Ensuring self-sustainability in decentralized communities through solar cookers combined with food dryers

In the background of the COVID 2019 pandemic, the importance of developing realistic and efficient decentralized energy solutions is one of the essential requirements. This paper presents the performance of a small-scale solar box cooker cum dryer (SBCD) for decentralized communities and domestic scale applications. The drying process in SBCD uses a simple and effective method for moisture removal. It enables de-moisturization of the cooker interior, allowing efficient use of SBCD for the simultaneous dual-mode operation of cooking and drying. Cooker Opto-thermal Ratio (COR) as a thermal performance parameter and glycerin as a test load enable cooking process analysis. COR-based objective parameters (OPs) realistically comment on the cooker performance in the dual-mode operation. Drying kinetic studies describe the drying performance of the device. The levelized cost of cooking meals (LCCM) allows understating of the economics of SBCD. The mean value of COR for the cooker is 0.104 ± 0.0028 (m2·°C)/W with a percentage standard deviation of 2.69%. The experimental values of OPs, reference cooking time, and maximum achievable load temperature varies between 74 and 86 min and 103–111 °C, respectively. Thus, SBCD cooks in approximately 80–90 min and dries ∼ 100 g of food products simultaneously with 70–80 % moisture removal. The LCCM for SBCD is $ 0.0174 per meal. Thus, SBCD depicts a realistic solution for ensuring self-sustainability in decentralized communities.

Consultant psychiatrists' experience of the impact of the COVID-19 pandemic on mental health services.

OBJECTIVES: The novel coronavirus 2019 (COVID-19) has spread worldwide threatening human health. To reduce transmission, a 'lockdown' was introduced in Ireland between March and May 2020. The aim of this study is to capture the experiences of consultant psychiatrists during lockdown and their perception of it's impact on mental health services. METHODS: A questionnaire designed by the Royal College of Psychiatrists was adapted and circulated to consultant members of the College of Psychiatrists of Ireland following the easing of restrictions. The questionnaire assessed the perceived impact on referral rates, mental health act provision, availability of information technology (IT), consultant well-being and availability of personal protective equipment (PPE). Thematic analysis was employed to analyse free-text sections. RESULTS: Response rate was 32% (n = 197/623). Consultants reported an initial decrease/significant decrease in referrals in the first month of lockdown (68%, n = 95/140) followed by an increase/significant increase in the second month for both new (83%, n = 100/137) and previously attending patients (65%, n = 88/136). Social isolation and reduced face-to-face mental health supports were among the main reasons identified. The needs of children and older adults were highlighted. Most consultants (76%, n = 98/129) felt their working day was affected and their well-being reduced (52%, n = 61/119). The majority felt IT equipment availability was inadequate (67%, n = 88/132). Main themes identified from free-text sections were service management, relationship between patients and healthcare service and effects on consultants' lives. CONCLUSIONS: The COVID-19 pandemic has placed increased pressure on service provision and consultant wellness. This further supports the longstanding need to increase mental health service investment.

COVID-19 Detection and Remote Tracking System Using IoT-Based Wearable Bracelet

A new type of virus was discovered in China in the year 2019, known as COVID-19. One of the main symptoms that are easy to spot is high body temperature. The recent virus outbreak necessitates infrared thermometers used for thermal screening at public places to test the body temperature. However, this protection method still lacks because it requires a significant amount of time to monitor large numbers of people's body temperatures. Moreover, direct contact with people infected with coronavirus may spread it to the person doing the screening. In addition, this method cannot detect the infection early without visiting the infected person to a screening place. This study proposed a new system for automatically detecting the coronavirus in early time through the body temperature with no human interactions using IoT-based wearable bracelets. The body temperature sensor is integrated into the wearable bracelet with IoT technology for monitoring the body temperature and reading the current bodily temperature. The system is additionally equipped with a GPS module. It can capture the location of the person automatically. Suppose the person is suffering from high body temperature. In that case, the system will send it with location through Wi-Fi module or GSM module over the internet to cloud database and notify medical officer at the same moment to do the immediate procedures for that person. Health officers use smartphone applications for monitoring and remote tracking using the application map. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Multi-features Extraction for Automating Covid-19 Detection from Cough Sound using Deep Neural Networks

This research paper proposed a smart system based on deep learning to detect COVID-19 patient's using the cough sound. The deep neural networks are used to distinguish between different types of cough COVID-19 positive or negative coughs. The proposed system is segmented into three stages: Audio pre-processing by noise reduction, segmentation, feature extraction, classification, and model deployment. Eight features have been extracted from 1635 sound subjects: 573 COVID-19 positive and 1062 negative coughs. The feature's extracted data have trained using two models;first model Cough detection based on ANN used to distinguish if there is cough or not, the second model to detect the covid-19 using Convolutional Neural Network. The overall accuracy for both models is 98.1% for the Cough model and 98.5% for the Covid-19 model. The models were compiled after deployment to work together as a web service based on flask. Cough model receives cough sound from the mobile app or web interface and discriminates if there is cough then passe it coivd1-9 model that will analyze if cough is positive or negative.and send the result back to the mobile app. © 2022 IEEE

Novel piperazine based compounds as potential inhibitors for SARS-CoV-2 Protease Enzyme: Synthesis and molecular docking study.

Structurally diverse piperazine-based compounds hybrid with thiadiazole, isatin or with sulfur/nitrogen, functionalities were synthesized. The structures of the new compounds were established based on their spectral data and elemental analysis. The physicochemical, bioactivity scores and pharmacokinetic behavior of all the prepared ligands were evaluated using in silico computational tools. The new piperazine ligands have been screened for their inhibition activity against SARS-CoV-2 protease enzyme using molecular docking analysis. The docking studies showed that all the ligands have been docked with negative dock energy onto the target protease protein. Moreover, Molecular interaction studies revealed that SARS-CoV-2 protease enzyme had strong hydrogen bonding interactions with piperazine ligands. The present in silico study thus, provided some guidance to facilitate drug design targeting the SARS-CoV-2 main protease.