An Efficient Emergency Patient Monitoring based on Mobile Ad Hoc Networks

Medical sensors are implanted within the vital organs of human body to record and monitor the vital signs of pulse rate, heartbeat, electrocardiogram, body mass index, temperature, blood pressure, etc. to ensure their effective functioning. These are monitored to detect patient’s health from anywhere and at any time. The Wireless Sensor Networks are embedded in the form of Body Area Nets and are capable of sensing and storing the information on a digital device. Later this information could be inspected or even sent to a remotely located storage device specifically (server or any public or private cloud for analysis) so that a medical doctor can diagnose the present medical condition of a person or a patient. Such a facility would be of immense help in the event of an emergency such as a sudden disaster or natural calamity where communication is damaged, and the potential sources become inaccessible. The aim of this paper is to create a mobile platform using Mobile Ad hoc Network to support healthcare connectivity and treatment in emergency situations.

Why Do Smart Contracts Self-Destruct? Investigating the Selfdestruct Function on Ethereum

The selfdestruct function is provided by Ethereum smart contracts to destroy a contract on the blockchain system. However, it is a double-edged sword for developers. On the one hand, using the selfdestruct function enables developers to remove smart contracts ( SCs ) from Ethereum and transfers Ethers when emergency situations happen, e.g., being attacked. On the other hand, this function can increase the complexity for the development and open an attack vector for attackers. To better understand the reasons why SC developers include or exclude the selfdestruct function in their contracts, we conducted an online survey to collect feedback from them and summarize the key reasons. Their feedback shows that 66.67% of the developers will deploy an updated contract to the Ethereum after destructing the old contract. According to this information, we propose a method to find the self-destructed contracts (also called predecessor contracts) and their updated version (successor contracts) by computing the code similarity. By analyzing the difference between the predecessor contracts and their successor contracts, we found five reasons that led to the death of the contracts; two of them (i.e., Unmatched ERC20 Token and Limits of Permission ) might affect the life span of contracts. We developed a tool named LifeScope to detect these problems. LifeScope reports 0 false positives or negatives in detecting Unmatched ERC20 Token . In terms of Limits of Permission , LifeScope achieves 77.89% of F-measure and 0.8673 of AUC in average. According to the feedback of developers who exclude selfdestruct functions, we propose suggestions to help developers use selfdestruct functions in Ethereum smart contracts better.

Emergency and Elective Surgery for Colorectal Cancer: A Single-Center Experience

Objective: Emergency surgical interventions due to colorectal cancer (CRC) obstruction are risk factors for poor prognosis. This study aims to compare emergency and elective surgeries for colorectal tumours performed in a single center. Material and Methods: CRC patients operated on between November 2014 and November 2019 were included in the study. Patients were divided into two groups; Patients operated under elective conditions, and patients operated under the emergency diagnosis of ileus or acute abdomen. Results: A total of 103 CRC patients were included in the study. Forty-five (43.7%) were operated in emergency situations, and 58 (56.3%) electively. 45.6% of the emergency cases were found to be Stage 3B and 4 (p=0.009). Bleeding and constipation were more common in elective cases, whereas in emergency cases, applications related to ileus and perforation were quite frequent (p<0.001). It was found that 62.3% of the tumors in emergency cases were seen in sigmoid and rectosigmoid regions (p=0.015). There was no anastomosis in 60.0% of emergency cases (p<0.001). Conclusion: In the hospital area where the study was applied, compared to other countries, more patients with CRC underwent emergency surgery for intestinal obstruction. Therefore, necessary measures must be taken to prevent further increases in these rates.

Load switching between main power grid to the backup grid by standard automatic transfer switch

PURPOSE. To develop a variant of the algorithm for the automatic input of the reserve, which consists in transferring the load in case of emergency situations, to make a simulation model in the MatLab® environment corresponding to the developed generalized electrical scheme of the system.METHODS. When solving the problem, the method of digital modeling was used, which consists in the maximum approximation of the system under study to a real object, implemented by means of MatLab.RESULTS. It is proposed to study the methods of synthesis of digital models of compensation of voltage drops by the example of a study of an automatic reserve transfer system, demonstrating an approach to modeling this system. When developing models in the MatLab environment, the parameters of real technical elements and devices and their digital analogues are taken into account. The issue of creating a digital model of an electric drive system, including a model of an asynchronous motor with a short-circuited rotor, is considered. The result, after final refinement, can be used to design a real system in production conditions.CONCLUSIONS. The developed model of the automatic transfer switch system is operable, the time indicators are satisfactory for systems that do not make excessive demands on performances and time intervals. For systems that are sensitive to current inrushes during load transfer, some improvements are required, which are reduced to the implementation of a high-speed automatic switch system. The development of this system is currently at the research stage, namely, the compilation of a load transfer logic that takes into account the phase matching of electrical circuits.

Thyroid Hormone Changes in the Northern Area of Tianjin during the COVID-19 Pandemic

Purpose. This study aimed to determine whether and how stress-induced thyroid hormone changes occur during the COVID-19 pandemic in the northern area of Tianjin. Methods. This study comprised two groups of study subjects in Tianjin: before (2019) and during (2020) the COVID-19 outbreak. Subjects were included if they had FT3, FT4, and TSH concentrations and thyroid TPOAb or TgAb information available. People who were pregnant, were lactating, or had mental illness were excluded. We used propensity score matching to form a cohort in which patients had similar baseline characteristics, and their anxiety level was measured by the Hamilton Anxiety Rating Scale (HAMA). Results. Among the 1395 eligible people, 224 in Group A and 224 in Group B had similar propensity scores and were included in the analyses. The detection rate of abnormal thyroid function was decreased in pandemic Group B (69.2% vs. 93.3%, χ2 = 42.725, p < 0.01 ), especially for hypothyroidism (14.29% vs. 35.71%, χ2 = 27.429, p < 0.01 ) and isolated thyroid-related antibodies (25.89% vs. 38.39%, χ2 = 8.023, p < 0.01 ). The level of FT4 (z = −2.821, p < 0.01 ) and HAMA score (7.63 ± 2.07 vs. 5.40 ± 1.65, t = 16.873, p < 0.01 ) went up in Group B; however, TSH (z = −5.238, p < 0.01 ), FT3 (z = −3.089, p = 0.002 ), TgAb (z = −11.814, p < 0.01 ), and TPOAb (z = −9.299, p < 0.01 ) were lower, and HAMA was positive with FT3 (r = 0.208, p < 0.01 ) and FT4 (r = 0.247, p < 0.01 ). Conclusion. People in the northern area of Tianjin during the COVID-19 outbreak were at an increased risk of higher FT4, lower FT3, and lower TSH. The HAMA scores increased in emergency situations and were positively correlated with the levels of FT3 and FT4.

Global User-Level Perception of COVID-19 Contact Tracing Applications: A Data-Driven Approach Using Natural Language Processing (Preprint)

BACKGROUND Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. Thanks to digital technologies, such as smartphones and wearable devices, contacts of COVID-19 patients can be easily traced and informed about their potential exposure to the virus. To this aim, several mobile applications have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community’s response to the applications by analyzing information from different sources, such as news and users’ reviews of the applications. However, to the best of our knowledge, there is no existing solution that automatically analyzes users’ reviews and extracts the evoked sentiments. We believe such solutions combined with a user-friendly interface can be used as a rapid surveillance tool to monitor how effective an application is and to make immediate changes without going through an intense participatory design method which, although in normal circumstances is optimal, but not optimal in emergency situations where a mobile device needs to be deployed immediately with little to no user input from the beginning for the greater public good. OBJECTIVE In this paper, we aim to analyze the efficacy of AI models and Natural Language Processing (NLP) techniques in automatically extracting and classifying the polarity of users’ sentiments by proposing a sentiment analysis framework to automatically analyze users’ reviews on COVID-19 contact tracing mobile applications. We also aim to provide a large-scale annotated benchmark dataset to facilitate future research in the domain. As a proof of concepts, we also develop a potential web application, based on the proposed solutions, with a user-friendly interface to automatically analyze and classify users’ reviews on the COVID-19 contact tracing applications. The proposed framework combined with the interface which is expected to help the community in quickly analyzing users’ perception about such mobile applications and can be used as a rapid surveillance tool to monitor effectiveness of mobile applications and to make immediate changes without going through an intense participatory design method in emergency situations. METHODS We propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users’ reviews. In detail, we collected and annotated a large- scale dataset of Android and iOS mobile applications users’ reviews for COVID-19 contact tracing. After manually analyzing and annotating users’ reviews, we employed both classical (i.e., Naïve Bayes, SVM, Random Forest) and deep learning (i.e., fastText, and different transformers) methods for classification experiments. This resulted in eight different classification models. RESULTS We employed eight different methods on three different tasks achieving up to an average F1-Scores 94.8% indicating the feasibility and applicability of automatic sentiment analysis of users’ reviews on the COVID-19 contact tracing applications. Moreover, the crowd-sourcing activity resulted in a large-scale benchmark dataset composed of 34,534 reviews manually annotated from the contract tracing applications of 46 distinct countries. The resulted dataset is also made publicly available for research usage. CONCLUSIONS The existing literature mostly relies on the manual/exploratory analysis of users’ reviews on the application, which is a tedious and time-consuming process. Moreover, in the existing studies, generally, data from fewer applications are analyzed. In this work, we showed that AI and NLP techniques provide good results in analyzing and classifying users’ sentiments’ polarity, and that the automatic sentiment analysis can help in analyzing users’ responses to the application more quickly with a significant accuracy. Moreover, we also provided a large-scale benchmark dataset composed of 34,534 reviews from 47 different applications. We believe the presented analysis, dataset, and the proposed solutions combined with a user-friendly interface can be used as a rapid surveillance tool to analyze and monitor mobile applications deployed in emergency situations leading to rapid changes in the applications without going through an intense participatory design method.

Psychological training of fire service employees

The problem of premature professional burnout among employees, including firefighters is of scientific and practical significance. The article describes the nature and types of professional stress, as well as activities of the Training Center of the Federal Fire Service in Chelyabinsk Region. It aims to identify guidelines in the normative labor functions of rescuers of the Ministry of Emergency Situations of Russia and firefighters for training programs to prepare fire service employees for stressful activities. To achieve this goal, labor functions of rescuers of the Ministry of Emergency Situations of Russia and firefighters were analyzed; training programs developed by the Training Center of the Federal Fire Service in Chelyabinsk region were assessed. The methods of comparative analysis and conversation with experts were used to identify pedagogical aspects of labor functions of fire service employees associated with their willingness to work under stress. The article established a relationship between the training programs and the development of readiness to perform labor functions under stress. The condition required for the training program to be efficient was determined. It involves attracting the psychological service of the Ministry of Emergency Situations to hold methodical meetings and seminars. The positive result of this training is an adequate response to stress factors. The research results may be used by teachers of training centers and universities and methodologists.

The Main Threats and Challenges for Russia in the First Half of the XXI Century and Possible Areas of Work to Mitigate their Consequences

This article analyzes threats and challenges for the Russian Federation in the first half of the XXI century and identifies possible directions for improving security in emergency situations based on the use of new approaches in organizational, legal, scientific and technical support of measures, management bodies and civil defense forces and the unified state system of emergency prevention and response (RSChS).

Challenges in Pharmacy Education With Limited Resources During COVID-19

The rapidly emerging COVID-19 pandemic resulted in the need for rapid and extensive changes in the education programs of universities. This chapter reviews the changes in teaching and learning made by pharmaceutical faculties in six universities located in the Association of Southeast Asian Nations (ASEAN): Mahasarakham University (Thailand), Taylor's University (Malaysia), University of the Philippines-Manilla (Philippines), Hai Phong University of Medicine and Pharmacy (Vietnam), University of Health Sciences (Lao PDR), and Sanata Dharma University (Indonesia). The authors discuss adjustments that were made based on educational contexts, planning and infrastructure, educational processes, and products and outcomes. Each university provides a specific story concerning lessons learned in responding to the pandemic. The chapter concludes with changes that will be employed in future emergency situations, as well as those that will continue to be incorporated with the resumption of normal operations.