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in Architecture & Planning
Periodicals

The physical environment of Kongsfjorden–Krossfjorden, an Arctic fjord system in Svalbard

Kongsfjorden-Krossfjorden and the adjacent West Spitsbergen Shelf meet at the common mouth of the two fjord arms. This paper presents our most up-to-date information about the physical environment of this fjord system and identifies important gaps in knowledge. Particular attention is given to the steep physical gradients along the main fjord axis, as well as to seasonal environmental changes. Physical processes on different scales control the large-scale circulation and small-scale (irreversible) mixing of water and its constituents. It is shown that, in addition to the tide, run-off (glacier ablation, snowmelt, summer rainfall and ice calving) and local winds are the main driving forces acting on the upper water masses in the fjord system. The tide is dominated by the semi-diurnal component and the freshwater supply shows a marked seasonal variation pattern and also varies interannually. The wind conditions are characterized by prevailing katabatic winds, which at times are strengthened by the geostrophic wind field over Svalbard. Rotational dynamics have a considerable influence on the circulation patterns within the fjord system and give rise to a strong interaction between the fjord arms. Such dynamics are also the main reason why variations in the shelf water density field, caused by remote forces (tide and coastal winds), propagate as a Kelvin wave into the fjord system. This exchange affects mainly the intermediate and deep water, which is also affected by vertical convection processes driven by cooling of the surface and brine release during ice formation in the inner reaches of the fjord arms. Further aspects covered by this paper include the geological and geomorphological characteristics of the Kongsfjorden area, climate and meteorology, the influence of glaciers, freshwater supply, sea ice conditions, sedimentation processes as well as underwater radiation conditions. The fjord system is assumed to be vulnerable to possible climate changes, and thus is very suitable as a site for the demonstration and investigation of phenomena related to climate change.

Architecture & Planning• 2002• Wiley
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Periodicals

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from “connected things” to “connected intelligence”. However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion, and privacy leakage in both of the training and inference processes. By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence, thereby improving the efficiency, effectiveness, privacy, and security of 6G networks. In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described. Standardization, software and hardware platforms, and application scenarios are also discussed to facilitate the industrialization and commercialization of edge AI systems.

Architecture & Planning• 2021• Institute of Electrical and Electronics Engineers
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Internet of Things (IoT) and the Energy Sector

Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers with an overview of the role of IoT in optimization of energy systems.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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Periodicals

Routines and Other Recurring Action Patterns of Organizations: Contemporary Research Issues

This paper reports and extends discussions carried out during a workshop held at the Santa Fe Institute in August 1995 by the authors. It treats eight major topics: (i) the importance of carefully examining research on routine, (ii) the concept of ‘action patterns’ in general and in terms of routine, (iii) the useful categorization of routines and other recurring patterns, (iv) the research implications of recent cognitive results, (v) the relation of evolution to action patterns, (vi) the contributions of simulation modeling for theory in this area, (vii) examples of various approaches to empirical research that reveal key problems, and (viii) a possible definition of ‘routine’. An extended appendix by Massimo Egidi provides a lexicon of synonyms and opposites covering use of the word ‘routine’ in such areas as economics, organization theory and artificial intelligence.

Architecture & Planning• 1996• Oxford University Press
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Periodicals

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past two decades, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of NLP, with an emphasis on different evaluation methods and the relationships between them.

Architecture & Planning• 2018• AI Access Foundation
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Periodicals

The Geomechanics of CO2 Storage in Deep Sedimentary Formations

This paper provides a review of the geomechanics and modeling of geomechanics associated with geologic carbon storage (GCS), focusing on storage in deep sedimentary formations, in particular saline aquifers. The paper first introduces the concept of storage in deep sedimentary formations, the geomechanical processes and issues related with such an operation, and the relevant geomechanical modeling tools. This is followed by a more detailed review of geomechanical aspects, including reservoir stress-strain and microseismicity, well integrity, caprock sealing performance, and the potential for fault reactivation and notable (felt) seismic events. Geomechanical observations at current GCS field deployments, mainly at the In Salah CO2 storage project in Algeria, are also integrated into the review. The In Salah project, with its injection into a relatively thin, low-permeability sandstone is an excellent analogue to the saline aquifers that might be used for large scale GCS in parts of Northwest Europe, the U.S. Midwest, and China. Some of the lessons learned at In Salah related to geomechanics are discussed, including how monitoring of geomechanical responses is used for detecting subsurface geomechanical changes and tracking fluid movements, and how such monitoring and geomechanical analyses have led to preventative changes in the injection parameters. Recently, the importance of geomechanics has become more widely recognized among GCS stakeholders, especially with respect to the potential for triggering notable (felt) seismic events and how such events could impact the long-term integrity of a CO2 repository (as well as how it could impact the public perception of GCS). As described in the paper, to date, no notable seismic event has been reported from any of the current CO2 storage projects, although some unfelt microseismic activities have been detected by geophones. However, potential future commercial GCS operations from large power plants will require injection at a much larger scale. For such large-scale injections, a staged, learn-as-you-go approach is recommended, involving a gradual increase of injection rates combined with continuous monitoring of geomechanical changes, as well as siting beneath a multiple layered overburden for multiple flow barrier protection, should an unexpected deep fault reactivation occur.

Architecture & Planning• 2012• Springer Science+Business Media
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Periodicals

Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence

Abstract This paper explores the important role of critical science, and in particular of post-colonial and decolonial theories, in understanding and shaping the ongoing advances in artificial intelligence. Artificial intelligence (AI) is viewed as amongst the technological advances that will reshape modern societies and their relations. While the design and deployment of systems that continually adapt holds the promise of far-reaching positive change, they simultaneously pose significant risks, especially to already vulnerable peoples. Values and power are central to this discussion. Decolonial theories use historical hindsight to explain patterns of power that shape our intellectual, political, economic, and social world. By embedding a decolonial critical approach within its technical practice, AI communities can develop foresight and tactics that can better align research and technology development with established ethical principles, centring vulnerable peoples who continue to bear the brunt of negative impacts of innovation and scientific progress. We highlight problematic applications that are instances of coloniality, and using a decolonial lens, submit three tactics that can form a decolonial field of artificial intelligence: creating a critical technical practice of AI, seeking reverse tutelage and reverse pedagogies, and the renewal of affective and political communities. The years ahead will usher in a wave of new scientific breakthroughs and technologies driven by AI research, making it incumbent upon AI communities to strengthen the social contract through ethical foresight and the multiplicity of intellectual perspectives available to us, ultimately supporting future technologies that enable greater well-being, with the goal of beneficence and justice for all.

Architecture & Planning• 2020• Springer Nature (Netherlands)
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Periodicals

Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids

Battery energy storage systems have gained increasing interest for serving grid support in various application tasks. In particular, systems based on lithium-ion batteries have evolved rapidly with a wide range of cell technologies and system architectures available on the market. On the application side, different tasks for storage deployment demand distinct properties of the storage system. This review aims to serve as a guideline for best choice of battery technology, system design and operation for lithium-ion based storage systems to match a specific system application. Starting with an overview to lithium-ion battery technologies and their characteristics with respect to performance and aging, the storage system design is analyzed in detail based on an evaluation of real-world projects. Typical storage system applications are grouped and classified with respect to the challenges posed to the battery system. Publicly available modeling tools for technical and economic analysis are presented. A brief analysis of optimization approaches aims to point out challenges and potential solution techniques for system sizing, positioning and dispatch operation. For all areas reviewed herein, expected improvements and possible future developments are highlighted. In order to extract the full potential of stationary battery storage systems and to enable increased profitability of systems, future research should aim to a holistic system level approach combining not only performance tuning on a battery cell level and careful analysis of the application requirements, but also consider a proper selection of storage sub-components as well as an optimized system operation strategy.

Architecture & Planning• 2017• Multidisciplinary Digital Publishing Institute
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Periodicals

From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz

The next few years will be exciting as prototype universal quantum processors emerge, enabling the implementation of a wider variety of algorithms. Of particular interest are quantum heuristics, which require experimentation on quantum hardware for their evaluation and which have the potential to significantly expand the breadth of applications for which quantum computers have an established advantage. A leading candidate is Farhi et al.’s quantum approximate optimization algorithm, which alternates between applying a cost function based Hamiltonian and a mixing Hamiltonian. Here, we extend this framework to allow alternation between more general families of operators. The essence of this extension, the quantum alternating operator ansatz, is the consideration of general parameterized families of unitaries rather than only those corresponding to the time evolution under a fixed local Hamiltonian for a time specified by the parameter. This ansatz supports the representation of a larger, and potentially more useful, set of states than the original formulation, with potential long-term impact on a broad array of application areas. For cases that call for mixing only within a desired subspace, refocusing on unitaries rather than Hamiltonians enables more efficiently implementable mixers than was possible in the original framework. Such mixers are particularly useful for optimization problems with hard constraints that must always be satisfied, defining a feasible subspace, and soft constraints whose violation we wish to minimize. More efficient implementation enables earlier experimental exploration of an alternating operator approach, in the spirit of the quantum approximate optimization algorithm, to a wide variety of approximate optimization, exact optimization, and sampling problems. In addition to introducing the quantum alternating operator ansatz, we lay out design criteria for mixing operators, detail mappings for eight problems, and provide a compendium with brief descriptions of mappings for a diverse array of problems.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
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A Critical Review of the Use of Virtual Reality in Construction Engineering Education and Training

Virtual Reality (VR) has been rapidly recognized and implemented in construction engineering education and training (CEET) in recent years due to its benefits of providing an engaging and immersive environment. The objective of this review is to critically collect and analyze the VR applications in CEET, aiming at all VR-related journal papers published from 1997 to 2017. The review follows a three-stage analysis on VR technologies, applications and future directions through a systematic analysis. It is found that the VR technologies adopted for CEET evolve over time, from desktop-based VR, immersive VR, 3D game-based VR, to Building Information Modelling (BIM)-enabled VR. A sibling technology, Augmented Reality (AR), for CEET adoptions has also emerged in recent years. These technologies have been applied in architecture and design visualization, construction health and safety training, equipment and operational task training, as well as structural analysis. Future research directions, including the integration of VR with emerging education paradigms and visualization technologies, have also been provided. The findings are useful for both researchers and educators to usefully integrate VR in their education and training programs to improve the training performance.

Architecture & Planning• 2018• Multidisciplinary Digital Publishing Institute
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Precision Agriculture Techniques and Practices: From Considerations to Applications

Internet of Things (IoT)-based automation of agricultural events can change the agriculture sector from being static and manual to dynamic and smart, leading to enhanced production with reduced human efforts. Precision Agriculture (PA) along with Wireless Sensor Network (WSN) are the main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure that the crops receive exactly what they need to optimize productivity and sustainability. PA includes retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned or unmanned), which are further processed to extract information used to provide future decisions. In this paper, a review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges. This survey includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyse spectral images and applications of WSN in agriculture. As a proof of concept, we present a case study showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor network-based system to monitor real-time crop health status. The second module uses a low altitude remote sensing platform to obtain multi-spectral imagery, which is further processed to classify healthy and unhealthy crops. We also highlight the results obtained using a case study and list the challenges and future directions based on our work.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
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Strategy making in novel and complex worlds: the power of analogy

Abstract We examine how firms discover effective competitive positions in worlds that are both novel and complex. In such settings, neither rational deduction nor local search is likely to lead a firm to a successful array of choices. Analogical reasoning, however, may be helpful, allowing managers to transfer useful wisdom from similar settings they have experienced in the past. From a long list of observable industry characteristics, analogizing managers choose a subset they believe distinguishes similar industries from different ones. Faced with a novel industry, they seek a familiar industry which matches the novel one along that subset of characteristics. They transfer from the matching industry high‐level policies that guide search in the novel industry. We embody this conceptualization of analogy in an agent‐based simulation model. The model allows us to examine the impact of managerial and structural characteristics on the effectiveness of analogical reasoning. With respect to managerial characteristics, we find, not surprisingly, that analogical reasoning is especially powerful when managers pay attention to characteristics that truly distinguish similar industries from different ones. More surprisingly, we find that the marginal returns to depth of experience diminish rapidly while greater breadth of experience steadily improves performance. Both depth and breadth of experience are useful only when one accurately understands what distinguishes similar industries from different ones. We also discover that following an analogy in too orthodox a manner—strictly constraining search efforts to what the analogy suggests—can be dysfunctional. With regard to structural characteristics, we find that a well‐informed analogy is particularly powerful when interactions among decisions cross policy boundaries so that the underlying decision problem is not easily decomposed. Overall, the results shed light on a form of managerial reasoning that we believe is prevalent among practicing strategists yet is largely absent from scholarly analysis of strategy. Copyright © 2005 John Wiley & Sons, Ltd.

Architecture & Planning• 2005• Wiley
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Periodicals

Graph neural networks for materials science and chemistry

Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.

Architecture & Planning• 2022• Nature Portfolio
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Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models

Digital twin (DT) is an emerging concept that is gaining attention in various industries. It refers to the ability to clone a physical object (PO) into a software counterpart. The softwarized object, termed logical object, reflects all the important properties and characteristics of the original object within a specific application context. To fully determine the expected properties of the DT, this article surveys the state-of-the-art starting from the original definition within the manufacturing industry. It takes into account related proposals emerging in other fields, namely augmented and virtual reality (e.g., avatars), multiagent systems, and virtualization. This survey thereby allows for the identification of an extensive set of DT features that point to the “softwarization” of POs. To properly consolidate a shared DT definition, a set of foundational properties is identified and proposed as a common ground outlining the essential characteristics (must-haves) of a DT. Once the DT definition has been consolidated, its technical and business value is discussed in terms of applicability and opportunities. Four application scenarios illustrate how the DT concept can be used and how some industries are applying it. The scenarios also lead to a generic DT architectural model. This analysis is then complemented by the identification of software architecture models and guidelines in order to present a general functional framework for the DT. This article, eventually, analyses a set of possible evolution paths for the DT considering its possible usage as a major enabler for the softwarization process.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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The Design of SimpleITK

SimpleITK is a new interface to the Insight Segmentation and Registration Toolkit (ITK) designed to facilitate rapid prototyping, education and scientific activities via high level programming languages. ITK is a templated C++ library of image processing algorithms and frameworks for biomedical and other applications, and it was designed to be generic, flexible and extensible. Initially, ITK provided a direct wrapping interface to languages such as Python and Tcl through the WrapITK system. Unlike WrapITK, which exposed ITK's complex templated interface, SimpleITK was designed to provide an easy to use and simplified interface to ITK's algorithms. It includes procedural methods, hides ITK's demand driven pipeline, and provides a template-less layer. Also SimpleITK provides practical conveniences such as binary distribution packages and overloaded operators. Our user-friendly design goals dictated a departure from the direct interface wrapping approach of WrapITK, toward a new facade class structure that only exposes the required functionality, hiding ITK's extensive template use. Internally SimpleITK utilizes a manual description of each filter with code-generation and advanced C++ meta-programming to provide the higher-level interface, bringing the capabilities of ITK to a wider audience. SimpleITK is licensed as open source software library under the Apache License Version 2.0 and more information about downloading it can be found at http://www.simpleitk.org.

Architecture & Planning• 2013• Frontiers Media
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A Bibliometric Analysis of Heart Disease Detection using Artificial Intelligence Techniques: Trends, Influential Works, and Research Gaps

Advanced diagnostic techniques are required as cardiovascular diseases continue to pose a serious threat to global health. The scientific community has recently shown a great deal of interest in the application of deep learning techniques to the detection of heart disease. In order to synthesize the body of research on the use of deep learning in the detection of heart disease, this study provides a thorough bibliometric analysis. A wide variety of publications, including articles, conference papers, and reviews, are included in the analysis. These were obtained from Scopus and WoS databases. Total 662 documents are analyzed from these databases. The study looks at geographic distributions, historical trends, and influential figures in the field. We uncover key papers and authors through quantitative analyses, providing insight into the way research themes have changed over time. The study delves into co-authorship networks and institutional collaborations, offering valuable perspectives on the collaborative environment among scholars operating within this field. To find popular terms and hot topics, keyword analysis is used, which helps to provide a more sophisticated understanding of the main ideas guiding the research that is being done today.

Architecture & Planning• 2023
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Informatics and Data Mining Tools and Strategies for the Human Connectome Project

The Human Connectome Project (HCP) is a major endeavor that will acquire and analyze connectivity data plus other neuroimaging, behavioral, and genetic data from 1,200 healthy adults. It will serve as a key resource for the neuroscience research community, enabling discoveries of how the brain is wired and how it functions in different individuals. To fulfill its potential, the HCP consortium is developing an informatics platform that will handle: (1) storage of primary and processed data, (2) systematic processing and analysis of the data, (3) open-access data-sharing, and (4) mining and exploration of the data. This informatics platform will include two primary components. ConnectomeDB will provide database services for storing and distributing the data, as well as data analysis pipelines. Connectome Workbench will provide visualization and exploration capabilities. The platform will be based on standard data formats and provide an open set of application programming interfaces (APIs) that will facilitate broad utilization of the data and integration of HCP services into a variety of external applications. Primary and processed data generated by the HCP will be openly shared with the scientific community, and the informatics platform will be available under an open source license. This paper describes the HCP informatics platform as currently envisioned and places it into the context of the overall HCP vision and agenda.

Architecture & Planning• 2011• Frontiers Media
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Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

Architecture & Planning• 2017• Multidisciplinary Digital Publishing Institute
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Measuring Risk Literacy: The Berlin Numeracy Test

Abstract We introduce the Berlin Numeracy Test, a new psychometrically sound instrument that quickly assesses statistical numeracy and risk literacy. We present 21 studies ( n =5336) showing robust psychometric discriminability across 15 countries (e.g., Germany, Pakistan, Japan, USA) and diverse samples (e.g., medical professionals, general populations, Mechanical Turk web panels). Analyses demonstrate desirable patterns of convergent validity (e.g., numeracy, general cognitive abilities), discriminant validity (e.g., personality, motivation), and criterion validity (e.g., numerical and non-numerical questions about risk). The Berlin Numeracy Test was found to be the strongest predictor of comprehension of everyday risks (e.g., evaluating claims about products and treatments; interpreting forecasts), doubling the predictive power of other numeracy instruments and accounting for unique variance beyond other cognitive tests (e.g., cognitive reflection, working memory, intelligence). The Berlin Numeracy Test typically takes about three minutes to complete and is available in multiple languages and formats, including a computer adaptive test that automatically scores and reports data to researchers ( http://www.riskliteracy.org ). The online forum also provides interactive content for public outreach and education, and offers a recommendation system for test format selection. Discussion centers on construct validity of numeracy for risk literacy, underlying cognitive mechanisms, and applications in adaptive decision support.

Architecture & Planning• 2012• Cambridge University Press
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A Simple, Low-Cost Conductive Composite Material for 3D Printing of Electronic Sensors

3D printing technology can produce complex objects directly from computer aided digital designs. The technology has traditionally been used by large companies to produce fit and form concept prototypes ('rapid prototyping') before production. In recent years however there has been a move to adopt the technology as full-scale manufacturing solution. The advent of low-cost, desktop 3D printers such as the RepRap and Fab@Home has meant a wider user base are now able to have access to desktop manufacturing platforms enabling them to produce highly customised products for personal use and sale. This uptake in usage has been coupled with a demand for printing technology and materials able to print functional elements such as electronic sensors. Here we present formulation of a simple conductive thermoplastic composite we term 'carbomorph' and demonstrate how it can be used in an unmodified low-cost 3D printer to print electronic sensors able to sense mechanical flexing and capacitance changes. We show how this capability can be used to produce custom sensing devices and user interface devices along with printed objects with embedded sensing capability. This advance in low-cost 3D printing with offer a new paradigm in the 3D printing field with printed sensors and electronics embedded inside 3D printed objects in a single build process without requiring complex or expensive materials incorporating additives such as carbon nanotubes.

Architecture & Planning• 2012• Public Library of Science
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