Neural network-based vehicular channel estimation performance: Effect of noise in the training set.

Vehicular communication systems face significant challenges due to high mobility and rapidly changing environments, which affect the channel over which the signals travel. To address these challenges, neural network (NN)-based channel estimation methods have been suggested. These methods are primarily trained on high signal-to-noise ratio (SNR) with the assumption that training a NN in less noisy conditions can result in good generalisation. This study examines the effectiveness of training NN-based channel estimators on mixed SNR datasets compared to training solely on high SNR datasets, as seen in several related works. Estimators evaluated in this work include an architecture that uses convolutional layers and self-attention mechanisms; a method that employs temporal convolutional networks and data pilot-aided estimation; two methods that combine classical methods with multilayer perceptrons; and the current state-of-the-art model that combines Long-Short- Term Memory networks with data pilot-aided and temporal averaging methods as post processing. Our results indicate that using only high SNR data for training is not always optimal, and the SNR range in the training dataset should be treated as a hyperparameter that can be adjusted for better performance. This is illustrated by the better performance of some models in low SNR conditions when trained on the mixed SNR dataset, as opposed to when trained exclusively on high SNR data

Sequence Based Deep Neural Networks for Channel Estimation in Vehicular Communication Systems

Channel estimation is a critical component of vehicular communications systems, especially in high-mobility scenarios. The IEEE 802.11p standard uses preamble-based channel estimation, which is not sufficient in these situations. Recent work has proposed using deep neural networks for channel estimation in IEEE 802.11p. While these methods improved on earlier baselines they still can perform poorly, especially in very high mobility scenarios. This study proposes a novel approach that uses two independent LSTM cells in parallel and averages their outputs to update cell states. The proposed approach improves normalised mean square error, surpassing existing deep learning approaches in very high mobility scenario

Livelihood benefits and costs from an invasive alien tree (Acacia dealbata) to rural communities in the Eastern Cape, South Africa

The negative effects of invasive alien species (IAS) are increasingly invoked to justify widespread and usually top-down approaches for their management or eradication. However, very little of the research or discourse is based on investigating local perceptions, uses and struggles with IAS, and how their presence influences and changes local livelihoods. The objective of this study was to assess the perceptions and livelihood uses of Acacia dealbata by local communities at three localities in the montane grasslands of the Eastern Cape, South Africa, using a combination of random household interviews, focus group discussions and participatory tools. We calculated direct-use values for each product and household (based on quantity used and local prices) and disaggregated these by gender of the household head and wealth quartiles. The results revealed the dualistic role of A. dealbata in local livelihoods. On the one hand, A. dealbata was widely used for firewood (100% of households), tools (77%) and construction timber (73%), with limited use for traditional medicines and forage. The cumulative value of approximately ZAR 2870 (±US$224) per household per year (across all households) represents considerable cash saving to households, most of whom are quite poor by national and international measures. On the other hand, the increasing extent of A. dealbata (93% said it was increasing) exacerbates local household vulnerability though reported reductions in cultivated areas, crop yields and forage production, and allegedly higher risks of crime. This quandary is well encapsulated by the considerable majority of respondents (84%) not wanting higher extents and densities of A. dealbata, but an equally high majority not wanting its total removal from local landscapes. Most respondents disliked A. dealbata in fields, close to homesteads or along primary access routes, and were more tolerant of it away from such sites. Institutional and use dynamics have varied over several decades in response to the changing extent and densities of A. dealbata and the broader political and socio-economic contexts. These results indicate that greater efforts are required to understand perceptions and uses of IAS by the people who live with them, and to direct such understanding into more spatially and temporally contextualised response strategies where required

South African Population Immunity and Severe Covid-19 with Omicron Variant

ABSTRACTBackgroundWe conducted a seroepidemiological survey from October 22 to December 9, 2021, in Gauteng Province, South Africa, to determine SARS-CoV-2 immunoglobulin G (IgG) seroprevalence primarily before the fourth wave of coronavirus disease 2019 (Covid-19), in which the B.1.1.529 (Omicron) variant was dominant. We evaluated epidemiological trends in case rates and rates of severe disease through to January 12, 2022, in Gauteng.MethodsWe contacted households from a previous seroepidemiological survey conducted from November 2020 to January 2021, plus an additional 10% of households using the same sampling framework. Dry blood spot samples were tested for anti-spike and anti-nucleocapsid protein IgG using quantitative assays on the Luminex platform. Daily case, hospital admission, and reported death data, and weekly excess deaths, were plotted over time.ResultsSamples were obtained from 7010 individuals, of whom 1319 (18.8%) had received a Covid-19 vaccine. Overall seroprevalence ranged from 56.2% (95% confidence interval [CI], 52.6 to 59.7) in children aged &lt;12 years to 79.7% (95% CI, 77.6 to 81.5) in individuals aged &gt;50 years. Seropositivity was more likely in vaccinated (93.1%) vs unvaccinated (68.4%) individuals. Epidemiological data showed SARS-CoV-2 infection rates increased and subsequently declined more rapidly than in previous waves. Infection rates were decoupled from Covid-19 hospitalizations, recorded deaths, and excess deaths relative to the previous three waves.ConclusionsWidespread underlying SARS-CoV-2 seropositivity was observed in Gauteng Province before the Omicron-dominant wave. Epidemiological data showed a decoupling of hospitalization and death rates from infection rate during Omicron circulation.</jats:sec

The effectiveness and cost-effectiveness of 3- vs. 6-monthly dispensing of antiretroviral treatment (ART) for stable HIV patients in community ART-refill groups in Zimbabwe: study protocol for a pragmatic, cluster-randomized trial

Abstract Background Sub-Saharan Africa is the world region with the greatest number of people eligible to receive antiretroviral treatment (ART). Less frequent dispensing of ART and community-based ART-delivery models are potential strategies to reduce the load on overburdened healthcare facilities and reduce the barriers for patients to access treatment. However, no large-scale trials have been conducted investigating patient outcomes or evaluating the cost-effectiveness of extended ART-dispensing intervals within community ART-delivery models. This trial will assess the clinical effectiveness, cost-effectiveness and acceptability of providing ART refills on a 3 vs. a 6-monthly basis within community ART-refill groups (CARGs) for stable patients in Zimbabwe. Methods In this pragmatic, three-arm, parallel, unblinded, cluster-randomized non-inferiority trial, 30 clusters (healthcare facilities and associated CARGs) are allocated using stratified randomization in a 1:1:1 ratio to either (1) ART refills supplied 3-monthly from the health facility (control arm), (2) ART refills supplied 3-monthly within CARGs, or (3) ART refills supplied 6-monthly within CARGs. A CARG consists of 6–12 stable patients who meet in the community to receive ART refills and who provide support to one another. Stable adult ART patients with a baseline viral load < 1000 copies/ml will be invited to participate (1920 participants per arm). The primary outcome is the proportion of participants alive and retained in care 12 months after enrollment. Secondary outcomes (measured at 12 and 24 months) are the proportions achieving virological suppression, average provider cost per participant, provider cost per participant retained, cost per participant retained with virological suppression, and average patient-level costs to access treatment. Qualitative research will assess the acceptability of extended ART-dispensing intervals within CARGs to both providers and patients, and indicators of potential facility-level decongestion due to the interventions will be assessed. Discussion Cost-effective health system models that sustain high levels of patient retention are urgently needed to accommodate the large numbers of stable ART patients in sub-Saharan Africa. This will be the first trial to evaluate extended ART-dispensing intervals within a community-based ART distribution model, and results are intended to inform national and regional policy regarding their potential benefits to both the healthcare system and patients. Trial registration ClinicalTrials.gov, ID: NCT03238846. Registered on 27 July 2017