Management factors affecting the use of pasture by table chickens in extensive production systems

Whether chickens will make proper use of pasture is a problem experienced by producers of free-range and organic chickens. The aims of this project are to identify husbandry techniques and aspects of system design that encourage good pasture use. Two studies have been conducted comprising a winter and a summer flock. The aim of the winter flock was to examine the effect of outdoor artificial shelter on pasture usage. This was done for female Ross 308 birds grown to day 56, and ISA 657 birds grown to day 81. In summer, ISA 657 birds were grown to day 81. Treatments were either standard or enriched brooding, with pasture only or enriched pasture. Standard brooding was in a controlled environment house until day 42. Enriched brooding was in naturally ventilated houses in which birds had sight of pasture from an early age and access from day 21. Enriched pasture included artificial shelter, with straw bales and a conifer “wigwam” used to provide natural shelter. Chickens may be encouraged to go outdoors by brooding in a less “controlled” environment than that used for intensive broilers, and by allowing access to pasture when young. However, mortality was higher. Conifer wigwams may offer a means for more even use of pasture and better distribution of droppings

Government and interest group relations: An analysis of the Canadian Association for the Advancement of Women and Sport

The Canadian Association for the Advancement of Women and Sport (CAAWS) is a non-profit feminist advocacy organization established to work towards enhancing the position of women and girls in sport in Canada. Imbedded in a sport system which reflects societal values, CAAWS has undertaken activities to eliminate gender inequity, such as lack of acknowledgment, inadequate financial support and limited opportunities for participation, in Canadian sport. This study has addressed the question of whether or not CAAWS has been coopted as a result of its relationship with the federal government agencies responsible for sport in Canada (i.e., Sport Canada and Sport Canada\u27s Women\u27s Program), as well as with the Secretary of State‘s Women\u27s Program. In the process of examining this relationship, consideration was given to CAAWS as a hybrid organization, combining elements of a feminist organization and an interest group. This evaluation was based on a review of literature related to interest groups and feminist organizations. The question of the impact of government funding on this group was also studied. Consideration was given to how this funding can change an organization. This analysis drew on literature related to government and interest group relations. In analyzing the consequences of the relationship between CAAWS and Sport Canada and Sport Canada’s Women’s Program, a conceptual model of cooptation was utilized which allowed this researcher to consider all questions related to the process of cooptation, such as the conditions and/ or (actors at work, the types of tactics used, the forms of control exercised and the consequences of these actions. This model was developed based on the literature. The analysis found that CAAWS has not been coopted as a result of its relationship with these government agencies. None the less the outcome of this group\u27s relationship with Sport Canada‘s Women’s Program and, to a lesser degree, the Women\u27s Program of the Secretary of State has resulted in: 1) constrained advocacy; 2) alteration of the organizational structure; 3) a shift from a collectivist group to a more institutionalized form; 4) a change in objectives; 5) financial insecurity, and, 6) undermined organizational autonomy. These consequences have come about as a result of internal conflicts and organizational decisions regarding group priorities. External influences from government, such as the provision of financial support, have intensified some of the internal strife, but are not solely responsible for the changes in the organization. These changes have come about as a result of numerous internal and external factors

Wifi-based human activity recognition using Raspberry Pi.

Ambient, non-intrusive approaches to smart home health monitoring, while limited in capability, are preferred by residents. More intrusive methods of sensing, such as video and wearables, can offer richer data but at the cost of lower resident uptake, in part due to privacy concerns. A radio frequency-based approach to sensing, Channel State Information (CSI),can make use of low cost off-the-shelf WiFi hardware. We have implemented an activity recognition system on the Raspberry Pi 4, one of the world’s most popular embedded boards. We have implemented an classification system using the Pi to demonstrate its capability for activity recognition. This involves performing data collection, interpretation and windowing, before supplying the data to a classification model. In this paper, the capabilities of the Raspberry Pi 4 at performing activity recognition on CSI data are investigated. We have developed and publicly released a data interaction framework, capable of interpreting, processing and visualising data from a range of CSI-capable hardware. Furthermore, CSI data captured for these experiments during various activity performances have also been made publically available. We then train a Deep Convolutional LSTM model to classify the activities. Our experiments, performed in a small apartment, achieve 92% average accuracy on 11 activity classes

Fall prediction using behavioural modelling from sensor data in smart homes.

The number of methods for identifying potential fall risk is growing as the rate of elderly fallers continues to rise in the UK. Assessments for identifying risk of falling are usually performed in hospitals and other laboratory environments, however these are costly and cause inconvenience for the subject and health services. Replacing these intrusive testing methods with a passive in-home monitoring solution would provide a less time-consuming and cheaper alternative. As sensors become more readily available, machine learning models can be applied to the large amount of data they produce. This can support activity recognition, falls detection, prediction and risk determination. In this review, the growing complexity of sensor data, the required analysis, and the machine learning techniques used to determine risk of falling are explored. The current research on using passive monitoring in the home is discussed, while the viability of active monitoring using vision-based and wearable sensors is considered. Methods of fall detection, prediction and risk determination are then compared

Visualisation to explain personal health trends in smart homes.

An ambient sensor network is installed in Smart Homes to identify low-level events taking place by residents, which are then analysed to generate a profile of activities of daily living. These profiles are compared to both the resident's typical profile and to known 'risky' profiles to support recommendation of evidence-based interventions. Maintaining trust presents an XAI challenge because the recommendations are not easily interpretable. Trust in the system can be improved by making the decision-making process more transparent. We propose a visualisation workflow which presents the data in clear, colour-coded graphs

A Hierarchical Taxonomy of Psychopathology (HiTOP) Primer for Mental Health Researchers

Mental health research is at an important crossroads as the field seeks more reliable and valid phenotypes to study. Dimensional approaches to quantifying mental illness operate outside the confines of traditional categorical diagnoses, and they are gaining traction as a way to advance research on the causes and consequences of mental illness. The Hierarchical Taxonomy of Psychopathology (HiTOP) is a leading dimensional research paradigm that synthesizes decades of data on the major dimensions of psychological disorders. In this article, we demonstrate how to use the HiTOP model to formulate and test research questions through a series of tutorials. To boost accessibility, data and annotated code for each tutorial are included at OSF ( https://osf.io/8myzw ). After presenting the tutorials, we outline how investigators can use these ideas and tools to generate new insights in their own substantive research programs

Monitoring health in smart homes using simple sensors.

We consider use of an ambient sensor network, installed in Smart Homes, to identify low level events taking place which can then be analysed to generate a resident's profile of activities of daily living (ADLs). These ADL profiles are compared to both the resident's typical profile and to known 'risky' profiles to support evidence-based interventions. Human activity recognition to identify ADLs from sensor data is a key challenge, a windowbased representation is compared on four existing datasets. We find that windowing works well, giving consistent performance. We also introduce FITsense, which is building a Smart Home environment to specifically identify increased risk of falls to allow interventions before falls occurs

Representing temporal dependencies in human activity recognition.

Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident’s behaviour can be produced from sensor data, and then compared over time. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs and consider the temporal dependencies that exist in binary ambient sensor data in order to produce case-based representations. These LSTM approaches are compared to the performance of a selection of baseline classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier

Representing temporal dependencies in smart home activity recognition for health monitoring.

Long term health conditions, such as fall risk, are traditionally diagnosed through testing performed in hospital environments. Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident’s behaviour can be produced from sensor data, and then compared overtime. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs which consider the temporal dependencies present in the sensor data in order to produce richer representations and improved classification accuracy. The LSTM approaches are compared to the performance of a selection of base line classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier

Motivational Changes in Reading Recovery Children: A Pre and Post Analysis

This investigation set out to determine whether Reading Recovery students demonstrate positive responses in regard to motivational constructs of self-concept, competence beliefs, and/or valuing of reading within Reading Recovery