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

Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey

Cutting-edge technologies, with a special emphasis on the Internet of Things (IoT), tend to operate as game changers, generating enormous alterations in both traditional and modern enterprises. Understanding multiple uses of IoT has become vital for effective financial management, given the ever-changing nature of organizations and the technological disruptions that come with this paradigm change. IoT has proven to be a powerful tool for improving operational efficiency, decision-making processes, overall productivity, and data management. As a result of the continuously expanding data volume, there is an increasing demand for a robust IT system capable of adeptly handling all enterprise processes. Consequently, businesses must develop suitable IoT architectures that can efficiently address these continually evolving requirements. This research adopts an incremental explanatory approach, guided by the principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). A rigorous examination of 84 research papers has allowed us to delve deeply into the current landscape of IoT research. This research aims to provide a complete and cohesive overview of the existing body of knowledge on IoT. This is accomplished by combining a rigorous empirical approach to categorization with ideas from specialized literature in the IoT sector. This study actively contributes to the ongoing conversation around IoT by recognizing and critically examining current difficulties. This, consequently, opens new research possibilities and promotes future developments in this ever-changing sector.

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

Applications of Blockchain Technology in Medicine and Healthcare: Challenges and Future Perspectives

Blockchain technology has gained considerable attention, with an escalating interest in a plethora of numerous applications, ranging from data management, financial services, cyber security, IoT, and food science to healthcare industry and brain research. There has been a remarkable interest witnessed in utilizing applications of blockchain for the delivery of safe and secure healthcare data management. Also, blockchain is reforming the traditional healthcare practices to a more reliable means, in terms of effective diagnosis and treatment through safe and secure data sharing. In the future, blockchain could be a technology that may potentially help in personalized, authentic, and secure healthcare by merging the entire real-time clinical data of a patient’s health and presenting it in an up-to-date secure healthcare setup. In this paper, we review both the existing and latest developments in the field of healthcare by implementing blockchain as a model. We also discuss the applications of blockchain, along with the challenges faced and future perspectives.

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

Enhancing Streamflow Forecast and Extracting Insights Using Long‐Short Term Memory Networks With Data Integration at Continental Scales

Abstract Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short‐term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental‐scale median Nash‐Sutcliffe Efficiency coefficient value of 0.86. Integrating moving‐average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow‐dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation‐energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater‐dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high‐aridity basins with 1‐day flash peaks. Despite this limitation, there is much promise for a deep‐learning‐based forecast paradigm due to its performance, automation, efficiency, and flexibility.

Architecture & Planning• 2020• Wiley
Access Full TextView Details
Periodicals

Neurocognitive impairment in drug-free patients with major depressive disorder

BACKGROUND: Although neurocognitive impairment has been widely reported in major depressive disorder (MDD), confounding factors, such as the effects of psychotropic medication, have rarely been controlled for. AIMS: To examine neurocognitive function in medication-free patients with MDD and healthy controls. METHOD: Forty-four patients meeting DSM-IV criteria for MDD, all psychotropic-medication-free for at least 6 weeks, and 44 demographically matched, healthy comparison subjects completed a comprehensive neurocognitive battery. RESULTS: Patients with depression were impaired significantly in a range of cognitive domains, including attention and executive function and visuospatial learning and memory, compared with controls. Motor and psychomotor functions were intact. Severity of depression correlated with learning and memory performance, but not executive function. CONCLUSIONS: Pronounced neurocognitive impairment was found in this sample of young adult out-patients with MDD. This is not attributable to the confounding effects of psychotropic medication and could therefore provide an objective marker of brain dysfunction in depression.

Architecture & Planning• 2003• Cambridge University Press
Access Full TextView Details
Periodicals

Human Mobility Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic

After the emergence of the H1N1 influenza in 2009, some countries responded with travel-related controls during the early stage of the outbreak in an attempt to contain or slow down its international spread. These controls along with self-imposed travel limitations contributed to a decline of about 40% in international air traffic to/from Mexico following the international alert. However, no containment was achieved by such restrictions and the virus was able to reach pandemic proportions in a short time. When gauging the value and efficacy of mobility and travel restrictions it is crucial to rely on epidemic models that integrate the wide range of features characterizing human mobility and the many options available to public health organizations for responding to a pandemic. Here we present a comprehensive computational and theoretical study of the role of travel restrictions in halting and delaying pandemics by using a model that explicitly integrates air travel and short-range mobility data with high-resolution demographic data across the world and that is validated by the accumulation of data from the 2009 H1N1 pandemic. We explore alternative scenarios for the 2009 H1N1 pandemic by assessing the potential impact of mobility restrictions that vary with respect to their magnitude and their position in the pandemic timeline. We provide a quantitative discussion of the delay obtained by different mobility restrictions and the likelihood of containing outbreaks of infectious diseases at their source, confirming the limited value and feasibility of international travel restrictions. These results are rationalized in the theoretical framework characterizing the invasion dynamics of the epidemics at the metapopulation level.

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

Oscillatory Control of Factors Determining Multipotency and Fate in Mouse Neural Progenitors

The basic helix-loop-helix transcription factors Ascl1/Mash1, Hes1, and Olig2 regulate fate choice of neurons, astrocytes, and oligodendrocytes, respectively. These same factors are coexpressed by neural progenitor cells. Here, we found by time-lapse imaging that these factors are expressed in an oscillatory manner by mouse neural progenitor cells. In each differentiation lineage, one of the factors becomes dominant. We used optogenetics to control expression of Ascl1 and found that, although sustained Ascl1 expression promotes neuronal fate determination, oscillatory Ascl1 expression maintains proliferating neural progenitor cells. Thus, the multipotent state correlates with oscillatory expression of several fate-determination factors, whereas the differentiated state correlates with sustained expression of a single factor.

Architecture & Planning• 2013• American Association for the Advancement of Science
Access Full TextView Details
Periodicals

Sustainable Management of Digital Transformation in Higher Education: Global Research Trends

Digital transformation in the education sector has implied the involvement of sustainable management, in order to adapt to the changes imposed by new technologies. Trends in global research on this topic have been analyzed and studied, during the 1986–2019 period. To achieve this purpose, a bibliometric study of 1590 articles from the Scopus database has been applied. The results provided data on the scientific productivity of authors, journals, institutions, and countries that contribute to the development of this research area. The evidence reveals an exponential trend, with special interest in the last five years. The main categories are Social Sciences and Environmental Science. The most productive journal is Sustainability. The author with more articles is Mulder, from The Hague University of Applied Sciences. The most productive institution is Delft University of Technology. The USA is the country with the most academic publications and international collaborations in its studies. The main keywords used in the articles are “sustainability”, “sustainable development”, “higher education”, “innovation”, “technology”, “environmental technology”, “technological development”, and “environmental management”. Global research has followed a growing trend, with optimal publication levels in recent years.

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

Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis

<ns4:p> <ns4:italic>Background:</ns4:italic> Given the demonstrated utility of Third Generation Sequencing [Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT)] long reads in many studies, a comprehensive analysis and comparison of their data quality and applications is in high demand. <ns4:italic>Methods:</ns4:italic> Based on the transcriptome sequencing data from human embryonic stem cells, we analyzed multiple data features of PacBio and ONT, including error pattern, length, mappability and technical improvements over previous platforms. We also evaluated their application to transcriptome analyses, such as isoform identification and quantification and characterization of transcriptome complexity, by comparing the performance of size-selected PacBio, non-size-selected ONT and their corresponding Hybrid-Seq strategies (PacBio+Illumina and ONT+Illumina). <ns4:italic>Results:</ns4:italic> PacBio shows overall better data quality, while ONT provides a higher yield. As with data quality, PacBio performs marginally better than ONT in most aspects for both long reads only and Hybrid-Seq strategies in transcriptome analysis. In addition, Hybrid-Seq shows superior performance over long reads only in most transcriptome analyses. <ns4:italic>Conclusions:</ns4:italic> Both PacBio and ONT sequencing are suitable for full-length single-molecule transcriptome analysis. As this first use of ONT reads in a Hybrid-Seq analysis has shown, both PacBio and ONT can benefit from a combined Illumina strategy. The tools and analytical methods developed here provide a resource for future applications and evaluations of these rapidly-changing technologies. </ns4:p>

Architecture & Planning• 2017• Faculty of 1000
Access Full TextView Details
Periodicals

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

In recent years, due to the wide utilization of direct current (DC) power sources, such as solar photovoltaic (PV), fuel cells, different DC loads, high-level integration of different energy storage systems such as batteries, supercapacitors, DC microgrids have been gaining more importance. Furthermore, unlike conventional AC systems, DC microgrids do not have issues such as synchronization, harmonics, reactive power control, and frequency control. However, the incorporation of different distributed generators, such as PV, wind, fuel cell, loads, and energy storage devices in the common DC bus complicates the control of DC bus voltage as well as the power-sharing. In order to ensure the secure and safe operation of DC microgrids, different control techniques, such as centralized, decentralized, distributed, multilevel, and hierarchical control, are presented. The optimal planning of DC microgrids has an impact on operation and control algorithms; thus, coordination among them is required. A detailed review of the planning, operation, and control of DC microgrids is missing in the existing literature. Thus, this article documents developments in the planning, operation, and control of DC microgrids covered in research in the past 15 years. DC microgrid planning, operation, and control challenges and opportunities are discussed. Different planning, control, and operation methods are well documented with their advantages and disadvantages to provide an excellent foundation for industry personnel and researchers. Power-sharing and energy management operation, control, and planning issues are summarized for both grid-connected and islanded DC microgrids. Also, key research areas in DC microgrid planning, operation, and control are identified to adopt cutting-edge technologies. This review explicitly helps readers understand existing developments on DC microgrid planning, operation, and control as well as identify the need for additional research in order to further contribute to the topic.

Architecture & Planning• 2021• Institute of Electrical and Electronics Engineers
Access Full TextView Details
Periodicals

A Market-Oriented Programming Environment and its Application to Distributed Multicommodity Flow Problems

Market price systems constitute a well-understood class of mechanisms that under certain conditions provide effective decentralization of decision making with minimal communication overhead. In a market-oriented programming approach to distributed problem solving, we derive the activities and resource allocations for a set of computational agents by computing the competitive equilibrium of an artificial economy. WALRAS provides basic constructs for defining computational market structures, and protocols for deriving their corresponding price equilibria. In a particular realization of this approach for a form of multicommodity flow problem, we see that careful construction of the decision process according to economic principles can lead to efficient distributed resource allocation, and that the behavior of the system can be meaningfully analyzed in economic terms.

Architecture & Planning• 1993• AI Access Foundation
Access Full TextView Details
Periodicals

Recent Advances in Biomaterials for 3D Printing and Tissue Engineering

Three-dimensional printing has significant potential as a fabrication method in creating scaffolds for tissue engineering. The applications of 3D printing in the field of regenerative medicine and tissue engineering are limited by the variety of biomaterials that can be used in this technology. Many researchers have developed novel biomaterials and compositions to enable their use in 3D printing methods. The advantages of fabricating scaffolds using 3D printing are numerous, including the ability to create complex geometries, porosities, co-culture of multiple cells, and incorporate growth factors. In this review, recently-developed biomaterials for different tissues are discussed. Biomaterials used in 3D printing are categorized into ceramics, polymers, and composites. Due to the nature of 3D printing methods, most of the ceramics are combined with polymers to enhance their printability. Polymer-based biomaterials are 3D printed mostly using extrusion-based printing and have a broader range of applications in regenerative medicine. The goal of tissue engineering is to fabricate functional and viable organs and, to achieve this, multiple biomaterials and fabrication methods need to be researched.

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

AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy

PURPOSE: Radiation therapy (RT) is a common treatment option for head and neck (HaN) cancer. An important step involved in RT planning is the delineation of organs-at-risks (OARs) based on HaN computed tomography (CT). However, manually delineating OARs is time-consuming as each slice of CT images needs to be individually examined and a typical CT consists of hundreds of slices. Automating OARs segmentation has the benefit of both reducing the time and improving the quality of RT planning. Existing anatomy autosegmentation algorithms use primarily atlas-based methods, which require sophisticated atlas creation and cannot adequately account for anatomy variations among patients. In this work, we propose an end-to-end, atlas-free three-dimensional (3D) convolutional deep learning framework for fast and fully automated whole-volume HaN anatomy segmentation. METHODS: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot. AnatomyNet is built upon the popular 3D U-net architecture, but extends it in three important ways: (a) a new encoding scheme to allow autosegmentation on whole-volume CT images instead of local patches or subsets of slices, (b) incorporating 3D squeeze-and-excitation residual blocks in encoding layers for better feature representation, and (c) a new loss function combining Dice scores and focal loss to facilitate the training of the neural model. These features are designed to address two main challenges in deep learning-based HaN segmentation: (a) segmenting small anatomies (i.e., optic chiasm and optic nerves) occupying only a few slices, and (b) training with inconsistent data annotations with missing ground truth for some anatomical structures. RESULTS: We collected 261 HaN CT images to train AnatomyNet and used MICCAI Head and Neck Auto Segmentation Challenge 2015 as a benchmark dataset to evaluate the performance of AnatomyNet. The objective is to segment nine anatomies: brain stem, chiasm, mandible, optic nerve left, optic nerve right, parotid gland left, parotid gland right, submandibular gland left, and submandibular gland right. Compared to previous state-of-the-art results from the MICCAI 2015 competition, AnatomyNet increases Dice similarity coefficient by 3.3% on average. AnatomyNet takes about 0.12 s to fully segment a head and neck CT image of dimension 178 × 302 × 225, significantly faster than previous methods. In addition, the model is able to process whole-volume CT images and delineate all OARs in one pass, requiring little pre- or postprocessing. CONCLUSION: Deep learning models offer a feasible solution to the problem of delineating OARs from CT images. We demonstrate that our proposed model can improve segmentation accuracy and simplify the autosegmentation pipeline. With this method, it is possible to delineate OARs of a head and neck CT within a fraction of a second.

Architecture & Planning• 2018• Wiley
Access Full TextView Details
Periodicals

How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

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

Optimization‐based autonomous racing of 1:43 scale RC cars

Summary This paper describes autonomous racing of RC race cars based on mathematical optimization. Using a dynamical model of the vehicle, control inputs are computed by receding horizon based controllers, where the objective is to maximize progress on the track subject to the requirement of staying on the track and avoiding opponents. Two different control formulations are presented. The first controller employs a two‐level structure, consisting of a path planner and a nonlinear model predictive controller (NMPC) for tracking. The second controller combines both tasks in one nonlinear optimization problem (NLP) following the ideas of contouring control. Linear time varying models obtained by linearization are used to build local approximations of the control NLPs in the form of convex quadratic programs (QPs) at each sampling time. The resulting QPs have a typical MPC structure and can be solved in the range of milliseconds by recent structure exploiting solvers, which is key to the real‐time feasibility of the overall control scheme. Obstacle avoidance is incorporated by means of a high‐level corridor planner based on dynamic programming, which generates convex constraints for the controllers according to the current position of opponents and the track layout. The control performance is investigated experimentally using 1:43 scale RC race cars, driven at speeds of more than 3 m/s and in operating regions with saturated rear tire forces (drifting). The algorithms run at 50 Hz sampling rate on embedded computing platforms, demonstrating the real‐time feasibility and high performance of optimization‐based approaches for autonomous racing. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Architecture & Planning• 2014• Wiley
Access Full TextView Details
Periodicals

Commercial Applications of Metal Foams: Their Properties and Production

This work gives an overview of the production, properties and industrial applications of metal foams. First, it classifies the most relevant manufacturing routes and methods. Then, it reviews the most important properties, with special interest in the mechanical and functional aspects, but also taking into account costs and feasibility considerations. These properties are the motivation and basis of related applications. Finally, a summary of the most relevant applications showing a large number of actual examples is presented. Concluding, we can forecast a slow, but continuous growth of this industrial sector.

Architecture & Planning• 2016• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

Raising Awareness on Solid Waste Management through Formal Education for Sustainability: A Developing Countries Evidence Review

Solid Waste Management (SWM) is a multifaceted problem comprising political, socioeconomic, institutional, and environmental aspects. Due to exponential urban growth, it has become one of the most significant issues faced by urban spaces in developing countries. The gap in environmental knowledge among the youth and the old within developing countries contribute to ecological issues or waste management problems, resulting in unsustainable development, with important consequences in low-income countries. For that matter, a systematic review was conducted aiming to identify and analyse environmental knowledge, awareness, attitudes, and practice studies on SWM from 2010 to 2019 in developing countries. The evidence suggests that students at both secondary and tertiary levels have positive environmental attitudes, and high awareness of environmental issues, but there is a lack of practical education of teachers to guide students to put SWM into practice. Student’s low environmental knowledge is related to a deficiency in teachers’ practical experience in SWM for environmental sustainability. A relationship between teachers’ and students’ knowledge and attitudes towards SWM, as well as differences in awareness, attitude, and practices of SWM linked with education and age, were also found. This review also revealed that the lack of environmental education in most developing countries is caused by fragilities in practical environmental curricula of teachers to respond to modern-day environmental issues for sustainable development and cleaner production (CP). To bridge the knowledge gap between the youth and older people in SWM, environmental sustainability education should be integrated into schools at all levels within developing countries.

Architecture & Planning• 2021• Multidisciplinary Digital Publishing Institute
Access Full TextView Details
Periodicals

Over my fake body: body ownership illusions for studying the multisensory basis of own-body perception

Which is my body and how do I distinguish it from the bodies of others, or from objects in the surrounding environment? The perception of our own body and more particularly our sense of body ownership is taken for granted. Nevertheless, experimental findings from body ownership illusions (BOIs), show that under specific multisensory conditions, we can experience artificial body parts or fake bodies as our own body parts or body, respectively. The aim of the present paper is to discuss how and why BOIs are induced. We review several experimental findings concerning the spatial, temporal, and semantic principles of crossmodal stimuli that have been applied to induce BOIs. On the basis of these principles, we discuss theoretical approaches concerning the underlying mechanism of BOIs. We propose a conceptualization based on Bayesian causal inference for addressing how our nervous system could infer whether an object belongs to our own body, using multisensory, sensorimotor, and semantic information, and we discuss how this can account for several experimental findings. Finally, we point to neural network models as an implementational framework within which the computational problem behind BOIs could be addressed in the future.

Architecture & Planning• 2015• Frontiers Media
Access Full TextView Details
Periodicals

A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations

The commercial market for technologies to monitor and improve personal health and sports performance is ever expanding. A wide range of smart watches, bands, garments, and patches with embedded sensors, small portable devices and mobile applications now exist to record and provide users with feedback on many different physical performance variables. These variables include cardiorespiratory function, movement patterns, sweat analysis, tissue oxygenation, sleep, emotional state, and changes in cognitive function following concussion. In this review, we have summarized the features and evaluated the characteristics of a cross-section of technologies for health and sports performance according to what the technology is claimed to do, whether it has been validated and is reliable, and if it is suitable for general consumer use. Consumers who are choosing new technology should consider whether it (1) produces desirable (or non-desirable) outcomes, (2) has been developed based on real-world need, and (3) has been tested and proven effective in applied studies in different settings. Among the technologies included in this review, more than half have not been validated through independent research. Only 5% of the technologies have been formally validated. Around 10% of technologies have been developed for and used in research. The value of such technologies for consumer use is debatable, however, because they may require extra time to set up and interpret the data they produce. Looking to the future, the rapidly expanding market of health and sports performance technology has much to offer consumers. To create a competitive advantage, companies producing health and performance technologies should consult with consumers to identify real-world need, and invest in research to prove the effectiveness of their products. To get the best value, consumers should carefully select such products, not only based on their personal needs, but also according to the strength of supporting evidence and effectiveness of the products.

Architecture & Planning• 2018• Frontiers Media
Access Full TextView Details
Periodicals

The Framework for 0-D Atmospheric Modeling (F0AM) v3.1

Abstract. The Framework for 0-D Atmospheric Modeling (F0AM) is a flexible and user-friendly MATLAB-based platform for simulation of atmospheric chemistry systems. The F0AM interface incorporates front-end configuration of observational constraints and model setups, making it readily adaptable to simulation of photochemical chambers, Lagrangian plumes, and steady-state or time-evolving solar cycles. Six different chemical mechanisms and three options for calculation of photolysis frequencies are currently available. Example simulations are presented to illustrate model capabilities and, more generally, highlight some of the advantages and challenges of 0-D box modeling.

Architecture & Planning• 2016• Copernicus Publications
Access Full TextView Details
Periodicals

S<scp>iesta</scp>: Recent developments and applications

A review of the present status, recent enhancements, and applicability of the Siesta program is presented. Since its debut in the mid-1990s, Siesta's flexibility, efficiency, and free distribution have given advanced materials simulation capabilities to many groups worldwide. The core methodological scheme of Siesta combines finite-support pseudo-atomic orbitals as basis sets, norm-conserving pseudopotentials, and a real-space grid for the representation of charge density and potentials and the computation of their associated matrix elements. Here, we describe the more recent implementations on top of that core scheme, which include full spin-orbit interaction, non-repeated and multiple-contact ballistic electron transport, density functional theory (DFT)+U and hybrid functionals, time-dependent DFT, novel reduced-scaling solvers, density-functional perturbation theory, efficient van der Waals non-local density functionals, and enhanced molecular-dynamics options. In addition, a substantial effort has been made in enhancing interoperability and interfacing with other codes and utilities, such as wannier90 and the second-principles modeling it can be used for, an AiiDA plugin for workflow automatization, interface to Lua for steering Siesta runs, and various post-processing utilities. Siesta has also been engaged in the Electronic Structure Library effort from its inception, which has allowed the sharing of various low-level libraries, as well as data standards and support for them, particularly the PSeudopotential Markup Language definition and library for transferable pseudopotentials, and the interface to the ELectronic Structure Infrastructure library of solvers. Code sharing is made easier by the new open-source licensing model of the program. This review also presents examples of application of the capabilities of the code, as well as a view of on-going and future developments.

Architecture & Planning• 2020• American Institute of Physics
Access Full TextView Details
Periodicals

Fuzzy Multicriteria Decision-Making: A Literature Review

Multicriteria decision-making (MCDM) refers to making decisions in the presence of multiple and usually conflicting criteria. Fuzzy decision-making is used where vague and incomplete data exist for the solution. Fuzzy multicriteria decision-making is one of the most popular problems handled by the researchers in the literature. In this paper, we survey the latest status of fuzzy multicriteria decision-making methods and classify these methods dividing into two parts: fuzzy multiattribute decision-making (MADM) and fuzzy multiobjective decision-making (MODM). Most of the publications are on fuzzy MADM since there are a plenty of classical multiattribute decision-making methods in the literature. Tabular and graphical illustrations for each method are given.

Architecture & Planning• 2015• Springer Nature
Access Full TextView Details
Periodicals

On the Essential Contexts of Artifacts or on the Proposition That "Design Is Making Sense (Of Things)"

1) Part of this work was supported by the The etymology of design goes back to the Latin de + signare and design firm RichardsonSmith, Worthmeans making something, distinguishing it by a sign, giving it ington, Ohio, and Ohio State University, Columbus, while on sabbatical leave in significance, designating its relation to other things, owners, 1986-87 from the University of Pennsylusers, or gods. Based on this original meaning, one could say: vania, Philadelphia. design is making sense (of things).

Architecture & Planning• 1989• The MIT Press
Access Full TextView Details
Previous1...2223242526...52Next