Modeling and Speed Control for Sensorless DC Motor BLDC Based on Real Time Experiement

This paper presents a modeling of the Brushless DC motor based on the system identification method. The input and output data were collected and simulated based on the real-time experiment. Taking a continues time form for the system model, a transfer function was selected in this work. The potentiometer has been used to send  Pulse Width Modulation (PWM) signals as input signal to the Brushless DC motor to determine the open-loop model of brushless DC motor (BLDC). LM2907 Tachometer attached with Brushless DC motor driver to measure the output speed. The input signal and measured output data were interfaced to plant by C code generation Matlab/Simulink through Arduino Mega controller. System identification toolbox was used for collecting data to obtain the estimates model. The best fit found for the system was 90.2%. The PID controller was developed to control the desired speed based on the given speed to demonstrate the feasibility of the given method.  &nbsp

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution-individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.Published versio

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multi-national data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar was found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-negligible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic

National identity predicts public health support during a global pandemic

Understanding collective behaviour is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviours and support for public health policies in the context of the pandemic.Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = -0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics

Interpretable Transformers for Alzheimer Disease Diagnosis on Multi modal Data

Alzheimer s disease (AD), a neurological condition, primarily affects brain cells. Historically, the detection of this disease has focused on single modality data using machine learning and artificial intelligence algorithms. However, recent advancements in machine learning have allowed for the analysis of multi-modal data sources and input types, thereby enhancing the ability to predict Alzheimer s disease. This research introduces an innovative approach to enhance Alzheimer s disease research by utilizing a multi-modal data-specific method that applies transformers to both image and text data. In the initial stage, the challenge of data pre-processing for multi-modal datasets is addressed through the use of a U-net based segmentation technique, effectively isolating the Region of Interests (ROIs) in MRI images. The second stage involves the deployment of vision transformers (ViT) and BERT to process the pre-processed data. This application is crucial in handling the complexities associated with multi-modal datasets, particularly those that combine image and textual information. Lastly, our method prioritizes data explainability and interpretability by incorporating advanced Explainable AI (XAI) techniques, specifically Local Interpretable Model-Agnostic Explanations (LIME) and Layer-wise Relevance Propagation (LRP). These techniques provide a deeper understanding of the model s decision-making process, enabling a more comprehensive interpretation of the multi-modal datasets. We used medical demographic and image data of Alzheimer s patients from Kaggle for our study. Our proposed method achieved an accuracy of 86 , outperforming other methods

Effect of methanol extract of Dicranopteris linearis against carbon tetrachloride- induced acute liver injury in rats

Background: Dicranopteris linearis (family Gleicheniaceae) has been reported to possess anti-inflammatory and antioxidant activities but no attempt has been made to study its hepatoprotective potential. The aim of the present study was to determine the hepatoprotective effect of methanol extracts of D. linearis (MEDL) against carbon tetrachloride (CCl4)-induced acute liver injury in rats. Methods: 6 groups (n = 6) of rats received oral test solutions: 10% dimethyl sulfoxide (DMSO), 200 mg/kg silymarin, or MEDL (50, 250, and 500 mg/kg), once daily for 7 consecutive days, followed by hepatotoxicity induction with CCl4. Blood and liver were collected for biochemical and microscopic analysis. The extract was also subjected to antioxidant studies (e.g. 2, 2-diphenyl-1-picrylhydrazyl (DPPH)- and superoxide anion-radical scavenging assays, oxygen radical absorbance capacity (ORAC) test and total phenolic content (TPC) determination), phytochemical screening and HPLC analysis. Results: Pretreatment with MEDL and silymarin significantly (P < 0.05) reduced the serum levels of AST, ALT and ALP, which were increased significantly (P < 0.05) in DMSO-pretreated group following treatment with CCl4. Histological analysis of liver tissues in groups pretreated with MEDL and silymarin showed mild necrosis and inflammation of the hepatocytes compared to the DMSO-pretreated group (negative control group). The MEDL showed higher DPPH- and superoxide anion-radical scavenging activity as well as high TPC and ORAC values indicating high antioxidant activity. Conclusions: MEDL exerts hepatoprotective activity that could be partly contributed by its antioxidant activity and high phenolic content, and hence demands further investigation