513 research outputs found
Impact of malaria related messages on insecticide-treated net (ITN) use for malaria prevention in Ghana
Background: Media messages have been used in Ghana to promote insecticide-treated net (ITN)/bed net usage in an effort to impact on malaria prevention. The aim of this study was to assess the effect of such malaria-related messages delivered through electronic/print media and by volunteers/health workers on the use of ITNs by children living in a household. Methods: Data was collected from September to November of 2008 using a structured, interviewer-administered questionnaire by the Ghana Statistical Service as part of a national demographic and health survey (DHS). Secondary data analysis was performed on the collected data using multivariate logistic regression for both individual messages and a composite (any of) message variable. Results: From the 11,788 households surveyed, 45% had at least one net. Households with male heads were more likely to have a child sleeping under a bed net the previous night (p = 0.0001). Individual Messages delivered by a health worker or a dedicated radio programme, had the highest effect for one or more children sleeping under a net the night before (OR adjusted = 1.65; 95% CI = 1.44 to 1.88 and OR adjusted = 1.26; 95% CI =1.12 to 1.42 respectively) while hearing any of the eight messages (composite score) resulted in the highest odds for one or more children (OR adjusted = 3.06; 95% CI = 2.27 to 4.12) sleeping under a bed net. Conclusion: Efforts to relate ITN messages to the public are very useful in increasing use of bed nets and having multiple ways of reaching the public increases their effect, with the biggest effect seen when health workers and volunteers were used to deliver malaria-related messages to the public
Recommended from our members
Cyber insurance of information systems: Security and privacy cyber insurance contracts for ICT and helathcare organizations
Nowadays, more-and-more aspects of our daily activities are digitalized. Data and assets in the cyber-space, both for individuals and organizations, must be safeguarded. Thus, the insurance sector must face the challenge of digital transformation in the 5G era with the right set of tools. In this paper, we present CyberSure-an insurance framework for information systems. CyberSure investigates the interplay between certification, risk management, and insurance of cyber processes. It promotes continuous monitoring as the new building block for cyber insurance in order to overcome the current obstacles of identifying in real-time contractual violations by the insured party and receiving early warning notifications prior the violation. Lightweight monitoring modules capture the status of the operating components and send data to the CyberSure backend system which performs the core decision making. Therefore, an insured system is certified dynamically, with the risk and insurance perspectives being evaluated at runtime as the system operation evolves. As new data become available, the risk management and the insurance policies are adjusted and fine-tuned. When an incident occurs, the insurance company possesses adequate information to assess the situation fast, estimate accurately the level of a potential loss, and decrease the required period for compensating the insured customer. The framework is applied in the ICT and healthcare domains, assessing the system of medium-size organizations. GDPR implications are also considered with the overall setting being effective and scalable
Data-efficient Online Classification with Siamese Networks and Active Learning
An ever increasing volume of data is nowadays becoming available in a
streaming manner in many application areas, such as, in critical infrastructure
systems, finance and banking, security and crime and web analytics. To meet
this new demand, predictive models need to be built online where learning
occurs on-the-fly. Online learning poses important challenges that affect the
deployment of online classification systems to real-life problems. In this
paper we investigate learning from limited labelled, nonstationary and
imbalanced data in online classification. We propose a learning method that
synergistically combines siamese neural networks and active learning. The
proposed method uses a multi-sliding window approach to store data, and
maintains separate and balanced queues for each class. Our study shows that the
proposed method is robust to data nonstationarity and imbalance, and
significantly outperforms baselines and state-of-the-art algorithms in terms of
both learning speed and performance. Importantly, it is effective even when
only 1% of the labels of the arriving instances are available.Comment: 2020 International Joint Conference on Neural Networks (IJCNN),
Glasgow, UK, 202
Cooperative Simultaneous Tracking and Jamming for Disabling a Rogue Drone
This work investigates the problem of simultaneous tracking and jamming of a
rogue drone in 3D space with a team of cooperative unmanned aerial vehicles
(UAVs). We propose a decentralized estimation, decision and control framework
in which a team of UAVs cooperate in order to a) optimally choose their
mobility control actions that result in accurate target tracking and b) select
the desired transmit power levels which cause uninterrupted radio jamming and
thus ultimately disrupt the operation of the rogue drone. The proposed decision
and control framework allows the UAVs to reconfigure themselves in 3D space
such that the cooperative simultaneous tracking and jamming (CSTJ) objective is
achieved; while at the same time ensures that the unwanted inter-UAV jamming
interference caused during CSTJ is kept below a specified critical threshold.
Finally, we formulate this problem under challenging conditions i.e., uncertain
dynamics, noisy measurements and false alarms. Extensive simulation experiments
illustrate the performance of the proposed approach.Comment: 2020 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS
Joint Estimation and Control for Multi-Target Passive Monitoring with an Autonomous UAV Agent
This work considers the problem of passively monitoring multiple moving
targets with a single unmanned aerial vehicle (UAV) agent equipped with a
direction-finding radar. This is in general a challenging problem due to the
unobservability of the target states, and the highly non-linear measurement
process. In addition to these challenges, in this work we also consider: a)
environments with multiple obstacles where the targets need to be tracked as
they manoeuvre through the obstacles, and b) multiple false-alarm measurements
caused by the cluttered environment. To address these challenges we first
design a model predictive guidance controller which is used to plan
hypothetical target trajectories over a rolling finite planning horizon. We
then formulate a joint estimation and control problem where the trajectory of
the UAV agent is optimized to achieve optimal multi-target monitoring
Integrated Ray-Tracing and Coverage Planning Control using Reinforcement Learning
In this work we propose a coverage planning control approach which allows a
mobile agent, equipped with a controllable sensor (i.e., a camera) with limited
sensing domain (i.e., finite sensing range and angle of view), to cover the
surface area of an object of interest. The proposed approach integrates
ray-tracing into the coverage planning process, thus allowing the agent to
identify which parts of the scene are visible at any point in time. The problem
of integrated ray-tracing and coverage planning control is first formulated as
a constrained optimal control problem (OCP), which aims at determining the
agent's optimal control inputs over a finite planning horizon, that minimize
the coverage time. Efficiently solving the resulting OCP is however very
challenging due to non-convex and non-linear visibility constraints. To
overcome this limitation, the problem is converted into a Markov decision
process (MDP) which is then solved using reinforcement learning. In particular,
we show that a controller which follows an optimal control law can be learned
using off-policy temporal-difference control (i.e., Q-learning). Extensive
numerical experiments demonstrate the effectiveness of the proposed approach
for various configurations of the agent and the object of interest.Comment: 2022 IEEE 61st Conference on Decision and Control (CDC), 06-09
December 2022, Cancun, Mexic
Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents
In this work, a novel distributed search-planning framework is proposed,
where a dynamically varying team of autonomous agents cooperate in order to
search multiple objects of interest in three-dimension (3-D). It is assumed
that the agents can enter and exit the mission space at any point in time, and
as a result the number of agents that actively participate in the mission
varies over time. The proposed distributed search-planning framework takes into
account the agent dynamical and sensing model, and the dynamically varying
number of agents, and utilizes model predictive control (MPC) to generate
cooperative search trajectories over a finite rolling planning horizon. This
enables the agents to adapt their decisions on-line while considering the plans
of their peers, maximizing their search planning performance, and reducing the
duplication of work.Comment: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 202
- …