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Abstract Introduction Strengthening and reforming the urban primary healthcare (PHC) system is essential to efficiently deliver need-based healthcare services to the rapidly increasing urban poor population. Such reforms of PHC system need to emphasize the opinion of patients in co-designing services in order that delivery of services can be accessed effectively by the urban population in a timely and low-cost way. Hence, it is important to identify the preference of urban population while choosing healthcare providers. The aim of this proposed protocol is to summarize a planned systematic review of existing evidence on the attributes considered for choosing PHC providers in urban settings of low- and middle-income countries (LMICs), as classified by the World Bank. Methods and analyses An inclusive literature search will be conducted in electronic databases including Pubmed/MEDLINE, Embase, Global Health, Cochrane Library, Web of Science, and Scopus. Databases will be searched from the earliest date of entry until March 30, 2024. Database search will be supplemented by manual search of citations, reference lists, and grey literature sources. Following the pre-set inclusion and exclusion criterion, two researchers will independently screen all the retrieved studies in Covidence. Any discrepancies will be resolved through a discussion between two researchers, and if disagreements persist, a third reviewer will be consulted. The methodological quality of included studies will be appraised using checklist for Conjoint Analysis studies and the Mixed Methods Appraisal Tool (MMAT). An Excel-based data extraction table will be developed, piloted, and refined during the review process. Preference attributes will be identified and analyzed according to their types. The systematic review will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta‑Analyses (PRISMA) guidelines. Discussion The identification of attributes, their influence on preference, and heterogeneity with socioeconomic characteristics of the population will help the policymakers and researchers to design targeted PHC interventions. Such evidence will be also useful to design choice experiment studies to quantify the preferred attributes of PHC providers in urban context of LMICs. Systematic review registration PROSPERO CRD42023409720.

Increasing crop resilience to drought stress through microorganisms is a sustainable approach. This study evaluated the efficacy of the endophytic fungus Metarhizium anisopliae MetA1 (MA) for improving drought tolerance of barley by analyzing various morphological, physiological, biochemical, and yield factors. Barley grains were treated with MA (1 × 108 spore/ml) and a pot experiment was conducted with three high-yielding barley genotypes: BARI Barley-10, BARI Barley-6, and BARI Barley-9 under three drought conditions: no drought (100 % field capacity, FC), moderate drought (50 % FC), and severe drought (25 % FC). Under drought conditions, MA priming significantly enhanced shoot and root biomass, leaf characteristics, photosynthetic pigment content, and activities of various antioxidant enzymes including superoxide dismutase, catalase, glutathione S-transferase, ascorbate peroxidase, peroxidase, glutathione peroxidase, glutathione reductase, dehydroascorbate reductase, monodehydroascorbate reductase, glyoxalase-I, and glyoxalase-II in all barley genotypes. Furthermore, there was an observed increase in the levels of non-enzymatic antioxidants such as ascorbate and glutathione. MA treatment also led to a significant reduction in stress markers like methylglyoxal, malondialdehyde, lipoxygenase, hydrogen peroxide, and calcium influx along with an increase in proline and potassium content in barley leaves in stressed conditions. Crop growth and yield related attributes in barley were improved, which is evidence of better physiological and biochemical changes under both stress and non-stressed conditions. As such, the study provides evidence suggesting that MA-mediated seed priming is an effective strategy for improving drought tolerance not only in barley but also possibly other crops.

In recent decades, breast cancer has become the most prevalent type of cancer that impacts women in the world, which shows a significant risk to the death rates of women. Early identification of breast cancer might drastically decrease patient mortality and greatly improve the chance of an effective treatment. In modern times, machine learning models have become crucial for classifying cancer and strengthening both the accuracy and efficiency of diagnostic and medical treatment strategies. Therefore, this study is focused on early detection of breast cancer using a variety of machine learning algorithms and desires to identify the most effective feature selection process with an amalgamated dataset. Initially, we evaluated five traditional models and two meta-models on separate datasets. To find the most valuable features, the study used the Least Absolute Shrinkage and Selection Operator (LASSO) as well as SHapley Additive exPlanations (SHAP) selection methods and analyzed them through a wide range of performance regulations. Additionally, we applied these models to the combined dataset and observed that the mergeddataset was significantly beneficial for breast cancer diagnosis. After analyzing the feature selection strategies, it was demonstrated that the majority of models performed more accurately when utilizing SHAP methodologies. Notably, three traditional models and two meta-classifiers obtained an accuracy of 99.82%, demonstrating superior performance compared to state-of-the-art methods. This advancement holds a crucial role as it lays the foundation for refining diagnostic tools and enhancing the progression of medical science in this field.

Abstract Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/ .

This article presents in-depth information on the structural and optoelectronic properties of MgLiX3 (X = Cl, Br, and I) perovskites, and it suggests that MgLiX3 perovskites are promising materials for use in a variety of optoelectronic gadgets. The structural and optoelectronic properties of the compounds are determined utilizing first-principles calculations, with the density functional theory applied through the WIEN2k code. The structural stability was verified by computing the formation energy and binding energy. This study investigated the behavior of electronic conductivity and determined the bandgap values by employing TB-mBJ, which are 3.354 eV (MgLiCl3), 1.728 eV (MgLiBr3), and 0.067 eV (MgLiI3). Furthermore, optical properties such as absorption coefficient, reflectivity, conductivity, loss function, dielectric function, refractive index, and extinction coefficient were calculated and analyzed. In the visible range, MgLiBr3 and MgLiI3 exhibit their primary highest peaks of the absorption coefficient, which are 8.8 × 104 cm−1 for MgLiBr3 and 7.7 × 104 cm−1 for MgLiI3. On the other hand, MgLiCl3 demonstrates its initial highest peaks in the UV range, that is, 9 × 104 cm−1. The findings indicate that among the compounds studied, MgLiBr3 shows promise as a candidate for manufacturing solar cell devices based on the SQ limit, bandgap for typical perovskites (within 0.8–2.2 eV), and absorption in the visible range. MgLiCl3 is suitable for manufacturing several optoelectronic devices, such as laser diodes (LDs) and UV sensors due to having a high absorption coefficient in the ultraviolet region. With its low energy bandgap and high absorption coefficient in the IR to VR regions, MgLiI3 is well-suited for manufacturing photodetectors, LEDs, and other optoelectronic devices.

Infectious fungi have been an increasing global concern in the present era. A promising approach to tackle this pressing concern involves utilizing Antifungal peptides (AFP) to develop an antifungal drug that can selectively eliminate fungal pathogens from a host with minimal toxicity to the host. Accordingly, identifying precise therapeutic antifungal peptides is crucial for developing effective drugs and treatments. This study proposed MLAFP-XN, a neural network-based strategy for accurately detecting active AFP in sequencing data to achieve this objective. In this work, eight feature extraction techniques and the XGB feature selection strategy are utilized together to present an enhanced methodology. A total of 24 classification models were evaluated, and the most effective four have been selected. Each of these models demonstrated superior accuracy on independent test sets, with respective scores of 97.93 %, 99.47 %, and 99.48 %. Our model outperforms current state of the art methods. In addition, we created a companion website to demonstrate our AFP recognition process and use SHAP to identify the most influential properties.

Healthy skin reflects a healthy microbiome and vice versa. The contemporary society, marked by a sharp increase in skin irritation cases, has compelled researchers, dermatologists, and the cosmetics industry to investigate the correlation between skin microbiomes and the use of skincare products. Different cosmetics can change skin's normal flora to a varying degree –some changes can be detrimental, there are also instances where these alterations aid in restoring the skin microbiome. Previous studies using artificial skin models, metagenomic analysis, and culture-based approaches have suggested that skincare products play an important role in skin microbial alteration. This article assessed current knowledge on microbial shifts from daily use of various personal and skincare products. We have also introduced a readily applicable framework, synthesized from various observations, which can be employed to identify the normal skin microbiome and evaluate the impact of personal care and skincare products on it. We also discussed how lifestyle choice remake skin microbial makeup. Future studies are warranted to examine the effect of personal and skincare product usage on skin microbiome across various age groups, genders, and body sites with a multi-study approach.

Graph neural network (GNN) is a formidable deep learning framework that enables the analysis and modeling of intricate relationships present in data structured as graphs. In recent years, a burgeoning interest has arisen in exploiting the latent capabilities of GNN for healthcare-based applications, capitalizing on their aptitude for modeling complex relationships and unearthing profound insights from graph-structured data. However, to the best of our knowledge, no study has systemically reviewed the GNN studies conducted in the healthcare domain. This study has furnished an all-encompassing and erudite overview of the prevailing cutting-edge research on GNN in healthcare. Through analysis and assimilation of studies, current research trends, recurrent challenges, and promising future opportunities in GNN for healthcare applications have been identified. China emerged as the leading country to conduct GNN-based studies in the healthcare domain, followed by the USA, UK, and Turkey. Among various aspects of healthcare, disease prediction and drug discovery emerge as the most prominent areas of focus for GNN application, indicating the potential of GNN for advancing diagnostic and therapeutic approaches. This study proposed research questions regarding diverse aspects of GNN in the healthcare domain and addressed them through an in-depth analysis. This study can provide practitioners and researchers with profound insights into the current landscape of GNN applications in healthcare and can guide healthcare institutes, researchers, and governments by demonstrating the ways in which GNN can contribute to the development of effective and efficient healthcare systems.

Knee osteoarthritis (KOA) is a prevalent condition characterized by gradual progression, resulting in observable bone alterations in X-ray images. X-rays are the preferred diagnostic tool for their ease of use and cost-effectiveness. Physicians use the Kellgren and Lawrence (KL) grading system to understand the severity of an individual condition of KOA. This system categorizes the disease from normal to a severe stage. Early detection of the condition with this approach enables knee deterioration to be slowed down with therapy. In this study, we aggregated four datasets to generate an extensive dataset comprising 110,232 raw images by applying an augmentation technique called deep convolutional generative adversarial network (DCGAN). We employed advanced image pre-processing methods (adaptive histogram equalization (AHE), fast non-local means), including image resizing, to generate a substantial dataset and enhance image quality. Our proposed approach involved developing a modified compact convolutional transformer (CCT) model known as KOA-CCTNet as the foundational model. We further investigated optimal configurations by adjusting various parameters and hyperparameters in the final model to handle large datasets and address training time concerns efficiently. We investigated optimizing its configurations by adjusting numerous parameters and hyperparameters to efficiently manage extensive data and address concerns related to training time. Simulation results indicated that our proposed model outperforms other transfer learning models (Swin Transformer, Vision Transformer, Involutional Neural Network) in terms of accuracy. The test accuracy for the ResNet50, MobileNetv2, DenseNet201, InceptionV3, and VGG16 was 80.77%, 79.98%, 80.23%, 76.89%, and 79.58%, respectively. All of them were surpassed by our proposed KOA-CCTNet model, which had a test accuracy of 94.58% while classifying KOA X-ray images. Furthermore, we reduced the number of images to assess the model’s performance and compared it to existing models. However, by employing a large datahub, our proposed approach provides a unique and effective way to diagnose KOA grades with satisfying results.

A novel automated multi-classification approach is proposed for the anticipation of lung abnormalities using chest X-ray and CT images. The study leverages a publicly accessible dataset with an insufficient and unbalanced number of images, addressing this issue by employing the data augmentation approach DCGAN to balance the dataset. Various preprocessing procedures are applied to improve features and reduce noise in lung pictures. As the base for the model, the vision trans-former and convolution-based compact convolutional transformer (CCT) model is utilized. To determine the best model configuration, an ablation study is performed on the original CCT model using a CT scan dataset with image dimensions of $32\times32$ . Following that, this model is trained on the X-ray dataset to evaluate performance on an entirely other modality. The performances are compared to six pre-trained models with $32\times 32$ images. While traditional models achieved modest performance, with test accuracies ranging from 43% to 77% and 49% to 73% requiring lengthy training times, the suggested model performed exceptionally well, obtaining test accuracies of 99.77% and 95.37% for CT and X-ray, respectively with a short training duration of 10–12 and 40–42 seconds/epoch. Robustness is demonstrated through the progressive reduction of the number of training images, with findings indicating that the model maintains good performance even on a reduced dataset. An explainable AI technique Grad-CAM is used to explain the model’s judgment. Grad-CAM-based color visualization is shown to explain model assessments and help health specialists make quick, confident decisions. This study used image preprocessing and deep learning techniques to detect lung anomalies, and it addressed the challenges of training time and computational complexity.

Eye diseases are defined as disorders or diseases that damage the tissue and related parts of the eyes. They appear in various types and can be either minor, meaning that they do not last long, or permanent blindness. Cataracts, glaucoma, and diabetic retinopathy are all eye illnesses that can cause vision loss if not discovered and treated early on. Automated classification of these diseases from fundus images can empower quicker diagnoses and interventions. Our research aims to create a robust model, BayeSVM500, for eye disease classification to enhance medical technology and improve patient outcomes. In this study, we develop models to classify images accurately. We start by preprocessing fundus images using contrast enhancement, normalization, and resizing. We then leverage several state-of-the-art deep convolutional neural network pre-trained models, including VGG16, VGG19, ResNet50, EfficientNet, and DenseNet, to extract deep features. To reduce feature dimensionality, we employ techniques such as principal component analysis, feature agglomeration, correlation analysis, variance thresholding, and feature importance rankings. Using these refined features, we train various traditional machine learning models as well as ensemble methods. Our best model, named BayeSVM500, is a Support Vector Machine classifier trained on EfficientNet features reduced to 500 dimensions via PCA, achieving 93.65 ± 1.05% accuracy. Bayesian hyperparameter optimization further improved performance to 95.33 ± 0.60%. Through comprehensive feature engineering and model optimization, we demonstrate highly accurate eye disease classification from fundus images, comparable to or superior to previous benchmarks.

Different physical properties of ternary intermetallic La-based superconducting materials LaM2Si2 (M = Co, Cu, Rh, Pd, Ag, Ir, Pt, Au) are examined through the self consistent ab-initio scheme depending on the DFT replication. The main purpose of this study is to comprehend several different crucial characteristics of these compounds, including their crystal phase, stability manner, bulk features, band configuration, dielectric, conductivity as well as thermal features. Good accordance in the structural analysis of superconducting materials LaM2Si2 (M = Co, Cu, Rh, Pd, Ag, Ir, Pt, Au) has been observed between the optimized and synthesized lattice parameters. The phonon dispersion curve indicates that the compounds LaCo2Si2, LaCu2Si2, LaRh2Si2, LaPd2Si2, and LaIr2Si2 are dynamically stable, but LaAg2Si2, LaPt2Si2, and LaAu2Si2 are dynamically unstable in nature. Mechanical crystal constancy of the selected materials is verified from the investigated elastic constants. Ductility behaviors of these compounds are ensured by the values of Pugh's and Poisson's ratios. High Young's modulus of LaIr2Si2 ensured its elevated hardness along with huge melting temperature compared than other phases. Anisotropic character of these materials can be ensured through the calculations of elastic anisotropy. The electrical properties of the superconducting LaM2Si2 (M = Co, Cu, Rh, Pd, Ag, Ir, Pt, Au) compounds were also studied in details. Metallic character of the selected solids is observed through the investigation of DOS and band structure calculations. The investigated dielectric function, conductivity, and absorption also reflect the conductive behavior of superconducting LaM2Si2 (M = Co, Cu, Rh, Pd, Ag, Ir, Pt, Au) materials. The optical phenomena ensured their huge absorption as well as optical conductive nature at the site of ultraviolet energy region. Moreover, high reflectivity in the UV region makes these superconductors good solar reflectors for this energy site. Very low minimum thermal conductivity of these compounds especially of LaPt2Si2 and LaAu2Si2 makes them useful and fascinating for thermal barrier coating (TBC) materials.

Background: Xanthomonas oryzae pv. oryzae is a plant pathogen responsible for causing one of the most severe bacterial diseases in rice, known as bacterial leaf blight that poses a major threat to global rice production. Even though several experimental compounds and chemical agents have been tested against X. oryzae pv. oryzae, still no approved drug is available. In this study, a subtractive genomic approach was used to identify potential therapeutic targets and repurposible drug candidates that could control of bacterial leaf blight in rice plants. Results: The entire proteome of the pathogen underwent an extensive filtering process which involved removal of the paralogous proteins, rice homologs, non-essential proteins. Out of the 4382 proteins present in Xoo proteome, five hub proteins such as dnaA, dnaN, recJ, ruvA, and recR were identified for the druggability analysis. This analysis led to the identification of dnaN-encoded Beta sliding clamp protein as a potential therapeutic target and one experimental drug named [(5R)-5-(2,3-dibromo-5-ethoxy-4hydroxybenzyl)-4-oxo-2-thioxo-1,3-thiazolidin-3-yl]acetic acid that can be repurposed against it. Molecular docking and 100 ns long molecular dynamics simulation suggested that the drug can form stable complexes with the target protein over time. Conclusion: Findings from our study indicated that the proposed drug showed potential effectiveness against bacterial leaf blight in rice caused by X. oryzae pv. oryzae. It is essential to keep in consideration that the procedure for developing novel drugs can be challenging and complicated. Even the most promising results from in silico studies should be validated through further in vitro and in vivo investigation before approval.

The adsorption performance, kinetics, and thermodynamic parameters of reactive dyes on chitosan-treated cotton fabric are described in this research. The batch experiment was carried out to investigate the influence of pH, time, temperature, dye concentration, and material to liquid ratio of Remazol Red RR and Remazol Yellow RR, two widely used reactive dyes. The adsorption of red and yellow dyes was observed at λmax 517 and 419 nm respectively. For both Remazol Red RR and Remazol Yellow RR reactive dyes with starting dye concentrations of 0.0667 and 40 mg/L, the greatest dye adsorption was reported at pH 11 (0.0009 and 0.1284 mg/g) and pH 4 (0.0004 and 0.1038 mg/g). The adsorption performance was investigated using the Langmuir and Freundlich isotherms. The adsorption behaviours of the two reactive dyes closely matched to the Langmuir adsorption isotherm (R2>0.98) than Freundlich isotherm (R2>0.92). The pseudo-second-order kinetic model (R2>0.99) was followed by the dyeing kinetics of modified cotton sample. Thermodynamic investigation was covered with enthalpy change (ΔH⁰), Gibbs free energy (ΔG⁰) and entropy change (ΔS⁰) where all the values were reported positive confirming that the adsorption process was non-spontaneous, endothermic and the higher affinity of reactive dye on chitosan treated cotton.

Abstract The entomopathogenic fungus (EPF), Beauveria bassiana, is reported as the most potent biological control agent against a wide range of insect families. This study aimed to isolate and characterize the native B. bassiana from various soil habitats in Bangladesh and to evaluate the bio-efficacy of these isolates against an important vegetable insect pest, Spodoptera litura. Seven isolates from Bangladeshi soils were characterized as B. bassiana using genomic analysis. Among the isolates, TGS2.3 showed the highest mortality rate (82%) against the 2nd instar larvae of S. litura at 7 days after treatment (DAT). This isolate was further bioassayed against different stages of S. litura and found that TGS2.3 induced 81, 57, 94, 84, 75, 65, and 57% overall mortality at egg, neonatal 1st, 2nd, 3rd, 4th, and 5th instar larvae, respectively, over 7 DAT. Interestingly, treatment with B. bassiana isolate TGS2.3 resulted in pupal and adult deformities as well as decreased adult emergence of S. litura. Taken together, our results suggest that a native isolate of B. bassiana TGS2.3 is a potential biocontrol agent against the destructive insect pest S. litura. However, further studies are needed to evaluate the bio-efficacy of this promising native isolate in planta and field conditions.

COVID-19, pneumonia, and tuberculosis have had a significant effect on recent global health. Since 2019, COVID-19 has been a major factor underlying the increase in respiratory-related terminal illness. Early-stage interpretation and identification of these diseases from X-ray images is essential to aid medical specialists in diagnosis. In this study, (COV-X-net19) a convolutional neural network model is developed and customized with a soft attention mechanism to classify lung diseases into four classes: normal, COVID-19, pneumonia, and tuberculosis using chest X-ray images. Image preprocessing is carried out by adjusting optimal parameters to preprocess the images before undertaking training of the classification models. Moreover, the proposed model is optimized by experimenting with different architectural structures and hyperparameters to further boost performance. The performance of the proposed model is compared with eight state-of-the-art transfer learning models for a comparative evaluation. Results suggest that the COV-X-net19 outperforms other models with a testing accuracy of 95.19%, precision of 96.49% and F1-score of 95.13%. Another novel approach of this study is to find out the probable reason behind image misclassification by analyzing the handcrafted imaging features with statistical evaluation. A statistical analysis known as analysis of variance test is performed, to identify at which point the model can identify a class accurately, and at which point the model cannot identify the class. The potential features responsible for the misclassification are also found. Moreover, Random Forest Feature importance technique and Minimum Redundancy Maximum Relevance technique are also explored. The methods and findings of this study can benefit in the clinical perspective in early detection and enable a better understanding of the cause of misclassification.

Farmers in Bangladesh are enthusiastic in dragon fruit farming as a new and promising crop that offers both difficulties and potential. The purpose of the study was to examine the advantages and disadvantages of this novel fruit from the perspective of growers. It is a descriptive survey research which was conducted in purposively selected four Upazilas of Chapainawabganj region of Bangladesh including Chapainawabganj Sadar, Gomastapur, Nachol, and Godagari from 16th April to 25th May, 2022. Pearson’s correlation test was administered to determine the relationship between socio-demographic characteristics of the growers and their knowledge on dragon fruit production. Socio-demographic profile of the randomly selected growers of the study revealed that majority of the respondents were in their middle age, highly educated, had a medium annual income with medium sized land, little farming experience, less training and higher cosmopolitan behavior. Majority of the dragon fruit growers showed high level of understanding regarding dragon fruit cultivation and their fellow farmers and mass media were determined to be the most effective sources of information. According to the study’s findings, the majority of farmers were inspired to grow dragon fruit because it is more resistant to pests and diseases, promotes excellent health, yields for a long time, and can be grown from both seeds and cuttings. Furthermore, the fruit has a higher profit margin than other field crops. Weeds, bird invasions, high labor expenses, lack of technical knowledge of farmers on dragon fruit farming were recognized as major obstacles to fruit production. According to a correlation analysis, farmers’ age, extension contacts, and cosmopolitan nature were significantly and positively correlated with their knowledge of growing dragon fruit. More government support, especially from the Department of Agricultural Extension (DAE), and fixing the issues described above could increase the fruit’s future prospects in the country.

Rhizoctonia solani is an important necrotrophic pathogenic fungus that causes okra root disease and results in severe yield reduction. Many biocontrol agents are being studied with the intent of improving plant growth and defense systems and reducing crop loss by preventing fungal infections. Recently, a member of the Hypocrealean family, Metarhizium anisopliae, has been reported for insect pathogenicity, endophytism, plant growth promotion, and antifungal potentialities. This research investigated the role of M. anisopliae (MetA1) in growth promotion and root disease suppression in okra. The antagonism against R. solani and the plant growth promotion traits of MetA1 were tested in vitro. The effects of endophytic MetA1 on promoting plant growth and disease suppression were assessed in planta. Dual culture and cell-free culture filtrate assays showed antagonistic activity against R. solani by MetA1. Some plant growth promotion traits, such as phosphate solubilization and catalase activity were also exhibited by MetA1. Seed primed with MetA1 increased the shoot, root, leaves, chlorophyll content, and biomass content compared to control okra plants. The plants challenged with R. solani showed the highest hydrogen peroxide (H2O2) and lipid peroxidation (MDA) contents in the leaves of okra. Whereas MetA1 applied plants showed a reduction of H2O2 and MDA by 5.21 and 14.96%, respectively, under pathogen-inoculated conditions by increasing antioxidant enzyme activities, including catalase (CAT), peroxidase (POD), glutathione S-transferase (GST), and ascorbate peroxidase (APX), by 30.11, 10.19, 5.62, and 5.06%, respectively. Moreover, MetA1 increased soluble sugars, carbohydrates, proline, and secondary metabolites, viz., phenol and flavonoid contents in okra resulting in a better osmotic adjustment of diseases infecting plants. MetA1 reduced disease incidence by 58.33% at 15 DAI compared to the R. solani inoculated plant. The results revealed that MetA1 improved plant growth, elevated the plant defense system, and suppressed root diseases caused by R. solani. Thus, MetA1 was found to be an effective candidate for the biological control program.

Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification.

Though different types of commercial probiotics are supplemented in biofloc technology (BFT), very little information is available on their effects on the farmed fish. Therefore, this study focused on evaluating the effects of three most commonly used commercial probiotics on the growth performance, intestinal histomorphology, and intestinal microbiota of Nile tilapia (Oreochromis niloticus) reared in BFT. Tilapia fry, with an average weight of 3.02 ± 0.50 g, were stocked at a density of 60 fry/0.2 m3, and cultured for 90 days. Three commercial probiotics were administered, with three replications for each: a single-genus multi-species probiotic (Bacillus spp.) (T1), a multi-genus multi-species probiotic (Bacillus sp., Lactobacillus sp., Nitrosomonas sp., Nitrobacter sp.) (T2), and a multi-species probiotic (Bacillus spp.) combined with enzymes including amylase, protease, cellulase, and xylanase (T3). The results showed significant variations in growth and feed utilization, with T3 outperforming other treatments in terms of weight gain, liver weight, and intestine weight. Adding Bacillus spp. with enzymes (T3) to water significantly increased the histomorphological parameters (villi length, villi depth, crypt depth, muscle thickness, intestinal thickness) as well as microbes (total viable count and total lactic acid bacteria) of intestine of fish compared to T1 and T2, leading to improved digestion and absorption responses. It is concluded that the supplementation of commercial probiotics has potential benefits on farmed fish species in BFT.

Abstract Background Financing healthcare through out-of-pocket (OOP) payment is a major barrier in accessing healthcare for the poor people. The Health Economics Unit (HEU) of the Ministry of Health and Family Welfare of the government of Bangladesh has developed Shasthyo Suroksha Karmasuchi (SSK), a health protection scheme, with the aim of reducing OOP expenditure and improving access of the below-poverty-line (BPL) population to healthcare. The scheme started piloting in 2016 at Kalihati sub-district of Tangail District. Our objective was to assess healthcare utilization by the enrolled BPL population and to identify the factors those influencing their utilization of the scheme. Method A cross-sectional household survey was conducted from July to September 2018 in the piloting sub-district. A total of 806 households were surveyed using a semi-structured questionnaire. Information on illness and sources of healthcare service were captured for the last 90 days before the survey. Multiple logistic regression models were applied to determine the factors related to utilization of healthcare from the SSK scheme and other medically trained providers (MTPs) by the SSK members for both inpatient and outpatient care. Result A total of 781 (24.6%) people reported of suffering from illness of which 639 (81.8%) sought healthcare from any sources. About 8.0% (51 out of 639) of them sought healthcare from SSK scheme and 28.2% from other MTPs within 90 days preceding the survey. Households with knowledge about SSK scheme were more likely to utilize healthcare from the scheme and less likely to utilize healthcare from other MTPs. Non-BPL status and suffering from an accident/injury were significantly positively associated with utilization of healthcare from SSK scheme. Conclusion Among the BPL population, healthcare utilization from the SSK scheme was very low compared to that of other MTPs. Effective strategies should be in place for improving knowledge of BPL population on SSK scheme and the benefits package of the scheme should be updated as per the need of the target population. Such initiative can be instrumental in increasing utilization of the scheme and ultimately will reduce the barriers of OOP payment among BPL population for accessing healthcare.

Natural colourants were widely employed in the ancient world to colour cotton garments. With the revolution of synthetic dyes, natural dyes have lost their position in respect of some performance. However, nowadays the demand for sustainable and eco-friendly products is increasing worldwide due to the awareness of ecological balance. The purpose of this study is to utilize natural colourants in printing on cotton fabric. In this regard, the colourants were extracted from myrobalan fruit, pomegranate peel and catechu, which were eventually applied to the fabric by screen printing technique. The print paste was prepared with carboxymethyl cellulose (CMC) as a natural thickener and polyvinyl alcohol (PVA) as a synthetic thickener. The stability of the print paste was studied with respect to time elapse by viscosity measurement. The depth of shade and colour fastness of the printed fabric were measured. K/S value was evaluated with respect to the stability of print paste. The viscosity was changed slightly in both the CMC and PVA thickener with respect to the stability of the print paste. The washing fastness grades for colour staining wash ranged from 4 to 5, rubbing fastness grades of dry rubbing also ranged from 4 to 5, wet rubbing fastness was 3/4 to 4/5 as well as perspiration fastness grades in alkaline, and in acidic it was 3/4 to 5. Thus, myrobalan fruit, pomegranate peel and catechu have shown excellent performance in printing on cotton textiles, which could be an alternative to the synthetic counterpart.

This article addresses the response of GLAss fiber REinforced aluminum to hypervelocity impacts of micrometeoroid analogs at impact velocities of 7 km/s and beyond. In relation, the damage modes of different GLAss fiber REinforced aluminum configurations have been exemplified. The GLAss fiber REinforced aluminum configurations comprised six to twelve variably thick aluminum layers and up to four plies of glass fiber reinforced epoxy per composite laminate. Hypervelocity impact experiments have been conducted with the help of a two-stage light-gas gun, wherein aluminum- and stainless steel projectiles were launched at velocities up to 7.15 km/s. Visual inspection of the damage area suggested the dissipation of impact energy in elastic-plastic deformation, petalling, delamination, debonding, tensile failure of fibers, and pyrolysis of epoxy. A prevailing damage mode was not apparent albeit. The quasi-isotropic ply orientation of S2-glass/FM94-epoxy laminates promoted the interference of shock- and rarefaction waves and suppressed the damage area of GLAss fiber REinforced aluminum. To discriminate between the impact performance of different GLAss fiber REinforced aluminum configurations, the energy dissipated in different damage modes of GLAss fiber REinforced aluminum has been assessed quantitatively. In terms of normalized energy, the cross-ply GLAss fiber REinforced aluminum dissipated higher energy in petal formation than in other primary damage modes. The normalized petalling energy was found to decline with the increase of impact energy. The outcomes of this study will help to optimize the GLAss fiber REinforced aluminum laminate, which will be employed as a bumper shield to prevent the fatal damage and the unzipping of a spacecraft pressure bulkhead.

Identification of brain tumors at an early stage is crucial in cancer diagnosis, as a timely diagnosis can increase the chances of survival. Considering the challenges of tumor biopsies, three dimensional (3D) Magnetic Resonance Imaging (MRI) are extensively used in analyzing brain tumors using deep learning. In this study, three BraTS datasets are employed to classify brain tumor into two classes where each of the datasets contains four 3D MRI sequences for a single patient. This research is composed of two approaches. In the first part, we propose a hybrid model named TimeDistributed-CNN-LSTM (TD- CNN-LSTM) combining 3D Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) where each layer is wrapped with a TimeDistributed function. The objective is to consider all the four MRI sequences of each patient as a single input data because every sequence contains necessary information of tumor. Therefore, the model is developed with optimal configuration performing ablation study for layer architecture and hyper-parameters. In the second part, a 3D CNN model is trained respectively with each of the MRI sequences to compare the performance. Moreover, the datasets are preprocessed to ensure highest performance. Results demonstrate that the TD-CNN-LSTM network outperforms 3D CNN achieving the highest test accuracy of 98.90%. Later, to evaluate the performance consistency, the TD-CNN-LSTM model is evaluated with K-fold cross validation. The approach of putting together all the MRI sequences at a time with good generalization capability can be used in future medical research which can aid radiologists in tumor diagnostics effectively.

The several physical features including structural, mechanical, electronic, optical and thermal properties of Pt3Ti and Pt3Cu have investigated here hypothetically for the first time. The possible results of Pt3Ti and Pt3Cu have been compared to other pt-based compounds for reliability of this work. The DFT simulation depends on ab-initio manner through CASTEP regulation is utilized for all of the investigation. The structural properties of these materials are in better agreement with the test data already available. The calculated elastic constants demonstrate mechanical stability for all the compounds. The computed Poisson's as well as Pugh's ratios demonstrate the behavior of ductility of these phases, with Pt3Cu is being far more ductile over Pt3Ti. The large Young’s modulus and hardness value of Pt3Ti ensured that Pt3Ti is harder material than Pt3Cu. The high machinability index of Pt3Cu ensured that this phase has high damage-tolerant capability and useful application in industry. The anisotropic factor and universal index of anisotropy ensure the elastic nature of anisotropic for all of these phases. The study of density of states (both TDOS and PDOS) as well as electronic band structures evidences the metallic behavior for these materials. The optical features of compounds Pt3Ti and Pt3Cu have also been investigated through CASTEP code for the first time and recommended that these phases have huge application in solar cell to reduce extra solar heat. The calculated Debye and melting temperature ensured that Pt3Ti is more thermally conductive and can be used in high temperature structural materials. The very low minimum thermal conductivity ensured that Pt3Cu can be used as a better Thermal Barrier Coating (TBC) material than Pt3Ti.

Early identification and adequate treatment can help prevent lung disorders from becoming chronic, severe, and life-threatening. X-ray images are commonly used and an automated and effective method involving deep learning techniques can potentially contribute to quick and accurate diagnosis of lung disorders. However, in the study of medical imaging using deep learning, two obstacles limit interpretability. One is an insufficient and imbalanced number of training samples in most medical datasets. The other is excessive training time. Although training time can be reduced by decreasing the number of pixels in the images, training with low resolution images tends to result in poor performance. This study represents a solution to overcome these impediments by balancing the number of images and reducing overall processing time while preserving accuracy. The dataset used in this research contains an unequal number of images in the different classes. The quantity of data in the classes is balanced by creating synthetic images based on the patterns and characteristics of the original images, using a Deep Convolutional Generative Adversarial Network (DCGAN). Unwanted regions are removed from the X-ray images, the brightness and contrast of the images are enhanced, and the abnormalities are highlighted by using different artifact removal, noise reduction, and enhancement techniques. We propose a Modified Compact Convolutional Transformer (MCCT) model using 32 × 32 sized images for the categorization of lung disorders into four classes. An ablation study of eleven cases is employed to adjust several hyper parameters and layer topologies. This reduces training time while preserving accuracy. Six transfer learning models, VGG19, VGG16, ResNet152, ResNet50, ResNet50V2, and MobileNet are applied with the same image size the performance is compared with the proposed MCCT model. Our MCCT model records the greatest test accuracy of 95.37%, requiring a short training time, 10-12 s/epoch, whereas the other models only reach near-moderate performance with accuracies ranging from 43% to 79% and training times of 80-90 s/epoch. The robustness of the model with regards to the number of training samples is validated by training the model multiple times reducing the number of training images gradually from 49621 images to 6204 images. Results suggest that even with a smaller dataset, the performance is sustained. Our proposed approach may contribute to an effective CAD based diagnostic system by addressing the issues of insufficient and imbalanced numbers of medical images, excessive training times and low-resolution images.

Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets. The impact of photometric and geometric augmentation techniques on different datasets is also evaluated. We selected the mammogram dataset to proceed with the ablation study for being one of the most challenging imaging modalities. Before generating the model, the dataset is augmented using the two approaches. A base CNN model is constructed first and applied to both the augmented and non-augmented mammogram datasets where the highest accuracy is obtained with the photometric dataset. Therefore, the architecture and hyper-parameters of the model are determined by performing an ablation study on the base model using the mammogram photometric dataset. Afterward, the robustness of the network and the impact of different augmentation techniques are assessed by training the model with the rest of the seven datasets. We obtain a test accuracy of 97.34% on the mammogram, 98.43% on the skin cancer, 99.54% on the brain tumor magnetic resonance imaging (MRI), 97.29% on the COVID chest X-ray, 96.31% on the tympanic membrane, 99.82% on the chest computed tomography (CT) scan, and 98.75% on the breast cancer ultrasound datasets by photometric augmentation and 96.76% on the breast cancer microscopic biopsy dataset by geometric augmentation. Moreover, some elastic deformation augmentation methods are explored with the proposed model using all the datasets to evaluate their effectiveness. Finally, VGG16, InceptionV3, and ResNet50 were trained on the best-performing augmented datasets, and their performance consistency was compared with that of the MNet-10 model. The findings may aid future researchers in medical data analysis involving ablation studies and augmentation techniques.

Background: Diarrhea is a leading cause of morbidity and mortality among under-five children in Bangladesh. Hospitalization for diarrhea can pose a significant burden on households and health systems. The aim of this study was to estimate the cost of illness due to diarrhea from the healthcare facility, caregiver, and societal perspectives in Bangladesh. Method: A cross-sectional study with an ingredient-based costing approach was conducted in 48 healthcare facilities in Bangladesh. In total, 899 caregivers of under-five children with diarrhea were interviewed face-to-face between August 2017 and May 2018, followed up over phone after 7–14 days of discharge, to capture all expenses and time costs related to the entire episode of diarrhea. Results: The average cost per episode for caregivers was US$62, with $29 direct and $34 indirect costs. From the societal perspective, average cost per episode of diarrhea was $71. In 2018, an estimated $79 million of economic costs were incurred for treating diarrhea in Bangladesh. Using 10% of income as threshold, over 46% of interviewed households faced catastrophic expenditure from diarrheal disease. Conclusion: The economic costs incurred by caregivers for treating per-episode of diarrhea was around 4% of the annual national gross domestic product per-capita. Investment in vaccination can help to reduce the prevalence of diarrheal diseases and avert this public health burden.

The key work of this research is to implement the ‘Fourth Industrial Revolution’ by using the internet of things (IoT) which can be applied to flammable gas condition monitoring and detection of gas leakage and activate fire extinguisher in case of fire accidents. Internet of things enabled the sensors to transmit LPG concentration value reading to the mobile app or cloud server and control actuators by connecting all in the same network. In case of a fire accident, the solenoid valve will be opened automatically or can be opened by an android application manually which will be pre-installed in mobile phones. Another advantage of this system is, the gas cylinder or flammable particle source can be closed by closing the solenoid valves attached to their outlets. The first challenge of industry 4.0 is to develop a cyber-physical system where all physical entities can be monitored and controlled over the internet or another way remotely or from a single point. This fire extinguisher system can be used everywhere and in all types of fire-fighting because all types of fire extinguishers are commercially available in cylinders where solenoid valves can be used instead of conventional valves. Use of robust automatic control system will eliminate the effort of human for continuous monitoring of LPG condition, leakage and in the activation of fire extinguishers by cyber-physical system.

Abstract Background This study estimated the economic cost of treating measles in children under-5 in Bangladesh from the caregiver, government, and societal perspectives. Method We conducted an incidence-based study using an ingredient-based approach. We surveyed the administrative staff and the healthcare professionals at the facilities, recording their estimates supported by administrative data from the healthcare perspective. We conducted 100 face-to-face caregiver interviews at discharge and phone interviews 7 to 14 days post-discharge to capture all expenses, including time costs related to measles. All costs are in 2018 USD ($). Results From a societal perspective, a hospitalized and ambulatory case of measles cost $159 and $18, respectively. On average, the government spent $22 per hospitalized case of measles. At the same time, caregivers incurred $131 and $182 in economic costs, including $48 and $83 in out-of-pocket expenses in public and private not-for-profit facilities, respectively. Seventy-eight percent of the poorest caregivers faced catastrophic health expenditures compared to 21% of the richest. In 2018, 2263 cases of measles were confirmed, totaling $348,073 in economic costs to Bangladeshi society, with $121,842 in out-of-pocket payments for households. Conclusion The resurgence of measles outbreaks is a substantial cost for society, requiring significant short-term public expenditures, putting households into a precarious financial situation. Improving vaccination coverage in areas where it is deficient (Sylhet division in our study) would likely alleviate most of this burden.

The first COVID-19 case in Bangladesh was detected on March 8, 2020. Since then, efforts are being made across the country to raise awareness among the population for preventing the spread of this virus. We aimed to examine the urban slum dwellers’ knowledge, attitude, and practice (KAP) towards COVID-19 transmission-prevention. A phone-based cross-sectional survey was conducted in five slums of Dhaka City. Total 476 adult slum dwellers were interviewed between October 31 to December 1, 2020 using a pre-tested questionnaire. During an interview, information was collected on participants’ demographic characteristics and KAP items towards COVID-19. We used quartiles for categorization of knowledge and practice score where the first quartile represents poor, the second and third quartiles represent average while the fourth quartile represents good. Attitude score was standardized using z-score and identified as positive and negative attitude. Multiple linear regression models were used separately to identify the socioeconomic predictors of the KAP scores. The results showed that 25% of the respondents had good knowledge and 25% had poor knowledge, 48% had a positive attitude and 52% had a negative attitude, and 21% maintained good practice and 33% maintained poor practice towards COVID-19 transmission-prevention. About 75% respondents relied on television for COVID-19 related information. Regression results showed that knowledge and attitude scores were significantly higher if respondents had primary or secondary and above level of education compared to the uneducated group. Female respondents maintained significantly good practice compared to their male counterparts (β = 6.841; p

Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases.

Background: Breast cancer, behind skin cancer, is the second most frequent malignancy among women, initiated by an unregulated cell division in breast tissues. Although early mammogram screening and treatment result in decreased mortality, differentiating cancer cells from surrounding tissues are often fallible, resulting in fallacious diagnosis. Method: The mammography dataset is used to categorize breast cancer into four classes with low computational complexity, introducing a feature extraction-based approach with machine learning (ML) algorithms. After artefact removal and the preprocessing of the mammograms, the dataset is augmented with seven augmentation techniques. The region of interest (ROI) is extracted by employing several algorithms including a dynamic thresholding method. Sixteen geometrical features are extracted from the ROI while eleven ML algorithms are investigated with these features. Three ensemble models are generated from these ML models employing the stacking method where the first ensemble model is built by stacking ML models with an accuracy of over 90% and the accuracy thresholds for generating the rest of the ensemble models are >95% and >96. Five feature selection methods with fourteen configurations are applied to notch up the performance. Results: The Random Forest Importance algorithm, with a threshold of 0.045, produces 10 features that acquired the highest performance with 98.05% test accuracy by stacking Random Forest and XGB classifier, having a higher than >96% accuracy. Furthermore, with K-fold cross-validation, consistent performance is observed across all K values ranging from 3–30. Moreover, the proposed strategy combining image processing, feature extraction and ML has a proven high accuracy in classifying breast cancer.

Abstract Background District hospitals (DHs) provide secondary level of healthcare to a wide range of population in Bangladesh. Efficient utilization of resources in these secondary hospitals is essential for delivering health services at a lower cost. Therefore, we aimed to estimate the technical efficiency of the DHs in Bangladesh. Methods We used input-oriented data envelopment analysis method to estimate the variable returns to scale (VRS) and constant returns to scale (CRS) technical efficiency of the DHs using data from Local Health Bulletin, 2015. In this model, we considered workforce as well as number of inpatient beds as input variables and number of inpatient, outpatient, and maternal services provided by the DHs as output variables. A Tobit regression model was applied for assessing the association of institutional and environmental characteristics with the technical efficiency scores. Results The average scale, VRS, and CRS technical efficiency of the DHs were estimated to 85%, 92%, and 79% respectively. Population size, poverty headcount, bed occupancy ratio, administrative divisions were significantly associated with the technical efficiency of the DHs. The mean VRS and CRS technical efficiency demonstrated that the DHs, on an average, could reduce their input mix by 8% and 21% respectively while maintaining the same level of output. Conclusion Since the average technical efficiency of the DHs was 79%, there is little scope for overall improvements in these facilities by adjusting inputs. Therefore, we recommend to invest further in the DHs for improvement of services. The Ministry of Health and Family Welfare (MoHFW) should improve the efficiency in resource allocation by setting an input-mix formula for DHs considering health and socio-economic indicators (e.g., population density, poverty, bed occupancy ratio). The formula can be designed by learning from the input mix in the more efficient DHs. The MoHFW should conduct this kind of benchmarking study regularly to assess the efficiency level of health facilities which may contribute to reduce the wastage of resources and consequently to provide more affordable and accessible public hospital care.

Abstract Background In recent years, developing countries like Bangladesh are facing a higher burden of non-communicable diseases such as hypertension as a result of demographic transition. Prevalence of hypertension is often studied in this setting. However, evidence on undiagnosed hypertension is not widely available in the existing literature. Therefore, the current study focuses on inequalities in the prevalence of undiagnosed hypertension in Bangladesh. Methods A total of 8835 participants aged 35+ years were included in this study using nationally representative Bangladesh Demographic and Health Survey 2011 (BDHS). In the survey, systolic blood pressure (SBP) and diastolic blood pressure (DBP) of these participants were measured three times with approximately 10 minutes of an interval between each measurement. Any respondent with either SBP ≥ 140 mmHg or DBP ≥ 90 mmHg was considered as patient with hypertension as per the guidelines from American Heart Association. Among the participants, undiagnosed hypertension was defined as having SBP > =140 mmHg or DBP > =90 mmHg and never taking prescribed medicine or being told by health professionals to lower/control blood pressure. Multiple logistic regression analysis was applied for identifying factors associated with undiagnosed hypertension. Further, socioeconomic inequalities in the prevalence of undiagnosed hypertension were estimated using Concentration Index (C). Results We found 978 (59.9% of the total) were undiagnosed among 1685 hypertensive patients studied. Regression analysis showed individuals with being underweight, having poor socioeconomic conditions, and lower educational qualifications were more likely to have undiagnosed hypertension. A similar association between undiagnosed hypertension and socioeconomic quintiles was observed using concentration index (C = − 0.07). On the other hand, individuals from higher age group (50–64 or above), female sex, and Sylhet region were at lower risk of undiagnosed hypertension. Conclusions This study showed that a large proportion of the cases with hypertension are remained undiagnosed in Bangladesh, especially among the poor and low educated population. Screening and awareness building initiatives on hypertension should be taken for this group of population to reduce the burden of undiagnosed hypertension.

Bacterial biofilms play a key role in metal biosorption from wastewater. Recently, Enterobacter asburiae ENSD102, Enterobacter ludwigii ENSH201, Vitreoscilla sp. ENSG301, Acinetobacter lwoffii ENSG302, and Bacillus thuringiensis ENSW401 were shown to form air–liquid (AL) and solid–air–liquid (SAL) biofilms in a static condition at 28 and 37°C, respectively. However, how environmental and nutritional conditions affect biofilm formation; production of curli and cellulose; and biosorption of copper (Cu), nickel (Ni), and lead (Pb) by these bacteria have not been studied yet. In this study, E. asburiae ENSD102, E. ludwigii ENSH201, and B. thuringiensis ENSW401 developed the SAL biofilms at pH 8, while E. asburiae ENSD102 and Vitreoscilla sp. ENSG301 constructed the SAL biofilms at pH 4. However, all these strains produced AL biofilms at pH 7. In high osmolarity and ½-strength media, all these bacteria built fragile AL biofilms, while none of these strains generated the biofilms in anaerobic conditions. Congo red binding results showed that both environmental cues and bacterial strains played a vital role in curli and cellulose production. Calcofluor binding and spectrophotometric results revealed that all these bacterial strains produced significantly lesser amounts of cellulose at 37°C, pH 8, and in high osmotic conditions as compared to the regular media, at 28°C, and pH 7. Metal biosorption was drastically reduced in these bacteria at 37°C than at 28°C. Only Vitreoscilla sp. ENSG301 and B. thuringiensis ENSW401 completely removed (100%) Cu and Ni at an initial concentration of 12.5 mg l–1, while all these bacteria totally removed (100%) Pb at concentrations of 12.5 and 25 mg l–1 at pH 7 and 28°C. At an initial concentration of 100 mg l–1, the removal of Cu (92.5 to 97.8%) and Pb (89.3 to 98.3%) was the highest at pH 6, while it was higher (84.7 to 93.9%) for Ni at pH 7. Fourier transform infrared spectroscopy results showed metal-unloaded biomass biofilms contained amino, hydroxyl, carboxyl, carbonyl, and phosphate groups. The peak positions of these groups were shifted responding to Cu, Ni, and Pb, suggesting biosorption of metals. Thus, these bacterial strains could be utilized to remove Cu, Ni, and Pb from aquatic environment.

This study aimed at developing a numerical model to estimate the power consumption of a GLAss fiber REinforced aluminum-based electro-thermal ice protection system for anti-icing. The GLAss fiber REinforced aluminum laminate was embedded with recurred S-shaped copper meshes thermally energized by a surface power density. The model determined the influence of a forced convective heat loss and copper-mesh dimension on anti-icing. As a test case, the numerical model addressed the anti-icing performance of a GLAss fiber REinforced aluminum 5A-3/2 laminate at a sub-freezing ambient temperature of 267.95 K. The model demanded a surface power density of 13.03 kW/m2 in order to keep the temperature of the outer aluminum-skin at 285.44 K, which was clearly beyond the water freezing point. The numerically predicted power consumption for anti-icing was in excellent agreement with that from the experiment. The experiment was conducted in a miniature icing wind tunnel by incorporating the comparable icing condition of the numerical analysis. In the experiment, an infrared thermography camera determined the spatio-temporal temperature history of the outer aluminum skin in contact with the free-stream. The infrared thermography camera measurements of surface temperature collapsed in close proximity to that from the numerical analysis. The outcomes of this study will consolidate the confidence of aircraft manufacturers on the proposed anti-icing system.

This study uses density functional theory to investigate the structural, mechanical, electronic, optical, and thermodynamic properties of tetragonal LaRhGe3, LaIrGe3, and LaPdGe3 compounds. The investigated lattice parameter showed similar results to the experimental data, justifying the accuracy of our calculations. The negative values of formation enthalpy confirmed the thermodynamic stability of LaTGe3 (T = Rh, Ir, or Pd). The mechanical stability of these compounds was also verified by their single independent elastic constants. Poisson’s and Pugh’s ratios revealed that all the compounds have a ductile nature. The metallic nature of these phases was found from their band structure calculations. The study of Mulliken bond populations and charge density maps ensured the existence of a mixed character of ionic, covalent, and metallic nature in LaRhGe3, LaIrGe3, and LaPdGe3 compounds. Detailed investigation was also performed on optical properties, and the dielectric function, absorption, and conductivity again ensured the metallic feature of all these phases. The calculated optical functions suggested their potential application in quantum-dot light emitting diodes, organic light emitting diodes, solar cells, waveguides, and solar heating reduction. Moreover, the very low values of minimum thermal conductivity and the Debye temperature are indicative of their suitability for thermal barrier coating materials.

Background National healthcare financing strategy recommends tax-based equity funds and insurance schemes for the poor and extreme poor living in urban slums and pavements as the majority of these population utilise informal providers resulting in adverse health effects and financial hardship. We assessed the effect of a health voucher scheme (HVS) and micro-health insurance (MHI) scheme on healthcare utilisation and out-of-pocket (OOP) payments and the cost of implementing such schemes. Methods HVS and MHI schemes were implemented by Concern Worldwide through selected NGO health centres, referral hospitals, and private healthcare facilities in three City Corporations of Bangladesh from December 2016 to March 2020. A household survey with 1,294 enrolees, key-informant interviews, focus group discussions, consultative meetings, and document reviews were conducted for extracting data on healthcare utilisation, OOP payments, views of enrolees, and suggestions of implementers, and costs of services at the point of care. Results Healthcare utilisation including maternal, neonatal and child health (MNCH) services, particularly from medically trained providers, was higher and OOP payments were lower among the scheme enrolees compared to corresponding population groups in general. The beneficiaries were happy with their access to healthcare, especially for MNCH services, and their perceived quality of care was fair enough. They, however, suggested expanding the benefits package, supported by an additional workforce. The cost per beneficiary household for providing services per year was €32 in HVS and €15 in MHI scheme. Conclusion HVS and MHI schemes enabled higher healthcare utilisation at lower OOP payments among the enrolees, who were happy with their access to healthcare, particularly for MNCH services. However, they suggested a larger benefits package in future. The provider’s costs of the schemes were reasonable; however, there are potentials of cost containment by purchasing the health services for their beneficiaries in a competitive basis from the market. Scaling up such schemes addressing the drawback would contribute to achieving universal health coverage.

Perovskite halides are the most promising current candidates for the construction of solar cells and other photovoltaic devices. This is the first theoretical approach to explore the effects of Mn-doping on the optoelectronic performance of the lead-free halide CsGeBr3 and the lead-bearing halide CsPbBr3. We have performed the first-principles calculations to investigate the structural, mechanical, electronic, and optical properties of pure and Mn-doped CsGeBr3 and CsPbBr3 perovskite halides in detail. The lattice constants of Mn-doped halides were slightly reduced compared to their pure phases, which is common in materials after doping. The structural stability of both undoped and doped halides was confirmed by their formation enthalpy. Analysis of the mechanical properties revealed the mechanical stability of both undoped and Mn-doped CsGeBr3 and CsPbBr3. The lower values of the bulk modulus suggested potential optoelectronic applications for the halides being studied. Remarkably, the partial substitution of Ge with Mn narrows the bandgap of both Pb-free and Pb halides, enhancing the electron transfer from the valence band to the conduction band, which increased the absorption and conductivity, essential for superior optoelectronic device applications. The combined analysis of mechanical, electronic, and optical properties indicated that the Mn-doped halides, CsGeBr3 and CsPbBr3, are more suitable for the solar cells and optoelectronic applications than undoped CsGeBr3 and CsPbBr3.

The structural, mechanical, and electronic properties of perovskite molybdates are a topic of frequent study in materials science. In this study, the influence of Mg doping on the physical metallurgy of perovskite molybdates is investigated using first-principles calculations based on density functional theory (DFT) and molecular dynamics (MD) simulation. Our calculated optimized lattice parameters (3.9945, 3.8964, 3.8634, 3.8440, and 3.7952 Å [mentioned only DFT data, MD data listed in Table 2] for x = 0, 0.1, 0.2, 0.3, and 0.4 respectively) of SrMo1−xMgxO3 are highly consistent with other experimental results (3.9762, 3.9695, and 3.9649 Å for x = 0, 0.1, and 0.2 respectively) and some available theoretical results (3.9720 Å for x = 0, no previous data available for Mg-doped systems). The calculated elastic constants satisfied the Born stability criteria, indicating that our studied materials are mechanically stable at all doping concentrations, which was also confirmed by the calculated negative values of Cohesive energy. The mechanical behaviors of perovskite, including elastic constants, elastic moduli, ductility, and elastic anisotropy, were investigated and discussed. Our computed results suggest that Mg doping can increase elastic moduli. The calculated Pugh’s ratio increased from 0.42 to 0.71 [DFT] and from 0.47 to 0.75 [MD] as well as the Poisson’s ratio decreased from 0.31 to 0.21 [DFT] and from 0.30 to 0.21 [MD] which transformed the compound from ductile to brittle due to the addition of Mg at Mo-site. The band structures, density of states, and charge density redistributions of the undoped and Mg-doped materials were predicted. Our simulation outputs clearly illustrated the importance of accounting for Mg doping’s influence in theoretical simulations of the physical properties of the presently studied perovskite material.

The statistical probabilistic Direct Simulation Monte Carlo (DSMC) method was developed to interrogate the flow regime, in which thermal non-equilibrium exists and flow properties are non-continuous. Unfortunately, the DSMC method had not been preferred for the pure continuum flow regime ascribed to the computational burden arises with the computation of a large number of simulated molecule trajectories. However, this study showed that the DSMC method can handle the number density of real molecules of the pure continuum flow regime. The DSMC predictions were in close proximity to the experimental measurements taken on an Orion aeroshell model at Mach 11.4, 12.4, and 12.5 of reacting air in an expansion tunnel facility. The measurements corresponded to laminar boundary layer conditions, whereby Schlieren images were taken to assess the shockwave encapsulating the Orion aeroshell. The DSMC model predicted no isolated bump of the stagnation point heat flux, which was in line with the experiments. At a 20° angle of attack of the aeroshell, the model could capture the non-axisymmetric flow-field, in which the shear layer was attached at the windward side aftbody and detached at the leeward side aftbody. Thinning of the shockwave with the increase of flow-field temperature and molecular dissociation was also visibly distinct. The outcomes suggest the fidelity of the DSMC method in capturing the molecular interactions and the associated reaction kinetics in regimes varying from continuum to a rarefied gas without any substantial compromise of accuracy.

B2C, or business-to-consumer, is the type of commerce transaction in which businesses sell products or services to consumers. Small businesses are the most suitable for part-time or entrepreneurial experiences. E-Business has become very popular in this era of high-speed internet & fast-growing demands of the customers. It can be run at a very low cost & manpower. There are so many risks in a startup e-commerce business which can be minimized by proper selection or decision makings. Proper planning can ensure preventive maintenance of the business & enables the entrepreneur to do the right thing the first time. The analytic hierarchy process (AHP) is a structured technique for organizing and analyzing complex decisions, based on mathematics and psychology. It is one of the world-wide popular methods of decision making. This research is based on business selection by applying AHP. The E-Business decision was taken based on a deep analysis using AHP.

In the present world, nuclear energy is a must need for various purposes. The main cause of nuclear energy is because of the increasing energy demand, which is not possible to provide by using convenient energy generation. Bangladesh is a lower income country and the energy sector is not so developed here though there is a very high demand for energy. Rooppur Nuclear Power Plant is the only one nuclear generation plant which can provide this kind of huge energy within a very short time. This paper mainly depicts the ins and outs of this plant and discusses it’s feasibility in Bangladesh. It also focuses on the worlds various power generation methods and comprises it with nuclear energy generation. A detailed technical brief is presented in this paper along with advantages, location selection, financial and environmental impacts. This will help researchers to do further researches about nuclear energy in Bangladesh.

Abstract Background Rapidly increasing healthcare costs and the growing burden of non-communicable diseases have increased the out-of-pocket (OOP) spending (63.3% of total health expenditure) in Bangladesh. This increasing OOP spending for healthcare has catastrophic economic impact on households. To reduce this burden, the Health Economics Unit (HEU) of the Ministry of Health and Family Welfare has developed the Shasthyo Surokhsha Karmasuchi (SSK) health protection scheme for the below-poverty line (BPL) population. The key actors in the scheme are HEU, contracted scheme operator and hospital. Under this scheme, each enrolled household is provided 50,000 BDT (620 USD) coverage per year for healthcare services against a government financed premium of 1000 BDT (12 USD). This initiative faces some challenges e.g., delays in scheme activities, registering the targeted population, low utilization of services, lack of motivation of the providers, and management related difficulties. It is also important to estimate the financial requirement for nationwide scale-up of this project. We aim to identify these implementation-related challenges and provide feedback to the project personnel. Methods This is a concurrent process documentation using mixed-method approaches. It will be conducted in the rural Kalihati Upazila where the SSK is being implemented. To validate the BPL population selection process, we will estimate the positive predictive value. A community survey will be conducted to assess the knowledge of the card holders about SSK services. From the SSK information management system, numbers of different services utilized by the card holders will be retrieved. Key-informant interviews with personnel from three key actors will be conducted to understand the barriers in the implementation of the project as per plan and gather their suggestions. To estimate the project costs, all inputs to be used will be identified, quantified and valued. The nationwide scale-up cost of the project will be estimated by applying economic modeling. Discussion SSK is the first ever government initiated health protection scheme in Bangladesh. The study findings will enable decision makers to gain a better understanding of the key challenges in implementation of such scheme and provide feedback towards the successful implementation of the program.

Background: Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone. Methods: Six pre-trained and fine-tuned deep CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, DenseNet201, and InceptionV3 are evaluated to determine which model yields the best performance. We propose a BreastNet18 model using VGG16 as foundational base, since VGG16 performs with the highest accuracy. An ablation study is performed on BreastNet18, to evaluate its robustness and achieve the highest possible accuracy. Various image processing techniques with suitable parameter values are employed to remove artefacts and increase the image quality. A total dataset of 1442 preprocessed mammograms was augmented using seven augmentation techniques, resulting in a dataset of 11,536 images. To investigate possible overfitting issues, a k-fold cross validation is carried out. The model was then tested on noisy mammograms to evaluate its robustness. Results were compared with previous studies. Results: Proposed BreastNet18 model performed best with a training accuracy of 96.72%, a validating accuracy of 97.91%, and a test accuracy of 98.02%. In contrast to this, VGGNet19 yielded test accuracy of 96.24%, MobileNetV2 77.84%, ResNet50 79.98%, DenseNet201 86.92%, and InceptionV3 76.87%. Conclusions: Our proposed approach based on image processing, transfer learning, fine-tuning, and ablation study has demonstrated a high correct breast cancer classification while dealing with a limited number of complex medical images.