Human–agent collaboration for disaster response

In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a multi-agent Markov decision process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked

Transcriptomes and expression profiling of deep-sea corals from the Red Sea provide insight into the biology of azooxanthellate corals

Despite the importance of deep-sea corals, our current understanding of their ecology and evolutionis limited due to difficulties in sampling and studying deep-sea environments. Moreover, a recent reevaluation of habitat limitations has been suggested after characterization of deep-sea corals in the Red Sea, where they live at temperatures of above 20 °C at low oxygen concentrations. To gain further insight into the biology of deep-sea corals, we produced reference transcriptomes and studied gene expression of three deep-sea coral species from the Red Sea, i.e. Dendrophyllia sp., Eguchipsammia fistula, and Rhizotrochus typus. Our analyses suggest that deep-sea coral employ mitochondrial hypometabolism and anaerobic glycolysis to manage low oxygen conditions present in the Red Sea. Notably, we found expression of genes related to surface cilia motion that presumably enhance small particle transport rates in the oligotrophic deep-sea environment. This is the first study to characterize transcriptomes and in situ gene expression for deep-sea corals. Our work offers several mechanisms by which deep-sea corals might cope with the distinct environmental conditions present in the Red Sea. As such, our data provides direction for future research and further insight to organismal response of deep sea coral to environmental change and ocean warming.Tis work was supported by King Abdullah University of Science and Technology (KAUST), baseline funds to CRV and Center Competitive Funding (CCF) Program FCC/1/1973-18-01

eHealth and mHealth initiatives in Bangladesh: A scoping study

BACKGROUND: The health system of Bangladesh is haunted by challenges of accessibility and affordability. Despite impressive gains in many health indicators, recent evidence has raised concerns regarding the utilization, quality and equity of healthcare. In the context of new and unfamiliar public health challenges including high population density and rapid urbanization, eHealth and mHealth are being promoted as a route to cost-effective, equitable and quality healthcare in Bangladesh. The aim of this paper is to highlight such initiatives and understand their true potential. METHODS: This scoping study applies a combination of research tools to explore 26 eHealth and mHealth initiatives in Bangladesh. A screening matrix was developed by modifying the framework of Arksey & O’Malley, further complemented by case study and SWOT analysis to identify common traits among the selected interventions. The WHO health system building blocks approach was then used for thematic analysis of these traits. RESULTS: Findings suggest that most eHealth and mHealth initiatives have proliferated within the private sector, using mobile phones. The most common initiatives include tele-consultation, prescription and referral. While a minority of projects have a monitoring and evaluation framework, less than a quarter have undertaken evaluation. Most of the initiatives use a health management information system (HMIS) to monitor implementation. However, these do not provide for effective sharing of information and interconnectedness among the various actors. There are extremely few individuals with eHealth training in Bangladesh and there is a strong demand for capacity building and experience sharing, especially for implementation and policy making. There is also a lack of research evidence on how to design interventions to meet the needs of the population and on potential benefits. CONCLUSION: This study concludes that Bangladesh needs considerable preparation and planning to sustain eHealth and mHealth initiatives successfully. Additional formative and operational research is essential to explore the true potential of the technology. Frameworks for regulation in regards to eHealth governance should be the aim of future research on the integration of eHealth and mHealth into the Bangladesh health system.DFI

Metabolic suppression in thecosomatous pteropods as an effect of low temperature and hypoxia in the eastern tropical North Pacific

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Marine Biology 159 (2012): 1955-1967, doi:10.1007/s00227-012-1982-x.Many pteropod species in the eastern tropical north Pacific Ocean migrate vertically each day, transporting organic matter and respiratory carbon below the thermocline. These migrations take species into cold (15-10ºC) hypoxic water (< 20 µmol O2 kg-1) at depth. We measured the vertical distribution, oxygen consumption and ammonia excretion for seven species of pteropod, some of which migrate and some which remain in oxygenated surface waters throughout the day. Within the upper 200 meters of the water column, changes in water temperature result in a ~60-75% reduction in respiration for most species. All three species tested under hypoxic conditions responded to low O2 with an additional ~35-50% reduction in respiratory rate. Combined, low temperature and hypoxia suppress the metabolic rate of pteropods by ~80-90%. These results shed light on the ways in which expanding regions of hypoxia and surface ocean warming may impact pelagic ecology.This work was funded by National Science Foundation grants to K. Wishner and B. Seibel (OCE – 0526502 and OCE – 0851043) and to K. Daly (OCE – 0526545), the University of Rhode Island, and the Rhode Island Experimental Program to Stimulate Competitive Research Fellowship program.2013-06-3

Decision Making in Agent-Based Models

Agent-Based Models (ABM) are being increasingly applied to the study of a wide range of social phenomena, often putting the focus on the macroscopic patterns that emerge from the interaction of a number of agents programmed to behave in a plausible manner. This agent behavior, however, is all too often encoded as a small set of rules that produces a somewhat simplistic behavior. In this short paper, we propose to explore the impact of decision-making processes on the outcome of simulations, and introduce a type of agent that uses a more systematic and principled decision-making approach, based on casting the simulation environment as a Markov Decision Process. We compare the performance of this type of agent to that of more simplistic agents on a simple ABM simulation, and examine the interplay between the decision-making mechanism and other relevant simulation parameters such as the distribution and scarcity of resources. Our preliminary findings show that our novel agent outperforms the rest of agents, and, more generally, that the process of decision-making needs to be acknowledged as a first-class parameter of ABM simulations with a significant impact on the simulation outcome.This research is part of the SimulPast Project (CSD2010-00034) funded by the CONSOLIDER-INGENIO2010 program of the Spanish Ministry of Science and Innovation.Peer Reviewe