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Periodicals

A Survey of LoRaWAN for IoT: From Technology to Application

LoRaWAN is one of the low power wide area network (LPWAN) technologies that have received significant attention by the research community in the recent years. It offers low-power, low-data rate communication over a wide range of covered area. In the past years, the number of publications regarding LoRa and LoRaWAN has grown tremendously. This paper provides an overview of research work that has been published from 2015 to September 2018 and that is accessible via Google Scholar and IEEE Explore databases. First, a detailed description of the technology is given, including existing security and reliability mechanisms. This literature overview is structured by categorizing papers according to the following topics: (i) physical layer aspects; (ii) network layer aspects; (iii) possible improvements; and (iv) extensions to the standard. Finally, a strengths, weaknesses, opportunities and threats (SWOT) analysis is presented along with the challenges that LoRa and LoRaWAN still face.

Architecture & Planning• 2018• Multidisciplinary Digital Publishing Institute
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The Prevalence of Depression, Anxiety and Stress and Their Associated Factors in College Students

AIM: To estimate the prevalence of symptoms of depression, anxiety, stress and associated factors in a population of college students. METHOD: Cross-sectional study of psychological distress measured through the Depression, Anxiety and Stress Scale (DASS-21) in a sample of 1074 college students. RESULTS: We found a moderate prevalence of depression (18.4%), anxiety (23.6%) and stress (34.5%) symptoms in our study population. Being <21, having problematic Internet use behavior, smoking, presenting insomnia and having a low self-esteem were independently associated with symptoms of depression, anxiety and stress. Being a woman, living with their family, having a stable partner, consuming alcohol frequently and having poor nutritional habits were significantly associated with symptoms of stress; lacking a stable partner was significantly associated with depressive symptoms; and frequent consumption of alcohol was significantly associated with symptoms of anxiety. CONCLUSION: We found a moderate prevalence of depression, anxiety and stress symptoms in our population. Interventions aimed at promoting mental health among college students should be implemented.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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Design of A Sustainable Building: A Conceptual Framework for Implementing Sustainability in the Building Sector

This paper presents a conceptual framework aimed at implementing sustainability principles in the building industry. The proposed framework based on the sustainable triple bottom line principle, includes resource conservation, cost efficiency and design for human adaptation. Following a thorough literature review, each principle involving strategies and methods to be applied during the life cycle of building projects is explained and a few case studies are presented for clarity on the methods. The framework will allow design teams to have an appropriate balance between economic, social and environmental issues, changing the way construction practitioners think about the information they use when assessing building projects, thereby facilitating the sustainability of building industry.

Architecture & Planning• 2012• Multidisciplinary Digital Publishing Institute
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Periodicals

The Role of Advanced Sensing in Smart Cities

In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. Advanced systems to improve and automate processes within a city will play a leading role in smart cities. From smart design of buildings, which capture rain water for later use, to intelligent control systems, which can monitor infrastructures autonomously, the possible improvements enabled by sensing technologies are immense. Ubiquitous sensing poses numerous challenges, which are of a technological or social nature. This paper presents an overview of the state of the art with regards to sensing in smart cities. Topics include sensing applications in smart cities, sensing platforms and technical challenges associated with these technologies. In an effort to provide a holistic view of how sensing technologies play a role in smart cities, a range of applications and technical challenges associated with these applications are discussed. As some of these applications and technologies belong to different disciplines, the material presented in this paper attempts to bridge these to provide a broad overview, which can be of help to researchers and developers in understanding how advanced sensing can play a role in smart cities.

Architecture & Planning• 2012• Multidisciplinary Digital Publishing Institute
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Periodicals

Management and Control of Domestic Smart Grid Technology

Emerging new technologies like distributed generation, distributed storage, and demand-side load management will change the way we consume and produce energy. These techniques enable the possibility to reduce the greenhouse effect and improve grid stability by optimizing energy streams. By smartly applying future energy production, consumption, and storage techniques, a more energy-efficient electricity supply chain can be achieved. In this paper a three-step control methodology is proposed to manage the cooperation between these technologies, focused on domestic energy streams. In this approach, (global) objectives like peak shaving or forming a virtual power plant can be achieved without harming the comfort of residents. As shown in this work, using good predictions, in advance planning and real-time control of domestic appliances, a better matching of demand and supply can be achieved.

Architecture & Planning• 2010• Institute of Electrical and Electronics Engineers
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Periodicals

A Review of Machine Learning and IoT in Smart Transportation

With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
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Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

Architecture & Planning• 2017• Multidisciplinary Digital Publishing Institute
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The multifaceted roles of antibiotics and antibiotic resistance in nature

Antibiotics are chemotherapeutic agents, which have been a very powerful tool in the clinical management of bacterial diseases since the 1940s. However, benefits offered by these magic bullets have been substantially lost in subsequent days following the widespread emergence and dissemination of antibiotic-resistant strains. While it is obvious that excessive and imprudent use of antibiotics significantly contributes to the emergence of resistant strains, antibiotic resistance is also observed in natural bacteria of remote places unlikely to be impacted by human intervention. Both antibiotic biosynthetic genes and resistance-conferring genes have been known to evolve billions of years ago, long before clinical use of antibiotics. Hence it appears that antibiotics and antibiotics resistance determinants have some other roles in nature, which often elude our attention because of overemphasis on the therapeutic importance of antibiotics and the crisis imposed by the antibiotic resistance in pathogens. In the natural milieu, antibiotics are often found to be present in sub-inhibitory concentrations acting as signaling molecules supporting the process of quorum sensing and biofilm formation. They also play an important role in the production of virulence factors and influence host-parasite interactions (e.g., phagocytosis, adherence to the target cell, and so on). The evolutionary and ecological aspects of antibiotics and antibiotic resistance in the naturally occurring microbial community are little understood. Therefore, the actual role of antibiotics in nature warrants in-depth investigations. Studies on such an intriguing behavior of the microorganisms promise insight into the intricacies of the microbial physiology and are likely to provide some lead in controlling the emergence and subsequent dissemination of antibiotic resistance. This article highlights some of the recent findings on the role of antibiotics and the genes that confer resistance to antibiotics in nature.

Architecture & Planning• 2013• Frontiers Media
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Periodicals

Human motion trajectory prediction: a survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots, and advanced surveillance systems. This article provides a survey of human motion trajectory prediction. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.

Architecture & Planning• 2020• SAGE Publishing
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Periodicals

Q-Learning Algorithms: A Comprehensive Classification and Applications

Q-learning is arguably one of the most applied representative reinforcement learning approaches and one of the off-policy strategies. Since the emergence of Q-learning, many studies have described its uses in reinforcement learning and artificial intelligence problems. However, there is an information gap as to how these powerful algorithms can be leveraged and incorporated into general artificial intelligence workflow. Early Q-learning algorithms were unsatisfactory in several aspects and covered a narrow range of applications. It has also been observed that sometimes, this rather powerful algorithm learns unrealistically and overestimates the action values hence abating the overall performance. Recently with the general advances of machine learning, more variants of Q-learning like Deep Q-learning which combines basic Q learning with deep neural networks have been discovered and applied extensively. In this paper, we thoroughly explain how Q-learning evolved by unraveling the mathematical complexities behind it as well its flow from reinforcement learning family of algorithms. Improved variants are fully described, and we categorize Q-learning algorithms into single-agent and multi-agent approaches. Finally, we thoroughly investigate up-to-date research trends and key applications that leverage Q-learning algorithms.

Architecture & Planning• 2019• Institute of Electrical and Electronics Engineers
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Periodicals

Cortico-Striatal-Thalamic Loop Circuits of the Salience Network: A Central Pathway in Psychiatric Disease and Treatment

The salience network (SN) plays a central role in cognitive control by integrating sensory input to guide attention, attend to motivationally salient stimuli and recruit appropriate functional brain-behavior networks to modulate behavior. Mounting evidence suggests that disturbances in SN function underlie abnormalities in cognitive control and may be a common etiology underlying many psychiatric disorders. Such functional and anatomical abnormalities have been recently apparent in studies and meta-analyses of psychiatric illness using functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM). Of particular importance, abnormal structure and function in major cortical nodes of the SN, the dorsal anterior cingulate cortex (dACC) and anterior insula (AI), have been observed as a common neurobiological substrate across a broad spectrum of psychiatric disorders. In addition to cortical nodes of the SN, the network's associated subcortical structures, including the dorsal striatum, mediodorsal thalamus and dopaminergic brainstem nuclei, comprise a discrete regulatory loop circuit. The SN's cortico-striato-thalamo-cortical loop increasingly appears to be central to mechanisms of cognitive control, as well as to a broad spectrum of psychiatric illnesses and their available treatments. Functional imbalances within the SN loop appear to impair cognitive control, and specifically may impair self-regulation of cognition, behavior and emotion, thereby leading to symptoms of psychiatric illness. Furthermore, treating such psychiatric illnesses using invasive or non-invasive brain stimulation techniques appears to modulate SN cortical-subcortical loop integrity, and these effects may be central to the therapeutic mechanisms of brain stimulation treatments in many psychiatric illnesses. Here, we review clinical and experimental evidence for abnormalities in SN cortico-striatal-thalamic loop circuits in major depression, substance use disorders (SUD), anxiety disorders, schizophrenia and eating disorders (ED). We also review emergent therapeutic evidence that novel invasive and non-invasive brain stimulation treatments may exert therapeutic effects by normalizing abnormalities in the SN loop, thereby restoring the capacity for cognitive control. Finally, we consider a series of promising directions for future investigations on the role of SN cortico-striatal-thalamic loop circuits in the pathophysiology and treatment of psychiatric disorders.

Architecture & Planning• 2016• Frontiers Media
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Periodicals

Insights into the Recent 2019 Novel Coronavirus (SARS-CoV-2) in Light of Past Human Coronavirus Outbreaks

Coronaviruses (CoVs) are RNA viruses that have become a major public health concern since the Severe Acute Respiratory Syndrome-CoV (SARS-CoV) outbreak in 2002. The continuous evolution of coronaviruses was further highlighted with the emergence of the Middle East Respiratory Syndrome-CoV (MERS-CoV) outbreak in 2012. Currently, the world is concerned about the 2019 novel CoV (SARS-CoV-2) that was initially identified in the city of Wuhan, China in December 2019. Patients presented with severe viral pneumonia and respiratory illness. The number of cases has been mounting since then. As of late February 2020, tens of thousands of cases and several thousand deaths have been reported in China alone, in addition to thousands of cases in other countries. Although the fatality rate of SARS-CoV-2 is currently lower than SARS-CoV, the virus seems to be highly contagious based on the number of infected cases to date. In this review, we discuss structure, genome organization, entry of CoVs into target cells, and provide insights into past and present outbreaks. The future of human CoV outbreaks will not only depend on how the viruses will evolve, but will also depend on how we develop efficient prevention and treatment strategies to deal with this continuous threat.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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Rift Valley fever virus (<i>Bunyaviridae: Phlebovirus</i>): an update on pathogenesis, molecular epidemiology, vectors, diagnostics and prevention

Rift Valley fever(RVF) virus is an arbovirus in the Bunyaviridae family that, from phylogenetic analysis, appears to have first emerged in the mid-19th century and was only identified at the beginning of the 1930's in the Rift Valley region of Kenya. Despite being an arbovirus with a relatively simple but temporally and geographically stable genome, this zoonotic virus has already demonstrated a real capacity for emerging in new territories, as exemplified by the outbreaks in Egypt (1977), Western Africa (1988) and the Arabian Peninsula (2000), or for re-emerging after long periods of silence as observed very recently in Kenya and South Africa. The presence of competent vectors in countries previously free of RVF, the high viral titres in viraemic animals and the global changes in climate, travel and trade all contribute to make this virus a threat that must not be neglected as the consequences of RVF are dramatic, both for human and animal health. In this review, we present the latest advances in RVF virus research. In spite of this renewed interest, aspects of the epidemiology of RVF virus are still not fully understood and safe, effective vaccines are still not freely available for protecting humans and livestock against the dramatic consequences of this virus.

Architecture & Planning• 2010• BioMed Central
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Periodicals

Product platform design and customization: Status and promise

In an effort to improve customization for today's highly competitive global marketplace, many companies are utilizing product families and platform-based product development to increase variety, shorten lead times, and reduce costs. The key to a successful product family is the product platform from which it is derived either by adding, removing, or substituting one or more modules to the platform or by scaling the platform in one or more dimensions to target specific market niches. This nascent field of engineering design has matured rapidly in the past decade, and this paper provides a comprehensive review of the flurry of research activity that has occurred during that time to facilitate product family design and platform-based product development for mass customization. Techniques for identifying platform leveraging strategies within a product family are reviewed along with metrics for assessing the effectiveness of product platforms and product families. Special emphasis is placed on optimization approaches and artificial intelligence techniques to assist in the process of product family design and platform-based product development. Web-based systems for product platform customization are also discussed. Examples from both industry and academia are presented throughout the paper to highlight the benefits of product families and product platforms. The paper concludes with a discussion of potential areas of research to help bridge the gap between planning and managing families of products and designing and manufacturing them.

Architecture & Planning• 2004• Cambridge University Press
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Periodicals

Augmenting large language models with chemistry tools

Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. We introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery and materials design. By integrating 18 expert-designed tools and using GPT-4 as the LLM, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. Our agent autonomously planned and executed the syntheses of an insect repellent and three organocatalysts and guided the discovery of a novel chromophore. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow's effectiveness in automating a diverse set of chemical tasks. Our work not only aids expert chemists and lowers barriers for non-experts but also fosters scientific advancement by bridging the gap between experimental and computational chemistry.

Architecture & Planning• 2024• Nature Portfolio
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Periodicals

Human–Robot Collaboration in Manufacturing Applications: A Review

This paper provides an overview of collaborative robotics towards manufacturing applications. Over the last decade, the market has seen the introduction of a new category of robots—collaborative robots (or “cobots”)—designed to physically interact with humans in a shared environment, without the typical barriers or protective cages used in traditional robotics systems. Their potential is undisputed, especially regarding their flexible ability to make simple, quick, and cheap layout changes; however, it is necessary to have adequate knowledge of their correct uses and characteristics to obtain the advantages of this form of robotics, which can be a barrier for industry uptake. The paper starts with an introduction of human–robot collaboration, presenting the related standards and modes of operation. An extensive literature review of works published in this area is undertaken, with particular attention to the main industrial cases of application. The paper concludes with an analysis of the future trends in human–robot collaboration as determined by the authors.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
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Periodicals

Virtual Inertia: Current Trends and Future Directions

The modern power system is progressing from a synchronous machine-based system towards an inverter-dominated system, with large-scale penetration of renewable energy sources (RESs) like wind and photovoltaics. RES units today represent a major share of the generation, and the traditional approach of integrating them as grid following units can lead to frequency instability. Many researchers have pointed towards using inverters with virtual inertia control algorithms so that they appear as synchronous generators to the grid, maintaining and enhancing system stability. This paper presents a literature review of the current state-of-the-art of virtual inertia implementation techniques, and explores potential research directions and challenges. The major virtual inertia topologies are compared and classified. Through literature review and simulations of some selected topologies it has been shown that similar inertial response can be achieved by relating the parameters of these topologies through time constants and inertia constants, although the exact frequency dynamics may vary slightly. The suitability of a topology depends on system control architecture and desired level of detail in replication of the dynamics of synchronous generators. A discussion on the challenges and research directions points out several research needs, especially for systems level integration of virtual inertia systems.

Architecture & Planning• 2017• Multidisciplinary Digital Publishing Institute
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Periodicals

Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon at the right time and thus the early diagnosis of pneumonia is vital. The paper aims to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances in accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN): AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. A total of 5247 chest X-ray images consisting of bacterial, viral, and normal chest x-rays images were preprocessed and trained for the transfer learning-based classification task. In this study, the authors have reported three schemes of classifications: normal vs. pneumonia, bacterial vs. viral pneumonia, and normal, bacterial, and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial, and viral pneumonia were 98%, 95%, and 93.3%, respectively. This is the highest accuracy, in any scheme, of the accuracies reported in the literature. Therefore, the proposed study can be useful in more quickly diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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Strategies for Fermentation Medium Optimization: An In-Depth Review

Optimization of production medium is required to maximize the metabolite yield. This can be achieved by using a wide range of techniques from classical "one-factor-at-a-time" to modern statistical and mathematical techniques, viz. artificial neural network (ANN), genetic algorithm (GA) etc. Every technique comes with its own advantages and disadvantages, and despite drawbacks some techniques are applied to obtain best results. Use of various optimization techniques in combination also provides the desirable results. In this article an attempt has been made to review the currently used media optimization techniques applied during fermentation process of metabolite production. Comparative analysis of the merits and demerits of various conventional as well as modern optimization techniques have been done and logical selection basis for the designing of fermentation medium has been given in the present review. Overall, this review will provide the rationale for the selection of suitable optimization technique for media designing employed during the fermentation process of metabolite production.

Architecture & Planning• 2017• Frontiers Media
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A Molecular View of Kinetochore Assembly and Function

Kinetochores are large protein assemblies that connect chromosomes to microtubules of the mitotic and meiotic spindles in order to distribute the replicated genome from a mother cell to its daughters. Kinetochores also control feedback mechanisms responsible for the correction of incorrect microtubule attachments, and for the coordination of chromosome attachment with cell cycle progression. Finally, kinetochores contribute to their own preservation, across generations, at the specific chromosomal loci devoted to host them, the centromeres. They achieve this in most species by exploiting an epigenetic, DNA-sequence-independent mechanism; notable exceptions are budding yeasts where a specific sequence is associated with centromere function. In the last 15 years, extensive progress in the elucidation of the composition of the kinetochore and the identification of various physical and functional modules within its substructure has led to a much deeper molecular understanding of kinetochore organization and the origins of its functional output. Here, we provide a broad summary of this progress, focusing primarily on kinetochores of humans and budding yeast, while highlighting work from other models, and present important unresolved questions for future studies.

Architecture & Planning• 2017• Multidisciplinary Digital Publishing Institute
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Is bilingualism associated with enhanced executive functioning in adults? A meta-analytic review.

Because of enduring experience of managing two languages, bilinguals have been argued to develop superior executive functioning compared with monolinguals. Despite extensive investigation, there is, however, no consensus regarding the existence of such a bilingual advantage. Here we synthesized comparisons of bilinguals' and monolinguals' performance in six executive domains using 891 effect sizes from 152 studies on adults. We also included unpublished data, and considered the potential influence of a number of study-, task-, and participant-related variables. Before correcting estimates for observed publication bias, our analyses revealed a very small bilingual advantage for inhibition, shifting, and working memory, but not for monitoring or attention. No evidence for a bilingual advantage remained after correcting for bias. For verbal fluency, our analyses indicated a small bilingual disadvantage, possibly reflecting less exposure for each individual language when using two languages in a balanced manner. Moreover, moderator analyses did not support theoretical presuppositions concerning the bilingual advantage. We conclude that the available evidence does not provide systematic support for the widely held notion that bilingualism is associated with benefits in cognitive control functions in adults. (PsycINFO Database Record

Architecture & Planning• 2018• American Psychological Association
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Periodicals

Poly(lactic Acid): A Versatile Biobased Polymer for the Future with Multifunctional Properties—From Monomer Synthesis, Polymerization Techniques and Molecular Weight Increase to PLA Applications

Environmental problems, such as global warming and plastic pollution have forced researchers to investigate alternatives for conventional plastics. Poly(lactic acid) (PLA), one of the well-known eco-friendly biodegradables and biobased polyesters, has been studied extensively and is considered to be a promising substitute to petroleum-based polymers. This review gives an inclusive overview of the current research of lactic acid and lactide dimer techniques along with the production of PLA from its monomers. Melt polycondensation as well as ring opening polymerization techniques are discussed, and the effect of various catalysts and polymerization conditions is thoroughly presented. Reaction mechanisms are also reviewed. However, due to the competitive decomposition reactions, in the most cases low or medium molecular weight (MW) of PLA, not exceeding 20,000-50,000 g/mol, are prepared. For this reason, additional procedures such as solid state polycondensation (SSP) and chain extension (CE) reaching MW ranging from 80,000 up to 250,000 g/mol are extensively investigated here. Lastly, numerous practical applications of PLA in various fields of industry, technical challenges and limitations of PLA use as well as its future perspectives are also reported in this review.

Architecture & Planning• 2021• Multidisciplinary Digital Publishing Institute
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