SamACO: variable sampling ant colony optimization algorithm for continuous optimization

An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of extending ACO to solving continuous optimization problems by focusing on continuous variable sampling as a key to transforming ACO from discrete optimization to continuous optimization. The proposed SamACO algorithm consists of three major steps, i.e., the generation of candidate variable values for selection, the ants’ solution construction, and the pheromone update process. The distinct characteristics of SamACO are the cooperation of a novel sampling method for discretizing the continuous search space and an efficient incremental solution construction method based on the sampled values. The performance of SamACO is tested using continuous numerical functions with unimodal and multimodal features. Compared with some state-of-the-art algorithms, including traditional ant-based algorithms and representative computational intelligence algorithms for continuous optimization, the performance of SamACO is seen competitive and promising

An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks

Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs

Hierarchical event selection for video storyboards with a case study on snooker video visualization

Video storyboard, which is a form of video visualization, summarizes the major events in a video using illustrative visualization. There are three main technical challenges in creating a video storyboard, (a) event classification, (b) event selection and (c) event illustration. Among these challenges, (a) is highly application-dependent and requires a significant amount of application specific semantics to be encoded in a system or manually specified by users. This paper focuses on challenges (b) and (c). In particular, we present a framework for hierarchical event representation, and an importance-based selection algorithm for supporting the creation of a video storyboard from a video. We consider the storyboard to be an event summarization for the whole video, whilst each individual illustration on the board is also an event summarization but for a smaller time window. We utilized a 3D visualization template for depicting and annotating events in illustrations. To demonstrate the concepts and algorithms developed, we use Snooker video visualization as a case study, because it has a concrete and agreeable set of semantic definitions for events and can make use of existing techniques of event detection and 3D reconstruction in a reliable manner. Nevertheless, most of our concepts and algorithms developed for challenges (b) and (c) can be applied to other application areas. © 2010 IEEE

A Review of Discharge Medications in Patients Admitted with Acute Decompesated Heart Failure in a Tertiary Referral Centre

Background: National guidelines for heart failure recommend prescription of certain classes of drugs to improve prognosis in patients admitted with acute decompensated heart failure (ADHF). It has been noted during clinical follow up such patients are discharged with different treatment regimes. Objective: To determine the relationship between drug treatment regimes in patients admitted to a tertiary referral centre with ADHF and their medium term clinical outcomes post-discharge, defined as 90-day mortality and hospital readmissions. Methods: 94 cases with a discharge diagnosis of ADHF were recruited from October 2017 until August 2018. Cases were analyzed retrospectively for their medications at discharge. Patients were followed-up for 90 days via phone. Results: Out of 94 patients, 8 patients died during admission. 86 patients were being analysed for clinical outcomes. 22 (26%) patients were discharged without a single type of guideline recommended medication for heart failure (GRM). 33 (38%) patients were discharged on one type, 22 (26%) patients discharged with two types and 10 (12%) patients were discharged with three or more types of GRM. The main reasons for not being discharged with all GRM were chronic kidney disease, obstructive lung disease, bradycardia and hypotension. The 90 days mortality rate was higher in patients discharged with ≤1 class of GRM drugs compared to patients with discharged on ≥2 classes of GRM drugs. (14.5% vs 6.5%; OR 2.25; 95%CI 0.51, 9.96; p=0.28). The 90 days readmission rate for ADHF was also higher for patients discharged with ≤1 class of GRM drugs (20.0% vs 12.9%; OR 1.55; 95%CI 0.539, 4.457; p=0.416). Overall, patients with discharged with ≤1 class of GRM drugs had also a higher 90-day event rate (27.3% vs 19.4%; OR 1.78; 95%CI 0.797, 3.993; p=0.16). Conclusions: Discharging ADHF patients with ≥2 class of GRM drugs was associated with lower 90 days readmission rates and mortality

Client Service Receipt Inventory as a standardised tool for measurement of socio-economic costs in the rare genetic disease population (CSRI-Ra)

The measurement of costs is fundamental in healthcare decision-making, but it is often challenging. In particular, standardised methods have not been developed in the rare genetic disease population. A reliable and valid tool is critical for research to be locally meaningful yet internationally comparable. Herein, we sought to develop, contextualise, translate, and validate the Client Service Receipt Inventory for the RAre disease population (CSRI-Ra) to be used in cost-of-illness studies and economic evaluations for healthcare planning. Through expert panel discussions and focus group meetings involving 17 rare disease patients, carers, and healthcare and social care professionals from Hong Kong, we have developed the CSRI-Ra. Rounds of forward and backward translations were performed by bilingual researchers, and face validity and semantic equivalence were achieved through interviews and telephone communications with focus group participants and an additional of 13 healthcare professional and university students. Intra-class correlation coefficient (ICC) was used to assess criterion validity between CSRI-Ra and electronic patient record in a sample of 94 rare disease patients and carers, with overall ICC being 0.69 (95% CI 0.56–0.78), indicating moderate to good agreement. Following rounds of revision in the development, contextualisation, translation, and validation stages, the CSRI-Ra is ready for use in empirical research. The CSRI-Ra provides a sufficiently standardised yet adaptable method for collecting socio-economic data related to rare genetic diseases. This is important for near-term and long-term monitoring of the resource consequences of rare diseases, and it provides a tool for use in economic evaluations in the future, thereby helping to inform planning for efficient and effective healthcare. Adaptation of the CSRI-Ra to other populations would facilitate international research

Knowledge-assisted ranking: A visual analytic application for sports event data

© 2016 IEEE. Organizing sports video data for performance analysis can be challenging, especially in cases involving multiple attributes and when the criteria for sorting frequently changes depending on the user's task. The proposed visual analytic system enables users to specify a sort requirement in a flexible manner without depending on specific knowledge about individual sort keys. The authors use regression techniques to train different analytical models for different types of sorting requirements and use visualization to facilitate knowledge discovery at different stages of the process. They demonstrate the system with a rugby case study to find key instances for analyzing team and player performance. Organizing sports video data for performance analysis can be challenging in cases with multiple attributes, and when sorting frequently changes depending on the user's task. As this video shows, the proposed visual analytic system allows interactive data sorting and exploration

Evaluating the health-related quality of life of the rare disease population in Hong Kong using EQ-5D 3-level

Objectives This study aimed to establish a normative profile of health-related quality of life (HRQOL) of the rare disease (RD) population in Hong Kong (HK) and identify potential predictors. Methods Between March 2020 and October 2020, patients with RD and caregivers were recruited through Rare Disease Hong Kong, the largest RD patient group alliance in HK. HRQOL was derived using the EQ-5D 3-Level with reference to the established HK value set. Utility scores were stratified according to demographics and disease-related information. Multiple linear regression was performed to explore the associations between patient characteristics and HRQOL. Results A total of 286 patients, covering 107 unique RDs, reported a mean utility score of 0.53 (SD 0.36). Thirty patients (10.5%) reported negative utility scores, indicating worse-than-death health states. More problems were recorded in the “usual activities” and “self-care” dimensions. Univariate analyses revealed that neurologic diseases, high out-of-pocket expenditure, home modification, and living in public housing or subdivided flats/units were significantly associated with lower HRQOL. A total of 99 caregivers reported a mean utility score of 0.78 (SD 0.17), which was significantly associated with the utility score of patients they took care of (r = 0.32; P = .001). Conclusions The normative profile of the RD population was established, which revealed lower HRQOL in the RD population than other chronic disease groups and general population in HK. Findings were corroborated by evidence from other cohorts using EQ-5D, combined as part of a meta-analysis. Identifying predictors highlight areas that should be prioritized to improve HRQOL of RD population through clinical and psychosocial dimensions

Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop

Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance. © 1995-2012 IEEE

Integral effect non-loca test results for the integral type reactor SMART-P using the VISTA facility

Paper presented at the 5th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, South Africa, 1-4 July, 2007.The SMART-P a pilot plant of the integral type reactor SMART(System Integrated Modular Advanced Reactor) which has new innovative design features aimed at achieving a highly enhanced safety and improved economics. A test facility (VISTA) has been constructed to simulate the SMART-P which is a full height and 1/96 volume scaled test facility with respect to the SMART-P. The VISTA facility has been used to understand the thermal-hydraulic behavior including several operational transients and design basis accidents and finally it will contribute to verifying the system design of the SMART-P. During the past five years, several integral effect tests have been carried out and reported, including performance tests, MCP(Main Coolant Pump) transients, power transients and heatup or cooldown procedures. In the present study, the VISTA facility was subjected to the major safety related non-LOCA transient conditions in a primary and secondary system, including a power increase due to a CEDM (Control Element Drive Mechanism) withdrawal, a feedwater decrease and a steam flow increase in order to verify the safety analysis code for the SMART-P.cs201