Daylong acoustic recordings of grazing and rumination activities in dairy cows.

Monitoring livestock feeding behavior may help assess animal welfare and nutritional status, and to optimize pasture management. The need for continuous and sustained monitoring requires the use of automatic techniques based on the acquisition and analysis of sensor data. This work describes an open dataset of acoustic recordings of the foraging behavior of dairy cows. The dataset includes 708 h of daily records obtained using unobtrusive and non-invasive instrumentation mounted on five lactating multiparous Holstein cows continuously monitored for six non-consecutive days in pasture and barn. Labeled recordings precisely delimiting grazing and rumination bouts are provided for a total of 392 h and for over 6,200 ingestive and rumination jaw movements. Companion information on the audio recording quality and expert-generated labels is also provided to facilitate data interpretation and analysis. This comprehensive dataset is a useful resource for studies aimed at exploring new tools and solutions for precision livestock farming.

Modeling of leptospirosis outbreaks in relation to hydroclimatic variables in the northeast of Argentina.

The transmission of leptospirosis is conditioned by climatic variables. In northeastern Argentina leptospirosis outbreaks occur mainly in coincidence with periods of abundant precipitation and high hydrometric level. A Susceptible-Infectious-Recovered Epidemiological Model (SIR) is proposed, which incorporates hydroclimatic variables for the three most populated cities in the area (Santa Fe, Paraná and Rosario), during the 2009-2018 period. Results obtained by solving the proposed SIR model for the 2010 outbreak are in good agreement with the actual data, capturing the dynamics of the leptospirosis outbreak wave. However, the model does not perform very well in the last months of the year when isolated cases appear outside the outbreak periods, probably due to non- climatic factors not explicitly considered in the present version of the model. Nevertheless, the dynamic modeling of infectious diseases considering hydroclimatic variables constitutes a climatic service for the public health system, not yet available in Argentina.

Influenza epidemic model using dynamic social networks of individuals with cognition maps.

The dynamic of infectious disease is the result of the interplay between the spread of pathogens and individuals' behaviour. This interaction can be modelled through a network of interdependent dynamical blocks with multiple feedback connections. Epidemic outbreaks trigger behavioural responses, at the group and individual levels, which in turn influence the development of the epidemic. The interactions can be modelled through adaptive temporal networks whose nodes represent the individuals interconnected. Here we introduce an individual-based model where the behaviour of each agent is governed by its appreciation of the environment and external stimulus and its appreciation of its environment. It is built as a combination of three interacting blocks: (i) individual behaviour, (ii) social behaviour and (iii) health state.•Here, we introduce an individual-based model.•Individual's behaviour is modelled through the interplay of information of its health state as well as its neighbourhood (infected and recovered neighbours) and global epidemic situation;•Social behaviour is modelled through contact network that aggregates the behaviour and health state of the individuals;•The proposed model allows to use a wide range of alternatives for modelling each of these blocks, that provides flexibility to select the most adequate tool to model each component of the framework.

Addressing population heterogeneity and distribution in epidemics models using a cellular automata approach.

BACKGROUND: The spread of an infectious disease is determined by biological and social factors. Models based on cellular automata are adequate to describe such natural systems consisting of a massive collection of simple interacting objects. They characterize the time evolution of the global system as the emergent behaviour resulting from the interaction of the objects, whose behaviour is defined through a set of simple rules that encode the individual behaviour and the transmission dynamic. METHODS: An epidemic is characterized trough an individual-based-model built upon cellular automata. In the proposed model, each individual of the population is represented by a cell of the automata. This way of modeling an epidemic situation allows to individually define the characteristic of each individual, establish different scenarios and implement control strategies. RESULTS: A cellular automata model to study the time evolution of a heterogeneous populations through the various stages of disease was proposed, allowing the inclusion of individual heterogeneity, geographical characteristics and social factors that determine the dynamic of the desease. Different assumptions made to built the classical model were evaluated, leading to following results: i) for low contact rate (like in quarantine process or low density population areas) the number of infective individuals is lower than other areas where the contact rate is higher, and ii) for different initial spacial distributions of infected individuals different epidemic dynamics are obtained due to its influence on the transition rate and the reproductive ratio of disease. CONCLUSIONS: The contact rate and spatial distributions have a central role in the spread of a disease. For low density populations the spread is very low and the number of infected individuals is lower than in highly populated areas. The spacial distribution of the population and the disease focus as well as the geographical characteristic of the area play a central role in the dynamics of the desease.

Cooperative-feedback control.

In this work a control structure capable of handling controllability problems, which emerge from the presence of constraints, and improve the performance of the system by coordinating the use of several manipulated variables is introduced. In this scheme, the primary manipulated variable is used to handle the transient response while the auxiliary manipulated variable is used to keep the primary manipulated variable away from saturation. These two manipulated variables are coordinated through a user-defined non-linear function, which decides when and how the control structure changes. Its parameters determine the interaction between both inputs and the steady state value of each manipulated variable. The effectiveness of the proposed scheme is illustrated in two simulation examples.

Embedded estimator in predictive feedback control.

In this work, a new approach to design predictive feedback control for SISO systems is presented. The proposed formulation relies on the development of a single step predictor based on an autoregressive moving average with external input (ARMAX) model. Although no explicit observer is actually involved in the implementation, this predictor implicitly includes one since the input-output model subsumes an observer. Exploiting this idea the resulting ARMAX model is extended to include extra outputs to improve the quality of the prediction for systems with large time delay and nonmeasurable disturbances. The resulting predictor is used to develop a predictive feedback controller. This new formulation of predictive feedback control includes feedback and feedforward actions. Simulations of two linear systems illustrate the applicability of the control algorithm.

Flexible-structure control: a strategy for releasing input constraints.

In this paper output unreachability under input saturation phenomenon is studied: under a large disturbance or setpoint change, the process output may never reach the set point even when the manipulated variable has driven to saturation. The process output can be brought back to the set point only by activating an auxiliary manipulated variable. A new control structure for designing and implementing a control system capable of solving this problem is proposed by transferring the control from one variable to another and taking into account the different dynamics involved in the system. The control structure, called flexible-structure control due to its ability to adapt the control structure to the operating conditons, is a generalization of the split-range control. It can be summarized as two controllers connected through a piecewise linear function. This function decides, based on the value of one manipulated variable, when and how the control structure changes. Its parameters control the interaction between both manipulated variables and leave the capability for handling the balance between control quality and other goals to the operator.

Predictive feedback control.

In this work a new method for designing predictive controllers for linear single-input/single-output systems is presented. It uses only one prediction of the process output J time intervals ahead to compute the correspondent future error. Then, the predictive feedback controller is defined by introducing a filter which weights the last w predicted errors. In this way, the resulting control action is computed by observing the system future behavior and also by weighting present and past errors. This last feature improves the closed-loop performance to disturbance rejection as shown through simulations of two linear systems and a nonlinear continuous stirred tank reactor.

Model predictive control with amplitude and rate actuator saturation.

In this work we show that the anti-wind-up-bumpless-transfer controller emerges from the structure of model predictive control (MPC) with quadratic objective and input constrains. The key to establish that relationship is the application of optimality conditions to the equivalent optimal control problem. The proposed framework employs a model of physical constraints as part of the controller architecture to ensure that the commands sent to the actuator do not exceed their specific limits and the internal states of the controller are well updated. Numerical examples are presented for illustrating the proposed control design methodology.