A review of the welfare of dairy cows in continuously housed and pasture-based production systems.

There is increasing interest in the use of continuous housing systems for dairy cows, with various reasons put forward to advocate such systems. However, the welfare of dairy cows is typically perceived to be better within pasture-based systems, although such judgements are often not scientifically based. The aim of this review was to interrogate the existing scientific literature to compare the welfare, including health, of dairy cows in continuously housed and pasture-based systems. While summarising existing work, knowledge gaps and directions for future research are also identified. The scope of the review is broad, examining relevant topics under three main headings; health, behaviour, and physiology. Regarding health, cows on pasture-based systems had lower levels of lameness, hoof pathologies, hock lesions, mastitis, uterine disease, and mortality compared to cows on continuously housed systems. Pasture access also had benefits for dairy cow behaviour, in terms of grazing, improved lying / resting times, and lower levels of aggression. Moreover, when given the choice between pasture and indoor housing, cows showed an overall preference for pasture, particularly at night. However, the review highlighted the need for a deeper understanding of cow preference and behaviour. Potential areas for concern within pasture-based systems included physiological indicators of more severe negative energy balance, and in some situations, the potential for compromised welfare with exposure to unpredictable weather conditions. In summary, the results from this review highlight that there remain considerable animal welfare benefits from incorporating pasture access into dairy production systems.

Towards a knowledge-based approach for effective decision-making in railway safety

Purpose – This paper aims to contribute towards understanding how safety knowledge can be elicited from railway experts for the purposes of supporting effective decision-making. Design/methodology/approach – A consortium of safety experts from across the British railway industry is formed. Collaborative modelling of the knowledge domain is used as an approach to the elicitation of safety knowledge from experts. From this, a series of knowledge models is derived to inform decision-making. This is achieved by using Bayesian networks as a knowledge modelling scheme, underpinning a Safety Prognosis tool to serve meaningful prognostics information and visualise such information to predict safety violations. Findings – Collaborative modelling of safety-critical knowledge is a valid approach to knowledge elicitation and its sharing across the railway industry. This approach overcomes some of the key limitations of existing approaches to knowledge elicitation. Such models become an effective tool for prediction of safety cases by using railway data. This is demonstrated using passenger–train interaction safety data. Practical implications – This study contributes to practice in two main directions: by documenting an effective approach to knowledge elicitation and knowledge sharing, while also helping the transport industry to understand safety. Social implications – By supporting the railway industry in their efforts to understand safety, this research has the potential to benefit railway passengers, staff and communities in general, which is a priority for the transport sector. Originality/value – This research applies a knowledge elicitation approach to understanding safety based on collaborative modelling, which is a novel approach in the context of transport.

Field Testing and Development of Novel SHM systems at Queen’s University Belfast

Ageing and deterioration of infrastructure is a challenge facing transport authorities. In
particular, there is a need for increased bridge monitoring in order to provide adequate
maintenance and to guarantee acceptable levels of transport safety. The Intelligent
Infrastructure group at Queens University Belfast (QUB) are working on a number of aspects
of infrastructure monitoring and this paper presents summarised results from three distinct
monitoring projects carried out by this group. Firstly the findings from a project on next
generation Bridge Weight in Motion (B-WIM) are reported, this includes full scale field testing
using fibre optic strain sensors. Secondly, results from early phase testing of a computer
vision system for bridge deflection monitoring are reported on. This research seeks to exploit
recent advances in image processing technology with a view to developing contactless
bridge monitoring approaches. Considering the logistical difficulty of installing sensors on a
‘live’ bridge, contactless monitoring has some inherent advantages over conventional
contact based sensing systems. Finally the last section of the paper presents some recent
findings on drive by bridge monitoring. In practice a drive-by monitoring system will likely
require GPS to allow the response of a given bridge to be identified; this study looks at the
feasibility of using low-cost GPS sensors for this purpose, via field trials. The three topics
outlined above cover a spectrum of SHM approaches namely, wired monitoring, contactless
monitoring and drive by monitoring

An ether-functionalised cyclic sulfonium based ionic liquid as an electrolyte for electrochemical double layer capacitors

A novel cyclic sulfonium cation-based ionic liquid (IL) with an ether-group appendage and the bis{(trifluoromethyl)sulfonyl}imide anion was synthesised and developed for electrochemical double layer capacitor (EDLC) testing. The synthesis and chemical-physical characterisation of the ether-group containing IL is reported in parallel with a similarly sized alkyl-functionalised sulfonium IL. Results of the chemical-physical measurements demonstrate how important transport properties, i.e. viscosity and conductivity, can be promoted through the introduction of the ether-functionality without impeding thermal, chemical or electrochemical stability of the IL. Although the apparent transport properties are improved relative to the alkyl-functionalised analogue, the ether-functionalised sulfonium cation-based IL exhibits moderately high viscosity, and poorer conductivity, when compared to traditional EDLC electrolytes based on organic solvents (propylene carbonate and acetonitrile). Electrochemical testing of the ether-functionalised sulfonium IL was conducted using activated carbon composite electrodes to inspect the performance of the IL as a solvent-free electrolyte for EDLC application. Good cycling stability was achieved over the studied range and the performance was comparable to other solvent free,
IL-based EDLC systems. Nevertheless, limitations of the attainable performance are primarily the result of sluggish transport properties and a restricted operative voltage of the IL, thus highlighting key aspects of this field which require further attention.

Laboratory investigation of contactless displacement measurement using computer vision systems

Much of the bridge stock on major transport links in North America and Europe was constructed in the 1950’s and 1960’s and has since deteriorated or is carrying loads far in excess of the original design loads. Structural Health Monitoring Systems (SHM) can provide valuable information on the bridge capacity but the application of such systems is currently limited by access and system cost. This paper investigates the development of a low cost portable SHM system using commercially available cameras and computer vision techniques. A series of laboratory tests have been carried out to test the accuracy of displacement measurements using contactless methods. The results from each of the tests have been validated with established measurement methods, such as linear variable differential transformers (LVDTs). A video image of each test was processed using two different digital image correlation programs. The results obtained from the digital image correlation methods provided an accurate comparison with the validation measurements. The calculated displacements agree within 4% of the verified measurements LVDT measurements in most cases confirming the suitability full camera based SHM systems

Proposing the deep dynamic Bayesian network as a future computer based medical system

The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.