Undergraduate Student’s Stress, Anxiety, Depression and their Coping styles during Covid-19

Introduction: COVID-19 was responsible for widespread disturbance throughout the educational system and was harmful in a variety of ways. In particular with regard to the state of a student’s psychological health. Purpose: This study examines the relationship between psychological distress (depression, anxiety, and stress) and coping strategies among the students of a private university in Bangladesh during the COVID-19 pandemic. Methodology: A cross-sectional web-based survey was conducted from September 2020 to December 2021 on 951 respondents using the DASS-21 and Brief-COPE questionnaires. Results: The majority of students were experiencing mild to extremely severe depression (75.8%), anxiety (88.5%), and stress (79.1%). The level of stress (p<.001), anxiety (p<.001), and depression (p=.23) was significantly higher among the female students compared with male students. This study also highlights the coping strategies of students. However, sex differences were not profound in adopting coping strategies. The results also showed significant correlation between some Brief-COPE measures, such as acceptance and self-blame (r=.708, p<.001); use of informational support and emotional support (r=.599, p<.001). Conclusion: As the prevalence of mental health disorders was found to be higher among private university students, university authorities should seek necessary assistance from mental health professionals to support their students in overcoming psychological distress not only related to the pandemic but also related to their day-to-day life activities

Auxilio: A Sensor-Based Wireless Head-Mounted Mouse for People with Upper Limb Disability

Upper limb disability may be caused either due to accidents, neurological disorders, or even birth defects, imposing limitations and restrictions on the interaction with a computer for the concerned individuals using a generic optical mouse. Our work proposes the design and development of a working prototype of a sensor-based wireless head-mounted Assistive Mouse Controller (AMC), Auxilio, facilitating interaction with a computer for people with upper limb disability. Combining commercially available, low-cost motion and infrared sensors, Auxilio solely utilizes head and cheek movements for mouse control. Its performance has been juxtaposed with that of a generic optical mouse in different pointing tasks as well as in typing tasks, using a virtual keyboard. Furthermore, our work also analyzes the usability of Auxilio, featuring the System Usability Scale. The results of different experiments reveal the practicality and effectiveness of Auxilio as a head-mounted AMC for empowering the upper limb disabled community.Comment: 28 pages, 9 figures, 5 table

Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-squared Test and Hyper-parameter Optimization: A Retrospective Analysis

Bone Marrow Transplant, a gradational rescue for a wide range of disorders emanating from the bone marrow, is an efficacious surgical treatment. Several risk factors, such as post-transplant illnesses, new malignancies, and even organ damage, can impair long-term survival. Therefore, technologies like Machine Learning are deployed for investigating the survival prediction of BMT receivers along with the influences that limit their resilience. In this study, an efficient survival classification model is presented in a comprehensive manner, incorporating the Chi-squared feature selection method to address the dimensionality problem and Hyper Parameter Optimization (HPO) to increase accuracy. A synthetic dataset is generated by imputing the missing values, transforming the data using dummy variable encoding, and compressing the dataset from 59 features to the 11 most correlated features using Chi-squared feature selection. The dataset was split into train and test sets at a ratio of 80:20, and the hyperparameters were optimized using Grid Search Cross-Validation. Several supervised ML methods were trained in this regard, like Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbors, Gradient Boosting Classifier, Ada Boost, and XG Boost. The simulations have been performed for both the default and optimized hyperparameters by using the original and reduced synthetic dataset. After ranking the features using the Chi-squared test, it was observed that the top 11 features with HPO, resulted in the same accuracy of prediction (94.73%) as the entire dataset with default parameters. Moreover, this approach requires less time and resources for predicting the survivability of children undergoing BMT. Hence, the proposed approach may aid in the development of a computer-aided diagnostic system with satisfactory accuracy and minimal computation time by utilizing medical data records.Comment: 25 pages, 14 figures, 38 table

Undergraduate Student’s Stress, Anxiety, Depression, and their Coping styles during COVID-19

Introduction: COVID-19 was responsible for widespread disturbance throughout the educational system and was harmful in a variety of ways. In particular with regard to the state of a student’s psychological health

Empirical analysis of power quality using UPQC with hybrid control techniques

The Unified Power Quality Conditioner (UPQC), composed of both parallel active filters (PAF) and series active filters (SAF), recognized as a crucial device for enhancing the overall power quality of the system. In context, different control methods are employed to evaluate the performance of UPQC. This study aims to explore the performance of four distinct control techniques: dq, pq, Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) in hybrid combinations to manage both PAF and SAF in UPQC device. Four different hybrid cases are developed for addressing three power quality problems: harmonic distortion, voltage sag, and swell to evaluate the performance of these control techniques. The results revealed a significant reduction in Total Harmonic Distortion (THD) for hybrid ANFIS-ANN control method, achieving a THD value of 4.26 % for load voltage and 0.08 % for source current after compensation in comparison with other methods. Furthermore, this paper concludes by recommending the most suitable combination of control techniques for specific applications