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Background: The Diabetes Health Plan (DHP), a value-based insurance plan that reduces cost sharing, was previously shown to modestly increase employer-level medication adherence. However, it is unclear whether the DHP has a larger impact on individual-level medication adherence among lower- or higher-income beneficiaries. Methods: An employer-level propensity score match was done to identify suitable control employers, followed by individual-level propensity score weighting. These weights were applied to difference-in-difference (DID) models examining 1) the effect of the DHP and 2) the effect of income on changes in adherence to metformin, statins, and ACE/ARBs. The weights were then applied to a differences-in-differences-in-differences (DDD) model to estimate the differential impact of DHP status on changes in adherence by income group. This is a retrospective, quasi-experimental study.Results: There were no significant differences in changes to adherence for any medications between beneficiaries enrolled in the DHP versus standard plans. However, changes in adherence were higher for all medications among those in the highest income strata (>$75,000) versus those in the lowest income strata (Conclusion: We did not find significant associations between the DHP and changes in individual-level medication adherence, even for low-income beneficiaries. New strategies to improve consumer engagement may be needed in order to translate value-based insurance designs into changes in patient behavior.

High quality, readable health information is vital to mitigate the impact of the COVID-19 pandemic. The aim of this study was to assess the quality and readability of online COVID-19 information using 6 validated tools. This is a cross-sectional study. “COVID-19” was searched across the three most popular English language search engines. Quality was evaluated using the DISCERN score, Journal of the American Medical Association benchmark criteria and Health On the Net Foundation Code of Conduct. Readability was assessed using the Flesch Reading Ease Score, Flesch-Kincaid Grade Level and Gunning-Fog Index. 41 websites were suitable for analysis. 9.8% fulfilled all JAMA criteria. Only one website was HONCode certified. Mean DISCERN score was 47.8/80 (“fair”). This was highest in websites published by a professional society/medical journal/healthcare provider. Readability varied from an 8th to 12th grade level. The overall quality of online COVID-19 information was “fair”. Much of this information was above the recommended 5th to 6th grade level, impeding access for many.

Zero Trust security model permits to secure cloud native applications while encrypting all network communication, authenticating, and authorizing every request. The service mesh can enable Zero Trust using a side-car proxy without changes to the application code. To the best of our knowledge, no previous work has provided a performance analysis of Zero Trust in a multi-cloud environment. This paper proposes a multi-cloud framework and a testing workflow to analyze performance of the data plane under load and the impact on the control plane, when Zero Trust is enabled. The results of preliminary tests show that Istio has reduced latency variability in responding to sequential HTTP requests. Results also reveal that the overall CPU and memory usage can increase based on service mesh configuration and the cloud environment., This paper was submitted for the IEEE CLOUD 2021 Conference and is currently being reviewed

This project was completed as part of a project conducted with an industrial partner DellEMC

Artificial Intelligence (AI) is an enabling technology that when integrated into healthcare applications and smart wearable devices such as Fitbits etc. can predict the occurrence of health conditions in users by capturing and analysing their health data. The integration of AI and smart wearable devices has a range of potential applications in the area of smart healthcare but there is a challenge in the black box operation of decisions made by AI models which have resulted in a lack of accountability and trust in the decisions made. Explainable AI (XAI) is a domain in which techniques are developed to explain predictions made by AI systems. In this paper, XAI is discussed as a technique that can used in the analysis and diagnosis of health data by AI-based systems and a proposed approach presented with the aim of achieving accountability. transparency, result tracing, and model improvement in the domain of healthcare.

Encouraged by the success of network virtualization in enterprise networks, wireless sensor network (WSN) virtualization has been receiving increasing attention. WSN virtualization is seen as offering economic benefits through resource sharing. But the actual gain is not clear and its cost-benefit ratio has not been thoroughly analyzed. While the discussion about the economic benefits of network virtualization is often reduced to two key components, namely CAPEX and OPEX, the effect of radio interference in wireless multi-hop networks such as WSN has not been considered. In this paper, we analyze the influencing factors that contribute to the cost of providing a virtual WSN over a shared physical substrate. We model the CAPEX and OPEX associated with Virtualized WSNs (VWSNs) and introduce an additional element, which we call INEX, into our cost function which captures the future impact (i.e. the ”actual cost”) of interference. INEX results in a loss of income due to increased VN request rejection rates. Using this model we conduct extensive simulations that allow us to compare the cost difference between renting shared WSN resources and deploying a standalone network. The results indicate that shared WSNs result in higher cost when there is only one sink.

The Link Fabrication Attack (LFA) in Software-Defined Networking (SDN) involves an attacker forging a new link in the network, providing them with control over traffic which traverses the new malicious link. One method to perform this attack is through the relaying of topology discovery traffic, for which no comprehensive defense exists. This paper proposes to detect this attack using statistical analysis of link latencies. A novel solution has been designed requiring a new link to undergo a vetting period during which its latency is evaluated. This is subsequently compared against a baseline model for benign links. This solution is assessed against several implementations of the relay-type LFA. The trade-off between the length of the vetting period and the accuracy of the attack detection is analyzed. The results show how user-space relaying causes a sizable increase in latency which can be detected with a low number of samples, while relaying using kernel-space forwarding requires larger samples sets in order to be discovered.

Previous research has shown that centralized network control in Wireless Sensor Networks (WSNs) can lead to improved network lifetime, benefit reliability, help to diagnose and localize network failures, assist network recovery, and lead to optimal routing and transmission scheduling. A stepping stone to centralized network control is to build and maintain a complete network topology model that scales and reacts to the network dynamics that occur in low-power wireless networks. We propose SMOG as a mechanism to build and maintain a centralized full network topology model using probabilistic data structures. Extensive analysis of the proposed approach in both simulation and two testbeds shows that SMOG can build a complete model of a WSN of over 100 nodes with 98% accuracy in less than four minutes. Our approach also offers fast recovery from heavy network interference, recovering model accuracy to 98% in less than two and a half minutes.

Accommodating multiple different applications on a single sensor node or within the same wireless sensor network (WSN) offers numerous benefits including reduced deployment cost and improved utilization of the physical substrate. This paper presents a new middleware for WSNs, called Motley. Motley, enables a shared infrastructure and multi-application support for WSNs by providing services for the dissemination, installation and scheduling of applications with diverse QoS requirements. Implementation challenges and the measures employed in Motley to overcome these as part of a shared WSN substrate are discussed. Some initial findings on the cost in terms of code loading time to support multiple applications are presented as part of this paper.

The Public Cloud has enabled the provisioning & management of an organisations IT infrastructure to be handled by a third-party, otherwise referred to Infrastructure as a Service (IaaS). This offers the potential to create cost effective & scalable IT solutions. However, little work has been done on the processes involved in cloud migration of existing infrastructure. Specifically, there has been no framework or solution implemented in a case study setting to detail the processes necessary to perform a migration of existing infrastructure to the public cloud. This paper presents the architecture of an Environment Migration Framework (EMF), the purpose of which is to automate the migration of existing IT infrastructure to the IaaS platform. To prove the viability of this framework, an ongoing case study is described where the Environment Migration Framework is being implemented in an organisation seeking to migrate their existing virtual machine environments to the public cloud. The final sections of this paper detail the results obtained so far from the ongoing project and the future work.

The centralised control provided by Software- Defined Networking allows an increase in network security as all traffic can be vetted before leaving the attachment switch. Nevertheless, as in any complex system, there are implementation and policy compromises which lead to security vulnerabilities. This paper exploits such vulnerabilities to implement a suite of attacks, consisting of Address Resolution Protocol (ARP) cache poisoning, Man in the Middle, a firewall and access control bypassing port scan called a Phantom Host Scan, and a Distributed Denial of Service attack called a Phantom Storm which induces the participation of legitimate hosts. These attacks were successfully implemented in a Floodlight controlled network.

Objective Numerous double-blind studies have assessed the efficacy of antidepressants in treating chronic depressive disorder, including dysthymic disorder, low-grade chronic depression. However, there are no double-blind, placebo-controlled studies of serotonin-norepinephrine reuptake inhibitors in chronic depressive disorder. Method Outpatients with chronic depressive disorder, but without concurrent major depressive disorder (MDD), were randomly assigned to prospective double-blind duloxetine (beginning at 30 mg/d, increased to a maximum dose of 120 mg/d) versus placebo for 10 weeks. Inclusion criteria were current DSM-IV-TR diagnosis of dysthymic disorder or depression not otherwise specified, age 18-75 years, and a Hamilton Depression Rating Scale (HDRS) score ≥ 12. Exclusion criteria included current major depression. The study was conducted between August 2006 and December 2011. HDRS, Cornell Dysthymia Rating Scale (CDRS), Clinical Global Impressions (CGI), Beck Depression Inventory (BDI), Global Assessment of Functioning (GAF), Social Adjustment Scale (SAS), and other assessments were administered at each visit. We hypothesized that duloxetine would be superior to placebo in (1) 24-item HDRS total score, (2) the percentage of subjects classified as responders and remitters, and (3) secondary measures (CDRS, BDI, CGI). Response was defined as > 50% decrease in 24-item HDRS and CGI-Improvement scale score of 1 or 2 ("very much improved" or "much improved"). Remission was defined as HDRS-17 item score ≤ 4 and 0 on item 1 of the HDRS (depressed mood). Results 65 subjects were enrolled, of whom 57 began medication. They ranged in age from 19 to 70 years (mean ± SD = 41.63 ± 11.22) and included 24 women and 33 men. Baseline 24-item HDRS score (mean ± SD) for both groups was 20.75 ± 4.92. After 10 weeks, duloxetine-treated subjects had significantly lower 24-item HDRS scores than placebo-treated subjects (time-by-drug group effect on analysis of variance: F1,55 = 9.43, P = .003). Responder and remitter analyses significantly favored duloxetine treatment. The response rate was 65.5% for duloxetine versus 25.0% for placebo (χ(2)(1) = 9.43, P = .003); and the remitter rate was 55.2% for duloxetine versus 14.3% for placebo (χ(2)(1) = 10.46, P = .002). After 10 weeks, duloxetine-treated subjects did not differ significantly better from placebo-treated subjects on the SAS (time-by-drug group effect on analysis of variance: F(1,46) = 0.35, P = .555) or on the GAF (time-by-drug group effect on analysis of variance: F(1,51) = .01, P = .922). Conclusions Results on the 24-item HDRS, CGI, and CDRS suggest that duloxetine is efficacious in acute treatment of chronic nonmajor depressive disorder. Response and remission rates also differed significantly, favoring duloxetine treatment, but BDI, GAF, and social functioning (Social Adjustment Scale) did not. Duloxetine appears to be effective in acute treatment of nonmajor chronic depression. Trial registration ClinicalTrials.gov identifier: NCT00360724.

Software Defined Networking (SDN) is emerging as a key technology to deal with the ever increasing network management burden created by our increasingly interconnected world. Wireless sensor network (WSN) are part of this interconnection, enabling to connect the physical world to the cyber world of the Internet and its networks. This connection of physical items, “Things”, to the Internet in the form of an Internet of Things is creating many new challenges for the management of the Internet networks. SDN moves away from a distributed management approach that has been at the core of wireless sensor networks since their inception and introduces a centralised view and control of a network. We believe that the SDN concept as well as the general compute virtualisation enabled through infrastructure as a service can offer the required flexible management and control of the network of Things. While the application of SDN to WSN has already been proposed, a comprehensive architecture for Software Defined Wireless Sensor Networks (SD-WSN) is currently missing. This paper provides a survey of related work considering both SDN and centralised non-SDN approaches to network management and control, examines the challenges and opportunities for SD-WSNs, and provides an architectural proposal for SD-WSN.

The focus of this paper will present Charge Flow which is a workflow based approach to manage the issue of chargeback within a Cloud environment. This work has been driven by the need for both private and public cloud operators to manage their virtualized infrastructures to ensure proper accountability so that resource usage is metered, charged and billed to the respective user of the virtualized resource. Charge Flow essentially automates and orchestrates the process of chargeback and charge view reducing the administrative overhead and increasing utilization of underlying resources.

Introduction Only about a third of patients with an episode of major depressive disorder remit with a given treatment and few remissions occur within the first weeks of treatment. This study tested whether combining escitalopram and bupropion as initial treatment would result in quicker remission and a higher remission rate than monotherapy with either drug. Method Two hundred forty-five outpatients aged 18–65 having non-psychotic, non-bipolar major depression were randomly assigned to double-blind treatment with bupropion or escitalopram or the combination dosed to a maximum of bupropion 450 mg/d and/or escitalopram 40 mg/d for 12 weeks. A Montgomery–Asberg Depression Rating Scale score of 22 was required for randomization, while a Hamilton Rating Scale for Depression score ≤ 7 defined remission. We hypothesized that bupropion plus escitalopram would outperform both monotherapies in both earlier onset of remission and higher rate of remission. Results Primary analyses did not demonstrate that dual therapy outperformed both monotherapies in either timing of remission or remission rate. All three treatments were well tolerated. Discussion These results do not support initial use of bupropion plus escitalopram to speed or enhance antidepressant response. Clinical trials registration NCT00519428 .