89,510 research outputs found
The artificial university : decision support for universities in the COVID-19 era
Operating universities under pandemic conditions is a complex undertaking. )e Artificial University (TAU) responds to this need. TAU is a configurable, open-source computer simulation of a university using a contact network based on publicly available information about university classes, residences, and activities. )is study evaluates health outcomes for an array of interventions and testing protocols in an artificial university of 6,500 students, faculty, and staff. Findings suggest that physical distancing and centralized contact tracing are most effective at reducing infections, but there is a tipping point for compliance below which physical distancing is less effective. If student compliance is anything short of high, it helps to have separate buildings for quarantining infected students, thereby gracefully increasing compliance. Hybrid in-person and online classes and closing fitness centers do not significantly change cumulative infections but do significantly decrease the number of the infected at any given time, indicating strategies for “flattening the curve” to protect limited resources. Supplementing physical distancing with centralized contact tracing decreases infected individuals by an additional 14%; boosting frequency of testing for student-facing staff yields a further 7% decrease. A trade-off exists between increasing the sheer number of infection tests and targeting testing for key nodes in the contact network (i.e., student-facing staff). )ere are significant advantages to getting and acting on test results quickly. )e costs and benefits to universities of these findings are discussed. Artificial universities can be an important decision support tool for universities, generating useful policy insights into the challenges of operating universities under pandemic conditions
Volume-Enclosing Surface Extraction
In this paper we present a new method, which allows for the construction of
triangular isosurfaces from three-dimensional data sets, such as 3D image data
and/or numerical simulation data that are based on regularly shaped, cubic
lattices. This novel volume-enclosing surface extraction technique, which has
been named VESTA, can produce up to six different results due to the nature of
the discretized 3D space under consideration. VESTA is neither template-based
nor it is necessarily required to operate on 2x2x2 voxel cell neighborhoods
only. The surface tiles are determined with a very fast and robust construction
technique while potential ambiguities are detected and resolved. Here, we
provide an in-depth comparison between VESTA and various versions of the
well-known and very popular Marching Cubes algorithm for the very first time.
In an application section, we demonstrate the extraction of VESTA isosurfaces
for various data sets ranging from computer tomographic scan data to simulation
data of relativistic hydrodynamic fireball expansions.Comment: 24 pages, 33 figures, 4 tables, final versio
Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems
The first-ever Ukraine cyberattack on power grid has proven its devastation
by hacking into their critical cyber assets. With administrative privileges
accessing substation networks/local control centers, one intelligent way of
coordinated cyberattacks is to execute a series of disruptive switching
executions on multiple substations using compromised supervisory control and
data acquisition (SCADA) systems. These actions can cause significant impacts
to an interconnected power grid. Unlike the previous power blackouts, such
high-impact initiating events can aggravate operating conditions, initiating
instability that may lead to system-wide cascading failure. A systemic
evaluation of "nightmare" scenarios is highly desirable for asset owners to
manage and prioritize the maintenance and investment in protecting their
cyberinfrastructure. This survey paper is a conceptual expansion of real-time
monitoring, anomaly detection, impact analyses, and mitigation (RAIM) framework
that emphasizes on the resulting impacts, both on steady-state and dynamic
aspects of power system stability. Hypothetically, we associate the
combinatorial analyses of steady state on substations/components outages and
dynamics of the sequential switching orders as part of the permutation. The
expanded framework includes (1) critical/noncritical combination verification,
(2) cascade confirmation, and (3) combination re-evaluation. This paper ends
with a discussion of the open issues for metrics and future design pertaining
the impact quantification of cyber-related contingencies
The Potential Trajectory of Carbapenem-Resistant Enterobacteriaceae, an Emerging Threat to Health-Care Facilities, and the Impact of the Centers for Disease Control and Prevention Toolkit.
Carbapenem-resistant Enterobacteriaceae (CRE), a group of pathogens resistant to most antibiotics and associated with high mortality, are a rising emerging public health threat. Current approaches to infection control and prevention have not been adequate to prevent spread. An important but unproven approach is to have hospitals in a region coordinate surveillance and infection control measures. Using our Regional Healthcare Ecosystem Analyst (RHEA) simulation model and detailed Orange County, California, patient-level data on adult inpatient hospital and nursing home admissions (2011-2012), we simulated the spread of CRE throughout Orange County health-care facilities under 3 scenarios: no specific control measures, facility-level infection control efforts (uncoordinated control measures), and a coordinated regional effort. Aggressive uncoordinated and coordinated approaches were highly similar, averting 2,976 and 2,789 CRE transmission events, respectively (72.2% and 77.0% of transmission events), by year 5. With moderate control measures, coordinated regional control resulted in 21.3% more averted cases (n = 408) than did uncoordinated control at year 5. Our model suggests that without increased infection control approaches, CRE would become endemic in nearly all Orange County health-care facilities within 10 years. While implementing the interventions in the Centers for Disease Control and Prevention's CRE toolkit would not completely stop the spread of CRE, it would cut its spread substantially, by half
A multi-faceted approach to optimising a complex unplanned healthcare system
Unscheduled and urgent health care represents the largest area of activity and cost for the UK’s National Health Service (NHS). Like typical complex systems unplanned care has the features of interdependence and having structures at different scales which requires modelling at different levels. The aim of this paper is to discuss the development of a multifaceted approach to study and optimise this complex system. We aim to integrate four different methodologies to gain better understanding of the nature of the system and to develop ways to enhance its performance. These methodologies are: (a) Lean/ Flow theory to look at the process and patients and other flows; (b) Simulation/ System Dynamics to undertake analytical analysis and multi-level modelling; (c) stakeholder consultation and use of system thinking to analyse the system and identify options, barriers and good practice; and (d) visual analytic modelling to facilitate effective decision making in this complex environment. Of particular concern are the boundary issues i.e. how changes in unplanned care will impact on the adjacent facilities and ultimately on the whole Healthcare system
Soft computing and its use in risk management
New analytical methods have begun to be used in risk management. The methods such as fuzzy
logic, neural network and genetic algorithms rank among them. The article shortly describes these
methods and represents their possible applications in the risk management. These methods can contribute
to decreasing of the risk and thus the human and material losses
Potential function of simplified protein models for discriminating native proteins from decoys: Combining contact interaction and local sequence-dependent geometry
An effective potential function is critical for protein structure prediction
and folding simulation. For simplified models of proteins where coordinates of
only atoms need to be specified, an accurate potential function is
important. Such a simplified model is essential for efficient search of
conformational space. In this work, we present a formulation of potential
function for simplified representations of protein structures. It is based on
the combination of descriptors derived from residue-residue contact and
sequence-dependent local geometry. The optimal weight coefficients for contact
and local geometry is obtained through optimization by maximizing margins among
native and decoy structures. The latter are generated by chain growth and by
gapless threading. The performance of the potential function in blind test of
discriminating native protein structures from decoys is evaluated using several
benchmark decoy sets. This potential function have comparable or better
performance than several residue-based potential functions that require in
addition coordinates of side chain centers or coordinates of all side chain
atoms.Comment: 4 pages, 2 figures, Accepted by 26th IEEE-EMBS Conference, San
Francisc
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