Clinical outcomes and risk factors for COVID-19 among migrant populations in high-income countries: a systematic review.

Background: Migrants in high-income countries may be at increased risk of COVID-19 due to their health and social circumstances, yet the extent to which they are affected and their predisposing risk factors are not clearly understood. We did a systematic review to assess clinical outcomes of COVID-19 in migrant populations, indirect health and social impacts, and to determine key risk factors. Methods: We did a systematic review following PRISMA guidelines (PROSPERO CRD42020222135). We searched multiple databases to 18/11/2020 for peer-reviewed and grey literature on migrants (foreign-born) and COVID-19 in 82 high-income countries. We used our international networks to source national datasets and grey literature. Data were extracted on primary outcomes (cases, hospitalisations, deaths) and we evaluated secondary outcomes on indirect health and social impacts and risk factors using narrative synthesis. Results: 3016 data sources were screened with 158 from 15 countries included in the analysis (35 data sources for primary outcomes: cases [21], hospitalisations [4]; deaths [15]; 123 for secondary outcomes). We found that migrants are at increased risk of infection and are disproportionately represented among COVID-19 cases. Available datasets suggest a similarly disproportionate representation of migrants in reported COVID-19 deaths, as well as increased all-cause mortality in migrants in some countries in 2020. Undocumented migrants, migrant health and care workers, and migrants housed in camps have been especially affected. Migrants experience risk factors including high-risk occupations, overcrowded accommodation, and barriers to healthcare including inadequate information, language barriers, and reduced entitlement. Conclusions: Migrants in high-income countries are at high risk of exposure to, and infection with, COVID-19. These data are of immediate relevance to national public health and policy responses to the pandemic. Robust data on testing uptake and clinical outcomes in migrants, and barriers and facilitators to COVID-19 vaccination, are urgently needed, alongside strengthening engagement with diverse migrant groups

Evaluation of piezocision and laser-assisted flapless corticotomy in the acceleration of canine retraction: a randomized controlled trial

Abstract Background To evaluate the effectiveness of two minimally invasive surgical procedures in the acceleration of canine retraction: piezocision and laser-assisted flapless corticotomy (LAFC). Methods Trial design: A single-centre randomized controlled trial with a compound design (two-arm parallel-group design and a split-mouth design for each arm). Participants: 36 Class II division I patients (12 males, 24 females; age range: 15 to 27 years) requiring first upper premolars extraction followed by canine retraction. Interventions: piezocision group (PG; n = 18) and laser-assisted flapless corticotomy group (LG; n = 18). A split-mouth design was applied for each group where the flapless surgical intervention was randomly allocated to one side and the other side served as a control side. Outcomes: the rate of canine retraction (primary outcome), anchorage loss and canine rotation, which were assessed at 1, 2, 3 and 4 months following the onset of canine retraction. Also the duration of canine retraction was recorded. Random sequence: Computer-generated random numbers. Allocation concealment: sequentially numbered, opaque, sealed envelopes. Blinding: Single blinded (outcomes’ assessor). Results Seventeen patients in each group were enrolled in the statistical analysis. The rate of canine retraction was significantly greater in the experimental side than in the control side in both groups by two-fold in the first month and 1.5-fold in the second month (p  0.05). There were no significant differences between the two flapless techniques regarding the studied variables during all evaluation times (p > 0.05). Conclusions Piezocision and laser-assisted flapless corticotomy appeared to be effective treatment methods for accelerating canine retraction without any significant untoward effect on anchorage or canine rotation during rapid retraction. Trials registration ClinicalTrials.gov (Identifier: NCT02606331)

Integrated Security System (ISS) Design and Evaluation for Commercial Nuclear Power Plant

Physical security system, which is also called physical protection system, is very crucial in the nuclear industry for protecting staff, visitors, buildings, assets, and nuclear materials against theft, sabotage, and harmful activities. Theft of nuclear materials has a major impact on the essence of nuclear safeguards. Sabotage of a nuclear facility could endanger the public at large. Reviewing the published literature, it is found that there are no complete physical security system designs based on an integrated network of electronic devices that are devoted to commercial NPPs. And there is no definite evaluation factor that was set to approve such a system. This paper is an evolving solution to this deficiency by proposing an unpreceded integrated security system design applicable to a commonly structured physical layout of any commercial NPP. This proposal provides comprehensive security coverage for the NPP boundaries employing a high level of integration for all subsystems communicated via an IP data network controlled by central management software. This paper is proposing also testing procedures to be followed to evaluate the proposed design. The security system effectiveness will be calculated using mathematical codes by assuming external intrusion attack scenarios. Attributes of each attack scenario will be numerically introduced to the evaluation software EASI and ASSESS codes developed by Sandia Labs, USA. This paper also proposes a threshold value of such security system effectiveness which should be achieved by the commercial NPP security system to achieve the so-called security license

Green Demand Aware Fog Computing: A Prediction-Based Dynamic Resource Provisioning Approach

Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance

Green Demand Aware Fog Computing: A Prediction-Based Dynamic Resource Provisioning Approach

Fog computing could potentially cause the next paradigm shift by extending cloud services to the edge of the network, bringing resources closer to the end-user. With its close proximity to end-users and its distributed nature, fog computing can significantly reduce latency. With the appearance of more and more latency-stringent applications, in the near future, we will witness an unprecedented amount of demand for fog computing. Undoubtedly, this will lead to an increase in the energy footprint of the network edge and access segments. To reduce energy consumption in fog computing without compromising performance, in this paper we propose the Green-Demand-Aware Fog Computing (GDAFC) solution. Our solution uses a prediction technique to identify the working fog nodes (nodes serve when request arrives), standby fog nodes (nodes take over when the computational capacity of the working fog nodes is no longer sufficient), and idle fog nodes in a fog computing infrastructure. Additionally, it assigns an appropriate sleep interval for the fog nodes, taking into account the delay requirement of the applications. Results obtained based on the mathematical formulation show that our solution can save energy up to 65% without deteriorating the delay requirement performance