搜索结果：curfew

Pandemics involve the high transmission of a disease that impacts global and local health and economic patterns. The impact of a pandemic can be minimized by enforcing certain restrictions on a community. However, while minimizing infection and death rates, these restrictions can also lead to economic crises. Epidemiological models help propose pandemic control strategies based on non-pharmaceutical interventions such as social distancing, curfews, and lockdowns, reducing the economic impact of these restrictions. However, designing manual control strategies while considering disease spread and economic status is non-trivial. Optimal strategies can be designed through multi-objective reinforcement learning (MORL) models, which demonstrate how restrictions can be used to optimize the outcome of a pandemic. In this research, we utilized an epidemiological Susceptible, Exposed, Infected, Recovered, Deceased (SEIRD) model: a compartmental model for virtually simulating a pandemic day by day. We combined the SEIRD model with a deep double recurrent Q-network to train a reinforcement learning agent to enforce the optimal restriction on the SEIRD simulation based on a reward function. We

Region-wide restrictions on personal vehicle travel have a long history in the United States, from riot curfews in the late 1960s, to travel bans during snow events, to the 2013 shelter-in-place "lockdown" during the search for the perpetrator of the Boston Marathon bombing. Because lockdowns require tremendous resources to enforce, they are often limited in duration or scope. The introduction of automated driving systems may allow governments to quickly and cheaply effect large-area lockdowns by jamming wireless communications, spoofing road closures on digital maps, exploiting a vehicle's programming to obey all traffic control devices, or coordinating with vehicle developers. Future vehicles may lack conventional controls, rendering them undrivable by the public. As travel restrictions become easier to implement, governments may enforce them more frequently, over longer durations and wider areas. This article explores the practical, legal, and ethical implications of lockdowns when most driving is highly automated, and provides guidance for the development of lockdown policies.

The latest pandemic COVID-19 brought governments worldwide to use various containment measures to control its spread, such as contact tracing, social distance regulations, and curfews. Epidemiological simulations are commonly used to assess the impact of those policies before they are implemented. Unfortunately, the scarcity of relevant empirical data, specifically detailed social contact graphs, hampered their predictive accuracy. As this data is inherently privacy-critical, a method is urgently needed to perform powerful epidemiological simulations on real-world contact graphs without disclosing any sensitive~information. In this work, we present RIPPLE, a privacy-preserving epidemiological modeling framework enabling standard models for infectious disease on a population's real contact graph while keeping all contact information locally on the participants' devices. As a building block of independent interest, we present PIR-SUM, a novel extension to private information retrieval for secure download of element sums from a database. Our protocols are supported by a proof-of-concept implementation, demonstrating a 2-week simulation over half a million participants completed in 7 m

We assess the impact of COVID-19 response measures implemented in Germany and Switzerland on cumulative COVID-19-related hospitalization and death rates. Our analysis exploits the fact that the epidemic was more advanced in some regions than in others when certain lockdown measures came into force, based on measuring health outcomes relative to the region-specific start of the epidemic and comparing outcomes across regions with earlier and later start dates. When estimating the effect of the relative timing of measures, we control for regional characteristics and initial epidemic trends by linear regression (Germany and Switzerland), doubly robust estimation (Germany), or synthetic controls (Switzerland). We find for both countries that a relatively later exposure to the measures entails higher cumulative hospitalization and death rates on region-specific days after the outbreak of the epidemic, suggesting that an earlier imposition of measures is more effective than a later one. For Germany, we also evaluate curfews (as introduced in a subset of states) based on cross-regional variation. We do not find any effects of curfews on top of the federally imposed contact restriction that

Classic asset management approaches begin by inventorying all infrastructure assets and then assigning maintenance tasks and resources. Our approach collects similar data, but by starting with current personnel assignment and describing their job responsibilities and work processes, staff resistance in a railroad infrastructure owner-operator environment is minimized. Resulting "manning model" quantitatively measures signal maintenance burden including Federally mandated tests, trouble tickets, non-FRA maintenance, overhead and vacation coverage, location/shift assignment, administrative process, and work curfew productivity losses. It is capable of delivering immediate results by rightsizing allocation of workforce across shifts and maintenance base locations--even before all assets are formally inventoried. Typical data from a commuter passenger railroad shows that work curfews and shift assignment constraints have significant impacts on workforce productivity. Just over half of signal maintenance employee-hours are devoted to Federally mandated tests, whilst non-FRA and repair maintenance consumes about 25% each. These indicators provide intelligence driving strategic management

A recent work (DOI 10.1101/2020.05.06.20093310) indicated that temporarily splitting larger populations into smaller groups can efficiently mitigate the spread of SARS-CoV-2 virus. The fact that, soon afterwards, on May 15, 2020, the two million people Slovenia was the first European country proclaiming the end of COVID-19 epidemic within national borders may be relevant from this perspective. Motivated by this evolution, in this paper we investigate the time dynamics of coronavirus cases in Slovenia with emphasis on how efficient various containment measures act to diminish the number of COVID-19 infections. Noteworthily, the present analysis does not rely on any speculative theoretical assumption; it is solely based on raw epidemiological data. Out of the results presented here, the most important one is perhaps the finding that, while imposing drastic curfews and travel restrictions reduce the infection rate kappa by a factor of four with respect to the unrestricted state, they only improve the \k{appa}-value by ~15 % as compared to the much bearable state of social and economical life wherein (justifiable) wearing face masks and social distancing rules are enforced/followed. Si

Our ordinary life changed quite a bit in March of 2020 due to the global Covid-19 pandemic. While spring time in general well awaited and regarded as a synonym for rejuvenation the spring of 2020 brought lock-down, curfew, home office and digital education to the lives of many. The particle physics community was not an exception: research institutes and universities introduced home office and digital lecturing and all workshops, conferences and summer schools were canceled, got postponed or took place online. Using publicly available data from the INSPIRE and arXiv databases we investigate the effects of this dramatic change of life to the publishing trends of the high-energy physics community with an emphasis on particle phenomenology and theory. To get insights we gather information about publishing trends in the last 20 years, and analyse it in detail.

The last three years have been an extraordinary time with the Covid-19 pandemic killing millions, affecting and distressing billions of people worldwide. Authorities took various measures such as turning school and work to remote and prohibiting social relations via curfews. In order to mitigate the negative impact of the epidemics, researchers tried to estimate the future of the pandemic for different scenarios, using forecasting techniques and epidemics simulations on networks. Intending to better represent the real-life in an urban town in high resolution, we propose a novel multi-layer network model, where each layer corresponds to a different interaction that occurs daily, such as "household", "work" or "school". Our simulations indicate that locking down "friendship" layer has the highest impact on slowing down epidemics. Hence, our contributions are twofold, first we propose a parametric network generator model; second, we run SIR simulations on it and show the impact of layers.

A new study suggests the brain begins making decisions much earlier than scientists previously thought。 Researchers found that even primary sensory regions are influenced by higher brain areas through rapid feedback loops, rather than simply passing information forward。 This more dynamic view of brain function could help engineers design future AI

With the settlement withdrawn, Google is now bound by the court's full antitrust remedies

NASA's Perseverance rover has reached an impressive new milestone on Mars, completing the equivalent of a full marathon by driving 26。2 miles (42。195 kilometers) across the Red Planet

Scientists at Nanyang Technological University in Singapore have discovered a surprisingly simple way to create exotic light structures called optical skyrmions using a 200-year-old optical effect known as the Poisson spot。 Instead of relying on expensive, highly engineered materials, they simply shine a laser at a tiny circular disc, producing sta

FCC chairman claims power to repeal TV ownership limit set by Congress

Epidemic is a rapid and wide spread of infectious disease threatening many lives and economy damages. It is important to fore-tell the epidemic lifetime so to decide on timely and remedic actions. These measures include closing borders, schools, suspending community services and commuters. Resuming such curfews depends on the momentum of the outbreak and its rate of decay. Being able to accurately forecast the fate of an epidemic is an extremely important but difficult task. Due to limited knowledge of the novel disease, the high uncertainty involved and the complex societal-political factors that influence the widespread of the new virus, any forecast is anything but reliable. Another factor is the insufficient amount of available data. Data samples are often scarce when an epidemic just started. With only few training samples on hand, finding a forecasting model which offers forecast at the best efforts is a big challenge in machine learning. In the past, three popular methods have been proposed, they include 1) augmenting the existing little data, 2) using a panel selection to pick the best forecasting model from several models, and 3) fine-tuning the parameters of an individual

Researchers discovered that electricity can dramatically reshape how heat flows through certain ceramic materials, increasing heat conduction by almost threefold in a preferred direction。 The unexpected result could lead to much more efficient cooling technologies and energy-saving devices

Smartphone-based electronic contact tracing is currently considered an essential tool towards easing lockdowns, curfews, and shelter-in-place orders issued by most governments around the world in response to the 2020 novel coronavirus (SARS-CoV-2) crisis. While the focus on developing smartphone-based contact tracing applications or apps has been on privacy concerns stemming from the use of such apps, an important question that has not received sufficient attention is: How reliable will such smartphone-based electronic contact tracing be? This is a technical question related to how two smartphones reliably register their mutual proximity. Here, we examine in detail the technical prerequisites required for effective smartphone-based contact tracing. The underlying mechanism that any contact tracing app relies on is called Neighbor Discovery (ND), which involves smartphones transmitting and scanning for Bluetooth signals to record their mutual presence whenever they are in close proximity. The hardware support and the software protocols used for ND in smartphones, however, were not designed for reliable contact tracing. In this paper, we quantitatively evaluate how reliably can smart