Law at the Margins: The Displacement of Law as a Framework of Governance

Abstract included in tex

Law at the Margins: The Displacement of Law as a Framework of Governance

Abstract included in tex

A Cyber-Support System for Distributed Infrastructures

The Internet is now heavily relied upon by the Critical Infrastructures (CI). This has led to different security threats facing interconnected security systems. By understanding the complexity of critical infrastructure interdependency, and how to take advantage of it in order to minimize the cascading problem, enables the prediction of potential problems before they happen. Our proposed system, detailed in this paper, is able to detect cyber-attacks and share the knowledge with interconnected partners to create an immune system network. In order to demonstrate our approach, a realistic simulation is used to construct data and evaluate the system put forward. This paper provides a summary of the work to-date, on the development of a system titled Critical Infrastructure Auto-Immune Response System (CIAIRS). It provides a view of the main CIAIRS segments, which comprise the framework and illustrates the functioning of the system

Targeting the Microbiota to Address Diet-Induced Obesity: A Time Dependent Challenge

peer-reviewedLinks between the gut microbiota and host metabolism have provided new perspectives on obesity. We previously showed that the link between the microbiota and fat deposition is age- and time-dependent subject to microbial adaptation to diet over time. We also demonstrated reduced weight gain in diet-induced obese (DIO) mice through manipulation of the gut microbiota with vancomycin or with the bacteriocin-producing probiotic Lactobacillus salivarius UCC118 (Bac+), with metabolic improvement achieved in DIO mice in receipt of vancomycin. However, two phases of weight gain were observed with effects most marked early in the intervention phase. Here, we compare the gut microbial populations at the early relative to the late stages of intervention using a high throughput sequencing-based analysis to understand the temporal relationship between the gut microbiota and obesity. This reveals several differences in microbiota composition over the intervening period. Vancomycin dramatically altered the gut microbiota composition, relative to controls, at the early stages of intervention after which time some recovery was evident. It was also revealed that Bac+ treatment initially resulted in the presence of significantly higher proportions of Peptococcaceae and significantly lower proportions of Rikenellaceae and Porphyromonadaceae relative to the gut microbiota of L. salivarius UCC118 bacteriocin negative (Bac-) administered controls. These differences were no longer evident at the later time. The results highlight the resilience of the gut microbiota and suggest that interventions may need to be monitored and continually adjusted to ensure sustained modification of the gut microbiota.The authors are supported in part by Teagasc, Science Foundation Ireland (in the form of a research centre grant to the Alimentary Pharmabiotic Centre and PI awards to PWOT and PC) and by Alimentary Health Ltd

Hot of Not: Physiological versus Meteorological Heatwaves-Support for a Mean Temperature Threshold

The aim of this study was to determine whether a revised heat warning threshold provides an enhanced predictive tool for increases in Emergency Department heat-related presentations in Canberra, Australia. All Emergency Department triage records containing the word "heat", as well as those diagnosing a heat related illness for the summer periods 2013/2014, 2014/2015, and 2015/2016 were searched. Then a medical record review was conducted to confirm that the patient's presentation was related to environmental heat, which was defined by the final clinical diagnosis, presentation complaint and details of the patient's treatment. Researchers then compared this presentation data, to a mean threshold formula. The mean threshold formula included the past three consecutive daily mean temperatures and the last measured temperature upon presentation. This formula was designed to take into account the variance of night-time lows, with concurrent daily ambient temperatures, and was used to determine whether there was a correlation between heat-related presentations and increasing mean temperatures. Heat-related presentations appeared to occur when the mean threshold temperature reached 25 °C (77 °F), with significant increases when the mean threshold reached 30 °C (86 °F). These results confirm that a mean temperature of 30 °C corresponds to a relevant local public health heat-related threat

Profiling Users in the Smart Grid

The implementation of the smart grid brings with it many new components that are fundamentally different to traditional power grid infrastructures. The most important addition brought by the smart grid is the application of the Advanced Metering Infrastructure (AMI). As part of the AMI, the smart meter device provides real time energy usage about the consumer to all of the smart grids stakeholders. Detailed statistics about a consumer’s energy usage can be accessed by the end user, utility companies and other parties. The problem, however, is in how to analyse, present and make best use of the data. This paper focuses on the data collected from the smart grid and how it can be used to detect abnormal user behaviour for energy monitoring applications. The proposed system employs a data classification technique to identify irregular energy usage in patterns generated by smart meters. The results show that it is possible to detect abnormal behaviour with an overall accuracy of 99.45% with 0.100 for sensitivity, 0.989 for specificity and an error of 0.006 using the LDC classifier

A Smart Health Monitoring Technology

With the implementation of the Advanced Metering Infrastructure (AMI), comes the opportunity to gain valuable insights into an individual’s daily habits, patterns and routines. A vital part of the AMI is the smart meter. It enables the monitoring of a consumer’s electricity usage with a high degree of accuracy. Each device reports and records a consumer’s energy usage readings at regular intervals. This facilitates the identification of emerging abnormal behaviours and trends, which can provide operative monitoring for people living alone with various health conditions. Through profiling, the detection of sudden changes in behaviour is made possible, based on the daily activities a patient is expected to undertake during a 24-hour period. As such, this paper presents the development of a system which detects accurately the granular differences in energy usage which are the result of a change in an individual’s health state. Such a process provides accurate monitoring for people living with self-limiting conditions and enables an early intervention practice (EIP) when a patient’s condition is deteriorating. The results in this paper focus on one particular behavioural trend, the detection of sleep disturbances; which is related to various illnesses, such as depression and Alzheimer’s. The results demonstrate that it is possible to detect sleep pattern changes to an accuracy of 95.96% with 0.943 for sensitivity, 0.975 for specificity and an overall error of 0.040 when using the VPC Neural Network classifier. This type of behavioral detection can be used to provide a partial assessment of a patient’s wellbeing