Modelling of Johne's disease control options in beef cattle: a decision support approach

Johne's disease in cattle is a contagious wasting disease caused by Mycobacterium avium subspecies paratuberculosis (MAP). Johne's infection is characterised by a long subclinical phase and can therefore go undetected for long periods of time during which substantial production losses can occur. The protracted nature of Johne's infection therefore presents a challenge for both veterinarians and farmers when discussing control options due to a paucity of information and limited test performance when screening for the disease. The objectives were to model Johne's control decisions in suckler beef cattle using a decision support approach, thus implying equal focus on ‘end user’ (veterinarian) participation whilst still focusing on the technical disease modelling aspects during the decision support model development. The model shows how Johne's disease is likely to affect a herd over time both in terms of physical and financial impacts. In addition, the model simulates the effect on production from two different Johne's control strategies; herd management measures and test and cull measures. The article also provides and discusses results from a sensitivity analysis to assess the effects on production from improving the currently available test performance. Output from running the model shows that a combination of management improvements to reduce routes of infection and testing and culling to remove infected and infectious animals is likely to be the least-cost control strategy.

Real-time multi-model decadal climate predictions

We present the first climate prediction of the coming decade made with multiple models, initialized with prior observations. This prediction accrues from an international activity to exchange decadal predictions in near real-time, in order to assess differences and similarities, provide a consensus view to prevent over-confidence in forecasts from any single model, and establish current collective capability. We stress that the forecast is experimental, since the skill of the multi-model system is as yet unknown. Nevertheless, the forecast systems used here are based on models that have undergone rigorous evaluation and individually have been evaluated for forecast skill. Moreover, it is important to publish forecasts to enable open evaluation, and to provide a focus on climate change in the coming decade. Initialized forecasts of the year 2011 agree well with observations, with a pattern correlation of 0.62 compared to 0.31 for uninitialized projections. In particular, the forecast correctly predicted La Niña in the Pacific, and warm conditions in the north Atlantic and USA. A similar pattern is predicted for 2012 but with a weaker La Niña. Indices of Atlantic multi-decadal variability and Pacific decadal variability show no signal beyond climatology after 2015, while temperature in the Niño3 region is predicted to warm slightly by about 0.5 °C over the coming decade. However, uncertainties are large for individual years and initialization has little impact beyond the first 4 years in most regions. Relative to uninitialized forecasts, initialized forecasts are significantly warmer in the north Atlantic sub-polar gyre and cooler in the north Pacific throughout the decade. They are also significantly cooler in the global average and over most land and ocean regions out to several years ahead. However, in the absence of volcanic eruptions, global temperature is predicted to continue to rise, with each year from 2013 onwards having a 50 % chance of exceeding the current observed record. Verification of these forecasts will provide an important opportunity to test the performance of models and our understanding and knowledge of the drivers of climate change.

Real-time identification and modelling in pervasive mining

This paper introduces an architecture for identifying and modelling in real-time at a copper mine using new technologies as M2M and cloud computing with a server in the cloud and an Android client inside the mine. The proposed design brings up pervasive mining, a system with wider coverage, higher communication efficiency, better fault-tolerance, and anytime anywhere availability. This solution was designed for a plant inside the mine which cannot tolerate interruption and for which their identification in situ, in real time, is an essential part of the system to control aspects such as instability by adjusting their corresponding parameters without stopping the process.

The impact of number mismatch and passives on the real-time processing of relative clauses

Language processing plays a crucial role in language development, providing the ability to assign structural representations to input strings (e.g., Fodor, 1998). In this paper we aim at contributing to the study of children's processing routines, examining the operations underlying the auditory processing of relative clauses in children compared to adults. English-speaking children (6–8;11) and adults participated in the study, which employed a self-paced listening task with a final comprehension question. The aim was to determine (i) the role of number agreement in object relative clauses in which the subject and object NPs differ in terms of number properties, and (ii) the role of verb morphology (active vs. passive) in subject relative clauses. Even though children's off-line accuracy was not always comparable to that of adults, analyses of reaction times results support the view that children have the same structural processing reflexes observed in adults.

Simple telemedicine for developing regions: Camera phones and paper-based microfluidic devices for real-time, off-site diagnosis

This article describes a prototype system for quantifying bioassays and for exchanging the results of the assays digitally with physicians located off-site. The system uses paper-based microfluidic devices for running multiple assays simultaneously, camera phones or portable scanners for digitizing the intensity of color associated with each colorimetric assay, and established communications infrastructure for transferring the digital information from the assay site to an off-site laboratory for analysis by a trained medical professional; the diagnosis then can be returned directly to the healthcare provider in the field. The microfluidic devices were fabricated in paper using photolithography and were functionalized with reagents for colorimetric assays. The results of the assays were quantified by comparing the intensities of the color developed in each assay with those of calibration curves. An example of this system quantified clinically relevant concentrations of glucose and protein in artificial urine. The combination of patterned paper, a portable method for obtaining digital images, and a method for exchanging results of the assays with off-site diagnosticians offers new opportunities for inexpensive monitoring of health, especially in situations that require physicians to travel to patients (e.g., in the developing world, in emergency management, and during field operations by the military) to obtain diagnostic information that might be obtained more effectively by less valuable personnel.

An Agent-Based Approach to Real-Time Patient Identification for Clinical Trials

Patient recruitment for clinical trials is expensive and has been a significant challenge, with many trials not achieving their recruitment goals. One method that shows promise for improving recruitment is the use of interactive prompts that inform practitioners of patient eligibility for clinical trials during consultation. This paper presents the ePCRN-IDEA recruitment system, which utilises an agent-based infrastructure to enable real-time recruitment of patients. In essence, whenever patients enter a clinic, the system compares their details against eligibility criteria, which define the requirements of active clinical trials. If a patient is found to be eligible, a prompt is raised to notify the user. In this way, it becomes possible for recruitment to take place quickly in a cost effective manner, whilst maintaining patient trust through the involvement of their own health care practitioner.

On Real-Time Monitoring And Control For Efficient Management Of Drinking Water Networks: Barcelona Case Study

Drinking water utilities in urban areas are focused on finding smart solutions facing new challenges in their real-time operation because of limited water resources, intensive energy requirements, a growing population, a costly and ageing infrastructure, increasingly stringent regulations, and increased attention towards the environmental impact of water use. Such challenges force water managers to monitor and control not only water supply and distribution, but also consumer demand. This paper presents and discusses novel methodologies and procedures towards an integrated water resource management system based on advanced ICT technologies of automation and telecommunications for largely improving the efficiency of drinking water networks (DWN) in terms of water use, energy consumption, water loss minimization, and water quality guarantees. In particular, the paper addresses the first results of the European project EFFINET (FP7-ICT2011-8-318556) devoted to the monitoring and control of the DWN in Barcelona (Spain). Results are split in two levels according to different management objectives: (i) the monitoring level is concerned with all the aspects involved in the observation of the current state of a system and the detection/diagnosis of abnormal situations. It is achieved through sensors and communications technology, together with mathematical models; (ii) the control level is concerned with computing the best suitable and admissible control strategies for network actuators as to optimize a given set of operational goals related to the performance of the overall system. This level covers the network control (optimal management of water and energy) and the demand management (smart metering, efficient supply). The consideration of the Barcelona DWN as the case study will allow to prove the general applicability of the proposed integrated ICT solutions and their effectiveness in the management of DWN, with considerable savings of electricity costs and reduced water loss while ensuring the high European standards of water quality to citizens.

Real-Time Control Of Floods Along The Demer River, Belgium, By Means Of MPC In Combination With GA And A Fast Conceptual River Model

Climate model projections show that climate change will further increase the risk of flooding in many regions of the world. There is a need for climate adaptation, but building new infrastructure or additional retention basins has its limits, especially in densely populated areas where open spaces are limited. Another solution is the more efficient use of the existing infrastructure. This research investigates a method for real-time flood control by means of existing gated weirs and retention basins. The method was tested for the specific study area of the Demer basin in Belgium but is generally applicable. Today, retention basins along the Demer River are controlled by means of adjustable gated weirs based on fixed logic rules. However, because of the high complexity of the system, only suboptimal results are achieved by these rules. By making use of precipitation forecasts and combined hydrological-hydraulic river models, the state of the river network can be predicted. To fasten the calculation speed, a conceptual river model was used. The conceptual model was combined with a Model Predictive Control (MPC) algorithm and a Genetic Algorithm (GA). The MPC algorithm predicts the state of the river network depending on the positions of the adjustable weirs in the basin. The GA generates these positions in a semi-random way. Cost functions, based on water levels, were introduced to evaluate the efficiency of each generation, based on flood damage minimization. In the final phase of this research the influence of the most important MPC and GA parameters was investigated by means of a sensitivity study. The results show that the MPC-GA algorithm manages to reduce the total flood volume during the historical event of September 1998 by 46% in comparison with the current regulation. Based on the MPC-GA results, some recommendations could be formulated to improve the logic rules.

Application Of Real-Time Control Strategy To Improve Nitrogen Removal In Wastewater Treatment

Biological nitrogen removal is an important task in the wastewater treatment. However, the actual removal of total nitrogen (TN) in the wastewater treatment plant (WWTP) is often unsatisfactory due to several causes, one of which is the insufficient availability of carbon source. One possible approach to improve the nitrogen removal therefore is addition of external carbon source, while the amount of which is directly related to operation cost of a WWTP. It is obviously necessary to determine the accurate amount of addition of external carbon source according to the demand depending on the influent wastewater quality. This study focused on the real-time control of external carbon source addition based on the on-line monitoring of influent wastewater quality. The relationship between the influent wastewater quality (specifically the concentration of COD and ammonia) and the demand of carbon source was investigated through experiments on a pilot-scale A/O reactor (1m3) at the Nanjing WWTP, China. The minimum doses of carbon source addition at different situations of influent wastewater quality were determined to ensure the effluent wastewater quality meets the discharge standard. The obtained relationship is expected to be applied in the full-scale WWTPs. .

A Gradient-Type Method For Real-Time State Estimation Of Water Distribution Networks

Drinking water distribution networks risk exposure to malicious or accidental contamination. Several levels of responses are conceivable. One of them consists to install a sensor network to monitor the system on real time. Once a contamination has been detected, this is also important to take appropriate counter-measures. In the SMaRT-OnlineWDN project, this relies on modeling to predict both hydraulics and water quality. An online model use makes identification of the contaminant source and simulation of the contaminated area possible. The objective of this paper is to present SMaRT-OnlineWDN experience and research results for hydraulic state estimation with sampling frequency of few minutes. A least squares problem with bound constraints is formulated to adjust demand class coefficient to best fit the observed values at a given time. The criterion is a Huber function to limit the influence of outliers. A Tikhonov regularization is introduced for consideration of prior information on the parameter vector. Then the Levenberg-Marquardt algorithm is applied that use derivative information for limiting the number of iterations. Confidence intervals for the state prediction are also given. The results are presented and discussed on real networks in France and Germany.

Adaptive, Decentralized, And Real-Time Sampling Strategies For Resource Constrained Hydraulic And Hydrologic Sensor Networks

We discuss the development and performance of a low-power sensor node (hardware, software and algorithms) that autonomously controls the sampling interval of a suite of sensors based on local state estimates and future predictions of water flow. The problem is motivated by the need to accurately reconstruct abrupt state changes in urban watersheds and stormwater systems. Presently, the detection of these events is limited by the temporal resolution of sensor data. It is often infeasible, however, to increase measurement frequency due to energy and sampling constraints. This is particularly true for real-time water quality measurements, where sampling frequency is limited by reagent availability, sensor power consumption, and, in the case of automated samplers, the number of available sample containers. These constraints pose a significant barrier to the ubiquitous and cost effective instrumentation of large hydraulic and hydrologic systems. Each of our sensor nodes is equipped with a low-power microcontroller and a wireless module to take advantage of urban cellular coverage. The node persistently updates a local, embedded model of flow conditions while IP-connectivity permits each node to continually query public weather servers for hourly precipitation forecasts. The sampling frequency is then adjusted to increase the likelihood of capturing abrupt changes in a sensor signal, such as the rise in the hydrograph – an event that is often difficult to capture through traditional sampling techniques. Our architecture forms an embedded processing chain, leveraging local computational resources to assess uncertainty by analyzing data as it is collected. A network is presently being deployed in an urban watershed in Michigan and initial results indicate that the system accurately reconstructs signals of interest while significantly reducing energy consumption and the use of sampling resources. We also expand our analysis by discussing the role of this approach for the efficient real-time measurement of stormwater systems.

Inexpensive solution for real-time video and image stitching

Image stitching is the process of joining several images to obtain a bigger view of a scene. It is used, for example, in tourism to transmit to the viewer the sensation of being in another place. I am presenting an inexpensive solution for automatic real time video and image stitching with two web cameras as the video/image sources. The proposed solution relies on the usage of several markers in the scene as reference points for the stitching algorithm. The implemented algorithm is divided in four main steps, the marker detection, camera pose determination (in reference to the markers), video/image size and 3d transformation, and image translation. Wii remote controllers are used to support several steps in the process. The built‐in IR camera provides clean marker detection, which facilitates the camera pose determination. The only restriction in the algorithm is that markers have to be in the field of view when capturing the scene. Several tests where made to evaluate the final algorithm. The algorithm is able to perform video stitching with a frame rate between 8 and 13 fps. The joining of the two videos/images is good with minor misalignments in objects at the same depth of the marker,misalignments in the background and foreground are bigger. The capture process is simple enough so anyone can perform a stitching with a very short explanation. Although real‐time video stitching can be achieved by this affordable approach, there are few shortcomings in current version. For example, contrast inconsistency along the stitching line could be reduced by applying a color correction algorithm to every source videos. In addition, the misalignments in stitched images due to camera lens distortion could be eased by optical correction algorithm. The work was developed in Apple’s Quartz Composer, a visual programming environment. A library of extended functions was developed using Xcode tools also from Apple.

An online platform for real-time sensor data collection, visualization, and sharing

Sharing sensor data between multiple devices and users can be^challenging for naive users, and requires knowledge of programming and use of different communication channels and/or development tools, leading to non uniform solutions. This thesis proposes a system that allows users to access sensors, share sensor data and manage sensors. With this system we intent to manage devices, share sensor data, compare sensor data, and set policies to act based on rules. This thesis presents the design and implementation of the system, as well as three case studies of its use.

Método Fuzzy para auxílio ao diagnóstico de câncer de mama em ambiente inteligente de telediagnóstico colaborativo para apoio à tomada de decisão

Breast cancer, despite being one of the leading causes of death among women worldwide is a disease that can be cured if diagnosed early. One of the main techniques used in the detection of breast cancer is the Fine Needle Aspirate FNA (aspiration puncture by thin needle) which, depending on the clinical case, requires the analysis of several medical specialists for the diagnosis development. However, such diagnosis and second opinions have been hampered by geographical dispersion of physicians and/or the difficulty in reconciling time to undertake work together. Within this reality, this PhD thesis uses computational intelligence in medical decision-making support for remote diagnosis. For that purpose, it presents a fuzzy method to assist the diagnosis of breast cancer, able to process and sort data extracted from breast tissue obtained by FNA. This method is integrated into a virtual environment for collaborative remote diagnosis, whose model was developed providing for the incorporation of prerequisite Modules for Pre Diagnosis to support medical decision. On the fuzzy Method Development, the process of knowledge acquisition was carried out by extraction and analysis of numerical data in gold standard data base and by interviews and discussions with medical experts. The method has been tested and validated with real cases and, according to the sensitivity and specificity achieved (correct diagnosis of tumors, malignant and benign respectively), the results obtained were satisfactory, considering the opinions of doctors and the quality standards for diagnosis of breast cancer and comparing them with other studies involving breast cancer diagnosis by FNA.

Sistema Especialista para Supressão Online de Alarmes em Processos Industriais

Operating industrial processes is becoming more complex each day, and one of the factors that contribute to this growth in complexity is the integration of new technologies and smart solutions employed in the industry, such as the decision support systems. In this regard, this dissertation aims to develop a decision support system based on an computational tool called expert system. The main goal is to turn operation more reliable and secure while maximizing the amount of relevant information to each situation by using an expert system based on rules designed for a particular area of expertise. For the modeling of such rules has been proposed a high-level environment, which allows the creation and manipulation of rules in an easier way through visual programming. Despite its wide range of possible applications, this dissertation focuses only in the context of real-time filtering of alarms during the operation, properly validated in a case study based on a real scenario occurred in an industrial plant of an oil and gas refinery