Miracle-dz/UniFAR-FTUs huggingface dataset:
A review of the entire warning system , from prediction and detection to public response , reveals such fundamental needs as identifying acceptable risks , improving personal preparation , and personalizing warnings

In final form 16 April 2013 ©2013 American Meteorological Society O ne of the scientif ic community’s greatest achievements in meteorology during the twentieth century has been the development of a largely effective public tornado warning system. Between 1912 and 1936, tornadoes killed an average 260 persons per year, about 1.8 deaths per million people when normalized by population (Brooks and Doswell 2001). Between 1975 and 2000, that number had declined to 54 deaths per year, or 0.12 deaths per million people in 2000 (Brooks and Doswell 2001), a reduction of 93% from 1925. In 1986 the tornado warning lead time was approximately five minutes, with only 25% of tornadoes warned; by 2004, the mean lead time was 13 min, with about 75% of tornadoes warned (Erickson and Brooks 2006). Far from simple, the tornado warning process is a complex chain of events, encompassing institutional action and individual responses, that utilizes sensing technologies, conceptual models, numerical weather prediction (NWP), forecaster and emergency management (EM) decision making, warning dissemination technologies, and public experience and education (Fig. 1). The sequential steps of this process—forecast, detection, warning decision, dissemination, and public response—are known as the Integrated Warning System (IWS; Leik et al. 1981; Doswell et al. 1999). This article reviews the end-to-end tornado warning process and related research, considers the challenges to improving the current system, and explores possible next steps. While this article cannot provide a completely comprehensive review of all research in each specific area, the goal is to provide a broad overview of the tornado warning process and a brief summary of the many avenues of research that could contribute to improvements in the current system.
The woodchuck model of hepatitis B virus infection.

The woodchuck hepatitis virus (WHV) was the first of the mammalian and avian hepadnaviruses described after discovery of the virus of hepatitis B (HBV). Woodchucks chronically infected with WHV develop progressively severe hepatitis and hepatocellular carcinoma, which present as lesions that are remarkably similar to those associated with HBV infection in humans. The initial virological studies and studies of pathogenesis utilized woodchucks that had been trapped in the wild and had acquired WHV infection naturally. Research with wild woodchucks was complicated by lack of knowledge of their backgrounds (e.g., dietary history, exposure to parasites or environmental toxins, and source and duration of WHV infection). Breeding colonies of woodchucks have been established and maintained in laboratory animal facilities, and laboratory-reared woodchucks are superior for experimental studies of pathogenesis or hepatocarcinogenesis. It is possible to infect neonatal woodchucks born in the laboratory with standardized inocula and produce a high rate of chronic WHV carriers that are useful for controlled investigations. WHV has been shown experimentally to cause hepatocellular carcinoma, supporting conclusions based on epidemiological and molecular virological studies that HBV is an important etiological factor in human hepatocarcinogenesis. Chronic WHV carrier woodchucks have become a valuable animal model for the preclinical evaluation of antiviral therapy for HBV infection, providing useful pharmacokinetic and pharmacodynamic results in a relevant animal disease model. It also has been shown that the pattern of toxicity and hepatic injury observed in woodchucks treated with certain fluorinated pyrimidines is remarkably similar to that observed in humans that were treated with the same drugs, suggesting the woodchuck has significant potential for the preclincial assessment of antiviral drug toxicity.
Trust network-based filtering of aggregated claims

On the semantic web, assertions may be aggregated from many sources, those aggregations filtered, reasoned over, aggregated with other aggregators, displayed, scraped, extracted, recombined, and otherwise processed without significant human oversight. To preserve the connection between assertions and their source, various provenance schemes for semantic web data have been explored. However, the primary focus has been on authenticating the author of a particular statement, e.g., using digital signatures, but there is no provision for relating the authenticity of the source of the assertion and the trustworthiness of the assertion itself. This paper presents a method for using semantic web based trust networks to infer the reputation of sources for a statement and compose the reputation of several sources. By calculating a trust rating for each statement based on the ratings of its sources, the set of statements can be filtered based on the rating.
Expert PID control system for blood glucose control in critically ill patients

In this paper, the design of an expert proportional-integral-derivative (PID) control system for blood glucose regulation in patients, such as the intensive care population, is described. The control system applied the concept of expert system, and proportional, integral and derivative control in clinical sliding table technique, to effect a control algorithm in the form of an "active" sliding table that is used to prescribe insulin infusion rates. This combination provided dynamic properties to the sliding table control. Clinical results have shown a comparable performance between the control system and routine clinical treatment, in terms of blood glucose level maintained. Nevertheless, the control system is sensitive to sensor reading artefact, particularly in the lower ranges of blood glucose level, mandating manual intervention.
Evaluation of a Pilot Project to Introduce Simulation-Based Team Training to Pediatric Surgery Trauma Room Care

Introduction. Several studies in pediatric trauma care have demonstrated substantial deficits in both prehospital and emergency department management. Methods. In February 2015 the PAEDSIM collaborative conducted a one and a half day interdisciplinary, simulation based team-training course in a simulated pediatric emergency department. 14 physicians from the medical fields of pediatric surgery, pediatric intensive care and emergency medicine, and anesthesia participated, as well as four pediatric nurses. After a theoretical introduction and familiarization with the simulator, course attendees alternately participated in six simulation scenarios and debriefings. Each scenario incorporated elements of pediatric trauma management as well as Crew Resource Management (CRM) educational objectives. Participants completed anonymous pre- and postcourse questionnaires and rated the course itself as well as their own medical qualification and knowledge of CRM. Results. Participants found the course very realistic and selected scenarios highly relevant to their daily work. They reported a feeling of improved medical and nontechnical skills as well as no uncomfortable feeling during scenarios or debriefings. Conclusion. To our knowledge this pilot-project represents the first successful implementation of a simulation-based team-training course focused on pediatric trauma care in German-speaking countries with good acceptance.
Large-scale terrain modeling from multiple sensors with dependent Gaussian processes

Terrain modeling remains a challenging yet key component for the deployment of ground robots to the field. The difficulty arrives from the variability of terrain shapes, sparseness of the data, and high degree uncertainty often encountered in large, unstructured environments. This paper presents significant advances to data fusion for stochastic processes modeling spatial data, demonstrated in large-scale terrain modeling tasks. We explore dependent Gaussian processes to provide a multi-resolution representation of space and associated uncertainties, while integrating sensors from different modalities. Experiments performed on multiple multi-modal datasets (3D laser scans and GPS) demonstrate the approach for terrains of about 5 km2.
CCG: a prototype coagulating code generator

This paper describes the design and performance of CCG, a prototype code generator based on couguiation. Coagulation orders code generation using a run-time profile for the program being compiled. By treating busy parts of a program first and using the strategy of local optimality, CCG maximizes the benefit of careful instruction selection, register allocation and interprocedural optimization while avoiding unnecessary data movement in busy sections. Coagulation radically alters standard techniques for code generation, achieving highly efficient code without graph-coloring register allocation or peephole optimization. Experimental results showing an average 25% improvement over the GNU C compiler suggest, that compilation order is crucial and that coagulation can outperform current code generator technology.
Growth in Student Achievement : Issues of Measurement , Longitudinal Data Analysis , and Accountability

Management has been given the directive to serve as an independent catalyst and resource for the improvement of measurement and data systems to enhance student achievement. The widespread availability of annual student assessment results during the last decade has greatly expanded the use of assessment data nationwide. Fueled by federally mandated Grade 3 to 8 testing in reading and math, together with the ability to track individual student achievement over time, federal and state policy mandates (e.g., No Child Left Behind [NCLB] Act of 2001, Race to the Top, Colorado SB163‐08) have increased the stakes associated with student performance on state assessments. The intense interest in education accountability has led to a variety of methods to examine assessment outcomes for use in making judgments about education quality. Receiving particular interest are analyses of student academic growth. Growth models, as they are often called, have found favor as the preferred method for analyzing student achievement data for accountability purposes. The appeal of student growth is apparent and certainly not without merit. Because learning is demonstrated by changes in student achievement from one point in time to another, an interest in the process of student learning is an interest in academic growth. Applied to accountability systems based upon large‐scale annual assessment results, various methods have been put forward to examine student achievement over time, including value‐added analyses, growth‐to‐standard analyses, growth‐curve modeling, value tables, gain scores, and student growth percentiles. The variety of models reflects the various questions and purposes that growth analyses are designed to address. This plurality of purposes is frequently lost on stakeholders wanting to utilize growth analyses in their own specific context. Complicating stakeholder efforts to grasp the scope and purpose of measuring student growth is a myriad of technical documentation that often supplies details on how growth calculations are performed but lacks broader explanations of the underlying rationale and assumptions: What the growth model is, what questions the growth model can (and cannot) answer, what test assumptions (e.g., properties of the measurement scales) the growth model is based upon, and what valid inferences can be derived from the growth model results. The end result is often a system of data analysis that belies the promise that growth models bring for transforming educational accountability. In this paper we unpack issues related to student growth by situating the discussion within three larger, intersecting topics: Measurement, longitudinal data analysis, and accountability. …
Fracture Extraction from Colored X-Ray Images

A fracture is a crack or break in the bone. This can be easily detected by taking an X-ray in that area. But sometimes these images lack sufficient details needed to diagnose. So these images can be enhanced by adding the color map. To add the RGB color to the Destination image, the reference image ‘mood’ color is taken. Although adding color to the gray scale has no much impact, but the human labor is much reduced. After adding color to the original image, it adds up details to the target image. In the second part of this paper, Bit-Plane slicing method is used to extract the details of a Colored X-Ray Image. This method produces different bit level images. In this paper Bit Level 6 is evaluated for RGB colors of the Original image and it is evaluated with the Bit level 6 of the original image. The result shows that the colored X-Ray image Bit level6 yield more details than the Bit level6 of gray scale X-Ray image. Keywords: Color map, RGB Color Model Feature Extraction, Bit-Plane Slicing, Gray scale images.
An Evaluation of the NSGA-II and MOCell Genetic Algorithms for Self-Management Planning in a Pervasive Service Middleware

Planning (for example choosing most suitable servicesfor self-configuration) is one important task in selfmanagement for pervasive service computing, and can bereduced to the problem of multi-objective services selectionwith constraints. Genetic algorithms (GAs) are effectivein solving such multi-objective optimization problems, andare one of the most successful computational intelligenceapproaches currently available. GAs are beginning to beused in planning for self-management, but there is a lack ofcomprehensive work that evaluates GAs performance andsolution quality, and guides the setting of GAs’ parameters.This situation makes the application of GAs difficultin the pervasive service computing domain in which performance may be critical and the settings of parameters may have big consequences for performance. In this paper, wewill present our evaluations of two GAs, namely NSGA-IIand MOCell, in the GA framework JMetal2.1, for achievingmulti-objective selection of available services. From theseevaluations, suggestions on how and when to use NSGA-IIand MOCell are given in the planning for self-management.Our experiences show that to get a true Pareto front for aproblem, combining solutions set from different GAs is abetter way than using a single GA.
Rapid Eye Movement Sleep Deprivation Produces Long-Term Detrimental Effects in Spatial Memory and Modifies the Cellular Composition of the Subgranular Zone

Sleep deprivation (SD) affects spatial memory and proliferation in the dentate gyrus. It is unknown whether these deleterious effects persist in the long run. The aim of this study was to evaluate the proliferation, differentiation and maturation of neural progenitors as well as spatial memory 21 days after suffering SD. Sixty-day old male Balb/C mice were exposed to 72-h REM-SD. Spatial memory, cell fate, apoptosis and expression levels of insulin-like growth factor 1 receptor (IGF-1R) were evaluated in the hippocampus at 0, 14, and 21 days after SD or control conditions. After 21-days recovery period, memory performance was assessed with the Barnes maze, we found a significant memory impairment in SD mice vs. control (94.0 ± 10.2 s vs. 25.2 ± 4.5 s; p < 0.001). The number of BrdU+ cells was significantly decreased in the SD groups at day 14 (controls = 1.6 ± 0.1 vs. SD mice = 1.2 ± 0.1 cells/field; p = 0.001) and at day 21 (controls = 0.2 ± 0.03 vs. SD mice = 0.1 ± 0.02 cells/field; p < 0.001). A statistically significant decrease was observed in neuronal differentiation (1.4 ± 0.1 cells/field vs. 0.9 ± 0.1 cells/field, p = 0.003). Apoptosis was significantly increased at day 14 after SD (0.53 ± 0.06 TUNEL+ cells/field) compared to controls (0.19 ± 0.03 TUNEL+ cells/field p < 0.001) and at 21-days after SD (SD mice 0.53 ± 0.15 TUNEL+ cells/field; p = 0.035). At day 0, IGF-1R expression showed a statistically significant reduction in SD animals (64.6 ± 12.2 units) when compared to the control group (102.0 ± 9.8 units; p = 0.043). However, no statistically significant differences were found at days 14 and 21 after SD. In conclusion, a single exposition to SD for 72-h can induce deleterious effects that persist for at least 3 weeks. These changes are characterized by spatial memory impairment, reduction in the number of hippocampal BrdU+ cells and persistent apoptosis rate. In contrast, changes IGF-1R expression appears to be a transient event. Highlight Sleep deprivation affects spatial memory and proliferation in the dentate gyrus. To date it is unknown whether these deleterious effects are persistent over a long period of time. We analyzed the effects of sleep deprivation in the hippocampus after 21 days of recovery sleep. Our findings indicate that after sleep recovery, the detrimental effects of SD can be observed for at least 2 weeks, as shown by a reduction in memory performance, changes in the hippocampal cellular composition and higher apoptotic rate over a long period of time.
A study of reinforcement learning with knowledge sharing for distributed autonomous system

Reinforcement learning is one of effective controller for autonomous robots. Because it does not need priori knowledge and behaviors to complete given tasks are obtained automatically be repeating trial and error. However a large number of trials are required to realize complex tasks. So the task that can be obtained using the real robot is restricted to simple ones. Considering these points, various methods that prove the learning cost of reinforcement learning have been proposed. In the method that uses priori knowledge, the methods lose the autonomy that is most important feature of reinforcement learning in applying it to the robots. In the Dyna-Q, that is one of simple and effective reinforcement learning architecture integrating online planning, a model of environment is learned from real experience and by utilizing the model to learn, the learning time is decreased. In this architecture, the autonomy is held, however the model depends on the task, so acquired knowledge of environment cannot be reused to other tasks. In the real world, human beings can learn various behaviors to complete complex tasks without priori knowledge of the tasks. We can try to realize the task in our image without moving our body. After the training in the image, by trying to the real environment, we save time to learn. It means that we have model of environment and we utilize the model to learn. We consider that the key ability that makes the learning process faster is construction of environment model and utilization of it. In this paper, we have proposed a method to obtain an environment model that is independent of the task. And by utilizing the model we have decreased learning time. We consider distributed autonomous agents, and we show that the environment model is constructed quickly by sharing the experience of each agent, even when each agent has own independent task. To demonstrate the effectiveness of the proposed method, we have applied the method to the Q-learning and simulations of a puddle world are carried out. As a result effective behaviors have been obtained quickly.
Detecting Malicious Nodes For Secure Routing in MANETS Using Reputation Based Mechanism

A BSTRACT — Mobile ad-hoc networks (MANETS) are prone to a number of security threats. We incorporate our distributed reputation protocol within DSR and perform extensive simulations using a number of scenarios characterized by high node mobility, short pause time and highly sparse network in order to evaluate each of the design choices of our system. We focus on single and multiple black hole attacks but our design principles and results are applicable to a wider range of attacks such as gray hole, flooding attacks. Our implementation of black hole comprises active routing misbehavior and forwarding misbehavior. We design and build our prototype over DSR and test it in NS-2 in the presence of variable active black hole attacks in highly mobile and sparse networks.
Childhood Obesity, Overweight, Socio-Demographic and Life Style Determinants among Preschool Children in Babol, Northern Iran

Abstract Background Childhood obesity and overweight are a general public health concern in a transition society. Thus, the objective of the study was to determine the prevalence of overweight, obesity and their correlates with socio-demographic and life style related factors. Methods This cross-sectional study was conducted on 760 representative samples of preschool children aged 2 to 5 years in urban area of Babol, northern Iran. The weight and height were measured with standard method and the socio-demographic and life style data were collected by interviewing their parents. The diagnosis of overweight and obesity was based on CDC criteria. Results The prevalence rate of overweight and obesity were 11.8%, 15% respectively. There was no significant difference observed between sexes. The odds of overweight/obesity was elevated more than double in age 4-5 years compared with 2-3 years(OR=2.53, 95%CI:1.71-3.73). By parental education at university level, the odds ratio significant decreased compared with primary level (OR=0.34, 95%CI: 0.31-0.90 and OR=0.49, 95%CI: 0.34-0.98 for mothers and fathers respectively). Parental obesity is positively associated with overweight/obesity risk OR=2.67(95%CI: 1.75, 4.07) and 1.61(95% CI: 1.04-2.150) for mothers and fathers respectively). While spending ≥2 hours per day for Tv watching and ≥1 hours for playing with computer games tended to elevate the risk but not significant. Conclusion The findings indicate that overweight and obesity are high in preschool children in Babol. Increasing child age, parental obesity, spending more times on TV and playing with computer games are positively associated with overweight/obesity while parental education at university level is inversely associated.
Generalized Interference Alignment—Part I: Theoretical Framework

Interference alignment (IA) has attracted enormous research interest as it achieves optimal capacity scaling with respect to signal to noise ratio on interference networks. IA has also recently emerged as an effective tool in engineering interference for secrecy protection on wireless wiretap networks. However, despite the numerous works dedicated to IA, two of its fundamental issues, i.e., feasibility conditions and transceiver design, are not completely addressed in the literature. In this two part paper, a generalized interference alignment (GIA) technique is proposed to enhance the IA's capability in secrecy protection. A theoretical framework is established to analyze the two fundamental issues of GIA in Part I and then the performance of GIA in large-scale stochastic networks is characterized to illustrate how GIA benefits secrecy protection in Part II. The theoretical framework for GIA adopts methodologies from algebraic geometry, determines the necessary and sufficient feasibility conditions of GIA, and generates a set of algorithms for solving the GIA problem. This framework sets up a foundation for the development and implementation of GIA.