Effectiveness Of Countermeasures Research Articles

Evaluation of the Effectiveness of Countermeasures for Improving the Safety of Dilemma Zones: A Driving Simulator Study

Evaluation of the Effectiveness of Countermeasures for Improving the Safety of Dilemma Zones: A Driving Simulator Study

Effects of Various Muscle Disuse States and Countermeasures on Muscle Molecular Signaling.

Skeletal muscle is capable of changing its structural parameters, metabolic rate and functional characteristics within a wide range when adapting to various loading regimens and states of the organism. Prolonged muscle inactivation leads to serious negative consequences that affect the quality of life and work capacity of people. This review examines various conditions that lead to decreased levels of muscle loading and activity and describes the key molecular mechanisms of muscle responses to these conditions. It also details the theoretical foundations of various methods preventing adverse muscle changes caused by decreased motor activity and describes these methods. A number of recent studies presented in this review make it possible to determine the molecular basis of the countermeasure methods used in rehabilitation and space medicine for many years, as well as to identify promising new approaches to rehabilitation and to form a holistic understanding of the mechanisms of gravity force control over the muscular system.

Injury Metrics for Assessing the Risk of Acute Subdural Hematoma in Traumatic Events.

Worldwide, the ocurrence of acute subdural hematomas (ASDHs) in road traffic crashes is a major public health problem. ASDHs are usually produced by loss of structural integrity of one of the cerebral bridging veins (CBVs) linking the parasagittal sinus to the brain. Therefore, to assess the risk of ASDH it is important to know the mechanical conditions to which the CBVs are subjected during a potentially traumatic event (such as a traffic accident or a fall from height). Recently, new studies on CBVs have been published allowing much more accurate prediction of the likelihood of mechanical failure of CBVs. These new data can be used to propose new damage metrics, which make more accurate predictions about the probability of occurrence of ASDH in road crashes. This would allow a better assessement of the effects of passive safety countermeasures and, consequently, to improve vehicle restraint systems. Currently, some widely used damage metrics are based on partially obsolete data and measurements of the mechanical behavior of CBVs that have not been confirmed by subsequent studies. This paper proposes a revision of some existing metrics and constructs a new metric based on more accurate recent data on the mechanical failure of human CBVs.

Estimation of the Lethality Rate, Recovery Rate, and Case Fatality Ratio of Classical Swine Fever in Japanese Wild Boar: An Analysis of the Epidemics From September 2018 to March 2019.

Understanding the morbidity and lethality of diseases is necessary to evaluate the effectiveness of countermeasure against the epidemics (e.g., vaccination). To estimate them, detailed data on host population dynamics are required; however, estimating the population size for wildlife is often difficult. We aimed to elucidate the morbidity and lethality of classical swine fever (CSF) currently highly prevalent in the wild boar population in Japan. To this end, we estimated lethality rate, recovery rate, and case fatality ratio (CFR) of CSF without detailed data on the population estimates of wild boar. A mathematical model was constructed to describe the CSF dynamics and population dynamics of wild boar. We fitted the model to the (i) results of the reverse transcription polymerase chain reaction (RT-PCR) test for the CSFV gene and the (ii) results of the enzyme-linked immunosorbent assay (ELISA) test for the antibody against CSFV in sampled wild boar. In the 280 wild boar sampled from September 2018 to March 2019 in the major CSF-affected area in Japan, the lethality rate and recovery rate of CSF per week were estimated as 0.165 (95% confidence interval: 0.081–0.250) and 0.004 (0–0.009), respectively. While the estimate of lethality rate of CSF was similar with the estimates in previous studies, the recovery rate was lower than those reported previously. CFR was estimated as 0.959 (0.904–0.981) using our estimate of recovery rate. This study is the first to estimate lethality rate of CSF from the dynamics of CSF epidemics in the wild boar population. Since the value of CFR is sensitive to the value of recovery rate, the accuracy in the estimate of recovery rate is a key for the accurate estimation of CFR. A long-term transmission experiment of moderately virulent strains may lead to more accurate estimation of the recovery rate and CFR of CSF.

Prediction of Occurrence and Severity of Run-off-Roadway Crashes on Rural Two-Lane Roadways Using Bayesian Networks

Run-off-roadway (ROR) crashes are among the most common crash types on rural two-lane roadways. Current methodologies to predict their occurrence and severity by considering conditional nature and interactions between independent variables require complex mathematical procedures. This study employs Bayesian networks (BNs), a non-functional form graphical model, to determine factors associated with the occurrence and severity of ROR crashes. The study used five-year (2014–2018) crash data collected from 397 randomly selected road segments within Texas. Out of 397 segments, 279 did not experience ROR crashes. The first BN model used all 397 segments and explored factors associated with occurrences of ROR crashes. The second BN model used the remaining 118 segments that involved ROR crashes and focused on factors associated with different crash types (guardrail [GR], overturning [OT], and fixed object [FO] crashes) and their associated severity levels. Study results revealed that the presence of horizontal curves and utility poles within the clear zone on the road individually increased the chance of ROR crashes by about 35%. Moreover, FO crashes resulted in 36% more fatal and injury crashes than GR crashes, which showed the effectiveness of guardrails in reducing severity. This study also explored the combined influence of variables on ROR crash occurrence and severity, as well as the interrelation between several independent variables. The proposed methodology can be used to evaluate the effectiveness of countermeasures.

Polarization and tipping points

Research has documented increasing partisan division and extremist positions that are more pronounced among political elites than among voters. Attention has now begun to focus on how polarization might be attenuated. We use a general model of opinion change to see if the self-reinforcing dynamics of influence and homophily may be characterized by tipping points that make reversibility problematic. The model applies to a legislative body or other small, densely connected organization, but does not assume country-specific institutional arrangements that would obscure the identification of fundamental regularities in the phase transitions. Agents in the model have initially random locations in a multidimensional issue space consisting of membership in one of two equal-sized parties and positions on 10 issues. Agents then update their issue positions by moving closer to nearby neighbors and farther from those with whom they disagree, depending on the agents' tolerance of disagreement and strength of party identification compared to their ideological commitment to the issues. We conducted computational experiments in which we manipulated agents' tolerance for disagreement and strength of party identification. Importantly, we also introduced exogenous shocks corresponding to events that create a shared interest against a common threat (e.g., a global pandemic). Phase diagrams of political polarization reveal difficult-to-predict transitions that can be irreversible due to asymmetric hysteresis trajectories. We conclude that future empirical research needs to pay much closer attention to the identification of tipping points and the effectiveness of possible countermeasures.

Modelling the effect of a border closure between Switzerland and Italy on the spatiotemporal spread of COVID-19 in Switzerland

We present an approach to extend the endemic–epidemic (EE) modelling framework for the analysis of infectious disease data. In its spatiotemporal formulation, spatial dependencies have originally been captured by static neighbourhood matrices. These weight matrices are adjusted over time to reflect changes in spatial connectivity between geographical units. We illustrate this extension by modelling the spread of COVID-19 disease between Swiss and bordering Italian regions in the first wave of the COVID-19 pandemic. The spatial weights are adjusted with data describing the daily changes in population mobility patterns, and indicators of border closures describing the state of travel restrictions since the beginning of the pandemic. These time-dependent weights are used to fit an EE model to the region-stratified time series of new COVID-19 cases. We then adjust the weight matrices to reflect two counterfactual scenarios of border closures and draw counterfactual predictions based on these, to retrospectively assess the usefulness of border closures. Predictions based on a scenario where no closure of the Swiss-Italian border occurred increased the number of cumulative cases in Switzerland by a factor of 2.7 (10th to 90th percentile: 2.2 to 3.6) over the study period. Conversely, a closure of the Swiss-Italian border two weeks earlier than implemented would have resulted in only a 12% (8% to 18%) decrease in the number of cases and merely delayed the epidemic spread by a couple of weeks. Our study provides useful insight into modelling the effect of epidemic countermeasures on the spatiotemporal spread of COVID-19.

Economic Impacts of Spillover Effects of Terrorism Countermeasures at Public Assembly Sites

Abstract In recent years, there have been many high-profile attacks on large, relatively unprotected venues, including entertainment and shopping complexes in the U.S. and around the world. Public and private decision-makers can choose from a wide array of terrorism countermeasures. A question arises as to whether patrons’ complaints about delays, inconvenience and invasion of privacy actually translate into decisions to attend fewer such events. This paper presents the bottom-line economic impacts of terrorism countermeasures on business revenue at three public assembly venues and on their surrounding regional economic activity. These venues include an MLB Stadium, an NBA/NHL Arena, and a Convention Center. The analysis is based primarily on survey responses relating to changes in attendance that stem from public perception of the implementation of four major types of countermeasures. The surveys indicated that the majority of patrons were not affected either way by the presence of the countermeasures, but nearly all of the remainder felt more secure in the presence of the countermeasures to both terrorism and ordinary crime, which resulted in an increased likelihood of attendance. The economic impact estimates yield a small but notable positive impact on business revenues, though this outcome varies significantly across venue types.

Pandemic spread in communities via random graphs

Working in the multi-type Galton–Watson branching-process framework we analyse the spread of a pandemic via a general multi-type random contact graph. Our model consists of several communities, and takes, as input, parameters that outline the contacts between individuals in distinct communities. Given these parameters, we determine whether there will be an outbreak and if yes, we calculate the size of the giant-connected-component of the graph, thereby, determining the fraction of the population of each type that would be infected before it ends. We show that the pandemic spread has a natural evolution direction given by the Perron–Frobenius eigenvector of a matrix whose entries encode the average number of individuals of one type expected to be infected by an individual of another type. The corresponding eigenvalue is the basic reproduction number of the pandemic. We perform numerical simulations that compare homogeneous and heterogeneous spread graphs and quantify the difference between them. We elaborate on the difference between herd immunity and the end of the pandemic and the effect of countermeasures on the fraction of infected population.

In situ evaluation of contact stiffness in a slip interface with different roughness conditions using ultrasound reflectometry

Understanding the dynamic condition of the interface between a railway wheel and rail is important to reduce the risks and consider the effectiveness of countermeasures for tribological problems. Traditionally the difficulty in obtaining accurate non-destructive interfacial measurements has hindered systematic experimental investigations. Recently, an ultrasound reflectometry technique has been developed as a direct observation method of a rolling–sliding interface; however, the topography dependence under the high contact pressures in a wheel–rail contact has not been clarified. For this reason, a novel in situ measurement of the contact stiffness using ultrasound reflectometry was carried out for three different levels of roughness. A contact pressure equivalent to that in a wheel–rail interface was achieved by using a high-pressure torsion test approach. The dynamic change of contact stiffness with slip was measured using ultrasound and the influence of roughness was investigated. The measured changes were validated using a newly developed numerical simulation, and mechanisms to explain the observed behaviour were proposed in terms of fracture and plastic deformation of the asperity bonds. These findings could help in understanding the traction characteristics for different roughness conditions and also assist in understanding damage mechanisms better, such as wear and rolling contact fatigue.

Effectiveness of CKD Exacerbation Countermeasures in Izumo City.

To diagnose chronic kidney disease (CKD) at an early stage, it is important to promote appropriate health guidance and consultation recommendations through regular medical examinations and implementation of continuous high-quality and appropriate treatment. From fiscal year (FY) 2018, Izumo City has initiated the “Izumo City CKD Exacerbation Countermeasures” program. In this study, we aimed to report on the methods undertaken and the effects of this program. Residents aged 40–74 years who underwent specific health checkups from the Izumo City National Health Insurance in FY2018 and FY2019 were included. The rates of CKD re-examination candidates, re-examinations implementation, nephrologist referrals, and health guidance referrals between FY2018 and FY2019 were compared. The rate of CKD re-examination candidates in both years remained unchanged at approximately 7%. The rate of re-examination implementation in FY2019 significantly increased relative to that in FY2018 (p < 0.001). Subsequent re-examination candidate trends showed that the rate of nephrologist referrals did not increase. However, the rate of city health guidance referrals significantly increased (p < 0.001). Increase in the re-examination and health guidance examination rates indicate improved awareness of CKD among the public and family doctors, and it is expected to prevent CKD exacerbation in the future.

The effect of the response to the coronavirus disease pandemic on treatment outcomes in patients with lymphoma and multiple myeloma.

Background/AimsRelatively little data are available on how the response to the coronavirus disease 2019 (COVID-19) pandemic has affected treatment outcomes in patients receiving chemotherapy for lymphoma or multiple myeloma. We aimed to determine the effect of COVID-19 countermeasures on treatment outcomes in this patient population.MethodsWe retrospectively analyzed data on patients treated for lymphoma or multiple myeloma in two tertiary hospitals in Seoul. Patients were divided into two groups: group 1 included patients who received chemotherapy between September and December 2019 (the control period), and group 2 included patients who received chemotherapy between September and December 2020 (the study period). Countermeasures to COVID-19 were applied to the patients in group 2. The countermeasures implemented included mask wearing and regular handwashing at home and in hospital; COVID-19 risk assessments on all hospital visitors; and pre-emptive COVID-19 screening for all newly hospitalized patients and their resident guardians.ResultsNo differences in treatment outcomes, including treatment response, incidence and duration of neutropenia or neutropenic fever, delays in chemotherapy, or number of deaths during chemotherapy, were observed between the g roups. None of the patients in group 2 tested positive for COVID-19, and there were no COVID-19-related deaths during the study period.ConclusionsCountermeasures to COVID-19 did not affect treatment outcomes in patients receiving chemotherapy for lymphoma or multiple myeloma. Data on the effect of countermeasures to COVID-19 on treatment outcomes should continue to be analyzed to ensure that treatment outcomes are not adversely affected.

Predicting the effective reproduction number of COVID-19: inference using human mobility, temperature, and risk awareness

ObjectivesThe effective reproduction number (Rt) has been critical for assessing the effectiveness of countermeasures during the coronavirus disease 2019 (COVID-19) pandemic. Conventional methods using reported incidences are unable to provide timely Rt data due to the delay from infection to reporting. Our study aimed to develop a framework for predicting Rt in real time, using timely accessible data — i.e. human mobility, temperature, and risk awareness. MethodsA linear regression model to predict Rt was designed and embedded in the renewal process. Four prefectures of Japan with high incidences in the first wave were selected for model fitting and validation. Predictive performance was assessed by comparing the observed and predicted incidences using cross-validation, and by testing on a separate dataset in two other prefectures with distinct geographical settings from the four studied prefectures. ResultsThe predicted mean values of Rt and 95% uncertainty intervals followed the overall trends for incidence, while predictive performance was diminished when Rt changed abruptly, potentially due to superspreading events or when stringent countermeasures were implemented. ConclusionsThe described model can potentially be used for monitoring the transmission dynamics of COVID-19 ahead of the formal estimates, subject to delay, providing essential information for timely planning and assessment of countermeasures.

To Be or Not to Be Stuck, or Is It a Continuum?

Players can get stuck in video games, which impedes their process to their goal and results in unfavorable outcomes like negative emotions, impediments of flow, and obstacles for learning. Currently, it is not easily possible to assess if a player is stuck, as no widely accepted definition of "being stuck" in games exists. We conducted 13 expert interviews and a systematic literature review with 104 relevant papers selected from 4022 candidates. We present a definition of "being stuck" that conceptualizes the state as a continuum and contextualize it within related concepts. Our stuck continuum can be applied to regulate the player's stuck level. We propose a taxonomy of measures that are useful for the detection of the level of stuckness and discuss the effectiveness of countermeasures. Our stuck concept is crucial for game developers creating an optimal player experience in games.

Assessing Countermeasure Effectiveness in Controlling Cyanobacterial Exceedance in Riverine Systems Using Probabilistic Forecasting Alternatives

AbstractCyanobacterial blooms are expected to be more frequent over time as more favorable environmental conditions are created in a warming climate. This study proposes strategies to effectively m...

Countermeasures against economic crisis from COVID-19 pandemic in China: An analysis of effectiveness and trade-offs

The effectiveness of different countermeasures to economic crisis from the public health emergency is still inadequately understood. We establish an illustrative scenario, specifying the shocks of COVID-19 pandemic and countermeasures applying a general equilibrium model to analyze the effectiveness of countermeasures with a particular focus on trade-offs in the impacts of monetary and fiscal policies. We find that both monetary and fiscal countermeasures could effectively mitigate the economic damages to GDP and employment. However, they would also produce adverse side-effects such as an increase in consumer price by 1.05% and 0.57%, respectively, and a decline in exports by 2.61% and 1.05%, respectively. Monetary policies would exacerbate the damages to external demand by supply-side shocks of the pandemic, but they are more suitable for mitigating demand-side shocks. While fiscal policies would benefit nearly all producing sectors, monetary policies would mainly affect export-oriented manufacturing sectors negatively.

The Impact of Dormant Defects on Defect Prediction: A Study of 19 Apache Projects

Defect prediction models can be beneficial to prioritize testing, analysis, or code review activities, and has been the subject of a substantial effort in academia, and some applications in industrial contexts. A necessary precondition when creating a defect prediction model is the availability of defect data from the history of projects. If this data is noisy, the resulting defect prediction model could result to be unreliable. One of the causes of noise for defect datasets is the presence of “dormant defects,” i.e., of defects discovered several releases after their introduction. This can cause a class to be labeled as defect-free while it is not, and is, therefore “snoring.” In this article, we investigate the impact of snoring on classifiers' accuracy and the effectiveness of a possible countermeasure, i.e., dropping too recent data from a training set. We analyze the accuracy of 15 machine learning defect prediction classifiers, on data from more than 4,000 defects and 600 releases of 19 open source projects from the Apache ecosystem. Our results show that on average across projects (i) the presence of dormant defects decreases the recall of defect prediction classifiers, and (ii) removing from the training set the classes that in the last release are labeled as not defective significantly improves the accuracy of the classifiers. In summary, this article provides insights on how to create defects datasets by mitigating the negative effect of dormant defects on defect prediction.

Mathematical modeling of information leakage prevention processes on parametric channels in of management activity

It is noted that control bodies are installed in which the main and auxiliary technical means and systems (MTM and ATM, respectively) are installed, used in the process of management activities, as well as the so-called semi-active stowing devices introduced in these rooms serve as a source of informative signals. in the field of these signals of some parameters in the element database included in the MTM and (or) ATM. The necessity of a systematic presentation of formal conditions characterizing the dynamics of counteraction to the interception of electromagnetic radiation arising from changes in the parameters of the radioelectronic devices of the reconnaissance object under the influence of an informative signal on the element base of these devices and external high-frequency electromagnetic irradiation, as a prerequisite for ensuring a high level of adequacy of assessing the effectiveness of countermeasures to technical intelligence (CMTI) to prevent information leakage through parametric channels. The structure of a set of mathematical models for evaluating the performance indicator is described. Analytical models of the temporal characteristics of threats to intercept information through parametric channels and measures to prevent its leakage are given. The probabilistic format of the indicator of the effectiveness of such measures is substantiated. The results of a computational experiment to substantiate the requirements for the characteristics of the means of CMTI are presented.

Modelling the relationship of driver license and offense history with fatal and serious injury (FSI) crash involvement

Introduction: Previous research has indicated that increases in traffic offenses are linked to increased crash involvement rates, making reductions in offending an appropriate measure for evaluating road safety interventions in the short-term. However, the extent to which traffic offending predicts fatal and serious injury (FSI) crash involvement risk is not well established, prompting this new Victorian (Australia) study. Method: A preliminary cluster analysis was performed to describe the offense data and assess FSI crash involvement risk for each cluster. While controlling demographic and licensing variables, the key traffic offenses that predict future FSI crash involvement were then identified. The large sample size allowed the use of machine learning methods such as random forests, gradient boosting, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. This was done for the ‘all driver’ sample and five sometimes overlapping groups of drivers; the young, the elderly, and those with a motorcycle license, a heavy vehicle license endorsement and/or a history of license bans. Results: With the exception of the group of drivers who had a history of bans, offense history significantly improved the accuracy of models predicting future FSI crash involvement using demographic and licensing data, suggesting that traffic offenses may be an important factor to consider when analyzing FSI crash involvement risk and the effects of road safety countermeasures. Conclusions: The results are helpful for identifying driver groups to target with further road safety countermeasures, and for showing that machine learning methods have an important role to play in research of this nature. Practical Application: This research indicates with whom road safety interventions should particularly be applied. Changes to driver demerit policies to better target offenses related to FSI crash involvement and repeat traffic offenders, who are at greater risk of FSI crash involvement, are recommended.

Alterations in Saliva and Plasma Cytokine Concentrations During Long-Duration Spaceflight.

Long-duration spaceflight is known to cause immune dysregulation in astronauts. Biomarkers of immune system function are needed to determine both the need for and effectiveness of potential immune countermeasures for astronauts. Whereas plasma cytokine concentrations are a well-established biomarker of immune status, salivary cytokine concentrations are emerging as a sensitive indicator of stress and inflammation. For this study, to aid in characterizing immune dysregulation during spaceflight, plasma and saliva cytokines were monitored in astronauts before, during and after long-duration spaceflight onboard the International Space Station. Blood was collected from 13 astronauts at 3 timepoints before, 5 timepoints during and 3 timepoints after spaceflight. Saliva was collected from 6 astronauts at 2 timepoints before spaceflight, 2 timepoints during and 3 timepoints following spaceflight. Samples were analyzed using multiplex array technology. Significant increases in the plasma concentration of IL-3, IL-15, IL-12p40, IFN-α2, and IL-7 were observed during spaceflight compared to before flight baseline. Significant decreases in saliva GM-CSF, IL-12p70, IL-10 and IL-13 were also observed during spaceflight as compared to compared to before flight baseline concentrations. Additionally, plasma TGFβ1 and TGFβ2 concentrations tended to be consistently higher during spaceflight, although these did not reach statistical significance. Overall, the findings confirm an in-vivo hormonal dysregulation of immunity, appearing pro-inflammatory and Th1 in nature, persists during long-duration orbital spaceflight. These biomarkers may therefore have utility for monitoring the effectiveness of biomedical countermeasures for astronauts, with potential application in terrestrial research and medicine.