Towards an interoperable healthcare information infrastructure - working from the bottom up

Historically, the healthcare system has not made effective use of information technology. On the face of things, it would seem to provide a natural and richly varied domain in which to target benefit from IT solutions. But history shows that it is one of the most difficult domains in which to bring them to fruition. This paper provides an overview of the changing context and information requirements of healthcare that help to explain these characteristics.First and foremost, the disciplines and professions that healthcare encompasses have immense complexity and diversity to deal with, in structuring knowledge about what medicine and healthcare are, how they function, and what differentiates good practice and good performance. The need to maintain macro-economic stability of the health service, faced with this and many other uncertainties, means that management bottom lines predominate over choices and decisions that have to be made within everyday individual patient services. Individual practice and care, the bedrock of healthcare, is, for this and other reasons, more and more subject to professional and managerial control and regulation.One characteristic of organisations shown to be good at making effective use of IT is their capacity to devolve decisions within the organisation to where they can be best made, for the purpose of meeting their customers' needs. IT should, in this context, contribute as an enabler and not as an enforcer of good information services. The information infrastructure must work effectively, both top down and bottom up, to accommodate these countervailing pressures. This issue is explored in the context of infrastructure to support electronic health records.Because of the diverse and changing requirements of the huge healthcare sector, and the need to sustain health records over many decades, standardised systems must concentrate on doing the easier things well and as simply as possible, while accommodating immense diversity of requirements and practice. The manner in which the healthcare information infrastructure can be formulated and implemented to meet useful practical goals is explored, in the context of two case studies of research in CHIME at UCL and their user communities.Healthcare has severe problems both as a provider of information and as a purchaser of information systems. This has an impact on both its customer and its supplier relationships. Healthcare needs to become a better purchaser, more aware and realistic about what technology can and cannot do and where research is needed. Industry needs a greater awareness of the complexity of the healthcare domain, and the subtle ways in which information is part of the basic contract between healthcare professionals and patients, and the trust and understanding that must exist between them. It is an ideal domain for deeper collaboration between academic institutions and industry

Weight changes following lower limb arthroplasty : a prospective observational study

The aim of this study was to assess patterns of weight loss/gain following total hip or knee joint replacement. Four hundred and fifty primary lower limb arthroplasty patients, where the current surgery was the last limiting factor to improved mobility, were selected. Over a one year period 212 gained weight (mean 5.03kg), 92 remained static, and 146 lost weight. The median change was a weight gain of 0.50Kg (p=0.002). All patients had a significant improvement in Oxford outcome scores. Hip arthroplasty patients were statistically more likely to gain weight than knee arthroplasty patients. A successful arthroplasty, restoring a patient's mobility, does not necessarily lead to subsequent weight loss. The majority of patients put on weight with an overall net weight gain. No adverse effect on functional outcome was noted

Carbon accounting in the context of multi-criteria assessment for SLES: challenges and opportunities

In the UK, national carbon emission reduction targets aim to reach Net Zero by 2050, with a fully decarbonised electricity system by 2035. Smart Local Energy Systems (SLES) are being deployed to combine and intelligently control complementary low and zero carbon technologies within micro-gridsto amplify their impacts and accelerate this ambitious transition towards a decarbonized energy system and low-carbon society. Today, national and local governments monitor the potential carbon reduction of energy system retrofitting and policy implementation through simplified carbon accounting methods, which allow for calculation of the accumulated carbon emissions. This focus on carbon may, however, neglect broader socioeconomic impacts and benefits of these actions. This paper describes the how the application of a multi-criteria assessment tool focusing on SLES can be used to evaluate (i) the carbon emissions from an energy system and (ii) the carbon reduction potential of renewable and smart energy technology implementation. Alongside the carbon accounting this MCA-SLES tool provides assessment and insights into the local socioeconomic and environmental benefits and impacts of the SLES development. The application of such a complex assessment tool has challenges in application, such as data collection, the intensity of the stakeholder approach, and the large volume of information for user dissemination. This paper illustrates how the developed assessment tool mitigates for these challenges and highlights the opportunity for small-scale energy development projects to employ it to assess project feasibility and progress towards economic, social, and environmental co-benefits

Experiences of 4-H Japanese Exchange Program on Participants: An Evaluative Study

The study described here assessed the 4-H Japanese Exchange Program in terms of arrival and departure orientation programs, 4-H program expectations, host family expectations, program coordination, and school and community experience. Overall, findings indicated that participants rated their exchange experience as excellent. The service received from 4-H, friendliness, and professionalism were also rated highly by participants. Seventy-one percent indicated that they would recommend the 4-H exchange program to others in their home country. As a result of participating in the 4-H Japanese Exchange Program, participants agreed that they better understand intercultural sensitivity and global perspectives

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

Matrix controlled channel diffusion of sodium in amorphous silica

To find the origin of the diffusion channels observed in sodium-silicate glasses, we have performed classical molecular dynamics simulations of Na$_2$O--4SiO$_2$ during which the mass of the Si and O atoms has been multiplied by a tuning coefficient. We observe that the channels disappear and that the diffusive motion of the sodium atoms vanishes if this coefficient is larger than a threshold value. Above this threshold the vibrational states of the matrix are not compatible with those of the sodium ions. We interpret hence the decrease of the diffusion by the absence of resonance conditions.Comment: 5 pages, 4 figure

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US

Aging of CKN:Modulus Versus Conductivity Analysis

It was recently reported that the electrical modulus peaks narrows upon annealing of the ionic system CKN [Paluch et al., Phys. Rev. Lett. 110, 015702 (2013)], which was interpreted as providing evidence of dynamic heterogeneity of this glass-forming liquid. An analysis of the same data in terms of the ac conductivity shows no shape changes, however. We discuss the relation between both findings and show further that the ac conductivity conforms to the prediction of the random barrier model (RBM) at all times during the annealing

On the ubiquity of trivial torsion on elliptic curves

The purpose of this paper is to give a "down--to--earth" proof of the well--known fact that a randomly chosen elliptic curve over the rationals is most likely to have trivial torsion

Managing Vegetation In Grassland Habitats To Enhance Livestock Or Wildlife Objectives

Sustainably stewarding grassland systems involves applying various practices to manipulate forage interactions with other plants, the environment, and grazing animals to meet resource manager objectives. These interactions can result in invasion or encroachment and increased abundance of weeds which hinder attainment of management objectives. Weeds influence the structure and function of pasture ecosystems whether forages are grown in improved pastures, rangeland, or grassland communities. They degrade pasture quality and reduce livestock performance by interfering with forage establishment, yield, and quality by competing for resources. Weeds reduce the feed value of forage, decrease pasture carrying capacity, and can be toxic or unpalatable to livestock. Managing weeds requires use of vegetation management tools that favor desirable forages. Herbicides can be a catalyst that expedite grassland renovation, improve the forage resource, and increase carrying capacity. Corteva Agriscience has a variety of herbicide products that provide superior control of herbaceous and woody weeds, while maintaining the desirable vegetation. These herbicides were designed and developed specifically for selective broadleaf weed control in rangeland, pastures, rights-of-way, non-cropland, and natural areas. Active ingredients historically used include aminopyralid, triclopyr, fluroxypyr, clopyralid, and picloram. Rinskor™ active and Arylex™ active are new herbicide active ingredients from Corteva Agriscience™ and are members of a unique synthetic auxin chemotype, the arylpicolinates (HRAC group O / WSSA group 4). Members of the arylpicolinate family demonstrate novel and differentiated characteristics in terms of use rate, spectrum, weed symptoms, environmental fate, and molecular interaction as compared to other auxin chemotypes. When applied as a stand-alone treatment or in various mixes these products are safe to desirable grass species and control key herbaceous and woody weeds in the genera Ambrosia, Acacia, Carduus, Centaurea, Cirsium, Mimosa, Prosopis, Ranunculus, Rumex, Sida, Solanum, Taraxacum, and more