Nitrogen (N) presents an important challenge for sustainability. Human intervention in the global nitrogen cycle has been pivotal in in providing goods and services to society. However, release of N beyond its intended societal use has many negative health and environmental consequences. Several systems modeling approaches have been developed to understand the trade-offs between the beneficial and harmful effects of N. These efforts include life cycle modeling, integrated management practices and sustainability metrics for individuals and communities. However, these approaches do not connect economic and ecological N flows in physical units throughout the system, which could better represent these trade-offs for decision-makers. Physical Input-Output Table (PIOT) based models present a viable complementary solution to overcome this limitation. We developed a N-PIOT for Illinois representing the interdependence of sectors in 2002, using N mass units. This allows studying the total N flow required to produce a certain amount of N in the final product. An Environmentally Extended Input Output (EEIO) based approach was used to connect the physical economic production to environmental losses; allowing quantification of total environmental impact to support agricultural production in Illinois. A bottom up approach was used to develop the N-PIOT using Material Flow Analysis (MFA) tracking N flows associated with top 3 commodities (Corn, Soybean and Wheat). These three commodities cover 99% of N fertilizer use in Illinois. The PIOT shows that of all the N inputs to corn production the state exported 68% of N embedded in useful products, 9% went to animal feed manufacturing and only 0.03% was consumed directly within the state. Approximately 35% of N input to soybean farming ended up in animal feed. Release of N to the environment was highest from corn farming, at about 21.8% of total N fertilizer inputs, followed by soybean (9.2%) and wheat farming (4.2%). The model also allowed the calculation of life cycle N use efficiency for N based on physical flows in the economy. Hence, PIOTs prove to be a viable tool for developing a holistic approach to manage disrupted biogeochemical cycles, since these provide a detailed insight into physical flows in economic systems and allow physical coupling with ecological N flows.
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http://dx.doi.org/10.1016/j.ecolmodel.2017.06.015 | DOI Listing |
PLoS One
January 2025
Klab4Recovery Research Program, The City University of New York, Staten Island, New York, United States of America.
Recruitment input-output curves of transspinal evoked potentials that represent the net output of spinal neuronal networks during which cortical, spinal and peripheral inputs are integrated as well as motor evoked potentials and H-reflexes are used extensively in research as neurophysiological biomarkers to establish physiological or pathological motor behavior and post-treatment recovery. A comparison between different sigmoidal models to fit the transspinal evoked potentials recruitment curve and estimate the parameters of physiological importance has not been performed. This study sought to address this gap by fitting eight sigmoidal models (Boltzmann, Hill, Log-Logistic, Log-Normal, Weibull-1, Weibull-2, Gompertz, Extreme Value Function) to the transspinal evoked potentials recruitment curves of soleus and tibialis anterior recorded under four different cathodal stimulation settings.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
Permanent magnet synchronous motors (PMSMs) are widely used in a variety of fields such as aviation, aerospace, marine, and industry due to their high angular position accuracy, energy conversion efficiency, and fast response. However, driving errors caused by the non-ideal characteristics of the driver negatively affect motor control accuracy. Compensating for the errors arising from the non-ideal characteristics of the driver demonstrates substantial practical value in enhancing control accuracy, improving dynamic performance, minimizing vibration and noise, optimizing energy efficiency, and bolstering system robustness.
View Article and Find Full Text PDFPNAS Nexus
December 2024
Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Before we attempt to (approximately) learn a function between two sets of observables of a physical process, we must first decide what the and of the desired function are going to be. Here we demonstrate two distinct, data-driven ways of first deciding "the right quantities" to relate through such a function, and then proceeding to learn it. This is accomplished by first processing simultaneous heterogeneous data streams (ensembles of time series) from observations of a physical system: records of multiple of the system.
View Article and Find Full Text PDFEnviron Sci Technol
December 2024
Civil and Environmental Engineering Department, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.
Present understanding on virtual scarce water embedded in supply chains has primarily focused on water quantities. Yet assessing the potential economic cost of and resilience to water scarcity along the supply chain is critical for making informed water and supply chain decisions. We quantified water scarcity risks and resilience at the city level, since fine-grained studies can better capture the highly varied water resource availability, demand and socio-economic development across China.
View Article and Find Full Text PDFEntropy (Basel)
October 2024
Department of Computer Science and Engineering, INESC-ID & Instituto Superior Técnico, University of Lisbon, 2744-016 Porto Salvo, Portugal.
The book , by Peter Wittek, made quantum machine learning popular to a wider audience. The promise of quantum machine learning for big data is that it will lead to new applications due to the exponential speed-up and the possibility of compressed data representation. However, can we really apply quantum machine learning for real-world applications? What are the advantages of quantum machine learning algorithms in addition to some proposed artificial problems? Is the promised exponential or quadratic speed-up realistic, assuming that real quantum computers exist? Quantum machine learning is based on statistical machine learning.
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