polishing and Finishing of EDM-drilled holes of various materials to meet the desired goal of demands on the surface quality. This experimentation aims to develop a novel method with spiral polishing using abrasive flow finishing. To explore the search investigation and find out the better surface finishing for through holes made on EDM, the process parameters have been designed using the Taguchi L16 orthogonal array with input parameters such as Current (I), Pulse on time (Ton), and Pulse off time (Toff). The addition of Boron Carbide Powder with Handwash, Glycerine, Shampoo, and Liquid Soap is used to decrease the Surface Roughness (Ra) with a ratio of 5 : 1. The Taguchi technique is used to assess the P/M process parameter setting for a given signal to noise (S/N) ratio.

Seam strength is critical in achieving the proper and adequate quality seam, which eventually determines the overall quality of any garment. The target of this research was to investigate exactly how different stitch density affected seam strength for different stitch types. Commercially available, 100% cotton plain weave structured woven fabric with mass per unit area of 270 g/m2 was used in this analysis. Two different stitch types, such as lock stitch (ISO#301) and chain stitch (ISO#401), along with four stitch densities like 8, 10, 12 and 14 per inch were utilized. The seam strength of the prepared samples was determined using a tensile strength tester followed by the ASTM D1683 standard procedure. The impact of independent variables on superimposed seam strength was statistically analyzed using a regression correlation with the help of SPSS software to construct a regression equation to predict seam strength before the manufacturing process. The study found a non-linear relationship between seam strength and stitch density. When the stitch density is too low, the seam strength is shown low as well, because the cloth cannot be held under tensile load. In a very compact woven fabric, structural jamming or intrinsic puckering occurs if it is too high. As a result, the best stitch density should be chosen to achieve the best seam strength. This study will help the manufacturers choose a suitable SPI for lock and chain stitches from polynomial regression equations before the sewing production of cotton woven fabric, which was not predicted by any researcher before. Therefore, the optimum stitch density can be chosen using regression equations to get the best result for seam strength.

This study attempted to produce a cheap polyester composite material using an agricultural waste banana peel in the structure. Banana fibre has been used in composites as reinforcements, but banana peel has never been used with polyester before. The possibility of improved thermal and flammability properties of a composite due to increased moisture in the structure, and the char-forming ability of the cellulosic part of banana peel or the production of highly flammable material due to the presence of carbohydrates in the structure were the assumptions. To tackle the second assumption, aluminium trihydrate (ATH) was added. The handmade composites showed a drastic drop in tensile strength from 38.02 MPa to 16.72 MPa due to a lack of chemical bonding between the constituents. The impact and flexural strength showed some improvement with the addition of banana peel, along with ATH, to record results of 10.92 kg/cm and 49 MPa, respectively, after the initial drop that occurred when only ATH was added. However, these results were still inferior to the properties of pure polyester. The results of flammability and thermal resistance matched the second assumption, as flame retardancy was kept under control by the presence of ATH. The absorbency properties remained almost unaffected.

This review provides an account of fish species contaminated with microplastics (MPs) across the globe (seven continents). A total of 887 fish species were found contaminated with MPs based on MPs in the gastrointestinal tract/GI. The most MPs contaminated-fish species found were marine and demersal species. Globally 45 % of fish ingested MPs with an average concentration of 5.93 MPs particles per fish species. Among all the countries, China had the highest number of fish species contaminated with MPs in the followingorders: China (176 species), Brazil (84), the USA (48), India (35), the Atlantic Ocean (31), Iran (30), Bangladesh (28), Turkey (26), Indonesia (25), the UK (23), Saudi Arabia (23), Thailand (21), Portugal (20), Australia (20), Italy (18), South Africa (18), Argentina (15), Chile (14), Galapagos Islands (Ecuador) (14), the North Pacific Gyre (14), Samoa (13), Malaysia (12), Colombia (11), New Zealand (11), Fiji (10), Spain (10), the North Sea (09), South Korea (09), Tahiti (09), Vanuatu (09), Ghana (08), Canada (07), Japan (07) and Nigeria (07) and others. MPs ingestion in fishes varied (high, medium, and low) among the locations/countries. In several locations/countries, MPs ingestion/contamination occurred in up to 100 % of fish samples. Because of MPs contamination, seafood fisheries, and the livelihoods of people associated with fishing, aquaculture, and seafood business, can be threatened. It may also increase health risks to seafood fish consumers since there is a probability that high risks pollutants adsorbed in MPs can be transferred to humans via the food chain.

Introduction: Coronavirus disease 2019 (COVID-19), a respiratory disease caused by the coronavirus SARSCoV-2, has had an immense impact on a variety of sectors both worldwide and nationwide. Vast differences are observed among states within the United States of America in terms of COVID-19 cases and deaths. Objective: The objective of this paper is to present a means through which we can compare deaths between multiple states, using the index date approach applied by Middelburg and Rosendaal. Materials and Methods: Using the CDC COVID-19 tracker, we created two sets of ten states focusing on states with (1) the highest number of deaths and (2) the highest number of deaths per 100,000. We applied features of the authors’ technique in order to compare deaths between certain states through visualizations. We referred to the cumulative number of deaths on each day from January 21st, 2020 to September 30th, 2020, as a percentage of the cumulative deaths 40 days after the first death. Results and Discussion: Comparability was established by synchronizing each state to a baseline date, which allows us to adjust for issues that arise from the scales used within a standard cumulative deaths graph, such as a tendency to be driven by the states with the highest cumulative number of deaths. This technique also normalized each state to a standard start date. Conclusion: This paper shows the application of a technique that allows for clearer comparisons of COVID19 related deaths between states, as opposed to the use of a standard cumulative deaths graphs.

Background Sodium (Na+) in saline water may increase blood pressure (BP), but potassium (K+), calcium (Ca2+), and magnesium (Mg2+) may lower BP. We assessed the association between drinking water salinity and population BP. Methods and Results We pooled 6487 BP measurements from 2 cohorts in coastal Bangladesh. We used multilevel linear models to estimate BP differences across water salinity categories: fresh water (electrical conductivity,

Conservation payments are increasingly advocated as a way to meet both social and ecological objectives, particularly in developing countries, but these payments often fail to reach the 'right' individuals. The Government of Bangladesh runs a food compensation scheme that aims to contribute to hilsa (Tenualosa ilisha) conservation by improving the socioeconomic situation of households affected by hilsa sanctuary fishing bans. Analysing data from a household survey of compensation recipients and non-recipients, we identify the current correlates of compensation distribution and explore perceptions of fairness in this distribution. We find that distribution is largely spatial rather than based on the household characteristics that are supposed to determine eligibility for compensation, indicating political influence in the distribution process. We also find the compensation scheme is widely perceived to be unfair, which could be undermining its potential to compensate vulnerable fishers while improving compliance with fishing bans. The spatial distribution of compensation would shift substantially under alternative targeting scenarios that are likely to improve the cost-effectiveness of the scheme, such as targeting those who are most dependent on fishing for their livelihood. This study highlights a challenge for conservation payment schemes that aim to achieve the dual objectives of poverty reduction and ecological sustainability, particularly large-scale public schemes, and suggests that more effective targeting and transparency about the basis of payment distribution are prerequisites for schemes to be both cost-effective and socially acceptable.

Ensuring sustainable irrigation water quality is vital for agricultural productivity and environmental health, particularly in regions with variable seasonal water quality. This study introduces an enhanced Irrigation Water Quality Index (IWQI) specifically developed for the Old Brahmaputra River in Bangladesh to address local hydrochemical complexities. Unlike conventional IWQI methods that primarily assess salinity and sodicity without statistical validation, this approach integrates a broader range of parameters with multivariate statistical validation, enabling a more precise, seasonally adaptive assessment. Through combined multivariate statistical analysis and geographic information systems, results indicate the river water generally remains in the ''low restriction'' category for irrigation across seasons (dry season: 73.42–83.85; wet season: 77.63–80.57). Seasonal fluctuations were observed in certain areas, with elevated ion concentrations during the dry season due to reduced flow and evaporation, while monsoon rains provided natural pollutant dilution in the wet season. This seasonal variability highlights the importance of continuous monitoring. Principal component analysis identified three primary components, accounting for 77.16 % of the variance in the dry season and 66.30 % in the wet season. Strong correlations were observed between IWQI and indices like Permeability Index (r = 0.94, p < 0.01) and Sodium Percentage (r = 0.91, p < 0.01) in the dry season. This enhanced IWQI offers a promising tool for seasonally adaptive water management, promoting sustainable agriculture and soil health amidst climate change. Future research should expand the temporal and spatial scope to capture long-term trends and adapt this model for broader regional water management applications.

This article proposes some new estimators, namely Stein’s estimators for ridge regression and Kibria and Lukman estimator and compares their performance with some existing estimators, namely maximum likelihood estimator (MLE), ridge regression estimator, Liu estimator, almost unbiased ridge and Liu estimators, adjusted Liu estimator, James stein’s estimator, Kibria and Lukman estimator, Dorugade estimator, and modified ridge estimator for the logistic regression model to solve the multicollinearity problem. The bias, covariance matrix, and mean square error matrix for each of the estimators are provided. A Monte Carlo simulation has been conducted to compare the performance of different estimators. We consider the smaller mean squared error value as a performance criterion. From the simulation study, it is evident that all proposed estimators performed better than the MLE. Finally, a real-life data is analyzed to illustrate the findings of the article. Some promising estimators are recommended for the practitioners.

Introduction Since there is a paucity of data on the epidemiology of the non-alcoholic fatty liver disease (NAFLD), particularly in rural areas in Asia, we undertook such a study among the population of a rural community in Bangladesh with the aims to (1) determine the prevalence of non-obese and obese NAFLD, (2) compare the sociodemographic clinical and metabolic characteristics between non-obese and obese NAFLD subjects, and (3) determine the risk factors of NAFLD and no-nobese NAFLD.Methods In this door-to-door survey, clinical examination, anthropometric measurements, biochemical tests and ultrasonography were performed on the adult population (≥18 years) of three villages in Bangladesh.Results Of 1682, 1353 (80.44%) responded. After the exclusion of 48 subjects for alcohol consumption, HBsAg or anti-hepatitis C virus positivity, 1305 ((mean age 41.28±15.10 years, female 908 (69.6%)) were included in the final analysis. On ultrasonography, among the study population, 57 (4.4%) non-obese, 185 (14.2%) obese and, overall, 242 (18.5 %, (male 23.40%, female 16.4%, p=0. 003)) participants had NAFLD. NAFLD was detected in 57/804 (7.1%) of non-obese and 185/501 (36.93%) obese participants. Among the lean subjects, 24/592 (4.1%) had NAFLD. Among NAFLD subjects, 57 (23.55%) were non-obese, and 53 (22%) had raised alanine aminotransferase. On multivariate analysis, age >40 years, male gender, metabolic syndrome (MS), diabetes mellitus (DM), abdominal obesity, hypertension, dyslipidaemia and obesity were found as the risk factors for NAFLD. There were no differences in sociodemographic characteristics, DM, MS, abdominal obesity, hypertension and dyslipidaemia between non-obese and obese NAFLD (all p>0.05).Conclusion In this community study in Bangladesh, NAFLD was present in 18.5% participants, one-quarter of whom were non-obese. Apart from body mass index, the metabolic profile was comparable between obese and non-obese NAFLD. Public health measures are needed to control and prevent NAFLD and MS and their adverse health consequences.

Abstract Background Hepatocellular carcinoma (HCC), a leading cause of cancer-related deaths globally, poses significant challenges in early detection. Improved diagnostic accuracy can drastically influence patient outcomes, emphasizing the need for innovative, non-invasive biomarkers. Methods: This study utilized a cohort of 402 participants, including healthy controls, chronic hepatitis patients, and HCC patients from Bangladesh, to evaluate DNA methylation signatures in peripheral blood mononuclear cells (PBMC). We performed targeted next-generation sequencing on selected genes previously identified to assess their methylation dynamics. The development of M8 and M4 scores was based on these dynamics, using Receiver Operating Characteristic (ROC) analysis to determine their effectiveness in detecting early-stage HCC alongside existing markers such as epiLiver and alpha-fetoprotein (AFP). Results: Integration of M8 and M4 scores with epiLiver and AFP significantly enhances diagnostic sensitivity for early-stage HCC. The M4+epiLiver score achieves a sensitivity of 79.4% in Stage A HCC, while combining M4 with AFP increases sensitivity to 88.2–95.7% across all stages, indicating a superior diagnostic performance compared to each marker used alone. Conclusions: Our study confirms that combining gene methylation profiles with established diagnostic markers substantially improves the sensitivity of detecting early-stage HCC. This integrated diagnostic approach holds promise for advancing non-invasive cancer diagnostics, potentially leading to earlier treatment interventions and improved survival rates for high-risk patients.

Freshwater is becoming increasingly vulnerable to pollution due to both climate change and an escalation in water consumption. The management of water resource systems relies heavily on accurately predicting fluctuations in lake water levels. In this study, an artificial neural network (ANN), a deep learning (DL) neural network model, and an autoregressive integrated moving average (ARIMA) model were employed for the water level forecasting of the St. Clair and Ontario Lakes from 1981 to 2021. To develop the models, we utilized the average mutual information and incorporated lag periods of up to 6 months to identify the optimal inputs for the water level assessment in the lakes. The results were compared in terms of the root mean square error (RMSE), coefficient of correlation (r), and mean absolute percentage error (MAPE) and graphical criteria. Upon evaluating the results, it was observed that the error values for the deep learning models were insignificant at the designated stations: Lake St. Clair—0.16606 m < RMSE < 1.0467 m and Lake Ontario—0.0211 m < RMSE < 0.7436 m. The developed deep learning model increased the accuracy of the models by 5% and 3.5% for Lake St. Clair and Lake Ontario, respectively. Moreover, the violin plot of the deep learning model for each lake was most similar to the violin plot of the observed data. Hence, the deep learning model outperformed the ANN and ARIMA model in each lake.

Humanity faces a rising number of challenging disasters due to their increasing unpredictability and scale. While large-scale engineering solutions have been the mainstream approach, there is a growing acknowledgment that ecosystem-based disaster risk reduction (Eco-DRR) is a suitable approach in the long term due to its cost-effectiveness, social equity, and environmental sustainability. Governments worldwide are integrating Eco-DRR into policies and legislation. Here, we review the extent of integration of Eco-DRR into DRR policies and legislation in Nepal, India, and Bangladesh using content analysis based on five criteria: acknowledgment, inclusion, weighting, resources, and reflexivity. While Eco-DRR principles are acknowledged and included in the national policies of these three countries, they are not prioritized as compared to competing alternatives due to limited integration tools. Resource allocation is directed towards prevention and relief mitigation, with limited emphasis on research and capacity building, which are crucial for strengthening Eco-DRR. Notably, policy aspirations still need to be realized in legislative action. Strengthening Eco-DRR requires enhanced integration, capacity-building, and robust institutions to boost resilience.

Background: Mental illness is an important risk factor for self-harm behavior. However, the association between self-harm behavior and psychiatric disorders has been poorly studied in Bangladesh. Aims: To determine the prevalence of psychiatric disorders and life-events among patients with a self harm behavior. Methods: This cross-sectional study was conducted between March 2022 and November 2023. We collected data from 100 patients with self harms from two tertiary care hospital of Dhaka by semi-structured interviews. Results: Of the 100 cases, 72 were females, 63 % came from semi-urban background. Hanging was the commonest method (31 %) followed cutting and piercing instrument (22 %), and ingestion of Benzodiazepines (20 %). Among the cases, 71 % had at least one psychiatric disorder (Axis I 49 % and Axis II 35 %). In case of life events, 52 % had life-events within immediate 48 h, 15 % in the last one month and 17 % in the last year. Family conflict (30 %) was the commonest risk factor and increased argument with resident family members (38 %) was the commonest life event. Conclusions: This study revealed a similar proportion of psychiatric disorders revealed in previous studies among suicide attempts. It also unveiled life events in suicide attempts indicating a potential role of life events in suicide attempts in Bangladesh. However, a prudential interpretation is warranted while considering the study results as it was conducted among clinical samples.

Abstract COVID-19 vaccines have been effective in preventing severe illness, hospitalization and death, however, the effectiveness diminishes with time. Here, we evaluated the longevity of antibodies generated by COIVD-19 vaccines and the risk of (re)infection in Bangladeshi population. Adults receiving two doses of AstraZeneca, Pfizer, Moderna or Sinopharm vaccines were enrolled at 2–4 weeks after second dosing and followed-up at 4-monthly interval for 1 year. Data on COVID-like symptoms, confirmed COVID-19 infection, co-morbidities, and receipt of booster dose were collected; blood was collected for measuring spike (S)- and nucleocapsid (N)-specific antibodies. S-specific antibody titers reduced by ~ 50% at 1st follow-up visit and continued to decline unless re-stimulated by booster vaccine dose or (re)infection. Individuals infected between follow-up visits showed significantly lower S-antibody titers at preceding visits compared to the uninfected individuals. Pre-enrolment infection between primary vaccination dosing exhibited 60% and 50% protection against reinfection at 5 and 9 months, respectively. mRNA vaccines provided highest odds of protection from (re)infection up to 5 months (Odds Ratio (OR) = 0.08), however, protection persisted for 9 months in AstraZeneca vaccine recipients (OR = 0.06). In conclusion, vaccine-mediated protection from (re)infection is partially linked to elevated levels of S-specific antibodies. AstraZeneca vaccine provided the longest protection.

Organic matter (OM) shows a critical role in mobilization and uptake of arsenic (As) by rice, and water management practice can mitigate this problem. However, very few research highlighted the impact of management of water on rice as influenced by OM amendment. Therefore, this study has evaluated the changes in As mobilization in paddy soil under different OM amendment and water management practices. Here, rice was grown to maturity in a two-factorial pot experiment comprising two different water management practices [continuous flooding (CF) and alternate wetting drying (AWD)] and eight combinations of As and OM amendment [comprising two As treatments (0 and 20 ppm) and four OM amendments (0, 0.25%, 0.5% and 5.0% w/w)]. Application of OM in As contaminated soil caused a significant increase in As accumulation in rice, and exhibited decreased growth and yield of rice. However, the results showed that rice growth and yield was significantly higher under AWD practice compared to CF. Arsenic concentration in rice was the lowest in As and OM control pots (44.67 µg/kg in AWD and 62.13 µg/kg in CF), and higher in As treated pots. Moreover, As concentration in rice grain increased with increasing levels of OM amendment. The As concentration in rice grain (168.44 µg/kg in AWD and 183.85 µg/kg in CF) was significantly higher in As treated pots with 0.5% OM amendment compared to other treatment combinations. Application of 5% OM in As contaminated soil did not produce any grains due to extreme toxicity. Thus, As accumulation in rice can be decreased by AWD water management technique without compromising yield. The findings suggest that applying OM in paddy soils with high soil As content should be done with caution.

Abstract Background Depression, anxiety, and stress are the commonly encountered mental health conditions among medical students. Overseas environment may add additional burden to the existing environment. However, comparison of the rate and associated factors of depression, anxiety, stress among domestic and overseas students has not been attempted in Bangladesh. We aimed to assess the rate and associated factors of depression, anxiety, stress among domestic and overseas medical students in the country. Methods This cross‐sectional study was conducted among 360 undergraduate medical students in 2021. Data were collected by self‐reporting instruments by a stratified random sampling method by the Depression, Anxiety, and Stress Scale‐21 scale. Bivariable and multivariable binary logistic regression analyses were done by computing crude odds ratio and adjusted odds ratio with 95% confidence interval to identify the associated factors. Results Among the 360 undergraduate medical students, 181 were domestic and 179 overseas ones. 44.8% of domestic students reported depressive symptoms, 45.3% reported anxiety symptoms, and 33.1% reported stress. On the other hand, half of overseas students experienced depression (50.3%) and anxiety (52.5%), and 41.3% experienced stress. Female medical students were found vulnerable for developing depression, anxiety, and stress than males. Conclusions The study revealed higher rates of depression, anxiety, and stress among overseas undergraduate medical students enrolled in a private medical school of Bangladesh. Private medical college authorities could consider special services for overseas students to ensure adequate psychosocial support.

The linear regression model is a widely used statistical tool that forms most modelling concepts' basis. The ordinary least square estimator is often adopted to estimate the model's parameters. The estimator is considered efficient when there are no violations of the classical regression assumptions. However, the estimator underperforms when the model violates the underlying assumptions of regression. One of the violated assumptions is the problem of multicollinearity. The problem occurs when there is a correlation among the model's independent variables. Many estimators have been proposed to solve this problem but the search for a better estimator continues. This study proposes an almost unbiased modified ridge-type (AUMRT) estimator which has proved to be comparatively superior to the existing ones. The performance of AUMRT was proven through theoretical proofs, simulations, and practical application to real-life data. The theoretical findings underscore the superiority of the proposed method, a notion reinforced by the outcomes of the simulation study. Specifically, the simulation results unequivocally demonstrate that, under specific conditions, the proposed estimator outperforms all other methods considered in this study. Moreover, validation through real-life application with Portland cement corroborates both the theoretical assertions and the simulation findings.

In the realm of healthcare, an imperative necessity for all, institutions are increasingly recognizing the advantages of adopting lean strategies to enhance performance. Lean implementation in healthcare can lead to significant improvements in efficiency, patient care, and overall institutional performance. This paper aims to assess the readiness levels for implementing lean practices in healthcare institutes in Bangladesh, employing a fuzzy logic approach. The construction of a conceptual model is grounded in literature review and expert opinions, incorporating critical enablers, criteria, and attributes identified from extensive research. Factors measured include leadership commitment, workforce capability, operational processes, technological infrastructure, and organizational culture, each pivotal in determining readiness for lean implementation. The fuzzy logic approach is particularly useful in this context due to its ability to handle uncertainty and imprecision, which are common in complex environments like healthcare. This methodology not only provides a clear picture of current capabilities but also highlights specific areas that need enhancement, paving the way for more targeted and effective lean interventions. Data sourced from consultations with experts in three prominent hospitals in Bangladesh forms the basis of the analysis, enabling a detailed examination of readiness levels. The model's application of fuzzy logic facilitates a comprehensive assessment, revealing 12 critical attributes across the hospitals that require attention. Interestingly, the evaluation identifies varying levels of readiness, with two hospitals demonstrating moderate readiness and one showing a lower level. This conceptual approach has significant potential to assist top management in healthcare companies by providing a structured framework to prioritize crucial areas for improvement. By accurately assessing readiness levels and pinpointing weaker aspects before implementing lean strategies, this study aims to transform the healthcare industry. Ultimately, its implementation has the potential to enhance organizational performance and elevate standards in patient care, contributing to improved healthcare delivery in Bangladesh and beyond.

Despite several accomplishments in addressing malnutrition, the issue of food scarcity remains a persistent concern all over the globe, particularly in the South Asian region. One recommended solution to address this situation involves advocating for further liberalization of global food trading and opening employment opportunities. In this context, using panel data spanning 2000–2019, this study makes a novel attempt to quantify the impact of agricultural trade openness and agricultural employment on food security in countries belonging to the South Asian region while controlling the tariff and agricultural production. Using “Fully Modified Ordinary Least Square (FMOLS)” and “Dynamic Ordinary Least Square (DOLS)” modeling, this article concludes that increased agricultural trade openness hinders food security in this region. Because, the member countries of South Asia are heavily reliant on food imports to meet their domestic needs, implying that the expenses of food imports exceed the potential benefits of increasing exports. Moreover, tariffs have a detrimental impact on food security in this region. However, production and employment in the agricultural industry augment earnings, strengthen the capacity to buy food, and ensure adequate nutrition intake over the long term. The study's findings suggest that these nations should prioritize food self-sufficiency to expand agricultural exports and lessen their reliance on imported food. More than that, economies should provide rewards to broaden their agricultural production locally, which aids in reducing hunger and uplifting food security.

The most crucial industries for growth, particularly in developing countries, are agriculture, tourism, and the fertilizer industry. These businesses are also linked to environmental deterioration. The current study uses empirical research to inspect the links between carbon dioxide (CO2) secretions and Gross Domestic Product (GDP) progression, government expenditure, tourism, fertilizer usage, renewable and non-renewable energies, and agricultural production. Information commencing from six South Asian nations is used to produce the findings between 1991 and 2019. Following the establishment of stationary and cointegration, a static analysis using a Fixed Effect and Random Effect Model and a dynamic analysis using a One Step Difference and System GMM (Generalized Method of Moments) are performed. This article employs Mean Group (MG) and Augmented Mean Group (AMG) approaches to increase robustness, while multicollinearity, heteroscedasticity, autocorrelation, and Cross-Sectional Dependency (CSD) are used for post-estimation. According to the study, the selected South Asian region's agricultural, fertilizer use, non-renewable energy use, tourism, GDP growth, and government spending all result in a rise in CO2 emissions, while using clean energy reduces those emissions and is crucial to reducing those emissions. The study offers some policy ramifications concerning green farming and tourism.

Vascular-related diseases pose significant public health challenges and are a leading cause of mortality and disability. Understanding the complex structure of the vascular system and its processes is crucial for addressing these issues. Recent advancements in medical imaging technology have enabled the generation of high-resolution 3D images of vascular structures, leading to a diverse array of methods for vascular extraction. While previous research has often assumed a normal distribution of image data, this paper introduces a novel vessel extraction method that utilizes the skew normal distribution for more accurate probability distribution modeling. The proposed method begins with a preprocessing step to enhance vessel structures and reduce noise in Magnetic Resonance Angiography (MRA) images. The skew normal distribution, known for its ability to model skewed data, is then employed to characterize the intensity distribution of vessels. By estimating the parameters of the skew normal distribution using the Expectation-Maximization (EM) algorithm, the method effectively separates vessel pixels from the background and non-vessel regions. To extract vessels, a thresholding technique is applied based on the estimated skew normal distribution parameters. This segmentation process enables accurate vessel extraction, particularly in detecting thin vessels and enhancing the delineation of vascular edges with low contrast. Experimental evaluations on a diverse set of MRA images demonstrate the superior performance of the proposed method compared to previous approaches in terms of accuracy and computational efficiency. The presented vessel extraction method holds promise for improving the diagnosis and treatment of vascular-related diseases. By leveraging the skew normal distribution, it provides accurate and efficient vessel segmentation, contributing to the advancement of vascular imaging in the field of medical image analysis.

Objectives This study aimed to assess the desire for smoke-free housing, determine the choice of smoke-free policies for multiunit housing (MUH), and identify the factors associated with policy choice among MUH residents in Bangladesh.Design We conducted a cross-sectional study from April to November 2019 using a semi-structured survey questionnaire.Setting This study was conducted in seven divisional cities of Bangladesh: Dhaka, Chattogram, Rajshahi, Khulna, Sylhet, Barishal, and Rangpur.Participants A total of 616 adult individuals living in MUH for at least 2 years participated in the study.Primary outcome measure Multinomial logistic regression was used to identify the determinants of the choice of smoke-free policies for MUH.Results Overall, 94.8% of the respondents wanted smoke-free housing. Among those who wanted smoke-free housing, 44.9% preferred a smoke-free building policy, 28.3% preferred a smoke-free common area policy, 20.2% favoured a smoke-free unit policy, and 6.7% did not know what policy they should choose. Three factors were found to be significantly associated with the choice of a smoke-free building policy: staying at home for more than 12 hours (adjusted OR (aOR): 2.6; 95% CI 1.035 to 6.493), being a non-smoker (aOR: 3.2; 95% CI 1.317 to 7.582), and having at least one family member who smoked (aOR: 3.0; 95% CI 1.058 to 8.422). Results also showed that residents having at least one child under 15 in the family (aOR: 0.3; 95% CI 0.152 to 0.778) were less likely to choose a smoke-free common area policy and that women (aOR: 3.7; 95% CI 1.024 to 13.188) were more likely to choose a smoke-free unit policy.Conclusions MUH residents in urban Bangladesh highly demanded smoke-free housing. Most residents favoured a smoke-free building policy for MUH. Those who stayed at home for a longer time, were non-smokers, and had smoking family members were more likely to choose this policy.

The ordinary least square estimator is inefficient when there exists multicollinearity among regressors in linear regression model. There are many methods available in literature to solve the multicollinearity problem. In this study, we consider some one and two parameter estimators for estimating the regression parameters. We theoretically compared the estimators in terms of smaller mean squared error (MSE) criteria. A Monte Carlo simulation study has been conducted to compare the performance of the estimators numerically. Finally, for illustration purposes, a real-life data is analyzed.

Introduction COVID-19 has caused severe disruption to clinical services in Bangladesh but the extent of this, and the impact on healthcare professionals is unclear. We aimed to assess the perceived levels of anxiety, depression and burnout among doctors and nurses during COVID-19 pandemic.Methods We undertook an online survey using RedCap, directed at doctors and nurses across four institutions in Bangladesh (The Sheikh Russel Gastro Liver Institute & Hospital (SRNGIH), Dhaka Medical College Hospital (DMCH), Mugda Medical College Hospital (MMCH) and M Abdur Rahim Medical College (MARMC) Hospital). We collected information on demographics, awareness of well-being services, COVID-19-related workload, as well as anxiety, depression and burnout using two validated questionnaires: the Hospital Anxiety and Depression Scale (HADS) and the Maslach Burnout Inventory (MBI).Results Of the 3000 participants approached, we received responses from 2705 (90.2%). There was a statistically significant difference in anxiety, depression and burnout scores across institutions (p

BackgroundCoronavirus disease 2019 (COVID-19) is a global public health concern. Evidence shows that depression and anxiety are common among patients with COVID-19 after recovery. About one-third of the total COVID-19 cases in Bangladesh have been reported in Dhaka city. Therefore, the study aimed to evaluate the prevalence of depression and anxiety among COVID-19 survivors in Dhaka city as well as to identify the factors associated with these mental health conditions.MethodsA cross-sectional study was carried out among a total of 384 COVID-19 survivors aged 18 years or older. Data collection was done through face-to-face and telephone interviews using a semi-structured questionnaire. Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) scales were used to assess depression and anxiety, respectively. Binary logistic regression analysis was performed to identify the predictors of depression and anxiety among patients recovered from COVID-19.ResultsThe overall prevalence of depression and anxiety was 26.0% and 23.2%, respectively among COVID-19 survivors. The respondents who were ≥60 years were 2.62 and 3.02 times more likely to report depressive and anxiety symptoms, respectively than those aged 18 to 39 years. Hospitalised patients recovered from COVID-19 had a 2.18 times higher chance of developing anxiety than their non-hospitalised counterparts. COVID-19 recovered patients with comorbidities were at 3.35 and 2.97 times higher risk of depression and anxiety, respectively compared to those without comorbidities. Similarly, the respondents who had already passed a period of 15 days to 3 months after recovery showed 3.06 and 1.85 times higher odds of depression and anxiety, respectively than those who had already passed a period of above 3 to 6 months after recovery.ConclusionThe study reported a high prevalence of depression and anxiety among COVID-19 survivors living in Dhaka city. The findings suggest the need for appropriate interventions to reduce mental health complications in COVID-19 survivors.

This study endeavors to examine the validity of Wagner's Law, which has received considerable attention in recent years. We develop a panel dataset of 20 countries, taking five countries from each income group defined by the World Bank, for the 1991–2018 periods. Five different versions of the law are tested using this dataset. We add further depth to the model by involve the government's revenue and the volume of trade as independent variables. We inquire into the subsistence of cross-sectional dependence. To determine the order of integration, we conduct LLC, IPS, and CADF tests. The results show that the dataset has I (0) and I (1) series, and no series is found to be of I (2). Then we perform the panel ARDL test, and calculate PMG and DFE estimates. We use the Hausman test to choose among the estimates. In each version of the law, the error correction term indicates the presence of both long-term associations within the variables and an economic convergence process. However, we find no evidence to support the law for any version. Additionally, we conduct the panel cointegration tests, such as Westerlund, Pedroni and Kao. These cointegration tests generate results accordant with the ARDL findings.

Biomass valorization by photoreforming approach provides a promising and alternative strategy to generate value-added chemicals and fuels. In this work, we demonstrate the selective production of lactic acid from glucose photoreforming over pristine graphitic carbon nitride (g-C3N4) photocatalyst. Control experiments screen the best condition for the highest yield of lactic acid, including modulating pH, catalyst loading, and reaction time. 100% glucose conversion is achieved along with almost 100% lactic acid yield under the optimized condition. Density functional theory (DFT) calculations reveal that the rate-determining step (RDS) of the overall reaction on g-C3N4 is the conversion of pyruvaldehyde, where an electron transfer takes place. This present work provides experimental insights and theoretical understanding for selective lactic acid production from biomass photoreforming.

Abstract Linear regression models with correlated regressors can negatively impact the performance of ordinary least squares estimators. The Stein and ridge estimators have been proposed as alternative techniques to improve estimation accuracy. However, both methods are non-robust to outliers. In previous studies, the M-estimator has been used in combination with the ridge estimator to address both correlated regressors and outliers. In this paper, we introduce the robust Stein estimator to address both issues simultaneously. Our simulation and application results demonstrate that the proposed technique performs favorably compared to existing methods.

Abstract High-throughput tests for early cancer detection can revolutionize public health and reduce cancer morbidity and mortality. Here we show a DNA methylation signature for hepatocellular carcinoma (HCC) detection in liquid biopsies, distinct from normal tissues and blood profiles. We developed a classifier using four CpG sites, validated in TCGA HCC data. A single F12 gene CpG site effectively differentiates HCC samples from other blood samples, normal tissues, and non-HCC tumors in TCGA and GEO data repositories. The markers were validated in a separate plasma sample dataset from HCC patients and controls. We designed a high-throughput assay using next-generation sequencing and multiplexing techniques, analyzing plasma samples from 554 clinical study participants, including HCC patients, non-HCC cancers, chronic hepatitis B, and healthy controls. HCC detection sensitivity was 84.5% at 95% specificity and 0.94 AUC. Implementing this assay for high-risk individuals could significantly decrease HCC morbidity and mortality.

BackgroundWith the widespread outbreak of the coronavirus (COVID-19) pandemic, many countries, including Egypt, have tried to restrict the virus by applying social distancing and precautionary measures. Understanding the impact of COVID-19-induced risks and social distancing measures on individuals' mental health will help mitigate the negative effects of crises by developing appropriate mental health services. This study aimed to investigate the most contributing factors that affected individuals' mental health and how individuals' mental health has changed over the lockdown period in Egypt in 2021.MethodsThe study draws on a nationally representative sample from the combined COVID-19 MENA Monitor Household Survey conducted by the Economic Research Forum. The data were collected in Egypt by phone over two waves in February 2021 and June 2021. The total number of respondents is 4,007 individuals. The target population is mobile phone owners aged 18–64 years. The 5-item World Health Organization Well-Being Index (WHO-5) is used to assess the individuals' mental health over the past 2 weeks during the pandemic. Penalized models (ridge and LASSO regressions) are used to identify the key drivers of mental health status during the COVID-19 pandemic.ResultsThe mean value of mental health (MH) scores is 10.06 (95% CI: 9.90–10.23). The average MH score for men was significantly higher than for women by 0.87. Rural residents also had significantly higher MH scores than their urban counterparts (10.25 vs. 9.85). Middle-aged adults, the unemployed, and respondents in low-income households experienced the lowest MH scores (9.83, 9.29, and 9.23, respectively). Individuals' mental health has deteriorated due to the negative impacts of the COVID-19 pandemic. Regression analysis demonstrated that experiencing food insecurity and a decrease in household income were independent influencing factors for individuals' mental health (p < 0.001). Furthermore, anxiety about economic status and worrying about contracting the virus had greater negative impacts on mental health scores (p < 0.001). In addition, women, middle-aged adults, urban residents, and those belonging to low-income households were at increased risk of poor mental health (p < 0.05).ConclusionThe findings reveal the importance of providing mental health services to support these vulnerable groups during crises and activating social protection policies to protect their food security, incomes, and livelihoods. A gendered policy response to the pandemic is also required to address the mental pressures incurred by women.

Background: Nowadays, computer users are facing musculoskeletal disorders (MSDs) and visual symptoms. Prolonged sitting in inappropriate, awkward, and static postures on the computer workstation may cause musculoskeletal disorders (MSDs). Similarly, inappropriate placement of monitors, illumination, and other factors such as prolonged usage of computers are related to visual symptoms. Objective: This study aims to evaluate the ergonomic deficiencies of computer workstations and their correlation with MSDs and visual symptoms. Methods: This study involved 271 university employees from a Bangladeshi engineering university. Ergonomic deficiencies were evaluated through direct observations and Occupational Safety and Health Administration checklists. In addition, the Nordic Musculoskeletal Questionnaire was used to assess the prevalence of MSDs and visual discomforts. Binary Logistic Regression (BLR) analysis was also used to examine the correlation between musculoskeletal symptoms and ergonomic deficiencies. Results: Results showed serious deficiencies in workstation setup, seating arrangement, monitor orientations, keyboard orientations, other input device orientations, and accessory setup. Employees reported that the MSDs in different body regions during the last 12 months including lower back (62 %), upper back (53 %), shoulders (47 %), and neck (25 %). Moreover, itchy eyes (69 %), tired eyes (83 %), and unclear vision (56.83 %) were the most common visual discomforts or visual symptoms among the participants. Results also revealed that monitor ergonomics and its orientation deficits were significantly associated with visual discomforts. Gender, job type, age, BMI, work experience, duration of computer work, and beak taking after 2 h were the independent variables reliably predicting the MSDs and visual symptoms. Conclusion: It is evident that MSDs and visual symptoms were associated with computer workstation deficiencies and other work-related factors.

The process capability index (Cp) measures the amount of dispersion a process involves relative to the limits of specification. This paper considers sixteen different available confidence intervals for estimating the population process capability index. A simulation study under different conditions has been conducted to compare the performance of the estimators. Our vast simulation records reveal that both augmented large sample (ALS) and modified augmented large sample (MALS) intervals have better coverage probability and shorter average width in all simulation conditions. We expect that the results of this paper will contribute to the literature on process capability and will guide the researchers to select an interval estimator when they are interested to estimate the population process capability index.

Introduction: Most people in the United States live busy stressful lives which can lead to significant health challenges, especially to one’s mental health. After years of being ignored and stigmatized, mental illness has been given the recognition it deserves. It is now associated with the overall health of a person and treated seriously. The same could be said about substance abuse disorder though on a lesser scale. This paper attempts to understand the relationship between drug use and mental health illness differentiated across age groups. The understanding of these causations and relationships could help us better understand the rate and triggers of mental illness in the United States. Methods: We used data from a 2023 survey, which contains a total of 69,850 completed interviews. The data collected were taken from each state individually with each of them implementing the same survey to collect responses. Instead of looking at all fifty states, we decided to analyze only the highest twenty- one most populated states. Various non-parametric tests were used to analyze age groups and their rate of mental health illnesses and drug use within the country. Results: We found that tobacco use is associated with mental illness, while alcohol use is associated with attempted suicide. It appears that indicators of drug use are not homogeneous across states for either age group. We also found that the alcohol consumption is related to a declining proportion of attempted suicides. Conclusion: In this research we found a significant association between drug use and inidcators of mental illness. The association differed across different age groups and also across states. Since we found that the alcohol consumption is related to a declining proportion of attempted suicides, it will be interesting to explore why this is the case and what could be observed to decrease suicidal rates.

BackgroundSince March 2020, the coronavirus disease 2019 (COVID-19) pandemic has been a major shock to health systems across the world. We examined national usage patterns for selected basic, essential health services, before and during the COVID-19 pandemic in Uganda and Bangladesh, to determine whether COVID-19 affected reporting of service utilization and the use of health services in each country.MethodsWe used routine health information system data since January 2017 to analyze reporting and service utilization patterns for a variety of health services. Using time series models to replicate pre-COVID-19 trajectories over time we estimated what levels would have been observed if COVID-19 had not occurred during the pandemic months, starting in March 2020. The difference between the observed and predicted levels is the COVID-19 effect on health services.ResultsThe time trend models for Uganda and Bangladesh closely replicated the levels and trajectories of service utilization during the 38 months prior to the COVID-19 pandemic. Our results indicate that COVID-19 had severe effects across all services, particularly during the first months of the pandemic, but COVID-19 impacts on health services and subsequent recovery varied by service type. In general, recovery to expected levels was slow and incomplete across the most affected services.ConclusionOur analytical approach based on national information system data could be very useful as a form of surveillance for health services disruptions from any cause leading to rapid responses from health service managers and policymakers.

The relationship between ecosystem functions and plant functional traits has been well documented and is considered to be one of the most vital topics in ecology. However, the correlation between anthropogenic disturbance and tree functional trait diversity remains largely unclear. The present study investigates the role of anthropogenic disturbance on selected tree functional traits in Ratargul Swamp Forest (RSF) – the only remnant tropical freshwater swamp forest ecosystem in Bangladesh. We established 50 plots and collected six plant traits, i.e., tree height, specific leaf area (SLA), wood density (WD), leaf dry matter content (LDMC), seed mass, and bark thickness. A two-way analysis of variance (ANOVA) was carried out to test the interaction between plant functional traits and anthropogenic disturbance in RSF. Our study found that anthropogenic disturbance has a significant (p< 0.05) influence on deciduous swamp forest tree species’ functional traits but none on evergreen tree species’ functional traits. For deciduous trees, disturbance has a significant positive effect on CWM.SLA (p< 0.05) and CWM.Height (p< 0.05) and a negative impact on CWM.Bark-thickness and CWM.Seed-mass (p< 0.05). There were no significant effects of disturbance on CWM.WD and CWM.LDMC. We believe the present study will contribute toward improving our understanding of the effect of disturbances on tree functional trait diversity in tropical human-dominated landscapes where anthropogenic pressure is increasing at an unprecedented rate. It will also function as an essential conservation for related decision-making of Ratargul Swamp Forest in northeastern Bangladesh and elsewhere in the tropics with a similar context.

This study looks at how Bangladesh's human capital investment has affected unemployment from 1995 to 2019. To identify the study's unit root, we employed the ADF and PP tests. The short- term and long-term impacts of human capital investment on unemployment are estimated using the Autoregressive Distributive Lag (ARDL) model. The presence or absence of cointegration is assessed using the ARDL bound cointegration test. The Pairwise Granger Causality test, in contrast, is used to ascertain whether there exist causal relationships between variables. The study's findings demonstrate that government health spending on human capital has a significant impact on Bangladesh's long-term unemployment rate. Government spending on education and the unemployment rate are causally related in a single direction, according to the Pairwise Granger test. In the short term, the analysis showed no discernible relationship between human capital investment and unemployment rates. To build a healthy nation and eventually lower Bangladesh's unemployment rate, it is urged that the government should increase health spending and strengthen the health sector. To connect education with employment, the government may give vocational and career-focused education equal weight with general education.

Abstract Background As systematically developed statements regarding possible courses of action, health system guidance (HSG) can assist with making decisions about addressing problems or achieving goals in health systems. However, there are conceptual and methodological challenges in HSG implementation due to the complexity of health-system policy-making, the diversity of available evidence and vast differences in contexts. To address these gaps, we aim to develop a theoretical framework for supporting HSG implementation as part of a broader effort to promote evidence-informed policy-making in health systems. Methods To develop a theoretical framework about facilitators, barriers and strategies for HSG implementation, we will apply a critical interpretive synthesis (CIS) approach to synthesize the findings from a range of relevant literature. We will search 11 electronic databases and seven organizational websites to identify relevant published and grey literature. We will check the references of included studies and contact experts to identify additional eligible papers. Finally, we will conduct purposively sampling of the literature to fill any identified conceptual gaps. We will use relevance and five quality criteria to assess included papers. A standardized form will be developed for extracting information. We will use an interpretive analytic approach to synthesize the findings, including a constant comparative method throughout the analysis. Two independent reviewers will conduct the literature screening and relevance assessment, and disagreements will be resolved through discussion. The principal investigator will conduct data extraction and synthesis, and a second reviewer will check the sample of extracted data for consistency and accuracy. Discussion A new theoretical framework about facilitators, barriers and strategies for HSG implementation will be developed using a CIS approach. The HSG implementation framework could be widely used for supporting the implementation of HSG covering varied topics and in different contexts (including low-, middle- and high-income countries). In later work, we will develop a tool for supporting HSG implementation based on the theoretical framework. Registration PROSPERO CRD42020214072. Date of Registration: 14 December 2020.

Abstract Background Malaria remains endemic in Bangladesh, with the majority of cases occurring in forested, mountainous region in the Chittagong Hill Tracts (CHT). This area is home to Bengali and diverse groups of indigenous people (Pahari) residing largely in mono-ethnic villages. Methods 1002 individuals of the 9 most prominent Pahari and the Bengali population were randomly selected and screened by RDT and qPCR. Parasites were genotyped by msp2 and deep sequencing of 5 amplicons (ama1-D3, cpmp, cpp, csp, and msp7) for Plasmodium falciparum (n = 20), and by microsatellite (MS) typing of ten loci and amplicon sequencing of msp1 for Plasmodium vivax (n = 21). Population structure was analysed using STRUCTURE software. Identity-by-state (IBS) was calculated as a measure of parasite relatedness and used to generate relatedness networks. Results The prevalence of P. falciparum and P. vivax infection was 0.7% by RDT (P. falciparum 6/1002; P. vivax 0/1002, mixed: 1/1002) and 4% by qPCR (P. falciparum 21/1002; P. vivax 16/1002, mixed: 5/1002). Infections were highly clustered, with 64% (27/42) of infections occurring in only two Pahari groups, the Khumi and Mro. Diversity was high; expected heterozygosity was 0.93 for P. falciparum and 0.81 for P. vivax. 85.7% (18/21) of P. vivax and 25% (5/20) of P. falciparum infections were polyclonal. No population structure was evident for either species, suggesting high transmission and gene flow among Pahari groups. Conclusions High subclinical infection prevalence and genetic diversity mirror ongoing transmission. Control activities should be specifically directed to Pahari groups at greatest risk.

This paper considers the Ridge Feasible Generalized Least Squares Estimator (RFGLSE), Ridge Seemingly Unrelated Regression RSUR and proposes the Kibria-Lukman KLSUR estimator for the parameters of the Seemingly Unrelated Regression (SUR) model when the regressors of the models are collinear. A simulation study was conducted to compare the performance of the three different types of estimators for the SUR model. Different correlation levels (0.0, 0.1, 0.2, ..., 0.9) among the independent variables, sample sizes replicated 10000 times and contemporaneous error correlation (0.0, 0.1, 0.2, ..., 0.9) among the equations were assumed for the simulation study. The efficiency of the three (RFGLSE, RSUR, and KLSUR estimators for SUR, when the predictors are correlated, was investigated using the Trace Mean Square Error (TMSE). The results showed that the KLSUR estimator outperformed the other estimators except for a few cases when the sample size is small.

Recent years have seen a spike in the number of academics utilizing the ecological footprint as a stand-in for environmental depletion because of its extensive nature and its ability to capture the worsening of the ecosystem. Thus, this article brings a new effort to analyze the effect of Bangladesh's economic complexity and natural resources on its ecological footprint over a long period, from 1995 to 2018. Using a nonlinear autoregressive distributed lag (NARDL) model, this paper suggests that a more complex economy has a significantly positive effect on ecological footprint over the long term. If the economy is simplified, it has less impact on the environment. For Bangladesh, an increase in economic complexity of 1 unit leads to an ecological footprint increase of 0.13 units, while a drop in economic complexity of 1% causes an ecological footprint decrease of 0.41%. Results also demonstrate that both positive and negative changes in natural resources contribute to rises in environmental quality in Bangladesh, which negatively influences the country's ecological footprint. Quantitatively, a 1% increase in natural resources reduces the ecological footprint by 0.14%, whereas a 1% decrease in resources has the opposite effect, reducing it by 0.59%. In addition, an asymmetric Granger causality test confirms the existence of a unidirectional causal link from ecological footprint to a positive partial sum of natural resources and from a negative partial sum of natural resources to ecological footprint. Finally, the findings point to a two-way causal relationship between the size of an economy's ecological footprint and the complexity of its economy. Policymakers should boost technological advances and lessen operational costs by adopting an innovative Research and development framework and devoting more cash to natural resource policies that promote an adaptable ecological footprint.

Abstract Background Plasmodium vivax forms dormant liver stages that can reactivate weeks or months following an acute infection. Recurrent infections are often associated with a febrile illness and can cause a cumulative risk of severe anaemia, direct and indirect mortality, and onward transmission of the parasite. There is an increased risk of P. vivax parasitaemia following falciparum malaria suggesting a rationale for universal use of radically curative treatment in patients with P. falciparum malaria even in the absence of detectable P. vivax parasitaemia in areas that are co-endemic for both species. Methods This is a multicentre, health care facility-based, randomized, controlled, open-label trial in Bangladesh, Indonesia and Ethiopia. Patients with uncomplicated falciparum malaria, G6PD activity of ≥70% of the adjusted male median (AMM) and haemoglobin levels ≥8g/dl are recruited into the study and randomized to either receive standard schizonticidal treatment plus 7-day high dose primaquine (total dose 7mg/kg) or standard care in a 1:1 ratio. Patients are followed up weekly until day 63. The primary endpoint is the incidence risk of any P. vivax parasitemia on day 63. Secondary endpoints include incidence risk on day 63 of symptomatic P. vivax malaria and the risk of any P. falciparum parasitaemia. Secondary safety outcomes include the proportion of adverse events and serious adverse events, the incidence risk of severe anaemia (Hb

The aim of this study was to introduce novel dressing with different extracts of mangosteen for quick blood clotting and wound healing. Insufficient use of natural biomaterials for blood clotting and wound healing in the medical textile sector has been observed. In order to improve the situation, viscose woven fabrics treated with mangosteen leaf, peel and pericarp extract were examined. Fresh mangosteens were collected from local market and different pastes were prepared by using an electric blender. The pretreated viscose woven fabrics were dyed with these pastes using glycerin as mordant. After the sample preparation, an antibacterial test, odor test, blood clotting test, strength test, color fastness tests were done. The treated samples exhibited good antibacterial activity against gram negative bacteria and mild antibacterial activity against gram positive bacteria, especially the fabrics treated with mangosteen leaf and pericarp. Besides, the treated samples did not create annoying odors as like the untreated one. To justify healing properties, implementations of the samples were done on a rabbit. Leaf and pericarp treated fabrics exhibited good blood clotting in comparison with untreated fabric. All the treated samples exhibited average to good color fastness to wash and rubbing properties with sufficient tensile strength. Morphological changes of the treated and untreated fibers were examined by SEM and FTIR analysis and the results clearly showed the differences in the untreated and treated samples. The experiments were carried out in an environment friendly way which indicates the production and processing of these dressing materials can have an enormous contribution to sustainable operations and products for the development of medical textiles sector.

Beam hopping (BH) and carrier aggregation (CA) are two promising technologies for the next generation satellite communication systems to achieve several orders of magnitude increase in system capacity and to significantly improve the spectral efficiency. While BH allows a great flexibility in adapting the offered capacity to the heterogeneous demand, CA further enhances the user quality-of-service (QoS) by allowing it to pool resources from multiple adjacent beams. In this paper, we consider a multi-beam high throughput satellite (HTS) system that employs BH in conjunction with CA to capitalize on the mutual interplay between both techniques. Particularly, an innovative joint BH-CA scheme is proposed and analyzed in this work to utilize their individual competencies. This includes designing an efficient joint time-space beam illumination pattern for BH and multi-user aggregation strategy for CA. Through this, user-carrier assignment, transponder filling-rates, beams hopping pattern, and illumination duration are all simultaneously optimized by formulating a joint optimization problem as a multi-objective mixed integer linear programming problem (MINLP). Simulation results are provided to corroborate our analysis, demonstrate the design tradeoffs, and point out the potentials of the proposed joint BH-CA concept. Advantages of our BH-CA scheme versus the conventional BH method without employing CA are investigated and presented under the same system circumstances.

The Conway–Maxwell–Poisson (COMP) model is defined as a flexible count regression model used for over- and under-dispersion cases. In regression analysis, when the explanatory variables are highly correlated, this means that there is a multicollinearity problem in the model. This problem increases the standard error of maximum likelihood estimates. To manage the multicollinearity effects in the COMP model, we proposed a new modified Liu estimator based on two shrinkage parameters (k, d). To assess the performance of the proposed estimator, the mean squared error (MSE) criterion is used. The theoretical comparison of the proposed estimator with the ridge, Liu, and modified one-parameter Liu estimators is made. The Monte Carlo simulation and real data application are employed to examine the efficiency of the proposed estimator and to compare it with the ridge, Liu, and modified one-parameter Liu estimators. The results showed the superiority of the proposed estimator as it has the smallest MSE value.

BackgroundSecondhand smoke (SHS) poses a high health risk to those living in multiunit housing (MUH) since it can easily spread from unit to unit and throughout the building's communal areas. MUH residents in Bangladesh are particularly vulnerable to SHS due to the absence of smoking restrictions within a housing complex. Therefore, this study aimed to assess the prevalence of SHS exposure and its associated factors among MUH residents living in seven divisional cities of Bangladesh- Dhaka, Chattogram, Rajshahi, Khulna, Sylhet, Barishal, and Rangpur.MethodsFrom April 2019 to November 2019, a cross-sectional survey was conducted with 616 MUH residents aged 18 or older who had been residing in MUH for at least two years in the seven divisional cities of Bangladesh. A multivariable logistic regression model was performed to determine the associated factors of SHS exposure.ResultsIn MUH complexes, more than half (54.9%) of the 616 respondents were exposed to SHS. The key factors positively associated with SHS exposure were females (aOR: 1.8, 95% CI:1.236-2.681), residents with a low monthly family income (aOR: 1.9, 95% CI: 1.162-3.220), those whose family members smoked (aOR: 2.4, 95% CI: 1.537-3.746), and Dhaka city residents (aOR: 1.9, 95% CI: 1.013-3.440).ConclusionsThis study revealed a high prevalence of SHS exposure among Bangladeshi MUH residents. Therefore, a smoking ban is needed in and around MUH complexes to protect non-smoking residents from SHS exposure.