Future access to essential services in a growing smart city: The case of Surrey,British Columbia

The concept of accessibility – the ease with which people can reach places or opportunities –lies at the heart of what makes cities livable, workable and sustainable. As urban populations shift over time, predicting the changes to accessibility demand for certain services becomes crucial for responsible and ‘smart’ urban planning and infrastructure investment. In this study, we investigate how projected population change could affect accessibility to essential services in the City of Surrey, one of the fastest growing cities in Canada. Our objectives are two-fold: first, to quantify the additional pressure that Surrey's growing population will have on existing facilities; second, to investigate how changes in the spatial distribution of different age and income groups will impact accessibility equity across the city. We evaluated accessibility levels to healthcare facilities and schools across Surrey's multimodal transport network using origin-destination matrices, and combined this information with high-resolution longitudinal census data. Paying close attention to two vulnerable population groups – children and youth (0–19 years of age) and seniors (65+ years of age) – we analyzed shifts in accessibility demand from 2016 to 2022. The results show that population growth both within and outside the catchments of existing facilities will have varying implications for future accessibility demand in different areas of the city. By 2022, the city's hospitals and walk-in clinics will be accessible to ~9000 and ~124,000 more people (respectively) within a predefined threshold of 30 min by public transport. Schools will also face increased demand, as ~8000 additional children/youth in 2022 will move to areas with access to at least half of the city's schools. Conversely, over 27,000 more people – almost half of them seniors – will not be able to access a hospital in under 30 min by 2022. Since low-income and senior residents moving into poorly connected areas tend to be more reliant on public transport, accessibility equity may decline in some rural communities. Our study highlights how open-source data and code can be leveraged to conduct in-depth analysis of accessibility demand across a city, which is key for ensuring inclusive and ‘smart’ urban investment strategies.     

An integrated physical-social analysis of disrupted access to critical facilities and community service-loss tolerance in urban flooding

This paper presents an integrated framework that combines a community's physical vulnerability to access disruption to critical facilities and their tolerance for such access disruption to services in order to inform the targeted communities protection and build an equitable resilience enhancement plan. The first component of the proposed framework includes a percolation simulation model capable of integrating the road network disruption probability into the flood propagation and encapsulates the road network's access to critical facilities (i.e., healthcare facilities). The discovered spatial reach of an areas' physical vulnerability dependence is 9 miles. Besides, physical disruptions in road networks and loss of access to emergency services (such as healthcare) have varying impacts on different sub-populations. To consider this aspect, the second component of the proposed framework involves a disruption tolerance index (DTI) to examine communities' tolerance towards access disruption to healthcare facilities in the face of the flooding. The proposed framework recognizes the importance of both infrastructure and human perspective of the vulnerability assessment and is tested using empirical data from Harris County, Texas, in the case of road network disruptions due to fluvial flooding. Houston, the fourth-largest city in the United States, is within Harris County. Integrated spatial analysis result reveals different spatial clusters of vulnerability across the study region and provides important insights regarding the critical infrastructure protection prioritization and hazard mitigation planning. The spatial clusters also unveil the existence of a homogeneous spatial pattern where similar vulnerable areas stay together. The proposed framework could be adopted by other cities and different critical facilities to enable decision-makers, infrastructure managers, and city planners to better evaluate their community vulnerability.     

Comprehensibility of universal healthcare symbols for wayfinding in healthcare facilities

Healthcare facilities are often complex and overwhelming for visitors, and wayfinding in healthcare facilities can be challenging. As there is an increasing number of global citizens who travel to seek medical care in another country, it is critical to make wayfinding easy for visitors who are not familiar with the language in a foreign country. Among many wayfinding aids, symbols are helpful for those visitors who have limited ability to understand written language. This study tested universal healthcare symbols in the United States, South Korea, and Turkey to compare the comprehension of symbols cross-country and identify predictors of the correct comprehension. To explore statistically significant relationships between symbol comprehension and countries, Pearson's Chi-square tests, logistic regression, and ANOVA were conducted. The test results showed that ten symbols among 14 tested have significant relationship with countries. Results of this study demonstrate that symbol comprehension can be varied significantly in different countries.     

Web服务应用之初探

近年来,Web服务技术的应用发展得越来越快。Web服务是基于横跨Internet的分布式计算。它依赖于服务提供者描述他们的服务性能和服务请求者用一种明白的、理论上可解释的格式来描述。Web服务使用标准的如XML、SOAP和HTTP之类的网页协议。它最后向Web交互领域延伸。     

Constrained location of competitive facilities in the plane

This paper examines a competitive facility location problem occurring in the plane. A new gravity-based utility model is developed, in which the capacity of a facility serves as its measure of attractiveness. A new problem formulation is given, having elastic gravity-based demand, along with capacity, forbidden region, and budget constraints. Two solution algorithms are presented, one based on the big square small square method, and the second based on a penalty function formulation using fixed-point iteration. Computational testing is presented, comparing these two algorithms along with a general-purpose nonlinear solver.Scope and purposeIn a competitive business environment where products are not distinguishable, facility location plays an important role in an organization's success. This paper examines a firm's problem of selecting the locations in the plane for a set of new facilities such that market capture is maximized across all of the firm's facilities (both new and pre-existing). Customers are assumed to divide their demand among all competing facilities according to a utility function that considers facility attractiveness (measured by facility capacity for satisfying demand) and customer-facility distance. The level of customer demand is assumed to be a function of the facility configuration. Three types of constraints are introduced, involving facility capacity, forbidden regions for new facility location, and a budget function. Two solution algorithms are devised, one based on branch-and-bound methods and the other based on penalty functions. Computational testing is presented, comparing these two algorithms along with a general-purpose nonlinear solver.     

Visual discrimination on colour VDTs at two viewing distances

The paper describes a research study on visual discrimination of textual and graphic symbols on a visual display terminal (VDT) screen when viewed at eye-lo-screen distances of 61 cm and 152 cm (24 and 60 ins). Conducted as part of a development programme at McDonnell Douglas Corporation's St. Louis Aircraft Company for an Integrated Manufacturing Composites Centre (ICC), the study investigated symbol shapes, symbol sizes, symbol colours and background colours at the two viewing distances. The longer distance is representative of required placements of the VDTs at some manufacturing workstations to avoid interference with process or control equipment. Knowledge gained from the study was incorporated in the selection of the manufacturing computer information system (CIS) terminals.

All four treatments showed significant effects on visual discrimination at both viewing distances and, particularly at the 152 cm distance, the mix of symbol and background colours was highly significant. A black screen background colour with more luminous symbols such as orange, green, yellow provided much better visual discrimination at the extended viewing distance than less luminous symbols such as red and blue on a white background. Visual discrimination at the extended viewing distance, when compared with the shorter viewing distance and using symbols of equal size, was better than the loss in visual angle would suggest.

Sex and age (to age 65) did not significantly affect visual discrimination mean scores, but the variance among individuals in the 51-65 years age group was greater than for the younger age groups.

The visual discrimination scores for symbol sizes of 4 mm² were not significantly different from the 6 mm² symbols at the 61 cm viewing distance. The 4 mm² symbol size was therefore adequate for visual discrimination of standalone symbols at this distance. Although 8 mm symbols were not used in this study, projections from the data indicate that such symbol sizes at 152 cm would provide comparable discrimination scores to the 4 mm² at 61 cm.

Improved visual discrimination of standalone symbols occurs with gaps or changes in the angles of symbols, such as sets 'C' versus 'O' and 'Xl' versus'+'.     

Healthcare process modularization using design structure matrix

The healthcare industry is confronted with the challenge of offering customized services while in the meantime to control increasing healthcare costs. Modularization is an important approach to reduce healthcare costs and improve patient-centered services via decreasing process complexity and enhancing flexibility through configuring pre-identified service modules. Recognizing the importance of modularity for healthcare services, this paper introduces Design Structure Matrix (DSM) as a technique for healthcare process modularization. A DSM-based modularization and sequencing algorithm is developed to allocate healthcare activities to service modules using Genetic Algorithm (GA) and arrange sequences of services both within and across service modules to support modular clinical pathway design. The proposed algorithm is implemented with a case study, the results of which have demonstrated the feasibility and applicability of the proposed DSM-based modularization method for healthcare process design.     

A hybrid power line and visible light communication system for indoor hospital applications

Modern hospitals are beginning to adopt E-HEALTH as efficient complements to the traditional healthcare services. To support the E-HEALTH services, a locatable, radiation-free and high-capacity communication system is urgently needed in hospitals. Power line communication (PLC) systems can use the ubiquitous power line network to power the light-emitting diode (LED) lamps while serving as the backbone network for the indoor visible light communication (VLC) systems naturally. In this article, a hybrid broadband power line and visible light communication system with orthogonal frequency division multiplexing modulation is proposed for the indoor hospital applications, which gives a brand-new solution to replace the conventional wireless communication systems in hospitals. A general-purpose system model is provided and some basic techniques to enhance system performance are also investigated. Moreover, a feasible demonstration which supports over 48 Mbps data rate within a bandwidth of 8 MHz is implemented in the laboratory.     

Parametric graph drawing

A diagram is a drawing on the plane that represents a graph like structure, where nodes are represented by symbols and edges are represented by curves connecting pairs of symbols. An automatic layout facility is a tool that receives as input a graph like structure and is able to produce a diagram that nicely represents such a structure. Many systems use diagrams in the interaction with the users; thus, automatic layout facilities and algorithms for graphs layout have been extensively studied in the last years. We present a new approach in designing an automatic layout facility. Our approach is based on a modular management of a large collection of algorithms and on a strategy that, given the requirements of an application, selects a suitable algorithm for such requirements. The proposed approach has been used for designing the automatic layout facility of Diagram Server, a network server that offers to its clients several facilities for managing diagrams     

The sound may be sweeter by another name

A name change from "facilities management" to "outsourcing" could spur new interest in a concept once shunned by most of the healthcare information systems industry. Big-name contracts are legitimizing such arrangements, opening fresh avenues for organizations requiring flexible I/S services.     

iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization

Nowadays, crowd-sourced review websites provide decision support for various aspects of daily life, including shopping, local services, healthcare, etc. However, one of the most important challenges for existing healthcare review websites is the lack of personalized and professionalized guidelines for users to choose medical services. In this paper, we develop a novel healthcare recommendation system called iDoctor, which is based on hybrid matrix factorization methods. iDoctor differs from previous work in the following aspects: (1) emotional offset of user reviews can be unveiled by sentiment analysis and be utilized to revise original user ratings; (2) user preference and doctor feature are extracted by Latent Dirichlet Allocation and incorporated into conventional matrix factorization. We compare iDoctor with previous healthcare recommendation methods using real datasets. The experimental results show that iDoctor provides a higher predication rating and increases the accuracy of healthcare recommendation significantly.     

Lagrangean relaxation and decomposition in an uncapacitated 2-hierarchal location-allocation problem

We consider an uncapacitated 2-hierarchal location-allocation problem where p₁ level 1 facilities and p₂ level 2 facilities are to be located among n(?p₁ + p₂) potential locations so as to minimize the total weighted travel distance to the facilities when θ, (0 ? θ ? 1) fraction of the demand from a level 1 facility is referred to a level 2 facility. At most one facility may be located at any location. In this model, a level 2 facility provides services in addition to services provided by a level 1 facility.The problem is formulated as a mathematical programming problem, relaxed and solved by a subgradient optimization procedure. The proposed procedure is illustrated with an example.     

Adaptive middleware supporting scalable performance for high-end network services

Network service-based computation is a promising paradigm for both scientific and engineering, and enterprise computing. The network service allows users to focus on their application and obtain services when needed, simply by invoking the service across the network. In this paper, we show that an adaptive, general-purpose run-time infrastructure in support of effective resource management can be built for a wide range of high-end network services running in a single-site cluster and in a Grid. The primary components of the run-time infrastructure are: (1) dynamic performance prediction; (2) adaptive intra-site resource management; and (3) adaptive inter-site resource management. The novel aspect of our approach is that the run-time system is able to dynamically select the most appropriate performance predictor or resource management strategy over time. This capability not only improves the performance, but also makes the infrastructure reusable across different high-end services. To evaluate the effectiveness and applicability of our approach, we have transformed two different classes of high-end applications—data parallel and distributed applications—into network services using the infrastructure. The experimental results show that the network services running on the infrastructure significantly reduce the overall service times under dynamically varying circumstances.     

Artificial intelligence for public perception of drones as a tool for telecommunication technologies

Drones, a small aviation craft that can hold sensors and be directed to certain locations, have been explored and utilized in domains such as public safety, traffic monitoring, and healthcare. Yet, the scientific research of drone usage still in progress. This article investigates the following: (1) how the public perceives a drone as a healthcare tool, and (2) what the concerns are of public acceptance of drones in healthcare. A qualitative approach with demographic questions such as participants' age, education level, current knowledge of drones, as well as willingness to learn about the technology was used. The contribution is to have an understanding of how the public perceives drones. The results can be used in enhancing public views of drones through skilled personnel and an educated workforce. In a conservative community such as Saudi Arabia, the public holds a relatively positive attitude towards drones. Respondents are aware of such technology from different sources, mainly TV news. Informants also did show a decent level of perceived beneficial usage of drones in healthcare. There are few participants who disclosed concerns of safety, security, and privacy for the usage of drones in healthcare due to a lack of knowledge about the technology. The results propose that government and industry stakeholders should invest more in increasing public awareness of technology. Media coverage will likely influence the public view and help the community move forward.     

Grounded discovery of symbols as concept–language pairs

In human designer usage, symbols have a rich semantics, grounded on experience, which permits flexible usage — e.g. design ideation is improved by meanings triggered by contrastive words. In computational usage however, symbols are syntactic tokens whose semantics is mostly left to the implementation, resulting in brittle failures in many knowledge-based systems. Here we ask if one may define symbols in computational design as {label,meaning} pairs, as opposed to merely the label. We consider three questions that must be answered to bootstrap a symbol learning process: (a) which concepts are most relevant in a given domain, (b) how to define the semantics of such symbols, and (c) how to learn labels for these so as to form a grounded symbol. We propose that relevant symbols may be discovered by learning patterns of functional viability. The stable patterns are information-conserving codes, also called chunks in cognitive science, which relate to the process of acquiring expertise in humans. Regions of a design space that contain functionally superior designs can be mapped to a lower-dimensional manifold; the inter-relations of the design variables discovered thus constitute the chunks. Using these as the initial semantics for symbols, we show how the system can acquire labels for them by communicating with human designers. We demonstrate the first steps in this process in our baby designer approach, by learning two early grounded symbols, tight and loose.     

Scalable entity-based summarization of web search results using MapReduce

Although Web Search Engines index and provide access to huge amounts of documents, user queries typically return only a linear list of hits. While this is often satisfactory for focalized search, it does not provide an exploration or deeper analysis of the results. One way to achieve advanced exploration facilities exploiting the availability of structured (and semantic) data in Web search, is to enrich it with entity mining over the full contents of the search results. Such services provide the users with an initial overview of the information space, allowing them to gradually restrict it until locating the desired hits, even if they are low ranked. This is especially important in areas of professional search such as medical search, patent search, etc. In this paper we consider a general scenario of providing such services as meta-services (that is, layered over systems that support keywords search) without a-priori indexing of the underlying document collection(s). To make such services feasible for large amounts of data we use the MapReduce distributed computation model on a Cloud infrastructure (Amazon EC2). Specifically, we show how the required computational tasks can be factorized and expressed as MapReduce functions. A key contribution of our work is a thorough evaluation of platform configuration and tuning, an aspect that is often disregarded and inadequately addressed in prior work, but crucial for the efficient utilization of resources. Finally we report experimental results about the achieved speedup in various settings.     

BEAMR: An exact and approximate model for the p-median problem

The p-median problem is perhaps one of the most well-known location–allocation models in the location science literature. It was originally defined by Hakimi in 1964 and 1965 and involves the location of p facilities on a network in such a manner that the total weighted distance of serving all demand is minimized. This problem has since been the subject of considerable research involving the development of specialized solution approaches as well as the development of many different types of extended model formats. One element of past research that has remained almost constant is the original ReVelle–Swain formulation [ReVelle CS, Swain R. Central facilities location. Geographical Analysis 1970;2:30–42]. With few exceptions as detailed in the paper, virtually no new formulations have been proposed for general use in solving the classic p-median problem. This paper proposes a new model formulation for the p-median problem that contains both exact and approximate features. This new p-median formulation is called Both Exact and Approximate Model Representation (BEAMR). We show that BEAMR can result in a substantially smaller integer-linear formulation for a given application of the p-median problem and can be used to solve for either an exact optimum or a bounded, close to optimal solution. We also present a methodological framework in which the BEAMR model can be used. Computational results for problems found in the OR_library of Beasley [A note on solving large p-median problems. European Journal of Operational Research 1985;21:270–3] indicate that BEAMR not only extends the application frontier for the p-median problem using general-purpose software, but for many problems represents an efficient, competitive solution approach.     

Monitoring healthcare performance by analytic hierarchy process: a developing-country perspective

The formulation and solution of multi‐criteria healthcare decision problems is of critical importance to the health and socio‐economic betterment of developing countries. The study shows how the multi‐criteria decision‐making method could facilitate implementation of healthcare performance analysis, especially for the public healthcare system of Bangladesh, which operates mainly through thana health complexes (THCs). We include outreach services and rural facilities together with ongoing THC activities, and analyze their relative performance. The methodology uses a phase of the Delphi method and of the analytic hierarchy process (AHP) approach. The outcome of Delphi is used as input for the hierarchical processing procedure in AHP and determines performance order of the healthcare activities. Results from AHP are discussed for implementation in decision‐making and the managerial policymaking process, towards improvement of overall healthcare performance.