DocLite: A Docker-Based Lightweight Cloud Benchmarking Tool

Existing benchmarking methods are time consuming processes as they typically benchmark the entire Virtual Machine (VM) in order to generate accurate performance data, making them less suitable for real-time analytics. The research in this paper is aimed to surmount the above challenge by presenting DocLite - Docker Container-based Lightweight benchmarking tool. DocLite explores lightweight cloud benchmarking methods for rapidly executing benchmarks in near real-time. DocLite is built on the Docker container technology, which allows a user-defined memory size and number of CPU cores of the VM to be benchmarked. The tool incorporates two benchmarking methods - the first referred to as the native method employs containers to benchmark a small portion of the VM and generate performance ranks, and the second uses historic benchmark data along with the native method as a hybrid to generate VM ranks. The proposed methods are evaluated on three use-cases and are observed to be up to 91 times faster than benchmarking the entire VM. In both methods, small containers provide the same quality of rankings as a large container. The native method generates ranks with over 90% and 86% accuracy for sequential and parallel execution of an application compared against benchmarking the whole VM. The hybrid method did not improve the quality of the rankings significantly.

A tool for the evaluation of slaughter horse welfare during unloading

This study aimed to develop a scientific and practical tool to be used to assess horse welfare after commercial transport over long journeys. A set of physical, behavioural and environmental measures was selected, covering welfare aspects of both transport and unloading procedures. The protocol was field-tested on 51 intra-EU commercial transports arriving at different sites in Italy. Univariate analysis was implemented to look for associations between the input variables (environmental hazards potentially affecting the animal well-being during long transports) and the outcome variables (direct evaluation of the animal condition). No severe welfare impairments were recorded (ie dead on arrival, severe injuries, non-ambulatory animals), while milder ones were more frequent at unloading (eg slipping; 36.7%, reluctance to move; 9.6%). Correlations emerged between ramp slope and falling; type of ramp floor and slipping; fast gait and the presence of gaps between the ramp and the floor. The horses' behaviour was also related to the type of handling procedure used. The measures were repeatable and practical to apply and score during real-time unloading. This work provides a sound basis for a new and practical welfare assessment tool for horses travelling over long journeys. Careful and constant application of this protocol would provide stakeholders with the opportunity to track and monitor changes in the industry over time, as well as to identify high risk areas in transport routines.

Pig producer perspectives on the use of meat inspection as an animal health and welfare diagnostic tool in the Republic of Ireland and Northern Ireland

Background
Currently, there is growing interest in developing ante and post mortem meat inspection (MI) to incorporate measures of pig health and welfare for use as a diagnostic tool on pig farms. However, the success of the development of the MI process requires stakeholder engagement with the process. Knowledge gaps and issues of trust can undermine the effective exchange and utilisation of information across the supply chain. A social science research methodology was employed to establish stakeholder perspectives towards the development of MI to include measures of pig health and welfare. In this paper the findings of semi-structured telephone interviews with 18 pig producers from the Republic of Ireland and Northern Ireland are presented.

Results
Producers recognised the benefit of the utilisation of MI data as a health and welfare diagnostic tool. This acknowledgment, however, was undermined for some by dissatisfaction with the current system of MI information feedback, by trust and fairness concerns, and by concerns regarding the extent to which data would be used in the producers’ interests. Tolerance of certain animal welfare issues may also have a negative impact on how producers viewed the potential of MI data. The private veterinary practitioner was viewed as playing a vital role in assisting them with the interpretation of MI data for herd health planning.

Conclusions
The development of positive relationships based on trust, commitment and satisfaction across the supply chain may help build a positive environment for the effective utilisation of MI data in improving pig health and welfare. The utilisation of MI as a diagnostic tool would benefit from the development of a communication strategy aimed at building positive relationships between stakeholders in the pig industry.

Development of a multivariable online monitoring system for the thermoforming process

The thermoforming industry has been relatively slow to embrace modern measurement technologies. As a result researchers have struggled to develop accurate thermoforming simulations as some of the key aspects of the process remain poorly understood. For the first time, this work reports the development of a prototype multivariable instrumentation system for use in thermoforming. The system contains sensors for plug force, plug displacement, air pressure and temperature, plug temperature, and sheet temperature. Initially, it was developed to fit the tooling on a laboratory thermoforming machine, but later its performance was validated by installing it on a similar industrial tool. Throughout its development, providing access for the various sensors and their cabling was the most challenging task. In testing, all of the sensors performed well and the data collected has given a powerful insight into the operation of the process. In particular, it has shown that both the air and plug temperatures stabilize at more than 80C during the continuous thermoforming of amorphous polyethylene terephthalate (aPET) sheet at 110C. The work also highlighted significant differences in the timing and magnitude of the cavity pressures reached in the two thermoforming machines. The prototype system has considerable potential for further development. 

A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings

Gun related violence is a complex issue and accounts for a large proportion of violent incidents. In the research reported in this paper, we set out to investigate the pro-gun and anti-gun sentiments expressed on a social media platform, namely Twitter, in response to the 2012 Sandy Hook Elementary School shooting in Connecticut, USA. Machine learning techniques are applied to classify a data corpus of over 700,000 tweets. The sentiments are captured using a public sentiment score that considers the volume of tweets as well as population. A web-based interactive tool is developed to visualise the sentiments and is available at this http://www.gunsontwitter.com. The key findings from this research are: (i) There are elevated rates of both pro-gun and anti-gun sentiments on the day of the shooting. Surprisingly, the pro-gun sentiment remains high for a number of days following the event but the anti-gun sentiment quickly falls to pre-event levels. (ii) There is a different public response from each state, with the highest pro-gun sentiment not coming from those with highest gun ownership levels but rather from California, Texas and New York.