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The field of multi-robot systems (MRS) has recently been gaining increasing popularity among various research groups, practitioners, and a wide range of industries. Compared to single-robot systems, multi-robot systems are able to perform tasks more efficiently or accomplish objectives that are simply not feasible with a single unit. This makes such multi-robot systems ideal candidates for carrying out distributed tasks in large environments-e.g., performing object retrieval, mapping, or surveillance. However, the traditional approach to multi-robot systems using global planning and centralized operation is, in general, ill-suited for fulfilling tasks in unstructured and dynamic environments. Swarming multi-robot systems have been proposed to deal with such steep challenges, primarily owing to its adaptivity. These qualities are expressed by the system's ability to learn or change its behavior in response to new and/or evolving operating conditions. Given its importance, in this perspective, we focus on the critical importance of adaptivity for effective multi-robot system swarming and use it as the basis for defining, and potentially quantifying, swarm intelligence. In addition, we highlight the importance of establishing a suite of benchmark tests to measure a swarm's level of adaptivity. We believe that a focus on achieving increased levels of swarm intelligence through the focus on adaptivity will further be able to elevate the field of swarm robotics., Competing Interests: HLK was employed by Thales Solutions Asia Pte Ltd. The company was not involved in the writing of the manuscript or the decision to submit it for publication. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kwa, Kit, Horsevad, Philippot, Savari and Bouffanais.)

Introduction: This cross-sectional study aimed at evaluating impacts of healthcare simulation training, either in-situ or lab-based, on personal strengths of healthcare workers (HCWs) and organizational outcomes during the COVID-19 pandemic., Methods: COVID-19 Taskforce was established to formulate standardized scenario-based simulation training materials in late-January 2020. Post-training questionnaires made up of 5-point Likert scales were distributed to all participants to evaluate their personal strengths, in terms of i) assertiveness, ii) mental preparedness, iii) self-efficacy, iv) internal locus of control, and v) internal locus of responsibility. Independent sample t-tests were used to analyze between-group difference in "In-situ" and "Lab-based" group; and one-sample t-tests were used to compare change in personal strengths with reference point of 3 (Neutral). Kirkpatrick's Model served as the analytical framework for overall training effects., Results: Between 05 February and 18 March 2020, 101 sessions of simulation training were conducted in "In-Situ" at either Accident & Emergency Department (20, 20%) or Intensive Care Unit (15, 14%) and "Lab-based" for Isolation (30, 30%) and General Wards (36, 36%). 1,415 hospital staff members, including 1,167 nurses (82%), 163 doctors (12%) and 85 patient care assistants (6%), were trained. All domains of personal strengths were scored 4.24 or above and statistically significantly increased when comparing with reference population ( p < .001). However, no significant differences between in-situ and lab-based simulation were found ( p > .05), for all domains of personal strengths., Conclusion: Healthcare simulation training enhanced healthcare workers' personal strengths critical to operational and clinical outcomes during the COVID-19 pandemic., (© 2020 Korea Institute of Oriental Medicine. Publishing services by Elsevier B.V.)

Picochlorum celeri is a fast-growing marine microalga with high biomass productivity. Here, we report the use of PacBio sequencing to assemble the phased diploid genome of P. celeri ., (Copyright © 2020 Becker et al.)