Multirobot coverage of modular environments

Multirobot systems for covering environments are increasingly used in applications like cleaning, industrial inspection, patrolling, and precision agriculture. The problem of covering a given environment using multiple robots can be naturally formulated and studied as a multi-Traveling Salesperson Problem (mTSP). In a mTSP, the environment is represented as a graph and the goal is to find tours (starting and ending at the same depot) for the robots in order to visit all the vertices with minimum global cost, namely the length of the longest tour. The mTSP is an NP-hard problem for which several approximation algorithms have been proposed. These algorithms usually assume generic environments, but tighter approximation bounds can be reached focusing on specific environments. In this paper, we address the case of environments composed of sub-parts, called modules, that can be reached from each other only through some linking structures. Examples are multi-floor buildings, in which the modules are the floors and the linking structures are the staircases or the elevators, and floors of large hotels or hospitals, in which the modules are the rooms and the linking structures are the corridors. We focus on linear modular environments, with the modules organized sequentially, presenting an efficient (with polynomial worst-case time complexity) algorithm that finds a solution for the mTSP whose cost is within a bounded distance from the cost of the optimal solution. The main idea of our algorithm is to allocate disjoint "blocks" of adjacent modules to the robots, in such a way that each module is covered by only one robot. We experimentally compare our algorithm against some state-of-the-art algorithms for solving mTSPs in generic environments and show that it is able to provide solutions with lower makespan and spending a computing time several orders of magnitude shorter

Demystifying Drug Repurposing Domain Comprehension with Knowledge Graph Embedding

Drug repurposing is more relevant than ever due to drug development's rising costs and the need to respond to emerging diseases quickly. Knowledge graph embedding enables drug repurposing using heterogeneous data sources combined with state-of-the-art machine learning models to predict new drug-disease links in the knowledge graph. As in many machine learning applications, significant work is still required to understand the predictive models' behavior. We propose a structured methodology to understand better machine learning models' results for drug repurposing, suggesting key elements of the knowledge graph to improve predictions while saving computational resources. We reduce the training set of 11.05% and the embedding space by 31.87%, with only a 2% accuracy reduction, and increase accuracy by 60% on the open ogbl-biokg graph adding only 1.53% new triples

The Örebro Musculoskeletal Pain Questionnaire. Cross-cultural adaptation, reliability, and validity of the Italian version

BACKGROUND: Musculoskeletal disorders (DMS) are biomechanical overload diseases whose onset is attributable to multiple factors. In fact, in addition to physical factors, such as microtrauma, overloads or inadequate posture protracted over time, has been recognized a fundamental role in the etiology of DMS also to bio-psychosocial factors. There is currently no Italian version of the Örebro Musculoskeletal Pain Questionnaire (OMPQ). The aim of this study was to describe the process of translating the OMPQ into Italian and to evaluate the psychometric properties of the questionnaire. METHODS: Translation of the questionnaire through a multi-stage process. Two independent translators translated the original version into Italian and then an optimized version into Italian was created. A backward translation was made to check for any differences. During the first administration the OMPQ, VAS and ODI questionnaire was submitted. The inclusion criteria were current pain in the lumbar region of the back, over 18 years old, ability to understand and speak Italian. The exclusion criteria were current or previous neoplasms, metastases, infectious processes, rheumatic diseases, osteoporosis, fractures, cognitive problems, and refusal of the patient to participate in the study. The statistical analysis was performed to evaluate the reliability, internal consistency, correlation with the comparison scales and validity of the construct. RESULTS: The questionnaire was administered to 172 patients and after 1 week only 1/3 completed the re-test. From the data obtained from the statistical analysis it has been demonstrated that the questionnaire has a good internal consistency with a Cronbach’s α is 0.829 and an excellent reliability evaluated through the ICC with C.I 95% equal to 0.977. The correlation with ODI was high (r=0.733) and high with VAS (r=0.610). CONCLUSIONS: The Italian version of the OMPQ questionnaire is valid and reliable as an evaluation tool for patients with musculoskeletal pain at risk of developing chronic disability

A prospective cohort analysis of the prevalence and predictive factors of delayed discharge after laparoscopic cholecystectomy in Italy: the DeDiLaCo Study

Background: The concept of early discharge ≤24 hours after Laparoscopic Cholecystectomy (LC) is still doubted in Italy. This prospective multicentre study aims to analyze the prevalence of patients undergoing elective LC who experienced a delayed discharge >24 hours in an extensive Italian national database and identify potential limiting factors of early discharge after LC. Methods: This is a prospective observational multicentre study performed from January 1, 2021 to December 31, 2021 by 90 Italian surgical units. Results: A total of 4664 patients were included in the study. Clinical reasons were found only for 850 patients (37.7%) discharged >24 hours after LC. After excluding patients with nonclinical reasons for delayed discharge >24 hours, 2 groups based on the length of hospitalization were created: the Early group (≤24 h; 2414 patients, 73.9%) and the Delayed group (>24 h; 850 patients, 26.1%). At the multivariate analysis, ASA III class ( P <0.0001), Charlson's Comorbidity Index (P=0.001), history of choledocholithiasis (P=0.03), presence of peritoneal adhesions (P<0.0001), operative time >60 min (P<0.0001), drain placement (P<0.0001), pain ( P =0.001), postoperative vomiting (P=0.001) and complications (P<0.0001) were independent predictors of delayed discharge >24 hours. Conclusions: The majority of delayed discharges >24 hours after LC in our study were unrelated to the surgery itself. ASA class >II, advanced comorbidity, the presence of peritoneal adhesions, prolonged operative time, and placement of abdominal drainage were intraoperative variables independently associated with failure of early discharge