In devising an effective Hazard Identification, Risk Assessment and Determining Controls plan of a company, it is crucial to understand the factors that relate with the Inherent and Residual Risks to prioritize the im...
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This paper addresses the problem of input-to-state stability (ISS) and stabilization of linear ordinary differential equations (ODEs) coupled with a system of homogeneous linear hyperbolic partial differential equatio...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
This paper addresses the problem of input-to-state stability (ISS) and stabilization of linear ordinary differential equations (ODEs) coupled with a system of homogeneous linear hyperbolic partial differential equations (PDEs) through the boundaries. First, a Lyapunov result characterizing the ISS property for finite-dimensional systems is extended to deal with coupled ODE and PDE systems. The proposed ISS condition is then applied to derive stability and stabilization conditions in terms of linear matrix inequality constraints assuming magnitude bounded disturbances at the boundaries. Two convex optimization problems are also proposed in order to obtain either an optimized reachable set estimate or a boundary controller that minimizes the disturbance effects on the ${\mathcal{L}}_{2} \times \mathbb{R}$-norm of the system states. Numerical examples illustrate the potential of the proposed approach.
Currently, the device that helps small ships navigate is a plotter that displays a chart. For economic reasons, it is difficult for small ships to have more equipment to assist navigation. Therefore, this suggestion, ...
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The global agribusiness context faces at the same time challenges of feeding a growing global population that is used to safe and nutritious food, opportunities based on innovation, high technology and efficiency in t...
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Wireless Sensor Network (WSN) is a wireless self-organizing network based on sensor nodes. Small batteries or power supply with weak computing power are usually used, so how to effectively control the energy consumpti...
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Livestock are the largest source of anthropogenic methane (CH4) emissions, and in intensive dairy systems, manure management can contribute half of livestock CH4. Recent policies such as California’s short-lived clim...
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Livestock are the largest source of anthropogenic methane (CH4) emissions, and in intensive dairy systems, manure management can contribute half of livestock CH4. Recent policies such as California’s short-lived climate pollutant reduction law (SB 1383) and the Global Methane Pledge call for cuts to livestock CH4 by 2030. However, investments in CH4 reduction strategies are primarily aimed at liquid dairy manure, whereas stockpiled solids remain a large source of CH4. Here, we measure the CH4 and net greenhouse gas reduction potential of dairy manure biochar-composting, a novel manure management strategy, through a composting experiment and life-cycle analysis. We found that biochar-composting reduces CH4 by 79%, compared to composting without biochar. In addition to reducing CH4 during composting, we show that the added climate benefit from biochar production and application contributes to a substantially reduced life-cycle global warming potential for biochar-composting: −535 kg CO2e Mg–1 manure compared to −194 kg CO2e Mg–1 for composting and 102 kg CO2e Mg–1 for stockpiling. If biochar-composting replaces manure stockpiling and complements anaerobic digestion, California could meet SB 1383 with 132 less digesters. When scaled up globally, biochar-composting could mitigate 1.59 Tg CH4 yr–1 while doubling the climate change mitigation potential from dairy manure management.
The increasing demand for recycled poly(ethylene terephthalate) (rPET) has raised concerns about the environmental impact of the production process, particularly regarding water consumption during the lye washing proc...
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As additive manufacturing transitions from manufacturing prototypes to rapid manufacturing, more human factors considerations must be assessed and integrated for improved work design. This review paper provides an ove...
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Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation ...
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Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs);on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the ?S?goals of parallel driving.
This study comprehensively analyzes the application of innovative deep learning (DL) and machine learning (ML) techniques in smart energy managementsystems (EMSs), with an emphasis on load forecasting, demand respons...
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