Communication Scheduling by Deep Reinforcement Learning for Remote Traffic State Estimation with Bayesian Inference

Traffic awareness is the prerequisite of autonomous driving. Given the limitation of on-board sensors (e.g., precision and price), remote measurement from either infrastructure or other vehicles can improve traffic safety. However, the wireless communication carrying the measurement result undergoes fading, noise and interference and has a certain probability of outage. When the communication fails, the vehicle state can only be predicted by Bayesian filtering with a low precision. Higher communication resource utilization (e.g., transmission power) reduces the outage probability and hence results in an improved estimation precision. The power control subject to an estimate variance constraint is a difficult problem due to the complicated mapping from transmit power to vehicle-state estimate variance. In this paper, we develop an estimator consisting of several Kalman filters (KFs) or extended Kalman filters (EKFs) and an interacting multiple model (IMM) to estimate and predict the vehicle state. We propose to apply deep reinforcement learning (DRL) for the transmit power optimization. In particular, we consider an intersection and a lane-changing scenario and apply proximal policy optimization (PPO) and soft actor-critic (SAC) to train the DRL model. Testing results show satisfactory power control strategies confining estimate variances below given threshold. SAC achieves higher performance compared to PPO

Decentralized Scheduling for Cooperative Localization With Deep Reinforcement Learning

Cooperative localization is a promising solution to the vehicular high-accuracy localization problem. Despite its high potential, exhaustive measurement and information exchange between all adjacent vehicles are expensive and impractical for applications with limited resources. Greedy policies or hand-engineering heuristics may not be able to meet the requirement of complicated use cases. In this paper, we formulate a scheduling problem to improve the localization accuracy (measured through the Cram\ue9r-Rao lower bound) of every vehicle up to a given threshold using the minimum number of measurements. The problem is cast as a partially observable Markov decision process and solved using decentralized scheduling algorithms with deep reinforcement learning, which allow vehicles to optimize the scheduling (i.e., the instants to execute measurement and information exchange with each adjacent vehicle) in a distributed manner without a central controlling unit. Simulation results show that the proposed algorithms have a significant advantage over random and greedy policies in terms of both required numbers of measurements to localize all nodes and achievable localization precision with limited numbers of measurements

Cooperative localization with angular measurements and posterior linearization

The application of cooperative localization in vehicular networks is attractive to improve accuracy and coverage of the positioning. Conventional distance measurements between vehicles are limited by the need for synchronization and provide no heading information of the vehicle. To address this, we present a cooperative localization algorithm using posterior linearization belief propagation (PLBP) utilizing angle-of-arrival (AoA)-only measurements. Simulation results show that both directional and positional root mean squared error (RMSE) of vehicles can be decreased significantly and converge to a low value in a few iterations. Furthermore, the influence of parameters for the vehicular network, such as vehicle density, communication radius, prior uncertainty, and AoA measurements noise, is analyzed

Essential medicines management during emergencies in Pakistan

This paper illustrates the experiences of essential medicine management in providing cure and care to victims of Pakistan\u27s 2005 earthquake in a safe, rational and effective mode. The health interventions assured access to essential medicine, sustained supply, inventory control through a computerized logistic support system and rational use of medicines. World Health Organization Pakistan outlined modalities for acceptance of donated medicines, assisted in speedy procurement of medicines and designed customized kits. Proper storage of medicines at controlled temperature was ensured in warehousing facilities in 12 locations. A steady supply of medicines and their consumption without stock-outs in the 56 first-level care facilities of calamity-hit areas helped to ascertain the average consumption and cost of essential medicines and supplies for the catchment population. Tools for quantification and forecasting of medicines and supplies were developed and shared. Medicines and medical supplies were efficiently used resulting in minimum wastage

Using models to inform policies to meet multiple objectives. Sustainable development, climate change mitigation and biodiversity conservation in Central Africa

Land-use change models can help in developing a holistic understanding of the range of potential impacts of different land-use related policy options, and so strengthen the development and implementation of policies to meet a range of objectives; including sustainable development, climate change mitigation, food security and biodiversity conservation. Member countries of the Central Africa Forest Commission (COMIFAC) have committed to sustainable management of the region’s forests, including under the COMIFAC “Convergence Plan”, and to achieving the Sustainable Development Goals (SDGs). Achieving these objectives is dependent on the development, and implementation, of new and existing national policies and approaches. Projections from land-use modelling identify potential trade-offs and synergies in the achievement of the SDGs under different macro-economic and land-use policy related scenarios. In particular they highlight the importance of effective protected areas and forest concessions for the conservation of Great Apes and other threatened species, and show that maintaining these areas has negligible impact on agricultural production in the region. As development continues in the region, further increasing the extent of protected areas could play a role in greatly reducing the number of species losing a large proportion of their habitat. However, protected area expansion needs to be well planned to avoid adverse impacts on particular species and societal challenges such as food security

Creating a National Specimen Referral System in Guinea: Lessons From Initial Development and Implementation

In the wake of the 2014–2016, West Africa Ebola virus disease (EVD) outbreak, the Government of Guinea recognized an opportunity to strengthen its national laboratory system, incorporating capacity and investments developed during the response. The Ministry of Health (MOH) identified creation of a holistic, safe, secure, and timely national specimen referral system as a priority for improved detection and confirmation of priority diseases, in line with national Integrated Disease Surveillance and Response guidelines. The project consisted of two parts, each led by different implementing partners working collaboratively together and with the Ministry of Health: the development and approval of a national specimen referral policy, and pilot implementation of a specimen referral system, modeled on the policy, in three prefectures. This paper describes the successful execution of the project, highlighting the opportunities and challenges of building sustainable health systems capacity during and after public health emergencies, and provides lessons learned for strengthening national capabilities for surveillance and disease diagnosis

Effectiveness of Mechanisms and Models of Coordination between Organizations, Agencies and Bodies Providing or Financing Health Services in Humanitarian Crises: A Systematic Review.

BACKGROUND: Effective coordination between organizations, agencies and bodies providing or financing health services in humanitarian crises is required to ensure efficiency of services, avoid duplication, and improve equity. The objective of this review was to assess how, during and after humanitarian crises, different mechanisms and models of coordination between organizations, agencies and bodies providing or financing health services compare in terms of access to health services and health outcomes. METHODS: We registered a protocol for this review in PROSPERO International prospective register of systematic reviews under number PROSPERO2014:CRD42014009267. Eligible studies included randomized and nonrandomized designs, process evaluations and qualitative methods. We electronically searched Medline, PubMed, EMBASE, Cochrane Central Register of Controlled Trials, CINAHL, PsycINFO, and the WHO Global Health Library and websites of relevant organizations. We followed standard systematic review methodology for the selection, data abstraction, and risk of bias assessment. We assessed the quality of evidence using the GRADE approach. RESULTS: Of 14,309 identified citations from databases and organizations' websites, we identified four eligible studies. Two studies used mixed-methods, one used quantitative methods, and one used qualitative methods. The available evidence suggests that information coordination between bodies providing health services in humanitarian crises settings may be effective in improving health systems inputs. There is additional evidence suggesting that management/directive coordination such as the cluster model may improve health system inputs in addition to access to health services. None of the included studies assessed coordination through common representation and framework coordination. The evidence was judged to be of very low quality. CONCLUSION: This systematic review provides evidence of possible effectiveness of information coordination and management/directive coordination between organizations, agencies and bodies providing or financing health services in humanitarian crises. Our findings can inform the research agenda and highlight the need for improving conduct and reporting of research in this field

Ebola virus transmission initiated by systemic ebola virus disease relapse

During the 2018-2020 Ebola virus disease (EVD) outbreak in North Kivu province in the Democratic Republic of Congo, EVD was diagnosed in a patient who had received the recombinant vesicular stomatitis virus-based vaccine expressing a ZEBOV glycoprotein (rVSV-ZEBOV) (Merck). His treatment included an Ebola virus (EBOV)-specific monoclonal antibody (mAb114), and he recovered within 14 days. However, 6 months later, he presented again with severe EVD-like illness and EBOV viremia, and he died. We initiated epidemiologic and genomic investigations that showed that the patient had had a relapse of acute EVD that led to a transmission chain resulting in 91 cases across six health zones over 4 months. (Funded by the Bill and Melinda Gates Foundation and others.)