The blockchain: a new framework for robotic swarm systems

Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. However, several of the heterogeneous characteristics that make them ideal for certain future applications --- robot autonomy, decentralized control, collective emergent behavior, etc. --- hinder the evolution of the technology from academic institutions to real-world problems. Blockchain, an emerging technology originated in the Bitcoin field, demonstrates that by combining peer-to-peer networks with cryptographic algorithms a group of agents can reach an agreement on a particular state of affairs and record that agreement without the need for a controlling authority. The combination of blockchain with other distributed systems, such as robotic swarm systems, can provide the necessary capabilities to make robotic swarm operations more secure, autonomous, flexible and even profitable. This work explains how blockchain technology can provide innovative solutions to four emergent issues in the swarm robotics research field. New security, decision making, behavior differentiation and business models for swarm robotic systems are described by providing case scenarios and examples. Finally, limitations and possible future problems that arise from the combination of these two technologies are described

The American Association for the Surgery of Trauma renal injury grading scale: Implications of the 2018 revisions for injury reclassification and predicting bleeding interventions.

BackgroundIn 2018, the American Association for the Surgery of Trauma (AAST) published revisions to the renal injury grading system to reflect the increased reliance on computed tomography scans and non-operative management of high-grade renal trauma (HGRT). We aimed to evaluate how these revisions will change the grading of HGRT and if it outperforms the original 1989 grading in predicting bleeding control interventions.MethodsData on HGRT were collected from 14 Level-1 trauma centers from 2014 to 2017. Patients with initial computed tomography scans were included. Two radiologists reviewed the scans to regrade the injuries according to the 1989 and 2018 AAST grading systems. Descriptive statistics were used to assess grade reclassifications. Mixed-effect multivariable logistic regression was used to measure the predictive ability of each grading system. The areas under the curves were compared.ResultsOf the 322 injuries included, 27.0% were upgraded, 3.4% were downgraded, and 69.5% remained unchanged. Of the injuries graded as III or lower using the 1989 AAST, 33.5% were upgraded to grade IV using the 2018 AAST. Of the grade V injuries, 58.8% were downgraded using the 2018 AAST. There was no statistically significant difference in the overall areas under the curves between the 2018 and 1989 AAST grading system for predicting bleeding interventions (0.72 vs. 0.68, p = 0.34).ConclusionAbout one third of the injuries previously classified as grade III will be upgraded to grade IV using the 2018 AAST, which adds to the heterogeneity of grade IV injuries. Although the 2018 AAST grading provides more anatomic details on injury patterns and includes important radiologic findings, it did not outperform the 1989 AAST grading in predicting bleeding interventions.Level of evidencePrognostic and Epidemiological Study, level III

First steps toward a BIG change: A pilot study to implement the Brain Injury Guidelines across a 24-hospital system

INTRODUCTION: The modified Brain Injury Guidelines (mBIG) support a subset of low-risk patients to be managed without repeat head computed tomography (RHCT), neurosurgical consult (NSC), or hospital transfer/admission. This pilot aimed to assess mBIG implementation at a single facility to inform future systemwide implementation. METHODS: Single cohort pilot trial at a level I trauma center, December 2021-August 2022. Adult patients included if tICH meeting BIG 1 or 2 criteria. BIG 3 patients excluded. RESULTS: No patients required neurosurgical intervention. 72 RHCT and 83 NSC were prevented. 21 isolated BIG 1 were safely discharged home from the ED. No hospital readmissions for tICH. Protocol adherence rate was 92%. CONCLUSION: Implementation of the mBIG at a single trauma center is feasible and optimizes resource utilization. This pilot study will inform an implementation trial of the mBIG across a 24-hospital integrated health system