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Human illness due to contamination of food by pathogenic strains of Escherichia coli is a serious public health concern and can cause significant economic losses in the food industry. Recent outbreaks of such illness sourced from ground beef production motivates the work in this paper. Most ground beef is produced in large facilities where many carcasses are butchered and various pieces of them are ground together in sequential batches. Assuming that the source of contamination is a single carcass and that downstream from the production facility ground beef from a particular batch has been identified as contaminated by E. coli, the probability that previous and subsequent batches are also contaminated is modelled. This model may help the beef industry to identify the likelihood of contamination in other batches and potentially save money by not needing to cook or recall unaffected batches of ground beef.

Influenza A virus (IAV) in swine is a pathogen that causes a threat to the health as well as to the production of swine. Moreover, swine can spread this virus to other species including humans. The virus persists in different types of swine farms as evident in a number of studies. The core objectives of this study are (i) to analyze the dynamics of influenza infection of a farrow-to-finish swine farm, (ii) to explore the reinfection at the farm level, and finally (iii) to examine the effectiveness of two control strategies: vaccination and reduction of indirect contact. The analyses are conducted using a deterministic Susceptible-Exposed-Infectious-Recovered (SEIR) model. Simulation results show that the disease is maintained in gilts and piglets because of new susceptible pigs entering the population on a weekly basis. A sensitivity analysis shows that the results are not sensitive to variation in the parameters. The results of the reinfection simulation indicate that the virus persists in the entire farm. The control strategies studied in this work are not successful in eliminating the virus within the farm.

Cheyne-Stokes respiration is a distinct breathing pattern consisting of periods of hyperpnea followed by apnoeas, with unknown aetiology. One in two patients with congestive heart failure suffers from this condition. Researchers hypothesize that key factors in CSR are the fluid shift from the standing to supine position and the differences between genders. A mathematical model of the cardiorespiratory system was constructed using parameter values from real data. Hopf bifurcation analysis was used to determine regions of steady versus oscillatory breathing patterns. In the model, Cheyne-Stokes respiration is more likely to occur while in the supine position and males are more likely to develop Cheyne-Stokes than females. These findings, which are in agreement with clinical experience, suggest that both gender and fluid shift contribute to the pathogenesis of Cheyne-Stokes respiration and that physical quantities such as blood volumes and neural feedback may be sufficient to explain the observations of CSR.

Human illness due to contamination of food by pathogenic strains of Escherichia coli is a serious public health concern and can cause significant economic losses in the food industry. Undercooked ground beef is the primary means of transmission of pathogenic E. coli to humans. In the Western world, most ground beef is produced in large facilities where many carcasses are butchered and various pieces of them are ground together in large batches. Assuming that the source of contamination is a single carcass, the primary determinant of how many batches of ground beef from a particular production cycle are affected is the manner in which pieces of that carcass are spread about in the raw sources that contribute to the ground beef batches. Assuming that ground beef from a particular batch has been identified at the consumer end as contaminated by E. coli, we model the probability that previous and subsequent batches generated in the same production cycle are also contaminated. This model may help the beef industry to identify the likelihood of contamination in other batches and potentially save money by not needing to cook or recall unaffected batches of ground beef.

A compartmental model of the human circulatory system that illustrates Cheyne–Stokes respiration (CSR) is presented. Clinical evidence suggests that patient body position can influence the likelihood of experiencing CSR, and this model is analyzed to see if blood fluid shifts associated with body position could be the means of this influence. It is shown that lying down causes a shift in the location of the Hopf bifurcation curve associated with the onset of CSR, making it more likely.

I introduce publicly available software for accurate fitting of Hodgkin-Huxley models to voltage-clamp data. I describe the model and non-linear fitting procedure employed by the software and compare its results with the usual method of fitting such models using potassium A-current data from a pyloric dilator cell of the lobster Panulirus interruptus and sodium current data from an electrocyte cell of the electric fish Sternopygus macrurus. The set of parameter values for the model determined by this software yield current traces that are substantially closer to the observed data than those determined from the usual fitting method. This improvement is due to the fact that the software fits all of the parameters simultaneously utilizing all of the data rather than fitting steady-state and time constant parameters disjointly using peak currents and portions of the rising and falling phases. I analyze the convergence properties of the software's fitting algorithm using simulated data showing that accurate parameter values are obtained for most of the parameters using any reasonable initial values. The software also incorporates a linear pre-estimation procedure to help in determining reasonable initial values for the full non-linear algorithm. I illustrate and discuss some of the inadequacies of voltage-clamp data.

Background Hepatitis A (HA) is a low-incidence, non-endemic disease in Canada and the United States (US). However, a large difference in HA incidence between Canada and HA-endemic countries has made travel an important contributor to hepatitis A prevalence in Canada. There is also a (smaller) incidence differential between Canada and the US. Although the US has only moderately higher HA incidence, the volume of travel by Canadians to the US is many times higher than travel volume to endemic countries. Hence, travel to the US may constitute a source of low to moderate risk for Canadian travelers. To our knowledge, travel to the US has never been included as a potential risk factor for HA infection in Canadian epidemiologic analyses. The objective of this study was to use dynamic models to investigate the possible effects on hepatitis A incidence in Canada due to (1) implementing vaccination in the US, and (2) varying the volume of travel by Canadians to the US. Methods We developed and analyzed age-structured compartmental models for the transmission and vaccination of hepatitis A, for both Canada and the US. Models were parameterized using data on seroprevalence, case reporting, and travel patterns. The potential effect of hepatitis A prevalence in the US on hepatitis A prevalence in Canada was captured through a term representing infection of Canadians due to travel in the US. Results The model suggests that approximately 22% of HA cases in Canada in the mid 1990s may have been attributable to travel to the US. A universal vaccination programme that attained 70% coverage in young children in the US in the mid 1990s could have reduced Canadian incidence by 21% within 5 years. Conclusion Since not all necessary data were available to parameterize the model, the results should be considered exploratory. However, the analysis shows that, under plausible assumptions, the US may be more important for determining HA prevalence in Canada than is currently supposed. As international travel continues to grow, making vaccination policies ever more relevant to populations beyond a country's borders, such multi-country models will most likely come into wider use as predictive aids for policy development.

In this paper we extend our previously presented efficient distance-propagating dynamic system for real-time robot path planning in dynamic environments to the case where safety margins around obstacles are included. Inclusion of safety margins approximately triples the number of arithmetic operations, however, the distance-propagating dynamic system is still very computationally efficient. The algorithm uses a grid representation of the environment, which need not be regular, and is applicable to dynamic environments where both targets and obstacles are permitted to move. No prior knowledge of target or obstacle movement is assumed. Safety margins around obstacles are implemented as "soft" margins defined by local penalty functions around obstacles which represent the extra distance the robot is willing to travel in order to avoid passing through this margin. The path through which the robot travels minimizes the sum of the current known distance to a target and the cumulative local penalty functions along the path. The effectiveness of the algorithm is demonstrated through a number of simulations.

Necessary and sufficient conditions for impulsive controllability of linear dynamical systems are obtained, which provide a novel approach to problems that are basically defined by continuous dynamical systems, but on which only discrete-time actions are exercised. As an application, impulsive maneuvering of a spacecraft is discussed.

In this paper, a simple yet efficient dynamic system is proposed for real-time collision-free robot path planning. Like most robot path-planning approaches, the environment is represented by a topologically organized map. Each grid point on the map has only local connections to its neighboring grid points. Thus the computational complexity linearly depends on the number of grid points. Unlike the neural network based path-planning model proposed by Yang and Meng (2001), the proposed algorithm efficiently propagate the distance from the target to the entire robot workspace. Thus the real-time optimal robot path is generated through the dynamic distance landscape without explicitly searching over the global free workspace nor the collision paths, without explicitly optimizing any global cost functions, and without any prior knowledge of the dynamic environment. Therefore the model algorithm is computationally efficient. The effectiveness and efficiency of the proposed dynamic system are demonstrated through simulation and comparison studies