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Decarbonizing the Food System with Microbes and Carbon-Neutral Feedstocks

Annual Review of Food Science and Technology 2025年第1期16卷 81-104页

作者： Mahmud, Niaz Taiwo, Kayode J. Usack, Joseph G. Sustainable Food Systems Engineering Group Department of Food Science and Technology University of Georgia AthensGA United States Food and Nutritional Sciences Program North Carolina A&T State University GreensboroNC United States New Materials Institute University of Georgia AthensGA United States Institute for Integrative and Precision Agriculture University of Georgia AthensGA United States

Harnessing CO2 and CO2-derived C1-C2 compounds for microbial food production can mitigate greenhouse gas emissions and boost sustainability within the food sector. These innovative technologies support carbon neutrality by generating nutrient-rich edible microbial biomass and biocompounds using autotrophic and heterotrophic microbes. However, qualifying microbial food viability and future impacts in the food sector remains challenging due to their diversity, technical complexity, socioeconomic forces, and incipient markets. This review provides an overview of microbial food systems and then delves into the technical interplay among feedstocks, microbes, carbon fixation platforms, bioreactor operations, and downstream processes. The review further explores developing markets for microbial food products, the industrial landscape, economic drivers, and emerging trends in next-generation food products. The analysis suggests a transformative shift in the food industry is underway, yet significant challenges persist, such as securing cost-effective feedstocks, improving downstream processing efficiency, and gaining consumer acceptance. These challenges require innovative solutions and collaborative efforts to ensure the future commercial success of microbial foods - doing so will create myriad opportunities to transform and decarbonize our food system. © 2025 by the author(s).

关键词： Feedstocks

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Using the Guide to the systems engineering Body of Knowledge (SEBoK version 0.5) for Undergraduate System engineering program Assessment

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INCOSE International Symposium 2012年第1期22卷 2208-2220页

作者： Parnell, Gregory S. McCarthy, Daniel J. Hardman, Nicholas Yale, Gary E. United States Air Force Academy CO United States Military Academy West Point NY United States Department of Systems Engineering United States Military Academy West Point NY United States United States Air Force Academy CO United States Systems Engineering Program United States Air Force Academy CO United States

The draft of the Guide to the systems engineering Body of Knowledge (SEBoK version 0.5) was released in a wiki format on September 19, 2011. This paper uses SEBoK to assess the undergraduate systems engineering programs at the United States Military Academy at West Point and the United States Air Force Academy. Both ABET accredited engineering programs educate cadets in to be future leaders of character in their respective military services. We first review the rationale for undergraduate SE programs at the academies. Next, we compare the two programs including their program objectives, student outcomes, curricula, enrolments, and graduate assignments for each program. Then, we compare each of the SE programs to the SEBoK knowledge areas. We conclude with ideas on how undergraduate SE programs can use the SEBoK in planning and execution of their academic programs. © 2012 The Authors.

关键词： Students

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Generative assistant for digital twin simulations 12

Generative assistant for digital twin simulations

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12th CIRP Global Web Conference, CIRPe 2024

作者： Boareto, Pedro Antonio De Freitas Rocha Loures, Eduardo Portela Santos, Eduardo Alves Deschamps, Fernando Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná 1155 Imaculada Conceição St. Paraná Curitiba80215-901 Brazil Federal University of Paraná 632 Prefeito Lothário Meissner Av. Paraná Curitiba80210-170 Brazil

One of the key emerging technologies in Industry 4.0 is the Digital Twin (DT). Although it promises increased efficiency, productivity, and innovation, its adoption faces challenges such as high investment costs and the need for workforce requalification. Generative Artificial Intelligence (GAI) emerges as a promising solution, offering capabilities to accelerate development processes and reduce costs. This study aims to leverage GAI to enhance the development of DT and support decision-making in industrial environments by proposing a Generative Assistant for Digital Twin Simulations (GADTS). This proposal generates operational models quickly, offers greater customization, and facilitates the creation of efficient scenario simulations in natural language. The proposal was tested with artificial data. As a result, the development of highly personalized DT simulations with Key Performance Indicators (KPIs) was entirely abstracted into natural language requests. © 2025 Elsevier B.V.. All rights reserved.

关键词： Generative adversarial networks

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Towards an Integration of Augmented Reality in Industrial Assembly Processes: Issues and Perspectives

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Procedia Computer Science 2025年 253卷 1063-1072页

作者： Bruno J. Souza Anderson L. Szejka Roberto Z. Freire Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana (PUCPR) Curitiba 80215-901 Brazil Federal University of Technology – Parana (UTFPR) Curitiba 80050-540 Brazil

The evolution of manufacturing has led to Industry 5.0, where human creativity is integrated with advanced technology to achieve efficient and sustainable production. Given the high volume and diversity of assembly tasks, operator assistance systems have become essential tools in the industry, leveraging augmented reality (AR) and other new technologies. This literature review specifically examines 19 papers on AR applications within the context of industrial assembly tasks. The review focuses on identifying the types of AR technologies utilized, the methodologies for implementing AR systems in assembly, and the primary limitations and challenges encountered. The findings indicate a growing field with promising applications in assembly, training, and maintenance, predominantly using AR glasses. Furthermore, a gap is identified indicating the absence of a standardized framework for AR-based assembly assistance systems. Future research should aim at creating a foundational framework for AR system implementation, thereby accelerating the adoption of innovative technologies in the industry.

关键词： Industry 5.0 augmented reality assembly assistance

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Generative a bistant for digital twin simulations 1

Generative a bistant for digital twin simulations

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1st International Conference on Digital Sovereignty, ICDS 2024

作者： Boareto, Pedro Antonio De Freitas Rocha Loures, Eduardo Portela Santos, Eduardo Alves Deschamps, Fernando Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná 1155 Imaculada Conceição St. Curitiba Paraná80215-901 Brazil Federal University of Paraná 632 Prefeito Lothário Mei bner Av. Curitiba Paraná80210-170 Brazil

One of the key emerging technologies in Industry 4.0 is the Digital Twin (DT). Although it promises increased efficiency, productivity, and innovation, its adoption faces challenges such as high investment costs and the need for workforce requalification. Generative Artificial Intelligence (GAI) emerges as a promising solution, offering capabilities to accelerate development proce bes and reduce costs. This study aims to leverage GAI to enhance the development of DT and support decision-making in industrial environments by proposing a Generative A bistant for Digital Twin Simulations (GADTS). This proposal generates operational models quickly, offers greater customization, and facilitates the creation of efficient scenario simulations in natural language. The proposal was tested with artificial data. As a result, the development of highly personalized DT simulations with Key Performance Indicators (KPIs) was entirely abstracted into natural language requests. © 2025 Elsevier B.V.. All rights reserved.

关键词： Generative adversarial networks

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Deep Learning for Real-Time PPE Usage Monitoring Using Wearable IMU Sensors

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IEEE Access 2025年 13卷 82492-82502页

作者： Guimarães, Pedro Carvalho da Fonseca De Cristo, Leonardo Braga Monteiro, Marcos Eduardo Pivaro Rebelatto, João Luiz Peron, Guilherme de Santi Rayel, Ohara Kerusauskas Moritz, Guilherme Luiz Federal University of Technology - Paraná (UTFPR) Curitiba Brazil Federal University of Technology - Paraná (UTFPR) Graduate Program in Electrical and Computer Engineering (CPGEI-CT) Curitiba Brazil Federal University of Technology - Paraná (UTFPR) Graduate Program in Energy Systems (PPGSE-CT) Curitiba Brazil

Ensuring the proper use of Personal Protective Equipment (PPE) is critical for safeguarding workers in high-risk environments, such as power distribution systems. This work presents an innovative approach to monitoring PPE usage through Deep Neural Networks (DNNs). Using data from inertial measurement units (IMUs) integrated into PPE items, we propose a solution to classify three usage states—namely carrying, still, and wearing—using raw accelerometer and gyroscope data. More specifically, thiswork assesses the effectiveness of three distinct network architectures—Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and CNN-BiLSTM—on a publicly released dataset on PPE usage provided by this study, while also comparing them against a traditional baseline Multi- Layer Perceptron (MLP) architecture. Our results demonstrate the superiority of BiLSTM in balancing high accuracy with computational efficiency, achieving above 98% accuracy regardless of the PPE type. Thiswork represents the first application of DNNs for PPE monitoring using IMU data, offering significant implications for enhancing safety compliance and operational monitoring in power distribution. © 2013 IEEE.

关键词： AI BiLSTM CNN DNN IoT PPE

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Versatile Test Reactor Open Digital engineering Ecosystem

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Insight 2025年第1期28卷 64-68页

作者： Ritter, Christopher Browning, Jeren Suyderhoud, Peter Hays, Ross Marshall, AnnMarie Han, Kevin Ashbocker, Taylor Darrington, John Nelson, Lee Idaho National Laboratory Department of Digital Systems & Engineering Versatile Test Reactor Program Idaho FallsID83401 United States Pivotal Solutions 3 Grace Avenue Suite 162 Great NeckNY11021 United States North Carolina State University Department of Civil and Environmental Engineering United States Department of Computer Science University of Illinois at Urbana-Champaign 205 N. Mathews Ave. UrbanaIL61801 United States

Modern design of nuclear facilities represents unique challenges: enabling the design of complex advanced concepts, supporting geographically dispersed teams, and supporting first-of-a-kind system development. Errors made early in design can introduce silent errors. These errors can cascade causing unknown risk of complex engineering programs. The Versatile Test Reactor (VTR) program uses digital-engineering principles for design, procurement, construction, and operation to reduce risk and improve efficiencies. Digital engineering is an integrated, model-based approach which connects proven digital tools such as building information management (BIM), project controls, and systems-engineering software tools into a cohesive environment. The VTR team hypothesizes using these principals can lead to similar risk and cost reductions and schedule efficiencies observed in other engineering industries. This research investigates the use of a digital engineering ecosystem in the design of a 300-MWt sodium-cooled fast reactor. This ecosystem was deployed to over 200 engineers and used to deliver the conceptual design of the VTR. We conclude that initial results show significant reductions in user latency (1000x at peak use), the possibility of direct finite-element-analysis (FEA) integrations to computer-aided design (CAD) tools, and nuclear reactor system design descriptions (SDDs) that we can fully link throughout design in data-driven requirements-management software. These early results led to the VTR maintaining milestone performance during the COVID-19 pandemic. Copyright © 2022 by Idaho National Laboratory. Published by INCOSE with permission.

关键词： Sodium-cooled fast reactors

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Thévenin Network Approach for Modeling Mutual Coupling in Narrowband Uniform Linear Arrays

Thévenin Network Approach for Modeling Mutual Coupling in N...

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IEEE Latin American Symposium on Circuits and systems (LASCAS)

作者： José A. Apolinário Cláudio A. B. Saunders Filho Antonio L. L. Ramos Program of Electrical Engineering Military Institute of Engineering (IME) Rio de Janeiro Brazil Department of Science and Industry Systems (IRI) TNM University of South-Eastern Norway (USN) Kongsberg Norway

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This paper investigates mutual coupling in narrow-band uniform linear antenna arrays through Thévenin network modeling. After an in-depth analysis, we derive a Thévenin network representing a given mutual coupling matrix and develop a method to achieve an equivalent decoupled model that significantly reduces computational and memory requirements in practical applications.

关键词： Mutual coupling Analytical models Circuits and systems Array signal processing Computational modeling Memory management Linear antenna arrays Integrated circuit modeling Narrowband

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A General Framework for Clustering and Distribution Matching with Bandit Feedback

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IEEE Transactions on Information Theory 2025年第3期71卷 2116-2139页

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y. F. Scarlett, Jonathan CNRS@CREATE DesCartes Program CREATE Tower 138602 Singapore National University of Singapore Department of Computer Science Queenstown119077 Singapore National University of Singapore School of Computing Department of Information Systems and Analytics Queenstown119077 Singapore National University of Singapore Department of Mathematics Department of Electrical and Computer Engineering Queenstown119077 Singapore Institute of Data Science National University of Singapore Department of Computer Science The Department of Mathematics Queenstown119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank-Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ vanishes. © 1963-2012 IEEE.

关键词： Random variables

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Assessing Tropical Cyclone-Driven Aerosol Transport - A High-Resolution WRF-Chem Analysis of Major Hurricane Matthew and Implications for Future Climate Scenarios

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Environmental Science and Technology 2025年 2025 Apr 29页

作者： Lin, Xiajing Huang, Guohe Environmental Systems Engineering Program University of Regina ReginaSKS4S 0A2 Canada

Tropical cyclones (TCs) are traditionally viewed as atmospheric cleansers, removing pollutants through precipitation and winds. However, emerging evidence suggests that TCs also transport pollutants over long distances, with their full impact on pollutant dynamics still underexplored. In this study, aerosol transport and chemical transformation during Hurricane Matthew (2016) are analyzed using high-resolution WRF-Chem simulations. The Hurricane-Driven Aerosol Transport Analysis Framework (WHDA-TAF) is developed to assess aerosol behavior under both current and future climate scenarios. Simulations based on Pseudo Global Warming (PGW) projections indicate that aerosols, such as sea salt, particulate matter, and carbonaceous aerosols, are lifted to higher altitudes and dispersed far from the storm’s core. Photochemical reactions within the cyclone generate secondary pollutants such as ozone and nitrates, which can worsen air quality even after the hurricane has dissipated. Copula-based analysis reveals that stronger hurricanes in high-emission scenarios (SSP585) are likely to increase the frequency and severity of extreme compound pollution events, even in regions distant from the storm’s direct impact. These findings challenge the view of TCs as mere atmospheric cleansers, highlighting their significant role as carriers and reactors of pollutants. Enhancing model resolution is crucial to better capture such TC-aerosol interactions and address the risks of cyclone-driven aerosol transport under climate change. © 2025 American Chemical Society.

关键词： Air quality

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