On the use of intelligent models towards meeting the challenges of the edge mesh

Nowadays, we are witnessing the advent of the Internet of Things (IoT) with numerous devices performing interactions between them or with their environment. The huge number of devices leads to huge volumes of data that demand the appropriate processing. The “legacy” approach is to rely on Cloud where increased computational resources can realize any desired processing. However, the need for supporting real-time applications requires a reduced latency in the provision of outcomes. Edge Computing (EC) comes as the “solver” of the latency problem. Various processing activities can be performed at EC nodes having direct connection with IoT devices. A number of challenges should be met before we conclude a fully automated ecosystem where nodes can cooperate or understand their status to efficiently serve applications. In this article, we perform a survey of the relevant research activities towards the vision of Edge Mesh (EM), i.e., a “cover” of intelligence upon the EC. We present the necessary hardware and discuss research outcomes in every aspect of EC/EM nodes functioning. We present technologies and theories adopted for data, tasks, and resource management while discussing how machine learning and optimization can be adopted in the domain

Unusual exanthema combined with cerebral vasculitis in pneumococcal meningitis: a case report

<p>Abstract</p> <p>Introduction</p> <p>Bacterial meningitis is a complex, rapidly progressive disease in which neurological injury is caused in part by the causative organism and in part by the host's own inflammatory responses.</p> <p>Case presentation</p> <p>We present the case of a two-year-old Greek girl with pneumococcal meningitis and an atypical curvilinear-like skin eruption, chronologically associated with cerebral vasculitis. A diffusion-weighted MRI scan showed lesions with restricted diffusion, reflecting local areas of immunologically mediated necrotizing vasculitis.</p> <p>Conclusions</p> <p>Atypical presentations of bacterial meningitis may occur, and they can be accompanied by serious unexpected complications.</p

Intelligent tasks allocation at the edge based on machine learning and bio-inspired algorithms

Current advances in the Internet of Things (IoT) and Cloud involve the presence of an additional layer between them acting as mediator for data transfer and processing in close distance to end users. This mediator is the edge computing (EC) infrastructure. In EC, we can identify an ecosystem of heterogeneous nodes capable of interacting with IoT devices, collecting and locally processing the data they report. The ultimate goal is to eliminate the latency we face when relying on Cloud to perform the desired processing activities. In EC, any processing is performed over a number of geo-distributed datasets formulated by the collected data that exhibit specific statistical characteristics. Processing can have the form of tasks requested by end users or applications. It becomes obvious that in the EC ecosystem, we have to carefully decide the EC nodes that will host and execute any requested task. In this paper, we extend our previous research efforts on the conclusion of efficient task allocations into the available EC nodes. We go a step forward and propose a batch processing model executed over multiple tasks and study two allocation models: a scheme based on an unsupervised machine learning technique and a bio-inspired optimization algorithm. Our models enhance the autonomous behavior of entities performing the envisioned task allocations. We provide the analytical description of the problem, our solution and the advances over the state of the art. We present and evaluate the proposed algorithms and compare them with other efforts in the domain. The pros and cons of our models are revealed through the provided extensive experimental evaluation adopting real and synthetic data

Smart Cars and Over-the-Air Updates

Smart cars have started taking over the world and, sooner rather than later, they might become the new normal in the automotive industry. Over-the-air software updates have been developed and established in the telecommunications industry and proven their worth. Their adaptation to the automotive industry has its challenges seeing as human lives are at stake there. A lot of work has been done to shorten the gap, so much so that this technology has already been applied to car models in the market sometimes successfully and others not. Until the gap is non-existent, there is still a lot to learn and improve on. This paper has described the advantages, threats and vulnerabilities tied to smart cars and over-the-air updates; it has gathered a list of existing solutions to them and a short examination of the situation in today’s relevant research and industry

Decreasing trend of Legionella isolation in a long-term microbial monitoring program in Greek hospitals

Legionnaires’ disease is a known cause of severe pneumonia in a nosocomial setting. This study examined Legionella isolation in Greek hospitals. Water samples and swabs of showerheads from 41 hospitals were collected over a four-year period (2004-2007) from hot and cold water systems and cooling towers in Greece. In total, 1058 samples were examined for the presence of Legionella. From the hot water samples examined, 166 out of 607 (27.3%) were positive for Legionella, including 111 (18.3%) positive for Legionella pneumophila sg1 samples. The temperature of hot water samples less than 55 degrees C was statistically significant, associated with Legionella spp. isolation (RR 4.01, 95% CI 2.33-6.92). Ten out of 17 (58.8%) hospital cooling towers required remedial actions due to Legionella colonization. Reemergence of Legionella spp. colonization was evident in more than half of the hospitals where frequent monitoring and appropriate risk assessment plans were absent or lacking. Hospitals that were monitored continuously over the study period presented a decreasing trend of Legionella colonization. Environmental risk assessment together with Legionella isolation should be enforced systematically in hospitals

Pregnancy interval and delivery outcome among HIV-seropositive and HIV-seronegative women in Kisumu, Kenya.

OBJECTIVE: A short pregnancy interval (PI) has been associated with increased child mortality, but mechanisms are unclear. We studied factors associated with PI and the effect of PI on birthweight and haemoglobin. METHODS: Information was analysed from 2218 multigravidae who were recruited at the prenatal clinic (1758) or in the labour ward (460) of the Provincial Hospital in Kisumu between June 1996 and July 2000 for a study to assess the interaction between placental malaria and vertical HIV transmission. RESULTS: The HIV prevalence was 28.9%. HIV seropositivity, older age, being unmarried, and <8 years of education were associated with a prolonged PI; among all women, a stillbirth, abortion, or death of a liveborn child as outcome of the previous pregnancy, and death of a child other than the last born among HIV-seronegative women, were associated with a shortened PI. No significant effect of short PI (an interval <24 months) on low birth weight (LBW), prematurity, small-for-gestational-age infants or maternal anaemia was evident. An abortion, stillbirth, or death of a liveborn child as outcome of the previous pregnancy was associated at the present delivery with LBW among HIV-seronegative women [adjusted odds ratio (AOR) 3.33, 95% confidence interval (CI) 1.63-6.81], and a low haemoglobin (<11 g/dl) among HIV-seropositive women (AOR 2.01, 95% CI 1.05-4.03 in the third trimester). CONCLUSION: Public health efforts to ensure 'adequate' birth spacing may run contrary to family planning decisions to replace a deceased child and may be spent on prenatal issues like prevention of anaemia, and vertical HIV transmission

Seed Germination and Plant Growth under Drought Stress of Herbicide-Resistant and Herbicide-Susceptible Biotypes of <i>Conyza</i> Species and Smart Farming Approaches

Horseweed (C. canadensis) and fleabane (C. sumatrensis) are two annual or perennial herbaceous weeds present with high frequency and density in many parts of the world. Their response to water deficit was studied by means of seed germination tests and pot experiments. Seed germination was tested in solutions with different concentrations of polyethylene glycol (PEG). Two biotypes of each species were examined, one glyphosate resistant and the other susceptible. Growth responses were similar in the two species, both being more affected by lower (−1 MPa) than higher water potential (−0.2 MPa). The results revealed a significant effect of the biotype and the weed species on the drought stress response and adaptation. When high PEG concentrations were applied (−0.6 MPa), both C. sumatrensis biotypes had higher germination percentages (up to 88%) than the C. canadensis biotypes, while in most cases the seeds of the resistant biotypes germinated more (up to 72%) compared to the susceptible ones. These findings were confirmed by means of NDVI values, indicating that remote sensing can be used for a quick evaluation of the drought stress response of these weeds. The results obtained highlight the significant effect of species, biotypes and drought stress level on the germination, survival and growth of the weeds

Complement C3 inhibition in severe COVID-19 using compstatin AMY-101

Complement C3 activation contributes to COVID-19 pathology, and C3 targeting has emerged as a promising therapeutic strategy. We provide interim data from ITHACA, the first randomized trial evaluating a C3 inhibitor, AMY-101, in severe COVID-19 (PaO2/FiO2 &lt;= 300 mmHg). Patients received AMY-101 (n = 16) or placebo (n = 15) in addition to standard of care. AMY-101 was safe and well tolerated. Compared to placebo (8 of 15, 53.3%), a higher, albeit nonsignificant, proportion of AMY-101-treated patients (13 of 16, 81.3%) were free of supplemental oxygen at day 14. Three nonresponders and two placebo-treated patients succumbed to disease-related complications. AMY-101 significantly reduced CRP and ferritin and restrained thrombin and NET generation. Complete and sustained C3 inhibition was observed in all responders. Residual C3 activity in the three nonresponders suggested the presence of a convertase-independent C3 activation pathway overriding the drug's inhibitory activity. These findings support the design of larger trials exploring the potential of C3-based inhibition in COVID-19 or other complement-mediated diseases