Gesture - Controlled Contactless Switch With OLED Display

To avoid the chance of getting covid-19, it's vital not to touch surfaces as well as switches, door knobs and keys that are often employed by people. Hand movements in our world are the foremost well-liked non vocal ways in which of communication that are of agreeable significance. Gesture recognition is associated in Nursing interaction with human computers, normally used for functions of education, medicine and recreation. So, we came upon a contactless switch that works with hand gestures. Today with expanded mechanical progressions, switches also require refreshing with current technology. So, a non-contactless switch that works with sensors is the next step. Our keen contactless switch incorporates a sensor that is equipped for recognizing hand developments and interprets them into orders for controlling lights fans and different home machines. We are using Arduino IDE where we can create a setup function in which we can initialize the sensor and set the pin mode output or the light and fan control. © 2022 IEEE.

Mental Health Status among the Quarantined Population during COVID-19 Pandemic: A Cross-sectional Study from Western Rajasthan

Introduction: Although isolation and quarantine are important measures to curb the exponential growth of the prevailing Coronavirus disease 2019 (COVID-19) pandemic, but at same time this can impose psychological issues among the affected population and also to their family members. Aims: To evaluate the mental health problems, their severity and associated factors in quarantined population during the COVID-19 pandemic. Materials and Methods: This cross-sectional study was conducted among 207 quarantined subjects at different quarantine centres of Ajmer, associated with JLN Medical College, Ajmer, from August 2020 to October 2020,after getting approval from ethics committee of the centre. All the consenting quarantined subjects who were of age 18 years and above, irrespective of their gender were enrolled in the study. For the assessment of psychiatric morbidity, participants were screened using Mini-International Neuropsychiatric Interview (MINI) 6.0.0. Finally, the relevant psychiatric assessment tools like Hamilton Rating Scale for Depression (HAM-D),Hamilton Anxiety Rating Scale (HAM-A) and Yale-Brown Obsessive Compulsive Scale were applied to assess the severity of the disorders. Pearson correlation analysis was used to evaluate the relationship among various clinical variables.The level of significance was considered at p-value lt;0.05. Results: Majority 85 (41%) subjects belonged to the age group 31-40 years of age. Around 116 (77.3%) participants were male. Around 51 (24.6%) presented moderate depression and 25 (12%) presented with severe depression. Also 182 (87.9%) presented with moderate anxiety. The study showed a statistically significant association between depression/anxiety and substance abuse, insomnia, co-morbidities, suicidal ideation/attempts. Conclusion: The findings of the present study concluded that a significant proportion of the quarantined population suffered from psychological issues. So, the psychological impact of a mandatory quarantine should be weighed more thoughtful and in an evidence based manner.

Optimizing Control of IOT Device using Traditional Machine Learning Models and Deep Neural Networks

With the increasing threat of Covid-19 and now omicron infection across the world among people, there has been a significant surge in the demand for a fully-automated, self-controlled or mechanized ventilator which can provide sufficient air-pressure to weak human lungs continuously. It is our humble endeavor to mitigate the effects caused due to handful of trained-physicians over countless untreated patients and lack of enough health-infrastructure facilities to support in the time of dire need. We all dread losing another precious life on earth due to any one of the above mentioned reason. We have tried simulating the observations obtained from a lab-developed mechanical ventilator system under different lung settings. After preprocessing this dataset using NLP, training data is analysed to study the correlation between observations from numerous attributes. A couple of Machine Learning (LR, RF, SVM, LGBM) and Deep Learning (MLP, LSTM, Bi-LSTM) algorithms have been deployed to train our model individually, out of which Bi-LSTM performed exceptionally well above others. However, only after exhaustive clinical trials and recommendations a large of number of patients on life-support can get a new life through the large practical application of this device, in the near future. © 2022 IEEE.

Mental health problems in health-care workers working in COVID-19 quarantine centers

Background: The coronavirus disease-19 (COVID-19) pandemic in India has put health-care workers (HCWs) under intense pressure and has led to immense psychological stress due to factors including a high risk of infection, inadequate protection from contamination, overwork, cutting-off with families, and exhaustion. The unprecedented stressful conditions are causing mental health problems such as stress, anxiety, depression, disturbed sleep, irritability, fear, and panic. The present study was carried out to investigate the mental health problems among HCWs working in close proximity with COVID-19 patients in quarantine centers and to explore the relevant influencing factors for the development of psychological interventions for them. Subject and Methods: HCWs working in COVID-19 quarantine centers in Ajmer were interviewed for exploring mental health problems in a cross-sectional study carried out at JLN Medical College, Ajmer, India. Depressive symptoms, anxiety, and stress among HCWs were assessed using the Depression Anxiety Stress Scale and Perceived Stress Scale (PSS). Relevant data were analyzed, and comparisons between different groups were performed using the two independent sample t-tests. All statistical analyses were performed using the SPSS software version 22.0 for Windows. Results: 53.57% (n = 30) of participants were suffering from depressive symptoms, 64.28% (n = 36) were suffering from anxiety symptoms, and 78.6% (n = 47) were suffering from stress. HCWs living in joint families were more affected by depression (P = 0.02). Both anxiety and stress were more commonly reported by HCWs of age <30, females, nurses and those living in joint families, but difference was statistically insignificant. Conclusion: The present study demonstrated that a considerable number of HCWs were suffering from mental health problems such as depression, anxiety, and stress. It is important to have periodic mental health assessment of HCWs dealing with COVID-19 patients.

Buyback and risk-sharing contracts to mitigate the supply and demand disruption risks

Events like the recent COVID-19 create major disruptions in global supply chains. Companies find it difficult to manage business continuity under supply uncertainties and disruptions. This paper investigates the buyer's optimal ordering decisions under stochastic demand, supply uncertainty, and disruption risks. We consider a two-echelon supply chain consisting of a single buyer and two suppliers. The main supplier is cheaper, but exposed to the risks of random yield and disruption. The backup supplier is perfectly reliable, but relatively expensive. An analytical model is developed using contract-based mechanisms considering the risks of demand uncertainty, supply disruption, and random yield. Two typologies of contracts with suppliers are considered, namely, risks sharing contract and buyback contract. A numerical study is performed to explore the effects of different parameters on the supply chain members' profits, providing guidelines for managers regarding how the supply chain's risks and demand uncertainty influence the ordering decisions. [Received: 5 November 2019;Accepted: 22 August 2020]