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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 & Sons, Ltd.

Architecture & Planning• 2014• Wiley
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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
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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
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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
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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
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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
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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
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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
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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
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Periodicals

Aluminum, a Friend or Foe of Higher Plants in Acid Soils

Aluminum (Al) is the most abundant metal in the earth's crust, but its availability depends on soil pH. Despite this abundance, Al is not considered an essential element and so far no experimental evidence has been put forward for a biological role. In plants and other organisms, Al can have a beneficial or toxic effect, depending on factors such as, metal concentration, the chemical form of Al, growth conditions and plant species. Here we review recent advances in the study of Al in plants at physiological, biochemical and molecular levels, focusing mainly on the beneficial effect of Al in plants (stimulation of root growth, increased nutrient uptake, the increase in enzyme activity, and others). In addition, we discuss the possible mechanisms involved in improving the growth of plants cultivated in soils with acid pH, as well as mechanisms of tolerance to the toxic effect of Al.

Architecture & Planning• 2017• Frontiers Media
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Periodicals

Travel and Subjective Well-Being: A Focus on Findings, Methods and Future Research Needs

Subjectively experienced well-being has recently attracted increased attention in transport and mobility studies. However, these studies are still in their infancy and many of the multifarious links between travel behaviour and well-being are still under-examined; most studies only focus on one aspect of this link (i.e. travel satisfaction). In this paper, we give an overview of studies concerning travel and well-being, focusing on results, methods and gaps in present research. We suggest that travel behaviour affects well-being through experiences during (destination-oriented) travel, activity participation enabled by travel, activities during (destination-oriented) travel, trips where travel is the activity and through potential travel (or motility). The majority of empirical studies to date have been based on hedonic views of well-being, where pleasure and satisfaction are seen as the ultimate goal in life. They have paid little attention to eudaimonic views of well-being, which emphasise the realisation of one's true potential, although this form of well-being can also be influenced by travel behaviour. We also argue that longer-term decisions, such as residential location choices, can affect well-being through travel. Travel options differ between different kinds of neighbourhoods, which can result in different levels of (feelings of) freedom and consequently different levels of subjective well-being. Since studies at present only show a subset of the travel behaviour–well-being interactions, we conclude the paper with an agenda for future research.

Architecture & Planning• 2013• Taylor & Francis
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Periodicals

Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting

Power grids are transforming into flexible, smart, and cooperative systems with greater dissemination of distributed energy resources, advanced metering infrastructure, and advanced communication technologies. Short-term electric load forecasting for individual residential customers plays a progressively crucial role in the operation and planning of future grids. Compared to the aggregated electrical load at the community level, the prediction of individual household electric loads is legitimately challenging because of the high uncertainty and volatility involved. Results from previous studies show that prediction using machine learning and deep learning models is far from accurate, and there is still room for improvement. We herein propose a deep learning framework based on a combination of a convolutional neural network (CNN) and long short-term memory (LSTM). The proposed hybrid CNN-LSTM model uses CNN layers for feature extraction from the input data with LSTM layers for sequence learning. The performance of our developed framework is comprehensively compared to state-of-the-art systems currently in use for short-term individual household electric load forecasting. The proposed model achieved significantly better results compared to other competing techniques. We evaluated our proposed model with the recently explored LSTM-based deep learning model on a publicly available electrical load data of individual household customers from the Smart Grid Smart City (SGSC) project. We obtained an average mean absolute percentage error (MAPE) of 40.38% for individual household electric load forecasts in comparison with the LSTM-based model that obtained an average MAPE of 44.06%. Furthermore, we evaluated the effectiveness of the proposed model on different time horizons (up to 3 h ahead). Compared to the recently developed LSTM-based model tested on the same dataset, we obtained 4.01%, 4.76%, and 5.98% improvement for one, two, and six look-forward time steps, respectively (with 2 lookback time steps). Additionally, we have performed clustering analysis based on the power consumption behavior of the energy users, which indicate that prediction accuracy could be improved by grouping and training the representative model using large amount of data. The results indicated that the proposed model outperforms the LSTM-based model for both 1 h ahead and 3 h ahead in forecasting individual household electric loads.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Periodicals

Improving Nitrogen Use Efficiency in Crops for Sustainable Agriculture

In this review, we present the recent developments and future prospects of improving nitrogen use efficiency (NUE) in crops using various complementary approaches. These include conventional breeding and molecular genetics, in addition to alternative farming techniques based on no-till continuous cover cropping cultures and/or organic nitrogen (N) nutrition. Whatever the mode of N fertilization, an increased knowledge of the mechanisms controlling plant N economy is essential for improving NUE and for reducing excessive input of fertilizers, while maintaining an acceptable yield and sufficient profit margin for the farmers. Using plants grown under agronomic conditions, with different tillage conditions, in pure or associated cultures, at low and high N mineral fertilizer input, or using organic fertilization, it is now possible to develop further whole plant agronomic and physiological studies. These can be combined with gene, protein and metabolite profiling to build up a comprehensive picture depicting the different steps of N uptake, assimilation and recycling to produce either biomass in vegetative organs or proteins in storage organs. We provide a critical overview as to how our understanding of the agro-ecophysiological, physiological and molecular controls of N assimilation in crops, under varying environmental conditions, has been improved. We have used combined approaches, based on agronomic studies, whole plant physiology, quantitative genetics, forward and reverse genetics and the emerging systems biology. Long-term sustainability may require a gradual transition from synthetic N inputs to legume-based crop rotation, including continuous cover cropping systems, where these may be possible in certain areas of the world, depending on climatic conditions. Current knowledge and prospects for future agronomic development and application for breeding crops adapted to lower mineral fertilizer input and to alternative farming techniques are explored, whilst taking into account the constraints of both the current world economic situation and the environment.

Architecture & Planning• 2011• Multidisciplinary Digital Publishing Institute
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Periodicals

Brian: a simulator for spiking neural networks in Python

"Brian" is a new simulator for spiking neural networks, written in Python (http://brian. di.ens.fr). It is an intuitive and highly flexible tool for rapidly developing new models, especially networks of single-compartment neurons. In addition to using standard types of neuron models, users can define models by writing arbitrary differential equations in ordinary mathematical notation. Python scientific libraries can also be used for defining models and analysing data. Vectorisation techniques allow efficient simulations despite the overheads of an interpreted language. Brian will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations. With its easy and intuitive syntax, Brian is also very well suited for teaching computational neuroscience.

Architecture & Planning• 2008• Frontiers Media
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Periodicals

The Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric and oceanic flows: model formulation, recent developments, and future perspectives

Abstract. In this paper we present the current version of the Parallelized Large-Eddy Simulation Model (PALM) whose core has been developed at the Institute of Meteorology and Climatology at Leibniz Universität Hannover (Germany). PALM is a Fortran 95-based code with some Fortran 2003 extensions and has been applied for the simulation of a variety of atmospheric and oceanic boundary layers for more than 15 years. PALM is optimized for use on massively parallel computer architectures and was recently ported to general-purpose graphics processing units. In the present paper we give a detailed description of the current version of the model and its features, such as an embedded Lagrangian cloud model and the possibility to use Cartesian topography. Moreover, we discuss recent model developments and future perspectives for LES applications.

Architecture & Planning• 2015• Copernicus Publications
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Periodicals

Applications of artificial neural networks in health care organizational decision-making: A scoping review

Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. Applications of ANN to diagnosis are well-known; however, ANN are increasingly used to inform health care management decisions. We provide a seminal review of the applications of ANN to health care organizational decision-making. We screened 3,397 articles from six databases with coverage of Health Administration, Computer Science and Business Administration. We extracted study characteristics, aim, methodology and context (including level of analysis) from 80 articles meeting inclusion criteria. Articles were published from 1997-2018 and originated from 24 countries, with a plurality of papers (26 articles) published by authors from the United States. Types of ANN used included ANN (36 articles), feed-forward networks (25 articles), or hybrid models (23 articles); reported accuracy varied from 50% to 100%. The majority of ANN informed decision-making at the micro level (61 articles), between patients and health care providers. Fewer ANN were deployed for intra-organizational (meso- level, 29 articles) and system, policy or inter-organizational (macro- level, 10 articles) decision-making. Our review identifies key characteristics and drivers for market uptake of ANN for health care organizational decision-making to guide further adoption of this technique.

Architecture & Planning• 2019• Public Library of Science
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Periodicals

Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs

Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge in oncology. Here, we proposed molecular pathway-based algorithm for scoring of target drugs using high throughput mutation data to personalize their clinical efficacies. This algorithm was validated on 3,800 exome mutation profiles from The Cancer Genome Atlas (TCGA) project for 128 target drugs. The output values termed Mutational Drug Scores (MDS) showed positive correlation with the published drug efficiencies in clinical trials. We also used MDS approach to simulate all known protein coding genes as the putative drug targets. The model used was built on the basis of 18,273 mutation profiles from COSMIC database for eight cancer types. We found that the MDS algorithm-predicted hits frequently coincide with those already used as targets of the existing cancer drugs, but several novel candidates can be considered promising for further developments. Our results evidence that the MDS is applicable to ranking of anticancer drugs and can be applied for the identification of novel molecular targets.

Architecture & Planning• 2019• Frontiers Media
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The evolutionary neuroscience of musical beat perception: the Action Simulation for Auditory Prediction (ASAP) hypothesis

EVERY HUMAN CULTURE HAS SOME FORM OF MUSIC WITH A BEAT: a perceived periodic pulse that structures the perception of musical rhythm and which serves as a framework for synchronized movement to music. What are the neural mechanisms of musical beat perception, and how did they evolve? One view, which dates back to Darwin and implicitly informs some current models of beat perception, is that the relevant neural mechanisms are relatively general and are widespread among animal species. On the basis of recent neural and cross-species data on musical beat processing, this paper argues for a different view. Here we argue that beat perception is a complex brain function involving temporally-precise communication between auditory regions and motor planning regions of the cortex (even in the absence of overt movement). More specifically, we propose that simulation of periodic movement in motor planning regions provides a neural signal that helps the auditory system predict the timing of upcoming beats. This "action simulation for auditory prediction" (ASAP) hypothesis leads to testable predictions. We further suggest that ASAP relies on dorsal auditory pathway connections between auditory regions and motor planning regions via the parietal cortex, and suggest that these connections may be stronger in humans than in non-human primates due to the evolution of vocal learning in our lineage. This suggestion motivates cross-species research to determine which species are capable of human-like beat perception, i.e., beat perception that involves accurate temporal prediction of beat times across a fairly broad range of tempi.

Architecture & Planning• 2014• Frontiers Media
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Operations for Learning with Graphical Models

This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, andthe manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximizationalgorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented. The main original contributions here are the decompositiontechniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.

Architecture & Planning• 1994• AI Access Foundation
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Servitization of the manufacturing firm

Purpose: This paper aims to explore practices and technologies successfully servitised manufacturers employ in the delivery of advanced services. Design/methodology/approach: A case study methodology is applied across four manufacturing organisations successful in servitization. Through interviews with personnel across host manufacturers, their partners, and key customers, extensive data are collected about service delivery systems. Analyses identify convergence in their practices and technologies. Findings: Six distinct technologies and practices are revealed: facilities and their location, micro-vertical integration and supplier relationships, information and communication technologies (ICTs), performance measurement and value demonstration, people deployment and their skills, and business processes and customer relationships. These are then combined in an integrative framework that illustrates how operations are configured to successfully deliver advanced services. Research limitations/implications: The analyses are reductive and rationalising. Future studies could identify other technologies and practices. Case study as a method is inherently limited in the extent to which findings can be generalised. Practical implications: Awareness and interest in servitization is growing, yet adoption of a servitization strategy requires particular organisational capabilities on the part of the manufacturer. This study identifies technologies and practices that underpin these capabilities. Originality/value: This paper contributes to the understanding of the servitization process and, in particular, the implications to broader operations of the firm.

Architecture & Planning• 2013• Emerald Publishing Limited
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Cervical cancer in low and middle‑income countries (Review)

Cervical cancer is a malignant tumour that occurs in the cervix and is classified into two histological types, adenocarcinoma and squamous cell carcinoma (SCC); SCC is more common and accounts for 70% of all cases. In 2018 there were ~569,000 new cases of cervical cancer diagnosed worldwide and ~311,000 deaths were attributed to cervical cancer. Of these, between 84 and 90% occurred in low- and middle-income countries (LMICs) such as South Africa, India, China and Brazil. The most common cause of cervical cancer is persistent infection caused by the sexually transmitted human papilloma virus. Other factors that contribute to the incidence of cervical cancer include geography, traditional practices and beliefs, the screening levels, socioeconomic status, healthcare access, public awareness, use of oral contraceptives, smoking and co-infection with HIV. An estimated 11 million women from LMICs will be diagnosed with cervical cancer in the next 10-20 years. The aim of this review was to explore various types of genetic and epigenetic factors that influence the development, progression or suppression of cervical cancer.

Architecture & Planning• 2020• Spandidos Publishing
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Antibiotic development challenges: the various mechanisms of action of antimicrobial peptides and of bacterial resistance

Antimicrobial peptides (AMPs) are natural antibiotics produced by various organisms such as mammals, arthropods, plants, and bacteria. In addition to antimicrobial activity, AMPs can induce chemokine production, accelerate angiogenesis, and wound healing and modulate apoptosis in multicellular organisms. Originally, their antimicrobial mechanism of action was thought to consist solely of an increase in pathogen cell membrane permeability, but it has already been shown that several AMPs do not modulate membrane permeability in the minimal lethal concentration. Instead, they exert their effects by inhibiting processes such as protein and cell wall synthesis, as well as enzyme activity, among others. Although resistance to these molecules is uncommon several pathogens developed different strategies to overcome AMPs killing such as surface modification, expression of efflux pumps, and secretion of proteases among others. This review describes the various mechanisms of action of AMPs and how pathogens evolve resistance to them.

Architecture & Planning• 2013• Frontiers Media
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