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The Edge-Cloud Continuum refers to the dynamic provisioning of distributed computing and network resources that can be scaled to support the creation of secure, resource-efficient, and decentralised digital ecosystems, which also support federated topologies for collaborating and sharing resources. Trusted interaction and orchestration of distributed edge-cloud resources are the fundamental principles of distributed network infrastructure and service provisioning. The zero trust architecture (ZTA) paradigm is gaining momentum based on being able to ensure trusted and secure interaction for edge-cloud networks. However, ZTA’s strict authentication policy mandates devices to be authenticated for every session, leading to significant overhead for resource-constrained devices engaged in multiple sessions. To address this challenge, this paper proposes a ZTA that integrates a performance-based trust assessment mechanism, allowing a higher number of consecutive sessions without the need for costly authentication/authorisation while preserving system integrity. Reputation, viewed through the performance lens, is a metric to gauge a node’s trustworthiness, considering its past behaviour and interactions. The proposed trust assessment mechanism is evaluated for its feasibility within our conceptualised ZTA for edge computing environments with limited resources, and simulation results demonstrate the practicality of utilising this technique in zero trust environments.

The concept of smart contracts (SCs) is becoming more prevalent, and their application is gaining traction across many diverse scenarios. However, producing poorly constructed contracts carries significant risks, including the potential for substantial financial loss, a lack of trust in the technology, and the risk of exposure to cyber-attacks. Several tools exist to assist in developing SCs, but their limited functionality increases development complexity. Expert knowledge is required to ensure contract reliability, resilience, and scalability. To overcome these risks and challenges, tools and services based on modeling and formal techniques are required that offer a robust methodology for SC verification and life-cycle management. This study proposes an engineering framework for the generation of a robust and verifiable smart contract (GRV-SC) framework that covers the entire SC life-cycle from design to deployment stages. It adopts SC modeling and automated formal verification methodologies to detect security vulnerabilities and improve resilience, extensibility, and code optimization to mitigate risks associated with SC development. Initially, the framework includes the implementation of a formal approach, using colored Petri nets (CPNs), to model cross-platform Digital Asset Modeling Language (DAML) SCs. It also incorporates a specialized type safety dynamic verifier, which is designed to detect and address new vulnerabilities that can arise in DAML contracts, such as access control and insecure direct object reference (Idor) vulnerabilities. The proposed GRV-SC framework provides a holistic approach to SC life-cycle management and aims to enhance the security, reliability, and adoption of SCs.

Abstract Digitalisation creates new opportunities for businesses to implement and manage collaborative ecosystems both internally and externally. Digital twin (DT) is a rapidly emerging technology that can be used to facilitate new models of interaction and sharing of information. DT is the digital version of a physical process or asset that can be used to model, manage, and optimise its physical counterpart. Connecting multiple DTs is vital to provide a holistic integration and view across complex ecosystems. To create a DT‐based collaborative ecosystem architecture, the following concerns need to be addressed. Trust is a fundamental requirement because multiple parties will work together as part of a composite DT. Interoperability is essential, as DTs from various domains will be required to interconnect and operate seamlessly. Finally, the governance is challenging as different scenarios require various mechanisms and governance structures. This study presents an architecture to enable multiple DT‐based collaborative ecosystems, and example use case scenarios to demonstrate its applicability in collaborative manufacturing.

Objectives The Intermountain Risk Score (IMRS), composed using published sex-specific weightings of parameters in the complete blood count (CBC) and basic metabolic profile (BMP), is a validated predictor of mortality. We hypothesised that IMRS calculated from prepandemic CBC and BMP predicts COVID-19 outcomes and that IMRS using laboratory results tested at COVID-19 diagnosis is also predictive.Design Prospective observational cohort study.Setting Primary, secondary, urgent and emergent care, and drive-through testing locations across Utah and in sections of adjacent US states. Viral RNA testing for SARS-CoV-2 was conducted from 3 March to 2 November 2020.Participants Patients aged ≥18 years were evaluated if they had CBC and BMP measured in 2019 and tested positive for COVID-19 in 2020.Primary and secondary outcome measures The primary outcome was a composite of hospitalisation or mortality, with secondary outcomes being hospitalisation and mortality separately.Results Among 3883 patients, 8.2% were hospitalised and 1.6% died. Subjects with low, mild, moderate and high-risk IMRS had the composite endpoint in 3.5% (52/1502), 8.6% (108/1256), 15.5% (152/979) and 28.1% (41/146) of patients, respectively. Compared with low-risk, subjects in mild-risk, moderate-risk and high-risk groups had HR=2.33 (95% CI 1.67 to 3.24), HR=4.01 (95% CI 2.93 to 5.50) and HR=8.34 (95% CI 5.54 to 12.57), respectively. Subjects aged

Background The Laboratory-based Intermountain Validated Exacerbation (LIVE) Score is associated with mortality and chronic obstructive pulmonary disease (COPD) exacerbation risk across multiple health systems. However, whether the LIVE Score and its associated risk is a stable patient characteristic is unknown.Methods We validated the LIVE Score in a fourth health system. Then we determined the LIVE Score stability in a retrospective cohort of 98 766 patients with COPD in four health systems where it was previously validated. We assessed whether LIVE Scores changed or remained the same over time. Stability was defined as a majority of surviving patients having the same LIVE Score 4 years later.Results The LIVE Score separated patients into three LIVE Score risk groups of low, medium, and high mortality and LIVE Score stability. Mortality ranged from 6.2% for low-risk LIVE to 45.8% for high-risk LIVE (p

BACKGROUND:The Charlson and Elixhauser comorbidity indices are mortality predictors often used in clinical, administrative, and research applications. The Intermountain Mortality Risk Scores (IMRS) are validated mortality predictors that use all factors from the complete blood count and basic metabolic profile. How IMRS, Charlson, and Elixhauser relate to each other is unknown. METHODS:All inpatient admissions except obstetric patients at Intermountain Healthcare's 21 adult care hospitals from 2010-2014 (N = 197,680) were examined in a observational cohort study. The most recent admission was a patient's index encounter. Follow-up to 2018 used hospital death records, Utah death certificates, and the Social Security death master file. Three Charlson versions, 8 Elixhauser versions, and 3 IMRS formulations were evaluated in Cox regression and the one of each that was most predictive was used in dual risk score mortality analyses (in-hospital, 30-day, 1-year, and 5-year mortality). RESULTS:Indices with the strongest mortality associations and selected for dual score study were the age-adjusted Charlson, the van Walraven version of the acute Elixhauser, and the 1-year IMRS. For in-hospital mortality, Charlson (c = 0.719; HR = 4.75, 95% CI = 4.45, 5.07), Elixhauser (c = 0.783; HR = 5.79, CI = 5.41, 6.19), and IMRS (c = 0.821; HR = 17.95, CI = 15.90, 20.26) were significant predictors (p

Background: Identifying COPD patients at high risk for mortality or healthcare utilization remains a challenge. A robust system for identifying high-risk COPD patients using Electronic Health Record (EHR) data would empower targeting interventions aimed at ensuring guideline compliance and multimorbidity management. The purpose of this study was to empirically derive, validate, and characterize subgroups of COPD patients based on routinely collected clinical data widely available within the EHR.Methods: Cluster analysis was used in 5,006 patients with COPD at Intermountain to identify clusters based on a large collection of clinical variables. Recursive Partitioning (RP) was then used to determine a preferred tree that assigned patients to clusters based on a parsimonious variable subset. The mortality, COPD exacerbations, and comorbidity profile of the identified groups were examined. The findings were validated in an independent Intermountain cohort and in external cohorts from the United States Veterans Affairs (VA) and University of Chicago Medicine systems.Measurements and Main Results: The RP algorithm identified five LIVE Scores based on laboratory values: albumin, creatinine, chloride, potassium, and hemoglobin. The groups were characterized by increasing risk of mortality. The lowest risk, LIVE Score 5 had 8% 4-year mortality vs. 56% in the highest risk LIVE Score 1 (p < 0.001). These findings were validated in the VA cohort (n = 83,134), an expanded Intermountain cohort (n = 48,871) and in the University of Chicago system (n = 3,236). Higher mortality groups also had higher COPD exacerbation rates and comorbidity rates.Conclusions: In large clinical datasets across different organizations, the LIVE Score utilizes existing laboratory data for COPD patients, and may be used to stratify risk for mortality and COPD exacerbations.

Environment perception is crucial for the safe navigation of vehicles and robots to detect obstacles in their surroundings. It is also of paramount interest for navigation of human beings in reduced visibility conditions. Obstacle avoidance systems typically combine multiple sensing technologies (i.e., LiDAR, radar, ultrasound and visual) to detect various types of obstacles under different lighting and weather conditions, with the drawbacks of a given technology being offset by others. These systems require powerful computational capability to fuse the mass of data, which limits their use to high-end vehicles and robots. INSPEX delivers a low-power, small-size and lightweight environment perception system that is compatible with portable and/or wearable applications. This requires miniaturizing and optimizing existing range sensors of different technologies to meet the user’s requirements in terms of obstacle detection capabilities. These sensors consist of a LiDAR, a time-of-flight sensor, an ultrasound and an ultra-wideband radar with measurement ranges respectively of 10 m, 4 m, 2 m and 10 m. Integration of a data fusion technique is also required to build a model of the user’s surroundings and provide feedback about the localization of harmful obstacles. As primary demonstrator, the INSPEX device will be fixed on a white cane.

There are strong policy drivers for the promotion of energy efficiency in buildings. In the literature, Model Predictive Control (MPC) is seen as a promising solution to deal with the energy management problem in buildings. Model identification is the primary task involved in the design of MPC control and defining the good level of complexity for the thermal dynamic model is a critical question. This paper focuses on the development of reliable models that can be used to support the deployment of (Distributive (Di)) MPC application.

In this paper, we address mobility management for 4th generation heterogeneous networks from a quality of service (QoS), optimisation and cross layer design perspective. Users are classified as high profile, normal profile and low profile according to their differentiated service requirements. Congestion avoidance control and adaptive handover mechanisms are implemented for efficient cooperation within the mobile heterogeneous network environment consisting of a TDMA network, ad hoc network and relay nodes. A previous proposed routing algorithm is also revised to include mobility management.

The energy consumption of buildings lies often far above the performance objectives of the design phase. This is due to several factors among other serious deficits in the energy operation of building services. TOPAs adopts the principle of continuous performance auditing by not only considering energy use but also knowledge and understanding of the buildings use and their climatic state. Thus it provides a holistic performance auditing process through supporting tools and methodologies that try to minimise the gap between predicted and actual energy use. TOPAs offers an open BMS (Building Management System) IoT driven framework. This framework is composed of core services to connect to any BMS and aggregate all the information in an open platform. Add-on services are also available to improve the understanding of buildings and reduce further the gap.

Despite the importance of the renin-angiotensin (Ang) system in abdominal aortic aneurysm (AAA) pathogenesis, strategies targeting this system to prevent clinical aneurysm progression remain controversial and unproven. We compared the relative efficacy of two Ang II type 1 receptor blockers, telmisartan and irbesartan, in limiting experimental AAAs in distinct mouse models of aneurysm disease.AAAs were induced using either 1) Ang II subcutaneous infusion (1000 ng/kg/min) for 28 days in male ApoE(-/-) mice, or 2) transient intra-aortic porcine pancreatic elastase infusion in male C57BL/6 mice. One week prior to AAA creation, mice started to daily receive irbesartan (50 mg/kg), telmisartan (10 mg/kg), fluvastatin (40 mg/kg), bosentan (100 mg/kg), doxycycline (100 mg/kg) or vehicle alone. Efficacy was determined via serial in vivo aortic diameter measurements, histopathology and gene expression analysis at sacrifice. Aortic aneurysms developed in 67% of Ang II-infused ApoE(-/-) mice fed with standard chow and water alone (n = 15), and 40% died of rupture. Strikingly, no telmisartan-treated mouse developed an AAA (n = 14). Both telmisartan and irbesartan limited aneurysm enlargement, medial elastolysis, smooth muscle attenuation, macrophage infiltration, adventitial neocapillary formation, and the expression of proteinases and proinflammatory mediators. Doxycycline, fluvastatin and bosentan did not influence aneurysm progression. Telmisartan was also highly effective in intra-aortic porcine pancreatic elastase infusion-induced AAAs, a second AAA model that did not require exogenous Ang II infusion.Telmisartan suppresses experimental aneurysms in a model-independent manner and may prove valuable in limiting clinical disease progression.

We present an improved version of adaptive distributed cross-layer routing algorithm (ADCR) for hybrid wireless network with dedicated relay stations (HWN∗) in this paper. A mobile terminal (MT) may borrow radio resources that are available thousands mile away via secure multihop RNs, where RNs are placed at pre-engineered locations in the network. In rural places such as mountain areas, an MT may also communicate with the core network, when intermediate MTs act as relay node with mobility. To address cross-layer network layers routing issues, the cascaded ADCR establishes routing paths across MTs, RNs, and cellular base stations (BSs) and provides appropriate quality of service (QoS). We verify the routing performance benefits of HWN∗ over other networks by intensive simulation.