Interventions to improve healthcare workers’ hand hygiene compliance: a systematic review of systematic reviews

Objective: To synthesize the existing evidence base of systematic reviews of interventions to improve healthcare worker (HCW) hand hygiene compliance (HHC). Methods: PRISMA guidelines were followed, and 10 information sources were searched in September 2017, with no limits to language or date of publication, and papers were screened against inclusion criteria for relevance. Data were extracted and risk of bias was assessed. Results: Overall, 19 systematic reviews (n=20 articles) were included. Only 1 article had a low risk of bias. Moreover, 15 systematic reviews showed positive effects of interventions on HCW HHC, whereas 3 reviews evaluating monitoring technology did not. Findings regarding whether multimodal rather than single interventions are preferable were inconclusive. Targeting social influence, attitude, self-efficacy, and intention were associated with greater effectiveness. No clear link emerged between how educational interventions were delivered and effectiveness. Conclusions: This is the first systematic review of systematic reviews of interventions to improve HCW HHC. The evidence is sufficient to recommend the implementation of interventions to improve HCW HHC (except for monitoring technology), but it is insufficient to make specific recommendations regarding the content or how the content should be delivered. Future research should rigorously apply behavior change theory, and recommendations should be clearly described with respect to intervention content and how it is delivered. Such recommendations should be tested for longer terms using stronger study designs with clearly defined outcomes

Formalizing Size-Optimal Sorting Networks: Extracting a Certified Proof Checker

Since the proof of the four color theorem in 1976, computer-generated proofs have become a reality in mathematics and computer science. During the last decade, we have seen formal proofs using verified proof assistants being used to verify the validity of such proofs. In this paper, we describe a formalized theory of size-optimal sorting networks. From this formalization we extract a certified checker that successfully verifies computer-generated proofs of optimality on up to 8 inputs. The checker relies on an untrusted oracle to shortcut the search for witnesses on more than 1.6 million NP-complete subproblems.Comment: IMADA-preprint-c

Automatic generation of hardware Tree Classifiers

Machine Learning is growing in popularity and spreading across different fields for various applications. Due to this trend, machine learning algorithms use different hardware platforms and are being experimented to obtain high test accuracy and throughput. FPGAs are well-suited hardware platform for machine learning because of its re-programmability and lower power consumption. Programming using FPGAs for machine learning algorithms requires substantial engineering time and effort compared to software implementation. We propose a software assisted design flow to program FPGA for machine learning algorithms using our hardware library. The hardware library is highly parameterized and it accommodates Tree Classifiers. As of now, our library consists of the components required to implement decision trees and random forests. The whole automation is wrapped around using a python script which takes you from the first step of having a dataset and design choices to the last step of having a hardware descriptive code for the trained machine learning model

Twenty-Five Comparators is Optimal when Sorting Nine Inputs (and Twenty-Nine for Ten)

This paper describes a computer-assisted non-existence proof of nine-input sorting networks consisting of 24 comparators, hence showing that the 25-comparator sorting network found by Floyd in 1964 is optimal. As a corollary, we obtain that the 29-comparator network found by Waksman in 1969 is optimal when sorting ten inputs. This closes the two smallest open instances of the optimal size sorting network problem, which have been open since the results of Floyd and Knuth from 1966 proving optimality for sorting networks of up to eight inputs. The proof involves a combination of two methodologies: one based on exploiting the abundance of symmetries in sorting networks, and the other, based on an encoding of the problem to that of satisfiability of propositional logic. We illustrate that, while each of these can single handed solve smaller instances of the problem, it is their combination which leads to an efficient solution for nine inputs.Comment: 18 page

Solution of Linear Programming Problems using a Neural Network with Non-Linear Feedback

This paper presents a recurrent neural circuit for solving linear programming problems. The objective is to minimize a linear cost function subject to linear constraints. The proposed circuit employs non-linear feedback, in the form of unipolar comparators, to introduce transcendental terms in the energy function ensuring fast convergence to the solution. The proof of validity of the energy function is also provided. The hardware complexity of the proposed circuit compares favorably with other proposed circuits for the same task. PSPICE simulation results are presented for a chosen optimization problem and are found to agree with the algebraic solution. Hardware test results for a 2–variable problem further serve to strengthen the proposed theory