Animal-assisted interventions: making better use of the human-animal bond

In the third of Veterinary Record’s series of articles promoting One Health, Daniel Mills and Sophie Hall discuss the therapeutic effects of companion animals, the influence of pets on childhood development and how researchers are elucidating the true value of animal companionship. IT has been proposed that the One Health initiative should be extended to ‘One Welfare’, in recognition of the diverse links between the welfare of human beings and other animals (Anon 2012). This is particularly true for companion animals, with a growing body of evidence indicating the diverse stress-ameliorating effects of the relationships between people and pets; however, their importance to mental and physical health from a developmental perspective(particularly for people) is perhaps not given the attention it deserves. This is potentially a serious oversight for ealthcare professionals, policymakers and government, at a time when there are concerns over the growing cost of public healthcare in the industrialised world. Indeed, in the current economic climate, there is perhaps a greater need than ever to consider novel approaches to preventive healthcare, such as the value of animal companionship, since such approaches are potentially more cost-effective and socially acceptable than technological solutions. Companion animals should not be considered a luxury or unnecessary indulgence, but rather, when cared for appropriately, they should be seen as valuable contributors to human health and wellbeing and, as a result, society and the broader economy

Socio-demographic factors associated with pet ownership amongst adolescents from a UK birth cohort

Background: In developed nations, pet ownership is common within families. Both physical and psychological health benefits may result from owning a pet during childhood and adolescence. However, it is difficult to determine whether these benefits are due to pet ownership directly or to factors linked to both pet ownership and health. Previous research found associations between a range of socio-demographic factors and pet ownership in seven-year-old children from a UK cohort. The current study extends this research to adolescence, considering that these factors may be important to consider in future Human-Animal Interaction (HAI) research across childhood.Results:The Avon Longitudinal Study of Parents and Children (ALSPAC) collected pet ownership data prospectively via maternal reports from gestation up to age 10 years old and via self-report retrospectively at age 18 for ages 11(n= 3063) to 18 years old (n= 3098) on cats, dogs, rabbits, rodents, birds, fish, tortoise/turtles and horses. The dataset also contains a wide range of potential confounders, including demographic and socio-economic variables.The ownership of all pet types peaked at age 11 (80%) and then decreased during adolescence, with the exclusion of cats which remained constant (around 30%), and dogs which increased through 11–18 years (26–37%). Logistic regression was used to build multivariable models for ownership of each pet type at age 13 years, and the factors identified in these models were compared to previously published data for 7 year-olds in the same cohort. There was some consistency with predictors reported at age 7. Generally sex, birth order, maternal age, maternal education, number of people in the household, house type, and concurrent ownership of other pets were associated with pet ownership at both 7 and 13 years (the direction of association varied according to pet type).Factors that were no longer associated with adolescent pet ownership included child ethnicity, paternal education,and parental social class.Conclusions:A number of socio-demographic factors are associated with pet ownership in childhood and adolescence and they differ according to the type of pet, and age of child. These factors are potential confounders that must be considered in future HAI studies

Dog bite prevention:Effect of a short educational intervention for preschool children

This study aimed to investigate whether preschool children can learn how to interpret dogs' behaviours, with the purpose of helping avoid dog bites. Three- to five-year-old children (N = 70) were tested on their ability to answer questions about dogs' emotional states before and after participating in either an educational intervention about dog behaviour (intervention group) or an activity about wild animals (control group). Children who had received training about dog behaviour (intervention group) were significantly better at judging the dogs' emotional states after the intervention compared to before. The frequency with which they referred to relevant behaviours in justifying their judgements also increased significantly. In contrast, the control group's performance did not differ significantly between the two testing times. These results indicate that preschool children can be taught how to correctly interpret dogs' behaviours. This implies that incorporating such training into prevention programmes may contribute to reducing dog bite incidents

Model Network Methodology for Experimental Development of Industrial Monitoring Systems

Industrial systems enable modern life. They benefit tremendously by adapting digital communication technologies and leveraging automation algorithms and data availability. Their importance to basic human needs such as electricity, heating, food, transportation, clothing, and more also means that their constant availability and reliability is imperative in modern societies. Plant monitoring strategies that can collect information and use it to analyze and understand plant behavior is a key technology for optimizing industrial systems. Enabling plant monitoring insights to be communicated to human operators is essential to ensure the information can be used. While data-based methods continue to find new applications, model-based methods that incorporate unique plant characteristics and industry-specific considerations alone have the dual benefits of explainability and extrapolability. By developing plant monitoring systems that enable operators to understand plant state, quickly identify developing faults, and mitigate issues before they cause harm, designers can radically improve industrial system operations and management. Through thoughtful human-centered design of the interfaces between human and machine, they can elevate the role of industrial operators to orchestrate the plant monitoring system’s set of autonomous routines. This dissertation presents a methodology for the systematic design and implementation of a plant monitoring and operator support system running a fault diagnostic and decision support engine that can be adapted for a variety of industrial monitoring applications. It then demonstrates, by proof-of-concept application to an experimental thermal-hydraulic facility - the Compact Integral Effects Test (CIET) - and advanced control room testbed - the Advanced Reactor Control and Operations (ARCO) facility - the iterative plant monitoring system development process. The focus of this dissertation is the advanced nuclear power industry and the Fluoride salt-cooled High-temperature Reactor (FHR). This dissertation is organized into eight sections. The first section introduces the background and motivation for model-based industrial monitoring systems before the second section provides an overview of the state-of-the-art for nuclear and other industry plant monitoring systems before focusing on nuclear industry challenges and opportunities. The third section details the iterative fault diagnostic system development methodology and the fourth section describes one approach to decision support and fault mitigation algorithm design. These sections also walk the reader through an example application. The fifth section then introduces the ARCO-CIET facility used in the case study and the sixth section describes the operator support and human-machine interface design for ARCO. Finally, the seventh section presents the case study plant monitoring system design and results before the eighth section discusses promising applications of the overall design methodology. This dissertation presents a methodology with the potential to guide the plant monitoring system development process across a variety of industries with the following original contributions: a methodology for iterative fault diagnostic system development using interdisciplinary information, recommendations for choosing plant models to build context between different monitoring objectives, a methodology for developing decision support routines, and guiding principles for plant monitoring system human-machine interface design and implementation in modern industrial control rooms

Model Network Methodology for Experimental Development of Industrial Monitoring Systems

Industrial systems enable modern life. They benefit tremendously by adapting digi-tal communication technologies and leveraging automation algorithms and data availability.Their importance to basic human needs such as electricity, heating, food, transportation,clothing, and more also means that their constant availability and reliability is impera-tive in modern societies. Plant monitoring strategies that can collect information and useit to analyze and understand plant behavior is a key technology for optimizing industrialsystems. Enabling plant monitoring insights to be communicated to human operators isessential to ensure the information can be used. While data-based methods continue tofind new applications, model-based methods that incorporate unique plant characteristicsand industry-specific considerations alone have the dual benefits of explainability and ex-trapolability. By developing plant monitoring systems that enable operators to understandplant state, quickly identify developing faults, and mitigate issues before they cause harm,designers can radically improve industrial system operations and management. Throughthoughtful human-centered design of the interfaces between human and machine, they canelevate the role of industrial operators to orchestrate the plant monitoring system’s set ofautonomous routines.This dissertation presents a methodology for the systematic design and implementationof a plant monitoring and operator support system running a fault diagnostic and decisionsupport engine that can be adapted for a variety of industrial monitoring applications. It thendemonstrates, by proof-of-concept application to an experimental thermal-hydraulic facility- the Compact Integral Effects Test (CIET) - and advanced control room testbed - theAdvanced Reactor Control and Operations (ARCO) facility - the iterative plant monitoringsystem development process. The focus of this dissertation is the advanced nuclear powerindustry and the Fluoride salt-cooled High-temperature Reactor (FHR).This dissertation is organized into eight sections. The first section introduces the back-ground and motivation for model-based industrial monitoring systems before the secondsection provides an overview of the state-of-the-art for nuclear and other industry plant2monitoring systems before focusing on nuclear industry challenges and opportunities. Thethird section details the iterative fault diagnostic system development methodology and thefourth section describes one approach to decision support and fault mitigation algorithmdesign. These sections also walk the reader through an example application. The fifth sec-tion then introduces the ARCO-CIET facility used in the case study and the sixth sectiondescribes the operator support and human-machine interface design for ARCO. Finally, theseventh section presents the case study plant monitoring system design and results beforethe eighth section discusses promising applications of the overall design methodology.This dissertation presents a methodology with the potential to guide the plant moni-toring system development process across a variety of industries with the following originalcontributions: a methodology for iterative fault diagnostic system development using in-terdisciplinary information, recommendations for choosing plant models to build contextbetween different monitoring objectives, a methodology for developing decision support rou-tines, and guiding principles for plant monitoring system human-machine interface designand implementation in modern industrial control rooms