Academic Digital Library for institutions, students, and solo learners

Discovery

Content search and filters

This is the first search surface for the seven content types. Next we will connect full-text search and metadata-specific filters.

ACTIVE FILTERS:Dept: Architecture & Planning Clear All

Results 1,238

in Architecture & Planning
Periodicals

A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System

Traffic congestion is a perpetual problem for the sustainability of transportation development. Traffic congestion causes delays, inconvenience, and economic losses to drivers, as well as air pollution. Identification and quantification of traffic congestion are crucial for decision-makers to initiate mitigation strategies to improve the overall transportation system’s sustainability. In this paper, the currently available measures are detailed and compared by implementing them on a daily and weekly traffic historical dataset. The results showed each measure showed significant variations in congestion states while indicating a similar congestion trend. The advantages and disadvantages of each measure are identified from the data analysis. This study summarizes the current road traffic congestion measures and provides a constructive insight into the development of a sustainable and resilient traffic management system.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

In Vivo Acoustic Super-Resolution and Super-Resolved Velocity Mapping Using Microbubbles

Standard clinical ultrasound (US) imaging frequencies are unable to resolve microvascular structures due to the fundamental diffraction limit of US waves. Recent demonstrations of 2-D super-resolution both in vitro and in vivo have demonstrated that fine vascular structures can be visualized using acoustic single bubble localization. Visualization of more complex and disordered 3-D vasculature, such as that of a tumor, requires an acquisition strategy which can additionally localize bubbles in the elevational plane with high precision in order to generate super-resolution in all three dimensions. Furthermore, a particular challenge lies in the need to provide this level of visualization with minimal acquisition time. In this paper, we develop a fast, coherent US imaging tool for microbubble localization in 3-D using a pair of US transducers positioned at 90°. This allowed detection of point scatterer signals in 3-D with average precisions equal to [Formula: see text] in axial and elevational planes, and [Formula: see text] in the lateral plane, compared to the diffraction limited point spread function full-widths at half-maximum of 488, 1188, and [Formula: see text] of the original imaging system with a single transducer. Visualization and velocity mapping of 3-D in vitro structures was demonstrated far beyond the diffraction limit. The capability to measure the complete flow pattern of blood vessels associated with disease at depth would ultimately enable analysis of in vivo microvascular morphology, blood flow dynamics, and occlusions resulting from disease states.

Architecture & Planning• 2014• Institute of Electrical and Electronics Engineers
Access Full TextView Details
Periodicals

The Emergence of National Electronic Health Record Architectures in the United States and Australia: Models, Costs, and Questions

Emerging electronic health record models present numerous challenges to health care systems, physicians, and regulators. This article provides explanation of some of the reasons driving the development of the electronic health record, describes two national electronic health record models (currently developing in the United States and Australia) and one distributed, personal model. The US and Australian models are contrasted in their different architectures ("pull" versus "push") and their different approaches to patient autonomy, privacy, and confidentiality. The article also discusses some of the professional, practical, and legal challenges that health care providers potentially face both during and after electronic health record implementation.

Architecture & Planning• 2005• JMIR Publications
Access Full TextView Details
Periodicals

Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression

This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future.

Architecture & Planning• 2016• Frontiers Media
Access Full TextView Details
Periodicals

State of Industry 5.0—Analysis and Identification of Current Research Trends

The term Industry 4.0, coined to be the fourth industrial revolution, refers to a higher level of automation for operational productivity and efficiency by connecting virtual and physical worlds in an industry. With Industry 4.0 being unable to address and meet increased drive of personalization, the term Industry 5.0 was coined for addressing personalized manufacturing and empowering humans in manufacturing processes. The onset of the term Industry 5.0 is observed to have various views of how it is defined and what constitutes the reconciliation between humans and machines. This serves as the motivation of this paper in identifying and analyzing the various themes and research trends of what Industry 5.0 is using text mining tools and techniques. Toward this, the abstracts of 196 published papers based on the keyword “Industry 5.0” search in IEEE, science direct and MDPI data bases were extracted. Data cleaning and preprocessing were performed for further analysis to apply text mining techniques of key terms extraction and frequency analysis. Further topic mining i.e., unsupervised machine learning method was used for exploring the data. It is observed that the terms artificial intelligence (AI), big data, supply chain, digital transformation, machine learning, internet of things (IoT), are among the most often used and among several enablers that have been identified by researchers to drive Industry 5.0. Five major themes of Industry 5.0 addressing, supply chain evaluation and optimization, enterprise innovation and digitization, smart and sustainable manufacturing, transformation driven by IoT, AI, and Big Data, and Human-machine connectivity were classified among the published literature, highlighting the research themes that can be further explored. It is observed that the theme of Industry 5.0 as a gateway towards human machine connectivity and co-existence is gaining more interest among the research community in the recent years.

Architecture & Planning• 2022• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

A Review of Last Mile Logistics Innovations in an Externalities Cost Reduction Vision

In this paper, a review of the recent scientific literature contributions on innovative strategies for last mile logistics, focusing on externalities cost reduction, is presented. Transport is causing problems in urban areas, in particular in freight transport: modern cities need solutions to reduce externalities costs such as congestion, pollution and others, which have increased in the last few years, especially due to the growth of goods delivery. Online sales and globalization lead to new trends in freight transport, and moreover, a larger quantity of goods is expected to be delivered in the next future. In this context, most of the delivered goods end up in the city centers. Last mile logistics is the least efficient stage of the supply chain and comprises up to 28% of the total delivery cost. Therefore, the improvement of last mile logistics and a significant externalities reduction are very important challenges for researchers. New technologies and transport means, innovative techniques and organizational strategies allow handling in a more effective way the last mile delivery in urban areas. Based on the Systematic Literature Review (SLR) method, recent papers that significantly contributed, with original proposals, to the reduction of externalities in urban logistics are identified and analyzed in this work. Furthermore, a classification of the papers dealing with the externality reduction problem is presented. It is consistent with a general formulation proposed to evaluate external costs in urban area. The innovative contributions are classified into five main categories: innovative vehicles, proximity stations or points, collaborative and cooperative urban logistics, optimization of transport management and routing, innovations in public policies and infrastructures. The new paradigm of smart logistics is based on the combination of these concepts and on the proposed innovations.

Architecture & Planning• 2018• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.

Architecture & Planning• 2023• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

Context-oriented Programming.

Context-dependent behavior is becoming increasingly important for a wide range of application domains, from pervasive computing to common business applications. Unfortunately, mainstream programming languages do not provide mechanisms that enable software entities to adapt their behavior dynamically to the current execution context. This leads developers to adopt convoluted designs to achieve the necessary runtime flexibility. We propose a new programming technique called Context-oriented Programming (COP) which addresses this problem. COP treats context explicitly, and provides mechanisms to dynamically adapt behavior in reaction to changes in context, even after system deployment at runtime. In this paper, we lay the foundations of COP, show how dynamic layer activation enables multi-dimensional dispatch, illustrate the application of COP by examples in several language extensions, and demonstrate that COP is largely independent of other commitments to programming style.

Architecture & Planning• 2008• EtH Zurich
Access Full TextView Details
Periodicals

Nonviral delivery of synthetic siRNAs in vivo

Sequence-specific gene silencing using small interfering RNA (siRNA) is a Nobel prize-winning technology that is now being evaluated in clinical trials as a potentially novel therapeutic strategy. This article provides an overview of the major pharmaceutical challenges facing siRNA therapeutics, focusing on the delivery strategies for synthetic siRNA duplexes in vivo, as this remains one of the most important issues to be resolved. This article also highlights the importance of understanding the genocompatibility/toxicogenomics of siRNA delivery reagents in terms of their impact on gene-silencing activity and specificity. Collectively, this information is essential for the selection of optimally acting siRNA delivery system combinations for the many proposed applications of RNA interference.

Architecture & Planning• 2007• American Society for Clinical Investigation
Access Full TextView Details
Periodicals

Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows

The spatial arrangement of urban hubs and centers and how individuals interact with these centers is a crucial problem with many applications ranging from urban planning to epidemiology. We utilize here in an unprecedented manner the large scale, real-time 'Oyster' card database of individual person movements in the London subway to reveal the structure and organization of the city. We show that patterns of intraurban movement are strongly heterogeneous in terms of volume, but not in terms of distance travelled, and that there is a polycentric structure composed of large flows organized around a limited number of activity centers. For smaller flows, the pattern of connections becomes richer and more complex and is not strictly hierarchical since it mixes different levels consisting of different orders of magnitude. This new understanding can shed light on the impact of new urban projects on the evolution of the polycentric configuration of a city and the dense structure of its centers and it provides an initial approach to modeling flows in an urban system.

Architecture & Planning• 2011• Public Library of Science
Access Full TextView Details
Periodicals

A Review of the Artificial Neural Network Models for Water Quality Prediction

Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture

Intelligence has been considered as the major challenge in promoting economic potential and production efficiency of precision agriculture. In order to apply advanced deep-learning technology to complete various agricultural tasks in online and offline ways, a large number of crop vision datasets with domain-specific annotation are urgently needed. To encourage further progress in challenging realistic agricultural conditions, we present the CropDeep species classification and detection dataset, consisting of 31,147 images with over 49,000 annotated instances from 31 different classes. In contrast to existing vision datasets, images were collected with different cameras and equipment in greenhouses, captured in a wide variety of situations. It features visually similar species and periodic changes with more representative annotations, which have supported a stronger benchmark for deep-learning-based classification and detection. To further verify the application prospect, we provide extensive baseline experiments using state-of-the-art deep-learning classification and detection models. Results show that current deep-learning-based methods achieve well performance in classification accuracy over 99%. While current deep-learning methods achieve only 92% detection accuracy, illustrating the difficulty of the dataset and improvement room of state-of-the-art deep-learning models when applied to crops production and management. Specifically, we suggest that the YOLOv3 network has good potential application in agricultural detection tasks.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

The Internet of Things: Impact and Implications for Health Care Delivery

The Internet of Things (IoT) is a system of wireless, interrelated, and connected digital devices that can collect, send, and store data over a network without requiring human-to-human or human-to-computer interaction. The IoT promises many benefits to streamlining and enhancing health care delivery to proactively predict health issues and diagnose, treat, and monitor patients both in and out of the hospital. Worldwide, government leaders and decision makers are implementing policies to deliver health care services using technology and more so in response to the novel COVID-19 pandemic. It is now becoming increasingly important to understand how established and emerging IoT technologies can support health systems to deliver safe and effective care. The aim of this viewpoint paper is to provide an overview of the current IoT technology in health care, outline how IoT devices are improving health service delivery, and outline how IoT technology can affect and disrupt global health care in the next decade. The potential of IoT-based health care is expanded upon to theorize how IoT can improve the accessibility of preventative public health services and transition our current secondary and tertiary health care to be a more proactive, continuous, and coordinated system. Finally, this paper will deal with the potential issues that IoT-based health care generates, barriers to market adoption from health care professionals and patients alike, confidence and acceptability, privacy and security, interoperability, standardization and remuneration, data storage, and control and ownership. Corresponding enablers of IoT in current health care will rely on policy support, cybersecurity-focused guidelines, careful strategic planning, and transparent policies within health care organizations. IoT-based health care has great potential to improve the efficiency of the health system and improve population health.

Architecture & Planning• 2020• JMIR Publications
Access Full TextView Details
Periodicals

Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided Dialogue Dataset

Virtual assistants such as Google Assistant, Alexa and Siri provide a conversational interface to a large number of services and APIs spanning multiple domains. Such systems need to support an ever-increasing number of services with possibly overlapping functionality. Furthermore, some of these services have little to no training data available. Existing public datasets for task-oriented dialogue do not sufficiently capture these challenges since they cover few domains and assume a single static ontology per domain. In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains. Our dataset exceeds the existing task-oriented dialogue corpora in scale, while also highlighting the challenges associated with building large-scale virtual assistants. It provides a challenging testbed for a number of tasks including language understanding, slot filling, dialogue state tracking and response generation. Along the same lines, we present a schema-guided paradigm for task-oriented dialogue, in which predictions are made over a dynamic set of intents and slots, provided as input, using their natural language descriptions. This allows a single dialogue system to easily support a large number of services and facilitates simple integration of new services without requiring additional training data. Building upon the proposed paradigm, we release a model for dialogue state tracking capable of zero-shot generalization to new APIs, while remaining competitive in the regular setting.

Architecture & Planning• 2020
Access Full TextView Details
Periodicals

Stock Market Analysis: A Review and Taxonomy of Prediction Techniques

Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool’s game to try and predict them. Predicting stock prices is a challenging problem in itself because of the number of variables which are involved. In the short term, the market behaves like a voting machine but in the longer term, it acts like a weighing machine and hence there is scope for predicting the market movements for a longer timeframe. Application of machine learning techniques and other algorithms for stock price analysis and forecasting is an area that shows great promise. In this paper, we first provide a concise review of stock markets and taxonomy of stock market prediction methods. We then focus on some of the research achievements in stock analysis and prediction. We discuss technical, fundamental, short- and long-term approaches used for stock analysis. Finally, we present some challenges and research opportunities in this field.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

Serverification of Molecular Modeling Applications: The Rosetta Online Server That Includes Everyone (ROSIE)

The Rosetta molecular modeling software package provides experimentally tested and rapidly evolving tools for the 3D structure prediction and high-resolution design of proteins, nucleic acids, and a growing number of non-natural polymers. Despite its free availability to academic users and improving documentation, use of Rosetta has largely remained confined to developers and their immediate collaborators due to the code's difficulty of use, the requirement for large computational resources, and the unavailability of servers for most of the Rosetta applications. Here, we present a unified web framework for Rosetta applications called ROSIE (Rosetta Online Server that Includes Everyone). ROSIE provides (a) a common user interface for Rosetta protocols, (b) a stable application programming interface for developers to add additional protocols, (c) a flexible back-end to allow leveraging of computer cluster resources shared by RosettaCommons member institutions, and (d) centralized administration by the RosettaCommons to ensure continuous maintenance. This paper describes the ROSIE server infrastructure, a step-by-step 'serverification' protocol for use by Rosetta developers, and the deployment of the first nine ROSIE applications by six separate developer teams: Docking, RNA de novo, ERRASER, Antibody, Sequence Tolerance, Supercharge, Beta peptide design, NCBB design, and VIP redesign. As illustrated by the number and diversity of these applications, ROSIE offers a general and speedy paradigm for serverification of Rosetta applications that incurs negligible cost to developers and lowers barriers to Rosetta use for the broader biological community. ROSIE is available at http://rosie.rosettacommons.org.

Architecture & Planning• 2013• Public Library of Science
Access Full TextView Details
Periodicals

Examining the debt implications of the Belt and Road Initiative from a policy perspective

China’s Belt and Road Initiative (BRI) hopes to deliver trillions of dollars in infrastructure financing to Asia, Europe, and Africa. If the initiative follows Chinese practices to date for infrastructure financing, which often entail lending to sovereign borrowers, then BRI raises the risk of debt distress in some borrower countries. This paper assesses the likelihood of debt problems in the 68 countries identified as potential BRI borrowers. We conclude that eight countries are at particular risk of debt distress based on an identified pipeline of project lending associated with BRI.Because this indebtedness also suggests a higher concentration in debt owed to official and quasi-official Chinese creditors, we examine Chinese policies and practices related to sustainable financing and the management of debt problems in borrower countries. Based on this evidence, we offer recommendations to improve Chinese policy in these areas. The recommendations are offered to Chinese policymakers directly, as well as to BRI’s bilateral and multilateral partners, including the IMF and World Bank.

Architecture & Planning• 2019
Access Full TextView Details
Periodicals

Adaptive Transgenerational Plasticity in Plants: Case Studies, Mechanisms, and Implications for Natural Populations

Plants respond to environmental conditions not only by plastic changes to their own development and physiology, but also by altering the phenotypes expressed by their offspring. This transgenerational plasticity was initially considered to entail only negative effects of stressful parental environments, such as production of smaller seeds by resource- or temperature-stressed parent plants, and was therefore viewed as environmental noise. Recent evolutionary ecology studies have shown that in some cases, these inherited environmental effects can include specific growth adjustments that are functionally adaptive to the parental conditions that induced them, which can range from contrasting states of controlled laboratory environments to the complex habitat variation encountered by natural plant populations. Preliminary findings suggest that adaptive transgenerational effects can be transmitted by means of diverse mechanisms including changes to seed provisioning and biochemistry, and epigenetic modifications such as DNA methylation that can persist across multiple generations. These non-genetically inherited adaptations can influence the ecological breadth and evolutionary dynamics of plant taxa and promote the spread of invasive plants. Interdisciplinary studies that join mechanistic and evolutionary ecology approaches will be an important source of future insights.

Architecture & Planning• 2011• Frontiers Media
Access Full TextView Details
Periodicals

Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks

In this paper, we consider the sum power minimization problem via jointly optimizing user association, power control, computation capacity allocation, and location planning in a mobile edge computing (MEC) network with multiple unmanned aerial vehicles (UAVs). To solve the nonconvex problem, we propose a low-complexity algorithm with solving three subproblems iteratively. For the user association subproblem, the compressive sensing-based algorithm is accordingly proposed. For the computation capacity allocation subproblem, the optimal solution is obtained in closed form. For the location planning subproblem, the optimal solution is effectively obtained via one-dimensional search method. To obtain a feasible solution for this iterative algorithm, a fuzzy c-means clustering-based algorithm is proposed. The numerical results show that the proposed algorithm achieves better performance than the conventional approaches.

Architecture & Planning• 2019• Institute of Electrical and Electronics Engineers
Access Full TextView Details
Previous1...1314151617...52Next