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in Architecture & Planning
Periodicals

Survey on Network Slicing for Internet of Things Realization in 5G Networks

Internet of Things (IoT) is an emerging technology that makes people's lives smart by conquering a plethora of diverse application and service areas. In near future, the fifth-generation (5G) wireless networks provide the connectivity for this IoT ecosystem. It has been carefully designed to facilitate the exponential growth in the IoT field. Network slicing is one of the key technologies in the 5G architecture that has the ability to divide the physical network into multiple logical networks (i.e., slices) with different network characteristics. Therefore, network slicing is also a key enabler of realisation of IoT in 5G. Network slicing can satisfy the various networking demands by heterogeneous IoT applications via dedicated slices. In this survey, we present a comprehensive analysis of the exploitation of network slicing in IoT realisation. We discuss network slicing utilisation in different IoT application scenarios, along with the technical challenges that can be solved via network slicing. Furthermore, integration challenges and open research problems related to the network slicing in the IoT realisation are also discussed in this paper. Finally, we discuss the role of other emerging technologies and concepts, such as blockchain and Artificial Intelligence/Machine Learning (AI/ML) in network slicing and IoT integration.

Architecture & Planning• 2021• Institute of Electrical and Electronics Engineers
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All Together Now: A Conceptual Exploration of Integrated Care

Integrated care is a key strategy in reforming health systems around the world. Despite its importance, the concept's polymorphous nature and lack of specificity and clarity significantly hamper systematic understanding, successful application and meaningful evaluation. This article explores the many definitions, concepts, logics and methods found in health system and service integration. In addition to framing this evolving, albeit imprecise field, the article summarizes the main elements or building blocks of integrated care and suggests a way to address its various complexities and unknowns in a real-world sense.

Architecture & Planning• 2009• Longwoods Publishing
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The CIAM Discourse on Urbanism, 1928-1960

In this article, Eric Mumford focuses on CIAM’s discourse tracing the development and promotion of its influential concepts around the notion of the Functional City. By means of a complete analysis of its official documents, as well as of its member’s pronouncements, the author reveals the transformations occurred within the CIAM from its origin at the end of the 1920’s and until its dissolution in 1959. This text is a written version of the lecture given by the professor Mumford on May 7th of 2007 at the seminar Modern Urbanism: from the CIAM to Colombia, organized by the Master Program in Urbanism of the National University of Colombia.

Architecture & Planning• 2000
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Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer

The Fujitsu Digital Annealer is designed to solve fully connected quadratic unconstrained binary optimization (QUBO) problems. It is implemented on application-specific CMOS hardware and currently solves problems of up to 1024 variables. The Digital Annealer's algorithm is currently based on simulated annealing; however, it differs from it in its utilization of an efficient parallel-trial scheme and a dynamic escape mechanism. In addition, the Digital Annealer exploits the massive parallelization that custom application-specific CMOS hardware allows. We compare the performance of the Digital Annealer to simulated annealing and parallel tempering with isoenergetic cluster moves on two-dimensional and fully connected spin-glass problems with bimodal and Gaussian couplings. These represent the respective limits of sparse versus dense problems, as well as high-degeneracy versus low-degeneracy problems. Our results show that the Digital Annealer currently exhibits a time-to-solution speedup of roughly two orders of magnitude for fully connected spin-glass problems with bimodal or Gaussian couplings, over the single-core implementations of simulated annealing and parallel tempering Monte Carlo used in this study. The Digital Annealer does not appear to exhibit a speedup for sparse two-dimensional spin-glass problems, which we explain on theoretical grounds. We also benchmarked an early implementation of the Parallel Tempering Digital Annealer. Our results suggest an improved scaling over the other algorithms for fully connected problems of average difficulty with bimodal disorder. The next generation of the Digital Annealer is expected to be able to solve fully connected problems up to 8192 variables in size. This would enable the study of fundamental physics problems and industrial applications that were previously inaccessible using standard computing hardware or special-purpose quantum annealing machines.

Architecture & Planning• 2019• Frontiers Media
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Periodicals

Toward a Neural Basis of Music Perception – A Review and Updated Model

Music perception involves acoustic analysis, auditory memory, auditory scene analysis, processing of interval relations, of musical syntax and semantics, and activation of (pre)motor representations of actions. Moreover, music perception potentially elicits emotions, thus giving rise to the modulation of emotional effector systems such as the subjective feeling system, the autonomic nervous system, the hormonal, and the immune system. Building on a previous article (Koelsch and Siebel, 2005), this review presents an updated model of music perception and its neural correlates. The article describes processes involved in music perception, and reports EEG and fMRI studies that inform about the time course of these processes, as well as about where in the brain these processes might be located.

Architecture & Planning• 2011• Frontiers Media
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Swarm Robotic Behaviors and Current Applications

In swarm robotics multiple robots collectively solve problems by forming advantageous structures and behaviors similar to the ones observed in natural systems, such as swarms of bees, birds, or fish. However, the step to industrial applications has not yet been made successfully. Literature is light on real-world swarm applications that apply actual swarm algorithms. Typically, only parts of swarm algorithms are used which we refer to as basic swarm behaviors. In this paper we collect and categorize these behaviors into spatial organization, navigation, decision making, and miscellaneous. This taxonomy is then applied to categorize a number of existing swarm robotic applications from research and industrial domains. Along with the classification, we give a comprehensive overview of research platforms that can be used for testing and evaluating swarm behavior, systems that are already on the market, and projects that target a specific market. Results from this survey show that swarm robotic applications are still rare today. Many industrial projects still rely on centralized control, and even though a solution with multiple robots is employed, the principal idea of swarm robotics of distributed decision making is neglected. We identified mainly following reasons: First of all, swarm behavior emerging from local interactions is hard to predict and a proof of its eligibility for applications in an industrial context is difficult to provide. Second, current communication architectures often do not match requirements for swarm communication, which often leads to a system with a centralized communication infrastructure. Finally, testing swarms for real industrial applications is an issue, since deployment in a productive environment is typically too risky and simulations of a target system may not be sufficiently accurate. In contrast, the research platforms present a means for transforming swarm robotics solutions from theory to prototype industrial systems.

Architecture & Planning• 2020• Frontiers Media
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Collaborative Networks: Integrating Blockchain for Enhanced Trust and Transparency

Blockchain technology has become a disruptive force in many different industries because it provides unparalleled security, transparency, and efficiency. In the context of cooperative networks, Blockchain's decentralized architecture provides a solid basis for enhancing trust, streamlining processes, and fostering innovation. This chapter examines how Blockchain can be used with cooperative networks and how it could fundamentally alter the way entities interact and communicate. Important areas of focus include Blockchain's technological foundations, applications in collaborative environments, and case studies that highlight its impacts. By examining the challenges and opportunities presented by this convergence, we intend to provide a comprehensive understanding of how Blockchain can revolutionize cooperative ecosystems. The field of scholarly communication is undergoing a significant transition due to technological advancements. This chapter looks at how blockchain technology and collaborative networks could alter contemporary living. We examine the basic shortcomings of the existing centralized model and discuss how the core concepts of immutability, transparency, and decentralization found in Blockchain technology can assist in resolving these problems. This paper explores the relationship between collaborative networks and blockchain technology. We start by outlining the fundamental ideas behind Blockchain technology and how it has developed. The use of Blockchain is then examined in a variety of cooperative settings, with its advantages and possible drawbacks discussed. We demonstrate the real-world effects of Blockchain-enabled collaborative networks across various industries with a number of case studies.

Architecture & Planning• 2024
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Periodicals

The Natural Product Domain Seeker NaPDoS: A Phylogeny Based Bioinformatic Tool to Classify Secondary Metabolite Gene Diversity

New bioinformatic tools are needed to analyze the growing volume of DNA sequence data. This is especially true in the case of secondary metabolite biosynthesis, where the highly repetitive nature of the associated genes creates major challenges for accurate sequence assembly and analysis. Here we introduce the web tool Natural Product Domain Seeker (NaPDoS), which provides an automated method to assess the secondary metabolite biosynthetic gene diversity and novelty of strains or environments. NaPDoS analyses are based on the phylogenetic relationships of sequence tags derived from polyketide synthase (PKS) and non-ribosomal peptide synthetase (NRPS) genes, respectively. The sequence tags correspond to PKS-derived ketosynthase domains and NRPS-derived condensation domains and are compared to an internal database of experimentally characterized biosynthetic genes. NaPDoS provides a rapid mechanism to extract and classify ketosynthase and condensation domains from PCR products, genomes, and metagenomic datasets. Close database matches provide a mechanism to infer the generalized structures of secondary metabolites while new phylogenetic lineages provide targets for the discovery of new enzyme architectures or mechanisms of secondary metabolite assembly. Here we outline the main features of NaPDoS and test it on four draft genome sequences and two metagenomic datasets. The results provide a rapid method to assess secondary metabolite biosynthetic gene diversity and richness in organisms or environments and a mechanism to identify genes that may be associated with uncharacterized biochemistry.

Architecture & Planning• 2012• Public Library of Science
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A Survey of the Connected Vehicle Landscape—Architectures, Enabling Technologies, Applications, and Development Areas

This paper summarizes the state of the art in connected vehicles-from the need for vehicle data and applications thereof, to enabling technologies, challenges, and identified opportunities. Connectivity is increasing around the world and its expansion to vehicles is no exception. With improvements in connectivity, sensing, and computation, the future will see vehicles used as development platforms capable of generating rich data, acting based on inference, and effecting great change in transportation, the human-vehicle dynamic, the environment, and the economy. Connected vehicle technologies have already been used to improve fleet safety and efficiency, with emerging technologies additionally allowing data to be used to inform aspects of vehicle design, ownership, and use. While the demand for connected vehicles and its enabling technology has progressed significantly in recent years, there remain challenges to connected and collaborative vehicle application deployment before the full potential of connected cars may be realized. From extensibility and scalability to privacy and security, this paper informs the reader about key enabling technologies, opportunities, and challenges in the connected vehicle landscape.

Architecture & Planning• 2017• Institute of Electrical and Electronics Engineers
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Gene-Expressing Liposomes as Synthetic Cells for Molecular Communication Studies

The bottom-up branch of synthetic biology includes – among others – innovative studies that combine cell-free protein synthesis with liposome technology to generate cell-like systems of minimal complexity, often referred to as synthetic cells. The functions of this type of synthetic cell derive from gene expression, hence they can be programmed in a modular, progressive and customizable manner by means of ad hoc designed genetic circuits. This experimental scenario is rapidly expanding and synthetic cell research already counts numerous successes. Here we present a review focused on the exchange of chemical signals between liposome-based synthetic cells (operating by gene expression) and biological cells, as well as between two populations of synthetic cells. The review includes a short presentation of the “molecular communication technologies”, briefly discussing their promises and challenges.

Architecture & Planning• 2019• Frontiers Media
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Periodicals

Humans and Technology: Forms of Conjoined Agency in Organizations

Organizations are increasingly deploying technologies that have the ability to parse through large amounts of data, acquire skills and knowledge, and operate autonomously. These technologies diverge from prior technologies in their capacity to exercise intentionality over protocol development or action selection in the practice of organizational routines, thereby affecting organizations in new and distinct ways. In this article, we categorize four forms of conjoined agency between humans and technologies: (1) conjoined agency with assisting technologies, (2) conjoined agency with arresting technologies, (3) conjoined agency with augmenting technologies, and (4) conjoined agency with automating technologies. We then theorize on the different ways in which these forms of conjoined agency impact a routine’s change at a particular moment in time as well as a routine’s responsiveness to feedback over time. In doing so, we elaborate on how organizations may evolve in varied and diverse ways based on the form(s) of conjoined agency they deploy in their organizational design choices.

Architecture & Planning• 2020• Academy of Management
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Future Trends and Current State of Smart City Concepts: A Survey

Intelligent systems are wanting for cities to cope with limited spaces and resources across the world. As a result, smart cities emerged mainly as a result of highly innovative ICT industries and markets, and additionally, they have started to use novel solutions taking advantage of the Internet of Things (IoT), big data and cloud computing technologies to establish a profound connection between each component and layer of a city. Several key technologies congregate to build a working smart city considering human requirements. Even though the smart city concept is an advanced solution for today's cities, recently, more living spaces should be discovered, and the concept of a smart city could be moved to these alternative living spaces, namely floating cities. The concept of a floating city emerged as a novel solution due to rising sea levels and land scarcity in order to provide alternative living spaces for humanity. In this article, our main research question is to raise awareness on the current state of smart city concepts across the world by understanding the key future trends, including floating cities, by motivating researchers and scientists through new IoT technologies and applications. Therefore, we present a survey of smart city initiatives and analyze their key concepts and different data management techniques. We performed a detailed literature survey and review by applying a complex literature matrix including terms, like smart people, smart economy, smart governance, smart mobility, smart environment, and smart living. We also discuss multiple perspectives of smart floating cities in detail. With the proposed approach, recent advances and practical future opportunities for smart cities can be revealed.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Speech Production as State Feedback Control

Spoken language exists because of a remarkable neural process. Inside a speaker's brain, an intended message gives rise to neural signals activating the muscles of the vocal tract. The process is remarkable because these muscles are activated in just the right way that the vocal tract produces sounds a listener understands as the intended message. What is the best approach to understanding the neural substrate of this crucial motor control process? One of the key recent modeling developments in neuroscience has been the use of state feedback control (SFC) theory to explain the role of the CNS in motor control. SFC postulates that the CNS controls motor output by (1) estimating the current dynamic state of the thing (e.g., arm) being controlled, and (2) generating controls based on this estimated state. SFC has successfully predicted a great range of non-speech motor phenomena, but as yet has not received attention in the speech motor control community. Here, we review some of the key characteristics of speech motor control and what they say about the role of the CNS in the process. We then discuss prior efforts to model the role of CNS in speech motor control, and argue that these models have inherent limitations - limitations that are overcome by an SFC model of speech motor control which we describe. We conclude by discussing a plausible neural substrate of our model.

Architecture & Planning• 2011• Frontiers Media
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Periodicals

The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations

In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI's greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based, and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combatting climate change, while reducing its impact on the environment.

Architecture & Planning• 2021• Springer Nature
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Location-Aware Communications for 5G Networks: How location information can improve scalability, latency, and robustness of 5G

Fifth-generation (5G) networks will be the first generation to benefit from location information that is sufficiently precise to be leveraged in wireless network design and optimization. We argue that location information can aid in addressing several of the key challenges in 5G, complementary to existing and planned technological developments. These challenges include an increase in traffic and number of devices, robustness for mission-critical services, and a reduction in total energy consumption and latency. This article gives a broad overview of the growing research area of location-aware communications across different layers of the protocol stack. We highlight several promising trends, tradeoffs, and pitfalls.

Architecture & Planning• 2014• Institute of Electrical and Electronics Engineers
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https://journals.vgtu.lt/index.php/JCEM/article/view/5898

The paper presents risk assessment of construction projects. The assessment is based on the multi‐attribute decision‐making methods. The risk evaluation attributes are selected taking into consideration the interests and goals of the stakeholders as well as factors that have influence on the construction process efficiency and real estate value. Ranking of objects and determination of their optimality are determined by applying TOPSIS grey and COPRAS‐G methods with attributes values determined at intervals. A background and a description of the proposed model are provided and key findings of the analysis are presented. Santrauka Straipsnyje vertinama statybos projektu rizika. Vertinimas pagristas ivairiais daugiatikslio vertinimo metodais. Rizikos vertinimo rodikliai atrenkami, atsižvelgiant i suinteresuotu šaliu interesus, tikslus ir veiksnius, kurie turi itakos statybos proceso efektyvumui ir nekilnojamojo turto vertes didinimui. Projektai surikiuoti pagal naudinguma, nustatyti santykiniai ju optimalumo dydžiai. Uždavinio modeliui aprašyti ir jam išspresti taikomi TOPSIS grey ir COPRAS‐G metodai. Projektu savybes aprašomos efektyvumo rodikliu reikšmemis, apibrežiamomis intervaluose. Straipsnyje aprašomas taikomas modelis, atlikta uždavinio analize ir pateikiamos trumpos išvados.

Architecture & Planning• 2010• Taylor & Francis
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Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging

The rapid development of abnormal brain cells that characterizes a brain tumor is a major health risk for adults since it can cause severe impairment of organ function and even death. These tumors come in a wide variety of sizes, textures, and locations. When trying to locate cancerous tumors, magnetic resonance imaging (MRI) is a crucial tool. However, detecting brain tumors manually is a difficult and time-consuming activity that might lead to inaccuracies. In order to solve this, we provide a refined You Only Look Once version 7 (YOLOv7) model for the accurate detection of meningioma, glioma, and pituitary gland tumors within an improved detection of brain tumors system. The visual representation of the MRI scans is enhanced by the use of image enhancement methods that apply different filters to the original pictures. To further improve the training of our proposed model, we apply data augmentation techniques to the openly accessible brain tumor dataset. The curated data include a wide variety of cases, such as 2548 images of gliomas, 2658 images of pituitary, 2582 images of meningioma, and 2500 images of non-tumors. We included the Convolutional Block Attention Module (CBAM) attention mechanism into YOLOv7 to further enhance its feature extraction capabilities, allowing for better emphasis on salient regions linked with brain malignancies. To further improve the model's sensitivity, we have added a Spatial Pyramid Pooling Fast+ (SPPF+) layer to the network's core infrastructure. YOLOv7 now includes decoupled heads, which allow it to efficiently glean useful insights from a wide variety of data. In addition, a Bi-directional Feature Pyramid Network (BiFPN) is used to speed up multi-scale feature fusion and to better collect features associated with tumors. The outcomes verify the efficiency of our suggested method, which achieves a higher overall accuracy in tumor detection than previous state-of-the-art models. As a result, this framework has a lot of potential as a helpful decision-making tool for experts in the field of diagnosing brain tumors.

Architecture & Planning• 2023• Multidisciplinary Digital Publishing Institute
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Sensitivity and Responses of Chloroplasts to Heat Stress in Plants

Increased temperatures caused by global warming threaten agricultural production, as warmer conditions can inhibit plant growth and development or even destroy crops in extreme circumstances. Extensive research over the past several decades has revealed that chloroplasts, the photosynthetic organelles of plants, are highly sensitive to heat stress, which affects a variety of photosynthetic processes including chlorophyll biosynthesis, photochemical reactions, electron transport and CO2 assimilation. Important mechanisms by which plant cells respond to heat stress to protect these photosynthetic organelles have been identified and analyzed. More recent studies have made it clear that chloroplasts play an important role in inducing the expression of nuclear heat-response genes during the heat stress response. In this review, we summarize these important advances in plant-based research and discuss how the sensitivity, responses and signaling roles of chloroplasts contribute to plant heat sensitivity and tolerance.

Architecture & Planning• 2020• Frontiers Media
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Human Behavior Analysis by Means of Multimodal Context Mining

There is sufficient evidence proving the impact that negative lifestyle choices have on people's health and wellness. Changing unhealthy behaviours requires raising people's self-awareness and also providing healthcare experts with a thorough and continuous description of the user's conduct. Several monitoring techniques have been proposed in the past to track users' behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user's context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels.

Architecture & Planning• 2016• Multidisciplinary Digital Publishing Institute
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Disease Resistance Mechanisms in Plants

Plants have developed a complex defense system against diverse pests and pathogens. Once pathogens overcome mechanical barriers to infection, plant receptors initiate signaling pathways driving the expression of defense response genes. Plant immune systems rely on their ability to recognize enemy molecules, carry out signal transduction, and respond defensively through pathways involving many genes and their products. Pathogens actively attempt to evade and interfere with response pathways, selecting for a decentralized, multicomponent immune system. Recent advances in molecular techniques have greatly expanded our understanding of plant immunity, largely driven by potential application to agricultural systems. Here, we review the major plant immune system components, state of the art knowledge, and future direction of research on plant⁻pathogen interactions. In our review, we will discuss how the decentralization of plant immune systems have provided both increased evolutionary opportunity for pathogen resistance, as well as additional mechanisms for pathogen inhibition of such defense responses. We conclude that the rapid advances in bioinformatics and molecular biology are driving an explosion of information that will advance agricultural production and illustrate how complex molecular interactions evolve.

Architecture & Planning• 2018• Multidisciplinary Digital Publishing Institute
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A Review on Battery Market Trends, Second-Life Reuse, and Recycling

The rapid growth, demand, and production of batteries to meet various emerging applications, such as electric vehicles and energy storage systems, will result in waste and disposal problems in the next few years as these batteries reach end-of-life. Battery reuse and recycling are becoming urgent worldwide priorities to protect the environment and address the increasing need for critical metals. As a review article, this paper reveals the current global battery market and global battery waste status from which the main battery chemistry types and their management, including reuse and recycling status, are discussed. This review then presents details of the challenges, opportunities, and arguments on battery second-life and recycling. The recent research and industrial activities in the battery reuse domain are summarized to provide a landscape picture and valuable insight into battery reuse and recycling for industries, scientific research, and waste management.

Architecture & Planning• 2021• Multidisciplinary Digital Publishing Institute
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The Brian simulator

"Brian" is a simulator for spiking neural networks (http://www.briansimulator.org). The focus is on making the writing of simulation code as quick and easy as possible for the user, and on flexibility: new and non-standard models are no more difficult to define than standard ones. This allows scientists to spend more time on the details of their models, and less on their implementation. Neuron models are defined by writing differential equations in standard mathematical notation, facilitating scientific communication. Brian is written in the Python programming language, and uses vector-based computation to allow for efficient simulations. It is particularly useful for neuroscientific modelling at the systems level, and for teaching computational neuroscience.

Architecture & Planning• 2009• Frontiers Media
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