Right Place, Right Time:Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty

For many multi-robot problems, tasks are announced during execution, where task announcement times and locations are uncertain. To synthesise multi-robot behaviour that is robust to early announcements and unexpected delays, multi-robot task allocation methods must explicitly model the stochastic processes that govern task announcement. In this paper, we model task announcement using continuous-time Markov chains which predict when and where tasks will be announced. We then present a task allocation framework which uses the continuous-time Markov chains to allocate tasks proactively, such that robots are near or at the task location upon its announcement. Our method seeks to minimise the expected total waiting duration for each task, i.e. the duration between task announcement and a robot beginning to service the task. Our framework can be applied to any multi-robot task allocation problem where robots complete spatiotemporal tasks which are announced stochastically. We demonstrate the efficacy of our approach in simulation, where we outperform baselines which do not allocate tasks proactively, or do not fully exploit our task announcement models

a retrospective cohort analysis

Background Allergy immunotherapy is an effective treatment for patients with allergic rhinitis whose symptoms are unresolved with pharmacotherapy. Allergy immunotherapy for grass pollen-induced allergic rhinitis is available in three modalities: subcutaneous immunotherapy and sublingual immunotherapy as a tablet or drop. This study aimed to understand trends in allergy immunotherapy prescribing and practice patterns for grass allergies in adult and paediatric patients in Germany. Methods A retrospective cohort study was conducted using IMS Disease Analyzer in Germany. Patients with an allergy immunotherapy prescription for grass pollen (Anatomical Therapeutic Chemical [ATC] classification code V01AA02) from September 2005 to December 2012 were included in the study. General Practitioners (GPs), dermatologists, Ear, Nose and Throat (ENT)-specialists, paediatricians and pneumologists were included as the allergy immunotherapy prescribing physicians in the study. Descriptive analyses were conducted on patient characteristics at index and prescribing physician specialty; a test for trend was conducted for timing of initiation of first allergy immunotherapy prescription in each annual prescribing season. Results Eighteen thousand eight hundred fifty eligible patients were identified during the study period. The majority of patients received subcutaneous immunotherapy; however, the proportion of patients receiving sublingual immunotherapy tablets increased from 8 % in 2006/2007 to 29 % in 2011/2012 (p < 0.001). Initiation of subcutaneous immunotherapy and Oralair® generally peaked during each prescribing year in two seasons (September- October and January) while GRAZAX® prescriptions peaked in autumn (September- October). ENT-specialists and dermatologists were the largest allergy immunotherapy prescribers in adults, while paediatricians and ENT-specialists were the largest prescribers of allergy immunotherapy in paediatric patients. Conclusions Subcutaneous immunotherapy remained the dominant allergy immunotherapy modality for grass pollen-induced allergic rhinitis in Germany for adult and paediatric patients; however, there was a marked increase in proportion of patients receiving sublingual immunotherapy tablets from 2006/2007 to 2011/2012, after their introduction to the market in 2006. ENT- specialists, dermatologists and paediatricians were responsible for the majority of prescribing. The predominance of particular modalities within certain physician specialties likely reflects different treatment goals or needs

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Decision-making under uncertainty for multi-robot systems

In this overview paper, we present the work of the Goal-Oriented Long-Lived Systems Lab on multi-robot systems. We address multi-robot systems from a decision-making under uncertainty perspective, proposing approaches that explicitly reason about the inherent uncertainty of action execution, and how such stochasticity affects multi-robot coordination. To develop effective decision-making approaches, we take a special focus on (i) temporal uncertainty, in particular of action execution; (ii) the ability to provide rich guarantees of performance, both at a local (robot) level and at a global (team) level; and (iii) scaling up to systems with real-world impact. We summarise several pieces of work and highlight how they address the challenges above, and also hint at future research directions

Analysing the Effects of Congestion on Hybrid Order Picking Systems using a Discrete-Event Simulator

In hybrid order-picking systems (OPSs), human workers collaborate alongside autonomous guided vehicles (AGVs) to pick up and transport items in a warehouse. Congestion occurs when multiple humans and AGVs operate in an area simultaneously. Congestion decreases AGV navigation performance and may cause queuing delays at packing stations. In this paper, we study the impact of congestion on the performance of hybrid OPSs. We simulate a hybrid OPS using a discrete-event simulator and evaluate the throughput under different levels of congestion and the number of AGVs. Using 10 AGVs under no congestion, we observe a throughput increase of 105% compared to a manual OPS with zero AGVs. However, this improvement decreases as the effects of congestion become stronger. Under our heaviest congestion model, there was only a 3% throughput increase for 10 AGVs. We also analyse the economic impact of adding AGVs to a hybrid OPS. Under medium congestion, the optimal number of AGVs for maximising long-term profit is 20