Dynamic Metrics Research Articles

Dynamic Inherently Safer Modifications: Metric development and its validation for fire and explosion prevention

Of the numerous inherent safety assessment tools, a dynamic metric capable of investigating and incorporating the temporal risk evolution when conducting Inherently Safer Modifications (ISMs) is yet to be established. To this end, this work developed a Dynamic Inherent Safety Metric (DISM) and validated its functionality and viability through a case study. Firstly, the Information-Flow-based Accident-causing Model (IFAM) was adapted to construct the topology of Bayesian Networks (BN). Then, Bayesian deductive reasoning was executed to do crucial risk identification by ranking posterior probabilities. Finally, risk-based ISMs were performed to address the relatively contributing risk factors. The case study results show that the fire and explosion risk decreased by approximately a third after implementing ISMs, thus demonstrating that the modified processing scenario could be inherently safer than the original processing scenario. The newly developed inherent safety metric (i.e., DISM) can assist in temporal risk identification and assessment, and it is expected to function as a novel assessment tool for measuring and comparing the inherent safeness before and after implementing ISMs with simultaneous considerations on the time-varying risk factors.

Impact of intelligent control algorithms on demand response flexibility and thermal comfort in a smart grid ready residential building

The present paper investigates the impact of advanced control algorithms on harnessing building energy flexibility in a smart-grid ready full-electric residential building. The impact on thermal comfort is also analysed. The building is located in Ireland and is equipped with a geothermal heat pump and a thermal energy storage system. Two Energy Management systems, based on rule-based and intelligent optimisation algorithm approaches, are developed which use real-time building smart meter and weather data. This data is utilised by various dynamic flexibility metrics within the respective control algorithms. Different time of use tariffs, based on data from the Irish Commission for Energy Regulation and structured on the basis of peak, off-peak and night periods, are also used. Results show that energy cost reductions of up to 21% and 43% can be achieved by the rule-based and intelligent algorithm, respectively, without compromising the thermal comfort within the building. Moreover, total shifting and forcing flexibility potential of up to 34 and 54 kWh, respectively, based on the month of January, can be achieved by the adoption of the intelligent control algorithm.

Spatiotemporal Dynamics of Urban Green Space Influenced by Rapid Urbanization and Land Use Policies in Shanghai

Urbanization has led to the continuous expansion of built-up areas and the ever-growing urban population, threatening the quantity and quality of urban green space (UGS). Exploring the spatiotemporal variations of UGS is substantially conducive to the formulation of land-use policies to protect the ecosystems. As one of the largest megacities all around the world, Shanghai has experienced rapid urbanization in the past three decades. Insights into how UGS changes in response to urbanization and greening policies are essential for guiding sustainable urban development. This paper employed integrated approaches to characterize the changing patterns and intensities of green space in Shanghai, China from 1990 to 2015. The spatiotemporal dynamics of the UGS pattern were derived through four main methods: green space ratio, dynamic change degree (DCD), transition matrix and landscape metrics. The results showed that Shanghai’s green space decreased from 84.8% in 1990 to 61.9% in 2015 while the built-up areas increased from 15.0% to 36.5%. Among the green space sub-types, farmland was largely encroached and fragmented by urban sprawl, especially in the Outer Ring Expressway and Suburban Ring Expressway belts of the city. About 1522 km2 of the green space has transferred into built-up areas, followed by farmland, waterbody, forest, and grassland in descending order. The 2000–2010 period witnessed the strong urban expansion and dramatic changes in UGS, but then the change around 2015 turned down and stable. The landscape pattern metrics showed that the entire green space in Shanghai was growingly fragmented and isolated during the past 25 years. Combined with the green space-related planning and policies issued in 1990–2015, the results revealed that both rapid urbanization and greening policies accounted for the spatiotemporal dynamics of UGS. Based on the results, some implicants to new urban planning and policies of Shanghai were highlighted.

Integral impact of PVT variation with NBTI degradation on dynamic and static SRAM performance metrics

ABSTRACT Advanced CMOS technology is highly susceptible to ageing effects such as negative bias temperature instability (NBTI) and process variability. This article focuses on investigating the ‘combined impact’ of NBTI degradation along with the process, voltage and temperature (PVT) variations on SRAM metrics. These issues impact the performance of SRAM (Static Random Access Memory) cell in many ways but the two main dynamic metrics are critical read-stability and write-ability. The dynamic metrics can, in turn, be characterised by their static metrics, that determine the static noise margins (SNM) of SRAM. The dynamic metrics were analysed for symmetric and asymmetric NBTI degradation of two inverters of SRAM. NBTI degradations were incorporated on the pull-up transistors of SRAM cell. Furthermore, results of the correlated performance metrics are presented for varied data storage patterns in SRAM cell over its lifetime. This analysis aims to provide an insight into the effects of integrated PVT and NBTI degradation on SRAM cell performance, enabling robust designs and help designers at the early stage of design to mitigate system failure.

Different alternations of static and dynamic brain regional topological metrics in schizophrenia and obsessive-compulsive disorder

IntroductionThough schizophrenia (SZ) and obsessive-compulsive disorder (OCD) are conceptualized as distinct clinical entities, they do have notable symptom overlap and a tight association. Graph-theoretical analysis of the brain connectome provides more indicators to describe the functional organization of the brain, which may help us understand the shared and disorder-specific neural basis of the two disorders.ObjectivesTo explore the static and dynamic topological organization of OCD and SZ as well as the relationship between topological metrics and clinical variables.MethodsResting state functional magnetic resonance imaging data of 31 OCD patients, 49 SZ patients, and 45 healthy controls (HC) were involved in this study (Table 1). Using independent component analysis to obtain independent components (ICs) (Figure 1), which were defined as nodes for static and dynamic topological analysis.ResultsStatic analysis showed the global efficiency of SZ was higher than HC. For nodal degree centrality, OCD exhibited decreased degree centrality in IC59 (located in visiual network) (P = 0.03) and increased degree centrality in IC38 (located in salience network) (P = 0.002) compared with HC. Dynamic analysis showed OCD exhibited decreased dynamics of degree centrality in IC38 (P = 0.003) compared with HC, which showed a negative correlation with clinical scores in OCD. While SZ showed decreased dynamics of degree centrality in IC76 (located in sensory motor network) compared with OCD (P=0.009), which showed a positive correlation with clinical scores in SZ (Figure 2).ConclusionsThese changes are suggestive of disorder-specific alternation of static and dynamic brain topological organization in OCD and SZ.

Simulated dataset collection method of dynamic quality of services (QoS) metrics

The dynamic behavior of web services is critical for researchers to determine their performance at different times. Many existing studies present the static information of web services that are not so effective for estimating web services' future performance. This research article reveals the acquisition of throughput metric dataset for thirty timestamps by a simulation technique. A simulated setup has been proposed to acquire the dynamic throughput data of benchmark web services from workload modeling. The proposed method is aimed to collect dynamic web services metrics data of five web services (WS1–WS5), which are randomly selected from WS-Dream dataset. Additionally, the collected data has been verified by measuring computer resource usage metrics in four experiments. Experiment results indicate that the varying ‘Central Processing Unit’ (CPU) resource usage metrics do not show changes in the collected ‘Quality of Service’ QoS metric values. The experiment results showed that the proposed method achieves the consistent average response time and throughput metric values. The collected simulated data can be further applied to improve the quality of web services.

Technical note: Characterising and comparing different palaeoclimates with dynamical systems theory

Abstract. Numerical climate simulations produce vast amounts of high-resolution data. This poses new challenges to the palaeoclimate community – and indeed to the broader climate community – in how to efficiently process and interpret model output. The palaeoclimate community also faces the additional challenge of having to characterise and compare a much broader range of climates than encountered in other subfields of climate science. Here we propose an analysis framework, grounded in dynamical systems theory, which may contribute to overcoming these challenges. The framework enables the characterisation of the dynamics of a given climate through a small number of metrics. These may be applied to individual climate variables or to several variables at once, and they can diagnose properties such as persistence, active number of degrees of freedom and coupling. Crucially, the metrics provide information on instantaneous states of the chosen variable(s). To illustrate the framework's applicability, we analyse three numerical simulations of mid-Holocene climates over North Africa under different boundary conditions. We find that the three simulations produce climate systems with different dynamical properties, such as persistence of the spatial precipitation patterns and coupling between precipitation and large-scale sea level pressure patterns, which are reflected in the dynamical systems metrics. We conclude that the dynamical systems framework holds significant potential for analysing palaeoclimate simulations. At the same time, an appraisal of the framework's limitations suggests that it should be viewed as a complement to more conventional analyses, rather than as a wholesale substitute.

Altered patterns of brain dynamics linked with body mass index in youth with autism.

Children with autism spectrum disorder (ASD) have higher rates of overweight and obesity (OWOB) compared with typically developing (TD) children. Brain functional connectivity differences have been shown in both ASD and OWOB. However, only one study to date has examined ASD and OWOB concurrently, so little is known regarding the neural mechanisms associated with the higher prevalence of OWOB and its behavioral impacts in ASD. We investigated co-activation patterns (CAPs) of brain regions identified by independent component analysis in 129 children and adolescents between 6 and 18 years of age (n= 68 ASD). We examined the interaction between body mass index (BMI) and diagnosis in predicting dynamic brain metrics (dwell time, DT; frequency of occurrence, and transitions between states) as well as dimensional brain-behavior relationships. The relationship between BMI and brain dynamics was moderated by diagnosis (ASD, TD), particularly among the frequency of CAP 4, characterized by co-activation of lateral frontoparietal, temporal, and frontal networks. This pattern was negatively associated with parent-reported inhibition skills. Children with ASD had shorter CAP 1, characterized by co-activation of the subcortical, temporal, sensorimotor, and frontal networks, and CAP 4 DTs compared with TD children. CAP 1 DT was negatively associated with cognitive flexibility, inhibition, social functioning, and BMI. Cognitive flexibility moderated the relationship between BMI and brain dynamics in the visual network. Our findings provide novel evidence of neural mechanisms associated with OWOB in children with ASD. Further, poorer cognitive flexibility may result in increased vulnerability for children with ASD and co-occurring OWOB. LAY SUMMARY: Obesity is a societal epidemic and is common in autism, however, little is known about the neural mechanisms associated with the higher rates of obesity in autism. Here, we find unique patterns of brain dynamics associated with obesity in autism that were not observed in typically developing children. Further, the relationship between body mass index and brain dynamics depended on cognitive flexibility. These findings suggest that individuals with autism may be more vulnerable to the effects of obesity on brain function. Autism Res 2021, 14: 873-886. © 2021 International Society for Autism Research, Wiley Periodicals LLC.

Dynamic Panel Data Modeling and Surveillance of COVID-19 in Metropolitan Areas in the United States: Longitudinal Trend Analysis.

BackgroundThe COVID-19 pandemic has had profound and differential impacts on metropolitan areas across the United States and around the world. Within the United States, metropolitan areas that were hit earliest with the pandemic and reacted with scientifically based health policy were able to contain the virus by late spring. For other areas that kept businesses open, the first wave in the United States hit in mid-summer. As the weather turns colder, universities resume classes, and people tire of lockdowns, a second wave is ascending in both metropolitan and rural areas. It becomes more obvious that additional SARS-CoV-2 surveillance is needed at the local level to track recent shifts in the pandemic, rates of increase, and persistence.ObjectiveThe goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk and persistence, and weekly shifts, to better understand and manage risk in metropolitan areas. Existing surveillance measures coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until, and after, an effective vaccine is developed. Here, we provide values for novel indicators to measure COVID-19 transmission at the metropolitan area level.MethodsUsing a longitudinal trend analysis study design, we extracted 260 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in the 25 largest US metropolitan areas as a function of the prior number of cases and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.ResultsMinneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population (which we refer to as speed). Extreme behavior in Minneapolis showed an increase in speed from 17 to 30 (67%) in 1 week. The jerk and acceleration calculated for these areas also showed extreme behavior. The dynamic panel data model shows that Minneapolis, Chicago, and Detroit have the largest persistence effects, meaning that new cases pertaining to a specific week are statistically attributable to new cases from the prior week.ConclusionsThree of the metropolitan areas with historically early and harsh winters have the highest persistence effects out of the top 25 most populous metropolitan areas in the United States at the beginning of their cold weather season. With these persistence effects, and with indoor activities becoming more popular as the weather gets colder, stringent COVID-19 regulations will be more important than ever to flatten the second wave of the pandemic. As colder weather grips more of the nation, southern metropolitan areas may also see large spikes in the number of cases.

Surveillance Metrics of SARS-CoV-2 Transmission in Central Asia: Longitudinal Trend Analysis.

BackgroundSARS-CoV-2, the virus that caused the global COVID-19 pandemic, has severely impacted Central Asia; in spring 2020, high numbers of cases and deaths were reported in this region. The second wave of the COVID-19 pandemic is currently breaching the borders of Central Asia. Public health surveillance is necessary to inform policy and guide leaders; however, existing surveillance explains past transmissions while obscuring shifts in the pandemic, increases in infection rates, and the persistence of the transmission of COVID-19.ObjectiveThe goal of this study is to provide enhanced surveillance metrics for SARS-CoV-2 transmission that account for weekly shifts in the pandemic, including speed, acceleration, jerk, and persistence, to better understand the risk of explosive growth in each country and which countries are managing the pandemic successfully.MethodsUsing a longitudinal trend analysis study design, we extracted 60 days of COVID-19–related data from public health registries. We used an empirical difference equation to measure the daily number of cases in the Central Asia region as a function of the prior number of cases, level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.ResultsCOVID-19 transmission rates were tracked for the weeks of September 30 to October 6 and October 7-13, 2020, in Central Asia. The region averaged 11,730 new cases per day for the first week and 14,514 for the second week. Infection rates increased across the region from 4.74 per 100,000 persons to 5.66. Russia and Turkey had the highest 7-day moving averages in the region, with 9836 and 1469, respectively, for the week of October 6 and 12,501 and 1603, respectively, for the week of October 13. Russia has the fourth highest speed in the region and continues to have positive acceleration, driving the negative trend for the entire region as the largest country by population. Armenia is experiencing explosive growth of COVID-19; its infection rate of 13.73 for the week of October 6 quickly jumped to 25.19, the highest in the region, the following week. The region overall is experiencing increases in its 7-day moving average of new cases, infection, rate, and speed, with continued positive acceleration and no sign of a reversal in sight.ConclusionsThe rapidly evolving COVID-19 pandemic requires novel dynamic surveillance metrics in addition to static metrics to effectively analyze the pandemic trajectory and control spread. Policy makers need to know the magnitude of transmission rates, how quickly they are accelerating, and how previous cases are impacting current caseload due to a lag effect. These metrics applied to Central Asia suggest that the region is trending negatively, primarily due to minimal restrictions in Russia.

SARS-CoV-2 Wave Two Surveillance in East Asia and the Pacific: Longitudinal Trend Analysis.

BackgroundThe COVID-19 pandemic has had a profound global impact on governments, health care systems, economies, and populations around the world. Within the East Asia and Pacific region, some countries have mitigated the spread of the novel coronavirus effectively and largely avoided severe negative consequences, while others still struggle with containment. As the second wave reaches East Asia and the Pacific, it becomes more evident that additional SARS-CoV-2 surveillance is needed to track recent shifts, rates of increase, and persistence associated with the pandemic.ObjectiveThe goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk, persistence, and weekly shifts, to better understand country risk for explosive growth and those countries who are managing the pandemic successfully. Existing surveillance coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until an effective vaccine is developed. We provide novel indicators to measure disease transmission.MethodsUsing a longitudinal trend analysis study design, we extracted 330 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in East Asia and the Pacific as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.ResultsThe standard surveillance metrics for Indonesia, the Philippines, and Myanmar were concerning as they had the largest new caseloads at 4301, 2588, and 1387, respectively. When looking at the acceleration of new COVID-19 infections, we found that French Polynesia, Malaysia, and the Philippines had rates at 3.17, 0.22, and 0.06 per 100,000. These three countries also ranked highest in terms of jerk at 15.45, 0.10, and 0.04, respectively.ConclusionsTwo of the most populous countries in East Asia and the Pacific, Indonesia and the Philippines, have alarming surveillance metrics. These two countries rank highest in new infections in the region. The highest rates of speed, acceleration, and positive upwards jerk belong to French Polynesia, Malaysia, and the Philippines, and may result in explosive growth. While all countries in East Asia and the Pacific need to be cautious about reopening their countries since outbreaks are likely to occur in the second wave of COVID-19, the country of greatest concern is the Philippines. Based on standard and enhanced surveillance, the Philippines has not gained control of the COVID-19 epidemic, which is particularly troubling because the country ranks 4th in population in the region. Without extreme and rigid social distancing, quarantines, hygiene, and masking to reverse trends, the Philippines will remain on the global top 5 list of worst COVID-19 outbreaks resulting in high morbidity and mortality. The second wave will only exacerbate existing conditions and increase COVID-19 transmissions.

SARS-CoV-2 Surveillance in the Middle East and North Africa: Longitudinal Trend Analysis.

BackgroundThe COVID-19 pandemic has disrupted the lives of millions and forced countries to devise public health policies to reduce the pace of transmission. In the Middle East and North Africa (MENA), falling oil prices, disparities in wealth and public health infrastructure, and large refugee populations have significantly increased the disease burden of COVID-19. In light of these exacerbating factors, public health surveillance is particularly necessary to help leaders understand and implement effective disease control policies to reduce SARS-CoV-2 persistence and transmission.ObjectiveThe goal of this study is to provide advanced surveillance metrics, in combination with traditional surveillance, for COVID-19 transmission that account for weekly shifts in the pandemic speed, acceleration, jerk, and persistence to better understand a country’s risk for explosive growth and to better inform those who are managing the pandemic. Existing surveillance coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until an effective vaccine is developed.MethodsUsing a longitudinal trend analysis study design, we extracted 30 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in MENA as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.ResultsThe regression Wald statistic was significant (χ25=859.5, P<.001). The Sargan test was not significant, failing to reject the validity of overidentifying restrictions (χ2294=16, P=.99). Countries with the highest cumulative caseload of the novel coronavirus include Iran, Iraq, Saudi Arabia, and Israel with 530,380, 426,634, 342,202, and 303,109 cases, respectively. Many of the smaller countries in MENA have higher infection rates than those countries with the highest caseloads. Oman has 33.3 new infections per 100,000 population while Bahrain has 12.1, Libya has 14, and Lebanon has 14.6 per 100,000 people. In order of largest to smallest number of cumulative deaths since January 2020, Iran, Iraq, Egypt, and Saudi Arabia have 30,375, 10,254, 6120, and 5185, respectively. Israel, Bahrain, Lebanon, and Oman had the highest rates of COVID-19 persistence, which is the number of new infections statistically related to new infections in the prior week. Bahrain had positive speed, acceleration, and jerk, signaling the potential for explosive growth.ConclusionsStatic and dynamic public health surveillance metrics provide a more complete picture of pandemic progression across countries in MENA. Static measures capture data at a given point in time such as infection rates and death rates. By including speed, acceleration, jerk, and 7-day persistence, public health officials may design policies with an eye to the future. Iran, Iraq, Saudi Arabia, and Israel all demonstrated the highest rate of infections, acceleration, jerk, and 7-day persistence, prompting public health leaders to increase prevention efforts.

Dynamic Metric Accelerated Method for Fuzzy Clustering

Features in data samples usually need a unified dimension by a standardization process before clustering. However, there still exists a nonstandardized metric in which the distance between samples is greater than 1 after features are standardized. It is difficult to find the optimal search path if the data sample metrics are not standardized. To address this problem, we develop a dynamic-metric accelerated method for fuzzy clustering by introducing a metric matrix, whose diagonal elements consist of infinite norms of the metric matrix into the Fuzzy C-Means (FCM) clustering algorithm and its derived algorithms. More specifically, we focus on constructing a dynamic metric matrix that is used to unify the metric between data samples and updating cluster centers to optimize the search path of the cluster center. In addition, we propose a new evaluation index named the Coefficient of Variation Metric (CVM) to evaluate metric effectiveness. The dynamic metric accelerated method, whose complexity remains unchanged, can effectively accelerate the iteration speed of fuzzy clustering. The comparisons between the algorithm using the dynamic metric accelerated method and the corresponding algorithm on UCI, business district and COVID-19 CT image datasets show the superiority of the dynamic metric accelerated method in accelerating effect and clustering performance.

BottleNet: Hiding Network Bottlenecks Using SDN-Based Topology Deception

The robustness of a network’s connectivity to other networks is often highly dependent on a few critical nodes and links that tie the network to the larger topology. The failure or degradation to such network bottlenecks can result in outages that may propagate throughout the network. Unfortunately, the presence of the bottlenecks also offers opportunities for targeted link flooding attacks (LFAs) . Researchers have proposed a new and promising defense to counter LFAs, referred to as topology deception . This strategy centers on hindering the discovery of bottlenecks by presenting false trace responses to adversaries as they perform topological probing of the target network. Even though the goal of topology deception centers on obscuring critical links, node dependencies can be exploited by an adversary. However, current approaches do not consider a wide range of metrics that may reveal important and diverse aspects of network bottlenecks. Furthermore, existing approaches create a simple form of virtual topology, which is subject to relatively easy detection by the adversary, reducing its effectiveness. In this paper, we propose a comprehensive topology deception framework, which we refer to as BottleNet. Our suggested approach can analyze various network topology features both with respect to static and dynamic metrics and then use this information to identify bottlenecks, finally producing complex virtual topologies that are resilient to adversarial detection.

Regulation of broking activities based on assessment of its risks

Risk-oriented regulation of brokers is considered in the article. Its implementation helps to improve the quality of regulation and the development of the economy. Risk-based regulation of brokers in theory and practice faces the problem of a lack of indicators and risk assessment system. Therefore, the purpose of the work is to develop proposals for assessing the risks of brokers in regulation. The theoretical substantiation of the study is the approaches of smart and risk-oriented regulation. The method of comparative analysis, methods of description and interpretation, statistical analysis were used. The article identifies risk factors and their indicators based on the analysis of research, foreign and Russian regulatory experience. These indicators are divided into dynamic and constant. The study verifies, using binary logistic regressions, the actual impact of the developed indicators on the work of brokers. Thresholds are defined for dynamic metrics. Exceeding the threshold value indicates an increase in the risks of the broker’s activities. The paper establishes significant indicators and develops a broker risk assessment model. The analysis revealed that some of the current standards do not affect the risks of brokers. Indicators and methods for calculating brokers’ risks can be used by regulators, and the methodology for their development can be applied in other areas.

Fault-Tolerant Application-Specific Topology-Based NoC and Its Prototype on an FPGA

Application-Specific Networks-on-Chips (ASNoCs) are suitable communication platforms for meeting current application requirements. Interconnection links are the primary components involved in communication between the cores of an ASNoC design. The integration density in ASNoC increases with continuous scaling down of the transistor size. Excessive integration density in ASNoC can result in the formation of thermal hotspots, which can cause a system to fail permanently. As a result, fault-tolerant techniques are required to address the permanent faults in interconnection links of an ASNoC design. By taking into account link faults in the topology, this paper introduces a fault-tolerant application-specific topology-based NoC design and its prototype on an FPGA. To place spare links in the ASNoC topology, a meta-heuristic algorithm based on Particle Swarm Optimization (PSO) is proposed. By taking link faults into account in ASNoC design, we also propose an application mapping heuristic and a table-based fault-tolerant routing algorithm. Experiments are carried out for a specific link and any link fault in fault-tolerant topologies generated by our approach and approaches reported in the literature. For the experimentation, we used the multi-media applications Picture-in-Picture (PiP), Moving Pictures Expert Group (MPEG) - 4, MP3Encoder, and Video Object Plane Decoder (VOPD). Experiments are run on software and hardware platforms. The static performance metric communication cost and the dynamic performance metrics network latency, throughput, and router power consumption are examined using software platform. In the hardware platform, the Field Programmable Gate Array (FPGA) is used to validate proposed fault-tolerant topologies and analyze performance metrics such as application runtime, resource utilization, and power consumption. The results are compared with the existing approaches, specifically Ring topology and its modified versions on both software and hardware platforms. The experimental results obtained from software and hardware platforms for a specific link and any link fault show significant improvements in performance metrics using our approach when compared with the related works in the literature.

Spatial heterogeneity in temporal dynamics of Alpine bird communities along an elevational gradient

AbstractAimMountains are biodiversity hotspots and are among the most sensitive ecosystems to ongoing global change being thus of conservation concern. Under this scenario, assessing how biological communities vary over time along elevational gradients and the relative effects of niche‐based deterministic processes and stochastic events in structuring assemblages is essential. Here, we examined how the temporal trends of bird communities vary with elevation over a 20 year‐period (1999–2018). We also tested for differences in temporal dynamics among habitat types (among‐community variability) and functional groups (within‐community variability).Taxon97 species of common breeding birds.LocationSwiss Alps.MethodsWe used abundance data from the Swiss breeding bird survey to compute different temporal dynamic metrics (temporal turnover, synchrony, rate of community change and community‐level of covariance among species). We also examined the relative contribution of deterministic and stochastic processes in community assembly using the Raup‐Crick method and the normalized stochasticity ratio.ResultsWe found that, with greater elevation, temporal species turnover increased while the rate of overall community change over successive years decreased, suggesting that high‐elevation communities display more erratic dynamics with no clear trend. Despite this, we found a more deterministic assembly of alpine communities in comparison to those located at lower elevations. Deterministic processes had greater influence than stochastic processes on community assembly along the entire elevational gradient (80% of communities). Forest communities exhibited higher synchrony in comparison to the remaining habitats likely because they consisted of species with greater functional redundancy, whereas alpine communities were the least stable as a result of their low taxonomic richness (‘portfolio’ effect).Main conclusionsCommunity‐level synchrony was overall positive supporting the idea that compensatory mechanisms are rare in natural biological communities. Our results suggest that rather than competition, the existence of differences in the ecological strategies of species may have a stabilizing effect on bird communities by weakening the concordance of species responses to fluctuations in environmental conditions (i.e. enhanced interspecific temporal asynchrony). This study provides evidence that, although species turnover in metacommunities is frequent, a high temporal turnover does not necessarily imply the overriding importance of stochastic processes.

Early Neurological Change After Ischemic Stroke Is Associated With 90-Day Outcome.

Large-scale observational studies of acute ischemic stroke (AIS) promise to reveal mechanisms underlying cerebral ischemia. However, meaningful quantitative phenotypes attainable in large patient populations are needed. We characterize a dynamic metric of AIS instability, defined by change in National Institutes of Health Stroke Scale score (NIHSS) from baseline to 24 hours baseline to 24 hours (NIHSSbaseline - NIHSS24hours = ΔNIHSS6-24h), to examine its relevance to AIS mechanisms and long-term outcomes. Patients with NIHSS prospectively recorded within 6 hours after onset and then 24 hours later were enrolled in the GENISIS study (Genetics of Early Neurological Instability After Ischemic Stroke). Stepwise linear regression determined variables that independently influenced ΔNIHSS6-24h. In a subcohort of tPA (alteplase)-treated patients with large vessel occlusion, the influence of early sustained recanalization and hemorrhagic transformation on ΔNIHSS6-24h was examined. Finally, the association of ΔNIHSS6-24h with 90-day favorable outcomes (modified Rankin Scale score 0-2) was assessed. Independent analysis was performed using data from the 2 NINDS-tPA stroke trials (National Institute of Neurological Disorders and Stroke rt-PA). For 2555 patients with AIS, median baseline NIHSS was 9 (interquartile range, 4-16), and median ΔNIHSS6-24h was 2 (interquartile range, 0-5). In a multivariable model, baseline NIHSS, tPA-treatment, age, glucose, site, and systolic blood pressure independently predicted ΔNIHSS6-24h (R2=0.15). In the large vessel occlusion subcohort, early sustained recanalization and hemorrhagic transformation increased the explained variance (R2=0.27), but much of the variance remained unexplained. ΔNIHSS6-24h had a significant and independent association with 90-day favorable outcome. For the subjects in the 2 NINDS-tPA trials, ΔNIHSS3-24h was similarly associated with 90-day outcomes. The dynamic phenotype, ΔNIHSS6-24h, captures both explained and unexplained mechanisms involved in AIS and is significantly and independently associated with long-term outcomes. Thus, ΔNIHSS6-24h promises to be an easily obtainable and meaningful quantitative phenotype for large-scale genomic studies of AIS.

Dynamic Public Health Surveillance to Track and Mitigate the US COVID-19 Epidemic: Longitudinal Trend Analysis Study.

BackgroundThe emergence of SARS-CoV-2, the virus that causes COVID-19, has led to a global pandemic. The United States has been severely affected, accounting for the most COVID-19 cases and deaths worldwide. Without a coordinated national public health plan informed by surveillance with actionable metrics, the United States has been ineffective at preventing and mitigating the escalating COVID-19 pandemic. Existing surveillance has incomplete ascertainment and is limited by the use of standard surveillance metrics. Although many COVID-19 data sources track infection rates, informing prevention requires capturing the relevant dynamics of the pandemic.ObjectiveThe aim of this study is to develop dynamic metrics for public health surveillance that can inform worldwide COVID-19 prevention efforts. Advanced surveillance techniques are essential to inform public health decision making and to identify where and when corrective action is required to prevent outbreaks.MethodsUsing a longitudinal trend analysis study design, we extracted COVID-19 data from global public health registries. We used an empirical difference equation to measure daily case numbers for our use case in 50 US states and the District of Colombia as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.ResultsExamination of the United States and state data demonstrated that most US states are experiencing outbreaks as measured by these new metrics of speed, acceleration, jerk, and persistence. Larger US states have high COVID-19 caseloads as a function of population size, density, and deficits in adherence to public health guidelines early in the epidemic, and other states have alarming rates of speed, acceleration, jerk, and 7-day persistence in novel infections. North and South Dakota have had the highest rates of COVID-19 transmission combined with positive acceleration, jerk, and 7-day persistence. Wisconsin and Illinois also have alarming indicators and already lead the nation in daily new COVID-19 infections. As the United States enters its third wave of COVID-19, all 50 states and the District of Colombia have positive rates of speed between 7.58 (Hawaii) and 175.01 (North Dakota), and persistence, ranging from 4.44 (Vermont) to 195.35 (North Dakota) new infections per 100,000 people.ConclusionsStandard surveillance techniques such as daily and cumulative infections and deaths are helpful but only provide a static view of what has already occurred in the pandemic and are less helpful in prevention. Public health policy that is informed by dynamic surveillance can shift the country from reacting to COVID-19 transmissions to being proactive and taking corrective action when indicators of speed, acceleration, jerk, and persistence remain positive week over week. Implicit within our dynamic surveillance is an early warning system that indicates when there is problematic growth in COVID-19 transmissions as well as signals when growth will become explosive without action. A public health approach that focuses on prevention can prevent major outbreaks in addition to endorsing effective public health policies. Moreover, subnational analyses on the dynamics of the pandemic allow us to zero in on where transmissions are increasing, meaning corrective action can be applied with precision in problematic areas. Dynamic public health surveillance can inform specific geographies where quarantines are necessary while preserving the economy in other US areas.

Roller blinds characterization assessing discomfort glare, view outside and useful daylight illuminance with the sun in the field of view

This research presents an experiment where the relationship between view outside (VO), daylight availability (DA) and glare protection (GP) of nine shading fabrics were evaluated. This kind of shading device is placed on the market (Argentina) without a proper characterization of their lighting performance, leading to an inadequate application. Glare was evaluated through DGP model, by means of HDRI technique, calibrated through externally measured luminance and validating with vertical illuminance. Vision outside was evaluated through the View Clarity Index (VCI) and daylight availability was assessed through Useful Daylight llluminance (UDI) dynamic daylight metric. Finally, two roller blind selection procedure were proposed. The first one considering each parameter independently (GP, VO and DA) and the second criteria, weighing the scores through a ternary plot.