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in Architecture & Planning
Periodicals

A Review of the Microbial Production of Bioactive Natural Products and Biologics

A variety of organisms, such as bacteria, fungi, and plants, produce secondary metabolites, also known as natural products. Natural products have been a prolific source and an inspiration for numerous medical agents with widely divergent chemical structures and biological activities, including antimicrobial, immunosuppressive, anticancer, and anti-inflammatory activities, many of which have been developed as treatments and have potential therapeutic applications for human diseases. Aside from natural products, the recent development of recombinant DNA technology has sparked the development of a wide array of biopharmaceutical products, such as recombinant proteins, offering significant advances in treating a broad spectrum of medical illnesses and conditions. Herein, we will introduce the structures and diverse biological activities of natural products and recombinant proteins that have been exploited as valuable molecules in medicine, agriculture and insect control. In addition, we will explore past and ongoing efforts along with achievements in the development of robust and promising microorganisms as cell factories to produce biologically active molecules. Furthermore, we will review multi-disciplinary and comprehensive engineering approaches directed at improving yields of microbial production of natural products and proteins and generating novel molecules. Throughout this article, we will suggest ways in which microbial-derived biologically active molecular entities and their analogs could continue to inspire the development of new therapeutic agents in academia and industry.

Architecture & Planning• 2019• Frontiers Media
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SPHERE: the exoplanet imager for the Very Large Telescope

Observations of circumstellar environments that look for the direct signal of exoplanets and the scattered light from disks have significant instrumental implications. In the past 15 years, major developments in adaptive optics, coronagraphy, optical manufacturing, wavefront sensing, and data processing, together with a consistent global system analysis have brought about a new generation of high-contrast imagers and spectrographs on large ground-based telescopes with much better performance. One of the most productive imagers is the Spectro-Polarimetic High contrast imager for Exoplanets REsearch (SPHERE), which was designed and built for the ESO Very Large Telescope (VLT) in Chile. SPHERE includes an extreme adaptive optics system, a highly stable common path interface, several types of coronagraphs, and three science instruments. Two of them, the Integral Field Spectrograph (IFS) and the Infra-Red Dual-band Imager and Spectrograph (IRDIS), were designed to efficiently cover the near-infrared range in a single observation for an efficient search of young planets. The third instrument, ZIMPOL, was designed for visible polarimetric observation to look for the reflected light of exoplanets and the light scattered by debris disks. These three scientific instruments enable the study of circumstellar environments at unprecedented angular resolution, both in the visible and the near-infrared. In this work, we thoroughly present SPHERE and its on-sky performance after four years of operations at the VLT.

Architecture & Planning• 2019• EDP Sciences
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Periodicals

Waterborne Pathogens: Detection Methods and Challenges

Waterborne pathogens and related diseases are a major public health concern worldwide, not only by the morbidity and mortality that they cause, but by the high cost that represents their prevention and treatment. These diseases are directly related to environmental deterioration and pollution. Despite the continued efforts to maintain water safety, waterborne outbreaks are still reported globally. Proper assessment of pathogens on water and water quality monitoring are key factors for decision-making regarding water distribution systems' infrastructure, the choice of best water treatment and prevention waterborne outbreaks. Powerful, sensitive and reproducible diagnostic tools are developed to monitor pathogen contamination in water and be able to detect not only cultivable pathogens but also to detect the occurrence of viable but non-culturable microorganisms as well as the presence of pathogens on biofilms. Quantitative microbial risk assessment (QMRA) is a helpful tool to evaluate the scenarios for pathogen contamination that involve surveillance, detection methods, analysis and decision-making. This review aims to present a research outlook on waterborne outbreaks that have occurred in recent years. This review also focuses in the main molecular techniques for detection of waterborne pathogens and the use of QMRA approach to protect public health.

Architecture & Planning• 2015• Multidisciplinary Digital Publishing Institute
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Periodicals

The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions

A growing number of investigators have commented on the lack of models to inform the design of behavioral intervention technologies (BITs). BITs, which include a subset of mHealth and eHealth interventions, employ a broad range of technologies, such as mobile phones, the Web, and sensors, to support users in changing behaviors and cognitions related to health, mental health, and wellness. We propose a model that conceptually defines BITs, from the clinical aim to the technological delivery framework. The BIT model defines both the conceptual and technological architecture of a BIT. Conceptually, a BIT model should answer the questions why, what, how (conceptual and technical), and when. While BITs generally have a larger treatment goal, such goals generally consist of smaller intervention aims (the "why") such as promotion or reduction of specific behaviors, and behavior change strategies (the conceptual "how"), such as education, goal setting, and monitoring. Behavior change strategies are instantiated with specific intervention components or "elements" (the "what"). The characteristics of intervention elements may be further defined or modified (the technical "how") to meet the needs, capabilities, and preferences of a user. Finally, many BITs require specification of a workflow that defines when an intervention component will be delivered. The BIT model includes a technological framework (BIT-Tech) that can integrate and implement the intervention elements, characteristics, and workflow to deliver the entire BIT to users over time. This implementation may be either predefined or include adaptive systems that can tailor the intervention based on data from the user and the user's environment. The BIT model provides a step towards formalizing the translation of developer aims into intervention components, larger treatments, and methods of delivery in a manner that supports research and communication between investigators on how to design, develop, and deploy BITs.

Architecture & Planning• 2014• JMIR Publications
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Periodicals

Construction with digital twin information systems

Abstract The concept of a “digital twin” as a model for data-driven management and control of physical systems has emerged over the past decade in the domains of manufacturing, production, and operations. In the context of buildings and civil infrastructure, the notion of a digital twin remains ill-defined, with little or no consensus among researchers and practitioners of the ways in which digital twin processes and data-centric technologies can support design and construction. This paper builds on existing concepts of Building Information Modeling (BIM), lean project production systems, automated data acquisition from construction sites and supply chains, and artificial intelligence to formulate a mode of construction that applies digital twin information systems to achieve closed loop control systems. It contributes a set of four core information and control concepts for digital twin construction (DTC), which define the dimensions of the conceptual space for the information used in DTC workflows. Working from the core concepts, we propose a DTC information system workflow—including information stores, information processing functions, and monitoring technologies—according to three concentric control workflow cycles. DTC should be viewed as a comprehensive mode of construction that prioritizes closing the control loops rather than an extension of BIM tools integrated with sensing and monitoring technologies.

Architecture & Planning• 2020• Cambridge University Press
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A Survey on Green 6G Network: Architecture and Technologies

While 5G is being commercialized worldwide, research institutions around the world have started to look beyond 5G and 6G is expected to evolve into green networks, which deliver high Quality of Service and energy efficiency. To meet the demands of future applications, significant improvements need to be made in mobile network architecture. We envision 6G undergoing unprecedented breakthrough and integrating traditional terrestrial mobile networks with emerging space, aerial and underwater networks to provide anytime anywhere network access. This paper presents a detailed survey on wireless evolution towards 6G networks. In this survey, the prime focus is on the new architectural changes associated with 6G networks, characterized by ubiquitous 3D coverage, introduction of pervasive AI and enhanced network protocol stack. Along with this, we discuss related potential technologies that are helpful in forming sustainable and socially seamless networks, encompassing terahertz and visible light communication, new communication paradigm, blockchain and symbiotic radio. Our work aims to provide enlightening guidance for subsequent research of green 6G.

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

Phenotyping for drought tolerance of crops in the genomics era

Improving crops yield under water-limited conditions is the most daunting challenge faced by breeders. To this end, accurate, relevant phenotyping plays an increasingly pivotal role for the selection of drought-resilient genotypes and, more in general, for a meaningful dissection of the quantitative genetic landscape that underscores the adaptive response of crops to drought. A major and universally recognized obstacle to a more effective translation of the results produced by drought-related studies into improved cultivars is the difficulty in properly phenotyping in a high-throughput fashion in order to identify the quantitative trait loci that govern yield and related traits across different water regimes. This review provides basic principles and a broad set of references useful for the management of phenotyping practices for the study and genetic dissection of drought tolerance and, ultimately, for the release of drought-tolerant cultivars.

Architecture & Planning• 2012• Frontiers Media
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A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

Abstract. Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16–19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error), and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that it accounts for pollutant cross-sensitivities. This highlights the importance of developing multipollutant sensor packages (as opposed to single-pollutant monitors); we determined this is especially critical for NO2 and CO2. The evaluation reveals that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrate that the RF-model-calibrated sensors could detect differences in NO2 concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that combining RF models with carefully controlled state-of-the-art multipollutant sensor packages as in the RAMP monitors appears to be a very promising approach to address the poor performance that has plagued low-cost air quality sensors.

Architecture & Planning• 2018• Copernicus Publications
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Periodicals

The Human Pangenome Project: a global resource to map genomic diversity

The human reference genome is the most widely used resource in human genetics and is due for a major update. Its current structure is a linear composite of merged haplotypes from more than 20 people, with a single individual comprising most of the sequence. It contains biases and errors within a framework that does not represent global human genomic variation. A high-quality reference with global representation of common variants, including single-nucleotide variants, structural variants and functional elements, is needed. The Human Pangenome Reference Consortium aims to create a more sophisticated and complete human reference genome with a graph-based, telomere-to-telomere representation of global genomic diversity. Here we leverage innovations in technology, study design and global partnerships with the goal of constructing the highest-possible quality human pangenome reference. Our goal is to improve data representation and streamline analyses to enable routine assembly of complete diploid genomes. With attention to ethical frameworks, the human pangenome reference will contain a more accurate and diverse representation of global genomic variation, improve gene-disease association studies across populations, expand the scope of genomics research to the most repetitive and polymorphic regions of the genome, and serve as the ultimate genetic resource for future biomedical research and precision medicine.

Architecture & Planning• 2022• Nature Portfolio
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Periodicals

A Comprehensive Review of Power Converter Topologies and Control Methods for Electric Vehicle Fast Charging Applications

Wide-scale adoption and projected growth of electric vehicles (EVs) necessitate research and development of power electronic converters to achieve high power, low-cost, and reliable charging solutions for the EV battery. This paper presents a comprehensive review of EV off-board chargers that consist of ac-dc and dc-dc power stages from the power network to the EV battery. Although EV chargers are categorized into two types, namely, on-board and off-board chargers, it is essential to utilize off-board chargers for dc fast and ultra-fast charging so that volume and weight of EV can be reduced significantly. Here, we discuss the state-of-the-art topologies and control methods of both ac-dc and dc-dc power stages for off-board chargers, focusing on technical details, ongoing progress, and challenges. In addition, most of the recent multiport EV chargers integrating PV, energy storage, EV, and grid are presented. Moreover, comparative analysis has been carried out for the topologies and the control schemes of ac-dc rectifiers, dc-dc converters, and multiport converters in terms of architecture, power and voltage levels, efficiency, bidirectionality, control variables, advantages, and disadvantages which can be used as a guideline for future research directions in EV charging solutions.

Architecture & Planning• 2022• Institute of Electrical and Electronics Engineers
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Human Action Recognition From Various Data Modalities: A Review

Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasing attention in the field of computer vision. Human actions can be represented using various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal, which encode different sources of useful yet distinct information and have various advantages depending on the application scenarios. Consequently, lots of existing works have attempted to investigate different types of approaches for HAR using various modalities. In this article, we present a comprehensive survey of recent progress in deep learning methods for HAR based on the type of input data modality. Specifically, we review the current mainstream deep learning methods for single data modalities and multiple data modalities, including the fusion-based and the co-learning-based frameworks. We also present comparative results on several benchmark datasets for HAR, together with insightful observations and inspiring future research directions.

Architecture & Planning• 2022• IEEE Computer Society
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Periodicals

Types of Solar Cells and Application

A solar cell is an electronic device which directly converts sunlight into electricity. Light shining on the solar cell produces both a current and a voltage to generate electric power. This process requires firstly, a material in which the absorption of light raises an electron to a higher energy state, and secondly, the movement of this higher energy electron from the solar cell into an external circuit. The electron then dissipates its energy in the external circuit and returns to the solar cell. A variety of materials and processes can potentially satisfy the requirements for photovoltaic energy conversion, but in practice nearly all photovoltaic energy conversion uses semiconductor materials in the form of a p-n junction. With regard to the development of sustainable energy, such as solar energy, in this article we will Study types of solar cells and their applications

Architecture & Planning• 2015• Science Publishing Group
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The Collaborative Image of The City: Mapping the Inequality of Urban Perception

A traveler visiting Rio, Manila or Caracas does not need a report to learn that these cities are unequal; she can see it directly from the taxicab window. This is because in most cities inequality is conspicuous, but also, because cities express different forms of inequality that are evident to casual observers. Cities are highly heterogeneous and often unequal with respect to the income of their residents, but also with respect to the cleanliness of their neighborhoods, the beauty of their architecture, and the liveliness of their streets, among many other evaluative dimensions. Until now, however, our ability to understand the effect of a city's built environment on social and economic outcomes has been limited by the lack of quantitative data on urban perception. Here, we build on the intuition that inequality is partly conspicuous to create quantitative measure of a city's contrasts. Using thousands of geo-tagged images, we measure the perception of safety, class and uniqueness; in the cities of Boston and New York in the United States, and Linz and Salzburg in Austria, finding that the range of perceptions elicited by the images of New York and Boston is larger than the range of perceptions elicited by images from Linz and Salzburg. We interpret this as evidence that the cityscapes of Boston and New York are more contrasting, or unequal, than those of Linz and Salzburg. Finally, we validate our measures by exploring the connection between them and homicides, finding a significant correlation between the perceptions of safety and class and the number of homicides in a NYC zip code, after controlling for the effects of income, population, area and age. Our results show that online images can be used to create reproducible quantitative measures of urban perception and characterize the inequality of different cities.

Architecture & Planning• 2013• Public Library of Science
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The Co-Morbidity Burden of Children and Young Adults with Autism Spectrum Disorders

OBJECTIVES: Use electronic health records Autism Spectrum Disorder (ASD) to assess the comorbidity burden of ASD in children and young adults. STUDY DESIGN: A retrospective prevalence study was performed using a distributed query system across three general hospitals and one pediatric hospital. Over 14,000 individuals under age 35 with ASD were characterized by their co-morbidities and conversely, the prevalence of ASD within these comorbidities was measured. The comorbidity prevalence of the younger (Age<18 years) and older (Age 18-34 years) individuals with ASD was compared. RESULTS: 19.44% of ASD patients had epilepsy as compared to 2.19% in the overall hospital population (95% confidence interval for difference in percentages 13.58-14.69%), 2.43% of ASD with schizophrenia vs. 0.24% in the hospital population (95% CI 1.89-2.39%), inflammatory bowel disease (IBD) 0.83% vs. 0.54% (95% CI 0.13-0.43%), bowel disorders (without IBD) 11.74% vs. 4.5% (95% CI 5.72-6.68%), CNS/cranial anomalies 12.45% vs. 1.19% (95% CI 9.41-10.38%), diabetes mellitus type I (DM1) 0.79% vs. 0.34% (95% CI 0.3-0.6%), muscular dystrophy 0.47% vs 0.05% (95% CI 0.26-0.49%), sleep disorders 1.12% vs. 0.14% (95% CI 0.79-1.14%). Autoimmune disorders (excluding DM1 and IBD) were not significantly different at 0.67% vs. 0.68% (95% CI -0.14-0.13%). Three of the studied comorbidities increased significantly when comparing ages 0-17 vs 18-34 with p<0.001: Schizophrenia (1.43% vs. 8.76%), diabetes mellitus type I (0.67% vs. 2.08%), IBD (0.68% vs. 1.99%) whereas sleeping disorders, bowel disorders (without IBD) and epilepsy did not change significantly. CONCLUSIONS: The comorbidities of ASD encompass disease states that are significantly overrepresented in ASD with respect to even the patient populations of tertiary health centers. This burden of comorbidities goes well beyond those routinely managed in developmental medicine centers and requires broad multidisciplinary management that payors and providers will have to plan for.

Architecture & Planning• 2012• Public Library of Science
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Convolutional Neural Networks: A Survey

Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development.

Architecture & Planning• 2023• Multidisciplinary Digital Publishing Institute
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The HARMONIE–AROME Model Configuration in the ALADIN–HIRLAM NWP System

Abstract The aim of this article is to describe the reference configuration of the convection-permitting numerical weather prediction (NWP) model HARMONIE-AROME, which is used for operational short-range weather forecasts in Denmark, Estonia, Finland, Iceland, Ireland, Lithuania, the Netherlands, Norway, Spain, and Sweden. It is developed, maintained, and validated as part of the shared ALADIN–HIRLAM system by a collaboration of 26 countries in Europe and northern Africa on short-range mesoscale NWP. HARMONIE–AROME is based on the model AROME developed within the ALADIN consortium. Along with the joint modeling framework, AROME was implemented and utilized in both northern and southern European conditions by the above listed countries, and this activity has led to extensive updates to the model’s physical parameterizations. In this paper the authors present the differences in model dynamics and physical parameterizations compared with AROME, as well as important configuration choices of the reference, such as lateral boundary conditions, model levels, horizontal resolution, model time step, as well as topography, physiography, and aerosol databases used. Separate documentation will be provided for the atmospheric and surface data-assimilation algorithms and observation types used, as well as a separate description of the ensemble prediction system based on HARMONIE–AROME, which is called HarmonEPS.

Architecture & Planning• 2017• American Meteorological Society
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Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review

Ongoing debate exists within the resting-state functional MRI (fMRI) literature over how intrinsic connectivity is altered in the autistic brain, with reports of general over-connectivity, under-connectivity, and/or a combination of both. Classifying autism using brain connectivity is complicated by the heterogeneous nature of the condition, allowing for the possibility of widely variable connectivity patterns among individuals with the disorder. Further differences in reported results may be attributable to the age and sex of participants included, designs of the resting-state scan, and to the analysis technique used to evaluate the data. This review systematically examines the resting-state fMRI autism literature to date and compares studies in an attempt to draw overall conclusions that are presently challenging. We also propose future direction for rs-fMRI use to categorize individuals with autism spectrum disorder, serve as a possible diagnostic tool, and best utilize data-sharing initiatives.

Architecture & Planning• 2017• Frontiers Media
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Morphological approaches in studying fungi: collection, examination, isolation, sporulation and preservation

Traditionally, fungal taxonomy was based on observable phenotypic characters. Recent advances have driven taxonomic conclusions towards DNA-based approaches and these techniques have corresponding pros and cons. Species concepts must therefore rely on incorporated approaches of genotypic, phenotypic and physiological characters and chemotaxonomy. Examination and interpretation of morphological characters however vary from person to person. Standardized procedures are used in the taxonomic study of fungi and general practices of phenotypic approaches are herein outlined. It is not possible to detail all techniques for all fungi and thus, this paper emphasizes on microfungi. Specimen collection is the initial step in any

Architecture & Planning• 2020• Guizhou Academy of Agricultural Sciences
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CALLING: THE DEVELOPMENT OF A SCALE MEASURE

This study clarifies the definition of calling—a consuming, meaningful passion people experience toward a domain—and develops a 12‐item scale measure of calling. Drawing on multiwave longitudinal, 2‐wave longitudinal, and cross‐sectional data from 1,500 participants (2,278 observations) in 4 separate domains, music, art, business, and management, we demonstrate the scale's reliability and unidimensional structure across contexts and over time. We establish the scale's convergent validity and discriminant validity. We determine criterion‐related validity through the scale's relationship to satisfaction with the calling domain, career‐related self‐efficacy, clarity of professional identity, career insight, attending a calling‐oriented college program, professional pursuit of the calling domain, and differences across the 4 domains. We discuss implications of this reliable, valid measure for theory and research on calling, meaning of work, and careers.

Architecture & Planning• 2011• Wiley
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Climate Change, Firm Performance, and Investor Surprises

We link records of firm performance, equity analyst forecast errors, and stock returns around companies’ earnings announcements to firm-specific measures of heat exposure for more than 17,000 firms in 93 countries from 1995 to 2019. We find that increased exposure to extremely high temperatures reduces firms’ revenues and operating income. A one-standard-deviation increase in the number of hot days decreases revenues (operating income) by 0.6% (1.8%) of the average quarterly revenue (operating income). Moreover, we provide evidence that increased heat exposure impacts negatively on firm financial performance relative to analyst predictions and on earnings announcement returns. These findings indicate that capital market participants do not fully anticipate the economic consequences of heat as a first order physical climate risk. This paper was accepted by Colin Mayer, Special Section of Management Science on Business and Climate Change. Funding: N. Pankratz gratefully acknowledges financial support from the French Social Investment Forum and the Principles for Responsible Investment [PhD Research Grant 2017]. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2023.4685 .

Architecture & Planning• 2023• Institute for Operations Research and the Management Sciences
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Renewable Energy and Energy Storage Systems

The use of fossil fuels has contributed to climate change and global warming, which has led to a growing need for renewable and ecologically friendly alternatives to these. It is accepted that renewable energy sources are the ideal option to substitute fossil fuels in the near future. Significant progress has been made to produce renewable energy sources with acceptable prices at a commercial scale, such as solar, wind, and biomass energies. This success has been due to technological advances that can use renewable energy sources effectively at lower prices. More work is needed to maximize the capacity of renewable energy sources with a focus on their dispatchability, where the function of storage is considered crucial. Furthermore, hybrid renewable energy systems are needed with good energy management to balance the various renewable energy sources’ production/consumption/storage. This work covers the progress done in the main renewable energy sources at a commercial scale, including solar, wind, biomass, and hybrid renewable energy sources. Moreover, energy management between the various renewable energy sources and storage systems is discussed. Finally, this work discusses the recent progress in green hydrogen production and fuel cells that could pave the way for commercial usage of renewable energy in a wide range of applications.

Architecture & Planning• 2023• Multidisciplinary Digital Publishing Institute
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