Inferring Economic Condition Uncertainty from Electricity Big Data

Inferring the uncertainties in economic conditions are of significant importance for both decision makers as well as market players. In this paper, we propose a novel method based on Hidden Markov Model (HMM) to construct the Economic Condition Uncertainty (ECU) index that can be used to infer the economic condition uncertainties. The ECU index is a dimensionless index ranges between zero and one, this makes it to be comparable among sectors, regions and periods. We use the daily electricity consumption data of nearly 20 thousand firms in Shanghai from 2018 to 2020 to construct the ECU indexes. Results show that all ECU indexes, no matter at sectoral level or regional level, successfully captured the negative impacts of COVID-19 on Shanghai's economic conditions. Besides, the ECU indexes also presented the heterogeneities in different districts as well as in different sectors. This reflects the facts that changes in uncertainties of economic conditions are mainly related to regional economic structures and targeted regulation policies faced by sectors. The ECU index can also be easily extended to measure uncertainties of economic conditions in different fields which has great potentials in the future

“Lock-in” Effect of Emission Standard and Its Impact on the Choice of Market Based Instruments

A country’s existing emission standard policy will lead to a “lock in” effect. When the country plans to adopt new market-based instruments to control greenhouse gas emissions, it must consider this effect as it chooses among instruments to avoid larger efficiency loss. In this paper, we find that the “lock in” effect will cause a kink point to occur on the marginal abatement cost (MAC) curve. This change of shape for the MAC curve reminds us to be cautious in choosing market-based instruments when applying Weitzman’s rule. We also introduce this concept into a dynamic multi-regional computable general equilibrium (CGE) model for China and simulate MAC curves for all regions. After applying Weitzman’s rule, we propose a timeline for introducing price instruments under different marginal benefit (MB) curve scenarios

Multiphysics Investigation on Coolant Thermohydraulic Conditions and Fuel Rod Behavior During a Loss-of-Coolant Accident

This article simulates the multiphysics coolant thermohydraulic conditions and fuel performance of a pressurized water reactor (PWR) during a loss-of-coolant accident (LOCA). In the coolant channel of a PWR, the coolant undergoes a series of different boiling regimes along the axial direction. At the inlet of the coolant channel, heat exchange between the cladding wall and coolant is based on single-phase forced convection. As the coolant flow distance increases, the boiling regime gradually converts to nucleate boiling. When a LOCA occurs, on the one hand, the coolant flux and coolant pressure decrease sharply; on the other hand, the heat flux at the cladding wall decreases relatively slowly. They both contribute to a swift increase in coolant temperature. As a consequence, a boiling crisis may occur as critical heat flux (CHF) decreases. In this article, the void fraction along the length of coolant channel in a reactor and mechanical performance of Zr cladding enwrapping UO2 fuel are investigated by establishing a fully coupled multiphysics model based on the CAMPUS code. Physical models of coolant boiling regimes are implemented into the CAMPUS code by adopting different heat transfer models and void fraction models. Physical properties of the coolant are implemented into the CAMPUS code using curve-fitting results. All physical models and parameters related to solid heat transfer are implemented into the CAMPUS code with a 2D axisymmetric geometry. The modeling results help enhance our understanding of void fraction along the length of the coolant channel and mechanical performance of Zr cladding enwrapping UO2 fuel under different coolant pressure and mass flux conditions during a LOCA

A Facile and Generic Strategy to Synthesize Large-Scale Carbon Nanotubes

An easy method to prepare carbon nanotubes (CNTs) has been demonstrated using a two-step refluxing and calcination process. First, a readily available inorganic salt, Ni(NO3)2⋅6H2O, used as the catalyst precursor was dissolved in the high-boiling-point organic solvents (alcohols or polyhydric alcohol) by refluxing at 190∘C for 3 hours. After refluxing, NiO nanoparticles obtained in the solution act as the catalyst, and the organic refluxing solvents are used as the carbon source for the growth of CNTs. Second, CNTs are prepared by calcining the refluxed solution at 800∘C in an N2 atmosphere for 3 hours. Results show that CNT growth possibly originates from carbon rings, with the nanotube walls growing perpendicular to these rings and forming a closed tube at the end

Effect of quercetin on the transport of ritonavir to the central nervous system in vitro and in vivo

The aim of this study was to identify an effective flavonoid that could improve the intracellular accumulation of ritonavir in human brain-microvascular endothelial cells (HBMECs). An in vivo experiment on Sprague-Dawley rats was then designed to further determine the flavonoid’s impact on the pharmacokinetics and tissue distribution of ritonavir. In the accumulation assay, the intracellular level of ritonavir was increased in the presence of 25 mmol L–1of flavonoids in HBMECs. Quercetin showed the strongest effect by improving the intracellular accumulation of ritonavir by 76.9 %. In the pharmacokinetic study, the presence of quercetin in the co-administration group and in the pretreatment group significantly decreased the area under the plasma concentration-time curve (AUC0-t) of ritonavir by 42.2 % (p < 0.05) and 53.5 % (p < 0.01), and decreased the peak plasma concentration (Cmax) of ritonavir by 23.1 % (p < 0.05) and 45.8 % (p < 0.01), respectively, compared to the control group (ritonavir alone). In the tissue distribution study, the ritonavir concentration in the brain was significantly increased 2-fold (p < 0.01), during the absorption phase (1 h) and was still significantly higher (p < 0.05) during the distribution phase (6 h) in the presence of quercetin

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure

Artificial intelligence : A powerful paradigm for scientific research

Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.Peer reviewe