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Spatial aggregation using Bayesian aggregation (BA) is effective in combining multiple measurements to detect weak sources of gamma radiation in mobile source detection applications. To perform spatial aggregation of evidence, the position of the sensor must be estimated over time, in synchronization with gamma-ray measurements. Prevalent low-cost position estimation approaches often suffer from inaccuracies on a scale which can affect aggregation performance. Due to the presence of large buildings, positioning technology modalities such as GPS can show higher levels of error in urban environments. Additionally, urban environments can have highly varying structures, be crowded and dynamic, causing time-varying occlusions in the line-of-sight from the sensor to the source. Both occlusions and errors in sensor position estimation can degrade the source detection performance. We use asymptotic analysis to characterize the magnitude of this degradation. Natural maximum-likelihood and marginalization-based extensions to a BA framework are then used to improve robustness of aggregation in the face of these issues. The proposed approach shows substantially improved robustness to positioning errors and dynamic occlusions when compared to the baseline BA.

Edge computing is rapidly changing the IoT-Cloud landscape. Various testbeds are now able to run multiple Docker-like containers developed and deployed by end-users on edge devices. However, this capability may allow an attacker to deploy a malicious container on the host and compromise it. This paper presents a dataset based on the Linux Auditing System, which contains malicious and benign container activity. We developed two malicious scenarios, a denial of service and a privilege escalation attack, where an adversary uses a container to compromise the edge device. Furthermore, we deployed benign user containers to run in parallel with the malicious containers. Container activity can be captured through the host system via system calls. Our time series auditd dataset contains partial labels for the benign and malicious related system calls. Generating the dataset is largely automated using a provided AutoCES framework. We also present a semi-supervised machine learning use case with the collected data to demonstrate its utility. The dataset and framework code are open-source and publicly available.

'Diabetic foot' is a term used to describe a group of syndromes in which neuropathy and/or ischaemia lead to tissue breakdown and reduced resistance to infection in a patient with diabetes mellitus. Foot ulceration is a serious and potentially limb-threatening complication of diabetes that can lead to pain, tissue necrosis and amputation, and may significantly affect an individual's well-being and mobility. Foot complications also have wider economic implications for healthcare services. Therefore, it is important to reduce the risk of foot complications through regular foot assessments and providing patient education and appropriate referral to specialist services. This article discusses foot complications in people with diabetes, outlining the causes, signs and symptoms, and associated risk factors. It also outlines the assessment and prevention measures that patients and healthcare practitioners can take to reduce the risk of foot complications.

The National Diabetes Stakeholders Covid‐19 Response Group was formed in early April 2020 as a rapid action by the Joint British Diabetes Societies for Inpatient Care, Diabetes UK, the Association of British Clinical Diabetologists, and Diabetes Frail to address and support the special needs of residents with diabetes in UK care homes during Covid‐19. It was becoming obvious that the care home sector was becoming a second wave of Covid‐19 infection and that those with diabetes residing in care homes were at increased risk not only of susceptibility to infection but also to poorer outcomes. Its key purposes included minimising the morbidity and mortality associated with Covid‐19 and assisting care staff to identify those residents with diabetes at highest risk of Covid‐19 infection. The guidance was particularly created for care home managers, other care home staff, and specialist and non‐specialist community nursing teams. The guidance covers the management of hyperglycaemia by discussion of various clinical scenarios that could arise, the management of hypoglycaemia, foot care and end of life care. In addition, it outlines the conditions where hospital admission is required. The guidance should be regarded as interim and will be updated as further medical and scientific evidence becomes available.

The language used by healthcare professionals can have a profound impact on how people living with diabetes, and those who care for them, experience their condition and feel about living with it day-to-day. At its best, good use of language, both verbal and written, can lower anxiety, build confidence, educate and help to improve self-care. Conversely, poor communication can be stigmatizing, hurtful and undermining of self-care and can have a detrimental effect on clinical outcomes. The language used in the care of those with diabetes has the power to reinforce negative stereotypes, but it also has the power to promote positive ones. The use of language is controversial and has many perspectives. The development of this position statement aimed to take account of these as well as the current evidence base. A working group, representing people with diabetes and key organizations with an interest in the care of people with diabetes, was established to review the use of language. The work of this group has culminated in this position statement for England. It follows the contribution of Australia and the USA to this important international debate. The group has set out practical examples of language that will encourage positive interactions with those living with diabetes and subsequently promote positive outcomes. These examples are based on a review of the evidence and are supported by a simple set of principles.