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The past several decades have witnessed an increasing number of natural and manmade hazards with a dramatic impact on the normal operations of the society. The occurrences of these hazards manifest...

INTRODUCTION Heroin overdose is a leading cause of mortality among drug users. This paper aims to identify individual and contextual factors associated with lethal and non-lethal heroin-related overdoses on the basis of case reports and semi-structured proxy interviews. Typical patterns within these cases are determined by means of cluster analysis. METHODS Within the CaRe (Case Reports of heroin-related overdoses) study, case reports (100 proxy reports of overdose events from 36 different facilities) were gathered and evaluated as part of a nationwide survey of experts conducted in Germany in 2019. Following initial descriptive analyses a two-step cluster analysis with the four binary variables of gender, age, time and place was conducted to identify patterns within the reported cases. RESULTS The case reports grouped into five clusters: 1) Younger male drug users, found in a public space during the daytime; 2) Female drug users; 3) Older male drug users, found in a public space during the daytime; 4) Drug users found at home at night; 5) Drug users found outside at night. Overdoses by female drug users and those which occurred at home and/or at night were significantly more likely to have a fatal outcome. CONCLUSION Future prevention and intervention measures should aim to consider the context, i.e. typical constellations of risk, and attempt to inhibit this through appropriate counter measures.

PurposeWhile United States is among countries with the world’s highest coronavirus infections, its approaches and policies to reopen the economy vary by state. A lack of objective criteria and monitoring toward satisfying the criteria can lead to another COVID-19 outbreak and business closures. Considering the pressing need to return to normalcy without a rebound of COVID-19 infections and deaths, an index that provides a data-driven and objective insight is urgently needed. Hence, a method was devised to assess the severity of the COVID-19 pandemic and determine the degree of progress any state has made in containing the spread of COVID-19.Design/methodology/approachUsing measures such as the weekly averages of daily new deaths, ICU bed occupancy rates, positive cases and test positivity rates, two indexes were developed: COVID-19 reopening readiness and severity.FindingsA clear difference in the pandemic severity trends can be observed between states, which is possibly due to the disparity in the state’s response to coronavirus. A sharp upward trend in index values requires caution prior to moving to the next phase of reopening.Originality/valueThe composite indexes advanced in this study will provide a universal, standardized and unbiased view of each state’s readiness to reopen and allow comparisons between states. This in turn can help governments and health-care agencies take counter measures if needed as to the anticipated demand for future health-care services and minimize adverse consequences of opening.

Through the method of literature and online inquiry, this paper studies the connotation, harm and classification of bribery in competitive sports industry and puts forward countermeasures such as enhancing the sensitivity of judicial enforcement and strengthening the management of sports competitions, which provides the basis for the decision-making of competent departments.

Physical Unclonable Functions (PUFs) exploit the manufacturing process variations inherent in silicon-based chips to generate unique secret keys. Although PUFs are supposed to be unclonable or unbreakable, researchers have found that they are vulnerable to machine learning (ML) attacks. In this article, we analyze the vulnerability of different FPGA-based Ring Oscillator PUFs (ROPUFs) to machine learning attacks. The challenge-response pairs (CRPs) data obtained from different ROPUFs is trained using different machine learning algorithms. From the study, it is found that the Artificial Neural Network (ANN) models can be used to train the ROPUFs with a training accuracy of 99.9% and a prediction accuracy of 62% when 5,000 CRPs are used for a challenge-response ROPUF. In this article, we assume a realistic situation where a small set of the CRP dataset (approximately 15% maximum) is unscrupulously obtained by the hacker. A prediction accuracy of 62% makes the PUF vulnerable to machine learning attacks. Therefore, a secondary goal of this article is the design of a ROPUF capable of thwarting machine learning modeling attacks. The modified XOR-inverter ROPUF drastically reduces the prediction accuracy from 62% to 13.1%, thus making it increasingly difficult for hackers to attack the ROPUF.

The Antarctic environment is characterized by many of the same extreme stressors as long-duration space flight (LDSE), thereby providing a useful earth-based analog for examining changes in and predictors of mental health over time. At coastal (n = 88) and inland (n = 22) Antarctic stations we tracked mental health symptoms across a nine-month period including winter-over using the Mental Health Checklist (MHCL; Bower et al., 2019). Our monthly assessment battery also examined changes in physical complaints, biomarkers of stress, and the use of different emotion regulation strategies. MHCL positive adaptation scores showed linear decreases whereas MHCL poor self-regulation scores and severity of physical symptoms increased across the study period. During-mission use of emotion regulation strategies and dehydroepiandrosterone (DHEA) levels predicted end-of-study MHCL scores, whereas trait-based psychological measures collected at the start of the mission showed little predictive utility. Results suggest that interventions and counter measures aimed at enhancing positive affect/emotion during prolonged exposure to extreme environments may be useful in reducing psychological risk.

Objective:People with NCDs are particularly vulnerable to disasters. This research systematically reviewed reports describing studies on the status of patients with NCDs before, during and after disasters.Method:Relevant articles published from 1997 to 2019 were collected by searching the Scopus, PubMed, and Science Direct databases. We specifically examined reports describing NCDs and including the key words ‘Non-Communicable Disease and Disasters.’ NCDs include cardiovascular, respiratory, diabetes and cancer diseases.Results:The review identified 42 relevant articles. Most of the included studies were found to have described the conditions of patients with NCDs after disasters - 14 (13.3%), during disasters – 11 (26.2%), before disasters – 6 (14.3%), within all stages of disasters: before, during and after – 6 (14.3%), only during and after disasters – 4 (9.5%), and includes before and during disasters – 1 (2.4%).Conclusion:NCDs pose major health issues in disasters. Development of strong counter measures against the interruption of treatment, as well as surveillance systems to ascertain medical needs for NCDs are necessary as preparation for future disasters.

This paper, by means of quantitative and qualitative analysis based on a vast investigation, talks about the anomie in China’s academic publishing from the following three aspects: first, disorders...

Although it has been proven that various volatile organic compounds (VOCs) are produced during surgery, there have been no studies focusing specifically on endoscopy. Therefore, we aimed to investigate VOCs produced during endoscopic submucosal dissection (ESD). Thirty-one patients scheduled for ESD were enrolled in this study. Sorbent tubes were installed to collect gas at two sites: one in a suction bottle and the other at the level of the endoscopists’ upper chest. Gas collections were performed for up to 30 min during submucosal dissection. Quantitative analysis of the VOCs in gas samples was performed using gas chromatography and mass spectrometry. Concentrations of fifteen VOCs were measured. The total concentration of volatile organic compounds (TVOC) was also calculated. Among the five carcinogens—benzene, ethylbenzene, formaldehyde, tetrachloroethylene, and trichloroethylene (TCE)—measured, excess life-time risks of cancer for benzene and TCE were interpreted as unacceptable based on the New Jersey Department of Environmental Protection Division of Air Quality and the Environmental Protection Agency guidelines (5 × 10–4 and 7 × 10–5, respectively). Among the non-carcinogenic VOCs, the mean concentration of toluene was much higher than the reference value (260 μg/m3) in the Japanese guidelines for indoor air quality (IAQ) (1323.7 ± 2884.0 μg/m3 from the air at the upper chest level of endoscopists and 540.9 ± 1345.4 μg/m3 from the suction bottle). Mean TVOCs were at least 10 times higher than the reference value (400 μg/m3) issued by the Japanese guidelines for IAQ. Various carcinogenic and non-carcinogenic VOCs were detected at levels higher than the reference “safe” values during the submucosal dissection step of ESD. Implementation of counter measures is essential to protect medical personnel who are involved in ESD surgeries.

This article presents a review and a synthesis of basic strategies and approaches, which constitute the basis of modern strategies for combating extremist and terrorist threats or their prevention. The approaches to the matter are defined, and their social consequences are considered. It summarizes the issues, concerning prior aspects of counter-terrorism strategies, which should be taken into consideration. It also presents some critical expert views whether any universal foundations for counter-terrorism strategies could be found. The author pays attention to the existing general difficulties at this scientific field, which directly affect the studys success. It focuses on the importance of careful approach to the issue from both by the general public and policy makers.

Internet of Things (IoT) is undoubtedly a well-known research area. Security on IoT communication services is the major challenge with advanced technology and devices. This paper mainly focusing on Perceptron layer based attacks and counter measures based on Effective Authentication System (EAS). This paper is ordered as outlining IoT Architecture, Types of Threats ,Perceptron Layer based attacks, sensor based communication services ,RFID mechanism ,Tag identify and verification by back end server and Hash based Effective Authentication System (EAS) to avoid pseudonym attacks .This paper proposes EAS as security measure by preventing privacy attack, pseudonym attack, location tracking and asynchronous attack.

Internet of Things (IoT) technology is anticipated to pave the way for groundbreaking applications in a number of areas of current healthcare systems. Given the significant number of connected medical devices, the vital data generated by the patient is under several security threats. Selective Forwarding (SF) and Wormhole (WH) attacks are two critical threats that cause information deficit in the network. The SF attack drops critical data packets at compromised nodes while the WH attack creates fallacious routing tables due to the introduction of malicious routes in the network. In this paper, we focus on the security aspects of Healthcare-IoT and review the proposed counter-measures against the SF and WH attacks. We weigh the recently postulated counter-measures based on their significance and identify their limitations. Additionally, we propose a blockchain-based cryptographic framework for mitigating SF and WH attacks in H-IoT. We explore future research directions in mitigating these threats.

Automatic Speaker verification systems are vulnerable to spoofing attacks. We propose our anti-spoofing system. It uses some acoustic features. The set of binary classifiers includes XGBoost tree boosting algorithm. ASV Spoof 2015 corpus is utilized in the experiments as the main database for anti-spoofing systems training. The pretreatment of acoustic features is essential for better performance of the system. Obtained results demonstrate that the proposed system can provide a good accuracy. High evaluation performance can be obtained using the combination between MFCC, LogFBE and SSC features. The attained metrics values such 97.80% for the accuracy, 97.12% for the precision, 98.50% for the Recall and 97.81% for the F1-mesure validate the performance of the proposed technique.

Burnout among trainee doctors is common with as many as two-thirds reporting poor health. This study aimed to assess burnout in a cohort of UK core and higher general surgical trainees.The Maslach Burnout Inventory for Medical Personnel was distributed to 158 surgical trainees to evaluate emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA). High EE (≥27) and DP (≥10), low PA (≤33) scores were taken to indicate burnout.A single UK (Wales) Deanery.One hundred responses were received; 65 core surgical trainees, 31 Higher Surgical Trainees (HST), and 4 not specified.Median EE, DP, and PA scores were 22.0 (range 2-50), 7.5 (0-25), and 36.0 (19-47), respectively. High burnout by domain was: EE (n = 33), DP (n = 39), PA (n = 34), with 59% of trainees demonstrating burnout in ≥1 one domain, with strong interdomain correlation (EE:DP r = 0.351, p0.001; EE:PA r = -0.455, p0.001; DP:PA r = -0.446, p0.001). Female gender (p = 0.020), core surgical training grade (p = 0.012), and being childless (p = 0.033) were independently associated with higher levels of EE; whereas HST grade (p = 0.007), age30 years (p = 0.010), married/partner status (p = 0.001), and parenthood (p = 0.015), were associated with lower levels of burnout with regard to DP. Binary logistic regression revealed lower burnout in all domains to be associated with HST status (hazard ratio 0.116, 95% confidence interval 0.014-0.980, p = 0.048) and male gender (hazard ratio 4.365, (1.246-15.293), p = 0.021).Burnout among surgical trainees was common in at least 1 Maslach Burnout Inventory domain. Urgent counter measures are required to protect the health and wellbeing of trainees at risk, which ought to be associated with commensurate improvement in patient safety.

Treatment of patients with severe COVID-19, is challenging specifically when a patient carries high risk of mortality, such as old age, immune suppression or cancer. Also a patient who manifests the disease with severe symptoms, such as hypoxia, requiring supplemental oxygenation, or artificial ventilation has a poor prognosis. Here we review the scientific rationale used to design a very promising therapy based on existing literature, but in significantly different method and protocols, used to treat cases of severe COVID-19, and we conclude that although the effort on drug development has been enormous, but as of today, we do not have a therapy with specific characteristics as this protocol, to be used safely in human and yet potentially meet the expectations we would have for a so called “effective therapy”. Further clinical trials are needed to support this hypothesis and generate further hypothesis to prove the concept in larger cohort of patients.

In this paper, we present a time series forecasting framework that uses deep learning to predict the environmental attributes that affect the olive fruit farming. The proposed framework is composed of two phases: a data preprocessing phase and a prediction phase. In the prediction phase, we use both the Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) deep learning approaches to develop two models for predicting the environmental attributes. We evaluate the performance of the framework using real-life agriculture data collected for twenty years from a Spanish olive grove. Our results show that proposed LSTM and GRU models achieve remarkable accuracy, measured through different metrics, in predicting the temperature and relative humidity for the upcoming year based on historical data. We further use the predicted temperature in calculating the degree-day metric used to define the development phases of the olive fruit fly. This allows for foreseeing the best times to apply the counter measures to prevent the outbreak of such a fatal pest that affects a major Mediterranean crop: olive.

The most common problem with drinking water supply is Non Revenue Water (NRW). NRW can be grouped into two types, namely physical water loss and non-physical water loss. This study aims to determine the NRW size, NRW causes, counter measures that can be done to suppress NRW. The results of evaluation of distribution networks that occur in the Blahbatuh City Zone District Meter Area (DMA) PDAM Gianyar. The difference between the amount of water distributed and the amount of water recorded in the account is NRW. The way to find out the cause and control of NRW is to analyze the results of the step test, UFM test (Ultrasonic Flow Meter), and conduct the interviews with employees of PDAM. The evaluation of distribution networks is only done in DMA Pande that has completed data. The amount of NRW that occured in the Blahbatuh City Zone is 474,142.83 m3/year (59.91%). The water loss is caused by damage to pipe accessories, leaky pipes, inaccuracies in the accuracy of the water meter. The water loss can be overcame by establishing a DMA, replacing pipe accessories, calibrating and replacing water meters. The evaluation results for the existing conditions of the distribution network in DMA Pande of Blahbatuh City Zone have not worked optimally. It seen from the results of the waternet and from the number of SR’s that have not met the requirements for the formation of a DMA.

Indian roads have a sizeable amount of accidents happening in them. The transportation problems faced by various nations across the world have increased manifold, necessitating search for methods or alternatives that ensure efficient, safe, feasible and faster means of transport. Government of India is taking many efforts to minimize the road accidents. Amongst all traffic accident, road traffic accident bears largest number of human life and tends to be most serious problem world over. Road accidents are largely predictable and preventable, provided rational analysis and counter measures. The number of accidents in India is increasing rapidly every year due to addition of more number of vehicles on the streets. This project is to look for few accidental prone spots in our locality and analyze the causes of accidents and try to provide viable solutions for improving the safety in the accidental zones. In this study, the accident zones were taken and a particular spot was selected and suitable measures to control the accidents were suggested.

Firstly, this paper analyses the development status of Cross-border E-commerce in Fujian. Secondly, it explores the advantages, disadvantages, opportunities and challenges of the development of Cross-border E-commerce in Fujian, so as to understand the problems existing in the development of Cross-border E-commerce in Fujian. Finally, the paper puts forward corresponding counter measures.

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate medical counter measures in time. As a first step, we evaluated whether exposure to an opioid (fentanyl) or a nerve agent (VX) could be detected in freely moving guinea pigs using features from respiration, electrocardiography (ECG) and electroencephalography (EEG), where machine learning models were trained and tested on different sets (across subject classification). Results showed this to be possible with close to perfect accuracy, where respiratory features were most relevant. Exposure detection accuracy rose steeply to over 95% correct during the first five minutes after exposure. Additional models were trained to correctly classify an exposed state as being induced either by fentanyl or VX. This was possible with an accuracy of almost 95%, where EEG features proved to be most relevant. Exposure detection models that were trained on subsets of animals generalized to subsets of animals that were exposed to other dosages of different chemicals. While future work is required to validate the principle in other species and to assess the robustness of the approach under different, realistic circumstances, our results indicate that utilizing different continuously measured physiological signals for early detection and identification of toxic agents is promising.

The utilization of Wireless Sensor Networks (WSN) inside healthcare applications is developing quickly. Numerous e-health applications, such as heart monitors, blood pressure levels, computer monitors, and endoscopic capsule, are being used. To deal with the increasing utilization of sensor engineering inside this specific spot, a new area recognized as Wireless Body Area Networks (WBAN) has emerged. As virtually all equipment and the applications are wirelessly connected to it, privacy and security issues are probably the main aspects of concern. Because immediate participation of people likewise boosts the awareness. Whether or not the information gathered about individuals or maybe people are gotten with the individual's consent or may be lacking it because of the device's demand, misuse or even secrecy issues might limit individuals by benefiting from the complete advantages the product. Individuals might overlook the gadgets protected for everyday use. There might also be potential for severe public unrest because of the worry that these products might be utilized to check and monitor people by federal organizations or any other personal businesses. Within this paper, we talk about the problems and assess with information the issues and the possible measures.

The present generation war game requires precision engagement with agility. Embedded systems of Aerospace vehicle like Seekers/Sensors are used to provide the necessary Guidance in the terminal phase. Seeker-based guidance can shape the latex demand within the capability of the Aerospace vehicle for a precision impact in the terminal phase. In addition, the autonomous guidance of Passive Imaging Infrared (IIR) seeker is less susceptible to external counter measures. Thorough performance evaluation of IIR Seekers and Guidance schemes is very essential for the effectiveness of the mission. For terminal engagement, Aerospace vehicle dynamics and accuracy are the prime factors which can be met by appropriate homing guidance design. Latest advances in seeker/sensor technology for locating target need to be integrated with the guidance system for steering and stabilizing the guided vehicle. Hardware-in-loop Simulation (HILS) of IIR Seekers integrated with the Real-Time Six Degrees of Freedom plant & target model helps in the evaluation of Aerospace vehicle embedded system design. Establishing the HILS test-bed with seeker and target along with Dynamic Motion Simulators and other subsystems is a major challenge. Various tests and the detailed procedures adopted to evaluate the Embedded systems of Aerospace vehicle in HILS are explained in this paper. The delay issues associated with the HILS runs were also discussed at the end.

Data and information in business are the most valuable entities which help in decision-making and better business choices. This data is processed using business intelligence and customer relationship management tools to help gain vital information about the customers. It is no different when it comes to the telecommunication industry. In this chapter, we have dived into the discussion of not only the usage of big data in business but also the security challenges that comes along with it. We have discussed the security threats that any business organization faces while working with huge amount of private data and some counter measures to secure those data.

The fast-moving nature of information technology is causing frequent obsolescence of technologies and competences. Changes in the environment cause a reduction in the need and demand of old competences. This results in a depreciation of these old competences and a reduction in performance in comparison to individuals with up-to-date competences. Obsolescence is especially relevant for IT professionals because the technologies they work with, and thus the demanded competences, change particularly frequently. However, what effect does that have on the education and development of IT professionals and IT work? To answer that question, we have conducted a systematic literature review. We have analyzed 115 relevant hits and identified key aspects and issues for future research. Causes for obsolescence, consequences of obsolescence, and counter-measures against obsolescence are presented as the three central dimensions of the topic.

This report describes the research conducted by use of the ForHLRI within the publicly funded project ’Kersolife100’, in which the long-term performance of a fully ceramic solid oxide fuel cell (SOFC) concept is investigated. The project aims at modeling and understanding the dominant degradation mechanisms in SOFCs so that the required lifetime of the SOFC-stacks can be ensured. One major cause of ageing is the unfavourable microstructural evolution of the nickel-based anode that occurs upon SOFC operation. The associated mechanisms are modeled by use of phase field methods within ’Kersolife100’. For a successful outcome, the availability of accurate material parameters is crucial, but until now not given. Complementary to experimental efforts, the ab-initio research activities of this year therefore focused on the determination of the relevant nickel surface and interface properties. By combining experimental and simulation results, a deeper understanding of the anode aging mechanism can be generated and the identification of counter measures can be guided. In this report, the current results of the ab-initio activities are summarized.

This study aimed to quantify core surgical trainee (CST) differential attainment (DA) related to three cohorts; white UK graduate (White UKG) versus black and minority ethnic UKG (BME UKG) versus international medical graduates (IMGs). The primary outcome measures were annual review of competence progression (ARCP) outcome, intercollegiate Membership of the Royal College of Surgeons (iMRCS) examination pass and national training number (NTN) selection. Intercollegiate Surgical Curriculum Programme (ISCP) portfolios of 264 consecutive CSTs (2010–2017, 168 white UKG, 66 BME UKG, 30 IMG) from a single UK regional post graduate medical region (Wales) were examined. Data collected prospectively over an 8-year time period was analysed retrospectively. ARCP outcomes were similar irrespective of ethnicity or nationality (ARCP outcome 1, white UKG 60.7% vs BME UKG 62.1% vs IMG 53.3%, p=0.395). iMRCS pass rates for white UKG vs BME UKG vs IMG were 71.4% vs 71.2% vs 50.0% (p=0.042), respectively. NTN success rates for white UKG vs BME UKG vs IMG were 36.9% vs 36.4% vs 6.7% (p=0.023), respectively. On multivariable analysis, operative experience (OR 1.002, 95% CI 1.001 to 1.004, p=0.004), bootcamp attendance (OR 2.615, 95% CI 1.403 to 4.871, p=0.002), and UKG (OR 7.081, 95% CI 1.556 to 32.230, p=0.011), were associated with NTN appointment. Although outcomes related to BME DA were equitable, important DA variation was apparent among IMGs, with iMRCS pass 21.4% lower and NTN success sixfold less likely than UKG. Targeted counter measures are required to let equity prevail in UK CST programmes.

Public shaming is mass criticism of someone for perceived violation of an established social norm. These events are ever increasing in number and scope on online social media platforms. An individual instance of shaming is, as a matter of fact, an expression of opinion regarding the target or victim of the event. Public shaming events are more engaging and often evoke stronger opinions from users than word of mouth spreading of information. This work models polarity of a user’s opinion as a function of several factors, such as his/her recently held opinions, received opinions and the environment, in online public shaming events on Twitter. Specifically, we investigate the opinion change process of Twitter users by highlighting the influence of responsible factors. A semi-supervised representation learning model is used to get opinion vectors from tweets. Next, an opinion prediction model based on the user’s past opinions, received opinions and environment is designed. We show that opinion changes can be attributed to factors in varying proportions depending on the direction of the change. Thus, a strategy to stop a shaming event by inducing opinion changes in the participants needs to take into account the direction of the change. Efficacy of possible counter measures to shaming is analyzed based on our findings.

All over the world, chronic kidney disease (CKD) is a serious public health condition that needs to be detected in advance so that costly end-stage treatments like dialysis, kidney transplantations can be avoided. Neural network model and 10-fold cross-validation methodology under a single platform in proposed as well as implemented in order to classify patients with CKD. This will assist medical care fields so that counter measures can be suggested. The performance of proposed classifier is justified against other baseline classifiers such as Support Vector Machine, K-Nearest Neighbours, Decision tree and Gradient Boost classifier. Experimental results conclude that the performance of neural network with 10-fold cross-validation method reaches promising accuracy of 98.25%, f1-score of 0.98, and kappa score of 0.96 and MSE of 0.0175.

One of the major driving forces in the current technological world, is the concept of cloud computing. Many companies and organizations have their precious data stored on various cloud computing platforms. Organizations depend on cloud computing will effectively manage the excessive data quantity and handle them. In such a scenario, a breach of that data or any major threat can cause huge damage to the image and financial position of the company. Due to a security breach, if all of those data is leaked to an unauthorized intruder, it can lead to severe manipulations of that data and cause huge losses to the assets of the company. Hence, various counter measures to tackle those security issues are discussed in this paper. All the possible data issues are listed out and the various methods to counter them are discussed.

Internet of Things (IoT) is the technology made up of many inter-disciplinary technologies. It includes Radio Frequency Identification (RFID), sensors networks, actuators, IoT devices, smart phones and even physical things. Thus plenty of applications that could not exist earlier are emerged. The advantages of IoT brought many security and privacy issues. Therefore, it is essential to have comprehension of those issues and also countermeasures to overcome them. Towards this end, we focused on understanding and providing useful insights related to privacy and security besides giving some of the methods to ensure security. The security measures include biometrics based solutions, trust management, embedded security, RFID based security issues and CoAP with security features.

Background In preparation for the 2020 Tokyo Olympic/Paralympic Games, the Japanese government assessed the risks of infectious disease outbreaks and determined necessary preparations. This present study reviews efforts made during a previous measles epidemic and describes the roles of hospitals. Methods This descriptive study investigated the records of 198 children with measles. Each child was treated at a general hospital January 1997 and February 1998. The study also examined the actions of pediatricians during and after the measles outbreak in the community. Results Of the 198 children, 145 (73%) were hospitalized. The measles vaccination rate in the previous year was approximately 75%. Of the patients examined, 53% were below two years of age. The mean age was two years and nine months. Pneumonia and gastroenteritis accounted for 46% and 30% of the complications, respectively. Issues requiring attention included the number of hospital beds located in a negative pressure room or a private room with a window, obtaining gamma globulin preparation with a high measles antibody titer, increased vaccination opportunities, and extended working hours for doctors. Conclusions Measles outbreaks are possible in Japan as a result of visitors from abroad. Items to mitigate an outbreak were identified as maintenance of high vaccination rates, readily available information regarding the location of negative pressure hospital rooms, the status of measles development, and finally medical staffing issues. There is a continued risk of measles outbreaks occurring in infants and individuals who do not have a measles antibody titer.

Technological revolution has done wonders in Aeronautics. With the evolution Unmanned Aerial vehicle, not only the military sector but also the commercial and industrial sectors use drones for various purposes. The global Unmanned Aerial Vehicle (UAV) market size is projected to reach $133.5 billion by 2026, from $25.9 billion in 2019, at a compound annual growth rate of 26.4% during 2021-2026. As the technology usage increases, its security plays a vital role in research. The idea behind this paper is to discuss definite and efficient countermeasures to spoofing and jamming of drone signals. The detection and mitigation of spoofing and jamming using new cyber security techniques including blockchain, deep learning using multiclass SVM, Co-operative localisation, Hardware sandboxing and Visual Odometry are explored. It also presents a deep analysis of all techniques along with the pros and cons of each method.

This chapter aims to develop an understanding of sensor networks and the security threats posed to them, owing to the inherently insecure wireless nature. It also highlights the current security issues associated with the exchange of information and presents respective countermeasures that can be used to secure the network of malevolent behavior. It builds the reader's understanding of security threats by presenting an idealistic security mechanism and comparing it to currently practiced security mechanisms. Doing so, it identifies the security flaws in each mechanism, henceforth, enumerating a list of well-known security attacks that are connected to the respective security flaws. To provide a better understanding of security threats, the security attacks, in general, are discussed in the perspective of a network administrator, and an adversary. Their impact is also considered from the side of a network administrator and its respective benefits to the adversary. The chapter is later concluded along with future directives and an insight on requirements of forthcoming technologies.

The applications of a mobile ad hoc network (MANET) range from military and disaster response to commercial and civilian environments. The intrinsic characteristics like lack of infrastructure, self-organizing nature and deployment flexibility contributes to its usefulness but few other features like open media for communication and topological changes on-the-fly makes it vulnerable to security attacks. The focus of the current work is security mechanisms for a MANET against the various insider attacks targeting the data forwarding operations. It is accomplished through a trust management framework and a secure routing protocol relying upon the TMF to form the routes with most trusted nodes. The protocol computes a novel reliability metric termed as Path Allegiance metric (PAM) which acts as a selection criteria for route selection. The performance evaluation involves simulation results which prove the robustness of the proposed TMF and the effectiveness of the PAM routing protocol in reliable data delivery in the presence of packet droppers which disrupt the data transmission.

The twenty-first century is fraught with dangers like climate change and pollution, which impacts human health and mortality. As levels of pollution increase, respiratory illnesses and cardiovascular ailments become more prevalent. Less understood are the eye-related complaints, which are commonly associated with increasing pollution. Affected people may complain of irritation, redness, foreign body sensation, tearing, and blurring of vision. Sources of pollution are varied, ranging from gases (such as ozone and NO2) and particulate matter produced from traffic, to some other hazards associated with indoor environments. Mechanisms causing ocular surface disease involve toxicity, oxidative stress, and inflammation. Homeostatic mechanisms of the ocular surface may adapt to certain chronic changes in the environment, so affected people may not always be symptomatic. However there are many challenges associated with assessing effects of air pollution on eyes, as pollution is large scale and difficult to control. Persons with chronic allergic or atopic tendencies may have a pre-existing state of heightened mucosal immune response, hence they may have less tolerance for further environmental antigenic stimulation. It is beneficial to identify vulnerable people whose quality of life will be significantly impaired by environmental changes and provide counter measures in the form of protection or treatment. Better technologies in monitoring of pollutants and assessment of the eye will facilitate progress in this field.

Our society greatly depends on services and applications provided by mobile communication networks. As billions of people and devices become connected, it becomes increasingly important to guarantee security of interactions of all players. In this talk we address several aspects of this important, many-folded problem. First, we show how to design cryptographic primitives which can assure integrity and confidentiality of transmitted messages while satisfying resource constrains of low-end low-cost wireless devices such as sensors or RFID tags. Second, we describe countermeasures which can enhance the resistance of hardware implementing cryptographic algorithms to hardware Trojans.

The article is devoted to the study of the conditions for the use of countermeasures in the contemporary international law. The application of countermeasures is not sufficiently regulated by the written norms of international law and is predominantly non-codified in a customary manner. The type and size of the countermeasures must be proportional to the offense committed, and the use countermeasures may be recognized as lawful only if the injured State or an international organization puts the offender in a position to comply with its obligations and notify of possible countermeasures. The basic principles of the application of countermeasures are the following: countermeasures should apply only to the State which has committed an internationally wrongful act; the unlawful nature of the act must be determined solely on the basis of the norms of international law; the purpose of applying countermeasures is to restore legitimate rights and interests of the victim only; countermeasures should ensure the return to the previous state that existed prior to the commission of an international offense and should not cause irreparable damage; the application of countermeasures is temporary only. The use of countermeasures is permissible only in case of exhaustion by the victim of other parties not connected with the use of means of protection of their rights and should be terminated when the violated right will be restored. Certain international obligations cannot be terminated in the order of application of countermeasures against the offender. These include, in particular, the obligation to refrain from using force or threat of force, an obligation to protect fundamental human rights, humanitarian obligations and other obligations arising from imperative norms of general international law.

Most mountain torrent disasters are caused by heavy rains and pose a serious threat to the lives and property of people in mountainous and hilly areas. These risks affect the stability of social an...