Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Integrated Obstacle Avoidance and Path Following Through a Feedback Control Law

The article proposes a novel approach to path following in the presence of obstacles with unique characteristics. First, the approach proposes an integrated method for obstacle avoidance and path following based on a single feedback control law, which produces commands to actuators directly executable by a robot with unicycle kinematics. Second, the approach offers a new solution to the well-known dilemma that one has to face when dealing with multiple sensor readings, i.e., whether it is better, to summarize a huge amount of sensor data, to consider only the closest sensor reading, to consider all sensor readings separately to compute the resulting force vector, or to build a local map. The approach requires very little memory and computational resources, thus being implementable even on simpler robots moving in unknown environments

A framework for context-awareness in artificial systems

This paper introduces a context model to be used in context-aware cognitive artificial systems. The framework, that is aimed at integrating ontology and logic approaches to context modeling, assumes the availability of both an ontology (i.e., a representation of what exists) and a simple inference schema (i.e., subsumption). The context model is defined using a formal protocol, which describes contexts and situations as recursive structures grounded with respect to the ontology. Examples are presented to discuss the proposed model

Path following for unicycle robots with an arbitrary path curvature

A new feedback control model is provided that allows a wheeled vehicle to follow a prescribed path. Differently from all other methods in the literature, the method that is proposed neither requires the computation of a projection of the robot position on the path, nor does it need to consider a moving virtual target to be tracked. Nevertheless, it guarantees asymptotic convergence to a generic 2-D curve which can be represented through its implicit equation in the form f(x, y) = 0, and it puts no bounds on the initial position of the vehicle provided that grad f be nonzero

Describing and Recognizing Patterns of Events in Smart Environments With Description Logic

This paper describes a system for context awareness in smart environments, which is based on an ontology expressed in description logic and implemented in OWL 2 EL, which is a subset of the Web Ontology Language that allows for reasoning in polynomial time. The approach is different from all other works in the literature since the proposed system requires only the basic reasoning mechanisms of description logic, i.e., subsumption and instance checking, without any additional external reasoning engine. Experiments performed with data collected in three different scenarios are described, i.e., the CASAS Project at Washington State University, the assisted living facility Villa Basilea in Genoa, and the Merry Porter mobile robot at the Polyclinic of Modena

Programming Real Time Distributed Multiple Robotic Systems

This paper presents ETHNOS-IV - a real-time programming environment for the design of a system composed of different robots, devices and external supervising or control stations. ETHNOS is being used for different service robotics applications and it is has also been used successfully used in RoboCup in the Italian ART robot team during the Stockholm '99 competition. It provides support from three main point of views which will be addressed in detail: inter-robot and intra-robot communication, real-time task scheduling, and software engineering, platform independence and code-reuse. Experimental results will also be presented

Improving smart environments with knowledge ecosystems

This paper presents a distributed cognitive architecture suitable for Ambient Intelligence applications. The key idea is to model an intelligent space as an ecosystem composed by artificial entities which collaborate with each other to perform an intelligent multi-sensor data fusion of both numerical and symbolic information. The semantics associated with the knowledge representation can be used to aid intelligent systems or human supervisors to take decisions according to situations and events occurring within the intelligent space. Experimental results are presented showing how this approach has been successfully applied to smart environments for elderly and disabled