Behavior of early warnings near the critical temperature in the two-dimensional Ising model

Among the properties that are common to complex systems, the presence of critical thresholds in the dynamics of the system is one of the most important. Recently, there has been interest in the universalities that occur in the behavior of systems near critical points. These universal properties make it possible to estimate how far a system is from a critical threshold. Several early-warning signals have been reported in time series representing systems near catastrophic shifts. The proper understanding of these early-warnings may allow the prediction and perhaps control of these dramatic shifts in a wide variety of systems. In this paper we analyze this universal behavior for a system that is a paradigm of phase transitions, the Ising model. We study the behavior of the early-warning signals and the way the temporal correlations of the system increase when the system is near the critical point.Comment: 20 pages, 8 figures, Submitted to PLOS ONE on Oct. 20th 2014. PONE-D-14-4718

Barnes Hospital Bulletin

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Evaluating A Rapid Response Team Performance To Implement Best Practice in Rapid Response Team Protocol

Background Rapid Response Teams (RRT) provide clinical resources to improve patient safety outcomes at healthcare institutions. This team promptly responds to deteriorating patient conditions to prevent further deterioration and reduce mortality rates. Rapid response teams do not always perform optimally. Reasons for this performance failure include breakdowns in communication, team dynamics, or other variables that can often be adjusted when the team members understand the role these variables play in undermining the performance of the RRT. An understanding of the perceptions of the RRT members regarding their roles and potential areas of improvement did provide valuable data that was utilized to improve the efficiency and effectiveness of the RRT. Purpose The purpose of this project was to create an evidence-based protocol for best practices in RRT responses by evaluating the perceptions of the rapid response team members regarding RRT performance at a medical center in Michigan. Current RRT practices were compared to the evidence-based standards of care that influenced recommendations for improvement based on the gaps identified. -- Method. This project utilized a qualitative approach with the use of semi-structured guided interviews held via Zoom to gather data related to the experiences of RRT members, to gain an in-depth understanding on the issues concerning the performances of the RRT. Seventeen participants who met the research criteria were selected. Participants who consented to be interviewed were scheduled in chronological order in which they gave consent. Participants were recruited via hospital unit huddles and one on one encounters, based on the project inclusion criteria, and were then scheduled for individual interviews that were audio-recorded, transcribed, and analyzed for thematic contents. This project was guided by Kurt Lewin\u27s Change Theory, which is a change model geared at preparing team members to become change agents. Applying this model will ensure that team members will be equipped to implement the quality improvement changes in the rapid response system. Associates will be provided with the necessary strategies to unlearn the ineffective old ways of clinical practices and embrace the new evidenced based practice guidelines. Results Data analysis revealed major themes that have been affecting the performances of the RRT. They were ineffective team dynamics, activation barriers, inadequate competency training/skills validation, staffing challenges, and failure to debrief after RRT encounters. Other issues emerged during this study that were important issues affecting the performances of the RRT. They were delayed response time of RT, attitudes of providers, and unavailability of attending physicians. Conclusion Ineffective team dynamics, poorly defined roles, crowd control issues, and inadequate education and training were the most critical factors interfering with the efficiency of the RRT. A change in policy that has the potential to optimize the performance of the RRT was developed in accordance with the best practice guidelines. The rapid response team is an important player in early recognition of declining patient conditions outside of intensive care areas. There is documented evidence of what excellent rapid response teams need to maintain their efficient performance. Teams may not always function at the optimum levels they desire. The qualitative interview results derived from experienced rapid response team members was compared with evidence based standards of practice. Improvements and recommendations were developed and shared with the management team at the project site

Evacuation in the Social Force Model is not stationary

An evacuation process is simulated within the Social Force Model. Thousand pedestrians are leaving a room by one exit. We investigate the stationarity of the distribution of time lags between instants when two successive pedestrians cross the exit. The exponential tail of the distribution is shown to gradually vanish. Taking fluctuations apart, the time lags decrease in time till there are only about 50 pedestrians in the room, then they start to increase. This suggests that at the last stage the flow is laminar. In the first stage, clogging events slow the evacuation down. As they are more likely for larger crowds, the flow is not stationary. The data are investigated with detrended fluctuation analysis.Comment: 7 pages, 3 figures; PACS numbers: 89.75.Fb, 05.40.-a, 05.45.Tp, 89.40.B

UNDERSTANDING CROWD DYNAMICS AND PSYCHOLOGY FOR BETTER EMERGENCY RESPONSE

Large events around the world—including sporting matches, music festivals, religious events, and other outdoor gatherings—continue to result in crowd crush injuries and deaths. Examined closely, crowd crush incidents tend to have the same causal factors that could have been addressed and avoided. When they are not prevented, the cases are often not recognized as a crowd crush, so an effective response is delayed. In crowd crush fatalities, compressive asphyxia is the most common cause of death. The treatment of patients depends on the timely response of emergency medical services to resuscitate patients. Through case studies of the 1989 Hillsborough soccer match, the 2021 Astroworld music festival, and the 2022 Itaewon, South Korea, crowd crush, this thesis reveals deficiencies in responding to these tragedies. Event organizers and first responders share responsibility in handling these events and must coordinate their efforts to prevent injuries caused by dynamic crowds. This thesis recommends that fire departments participate directly in the planning process and management of the event as one of the primary stakeholders. Furthermore, training in crowd dynamics and safety should be a part of the first responder's knowledge base and education.Copyright is reserved by the copyright owner.Civilian, Los Angeles County Fire Departmen

Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting

Objective: Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. Although recent work has convincingly demonstrated that seizure risk assessment is in principle possible, these early approaches relied largely on complex, often invasive setups including intracranial electrocorticography, implanted devices, and multichannel electroencephalography, and required patient-specific adaptation or learning to perform optimally, all of which limit translation to broad clinical application. To facilitate broader adaptation of seizure forecasting in clinical practice, noninvasive, easily applicable techniques that reliably assess seizure risk without much prior tuning are crucial. Wristbands that continuously record physiological parameters, including electrodermal activity, body temperature, blood volume pulse, and actigraphy, may afford monitoring of autonomous nervous system function and movement relevant for such a task, hence minimizing potential complications associated with invasive monitoring and avoiding stigma associated with bulky external monitoring devices on the head. Methods: Here, we applied deep learning on multimodal wristband sensor data from 69 patients with epilepsy (total duration > 2311 hours, 452 seizures) to assess its capability to forecast seizures in a statistically significant way. Results: Using a leave-one-subject-out cross-validation approach, we identified better-than-chance predictability in 43% of the patients. Time-matched seizure surrogate data analyses indicated forecasting not to be driven simply by time of day or vigilance state. Prediction performance peaked when all sensor modalities were used, and did not differ between generalized and focal seizure types, but generally increased with the size of the training dataset, indicating potential further improvement with larger datasets in the future. Significance: Collectively, these results show that statistically significant seizure risk assessments are feasible from easy-to-use, noninvasive wearable devices without the need of patient-specific training or parameter optimization

Beacon Light: Spring 2001

Profile on the St. Cloud Hospital\u27s Sleep Program Story on the Octopus2 off-pump bypass machine List of warning signs for heart disease Various news and announcements concerning the Hospital Article on the 115th anniversary of the St. Cloud Hospital Feature on the St. Cloud Hospital\u27s Internship Program Story on William Boucher (volunteer) head of the Stearns County Sheriff\u27s Mounted Reserv

CheckUp

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