Improving Local Search for Minimum Weighted Connected Dominating Set Problem by Inner-Layer Local Search

The minimum weighted connected dominating set (MWCDS) problem is an important variant of connected dominating set problems with wide applications, especially in heterogenous networks and gene regulatory networks. In the paper, we develop a nested local search algorithm called NestedLS for solving MWCDS on classic benchmarks and massive graphs. In this local search framework, we propose two novel ideas to make it effective by utilizing previous search information. First, we design the restart based smoothing mechanism as a diversification method to escape from local optimal. Second, we propose a novel inner-layer local search method to enlarge the candidate removal set, which can be modelled as an optimized version of spanning tree problem. Moreover, inner-layer local search method is a general method for maintaining the connectivity constraint when dealing with massive graphs. Experimental results show that NestedLS outperforms state-of-the-art meta-heuristic algorithms on most instances

Using smartphone-based virtual patients to assess the quality of primary healthcare in rural China: protocol for a prospective multicentre study.

INTRODUCTION: Valid and low-cost quality assessment tools examining care quality are not readily available. The unannounced standardised patient (USP), the gold standard for assessing quality, is costly to implement while the validity of clinical vignettes, as a low-cost alternative, has been challenged. Computerised virtual patients (VPs) create high-fidelity and interactive simulations of doctor-patient encounters which can be easily implemented via smartphone at low marginal cost. Our study aims to develop and validate smartphone-based VP as a quality assessment tool for primary care, compared with USP. METHODS AND ANALYSIS: The study will be implemented in primary health centres (PHCs) in rural areas of seven Chinese provinces, and physicians practicing at township health centres and village clinics will be our study population. The development of VPs involves three steps: (1) identifying 10 VP cases that can best represent rural PHCs' work, (2) designing each case by a case-specific development team and (3) developing corresponding quality scoring criteria. After being externally reviewed for content validity, these VP cases will be implemented on a smartphone-based platform and will be tested for feasibility and face validity. This smartphone-based VP tool will then be validated for its criterion validity against USP and its reliability (ie, internal consistency and stability), with 1260 VP/USP-clinician encounters across the seven study provinces for all 10 VP cases. ETHICS AND DISSEMINATION: Sun Yat-sen University: No. 2017-007. Study findings will be published and tools developed will be freely available to low-income and middle-income countries for research purposes

Assessing the quality of primary healthcare in seven Chinese provinces with unannounced standardised patients: protocol of a cross-sectional survey.

INTRODUCTION: Primary healthcare (PHC) serves as the cornerstone for the attainment of universal health coverage (UHC). Efforts to promote UHC should focus on the expansion of access and on healthcare quality. However, robust quality evidence has remained scarce in China. Common quality assessment methods such as chart abstraction, patient rating and clinical vignette use indirect information that may not represent real practice. This study will send standardised patients (SP or healthy person trained to consistently simulate the medical history, physical symptoms and emotional characteristics of a real patient) unannounced to PHC providers to collect quality information and represent real practice. METHODS AND ANALYSIS: 1981 SP-clinician visits will be made to a random sample of PHC providers across seven provinces in China. SP cases will be developed for 10 tracer conditions in PHC. Each case will include a standard script for the SP to use and a quality checklist that the SP will complete after the clinical visit to indicate diagnostic and treatment activities performed by the clinician. Patient-centredness will be assessed according to the Patient Perception of Patient-Centeredness Rating Scale by the SP. SP cases and the checklist will be developed through a standard protocol and assessed for content, face and criterion validity, and test-retest and inter-rater reliability before its full use. Various descriptive analyses will be performed for the survey results, such as a tabulation of quality scores across geographies and provider types. ETHICS AND DISSEMINATION: This study has been reviewed and approved by the Institutional Review Board of the School of Public Health of Sun Yat-sen University (#SYSU 2017-011). Results will be actively disseminated through print and social media, and SP tools will be made available for other researchers

Shared Mobility Options for the Commute Trip: Opportunities for Employers and Employees

69A3551747110This multi-institutional research project consisted of two components that were conducted, respectively, by University of Washington (UW) team members and University of Idaho (UI) team members. The UW component explored the commuting experience of essential workers during the COVID-19 pandemic, using the UW as a case study. The empirical work started with a quantitative analysis of data from the UW transportation needs assessment survey to depict and model the commute mode choices of essential workers before and during the pandemic. It found that most pre-pandemic public transit riders switched to other modes, especially driving alone, whereas almost all the essential workers who had driven alone, biked, or walked before the pandemic continued to do so. The shift to driving alone was most pronounced among essential workers with high incomes, whereas public transit remained a primary mode choice of lower-income groups. A qualitative analysis, which was based on a series of focus group discussions with UW employees, was then performed to gain deeper insights into essential workers\u2019 travel constraints and corresponding decision making. It revealed that most participants switched away from transit at the beginning of the pandemic because of safety concerns related to virus infection and issues with transit frequency, schedules, and reliability. It showed that incentives such as a fully subsidized transit pass and free carpool parking would encourage a reversed mode shift from driving alone to transit or carpooling post-pandemic. Together, results of the UW study suggest the need for timely adjustments in TDM policies in response to the evolution of the pandemic, as well as to expand the mobility options for employees, especially essential workers. The UI component, which used the University of Idaho as a case study, investigated the travel behaviors of university students from rural and suburban communities and how their experience with non-automobile modes of transportation affected their mode choice. This research component was implemented through surveys, which were aimed at identifying any relationship between previous multi-modal experience and current travel behavior, and an experiment that took participants on a 90-minute tour of the community by bus, bike, and on foot and then evaluated the impact of the tour on the participants\u2019 travel behaviors. The results showed that students from rural communities who had frequently driven to high school and had had little experience with public and private transit were more likely to be driving currently and that participation in the experiment increased the students\u2019 bus and bike use and walking

Two Efficient Local Search Algorithms for Maximum Weight Clique Problem

The Maximum Weight Clique problem (MWCP) is an important generalization of the Maximum Clique problem with wide applications. This paper introduces two heuristics and develops two local search algorithms for MWCP. Firstly, we propose a heuristic called strong configuration checking (SCC), which is a new variant of a recent powerful strategy called configuration checking (CC) for reducing cycling in local search. Based on the SCC strategy, we develop a local search algorithm named LSCC. Moreover, to improve the performance on massive graphs, we apply a low-complexity heuristic called Best from Multiple Selection (BMS) to select the swapping vertex pair quickly and effectively. The BMS heuristic is used to improve LSCC, resulting in the LSCC+BMS algorithm. Experiments show that the proposed algorithms outperform the state-of-the-art local search algorithm MN/TS and its improved version MN/TS+BMS on the standard benchmarks namely DIMACS and BHOSLIB, as well as a wide range of real world massive graphs