Could Cultures Determine the Course of Epidemics and Explain Waves of COVID-19?

Coronavirus Disease (COVID-19), caused by the SARS-CoV-2 virus, is an infectious disease that quickly became a pandemic spreading with different patterns in each country. Travel bans, lockdowns, social distancing, and non-essential business closures caused significant economic disruptions and stalled growth worldwide in the pandemic’s first year. In almost every country, public health officials forced and/or encouraged Nonpharmaceutical Interventions (NPIs) such as contact tracing, social distancing, masks, and quarantine. Human behavioral decision-making regarding social isolation significantly impedes global success in containing the pandemic. This thesis focuses on human behaviors and cultures related to the decision-making of social isolation during the pandemic. Within a COVID-19 disease transmission model, we created a conceptual and deterministic model of human behavior and cultures. This study emphasizes the importance of human behavior in successful disease control strategies. Additionally, we introduce a back engineering approach to determine whether cultures are explained by the courses of COVID-19 epidemics. We used a deep learning technique based on a convolutional neural network (CNN) to predict cultures from COVID-19 courses. In this system, CNN is used for deep feature extraction with ordinary convolution and with residual blocks. Also, a novel concept is introduced that converts tabular data into an image using matrix transformation and image processing validated by identifying some well-known function. Despite having a small and novel data set, we have achieved an 80-95% accuracy, depending on the cultural measures

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

Improving spatial agreement in machine learning-based landslide susceptibility mapping

Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions

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