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Intrusion detection systems are essential for securing wireless sensor networks (WSNs) and Internet of Things (IoT) environments against various threats. This study presents a novel hybrid machine learning (ML) model that integrates KMeans-SMOTE (KMS) for data balancing and principal component analysis (PCA) for dimensionality reduction, evaluated using the WSN-DS and TON-IoT datasets. The model employs classifiers such as Decision Tree Classifier, Random Forest Classifier (RFC), and gradient boosting techniques like XGBoost (XGBC) to enhance detection accuracy and efficiency. The proposed hybrid (KMS + PCA + RFC) approach achieves remarkable performance, with an accuracy of 99.94% and an f1-score of 99.94% on the WSN-DS dataset. For the TON-IoT dataset, it achieves 99.97% accuracy and an f1-score of 99.97%, outperforming traditional SMOTE TomekLink and Generative Adversarial Network-based data balancing techniques. This hybrid approach addresses class imbalance and high-dimensionality challenges, providing scalable and robust intrusion detection. Complexity analysis reveals that the proposed model reduces training and prediction times, making it suitable for real-time applications.

Background Bangladesh is facing a higher maternal mortality and morbidity than many other developing countries in the world. The majority of these maternal deaths occur due to pregnancy related complications. Although health facilities in urban areas in Bangladesh are widely available, women living in underprivileged urban areas are least likely to receive the maternal health services and as a result, they face more pregnancy related complications. Unfortunately, there are only a few studies on complications during maternal and delivery period in these areas. We aim to investigate the factors responsible for pregnancy related complications in urban slum and non-slum areas. Methods Data from the Urban Health Survey (UHS), 2013 were analyzed applying mixed logistic regression model. The response variable was complications during pregnancy, during/after delivery at the last birth and the total sample size was 6137. The adjusted odds ratios (AORs) along with their 95% confidence intervals (CIs) were also calculated to compare the magnitude of different risk factors for the pregnancy related complications. Results Younger mothers (age < 18 years) at the birth of their children had 24% (OR = 1.24, 95% CI: 1.01, 1.54) more odds to experience complications during pregnancy/delivery or after delivery compared to older mothers aged 18 to 35 years. The increased risk of complications was found among primiparous women. Women living in urban slum areas had higher pregnancy related complications than women living elsewhere. Migrant mothers faced more complications than women-who were not migrants. Women had greater pregnancy related complications when they delivered boy child than girl child, presumably from an increased size of the baby and resultant birth obstruction, assisted delivery and post partum haemorrhage. Moreover, a wanted pregnancy had fewer significant complications during pregnancy/delivery or after delivery than an unwanted pregnancy. Conclusions The study associates early maternal age, primiparity, unwanted pregnancy, women living in slum areas, women migrating from other cities or non-urban areas and NGO membership with increased risk of pregnancy related complications among urban women in Bangladesh. It is likely that addressing these risk factors for complications to the policymakers may help to reduce the maternal mortality and morbidity in Bangladesh.

Overdispersion in count data analysis is very common in many practical fields of health sciences. Ignorance of the presence of overdispersion in such data analysis may cause misleading inferences and thus lead to incorrect interpretations of the results. Researchers should account for the consequences of overdispersion and need to select the correct choice of models for the analysis of such data. In this paper, Generalized Linear Models (GLMs) are applied in modelling and analysis of antenatal care (ANC) count data extracted from the Bangladesh Demographic and Health Survey (BDHS) 2014. Pearson chi-square and different score tests are used to investigate the effect of overdispersion in the analysis. Overdispersion is found to be significant in the antenatal health care count data and so appropriate modelling is used to produce valid inferences for the regression parameters. The zero-truncated negative binomial regression (0-NBR) is found to be the best choice for analysing such data while excluding zero counts. Study findings reveal that place of residence, order of birth, exposure to mass media, wealth index and education of mother have significant impacts on the ANC status of women during pregnancy in Bangladesh.

Cardiovascular arrhythmia, characterized by irregular heart rhythms, poses significant health risks, including stroke and heart failure, making accurate and early detection critical for effective treatment. Traditional detection methods often struggle with challenges such as imbalanced datasets, limiting their ability to identify rare arrhythmia types. This study proposes a novel hybrid approach that integrates ConvNeXt-X deep learning models with advanced data balancing techniques to improve arrhythmia classification accuracy. Specifically, we evaluated three ConvNeXt variants—ConvNeXtTiny, ConvNeXtBase, and ConvNeXtSmall—combined with Random Oversampling (RO) and SMOTE-TomekLink (STL) on the MIT-BIH Arrhythmia Database. Experimental results demonstrate that the ConvNeXtTiny model paired with STL achieved the highest accuracy of 99.75%, followed by ConvNeXtTiny with RO at 99.72%. The STL technique consistently enhanced minority class detection and overall performance across models, with ConvNeXtBase and ConvNeXtSmall achieving accuracies of 99.69% and 99.72%, respectively. These findings highlight the efficacy of ConvNeXt-X models, when coupled with robust data balancing techniques, in achieving reliable and precise arrhythmia detection. This methodology holds significant potential for improving diagnostic accuracy and supporting clinical decision-making in healthcare.

Allergic disease is one of the most important and common health problems worldwide. We have previously demonstrated that a fig leaf-derived lactic acid bacterium Lactobacillus (Lb.) paracasei IJH-SONE68 produces a novel exopolysaccharide (EPS). Furthermore, we have shown that the EPS inhibits the catalytic activity of hyaluronidase (EC 3.2.1.36) promoting inflammatory reactions. To evaluate the anti-allergy and anti-inflammatory effects of the EPS, in the present study, we employed the picryl-chloride-induced delayed-type (type IV) allergy model mice, which is used to evaluate the contact dermatitis. Oral administration of the EPS was observed to reduce the ear swelling in the model mice. We also observed that the overexpression of ear interleukin-4 (T helper (Th) 2 cytokine) mRNA and the increase in serum immunoglobulin E (IgE) are repressed. However, the expression of interferon-γ (Th1 cytokine) was not accelerated in all of the allergen-challenged model mice. The improvement may be responsible for the Th2 downregulation rather than the Th1 upregulation. In addition, the symptom of immediate-type (type I) allergy model mice was improved by oral administration of the IJH-SONE68 cell (data not shown). We can conclude that the IJH-SONE68-derived EPS is useful to improve the type I and IV allergies including atopic dermatitis.

In this report, we discussed rapid, facile one-pot green synthesis of gold and silver nanoparticles (AuNPs and AgNPs) by using tuber extract of Amorphophallus paeoniifolius, and evaluated their antibacterial activity. AuNPs and AgNPs were synthesized by mixing their respective precursors (AgNO3 and HAuCl4) with tuber extract of Amorphophallus paeoniifolius as the bio-reducing agent. Characterization of AuNPs and AgNPs were confirmed by applying UV-vis spectroscopy, field-emission scanning electron microscopy (FESEM), X-ray diffraction (XRD) analysis, Fourier transform infrared spectroscopy (FTIR), and energy dispersive X-ray spectroscopy (EDS). From UV-vis characterization, surface plasmon resonance spectra were found at 530 nm for AuNPs and 446 nm for AgNPs. XRD data confirmed that both synthesized nanoparticles were face-centered cubic in crystalline nature, and the average crystallite sizes for the assign peaks were 13.3 nm for AuNPs and 22.48 nm for AgNPs. FTIR data evaluated the characteristic peaks of different phytochemical components of tuber extract, which acted as the reducing agent, and possibly as stabilizing agents. The antibacterial activity of synthesized AuNPs and AgNPs were examined in Muller Hinton agar, against two Gram-positive and four Gram-negative bacteria through the disc diffusion method. AuNPs did not show any inhibitory effect, while AgNPs showed good inhibitory effect against both Gram-positive and Gram-negative bacteria.

The possession of diverse knowledge is vital for countries to maintain competitive advantages as new technologies and other disruptive changes emerge. Foreign Direct Investment (FDI) has become an important instrument to access knowledge and innovation available in other countries with the increasing trend of globalization. However, little is known about how the knowledge base of a country can be enriched in the context of knowledge sourcing through FDI, drawing upon general contexts of knowledge and innovation in countries. By adopting the social network analysis methodology to model and analyze the global outward FDI network for the period 2009-2016 and conducting longitudinal regression analyses, this study reveals that the global outward FDI network has a core-periphery structure, the network centrality position of a country is positively and significantly associated with the knowledge base of the country, and the absorptive capacity of a country positively moderates the association. Equipped with empirical evidence to support our theory, we develop the K-NACK framework to understand the context of knowledge sourcing through FDI. This empirical research article offering novel analysis and a theoretical and conceptual framework will be useful for policymakers at firms and in individual countries to find advanced as well as complementary knowledge from host countries to improve the knowledge base in home countries.

Objective: This study sought to determine the effectiveness of the cryodehydration technique in preserving the morphologic and morphometric attributes of the anatomical specimens of goats. Materials and methods: Different anatomical parts of a goat, i.e., heart, lungs, spleen, liver, kidney, and musculoskeletal specimens, were collected and fixed in 15% formalin. Later on, the fixed specimens were cryodehydrated by fast freezing (burning process) and repeated freezing-thawing sessions, followed by wood glue coating. Finally, the macroscopic (i.e., weight, color, texture, odor, and durability) and microscopic characteristics (by routine hematoxylin and eosin staining) of the cryodehydrated specimens were studied. Results: The resultant specimens produced excellent color and texture and were lightweight (60%–80% weight loss), soft, dry, odorless, durable, and easy to handle. The histoarchitectural details of the heart and skeletal muscle were well preserved, while some distinctive alterations were observed in the parenchymatous organs, i.e., breach in cellular integrity, loss of cell cytoplasm, loss of cytoplasmic and nuclear clarity, increased sinusoidal space, dilatation of the renal tubules, and reduction in glomerular size. Nevertheless, the basic histoarchitecture of each specimen was yet to be distinctly identifiable. Conclusion: The current study findings suggest that the cryodehydration technique can preserve gross anatomical features as well as histoarchitectural details and can be an effective tool for facilitating veterinary education and research.

The COVID-19 pandemic has been a major health concern in Bangladesh until very recently. Although the Bangladesh government has employed various infection control strategies, more targeted Non-Pharmaceutical interventions (NPIs), including school closure, mask-wearing, hand washing, and social distancing have gained special attention. Despite significant long-term adverse effects of school closures, authorities have opted to keep schools closed to curb the spread of COVID-19 infection. However, there is limited knowledge about the impact of reopening schools alongside other NPI measures on the course of the epidemic. In this study, we implemented a mathematical modeling framework developed by the CoMo Consortium to explore the impact of NPIs on the dynamics of the COVID-19 outbreak and deaths for Bangladesh. For robustness, the results of prediction models are then validated through model calibration with incidence and mortality data and using external sources. Hypothetical projections are made under alternative NPIs where we compare the impact of current NPIs with school closures versus enhanced NPIs with school openings. Results suggest that enhanced NPIs with schools opened may have lower COVID-19 related prevalence and deaths. This finding indicates that enhanced NPIs and school openings may mitigate the long-term negative impacts of COVID-19 in low- and middle-income countries. Potential shortcomings and ways to improve the research are also discussed.

Medicinal plants or their products are popular substitutes for antibiotics or other synthetic growth promoters for their beneficial effects and cost-effectiveness. Clove (Cv) and tulsi (Ts) are rich in phytochemicals and are used to augment the growth performance of poultry. The current investigation was carried out to evaluate the effects of Cv and Ts supplementation in drinking water on the morphologic and morphometric adaptations in the lymphoid organs. The experiment was conducted on 60 broiler chicks, divided into 4 homogenous groups (5 pens/group and 3 broilers/pen) named T0 (control), T1 (0.5% Cv and 2% Ts), T2 (1% Cv and 3% Ts) and T3 (1.5% Cv and 4% Ts). The treatments were administered from days 8 to 28. Lymphoid organs (thymus, spleen and bursa of Fabricius) were collected on days 14, 21 and 28 after sacrificing five broilers from each group. The treatment groups had higher thymus and spleen weights on day 21, whereas the weights of the bursa of Fabricius were higher on days 14, 21 and 28. In the histomorphologic study, no noticeable difference in the histoarchitectures of the lymphoid organs was noticed. However, substantial differences in the cortico-medullary ratio and the number of follicles in the thymus and bursa of Fabricius, respectively, were found in the histomorphometric investigation. The inclusion of Cv and Ts in drinking water augments the weight of lymphoid organs with histomorphometric adaptations that might improve the immune status in broilers.