ABSTRACT
The emergence of COVID-19 has drastically altered the lifestyle of people around the world, resulting in significant consequences on people's physical and mental well-being. Fear of COVID-19, prolonged isolation, quarantine, and the pandemic itself have contributed to a rise in hypertension among the general populace globally. Protracted exposure to stress has been linked with the onset of numerous diseases and even an increased frequency of suicides. Stress monitoring is a critical component of any strategy used to intervene in the case of stress. However, constant monitoring during activities of daily living using clinical means is not viable. During the current pandemic, isolation protocols, quarantines, and overloaded hospitals have made it physically challenging for subjects to be monitored in clinical settings. This study presents a proposal for a framework that uses unobtrusive wearable sensors, securely connected to an artificial intelligence (AI)-driven cloud-based server for early detection of hypertension and an intervention facilitation system. More precisely, the proposed framework identifies the types of wearable sensors that can be utilized ubiquitously, the enabling technologies required to achieve energy efficiency and secure communication in wearable sensors, and, finally, the proposed use of a combination of machine-learning (ML) classifiers on a cloud-based server to detect instances of sustained stress and all associated risks during times of a communicable disease epidemic like COVID-19. © 2001-2012 IEEE.
ABSTRACT
BACKGROUND: The impact of COVID-19 in families and patients with congenital diaphragmatic hernia (CDH) is unknown, this situation has generated uncertainty not only in family members but also in the optimal outpatient follow-up. Telehealth has become a fundamental tool for the follow-up during the pandemic. The objective of this survey is to evaluated the impact of SARS-CoV-2 in families and patients with CDH and the satisfaction with telematic follow-up. METHODS: Telephone survey of patient's caregivers with CHD, aged 1-16 years, followed in neonatal surgery outpatients, from January 31, 2020 to November 15, 2020. The ethical clearance for this study was taken from the Clinical Research Ethics Committee of our Research Institute vide letter number VHIR/239283/01.01.2021. RESULTS: 81 surveys of 100 patients with active follow-up were carried out. There were no refusals in any contacted parents. There were 30 contacts (37%), 44.8% at school and 27.6% from cohabiting family members. Four infections (4.9%) were diagnosed, half symptomatic. In 40 patients (49.4%) the follow-up was telematic, with a mean score of 3.1±1.3 out of 5. For future controls, 65% prefer presential follow-up, 25% alternate and 10% telematics. 50.6% reported greater anxiety and 34.6% (28/81) extreme measures of isolation, being more accentuated in the group of 3-6 years (p<0.05). CONCLUSION: The impact of COVID19 in patients with CHD is not greater than in the general pediatric population. Although the incorporation of the telehealth was well valued, most of the caregivers prefer the face-to-face outpatient follow-up.
ABSTRACT
The emergence of COVID-19 has drastically altered the lifestyle of people around the world, resulting in significant consequences in people’s physical and mental wellbeing. Fear of COVID-19, prolonged isolation, quarantine, and the pandemic itself have contributed to a rise in hypertension amongst the general populace globally. Protracted exposure to stress has been linked with the onset of numerous diseases and even increased frequency in suicides. Stress monitoring is a critical component of any strategy used to intervene in case of stress. However, constant monitoring during activities of daily living using clinical means is not viable. During the current pandemic, isolation protocols, quarantines and overloaded hospitals have made it physically challenging for subjects to be monitored in clinical settings. This study presents a proposal for a framework that uses unobtrusive wearable sensors, securely connected to an AI-driven cloud-based server for early detection of hypertension and an intervention facilitation system. More precisely, the proposed framework identifies the types of wearable sensors that can be utilized ubiquitously, the enabling technologies required to achieve energy efficiency and secure communication in wearable sensors and finally, the proposed use of a combination of machine learning classifiers on a cloud-based server to detect instances of sustained stress and all associated risks during times of a communicable disease epidemic like COVID-19. IEEE
ABSTRACT
Ridesharing services do not make data of their availability (supply, utilization, idle time, and idle distance) and surge pricing publicly available. It limits the opportunities to study the spatiotemporal trends of the availability and surge pricing of these services. Only a few research studies conducted in North America analyzed these features for only Uber and Lyft. Despite the interesting observations, the results of prior works are not generalizable or reproducible because: i) the datasets collected in previous publications are spatiotemporally sensitive, i.e., previous works do not represent the current availability and surge pricing of ridesharing services in different parts of the world;and ii) the analyses presented in previous works are limited in scope (in terms of countries and ridesharing services they studied). Hence, prior works are not generally applicable to ridesharing services operating in different countries. This paper addresses the issue of ridesharing-data unavailability by presenting Ridesharing Measurement Suite (RMS). RMS removes the barrier of entry for analyzing the availability and surge pricing of ridesharing services for ridesharing users, researchers from various scientific domains, and regulators. RMS continuously collects the data of the availability and surge pricing of ridesharing services. It exposes real-time data of these services through i) graphical user interfaces and ii) public APIs to assist various stakeholders of these services and simplify the data collection and analysis process for future ridesharing research studies. To signify the utility of RMS, we deployed RMS to collect and analyze the availability and surge pricing data of 10 ridesharing services operating in nine countries for eight weeks in pre and during pandemic periods. Using the data collected and analyzed by RMS, we identify that previous articles miscalculated the utilization of ridesharing services as they did not count in the vehicles driving in multiple categories of the same service. We observe that during COVID-19, the supply of ridesharing services decreased by 54%, utilization of available vehicles increased by 6%, and a 5 × increase in the surge frequency of services. We also find that surge occurs in a small geographical region, and its intensity reduces by 50% in about 0.5 miles away from the location of a surge. We present several other interesting observations on ridesharing services' availability and surge pricing. © 2022 ACM.
ABSTRACT
Law education is a must subject in many courses due to its importance. However, law is perceived as a complicated subject and consumes much time, particularly for non-law students who do not aim to study law throughout their course registration. Indeed, law education is a dull topic since it is usually taught in a conventional style. School of Business and Economics, Universiti Putra Malaysia, has offered one law subject to the business students, "Business Law". It was realized that most of the students enrolled in that course faced difficulty in learning law education. This paper aims to study the business students' experience of learning law and propose effective teaching methods to ensure that legal topics are taught more acceptably. This paper adopts a qualitative method in collecting data where three surveys and two short interviews were conducted. It was found that the students have found this law subject complicated. Still, they liked the subject and agreed to learn it with some appropriate learning activities. Thus, something must be done to contest these views and change students' perspective on legal courses. This subject needs to be taught in a manner acceptable to attract the students' attention and interest. During the Covid-19 pandemic, all learning activities are conducted through the online platform. Therefore, the lecturer must have a good teaching plan to ensure that students can learn and enjoy law. © 2021 Ascociacion Internacional de Economia Aplicada. All right reserved.
ABSTRACT
Domestic violence is a worldwide epidemic, and the UN lists it as the worst human rights violation before the Covid-19 outbreak. A large rise in domestic violence incidents occurred when governments adopted emergency lockdown measures due to a worldwide Covid-19 epidemic. The unprecedented rise in domestic violence cases under Covid-19 serves as a wake-up call for the states. This study aims to examine the reasons for the increase in domestic violence and the methods used by stakeholders to address the issue in South Asian and Far Eastern countries, specifically the Maldives, Pakistan, and Malaysia. This paper adopted the qualitative research method in collecting data. The statistics indicate an alarming surge in domestic violence cases during Covid-19, and clear links exist between Covid-19 and domestic violence, impacting the economic and social crisis. This study confirmes that the state interventions to deal with this social problem are inadequate. Domestic violence has been worsened by growing unemployment, financial stress, anxiety and lack of community services. Furthermore, states have significant difficulties in addressing such issues due to a lack of cooperation across government agencies and stakeholders. Finally, this research recommends policy initiatives and legislative reforms to decrease domestic violence during this crucial period. © 2021 Estudios de Economia Aplicada. All rights reserved.