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Periodicals

A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification

Brain cancer classification is an important step that depends on the physician's knowledge and experience. An automated tumor classification system is very essential to support radiologists and physicians to identify brain tumors. However, the accuracy of current systems needs to be improved for suitable treatments. In this paper, we propose a hybrid feature extraction method with a regularized extreme learning machine (RELM) for developing an accurate brain tumor classification approach. The approach starts by preprocessing the brain images by using a min-max normalization rule to enhance the contrast of brain edges and regions. Then, the brain tumor features are extracted based on a hybrid method of feature extraction. Finally, a RELM is used for classifying the type of brain tumor. To evaluate and compare the proposed approach, a set of experiments is conducted on a new public dataset of brain images. The experimental results proved that the approach is more effective compared with the existing state-of-the-art approaches, and the performance in terms of classification accuracy improved from 91.51% to 94.233% for the experiment of the random holdout technique.

Architecture & Planning2019Institute of Electrical and Electronics Engineers
Periodicals

Integrated genomics, physiology and breeding approaches for improving drought tolerance in crops

Drought is one of the most serious production constraint for world agriculture and is projected to worsen with anticipated climate change. Inter-disciplinary scientists have been trying to understand and dissect the mechanisms of plant tolerance to drought stress using a variety of approaches; however, success has been limited. Modern genomics and genetic approaches coupled with advances in precise phenotyping and breeding methodologies are expected to more effectively unravel the genes and metabolic pathways that confer drought tolerance in crops. This article discusses the most recent advances in plant physiology for precision phenotyping of drought response, a vital step before implementing the genetic and molecular-physiological strategies to unravel the complex multilayered drought tolerance mechanism and further exploration using molecular breeding approaches for crop improvement. Emphasis has been given to molecular dissection of drought tolerance by QTL or gene discovery through linkage and association mapping, QTL cloning, candidate gene identification, transcriptomics and functional genomics. Molecular breeding approaches such as marker-assisted backcrossing, marker-assisted recurrent selection and genome-wide selection have been suggested to be integrated in crop improvement strategies to develop drought-tolerant cultivars that will enhance food security in the context of a changing and more variable climate.

Architecture & Planning2012Springer Science+Business Media
Periodicals

Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series

A detailed and accurate knowledge of land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable. This accurate knowledge needs frequent updates. This paper presents a methodology for the fully automatic production of land cover maps at country scale using high resolution optical image time series which is based on supervised classification and uses existing databases as reference data for training and validation. The originality of the approach resides in the use of all available image data, a simple pre-processing step leading to a homogeneous set of acquisition dates over the whole area and the use of a supervised classifier which is robust to errors in the reference data. The produced maps have a kappa coefficient of 0.86 with 17 land cover classes. The processing is efficient, allowing a fast delivery of the maps after the acquisition of the image data, does not need expensive field surveys for model calibration and validation, nor human operators for decision making, and uses open and freely available imagery. The land cover maps are provided with a confidence map which gives information at the pixel level about the expected quality of the result.

Architecture & Planning2017Multidisciplinary Digital Publishing Institute
Periodicals

Sarcoidosis: A Clinical Overview from Symptoms to Diagnosis

Sarcoidosis is a multi-system disease of unknown etiology characterized by the formation of granulomas in various organs. It affects people of all ethnic backgrounds and occurs at any time of life but is more frequent in African Americans and Scandinavians and in adults between 30 and 50 years of age. Sarcoidosis can affect any organ with a frequency varying according to ethnicity, sex and age. Intrathoracic involvement occurs in 90% of patients with symmetrical bilateral hilar adenopathy and/or diffuse lung micronodules, mainly along the lymphatic structures which are the most affected system. Among extrapulmonary manifestations, skin lesions, uveitis, liver or splenic involvement, peripheral and abdominal lymphadenopathy and peripheral arthritis are the most frequent with a prevalence of 25-50%. Finally, cardiac and neurological manifestations which can be the initial manifestation of sarcoidosis, as can be bilateral parotitis, nasosinusal or laryngeal signs, hypercalcemia and renal dysfunction, affect less than 10% of patients. The diagnosis is not standardized but is based on three major criteria: a compatible clinical and/or radiological presentation, the histological evidence of non-necrotizing granulomatous inflammation in one or more tissues and the exclusion of alternative causes of granulomatous disease. Certain clinical features are considered to be highly specific of the disease (e.g., Löfgren's syndrome, lupus pernio, Heerfordt's syndrome) and do not require histological confirmation. New diagnostic guidelines were recently published. Specific clinical criteria have been developed for the diagnosis of cardiac, neurological and ocular sarcoidosis. This article focuses on the clinical presentation and the common differentials that need to be considered when appropriate.

Architecture & Planning2021Multidisciplinary Digital Publishing Institute
Periodicals

Taking Drones to the Next Level: Cooperative Distributed Unmanned-Aerial-Vehicular Networks for Small and Mini Drones

Unmanned aerial vehicles (UAVs) have been widely used in both military and civilian applications. However, the cooperation of small and mini drones in a network is capable of further improving the performance and the coverage area of UAVs. There are numerous new challenges to be solved before the widespread introduction of multi-UAV-based heterogeneous flying ad hoc networks (FANET), including the formulation of a stable network structure. Meanwhile, an efficient gateway-selection algorithm and management mechanism is required. However, the stability control of the hierarchical UAV network guarantees the efficient collaboration of the drones. In this article, we begin by surveying the FANET structure and its protocol architecture. Then, a variety of distributed gateway-selection algorithms and cloud-based stability-control mechanisms are addressed, complemented by a range of open challenges.

Architecture & Planning2017Institute of Electrical and Electronics Engineers
Periodicals

Behavioural nudges increase COVID-19 vaccinations

Abstract Enhancing vaccine uptake is a critical public health challenge 1 . Overcoming vaccine hesitancy 2,3 and failure to follow through on vaccination intentions 3 requires effective communication strategies 3,4 . Here we present two sequential randomized controlled trials to test the effect of behavioural interventions on the uptake of COVID-19 vaccines. We designed text-based reminders that make vaccination salient and easy, and delivered them to participants drawn from a healthcare system one day (first randomized controlled trial) ( n = 93,354 participants; clinicaltrials number NCT04800965) and eight days (second randomized controlled trial) ( n = 67,092 individuals; clinicaltrials number NCT04801524) after they received a notification of vaccine eligibility. The first reminder boosted appointment and vaccination rates within the healthcare system by 6.07 (84%) and 3.57 (26%) percentage points, respectively; the second reminder increased those outcomes by 1.65 and 1.06 percentage points, respectively. The first reminder had a greater effect when it was designed to make participants feel ownership of the vaccine dose. However, we found no evidence that combining the first reminder with a video-based information intervention designed to address vaccine hesitancy heightened its effect. We performed online studies ( n = 3,181 participants) to examine vaccination intentions, which revealed patterns that diverged from those of the first randomized controlled trial; this underscores the importance of pilot-testing interventions in the field. Our findings inform the design of behavioural nudges for promoting health decisions 5 , and highlight the value of making vaccination easy and inducing feelings of ownership over vaccines.

Architecture & Planning2021Nature Portfolio
Periodicals

Components of expertise

This article discusses frameworks for studying expertise at the knowledge level and knowledge-use level. It reviews existing approaches such as inference structures, the distinction between deep and surface knowledge, problem-solving methods, and generic tasks. A new synthesis is put forward in the form of a componential framework that stresses modularity and an analysis of the pragmatic constraints on the task. The analysis of a rule from an existing expert system (the Dipmeter Advisor) is used to illustrate the framework.

Architecture & Planning1990Consejo Superior de Investigaciones Científicas
Periodicals

The Simoa HD-1 Analyzer: A Novel Fully Automated Digital Immunoassay Analyzer with Single-Molecule Sensitivity and Multiplexing

Disease detection at the molecular level is driving the emerging revolution of early diagnosis and treatment. A challenge facing the field is that protein biomarkers for early diagnosis can be present in very low abundance. The lower limit of detection with conventional immunoassay technology is the upper femtomolar range (10(-13) M). Digital immunoassay technology has improved detection sensitivity three logs, to the attomolar range (10(-16) M). This capability has the potential to open new advances in diagnostics and therapeutics, but such technologies have been relegated to manual procedures that are not well suited for efficient routine use. We describe a new laboratory instrument that provides full automation of single-molecule array (Simoa) technology for digital immunoassays. The instrument is capable of single-molecule sensitivity and multiplexing with short turnaround times and a throughput of 66 samples/h. Singleplex and multiplexed digital immunoassays were developed for 16 proteins of interest in cardiovascular, cancer, infectious disease, neurology, and inflammation research. The average sensitivity improvement of the Simoa immunoassays versus conventional ELISA was >1200-fold, with coefficients of variation of <10%. The potential of digital immunoassays to advance human diagnostics was illustrated in two clinical areas: traumatic brain injury and early detection of infectious disease.

Architecture & Planning2015Elsevier BV
Periodicals

A Comprehensive Review of Blockchain Consensus Mechanisms

Since the advent of distributed ledger technologies, they have provided diverse opportunities in a wide range of application domains. This article brings a comprehensive review of the fundamentals of distributed ledger and its variants. Analyzing 185 publications, ranging from academic journals to industry websites, it provides a comparative analysis of 130 consensus algorithms using a novel architectural classification. The distribution of the reviewed algorithms is analyzed in terms of the proposed classification and different application domains, along with the applicability of each class among the top 10 platforms in the most prominent blockchain application domains. Additional conclusions are drawn from the evolution of consensus mechanisms, and the analysis concludes envisaging future prospects for consensus as an important part of distributed ledger technology.

Architecture & Planning2021Institute of Electrical and Electronics Engineers
Periodicals

Metabolism of Oligosaccharides and Starch in Lactobacilli: A Review

Oligosaccharides, compounds that are composed of 2-10 monosaccharide residues, are major carbohydrate sources in habitats populated by lactobacilli. Moreover, oligosaccharide metabolism is essential for ecological fitness of lactobacilli. Disaccharide metabolism by lactobacilli is well understood; however, few data on the metabolism of higher oligosaccharides are available. Research on the ecology of intestinal microbiota as well as the commercial application of prebiotics has shifted the interest from (digestible) disaccharides to (indigestible) higher oligosaccharides. This review provides an overview on oligosaccharide metabolism in lactobacilli. Emphasis is placed on maltodextrins, isomalto-oligosaccharides, fructo-oligosaccharides, galacto-oligosaccharides, and raffinose-family oligosaccharides. Starch is also considered. Metabolism is discussed on the basis of metabolic studies related to oligosaccharide metabolism, information on the cellular location and substrate specificity of carbohydrate transport systems, glycosyl hydrolases and phosphorylases, and the presence of metabolic genes in genomes of 38 strains of lactobacilli. Metabolic pathways for disaccharide metabolism often also enable the metabolism of tri- and tetrasaccharides. However, with the exception of amylase and levansucrase, metabolic enzymes for oligosaccharide conversion are intracellular and oligosaccharide metabolism is limited by transport. This general restriction to intracellular glycosyl hydrolases differentiates lactobacilli from other bacteria that adapted to intestinal habitats, particularly Bifidobacterium spp.

Architecture & Planning2012Frontiers Media
Periodicals

Technology Development of Electric Vehicles: A Review

To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of EVs and emerging technologies for their future application. Key technologies regarding batteries, charging technology, electric motors and control, and charging infrastructure of EVs are summarized. This paper also highlights the technical challenges and emerging technologies for the improvement of efficiency, reliability, and safety of EVs in the coming stages as another contribution.

Architecture & Planning2019Multidisciplinary Digital Publishing Institute
Periodicals

The future of Earth observation in hydrology

In just the past five years, the field of Earth observation has progressed beyond the offerings of conventional space agency based platforms to include a plethora of sensing opportunities afforded by CubeSats, Unmanned Aerial Vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically on the order of one billion dollars per satellite and with concept-to-launch timelines on the order of two decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturise sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist five years ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of the cost of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-meter resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen-scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the Internet of Things as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilise and exploit these new observing systems to enhance our understanding of the Earth and its linked processes.

Architecture & Planning2017Copernicus Publications
Periodicals

A conceptual framework for supply chain collaboration: empirical evidence from the agri‐food industry

Purpose The purpose of this paper is to analyse the concept of supply chain collaboration and to provide an overall framework that can be used as a conceptual landmark for further empirical research. In addition, the concept is explored in the context of agri‐food industry and particularities are identified. Finally, the paper submits empirical evidence from an exploratory case study in the agri‐food industry, at the grower‐processor interface, and information regarding the way the concept is actually applied in small medium‐sized enterprises (SMEs) is presented. Design/methodology/approach The paper employed case study research by conducting in‐depth interviews in the two companies. Findings Supply chain collaboration concept is of significant importance for the agri‐food industry however, some constraints arise due to the nature of industry's products, and the specific structure of the sector. Subsequently, collaboration in the supply chain is often limited to operational issues and to logistics‐related activities. Research limitations/implications Research is limited to a single case study and further qualitative testing of the conceptual model is needed in order to adjust the model before large scale testing. Practical implications Case study findings may be transferable to other similar dual relationships at the grower‐processor interface. Weaker parts in asymmetric relationships have opportunities to improve their position, altering the dependence balance, by achieving product/process excellence. Originality/value The paper provides evidence regarding the applicability of the supply chain collaboration concept in the agri‐food industry. It takes into consideration not relationships between big multinational companies, but SMEs.

Architecture & Planning2007Emerald Publishing Limited
Periodicals

Tumor Ablation with Irreversible Electroporation

We report the first successful use of irreversible electroporation for the minimally invasive treatment of aggressive cutaneous tumors implanted in mice. Irreversible electroporation is a newly developed non-thermal tissue ablation technique in which certain short duration electrical fields are used to permanently permeabilize the cell membrane, presumably through the formation of nanoscale defects in the cell membrane. Mathematical models of the electrical and thermal fields that develop during the application of the pulses were used to design an efficient treatment protocol with minimal heating of the tissue. Tumor regression was confirmed by histological studies which also revealed that it occurred as a direct result of irreversible cell membrane permeabilization. Parametric studies show that the successful outcome of the procedure is related to the applied electric field strength, the total pulse duration as well as the temporal mode of delivery of the pulses. Our best results were obtained using plate electrodes to deliver across the tumor 80 pulses of 100 micros at 0.3 Hz with an electrical field magnitude of 2500 V/cm. These conditions induced complete regression in 12 out of 13 treated tumors, (92%), in the absence of tissue heating. Irreversible electroporation is thus a new effective modality for non-thermal tumor ablation.

Architecture & Planning2007Public Library of Science
Periodicals

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitableConvolutional Neural Network(CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.

Architecture & Planning2020Institute of Electrical and Electronics Engineers
Periodicals

Deep reinforcement learning based mobile robot navigation: A review

Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement Learning (DRL) has received significant attention because of its strong representation and experience learning abilities. There is a growing trend of applying DRL to mobile robot navigation. In this paper, we review DRL methods and DRL-based navigation frameworks. Then we systematically compare and analyze the relationship and differences between four typical application scenarios: local obstacle avoidance, indoor navigation, multi-robot navigation, and social navigation. Next, we describe the development of DRL-based navigation. Last, we discuss the challenges and some possible solutions regarding DRL-based navigation.

Architecture & Planning2021Tsinghua University Press
Periodicals

Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing

An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV' UE-load and the overall energy consumption of UEs. The above optimization problem includes both integer and continues variables and it is challenging to solve. To address the above problem, a multi-agent deep reinforcement learning based trajectory control algorithm is proposed for managing the trajectory of each UAV independently, where the popular Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is applied. Given the UAVs' trajectories, a low-complexity approach is introduced for optimizing the offloading decisions of UEs. We show that our proposed solution has considerable performance over other traditional algorithms, both in terms of the fairness for serving UEs, fairness of UE-load at each UAV and energy consumption for all the UEs.

Architecture & Planning2020Institute of Electrical and Electronics Engineers
Periodicals

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.

Architecture & Planning2020Multidisciplinary Digital Publishing Institute
Periodicals

A Comparative Survey of VANET Clustering Techniques

A vehicular ad hoc network (VANET) is a mobile ad hoc network in which network nodes are vehicles-most commonly road vehicles. VANETs present a unique range of challenges and opportunities for routing protocols due to the semi-organized nature of vehicular movements subject to the constraints of road geometry and rules, and the obstacles which limit physical connectivity in urban environments. In particular, the problems of routing protocol reliability and scalability across large urban VANETs are currently the subject of intense research. Clustering can be used to improve routing scalability and reliability in VANETs, as it results in the distributed formation of hierarchical network structures by grouping vehicles together based on correlated spatial distribution and relative velocity. In addition to the benefits to routing, these groups can serve as the foundation for accident or congestion detection, information dissemination and entertainment applications. This paper explores the design choices made in the development of clustering algorithms targeted at VANETs. It presents a taxonomy of the techniques applied to solve the problems of cluster head election, cluster affiliation, and cluster management, and identifies new directions and recent trends in the design of these algorithms. Additionally, methodologies for validating clustering performance are reviewed, and a key shortcoming-the lack of realistic vehicular channel modeling-is identified. The importance of a rigorous and standardized performance evaluation regime utilizing realistic vehicular channel models is demonstrated.

Architecture & Planning2016Institute of Electrical and Electronics Engineers
Periodicals

The Parent's Capacity to Treat the Child as a Psychological Agent: Constructs, Measures and Implications for Developmental Psychopathology

Abstract Recent studies of the relationship between parenting and child development have included a focus on the parent's capacity to treat the child as a psychological agent. Several constructs have been developed to refer to this capacity, for example maternal mind‐mindedness, reflective functioning, and parental mentalizing. In this review article, we compare and contrast different constructs from diverse theoretical backgrounds that have been developed to operationalize parental mentalizing. We examine the empirical evidence to date in support of each of the constructs and review the relevant measures associated with each construct. Next, we discuss the possibility that these apparently diverse constructs may tap into the same underlying neurobiological socio‐cognitive system. We conclude by proposing a testable model for describing the links between parental mentalization, the development of mentalizing in children, and child psychopathology.

Architecture & Planning2008Wiley
Periodicals

From Axial to Road-Centre Lines: A New Representation for Space Syntax and a New Model of Route Choice for Transport Network Analysis

Axial analysis is one of the fundamental components of space syntax. The space syntax community has suggested that it picks up qualities of configurational relationships between spaces not illuminated by other representations. However, critics have questioned the absolute necessity of axial lines to space syntax, as well as the exact definition of axial lines. Why not another representation? In particular, why not road-centre lines, which are easily available in many countries for use within geographical information systems? Here I propose that a recently introduced method of analysis, angular segment analysis, can marry axial and road-centre line representations, and in doing so reflect a cognitive model of how route choice decisions may be made. I show that angular segment analysis can be applied generally to road-centre line segments or axial segments, through a simple length-weighted normalisation procedure that makes values between the two maps comparable. I make comparative quantitative assessments for a real urban system, not just investigating angular analysis between axial and road-centre line networks, but also including more intuitive measures based on metric (or block) distances between locations. I show that the new angular segment analysis algorithm produces better correlation with observed vehicular flow than both standard axial analysis and metric distance measures. The results imply that there is no reason why space syntax inspired measures cannot be combined with transportation network analysis representations in order to create a new, cognitively coherent, model of movement in the city.

Architecture & Planning2007SAGE Publishing