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in Architecture & Planning
Periodicals

New scholarly pathways on green gentrification: What does the urban ‘green turn’ mean and where is it going?

Scholars in urban political ecology, urban geography, and planning have suggested that urban greening interventions can create elite enclaves of environmental privilege and green gentrification, and exclude lower-income and minority residents from their benefits. Yet, much remains to be understood in regard to the magnitude, scope, and manifestations of green gentrification and the forms of contestation and resistance articulated against it. In this paper, we propose new questions, theoretical approaches, and research design approaches to examine the socio-spatial dynamics and ramifications of green gentrification and parse out why, how, where, and when green gentrification takes place.

Architecture & Planning• 2018• SAGE Publishing
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Periodicals

Oxidative Stress-Mediated Atherosclerosis: Mechanisms and Therapies

Atherogenesis, the formation of atherosclerotic plaques, is a complex process that involves several mechanisms, including endothelial dysfunction, neovascularization, vascular proliferation, apoptosis, matrix degradation, inflammation, and thrombosis. The pathogenesis and progression of atherosclerosis are explained differently by different scholars. One of the most common theories is the destruction of well-balanced homeostatic mechanisms, which incurs the oxidative stress. And oxidative stress is widely regarded as the redox status realized when an imbalance exists between antioxidant capability and activity species including reactive oxygen (ROS), nitrogen (RNS) and halogen species, non-radical as well as free radical species. This occurrence results in cell injury due to direct oxidation of cellular protein, lipid, and DNA or via cell death signaling pathways responsible for accelerating atherogenesis. This paper discusses inflammation, mitochondria, autophagy, apoptosis, and epigenetics as they induce oxidative stress in atherosclerosis, as well as various treatments for antioxidative stress that may prevent atherosclerosis.

Architecture & Planning• 2017• Frontiers Media
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Short-Term Photovoltaic Power Forecasting Based on Long Short Term Memory Neural Network and Attention Mechanism

Photovoltaic power generation forecasting is an important topic in the field of sustainable power system design, energy conversion management, and smart grid construction. Difficulties arise while the generated PV power is usually unstable due to the variability of solar irradiance, temperature, and other meteorological factors. In this paper, a hybrid ensemble deep learning framework is proposed to forecast short-term photovoltaic power generation in a time series manner. Two LSTM neural networks are employed working on temperature and power outputs forecasting, respectively. The forecasting results are flattened and combined with a fully connected layer to enhance forecasting accuracy. Moreover, we adopted the attention mechanism for the two LSTM neural networks to adaptively focus on input features that are more significant in forecasting. Comprehensive experiments are conducted with recently collected real-world photovoltaic power generation datasets. Three error metrics were adopted to compare the forecasting results produced by attention LSTM model with state-of-art methods, including the persistent model, the auto-regressive integrated moving average model with exogenous variable (ARIMAX), multi-layer perceptron (MLP), and the traditional LSTM model in all four seasons and various forecasting horizons to show the effectiveness and robustness of the proposed method.

Architecture & Planning• 2019• Institute of Electrical and Electronics Engineers
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Dorsoventral Patterning in Hemichordates: Insights into Early Chordate Evolution

We have compared the dorsoventral development of hemichordates and chordates to deduce the organization of their common ancestor, and hence to identify the evolutionary modifications of the chordate body axis after the lineages split. In the hemichordate embryo, genes encoding bone morphogenetic proteins (Bmp) 2/4 and 5/8, as well as several genes for modulators of Bmp activity, are expressed in a thin stripe of ectoderm on one midline, historically called "dorsal." On the opposite midline, the genes encoding Chordin and Anti-dorsalizing morphogenetic protein (Admp) are expressed. Thus, we find a Bmp-Chordin developmental axis preceding and underlying the anatomical dorsoventral axis of hemichordates, adding to the evidence from Drosophila and chordates that this axis may be at least as ancient as the first bilateral animals. Numerous genes encoding transcription factors and signaling ligands are expressed in the three germ layers of hemichordate embryos in distinct dorsoventral domains, such as pox neuro, pituitary homeobox, distalless, and tbx2/3 on the Bmp side and netrin, mnx, mox, and single-minded on the Chordin-Admp side. When we expose the embryo to excess Bmp protein, or when we deplete endogenous Bmp by small interfering RNA injections, these expression domains expand or contract, reflecting their activation or repression by Bmp, and the embryos develop as dorsalized or ventralized limit forms. Dorsoventral patterning is independent of anterior/posterior patterning, as in Drosophila but not chordates. Unlike both chordates and Drosophila, neural gene expression in hemichordates is not repressed by high Bmp levels, consistent with their development of a diffuse rather than centralized nervous system. We suggest that the common ancestor of hemichordates and chordates did not use its Bmp-Chordin axis to segregate epidermal and neural ectoderm but to pattern many other dorsoventral aspects of the germ layers, including neural cell fates within a diffuse nervous system. Accordingly, centralization was added in the chordate line by neural-epidermal segregation, mediated by the pre-existing Bmp-Chordin axis. Finally, since hemichordates develop the mouth on the non-Bmp side, like arthropods but opposite to chordates, the mouth and Bmp-Chordin axis may have rearranged in the chordate line, one relative to the other.

Architecture & Planning• 2006• Public Library of Science
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Periodicals

Understanding the Complex of Suicide in Depression: from Research to Clinics

OBJECTIVE: Amongst psychiatric disorders, major depressive disorder (MDD) is the most prevalent, by affecting approximately 15-17% of the population and showing a high suicide risk rate equivalent to around 15%. The present comprehensive overview aims at evaluating main research studies in the field of MDD at suicide risk, by proposing as well as a schematic suicide risk stratification and useful flow-chart for planning suicide preventive and therapeutic interventions for clinicians. METHODS: A broad and comprehensive overview has been here conducted by using PubMed/Medline, combining the search strategy of free text terms and exploded MESH headings for the topics of 'Major Depressive Disorder' and 'Suicide' as following: ((suicide [Title/Abstract]) AND (major depressive disorder [Title/Abstract])). All articles published in English through May 31, 2019 were summarized in a comprehensive way. RESULTS: Despite possible pathophysiological factors which may explain the complexity of suicide in MDD, scientific evidence supposed the synergic role of genetics, exogenous and endogenous stressors (i.e., interpersonal, professional, financial, as well as psychiatric disorders), epigenetic, the hypothalamic-pituitary-adrenal stress-response system, the involvement of the monoaminergic neurotransmitter systems, particularly the serotonergic ones, the lipid profile, neuro-immunological biomarkers, the Brain-derived neurotrophic factor and other neuromodulators. CONCLUSION: The present overview reported that suicide is a highly complex and multifaceted phenomenon in which a large plethora of mechanisms could be variable implicated, particularly amongst MDD subjects. Beyond these consideration, modern psychiatry needs a better interpretation of suicide risk with a more careful assessment of suicide risk stratification and planning of clinical and treatment interventions.

Architecture & Planning• 2020• Korean Neuropsychiatric Association
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Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM

Emotional state recognition of a speaker is a difficult task for machine learning algorithms which plays an important role in the field of speech emotion recognition (SER). SER plays a significant role in many real-time applications such as human behavior assessment, human-robot interaction, virtual reality, and emergency centers to analyze the emotional state of speakers. Previous research in this field is mostly focused on handcrafted features and traditional convolutional neural network (CNN) models used to extract high-level features from speech spectrograms to increase the recognition accuracy and overall model cost complexity. In contrast, we introduce a novel framework for SER using a key sequence segment selection based on redial based function network (RBFN) similarity measurement in clusters. The selected sequence is converted into a spectrogram by applying the STFT algorithm and passed into the CNN model to extract the discriminative and salient features from the speech spectrogram. Furthermore, we normalize the CNN features to ensure precise recognition performance and feed them to the deep bi-directional long short-term memory (BiLSTM) to learn the temporal information for recognizing the final state of emotion. In the proposed technique, we process the key segments instead of the whole utterance to reduce the computational complexity of the overall model and normalize the CNN features before their actual processing, so that it can easily recognize the Spatio-temporal information. The proposed system is evaluated over different standard dataset including IEMOCAP, EMO-DB, and RAVDESS to improve the recognition accuracy and reduce the processing time of the model, respectively. The robustness and effectiveness of the suggested SER model is proved from the experimentations when compared to state-of-the-art SER methods with an achieve up to 72.25%, 85.57%, and 77.02% accuracy over IEMOCAP, EMO-DB, and RAVDESS dataset, respectively.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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A Review of IoT Sensing Applications and Challenges Using RFID and Wireless Sensor Networks

Radio frequency identification (RFID) and wireless sensors networks (WSNs) are two fundamental pillars that enable the Internet of Things (IoT). RFID systems are able to identify and track devices, whilst WSNs cooperate to gather and provide information from interconnected sensors. This involves challenges, for example, in transforming RFID systems with identification capabilities into sensing and computational platforms, as well as considering them as architectures of wirelessly connected sensing tags. This, together with the latest advances in WSNs and with the integration of both technologies, has resulted in the opportunity to develop novel IoT applications. This paper presents a review of these two technologies and the obstacles and challenges that need to be overcome. Some of these challenges are the efficiency of the energy harvesting, communication interference, fault tolerance, higher capacities to handling data processing, cost feasibility, and an appropriate integration of these factors. Additionally, two emerging trends in IoT are reviewed: the combination of RFID and WSNs in order to exploit their advantages and complement their limitations, and wearable sensors, which enable new promising IoT applications.

Architecture & Planning• 2020• Multidisciplinary Digital Publishing Institute
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A Review on Artificial Intelligence in Education

The emergence of innovative technologies has an impact on the methods of teaching and learning. With the rapid development of artificial intelligence (AI) technology in recent years, using AI in education has become more and more apparent. This article first outlines the application of AI in the field of education, such as adaptive learning, teaching evaluation, virtual classroom, etc. And then analyzes its impact on teaching and learning, which has a positive meaning for improving teachers' teaching level and students' learning quality. Finally, it puts forward the challenges that AI applications may face in education in the future and provides references for AI to promote education reform. Received: 16 January 2021 / Accepted: 24 March 2021 / Published: 10 May 2021

Architecture & Planning• 2021• Richtmann Publishing
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Periodicals

Grid Impact of Electric Vehicle Fast Charging Stations: Trends, Standards, Issues and Mitigation Measures - An Overview

With growing concern on climate change, widespread adoption of electric vehicles (EVs) is important. One of the main barriers to EV acceptance is range anxiety, which can be alleviated by fast charging (FC). The main technology constraints for enabling FC consist of high-charging-rate batteries, high-power-charging infrastructure, and grid impacts. Although these technical aspects have been studied in literature individually, there is no comprehensive review on FC involving all the perspectives. Moreover, the power quality (PQ) problems of fast charging stations (FCSs) and the mitigation of these problems are not clearly summarized in the literature. In this paper, the state-of-the-art technology, standards for FC (CHAdeMO, GB/T, CCS, and Tesla), power quality issues, IEEE and IEC PQ standards, and mitigation measures of FCSs are systematically reviewed.

Architecture & Planning• 2021• Institute of Electrical and Electronics Engineers
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InSDN: A Novel SDN Intrusion Dataset

Software-Defined Network (SDN) has been developed to reduce network complexity through control and manage the whole network from a centralized location. Today, SDN is widely implemented in many data center's network environments. Nevertheless, emerging technology itself can lead to many vulnerabilities and threats which are still challenging for manufacturers to address it. Therefore, deploying Intrusion Detection Systems (IDSs) to monitor malicious activities is a crucial part of the network architecture. Although the centralized view of the SDN network creates new opportunities for the implementation of IDSs, the performance of these detection techniques relies on the quality of the training datasets. Unfortunately, there are no publicly available datasets that can be used directly for anomaly detection systems applied in SDN networks. The majority of the published studies use non-compatible and outdated datasets, such as the KDD'99 dataset. This manuscript aims to generate an attack-specific SDN dataset and it is publicly available to the researchers. To the best of our knowledge, our work is one of the first solutions to produce a comprehensive SDN dataset to verify the performance of intrusion detection systems. The new dataset includes the benign and various attack categories that can occur in the different elements of the SDN platform. Further, we demonstrate the use of our proposed dataset by performing an experimental evaluation using eight popular machine-learning-based techniques for IDSs.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Applications of Wireless Sensor Networks in Marine Environment Monitoring: A Survey

With the rapid development of society and the economy, an increasing number of human activities have gradually destroyed the marine environment. Marine environment monitoring is a vital problem and has increasingly attracted a great deal of research and development attention. During the past decade, various marine environment monitoring systems have been developed. The traditional marine environment monitoring system using an oceanographic research vessel is expensive and time-consuming and has a low resolution both in time and space. Wireless Sensor Networks (WSNs) have recently been considered as potentially promising alternatives for monitoring marine environments since they have a number of advantages such as unmanned operation, easy deployment, real-time monitoring, and relatively low cost. This paper provides a comprehensive review of the state-of-the-art technologies in the field of marine environment monitoring using wireless sensor networks. It first describes application areas, a common architecture of WSN-based oceanographic monitoring systems, a general architecture of an oceanographic sensor node, sensing parameters and sensors, and wireless communication technologies. Then, it presents a detailed review of some related projects, systems, techniques, approaches and algorithms. It also discusses challenges and opportunities in the research, development, and deployment of wireless sensor networks for marine environment monitoring.

Architecture & Planning• 2014• Multidisciplinary Digital Publishing Institute
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Flexible Cognitive Strategies during Motor Learning

Visuomotor rotation tasks have proven to be a powerful tool to study adaptation of the motor system. While adaptation in such tasks is seemingly automatic and incremental, participants may gain knowledge of the perturbation and invoke a compensatory strategy. When provided with an explicit strategy to counteract a rotation, participants are initially very accurate, even without on-line feedback. Surprisingly, with further testing, the angle of their reaching movements drifts in the direction of the strategy, producing an increase in endpoint errors. This drift is attributed to the gradual adaptation of an internal model that operates independently from the strategy, even at the cost of task accuracy. Here we identify constraints that influence this process, allowing us to explore models of the interaction between strategic and implicit changes during visuomotor adaptation. When the adaptation phase was extended, participants eventually modified their strategy to offset the rise in endpoint errors. Moreover, when we removed visual markers that provided external landmarks to support a strategy, the degree of drift was sharply attenuated. These effects are accounted for by a setpoint state-space model in which a strategy is flexibly adjusted to offset performance errors arising from the implicit adaptation of an internal model. More generally, these results suggest that strategic processes may operate in many studies of visuomotor adaptation, with participants arriving at a synergy between a strategic plan and the effects of sensorimotor adaptation.

Architecture & Planning• 2011• Public Library of Science
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Radiation hydrodynamics with neutrinos

Neutrino transport and neutrino interactions in dense matter play a crucial role in stellar core collapse, supernova explosions and neutron star formation. Here we present a detailed description of a new numerical code for treating the time and energy dependent neutrino transport in hydrodynamical simulations of such events. The code is based on a variable Eddington factor method to deal with the integro-differential character of the Boltzmann equation. The moments of the neutrino distribution function and the energy and lepton number exchange with the stellar medium are determined by iteratively solving the zeroth and first order moment equations in combination with a model Boltzmann equation. The latter is discretized on a grid of tangent rays. The integration of the transport equations and the neutrino source terms is performed in a time-implicit way. In the present version of the program, the transport part is coupled to an explicit hydrodynamics code which follows the evolution of the stellar plasma by a finite-volume method with piecewise parabolic interpolation, using a Riemann solver to calculate the hydrodynamic states. The neutrino source terms are implemented in an operator-split step. Neutrino transport and hydrodynamics can be calculated with different spatial grids and different time steps. The structure of the described code is modular and offers a high degree of flexibility for an application to relativistic and multi-dimensional problems at different levels of refinement and accuracy. We critically evaluate results for a number of test cases, including neutrino transport in rapidly moving stellar media and approximate relativistic core collapse, and suggest a path for generalizing the code to be used in multi-dimensional simulations of convection in neutron stars and supernovae.

Architecture & Planning• 2002• EDP Sciences
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The Use of MQTT in M2M and IoT Systems: A Survey

Nowadays billions of smart devices or things are present in Internet of Things (IoT) environments, such as homes, hospitals, factories, and vehicles, all around the world. As a result, the number of interconnected devices is continuously and rapidly growing. These devices communicate with each other and with other services using various communication protocols for the transportation of sensor or event data. These protocols enable applications to collect, store, process, describe, and analyze data to solve a variety of problems. IoT also aims to provide secure, bi-directional communication between interconnected devices, such as sensors, actuators, microcontrollers or smart appliances, and corresponding cloud services. In this paper we analyze the growth of M2M protocol research (MQTT, AMQP, and CoAP) over the past 20 years, and show how the growth in MQTT research stands out from the rest. We also gather relevant application areas of MQTT, as the most widespread M2M/IoT protocol, by performing a detailed literature search in major digital research archives. Our quantitative evaluation presents some of the important MQTT-related studies published in the past five years, which we compare to discuss the main features, advantages, and limitations of the MQTT protocol. We also propose a taxonomy to compare the properties and features of various MQTT implementations, i.e. brokers and libraries currently available in the public domain to help researchers and end-users to efficiently choose a broker or client library based on their requirements. Finally, we discuss the relevant findings of our comparison and highlight open issues that need further research and attention.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Nature-based solutions for hydro-meteorological risk reduction: a state-of-the-art review of the research area

Abstract. Hydro-meteorological risks due to natural hazards such as severe floods, storm surges, landslides and droughts are causing impacts on different sectors of society. Such risks are expected to become worse given projected changes in climate, degradation of ecosystems, population growth and urbanisation. In this respect, nature-based solutions (NBSs) have emerged as effective means to respond to such challenges. A NBS is a term used for innovative solutions that are based on natural processes and ecosystems to solve different types of societal and environmental challenges. The present paper provides a critical review of the literature concerning NBSs for hydro-meteorological risk reduction and identifies current knowledge gaps and future research prospects. There has been a considerable growth of scientific publications on this topic, with a more significant rise taking place from 2007 onwards. Hence, the review process presented in this paper starts by sourcing 1608 articles from Scopus and 1431 articles from the Web of Science. The full analysis was performed on 146 articles. The analysis confirmed that numerous advancements in the area of NBSs have been achieved to date. These solutions have already proven to be valuable in providing sustainable, cost-effective, multi-purpose and flexible means for hydro-meteorological risk reduction. However, there are still many areas where further research and demonstration are needed in order to promote their upscaling and replication and to make them become mainstream solutions.

Architecture & Planning• 2020• Copernicus Publications
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Cancer drug delivery in the nano era: An overview and perspectives

Nanomaterials are increasingly used as drug carriers for cancer therapy. Nanomaterials also appeal to researchers in the areas of cancer diagnosis and biomarker discovery. Several antitumor nanodrugs are currently being tested in preclinical and clinical trials and show promise in therapeutic and other settings. We review the development of nanomaterial drug carriers, including liposomes, polymer nanoparticles, dendritic polymers, and nanomicelles, for the diagnosis and treatment of various cancers. The prospects of nanomaterials as drug carriers for future clinical applications are also discussed.

Architecture & Planning• 2017• Elsevier BV
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Discovery of Sexual Dimorphisms in Metabolic and Genetic Biomarkers

Metabolomic profiling and the integration of whole-genome genetic association data has proven to be a powerful tool to comprehensively explore gene regulatory networks and to investigate the effects of genetic variation at the molecular level. Serum metabolite concentrations allow a direct readout of biological processes, and association of specific metabolomic signatures with complex diseases such as Alzheimer's disease and cardiovascular and metabolic disorders has been shown. There are well-known correlations between sex and the incidence, prevalence, age of onset, symptoms, and severity of a disease, as well as the reaction to drugs. However, most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by gender. This study investigated sex-specific differences of serum metabolite concentrations and their underlying genetic determination. For discovery and replication we used more than 3,300 independent individuals from KORA F3 and F4 with metabolite measurements of 131 metabolites, including amino acids, phosphatidylcholines, sphingomyelins, acylcarnitines, and C6-sugars. A linear regression approach revealed significant concentration differences between males and females for 102 out of 131 metabolites (p-values<3.8×10(-4); Bonferroni-corrected threshold). Sex-specific genome-wide association studies (GWAS) showed genome-wide significant differences in beta-estimates for SNPs in the CPS1 locus (carbamoyl-phosphate synthase 1, significance level: p<3.8×10(-10); Bonferroni-corrected threshold) for glycine. We showed that the metabolite profiles of males and females are significantly different and, furthermore, that specific genetic variants in metabolism-related genes depict sexual dimorphism. Our study provides new important insights into sex-specific differences of cell regulatory processes and underscores that studies should consider sex-specific effects in design and interpretation.

Architecture & Planning• 2011• Public Library of Science
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