Thermal reference points as an index for monitoring body temperature in marine mammals

BACKGROUND Monitoring body temperature is essential in veterinary care as minor variations may indicate dysfunction. Rectal temperature is widely used as a proxy for body temperature, but measuring it requires special equipment, training or restraining, and it potentially stresses animals. Infrared thermography is an alternative that reduces handling stress, is safer for technicians and works well for untrained animals. This study analysed thermal reference points in five marine mammal species: bottlenose dolphin (Tursiops truncatus); beluga whale (Delphinapterus leucas); Patagonian sea lion (Otaria flavescens); harbour seal (Phoca vitulina); and Pacific walrus (Odobenus rosmarus divergens). RESULTS The thermogram analysis revealed that the internal blowhole mucosa temperature is the most reliable indicator of body temperature in cetaceans. The temperatures taken during voluntary breathing with a camera held perpendicularly were practically identical to the rectal temperature in bottlenose dolphins and were only 1 °C lower than the rectal temperature in beluga whales. In pinnipeds, eye temperature appears the best parameter for temperature control. In these animals, the average times required for temperatures to stabilise after hauling out, and the average steady-state temperature values, differed according to species: Patagonian sea lions, 10 min, 31.13 °C; harbour seals, 10 min, 32.27 °C; Pacific walruses, 5 min, 29.93 °C. CONCLUSIONS The best thermographic and most stable reference points for monitoring body temperature in marine mammals are open blowhole in cetaceans and eyes in pinnipeds.

Monitoring system for small sized photovoltaic power plants

This paper presents a monitoring system devoted to small sized photovoltaic (PV) power plants. The system is characterized by: a high level of integration; a low cost, when compared to the cost of the PV system to be monitored; and an easy installation in the majority of the PV plants with installed power of some kW. The system is able to collect, store, process and display electrical and meteorological parameters that are crucial when monitoring PV facilities. The identification of failures in the PV system and the elaboration of performance analysis of such facilities are other important characteristics of the developed system. The access to the information about the monitored facilities is achieved by using a web application, which was developed with a focus on the mobile devices. In addition, there is the possibility of an integration between the developed monitoring system and the central supervision system of Martifer Solar (a company focused on the development, operation and maintenance of PV systems).

Monitoring and modelling the morphodynamic evolution of a breached barrier beach system

Predicting the evolution of a coastal cell requires the identification of the key drivers of morphology. Soft coastlines are naturally dynamic but severe storm events and even human intervention can accelerate any changes that are occurring. However, when erosive events such as barrier breaching occur with no obvious contributory factors, a deeper understanding of the underlying coastal processes is required. Ideally conclusions on morphological drivers should be drawn from field data collection and remote sensing over a long period of time. Unfortunately, when the Rossbeigh barrier beach in Dingle Bay, County Kerry, began to erode rapidly in the early 2000’s, eventually leading to it breaching in 2008, no such baseline data existed. This thesis presents a study of the morphodynamic evolution of the Inner Dingle Bay coastal system. The study combines existing coastal zone analysis approaches with experimental field data collection techniques and a novel approach to long term morphodynamic modelling to predict the evolution of the barrier beach inlet system. A conceptual model describing the long term evolution of Inner Dingle Bay in 5 stages post breaching was developed. The dominant coastal processes driving the evolution of the coastal system were identified and quantified. A new methodology of long term process based numerical modelling approach to coastal evolution was developed. This method was used to predict over 20 years of coastal evolution in Inner Dingle Bay. On a broader context this thesis utilised several experimental coastal zone data collection and analysis methods such as ocean radar and grain size trend analysis. These were applied during the study and their suitability to a dynamic coastal system was assessed.

Baran: an interaction-centred user monitoring framework

User Quality of Experience (QoE) is a subjective entity and difficult to measure. One important aspect of it, User Experience (UX), corresponds to the sensory and emotional state of a user. For a user interacting through a User Interface (UI), precise information on how they are using the UI can contribute to understanding their UX, and thereby understanding their QoE. As well as a user’s use of the UI such as clicking, scrolling, touching, or selecting, other real-time digital information about the user such as from smart phone sensors (e.g. accelerometer, light level) and physiological sensors (e.g. heart rate, ECG, EEG) could contribute to understanding UX. Baran is a framework that is designed to capture, record, manage and analyse the User Digital Imprint (UDI) which, is the data structure containing all user context information. Baran simplifies the process of collecting experimental information in Human and Computer Interaction (HCI) studies, by recording comprehensive real-time data for any UI experiment, and making the data available as a standard UDI data structure. This paper presents an overview of the Baran framework, and provides an example of its use to record user interaction and perform some basic analysis of the interaction.

Novel efficient technologies in Europe for axle bearing condition monitoring – the MAXBE project

Axle bearing damage with possible catastrophic failures can cause severe disruptions or even dangerous derailments, potentially causing loss of human life and leading to significant costs for railway infrastructure managers and rolling stock operators. Consequently the axle bearing damage process has safety and economic implications on the exploitation of railways systems. Therefore it has been the object of intense attention by railway authorities as proved by the selection of this topic by the European Commission in calls for research proposals. The MAXBE Project (http://www.maxbeproject.eu/), an EU-funded project, appears in this context and its main goal is to develop and to demonstrate innovative and efficient technologies which can be used for the onboard and wayside condition monitoring of axle bearings. The MAXBE (interoperable monitoring, diagnosis and maintenance strategies for axle bearings) project focuses on detecting axle bearing failure modes at an early stage by combining new and existing monitoring techniques and on characterizing the axle bearing degradation process. The consortium for the MAXBE project comprises 18 partners from 8 member states, representing operators, railway administrations, axle bearing manufactures, key players in the railway community and experts in the field of monitoring, maintenance and rolling stock. The University of Porto is coordinating this research project that kicked-off in November 2012 and it is completed on October 2015. Both on-board and wayside systems are explored in the project since there is a need for defining the requirement for the onboard equipment and the range of working temperatures of the axle bearing for the wayside systems. The developed monitoring systems consider strain gauges, high frequency accelerometers, temperature sensors and acoustic emission. To get a robust technology to support the decision making of the responsible stakeholders synchronized measurements from onboard and wayside monitoring systems are integrated into a platform. Also extensive laboratory tests were performed to correlate the in situ measurements to the status of the axle bearing life. With the MAXBE project concept it will be possible: to contribute to detect at an early stage axle bearing failures; to create conditions for the operational and technical integration of axle bearing monitoring and maintenance in different European railway networks; to contribute to the standardization of the requirements for the axle bearing monitoring, diagnosis and maintenance. Demonstration of the developed condition monitoring systems was performed in Portugal in the Northern Railway Line with freight and passenger traffic with a maximum speed of 220 km/h, in Belgium in a tram line and in the UK. Still within the project, a tool for optimal maintenance scheduling and a smart diagnostic tool were developed. This paper presents a synthesis of the most relevant results attained in the project. The successful of the project and the developed solutions have positive impact on the reliability, availability, maintainability and safety of rolling stock and infrastructure with main focus on the axle bearing health.

Energy harvesting for monitoring bridges over their operational life

The use of energy harvesting materials for large infrastructure is a promising and growing field. In this regard, the use of such harvesters for the purpose of structural health monitoring of bridges has been proposed in recent times as one of the feasible options since the deployment of them can remove the necessity of an external power source. This paper addresses the performance issue of such monitors over the life-cycle of a bridge as it deteriorates and the live load on the structure increases. In this regard, a Lead Zirconate Titanate (PZT) material is considered as the energy harvesting material and a comparison is carried out over the operational life of a reinforced concrete bridge. The evolution of annual average daily traffic (AADT) is taken into consideration, as is the degradation of the structure over time, due to the effects of corrosion. Evolution of such harvested energy is estimated over the life-cycle of the bridge and the sensitivity of harvested energy is investigated for varying rates of degradation and changes in AADT. The study allows for designing and understanding the potential of energy harvesters as a health monitor for bridges. This paper also illustrates how the natural growth of traffic on a bridge over time can accentuate the identification of damage, which is desirable for an ageing structure. The paper also assesses the impact and effects of deployment of harvesters in a bridge as a part of its design process, considering performance over the entire life-cycle versus a deployment at a certain age of the structure.

A transformation-based multivariate chart to monitor process dispersion

Multivariate monitoring techniques such as multivariate control charts are used to control the processes that contain more than one correlated characteristic. Although the majority of previous researches are focused on controlling only the mean vector of multivariate processes, little work has been performed to monitor the covariance matrix. In this research, a new method is presented to detect possible shifts in the covariance matrix of multivariate processes. The basis of the proposed method is to eliminate the correlation structure between the quality characteristics by transformation technique and then use an S chart for each variable. The performance of the proposed method is then compared to the ones from other existing methods and a real case is presented.

Screen Time Weight-loss Intervention Targeting Children at Home (SWITCH): process evaluation of a randomised controlled trial intervention.

BACKGROUND: The Screen Time Weight-loss Intervention Targeting Children at Home (SWITCH) trial tested a family intervention to reduce screen-based sedentary behaviour in overweight children. The trial found no significant effect of the intervention on children's screen-based sedentary behaviour. To explore these null findings, we conducted a pre-planned process evaluation, focussing on intervention delivery and uptake. METHODS: SWITCH was a randomised controlled trial of a 6-month family intervention to reduce screen time in overweight children aged 9-12 years (n = 251). Community workers met with each child's primary caregiver to deliver the intervention content. Community workers underwent standard training and were monitored once by a member of the research team to assess intervention delivery. The primary caregiver implemented the intervention with their child, and self-reported intervention use at 3 and 6 months. An exploratory analysis determined whether child outcomes at 6 months varied by primary caregiver use of the intervention. RESULTS: Monitoring indicated that community workers delivered all core intervention components to primary caregivers. However, two thirds of primary caregivers reported using any intervention component "sometimes" or less frequently at both time points, suggesting that intervention uptake was poor. Additionally, analyses indicated no effect of primary caregiver intervention use on child outcomes at 6 months, suggesting the intervention itself lacked efficacy. CONCLUSIONS: Poor uptake, and the efficacy of the intervention itself, may have played a role in the null findings of the SWITCH trial on health behaviour and body composition. TRIAL REGISTRATION: The trial was registered in the Australian and New Zealand Clinical Trials Registry (no. ACTRN12611000164998 ); registration date: 10/02/2011.

Application of artificial neural networks in linear profile monitoring

In many quality control applications the quality of process or product is characterized and summarized by a relation (profile) between a response variable and one or more explanatory variables. Such profiles can be modeled using linear or nonlinear regression models. In this paper we use artificial neural networks to detect and classify the shifts in linear profiles. Three monitoring methods based on artificial neural networks are developed to monitor linear profiles. Their efficacies are assessed using average run length criterion. © 2010 Elsevier Ltd. All rights reserved.

Process capability analysis in non normal linear regression profiles

When the distribution of a process characterized by a profile is non normal, process capability analysis using normal assumption often leads to erroneous interpretations of the process performance. Profile monitoring is a relatively new set of techniques in quality control that is used in situations where the state of product or process is represented by a function of two or more quality characteristics. Such profiles can be modeled using linear or nonlinear regression models. In some applications, it is assumed that the quality characteristics follow a normal distribution; however, in certain applications this assumption may fail to hold and may yield misleading results. In this article, we consider process capability analysis of non normal linear profiles. We investigate and compare five methods to estimate non normal process capability index (PCI) in profiles. In three of the methods, an estimation of the cumulative distribution function (cdf) of the process is required to analyze process capability in profiles. In order to estimate cdf of the process, we use a Burr XII distribution as well as empirical distributions. However, the resulted PCI with estimating cdf of the process is sometimes far from its true value. So, here we apply artificial neural network with supervised learning which allows the estimation of PCIs in profiles without the need to estimate cdf of the process. Box-Cox transformation technique is also developed to deal with non normal situations. Finally, a comparison study is performed through the simulation of Gamma, Weibull, Lognormal, Beta and student-t data.

Evaluation of process capability indices of linear profiles

Purpose: In profile monitoring, which is a growing research area in the field of statistical process control, the relationship between response and explanatory variables is monitored over time. The purpose of this paper is to focus on the process capability analysis of linear profiles. Process capability indices give a quick indication of the capability of a manufacturing process. Design/methodology/approach: In this paper, the proportion of the non-conformance criteria is employed to estimate process capability index. The paper has considered the cases where specification limits is constant or is a function of explanatory variable X. Moreover, cases where both equal and random design schemes in profile data acquisition is required (as the explanatory variable) is considered. Profiles with the assumption of deterministic design points are usually used in the calibration applications. However, there are other applications where design points within a profile would be i.i.d. random variables from a given distribution. Findings: Simulation studies using simple linear profile processes for both fixed and random explanatory variable with constant and functional specification limits are considered to assess the efficacy of the proposed method. Originality/value: There are many cases in industries such as semiconductor industries where quality characteristics are in form of profiles. There is no method in the literature to analyze process capability for theses processes, however recently quite a few methods have been presented in monitoring profiles. Proposed methods provide a framework for quality engineers and production engineers to evaluate and analyze capability of the profile processes. © Emerald Group Publishing Limited.

Seasonal dynamics and operational monitoring of hedgerow olive tree transpiration in response to applied water

We used 2012 sap flow measurements to assess the seasonal dynamics of daily plant transpiration (ETc) in a high-density olive orchard (Olea europaea L. cv. ‘Arbequina’) with a well-watered (HI) control treatment A to supply 100 % of the crop water needs, and a moderately (MI) watered treatment B that replaced 70% of crop needs. To assure that treatment A was well-watered, we compared field daily ETc values against ETc obtained with the Penman-Monteith (PM) combination equation incorporating the Orgaz et al. (2007) bulk daily canopy conductance (gc) model, validated for our non-limiting conditions. We then tested the hypothesis of indirectly monitoring olive ETc from readily available vegetation index (VI) and ground-based plant water stress indicator. In the process we used the FAO56 dual crop coefficient (Kc) approach. For the HI olive trees we defined Kcb as the basal transpiration coefficient, and we related Kcb to remotely sensed Soil Adjusted Vegetation Index (SAVI) through a Kcb-SAVI functional relationship. For the MI treatment, we defined the actual transpiration ETc as the product of Kcb and the stress reduction coefficient Ks obtained as the ratio of actual to crop ETc, and we correlated Ks with MI midday stem water potential (ψst) values through a Ks-ψ functional relationship. Operational monitoring of ETc was then implemented with the ETc = Kcb(SAVI)Ks(ψ)ETo relationship stemmed from the FAO56 approach and validated taking as inputs collected SAVI and ψst data reporting to year 2011. Low validation error (6%) and high goodness-of-fit of prediction were observed (R2 = 0.94, RSME = 0.2 mm day-1, P = 0.0015), allowing to consider that under field conditions it is possible to predict ETc values for our hedgerow olive orchards if SAVI and water potential (ψst) values are known.

Dynamical modeling of biogas production supported by an experimental anaerobic digestion process

This paper presents the development of a combined experimental and numerical approach to study the anaerobic digestion of both the wastes produced in a biorefinery using yeast for biodiesel production and the wastes generated in the preceding microbial biomass production. The experimental results show that it is possible to valorise through anaerobic digestion all the tested residues. In the implementation of the numerical model for anaerobic digestion, a procedure for the identification of its parameters needs to be developed. A hybrid search Genetic Algorithm was used, followed by a direct search method. In order to test the procedure for estimation of parameters, first noise-free data was considered and a critical analysis of the results obtain so far was undertaken. As a demonstration of its application, the procedure was applied to experimental data.

On The Use of Genetic Algorithms for Parameter Identification in Anaerobic Digestion Modelling

Anaerobic digestion (AD) of wastewater is a very interesting option for waste valorization, energy production and environment protection. It is a complex, naturally occurring process that can take place inside bioreactors. The capability of predicting the operation of such bioreactors is important to optimize the design and the operation conditions of the reactors, which, in part, justifies the numerous AD models presently available. The existing AD models are not universal, have to be inferred from prior knowledge and rely on existing experimental data. Among the tasks involved in the process of developing a dynamical model for AD, the estimation of parameters is one of the most challenging. This paper presents the identifiability analysis of a nonlinear dynamical model for a batch reactor. Particular attention is given to the structural identifiability of the model, which considers the uniqueness of the estimated parameters. To perform this analysis, the GenSSI toolbox was used. The estimation of the model parameters is achieved with genetic algorithms (GA) which have already been used in the context of AD modelling, although not commonly. The paper discusses its advantages and disadvantages.

Evaluation of DUALEM, OptRx and Grassmaster II sensors for monitoring the spatial variability and the temporal stability of a Mediterranean pasture

Site-specific management (SSM) is a form of precision agriculture whereby decisions on resource application and agronomic practices are improved to better match soil and crop requirements as they vary in the field. SSM enables the identification of regions (homogeneous management zones) within the area delimited by field boundaries. These subfield regions constitute areas that have similar permanent characteristics. Traditional soil and pasture sampling and the necessary laboratory analysis are time-consuming, labour-intensive and cost prohibitive, not viable from a SSM perspective because it needs a large number of soil and pasture samples in order to achieve a good representation of soil properties, nutrient levels and pasture quality and productivity. The main objective of this work was to evaluate technologies which have potential for monitoring aspects related to spatial and temporal variability of soil nutrients and pasture green and dry matter yield (respectively, GM and DM, in kg/ha) and support to decision making for the farmer. Three types of sensors were evaluated in a 7ha pasture experimental field: an electromagnetic induction sensor (“DUALEM 1S”, which measures the soil apparent electrical conductivity, ECa), an active optical sensor ("OptRx®", which measures the NDVI, “Normalized Difference Vegetation Index”) and a capacitance probe ("GrassMaster II" which estimates plant mass). The results indicate the possibility of using a soil electrical conductivity probe as, probably, the best tool for monitoring not only some of the characteristics of the soil, but also those of the pasture, which could represent an important help in simplifying the process of sampling and support SSM decision making, in precision agriculture projects. On the other hand, the significant and very strong correlations obtained between capacitance and NDVI and between any of these parameters and the pasture productivity shows the potential of these tools for monitoring the evolution of spatial and temporal patterns of the vegetative growth of biodiverse pasture, for identifying different plant species and variability in pasture yield in Alentejo dry-land farming systems. These results are relevant for the selection of an adequate sensing system for a particular application and open new perspectives for other works that would allow the testing, calibration and validation of the sensors in a wider range of pasture production conditions, namely the extraordinary diversity of botanical species that are characteristic of the Mediterranean region at the different periods of the year.