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The UN-led discussion about the post-2015 sustainable development agenda provides an opportunity to develop indicators and targets that show the importance of health as a precondition for and an outcome of policies to promote sustainable development. Health as a precondition for development has received considerable attention in terms of achievement of health-related Millennium Development Goals (MDGs), addressing growing challenges of non-communicable diseases, and ensuring universal health coverage. Much less attention has been devoted to health as an outcome of sustainable development and to indicators that show both changes in exposure to health-related risks and progress towards environmental sustainability. We present a rationale and methods for the selection of health-related indicators to measure progress of post-2015 development goals in non-health sectors. The proposed indicators show the ancillary benefits to health and health equity (co-benefits) of sustainable development policies, particularly those to reduce greenhouse gas emissions and increase resilience to environmental change. We use illustrative examples from four thematic areas: cities, food and agriculture, energy, and water and sanitation. Embedding of a range of health-related indicators in the post-2015 goals can help to raise awareness of the probable health gains from sustainable development policies, thus making them more attractive to decision makers and more likely to be implemented than before.

COVID-19 has been linked to hyperglycemia and diabetes, with noteworthy variation in outcomes. This study aimed to compare the sociodemographic factors, clinical characteristics, and in-hospital and short-term post-discharge outcomes between COVID-19 patients with new onset hyperglycemia and pre-existing diabetes patients in tertiary care hospitals in Bangladesh. A prospective observational study was conducted among adult COVID-19 patients with new onset hyperglycemia or pre-existing diabetes admitted to the COVID-19 unit of Dhaka Medical College Hospital between April 2021 and October 2021. Patients were conveniently selected from indoors. Bivariate analysis was used to compare sociodemographic and clinical characteristics at admission and short-term outcomes. The Cox proportional hazard model was used to examine factors associated with time to death in the hospital. All statistical analyses were performed using Stata Version 17. A total of 169 patients were included. Of these, 29 died in the hospital, and four left against medical advice. Out of the 136 survivors, 135 came for follow-up two weeks after discharge. At baseline, 30.18% of patients had new onset hyperglycemia, and 69.8% had pre-existing diabetes. The average age of patients was 56.38 ± 14.21 years, and 60.36% were male. A significantly higher proportion of COVID-19 patients with new onset hyperglycemia were smokers than those with pre-existing diabetes (p = 0.003). However, pre-existing diabetes was associated with higher lung involvement (p = 0.047) and comorbidities (p = 0.002). Age, income over 35,000 BDT (USD 335.5$), and a BMI over 25 kg/m2 emerged as significant predictors of prolonged hospital stay and mortality. Post-discharge follow-up indicated that new-onset hyperglycemia resolved in 8.89% of patients, whereas 19.26% continued to exhibit hyperglycemia, with smoking being a significant determinant of its persistence (p = 0.001). In conclusion, our investigation illuminates the clinical trajectory of new-onset hyperglycemia in the context of COVID-19 and reinforces the necessity for diligent monitoring and management post-discharge. Therefore, close monitoring and follow-up of COVID-19 patients is recommended for the early detection and management of hyperglycemia and the prevention of diabetes development in the long run.

The Department of Disaster and Human Security Management (DHSM) at Bangladesh University of Professionals (BUP) started its journey in 2015. This is one of the few programs at this university that began at the very beginning. At the onset, this study examined some of the existing undergraduate programs in Disaster Science and Management offered by various higher educational institutions around Bangladesh. Among these programs, a handful are well-organized and utilize an integrated curriculum responsive to the needs of the 21st century. Transforming the traditional undergraduate programs and curricula of Social Disaster Management into an integrated STEM program from policy to practice is a considerable challenge, and students have many expectations for this cutting-edge discipline. This study found that very few Bangladeshi academicians and professionals can develop dynamic suggestions regarding this matter and have the knowledge to design an effective program and curriculum for the future students of this discipline. As a result, certain challenges devising integrated STEM-based programs may jeopardize the development and implementation of disaster management programs at the university level. Hence, adequate qualified members, budget, laboratory, and equipment must implement a multidisciplinary STEM program. Moreover, an innovative STEM program requires additional support from diverse professional organizations to support projects and research. Very often, national higher education policy and regulatory institutions create obstacles. At the same, attempts are made to launch such innovative and integrated programs. This study recommends that a new integration be partially implemented, turning into a milestone of Bangladesh’s 21st-century higher education reformation process.

Human activity recognition is a significant area of research in artificial intelligence for surveillance, healthcare, sports, and human-computer interaction applications. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The dataset consists of 14,186 images across 19 activity classes, from dynamic activities such as running and swimming to static activities such as sitting and sleeping. Preprocessing included resizing all images to 512 512 pixels, annotating them in YOLO’s bounding box format, and applying data augmentation methods such as flipping, rotation, and cropping to enhance model generalization. The proposed model was trained for 100 epochs with adaptive learning rate methods and hyperparameter optimization for performance improvement, with a mAP@0.5 of 74.93% and a mAP@0.5-0.95 of 64.11%, outperforming previous versions of YOLO (v10, v9, and v8) and general-purpose architectures like ResNet50 and EfficientNet. It exhibited improved precision and recall for all activity classes with high precision values of 0.76 for running, 0.79 for swimming, 0.80 for sitting, and 0.81 for sleeping, and was tested for real-time deployment with an inference time of 8.9 ms per image, being computationally light. Proposed YOLOv11’s improvements are attributed to architectural advancements like a more complex feature extraction process, better attention modules, and an anchor-free detection mechanism. While YOLOv10 was extremely stable in static activity recognition, YOLOv9 performed well in dynamic environments but suffered from overfitting, and YOLOv8, while being a decent baseline, failed to differentiate between overlapping static activities. The experimental results determine proposed YOLOv11 to be the most appropriate model, providing an ideal balance between accuracy, computational efficiency, and robustness for real-world deployment. Nevertheless, there exist certain issues to be addressed, particularly in discriminating against visually similar activities and the use of publicly available datasets. Future research will entail the inclusion of 3D data and multimodal sensor inputs, such as depth and motion information, for enhancing recognition accuracy and generalizability to challenging real-world environments.

The global rise in mental health issues has become a significant public health challenge, exacerbated by the reluctance of many individuals to share their mental health concerns due to social stigma. Effective medical interventions and support systems are urgently needed. Researchers are increasingly turning to machine learning as a potential tool for diagnosing and addressing mental health conditions. This systematic review identifies and categorizes machine-learning techniques applied to mental health detection, examines studies predicting mental health states, compiles available datasets, and analyzes the most frequently used algorithms for mental health assessment. An extensive search was conducted across prominent databases such as Springer, ScienceDirect, IEEE, and PubMed, spanning the period from January 2015 to December 2024, using relevant keywords. Initially, 3,320 articles were selected based on their titles and abstracts. After careful examination, 35 articles met the inclusion criteria. Among the selected 35 studies, 14 leveraged data from online social networks to identify mental health issues, while 21 collected data through various manual means. These studies employed a diverse array of machine learning techniques, encompassing both supervised and unsupervised approaches. Machine learning exhibits promise in assisting with the diagnosis of mental health conditions and our studies show that machine learning is an effective and efficient way to detect mental health. However, further research is warranted in several key areas. Future studies should explore improved sampling methods, refine prediction algorithms, and address ethical considerations regarding using sensitive mental health data. Furthermore, incorporating image processing techniques could introduce a new dimension to this field. Collaboration with mental health specialists can augment the validity and impact of research outcomes in this critical domain. The systematic review underscores the potential of machine learning in addressing mental health issues and emphasizes the importance of ongoing research and collaboration to optimize its application in the field. It also shows that, although simpler and more interpretable models such as logistic regression are frequently used as baselines, the highest reported performances are usually achieved by more complex deep learning architectures, underscoring a central trade-off between model interpretability and predictive accuracy in this domain.

Novel SARS-CoV-2 variants are emerging at an alarming rate. The delta variant and other variants of concern (VoC) carry spike (S)-protein mutations, which have the potential to evade protective immunity, to trigger break-through infections after COVID-19 vaccination, and to propagate future waves of COVID-19 pandemic. To identify SARS CoV-2 variants in Bangladesh, patients who are RT-PCR-positive for COVID-19 infections in Dhaka were screened by a RT-PCR melting curve analysis for spike protein mutations. To assess the anti-SARS CoV-2 antibody responses, the levels of the anti-S -proteins IgA and IgG and the anti-N-protein IgG were measured by ELISA. Of a total of 36 RT-PCR positive samples (75%), 27 were identified as delta variants, with one carrying an additional Q677H mutation and two with single nucleotide substitutions at position 23029 (compared to Wuhan-Hu-1 reference NC 045512) in the genome sequence. Three (8.3%) were identified as beta variants, two (5.5%) were identified as alpha variants, three (8.3%) were identified as having a B.1.1.318 lineage, and one sample was identified as an eta variant (B.1.525) carrying an additional V687L mutation. The trend of higher viral load (lower Cp values) among delta variants than in the alpha and beta variants was of borderline statistical significance (p = 0.045). Prospective studies with larger Bangladeshi cohorts are warranted to confirm the emergence of S-protein mutations and their association with antibody response in natural infection and potential breakthrough in vaccinated subjects.

Unmanned Aerial Vehicles (UAVs) have transformed the process of data collection and analysis in a variety of research disciplines, delivering unparalleled adaptability and efficacy. This paper presents a thorough examination of UAV datasets, emphasizing their wide range of applications and progress. UAV datasets consist of various types of data, such as satellite imagery, images captured by drones, and videos. These datasets can be categorized as either unimodal or multimodal, offering a wide range of detailed and comprehensive information. These datasets play a crucial role in disaster damage assessment, aerial surveillance, object recognition, and tracking. They facilitate the development of sophisticated models for tasks like semantic segmentation, pose estimation, vehicle re-identification, and gesture recognition. By leveraging UAV datasets, researchers can significantly enhance the capabilities of computer vision models, thereby advancing technology and improving our understanding of complex, dynamic environments from an aerial perspective. This review aims to encapsulate the multifaceted utility of UAV datasets, emphasizing their pivotal role in driving innovation and practical applications in multiple domains.

Background The purpose of the study was to measure the prevalence of hyponatremia and its association with clinical and laboratory characteristics of hospitalized coronavirus disease 2019 (COVID‐19) patients at Dhaka Medical College and Hospital (DMCH). Methods This retrospective study was conducted in COVID‐19 dedicated wards at DMCH from June to August 2020. Demographic, clinical, and laboratory data were collected from patient treatment sheets. Two groups of COVID‐19 patients were retrospectively screened on the basis of plasma sodium level at admission: hyponatremic (sodium < 135 mM, n = 84) or normonatremic (sodium ≥ 135 mM, n = 48) patients. Severity was assessed using World Health Organization classification for COVID‐19 disease severity. To compare the two groups, Pearson's χ2 (qualitative variables) and Student's T tests (quantitative variables) were applied. The link between patients' clinical data and outcomes was investigated using logistic regression model. Results A total of 132 patients were included in the study, with a mean age of 51.41 (±14.13) years. Hyponatremia was found in 84 patients (63.6%) and the remaining 48 patients (36.4%) had normal plasma Na+ values. Among them, 74 (56.06%) presented with severe disease and 53 (40.15%) with moderate disease. At presentation, patients with moderate COVID‐19 disease had 2.15 (1.04–4.5) times higher odds of suffering from hyponatremia. Besides, hyponatremia was independently associated with on admission SpO2 (p = 0.038), hemoglobin (p = 0.004), and C‐reactive protein (p = 0.001). Conclusions The authors suggest that patients' serum electrolytes be measured during initial hospital admission and then monitored throughout the hospital stay to predict the probability for referral for invasive ventilation and for better management.

The clinical presentation of COVID-19 and the specific antibody responses associated with SARS-CoV-2 variants have not been investigated during the emergence of Omicron variants in Bangladesh. The Delta and Omicron variants were identified by post-PCR melting curve analysis of the spike (S) protein receptor binding domain amplicons. Anti-S-protein immunoglobulin-G anti-nucleocapsid (N)-protein immunoglobulin-G and immunoglobulin-A levels were measured by ELISA. The Delta variant was found in 40 out of 40 (100%) SARS-CoV-2 RT-PCR positive COVID-19 patients between 13 September and 23 October 2021 and Omicron variants in 90 out of 90 (100%) RT-PCR positive COVID-19 patients between 9 January and 10 February 2022. The Delta variant associated with hospitalization (74%, 80%, and 40%) and oxygen support (60%, 57%, and 40%) in the no vaccine, dose-1, and dose-2 vaccinated cases, respectively, whereas the Omicron COVID-19 required neither hospitalization nor oxygen support (0%, p < 0.0001). Fever, cough, and breathlessness were found at a significantly higher frequency among the Delta than Omicron variants (p < 0.001). The viral RNA levels of the Delta variant were higher than that of the Omicron variants (Ct median 19.9 versus 23.85; p < 0.02). Anti-spike protein immunoglobulin-G and anti-N-protein immunoglobulin-G within 1 week post onset of Delta variant COVID-19 symptoms indicate prior SARS-CoV-2 infection. The Delta variant and Omicron BA.1 and BA.2 breakthrough infections in the Dhaka region, at 240 days post onset of COVID-19 symptoms, negatively correlated with the time interval between the second vaccine dose and serum sampling. The findings of lower anti-spike protein immunoglobulin-G reactivity after booster vaccination than after the second vaccine dose suggest that the booster vaccine is not necessarily beneficial in young Bangladeshi adults having a history of repeated SARS-CoV-2 infections.

Over the past few decades, machine learning (ML) has demonstrated significant advancements in all areas of human existence. Machine learning and deep learning models rely heavily on data. Typically, basic machine learning (ML) and deep learning (DL) models receive input data and its matching output. Within the model, these models generate rules. In a physics-guided model, input and output rules are provided to optimize the model’s learning, hence enhancing the model’s loss optimization. The concept of the physics-guided neural network (PGNN) is becoming increasingly popular among researchers and industry professionals. It has been applied in numerous fields such as healthcare, medicine, environmental science, and control systems. This review was conducted using four specific research questions. We obtained papers from six different sources and reviewed a total of 81 papers, based on the selected keywords. In addition, we have specifically addressed the difficulties and potential advantages of the PGNN. Our intention is for this review to provide guidance for aspiring researchers seeking to obtain a deeper understanding of the PGNN.