Within the domain of risky decision making, there are a number of predictive models which are consistent with the hypothesis that human minds are molded for specific behavioral patterns based on environmental cues. Two models are the priority heuristic and risk sensitive foraging. Using a modified version of the traditional risky choice gambles paradigm, a study was designed to tease apart specific predictions made by each of these two models. It was discovered that the best explanation for choice behavior was consistent with risk sensitive foraging. This was true for risky preferences in gambles. Also, decision time predictions from the priority heuristic were not supported. Collectively, this may show additional support for risk-sensitivity driving some human behaviors. It may also carve out the boundaries for the proper "ecology" of the priority heuristic.
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http://dx.doi.org/10.1177/0033294117709786 | DOI Listing |
BMJ Open
January 2025
Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran (the Islamic Republic of).
Objectives: Microbial threats pose a growing concern worldwide. This paper reports the analysis of Iran's policy process against microbial threats.
Design: This is a qualitative study.
Biomimetics (Basel)
November 2024
National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China.
With the increasing number of space debris, the demand for telescopes to observe space debris is also constantly increasing. The telescope observation scheduling problem requires algorithms to schedule telescopes to maximize observation value within the visible time constraints of space debris, especially when dealing with large-scale problems. This paper proposes a practical heuristic algorithm to solve the telescope observation of space debris scheduling problem.
View Article and Find Full Text PDFSensors (Basel)
November 2024
College of Information Science and Engineering, Jiaxing University, Jiaxing 314001, China.
Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in real time, significantly reducing latency and dependency on remote cloud servers. Motivated by these advancements, this paper explores the application of LAPSs and edge computing in the agricultural IoT.
View Article and Find Full Text PDFJMIR Public Health Surveill
October 2024
Washington State Department of Health, Olympia, WA, United States.
Background: During the peak of the winter 2020-2021 surge, the number of weekly reported COVID-19 outbreaks in Washington State was 231; the majority occurred in high-priority settings such as workplaces, community settings, and schools. The Washington State Department of Health used automated address matching to identify clusters at health care facilities. No other systematic, statewide outbreak detection methods were in place.
View Article and Find Full Text PDFSensors (Basel)
September 2024
School of Computer Science and Technology, Xinjiang Normal University, Urumqi 830054, China.
The adoption of multiprocessor platforms is growing commonplace in Internet of Things (IoT) applications to handle large volumes of sensor data while maintaining real-time performance at a reasonable cost and with low power consumption. Partitioned scheduling is a competitive approach to ensure the temporal constraints of real-time sensor data processing tasks on multiprocessor platforms. However, the problem of partitioning real-time sensor data processing tasks to individual processors is strongly NP-hard, making it crucial to develop efficient partitioning heuristics to achieve high real-time performance.
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