Background/MethodsQualitative studies suggest that bed nets affect the thermal comfort of users. To understand and reduce this discomfort the effect of bed nets on temperature, humidity, and airflow was measured in rural homes in Asia and Africa, as well as in an experimental wind tunnel. Two investigators with architectural training selected 60 houses in The Gambia, Tanzania, Philippines, and Thailand. Data-loggers were used to measure indoor temperatures in hourly intervals over a 12 months period. In a subgroup of 20 houses airflow, temperature and humidity were measured at five-minute intervals for one night from 21.00 to 6.00 hrs inside and outside of bed nets using sensors and omni-directional thermo-anemometers. An investigator set up a bed net with a mesh size of 220 holes per inch2 in each study household and slept under the bed net to simulate a realistic environment. The attenuation of airflow caused by bed nets of different mesh sizes was also measured in an experimental wind tunnel.ResultsThe highest indoor temperatures (49.0 C) were measured in The Gambia. During the hottest months of the year the mean temperature at night (9 pm) was between 33.1 C (The Gambia) and 26.2 C (Thailand). The bed net attenuated the airflow from a minimum of 27% (Philippines) to a maximum of 71% (The Gambia). Overall the bed nets reduced airflow compared to un-attenuated airflow from 9 to 4 cm sec-1 or 52% (p < 0.001). In all sites, no statistically significant difference in temperature or humidity was detected between the inside and outside of the bed net. Wind tunnel experiments with 11 different mesh-sized bed nets showed an overall reduction in airflow of 64% (range 55 - 71%) compared to un-attenuated airflow. As expected, airflow decreased with increasing net mesh size. Nets with a mesh of 136 holes inch-2 reduced airflow by 55% (mean; range 51 - 73%). A denser net (200 holes inch-2) attenuated airflow by 59% (mean; range 56 - 74%).DiscussionDespite concerted efforts to increase the uptake of this intervention in many areas uptake remains poor. Bed nets reduce airflow, but have no influence on temperature and humidity. The discomfort associated with bed nets is likely to be most intolerable during the hottest and most humid period of the year, which frequently coincides with the peak of malaria vector densities and the force of pathogen transmission.ConclusionsThese observations suggest thermal discomfort is a factor limiting bed net use and open a range of architectural possibilities to overcome this limitation.

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This paper proposes a distributed deep reinforcement learning (DRL) methodology for autonomous mobile robots (AMRs) to manage radio resources in an indoor factory with no network infrastructure. Hence, deep neural networks (DNN) are used to optimize the decision policy of the robots, which will make decisions in a distributed manner without signalling exchange. To speed up the learning phase, a centralized training is adopted in which a single DNN is trained using the experience from all robots. Once completed, the pre-trained DNN is deployed at all robots for distributed selection of resources. The performance of this approach is evaluated and compared to 5G NR sidelink mode 2 via simulations. The results show that the proposed method achieves up to 5% higher probability of successful reception when the density of robots in the scenario is high.

Robotic swarms are becoming relevant across different industries. In an indoor factory, collective perception of the environment can be used for increased factory automatization. It requires reliable, high throughput and low latency communication of broadcasted video data among robots within proximity.We introduce two new decentralized resource allocation schemes that meet these stringent requirements. The two proposed decentralized schemes are denoted as: (i) device sequential, where robots take turns to allocate resources, and (ii) group scheduling, where robots select local group leaders who perform the resource allocation. A comparative evaluation is performed by simulation against a centralized resource allocation scheme and the current 3GPP release 16 NR sidelink mode 2 scheme.Our results show that the two proposed decentralized resource allocation schemes outperform sidelink mode 2 due to the mitigation of the half-duplex problem. The proposed schemes reach the throughput target of 10 Mbps with a reliability of 99.99% for a swarm size of 50 robots.