Implementation Of Countermeasures Research Articles

Automotive Ground Vehicles' Resilience to HEMP Attack: An Emergency Management Mitigation Plan

United States exposure to attack from a nuclear weapon of mass destruction (WMD) optimized to generate a high‐altitude electromagnetic pulse (HEMP) is a concern in national security forums. Such an attack could lead to widespread damage to the electrical components of many critical infrastructures. The purpose of this study was to develop a framework to better understand the hazard risks from a HEMP attack on non‐military (non‐MIL), “electronics‐heavy” automotive ground vehicles that could inform an effective emergency management response and recovery plan. A hybrid emergency management and engineering hazards risk analysis utilizing a Failure Modes and Effects Analysis (FMEA) format helped visualize the critical vehicle electronics subsystems and components, and their respective potential failure modes. A design of experiments test plan was developed to quantify the risks, and to develop a pathway to validate cost‐effective mitigation countermeasures based on present‐day best practices for HEMP hardening. The proposed emergency management plan emphasized strategic implementation of HEMP mitigation countermeasures to support continuity in emergency services and delivery of community lifelines to the public. The results of this study will serve as a foundation for future HEMP projects with automotive ground vehicles that will support the FEMA National Preparedness Goal, Presidential Executive Order 13865, and the recent FY2020 National Defense Authorization Act (NDAA) legislation. The methodologies can also apply to other critical infrastructure segments.

Current status of the environmental monitoring database on the accident at Fukushima Daiichi Nuclear Power Plant

An enormous amount of environmental monitoring data has been acquired by various organisations for the evaluation and implementation of countermeasures to mitigate the effects of the accident at the Fukushima Daiichi Nuclear Power Plant. However, it is difficult to collate, compare, and analyse this data because it was published in different formats at different sites according to the respective objectives of the publishing organisations. Moreover, these organisations have been accumulating data in large volumes for over nine years after the accident. We established procedures to collect this data, convert them into a unified format, classify them according to categories, and make the data accessible on a web-based database system. The database contains environmental monitoring data on air dose rates, ground deposition densities, and concentrations in various environmental samples such as soil, water, and food. This data is being provided not only in numerical format for quantitative analysis but also as distribution maps and time-series graphs for visual understanding. The database system enabled us to spatially and temporally compare large volumes of monitoring data. By using the database functions, characteristics of some representative data in the database was clarified.

FIVER – Robust Verification of Countermeasures against Fault Injections

Fault Injection Analysis is seen as a powerful attack against implementations of cryptographic algorithms. Over the last two decades, researchers proposed a plethora of countermeasures to secure such implementations. However, the design process and implementation are still error-prone, complex, and manual tasks which require long-standing experience in hardware design and physical security. Moreover, the validation of the claimed security is often only done by empirical testing in a very late stage of the design process. To prevent such empirical testing strategies, approaches based on formal verification are applied instead providing the designer early feedback.In this work, we present a fault verification framework to validate the security of countermeasures against fault-injection attacks designed for ICs. The verification framework works on netlist-level, parses the given digital circuit into a model based on Binary Decision Diagrams, and performs symbolic fault injections. This verification approach constitutes a novel strategy to evaluate protected hardware designs against fault injections offering new opportunities as performing full analyses under a given fault models.Eventually, we apply the proposed verification framework to real-world implementations of well-established countermeasures against fault-injection attacks. Here, we consider protected designs of the lightweight ciphers CRAFT and LED-64 as well as AES. Due to several optimization strategies, our tool is able to perform more than 90 million fault injections in a single-round CRAFT design and evaluate the security in under 50 min while the symbolic simulation approach considers all 2128 primary inputs.

Towards improving sustainability of rail transport by reducing traffic delays at level crossings: A case study for the State of Florida

Rail transport makes a significant contribution to the economy of many geographical locations around the globe. Along with numerous merits, rail transport has some demerits as well, such as traffic delays and safety issues at level crossings. Traffic delays at level crossings have various negative impacts, including economic losses, increasing number of violations because of delays, queue spillbacks at the adjacent intersections, and environmental impacts. This study reviews a number of level crossing delay estimation approaches found in the literature. Based on the state-of-the-art review and practical considerations, a new approach for estimation of the overall traffic delay per day at level crossings with the existing warning devices is proposed to improve the sustainability of rail transport. Moreover, in order to assist with the selection of countermeasures for level crossing upgrades, the proposed approach can be also used to estimate the overall traffic delay per day at level crossings after implementation of countermeasures. A case study is conducted for the public and private level crossings in the State of Florida, considering the countermeasures commonly used in the United States. It was revealed that traffic delays were higher at the public level crossings than at the private level crossings, both before and after implementation of the candidate countermeasures. The case study also indicated that low-cost countermeasures could be more advantageous in terms of safety effectiveness and traffic delays as compared to many high-cost conventional countermeasures. Furthermore, certain attributes of the level crossings in Florida with the highest traffic delays were investigated.

Policy and planning for large epidemics and pandemics - challenges and lessons learned from COVID-19.

Purpose of reviewThe COVID-19 pandemic is a global catastrophe that has led to untold suffering and death. Many previously identified policy challenges in planning for large epidemics and pandemics have been brought to the fore, and new ones have emerged. Here, we review key policy challenges and lessons learned from the COVID-19 pandemic in order to be better prepared for the future.Recent findingsThe most important challenges facing policymakers include financing outbreak preparedness and response in a complex political environment with limited resources, coordinating response efforts among a growing and diverse range of national and international actors, accurately assessing national outbreak preparedness, addressing the shortfall in the global health workforce, building surge capacity of both human and material resources, balancing investments in public health and curative services, building capacity for outbreak-related research and development, and reinforcing measures for infection prevention and control.SummaryIn recent years, numerous epidemics and pandemics have caused not only considerable loss of life, but billions of dollars of economic loss. The COVID-19 pandemic served as a wake-up call and led to the implementation of relevant policies and countermeasures. Nevertheless, many questions remain and much work to be done. Wise policies and approaches for outbreak control exist but will require the political will to implement them.

Evolution and Epidemic Spread of SARS-CoV-2 in Colombia: A Year into the Pandemic.

Current efforts to understand the epidemiology, transmission dynamics and emergence of novel SARS-CoV-2 variants worldwide has enabled the scientific community to generate critical information aimed at implementing disease surveillance and control measures, as well as to reduce the social, economic and health impact of the pandemic. Herein, we applied an epidemic model coupled with genomic analysis to assess the SARS-CoV-2 transmission dynamics in Colombia. This epidemic model allowed to identify the geographical distribution, Rt dynamics and predict the course of the pandemic considering current implementation of countermeasures. The analysis of the incidence rate per 100,000 inhabitants carried out across different regions of Colombia allowed visualizing the changes in the geographic distribution of cases. The cumulative incidence during the timeframe March 2020 to March 2021 revealed that Bogotá (8063.0), Quindío (5482.71), Amazonas (5055.68), Antioquia (4922.35) and Tolima (4724.41) were the departments with the highest incidence rate. The highest median Rt during the first period evaluated was 2.13 and 1.09 in the second period; with this model, we identified improving opportunities in health decision making related to controlling the pandemic, diagnostic testing capacity, case registration and reporting, among others. Genomic analysis revealed 52 circulating SARS-CoV-2 lineages in Colombia detected from 774 genomes sequenced throughout the first year of the pandemic. The genomes grouped into four main clusters and exhibited 19 polymorphisms. Our results provide essential information on the spread of the pandemic countrywide despite implementation of early containment measures. In addition, we aim to provide deeper phylogenetic insights to better understand the evolution of SARS-CoV-2 in light of the latent emergence of novel variants and how these may potentially influence transmissibility and infectivity.

A study of profit shifting using the Hines and Rice approach

Adopting and modifying the approach used by Hines and Rice (1994), we investigate the extent of crossborder profit shifting activities by foreign-owned Australian companies (FOACs) and evaluate the effectiveness of the measures implemented by the Australian government to combat base erosion and profit shifting (BEPS) by multinational enterprises (MNEs). Specifically, we measure the sensitivity of profit before tax reported in Australia by FOACs to the tax rate differentials between Australia and other countries where the related foreign-based MNE groups operate and examine whether such sensitivity decreases after the implementation of BEPS countermeasures. Overall, we find that profit shifting from Australia to low-tax countries took place throughout the 14-year study period, 2007 to 2020. The higher the Australian corporate tax rate relative to the tax rates of immediate parent entity, ultimate parent entity, and the higher the ranking of Australian tax rate relative to those of other countries where the foreign MNEs operate, the lower is the profit reported in Australia. In general, crossborder profit shifting from Australia to low-tax countries has not reduced in the post-BEPS period from 2013 to 2020 after the launch of the BEPS Project by the OECD and the implementation of BEPS countermeasures in Australia, although there is some evidence from breaking down the post-BEPS period by years which shows that profit shifting might have reduced in the year 2019. Such reduction, however, does not sustain in 2020.

A comprehensive analysis of factors that influence interstate highway crash severity in Alabama

This paper identifies factors that influence the severity of interstate crash outcomes and how they vary depending on the location and manner of collision. Four separate injury severity models were developed to explore the differences and similarities in crash factors between single-and multi-vehicle crashes that occurred in rural and urban areas of the state. Random parameters multinomial logit with heterogeneity in means and variances modeling approach was used to account for unobserved heterogeneity in the crash data. The model estimation results show that some driver behavioral factors such as speeding, aggressive driving, failure to use seatbelt, and driving without a valid license were found to significantly contribute to some form of injury outcome. The influence of roadway features such as type of opposing lane separation, collision type, temporal and lighting conditions on crash outcomes were also explored. Some differences and similarities in the associations between these factors and crash injury severity based on the manner and location of crash were unraveled. These findings are expected to guide the implementation of crash countermeasures on interstates. The findings of this study further support the evidence for the analysis of subsets of crash data to unravel underlying complex relationships within factors that influence crash injury severity.

A study on the association of functional hearing behaviors with semantics, morphology and syntax in cochlear-implanted preschool children

ObjectivesMonitoring children's development of auditory perception, speech and language after implant programming is critical to post-implantation success. Most of the time, it is not possible to perform an evaluation in clinical conditions, and the tests administered may not provide adequate data on children's everyday functional hearing skills. Therefore, administration of functional hearing measures alongside clinical tests will present supplementary data as to both language development and functional hearing performance of children. In addition, determining demographic and environmental variables affecting functional hearing skills and language development will facilitate the implementation of countermeasures. The main objective of this study was to investigate the relationship of functional hearing behaviors in cochlear-implanted children with semantics, morphology and syntax, and to determine how various variables affect functional hearing and language development. MethodThe study was conducted with 48 cochlear implant users (24 female and 24 male) aged between 3 years and 5 years 11 months with no additional disability. The Test of Early Language Development –Third Edition (TELD-3) was administered to the children participating in the study, and the Functioning after Pediatric Cochlear Implantation (FAPCI) and the demographic information form was completed pursuant to the information provided by the parents. ResultsData analysis in accordance with our hypotheses revealed a significant relationship between semantics, morphology, syntax and functional hearing skill (p < 0.05). The results indicated that language development and functional hearing skill were positively affected by duration of implant use, duration of auditory rehabilitation and kindergarten attendance, and a negative relationship between screen time and functional hearing skill. ConclusionsIn conclusion, the FAPCI is a suitable measure for early listening development and language assessment after cochlear implantation surgery, providing supplementary data on functional hearing and language development when administered in conjunction with other tests. Many factors are affecting functional hearing and language development that determine cochlear implant success. It is important to identify these factors.

Analysis of Risk Factors Regarding Short- and Long-Term Mortality of Critically Ill Patients with Hematologic Malignancies or Following Stem Cell Transplantation Requiring Treatment on the Intensive Care Unit

▪Introduction: Patients with hematologic malignancies or following hematopoietic stem cell transplantation (HSCT) are at an increased risk of suffering from severe complications requiring intensive care treatment. Despite improvement in antimicrobial therapy and supportive care, prognosis of these patients after transferal to the intensive care unit (ICU) is still often considered dismal and strategies to identify patients who might best benefit from ICU treatment are a matter of debate.Methods: We retrospectively analyzed clinical characteristics of patients with hematologic malignancies or following HSCT who were admitted to the ICU at Heidelberg University Hospital, a tertiary referral hospital and HSCT center, between 01/2009 and 12/2016. The impact of possible risk factors on ICU mortality as well as 1-year mortality was assessed by univariate Chi square tests and multivariate logistic regression.Results: In total, 346 patients were identified, 204 (59%) were male. Median age was 55.7 years (range 19-80). Regarding transplant status, 42 patients (12.5%) had received a prior autologous HSCT, 111 (32.1%) an allogeneic HSCT. Of the latter, 55.0% suffered from acute or chronic graft-versus-host disease (GVHD). Overall ICU mortality was 50.3% with a median time to death of 11.6 days (range 0-113), 1-year mortality of patients discharged from ICU was 47.6%.The main reason for ICU transferal was respiratory failure with 309 (89.3%) patients requiring mechanical ventilation. In univariate analysis, status post allogeneic HSCT (p<0.0001) and presence of GVHD (p<0.001) were significantly associated with higher ICU mortality, as was hospital stay prior to ICU admission > 4 days (p=0.04). Neither prior autologous HSCT nor prior conventional chemotherapy showed a negative impact on survival.Pneumonia at transferal or during ICU stay was diagnosed in 293 patients (84.7%). Microbiologically confirmed invasive pulmonary aspergillosis was associated with higher ICU mortality (p=0.02) in univariate analysis as was influenza-associated pneumonia (p=0.04). Regarding 1-year survival, this negative effect was no longer significantly discernible. Colonization by multi-resistant bacteria did not exert a significant impact on ICU mortality or 1-year survival.Regarding organ complications, use of mechanical ventilation (p<0.0001), especially requirement of nitric oxide ventilation (p<0.0001), requirement of vasopressors within the first hour of transferal (p=0.02) and requirement of hemodialysis (p=0.001) were significant risk factors for ICU mortality in univariate analysis. With the exception of vasopressor therapy, all these remained significant risk factors regarding 1-year survival. Regarding laboratory assessments, an initial pH value < 7.25 (p=0.004), serum creatinine > 1.1 mg/dL (p=0.004), serum bilirubin > 2mg/dL (p<0.001), and serum lactate > 2.2 mg/dL (p=0.02) as indicators of multi-organ failure were significantly associated with inferior ICU survival, a trend was seen for leukocytes < 1/nL (p=0.05). Elevated serum creatinine, bilirubin and lactate levels remained significant risk factors with respect to 1-year survival.Next we performed a multivariate analysis in 309 patients requiring mechanical ventilation evaluating parameters easily assessable by the treating physician at the time of ICU transferal. This revealed that presence of GHVD (p=0.009, OR 2.5), pH < 7.25 (p=0.03, OR 2.3), and bilirubin > 2 mg/dL (p=0.047, OR 1.8) exerted an independent negative prognostic impact on ICU mortality, a trend was seen for elevated serum creatinine (p=0.098, OR 1.5). Of interest, neither prior autologous HSCT nor prior allogeneic HSCT in the absence of GVHD showed an independent adverse influence.Conclusions: In this cohort of critically ill patients with hematologic malignancies, ICU survival of 49.7% could be achieved despite the high rate of requirement of mechanical ventilation. Severe ongoing immunosuppression in the setting of GVHD, and signs of organ failure such acidosis or hyperbilirubinemia at time of ICU transferal were significantly associated with an increased risk of ICU mortality. Early identification of patients at risk and implementation of counter-measures to avoid organ complications are therefore of the utmost importance. DisclosuresNo relevant conflicts of interest to declare.

Space-Time Evolutions of Land Subsidence in the Choushui River Alluvial Fan (Taiwan) from Multiple-Sensor Observations

Land subsidence is a significant problem around the world that can increase the risk of flooding, damage to infrastructure, and economic loss. Hence, the continual monitoring of subsidence is important for early detection, mechanism understanding, countermeasure implementation, and deformation prediction. In this study, we used multiple-sensor observations from the Continuous Global Positioning System (CGPS), the small baseline subset (SBAS) algorithm, interferometric synthetic-aperture radar (InSAR), precise leveling, multi-layer compaction monitoring wells (MLCWs), and groundwater observation wells (GWs) to show the spatial and temporal details of land subsidence in the Choushui River alluvial fan (CRAF), Taiwan, from 1993 to 2019. The results showed that significant land subsidence has occurred along the coastal areas in the CRAF, and most of the inland subsidence areas have also experienced higher subsidence rates (&gt;30 mm/yr). The analysis of subsidence along the Taiwan High Speed Rail (THSR) revealed a newly formed subsidence center between Tuku and Yuanchang Townships in Yunlin, with high subsidence rates ranging from 30 to 70 mm/yr. We propose a map showing, for the first time, the distribution of deep compactions occurring below 300 m depth in the CRAF.

Did the number of older adults with frailty increase during the COVID-19 pandemic? A prospective cohort study in Japan.

To clarify the actual frail status over 6months with the COVID-19 countermeasures. This prospective cohort study was conducted between the baseline assessment from May 11 to July 10 in 2020, and the follow-up assessment from November 11, 2020, to January 10, 2021, in Japan. The survey forms were distributed among 1953 community-dwelling older adults. Frailty status was assessed using the Frailty Screening Index. In total, 702 older adults (35.2%) returned the survey forms, and 593 (mean age = 78.8years, and 77.4% females) older adults without missing values for the survey forms were analyzed. Pre-frail and frail prevalence were 55.0 and 7.9% at the baseline, and 57.3 and 11.8% at the follow-up, respectively. Frailty transition that indicated transition from robust or pre-frail at the baseline to frail at the follow-up was 9.9%. Increase in frailty might indicate frailty related to implementation of COVID-19 countermeasures.

Synthetic Reproduction and Augmentation of COVID-19 Case Reporting Data by Agent-Based Simulation

We generate synthetic data documenting COVID-19 cases in Austria by the means of an agent-based simulation model. The model simulates the transmission of the SARS-CoV-2 virus in a statistical replica of the population and reproduces typical patient pathways on an individual basis while simultaneously integrating historical data on the implementation and expiration of population-wide countermeasures. The resulting data semantically and statistically aligns with an official epidemiological case reporting data set and provides an easily accessible, consistent and augmented alternative. Our synthetic data set provides additional insight into the spread of the epidemic by synthesizing information that cannot be recorded in reality.

Decision support for the quickest detection of critical COVID-19 phases

During the course of an epidemic, one of the most challenging tasks for authorities is to decide what kind of restrictive measures to introduce and when these should be enforced. In order to take informed decisions in a fully rational manner, the onset of a critical regime, characterized by an exponential growth of the contagion, must be identified as quickly as possible. Providing rigorous quantitative tools to detect such an onset represents an important contribution from the scientific community to proactively support the political decision makers. In this paper, leveraging the quickest detection theory, we propose a mathematical model of the COVID-19 pandemic evolution and develop decision tools to rapidly detect the passage from a controlled regime to a critical one. A new sequential test—referred to as MAST (mean-agnostic sequential test)—is presented, and demonstrated on publicly available COVID-19 infection data from different countries. Then, the performance of MAST is investigated for the second pandemic wave, showing an effective trade-off between average decision delay Delta and risk R, where R is inversely proportional to the time required to declare the need to take unnecessary restrictive measures. To quantify risk, in this paper we adopt as its proxy the average occurrence rate of false alarms, in that a false alarm risks unnecessary social and economic disruption. Ideally, the decision mechanism should react as quick as possible for a given level of risk. We find that all the countries share the same behaviour in terms of quickest detection, specifically the risk scales exponentially with the delay, R sim exp {(-omega Delta )}, where omega depends on the specific nation. For a reasonably small risk level, say, one possibility in ten thousand (i.e., unmotivated implementation of countermeasures every 27 years, on the average), the proposed algorithm detects the onset of the critical regime with delay between a few days to 3 weeks, much earlier than when the exponential growth becomes evident. Strictly from the quickest-detection perspective adopted in this paper, it turns out that countermeasures against the second epidemic wave have not always been taken in a timely manner. The developed tool can be used to support decisions at different geographic scales (regions, cities, local areas, etc.), levels of risk, instantiations of controlled/critical regime, and is general enough to be applied to different pandemic time-series. Additional analysis and applications of MAST are made available on a dedicated website.

Protein biomarker elucidation for the verification of biological agents in the taxonomic group of Gammaproteobacteria using tandem mass spectrometry.

Some pathogenic microbes can be used for nefarious applications and instigate population-based fear. In a bio-threat scenario, rapid and accurate methods to detect biological agents in a wide range of complex environmental and clinical matrices, is of paramount importance for the implementation of mitigation protocols and medical countermeasures. This study describes targeted and shot-gun tandem MS based approaches for the verification of biological agents from the environmental samples. The marker proteins and peptides were elucidated by an exhaustive literature mining, in silico analysis of prioritized proteins, and MS/MS analysis of abundant proteins from selected bacterial species. For the shot-gun methodology, tandem MS analysis of abundant peptides was carried from spiked samples. The validation experiments employing a combination of shot-gun tandem MS analysis and a targeted search reported here is a proof of concept to show the applicability of the methodology for the unambiguous verification of biological agents at sub-species level, even with limited fractionation of crude protein extracts from environmental samples.

A comparative study of machine learning classifiers for injury severity prediction of crashes involving three-wheeled motorized rickshaw

Motorcycles and motorcyclists have a variety of attributes that have been found to be a potential contributor to the high liability of vulnerable road users (VRUs). Vulnerable Road Users (VRUs) that include pedestrians, bicyclists, cycle-rickshaw occupants, and motorcyclists constitute by far the highest share of road traffic accidents in developing countries. Motorized three-wheeled Rickshaws (3W-MR) is a popular public transport mode in almost all Pakistani cities and is used primarily for short trips to carry passengers and small-scale goods movement. Despite being an important mode of public transport in the developing world, little work has been done to understand the factors affecting the injury severity of three-wheeled motorized vehicles. Crash injury severity prediction is a promising research target in traffic safety. Traditional statistical models have underlying assumptions and predefined associations, which can yield misleading results if flouted. Machine learning(ML) is an emerging non-parametric method that can effectively capture the non-linear effects of both continuous and discrete variables without prior assumptions and achieve better prediction accuracy. This research analyzed injury severity of three-wheeled motorized rickshaws (3W-MR) using various machine learning-based identification algorithms, i.e., Decision jungle (DJ), Random Forest (RF), and Decision Tree (DT). Three years of crash data (from 2017 to 2019) was collected from Provincial Emergency Response Service RESCUE 1122 for Rawalpindi city, Pakistan. A total of 2,743 3W-MR crashes were reported during the study period that resulted in 258 fatalities. The predictive performance of proposed ML models was assessed using several evaluation metrics such as overall accuracy, macro-average precision, macro-average recall, and geometric means of individual class accuracies. Results revealed that DJ with an overall accuracy of 83.7 % outperformed the DT and RF-based on a stratified 10-fold cross-validation approach. Finally, Spearman correlation analysis showed that factors such as the lighting condition, crashes involving young drivers (aged 20–30 years), facilities with high-speed limits (over 60 mph), weekday, off-peak, and shiny weather conditions were more likely to worsen injury severity of 3W-MR crashes. The outcomes of this study could provide necessary and essential guidance to road safety agencies, particularly in the study area, for proactive implementation of appropriate countermeasures to curb road safety issues pertaining to three-wheeled motorized vehicles.

Limiting Ship Accidents by Identifying Their Causes and Determining Barriers to Application of Preventive Measures

When analyzing ship accidents, there may be doubts whether appropriate countermeasures had been taken to prevent known types of accidents. This study aimed to suggest possible solutions by investigating the status and issues associated with the implementation of countermeasures using importance–performance analysis (IPA), Borich’s needs assessment, and locus for focus models based on previously identified causes of the ship accidents. As a result, firstly, we confirmed that there is a need to enhance education and training on specific knowledge, understanding, and proficiency (KUP) regarding ship stability, emergency response, and type specific training. Secondly, we confirmed that there is a need for a system of monitoring a seafarer’s KUP even while onboard a vessel—that is, after completion of identified training. Additionally, it is necessary to improve the seafarers’ working environment, which is subject to regulations. Thirdly, difficulties in solving wrong practice parts of safety and efficiency, such as the costs associated with implementation of safety regulations, were identified as the main reasons for the causes of the “not amended yet” sector after accidents. Lastly, the tools that were employed in this analysis can be used to confirm the implementation status of the actions to be taken after a ship accident.

A hierarchical Bayes approach to adjust for selection bias in before–after analyses of vision zero policies

American cities devote significant resources to the implementation of traffic safety countermeasures that prevent pedestrian fatalities. However, the before–after comparisons typically used to evaluate the success of these countermeasures often suffer from selection bias. This paper motivates the tendency for selection bias to overestimate the benefits of traffic safety policy, using New York City’s Vision Zero strategy as an example. The NASS General Estimates System, Fatality Analysis Reporting System and other databases are combined into a Bayesian hierarchical model to calculate a more realistic before–after comparison. The results confirm the before–after analysis of New York City’s Vision Zero policy did in fact overestimate the effect of the policy, and a more realistic estimate is roughly two-thirds the size.

The Vulnerability of the Production Line Using Industrial IoT Systems under DDoS Attack

Internet of Things (IoT) devices are not only finding increasing use in ordinary households, but they have also become a key element for the Industry 4.0 concept. The implementation of industrial IoT devices into production streamlines the production process and reduces production costs. On the other hand, connected IoT devices bring new security risks to production and expose an industrial environment to new types of attacks. The article analyzes the vulnerability of the production line with implemented industrial IoT devices with consideration of a possible Distributed Denial-of-service (DDoS) attack led by attackers from the internet. Various types of DDoS attacks abusing the presence of IoT devices in the system were performed on an automated production line implementing sorting, preparation, and dosing of bulk and liquid materials for filling into containers. The leading attacks caused failure of the production line during the production, as well as the dysfunction of communication with IoT devices. The article also demonstrates the implementation of countermeasures against DDoS attacks and possible strategies to protect and mitigate such attacks on the production line.

Countermeasures against COVID-19: how to navigate medical practice through a nascent, evolving evidence base — a European multicentre mixed methods study

ObjectivesIn a previously published Delphi exercise the European Pediatric Dialysis Working Group (EPDWG) reported widely variable counteractive responses to COVID-19 during the first week of statutory public curfews in 12...