The Experience and Impact of a Fitbit-Based Physical Activity and Sleep Hygiene Intervention for Older Adults Experiencing Cognitive Decline

Objectives: This study aimed to understand the experience and impact of a physical activity and sleep wrist-worn tracker (Fitbit)-based healthy lifestyle intervention for older patients attending a memory assessment service, who are experiencing cognitive impairment but do not receive a dementia diagnosis. Methods: A qualitative design was employed. Semi-structured interviews were conducted with a purposeful sample of thirteen participants recruited from a memory assessment service. Thematic analysis, that was data driven and inductive, was undertaken to analyse the data. Results: Two global themes were developed. “Understanding exercise and sleep as part of my lifestyle” was made up of themes representing how participants viewed exercise and sleep as part of their lifestyles in terms of acknowledging the positive impacts and the barriers to exercise and sleep. The second global theme “Understanding my experience of the healthy lifestyle intervention” was made up of themes that identified the positive impact of the intervention regarding improving health and wellbeing, enabling validation of proactive behaviours and motivation to engage in healthy lifestyle behaviours, so promoting positive behaviour change. Conclusion: Patients experiencing age-related cognitive impairment, applied and benefited from a healthy lifestyle Fitbit-based intervention to facilitate and promote physical activity, better sleep hygiene and healthy lifestyles.

“Active Feedback” Fitbit-Based Physical Activity and Sleep Hygiene Intervention for Memory Assessment Service (MAS) Patients with Cognitive Deficits: Feasibility, Acceptability, Sleep Quality, Stress, and Wellbeing Outcomes

Research Background: Compared to the general population, people experiencing age-related cognitive decline are more likely to have low levels of physical activity and sleep problems. Sufficient physical activity and quality sleep are protective factors against cognitive decline and poor health and can improve coping with stressors. The “Active Feedback” intervention comprises a wearable activity and sleep tracker (Fitbit), access to Fitbit software healthy lifestyle software apps;one session with Memory Assessment Service (MAS) staff providing physical activity and sleep hygiene advice and two further engagement, discussion, and feedback sessions. Purpose/Aim: This study investigates the acceptability and feasibility of Active Feedback and the effect on stress, mental wellbeing, and sleep quality, and the links between these factors. Methods: An open-label patient cohort design with no control group was used. Pre-intervention, 4-week and 8-week intervention assessments were performed using participant self-report measures: Perceived Stress Scale (PSS), Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS), and Sleep Conditioning Index (SCI). Twenty-five participants completed an eight-week three-session intervention (18 males and 7 females), with the age range of 66 - 84 years old, and average age of 73.8 years (SD = 5.09). Fifteen participants had a diagnosis of MCI, ten participants did not. Results: There were non-significant improvements in SCI scores from 21.0 (SD = 8.84) to 21.6 (SD = 6.20) at 8 weeks, PSS scores from 17.5 (SD = 5.89) to 17.0 (SD = 6.20) at 8 weeks, and WEMWBS scores from 46.9 (SD = 9.23) to 48.8 (SD = 9.69) at 8 weeks. There were negative correlations between WEMWBS and PSS. Conclusion: Active Feedback intervention was found to be feasible and acceptable. Active Feedback could be enhanced to include motivational interviewing and goal setting.

Introducing Weighted Nodes to Evaluate the Cloud Computing Topology

Typical data centers house several powerful ICT (Information and Communication Technology) equipment such as servers, storage devices and network equipment that are high-energy consuming. The nature of these high-energy consuming equipment is mostly accountable for the very large quantities of emissions which are harmful and unfriendly to the environment. The costs associated with energy consumption in data centers increases as the need for more computational resources increases, so also the appalling effect of CO2 (Carbon IV Oxide) emissions on the environment from the constituent ICT facilities-Servers, Cooling systems, Telecommunication systems, Printers, Local Area Network etc. Energy related costs would traditionally account for about 42% (forty-two per cent) of the total costs of running a typical data center. There is a need to have a good balance between optimization of energy budgets in any data center and fulfillment of the Service Level Agreements (SLAs), as this ensures continuity/profitability of business and customer’s satisfaction. A greener computing from what used to be would not only save/sustain the environment but would also optimize energy and by implication saves costs. This paper addresses the challenges of sustainable (or green computing) in the cloud and proffer appropriate, plausible and possible solutions. The idle and uptime of a node and the traffic on its links (edges) has been a concern for the cloud operators because as the strength and weights of the links to the nodes (data centres) increases more energy are also being consumed by and large. It is hereby proposed that the knowledge of centrality can achieve the aim of energy sustainability and efficiency therefore enabling efficient allocation of energy resources to the right path. Mixed-Mean centrality as a new measure of the importance of a node in a graph is introduced, based on the generalized degree centrality. The mixed-mean centrality reflects not only the strengths (weights) and numbers of edges for degree centrality but it combines these features by also applying the closeness centrality measures while it goes further to include the weights of the nodes in the consideration for centrality measures. We illustrate the benefits of this new measure by applying it to cloud computing, which is typically a complex system. Network structure analysis is important in characterizing such complex systems.

SDDNet:Infrared small and dim target detection network

This study focuses on developing deep learning methods for small and dim target detection.We model infrared images as the union of the target region and background region.Based on this model,the target detection problem is considered a two‐class segmentation problem that divides an image into the target and background.Therefore,a neural network called SDDNet for single‐frame images is constructed.The network yields target extraction results according to the original images.For multiframe images,a network called IC‐SDDNet,a combination of SDDNet and an interframe correlation network module is constructed.SDDNet and IC‐SDDNet achieve target detection rates close to 1 on typical datasets with very low false positives,thereby performing significantly better than current methods.Both models can be executed end to end,so both are very convenient to use,and their implementation efficiency is very high.Average speeds of 540+/230+FPS and 170+/60+FPS are achieved with SDDNet and IC‐SDDNet on a single Tesla V100 graphics processing unit and a single Jetson TX2 embedded module respectively.Additionally,neither network needs to use future information,so both networks can be directly used in real‐time systems.The well‐trained models and codes used in this study are available at https://github.com/LittlePieces/ObjectDetection.

Cyber risk at the edge:current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains

Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.

Cyber risk at the edge:current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains

Digital technologies have changed the way supply chain operations are structured.In this article,we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks.A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0,with a specific focus on the mitigation of cyber risks.An analytical framework is presented,based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies.This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning(AI/ML)and real-time intelligence for predictive cyber risk analytics.The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge.This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed,and when AI/ML technologies are migrated to the periphery of IoT networks.

Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory

As a result of noise and intensity non-uniformity,automatic segmentation of brain tissue in magnetic resonance imaging (MRI) is a challenging task.In this study a novel brain MRI segmentation approach is presented which employs Dempster-Shafer theory (DST) to perform information fusion.In the proposed method,fuzzy c-mean (FCM) is applied to separate features and then the outputs of FCM are interpreted as basic belief structures.The salient aspect of this paper is the interpretation of each FCM output as a belief structure with particular focal elements.The results of the proposed method are evaluated using Dice similarity and Accuracy indices.Qualitative and quantitative comparisons show that our method performs better and is more robust than the existing method.