Out of Distribution Performance of State of Art Vision Model

The vision transformer (ViT) has advanced to the cutting edge in the visual recognition task. Transformers are more robust than CNN, according to the latest research. ViT's self-attention mechanism, according to the claim, makes it more robust than CNN. Even with this, we discover that these conclusions are based on unfair experimental conditions and just comparing a few models, which did not allow us to depict the entire scenario of robustness performance. In this study, we investigate the performance of 58 state-of-the-art computer vision models in a unified training setup based not only on attention and convolution mechanisms but also on neural networks based on a combination of convolution and attention mechanisms, sequence-based model, complementary search, and network-based method. Our research demonstrates that robustness depends on the training setup and model types, and performance varies based on out-of-distribution type. Our research will aid the community in better understanding and benchmarking the robustness of computer vision models

Estimation of the Healthcare Waste Generation During COVID-19 Pandemic in Bangladesh

COVID-19 pandemic-borne wastes imposed a severe threat to human lives as well as the total environment. Improper handling of these wastes increases the possibility of future transmission. Therefore, immediate actions are required from both local and international authorities to mitigate the amount of waste generation and ensure proper disposal of these wastes, especially for low-income and developing countries where solid waste management is challenging. In this study, an attempt is made to estimate healthcare waste generated during the COVID-19 pandemic in Bangladesh. This study includes infected, ICU, deceased, isolated and quarantined patients as the primary sources of medical waste. Results showed that COVID-19 medical waste from these patients was 658.08 tons in March 2020 and increased to 16164.74 tons in April 2021. A top portion of these wastes was generated from infected and quarantined patients. Based on survey data, approximate daily usage of face masks and hand gloves is also determined. Probable waste generation from COVID-19 confirmatory tests and vaccination has been simulated. Finally, several guidelines are provided to ensure the country\u27s proper disposal and management of COVID-related wastes

Academic Use of Smartphones in Secondary Level Education in Bangladesh: A Non-Parametric Approach

This study aims to examine the use of smartphones for educational purposes and the acceptance of online learning among secondary students. To investigate the academic utilization of smartphones among secondary students in Bangladesh, a sample of 384 students from different districts of Bangladesh were surveyed. The survey was conducted using a selfadministered, semi-tailored computerized questionnaire. The collected data was analyzed using IBM SPSS statistics 26 and the Mann-Whitney U test. The findings indicate that male students used smartphones for educational purposes with greater confidence and less difficulty than female students. On the other hand, students in 8th to 10th grade classrooms reported a greater willingness to use smartphones for academic purposes, with urban students being more enthusiastic than their rural peers. The study’s findings have implications for the government, policymakers, educators, and non-governmental organizations (NGOs). They highlight the importance of ensuring equal access to resources and tools that support academic success, as well as addressing the adverse effects of excessive smartphone usage. In addition, the government and NGOs should prioritize the elimination of inequities between rural and urban areas and provide subsidies to rural students

Ophthalmic Biomarker Detection Using Ensembled Vision Transformers -- Winning Solution to IEEE SPS VIP Cup 2023

This report outlines our approach in the IEEE SPS VIP Cup 2023: Ophthalmic Biomarker Detection competition. Our primary objective in this competition was to identify biomarkers from Optical Coherence Tomography (OCT) images obtained from a diverse range of patients. Using robust augmentations and 5-fold cross-validation, we trained two vision transformer-based models: MaxViT and EVA-02, and ensembled them at inference time. We find MaxViT's use of convolution layers followed by strided attention to be better suited for the detection of local features while EVA-02's use of normal attention mechanism and knowledge distillation is better for detecting global features. Ours was the best-performing solution in the competition, achieving a patient-wise F1 score of 0.814 in the first phase and 0.8527 in the second and final phase of VIP Cup 2023, scoring 3.8% higher than the next-best solution

Current Scenario of Solar Energy Applications in Bangladesh: Techno-Economic Perspective, Policy Implementation, and Possibility of the Integration of Artificial Intelligence

Bangladesh is blessed with abundant solar resources. Solar power is considered the most desirable energy source to mitigate the high energy demand of this densely populated country. Although various articles deal with solar energy applications in Bangladesh, no detailed review can be found in the literature. Therefore, in this study, we report on the current scenario of renewable energy in Bangladesh and the most significant potential of solar energy’s contribution among multiple renewable energy resources in mitigating energy demand. One main objective of this analysis was to outline the overall view of solar energy applications in Bangladesh to date, as well as the ongoing development of such projects. The technical and theoretical solar energy potential and the technologies available to harvest solar energy were also investigated. A detailed techno-economic design of solar power applications for the garment industry was also simulated to determine the potential of solar energy for this specific scenario. Additionally, renewable energy policies applied in Bangladesh to date are discussed comprehensively, with an emphasis on various ongoing projects undertaken by the government. Moreover, we elaborate global insight into solar power applications and compare Bangladesh’s current solar power scenario with that of other regions worldwide. Furthermore, the potential of artificial intelligence to accelerate solar energy enhancement is delineated comprehensively. Therefore, in this study, we determined the national scenarios of solar power implementation in Bangladesh and projected the most promising approaches for large-scale solar energy applications using artificial intelligence approaches

Profile of Parkia speciosa hassk metabolites extracted with SFE using FTIR- PCA method

A rapid identification, classification and discrimination tool, using Fourier Transform Infrared (FTIR) spectroscopy combined with Principal Component Analysis (PCA), was developed and applied to determine the profile of the Supercritical Fluid Extraction (SFE) of Parkia speciosa seeds under various temperature and pressure conditions (313, 323, 333, 343, 353 and 363 K and 20.68, 27.58, 34.47, 41.37, 48.26, and 55.16MPa). The separation and identification of the compounds was carried out by Gas Chromatography coupled with Time of Flight Mass Spectrometry (GC/TOF-MS). This technique has made it possible to detect the variability obtained under different SFE conditions and the separation of different chemical compounds in P. speciosa seeds. The FTIR-PCA results were verified by GC/TOF-MS, and the FTIR-PCA method successfully identified the unsaturated carboxylic acids with the highest percentage area under the different conditions

An automated approach for the kidney segmentation and detection of kidney stones on computed tomography using YOLO algorithms

Background: For effective diagnosis and treatment planning, accurate segmentation of the kidneys and detection of kidney stones are crucial. Traditional procedures are time-consuming and subject to observer variation. This study proposes an automated method employing YOLO algorithms for renal segmentation and kidney stone detection on CT scans to address these issues. Methods: The dataset used in this study was sourced from the GitHub. The dataset contains a total of 1799 images, with 790 images labeled as 'containing kidney stones' and 1009 images labeled as 'not containing kidney stones'. U-Net architecture was utilized to precisely identify the region of interest, while YOLOv5 and YOLOv7 architecture was utilized to detect the stones. In addition, a performance comparison between the two YOLO models and other contemporary relevant models has been conducted. Results: We obtained a kidney segmentation IOU of 91.4% and kidney stone detection accuracies of 99.5% for YOLOv7 and 98.7% for YOLOv5. YOLOv5 and YOLOv7 outperform the best existing models, including CNN, KNN, SVM, Kronecker CNN, Xresnet50, VGG16, etc. YOLOv7 possesses superior accuracy than YOLOv5. The only issue we encountered with the YOLOv7 model was that it demanded more training time than the YOLOv5 model. Conclusion: The results demonstrate that the proposed AI-based method has the potential to improve clinical procedures, allowing radiologists and urologists to make well-informed decisions for patients with renal pathologies. As medical imaging technology progresses, the incorporation of deep learning techniques such as YOLO holds promise for additional advances in automated diagnosis and treatment planning

Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019

Background: Updated data on chronic respiratory diseases (CRDs) are vital in their prevention, control, and treatment in the path to achieving the third UN Sustainable Development Goals (SDGs), a one-third reduction in premature mortality from non-communicable diseases by 2030. We provided global, regional, and national estimates of the burden of CRDs and their attributable risks from 1990 to 2019. Methods: Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we estimated mortality, years lived with disability, years of life lost, disability-adjusted life years (DALYs), prevalence, and incidence of CRDs, i.e. chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease and pulmonary sarcoidosis, and other CRDs, from 1990 to 2019 by sex, age, region, and Socio-demographic Index (SDI) in 204 countries and territories. Deaths and DALYs from CRDs attributable to each risk factor were estimated according to relative risks, risk exposure, and the theoretical minimum risk exposure level input. Findings: In 2019, CRDs were the third leading cause of death responsible for 4.0 million deaths (95% uncertainty interval 3.6–4.3) with a prevalence of 454.6 million cases (417.4–499.1) globally. While the total deaths and prevalence of CRDs have increased by 28.5% and 39.8%, the age-standardised rates have dropped by 41.7% and 16.9% from 1990 to 2019, respectively. COPD, with 212.3 million (200.4–225.1) prevalent cases, was the primary cause of deaths from CRDs, accounting for 3.3 million (2.9–3.6) deaths. With 262.4 million (224.1–309.5) prevalent cases, asthma had the highest prevalence among CRDs. The age-standardised rates of all burden measures of COPD, asthma, and pneumoconiosis have reduced globally from 1990 to 2019. Nevertheless, the age-standardised rates of incidence and prevalence of interstitial lung disease and pulmonary sarcoidosis have increased throughout this period. Low- and low-middle SDI countries had the highest age-standardised death and DALYs rates while the high SDI quintile had the highest prevalence rate of CRDs. The highest deaths and DALYs from CRDs were attributed to smoking globally, followed by air pollution and occupational risks. Non-optimal temperature and high body-mass index were additional risk factors for COPD and asthma, respectively. Interpretation: Albeit the age-standardised prevalence, death, and DALYs rates of CRDs have decreased, they still cause a substantial burden and deaths worldwide. The high death and DALYs rates in low and low-middle SDI countries highlights the urgent need for improved preventive, diagnostic, and therapeutic measures. Global strategies for tobacco control, enhancing air quality, reducing occupational hazards, and fostering clean cooking fuels are crucial steps in reducing the burden of CRDs, especially in low- and lower-middle income countries

Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background Regularly updated data on stroke and its pathological types, including data on their incidence, prevalence, mortality, disability, risk factors, and epidemiological trends, are important for evidence-based stroke care planning and resource allocation. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) aims to provide a standardised and comprehensive measurement of these metrics at global, regional, and national levels. Methods We applied GBD 2019 analytical tools to calculate stroke incidence, prevalence, mortality, disability-adjusted life-years (DALYs), and the population attributable fraction (PAF) of DALYs (with corresponding 95% uncertainty intervals [UIs]) associated with 19 risk factors, for 204 countries and territories from 1990 to 2019. These estimates were provided for ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage, and all strokes combined, and stratified by sex, age group, and World Bank country income level. Findings In 2019, there were 12·2 million (95% UI 11·0–13·6) incident cases of stroke, 101 million (93·2–111) prevalent cases of stroke, 143 million (133–153) DALYs due to stroke, and 6·55 million (6·00–7·02) deaths from stroke. Globally, stroke remained the second-leading cause of death (11·6% [10·8–12·2] of total deaths) and the third-leading cause of death and disability combined (5·7% [5·1–6·2] of total DALYs) in 2019. From 1990 to 2019, the absolute number of incident strokes increased by 70·0% (67·0–73·0), prevalent strokes increased by 85·0% (83·0–88·0), deaths from stroke increased by 43·0% (31·0–55·0), and DALYs due to stroke increased by 32·0% (22·0–42·0). During the same period, age-standardised rates of stroke incidence decreased by 17·0% (15·0–18·0), mortality decreased by 36·0% (31·0–42·0), prevalence decreased by 6·0% (5·0–7·0), and DALYs decreased by 36·0% (31·0–42·0). However, among people younger than 70 years, prevalence rates increased by 22·0% (21·0–24·0) and incidence rates increased by 15·0% (12·0–18·0). In 2019, the age-standardised stroke-related mortality rate was 3·6 (3·5–3·8) times higher in the World Bank low-income group than in the World Bank high-income group, and the age-standardised stroke-related DALY rate was 3·7 (3·5–3·9) times higher in the low-income group than the high-income group. Ischaemic stroke constituted 62·4% of all incident strokes in 2019 (7·63 million [6·57–8·96]), while intracerebral haemorrhage constituted 27·9% (3·41 million [2·97–3·91]) and subarachnoid haemorrhage constituted 9·7% (1·18 million [1·01–1·39]). In 2019, the five leading risk factors for stroke were high systolic blood pressure (contributing to 79·6 million [67·7–90·8] DALYs or 55·5% [48·2–62·0] of total stroke DALYs), high body-mass index (34·9 million [22·3–48·6] DALYs or 24·3% [15·7–33·2]), high fasting plasma glucose (28·9 million [19·8–41·5] DALYs or 20·2% [13·8–29·1]), ambient particulate matter pollution (28·7 million [23·4–33·4] DALYs or 20·1% [16·6–23·0]), and smoking (25·3 million [22·6–28·2] DALYs or 17·6% [16·4–19·0]). Interpretation The annual number of strokes and deaths due to stroke increased substantially from 1990 to 2019, despite substantial reductions in age-standardised rates, particularly among people older than 70 years. The highest age-standardised stroke-related mortality and DALY rates were in the World Bank low-income group. The fastest-growing risk factor for stroke between 1990 and 2019 was high body-mass index. Without urgent implementation of effective primary prevention strategies, the stroke burden will probably continue to grow across the world, particularly in low-income countries.publishedVersio