bluuebunny/crossref_metadata_2025_split huggingface dataset:
Emergence of a finite-size-scaling function in the supervised learning of the Ising phase transition
Authors: Kim Dongkyu , Kim Dong-Hee 
Published: February 2021

Abstract
We investigate the connection between the supervised learning of the binary phase classification in the ferromagnetic Ising model and the standard finite-size-scaling theory of the second-order phase transition. Proposing a minimal one-free-parameter neural network model, we analytically formulate the supervised learning problem for the canonical ensemble being used as a training data set. We show that just one free parameter is capable enough to describe the data-driven emergence of the universal finite-size-scaling function in the network output that is observed in a large neural network, theoretically validating its critical point prediction for unseen test data from different underlying lattices yet in the same universality class of the Ising criticality. We also numerically demonstrate the interpretation with the proposed one-parameter model by providing an example of finding a critical point with the learning of the Landau mean-field free energy being applied to the real data set from the uncorrelated random scale-free graph with a large degree exponent.
Sensors track mobilization of ‘chemical cocktails’ in streams impacted by road salts in the Chesapeake Bay watershed
Authors: Galella Joseph G , Kaushal Sujay S , Wood Kelsey L , Reimer Jenna E , Mayer Paul M 
Published: February 2021

Abstract
Increasing trends in base cations, pH, and salinity of freshwaters have been documented in US streams over 50 years. These patterns, collectively known as freshwater salinization syndrome (FSS), are driven by multiple processes, including applications of road salt and human-accelerated weathering of impervious surfaces, reductions in acid rain, and other anthropogenic legacies of change. FSS mobilizes chemical cocktails of distinct elemental mixtures via ion exchange, and other biogeochemical processes. We analyzed impacts of FSS on streamwater chemistry across five urban watersheds in the Baltimore-Washington, USA metropolitan region. Through combined grab-sampling and high-frequency monitoring by USGS sensors, regression relationships were developed among specific conductance and major ion and trace metal concentrations. These linear relationships were statistically significant in most of the urban streams (e.g. R
2 = 0.62 and 0.43 for Mn and Cu, respectively), and showed that specific conductance could be used as a proxy to predict concentrations of major ions and trace metals. Major ions and trace metals analyzed via linear regression and principal component analysis showed co-mobilization (i.e. correlations among combinations of specific conductance (SC), Mn, Cu, Sr2+, and all base cations during certain times of year and hydrologic conditions). Co-mobilization of metals and base cations was strongest during peak snow events but could continue over 24 h after SC peaked, suggesting ongoing cation exchange in soils and stream sediments. Mn and Cu concentrations predicted from SC as a proxy indicated acceptable goodness of fit for predicted vs. observed values (Nash–Sutcliffe efficiency > 0.28). Metals concentrations remained elevated for days after SC decreased following snowstorms, suggesting lag times and continued mobilization after road salt use. High-frequency sensor monitoring and proxies associated with FSS may help better predict contaminant pulses and contaminant exceedances in response to salinization and impacts on aquatic life, infrastructure, and drinking water.
High-resolution forest carbon modelling for climate mitigation planning over the RGGI region, USA
Authors: Ma L , Hurtt G , Tang H , Lamb R , Campbell E , Dubayah R , Guy M , Huang W , Lister A , Lu J , O’Neil-Dunne J , Rudee A , Shen Q , Silva C 
Published: February 2021

Abstract
The inclusion of forest carbon in climate change mitigation planning requires the development of models able to project potential future carbon stocks—a step beyond traditional monitoring, reporting and verification frameworks. Here, we updated and expanded a high-resolution forest carbon modelling approach previously developed for the state of Maryland to 11 states in the Regional Greenhouse Gas Initiative (RGGI) domain, which includes Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. In this study, we employ an updated version of the Ecosystem Demography (ED) model, an improved lidar initialization strategy, and an expanded calibration/validation approach. High resolution (90 m) wall-to-wall maps of present aboveground carbon, aboveground carbon sequestration potential, aboveground carbon sequestration potential gap (CSPG), and time to reach sequestration potential were produced over the RGGI domain where airborne lidar data were available, including 100% of eight states, 62% of Maine, 12% of New Jersey, and 0.65% of New York. For the eight states with complete data, an area of 228 552 km2, the contemporary forest aboveground carbon stock is estimated to be 1134 Tg C, and the forest aboveground CSPG is estimated to be larger at >1770 Tg C. Importantly, these estimates of the potential for added aboveground carbon sequestration in forests are spatially resolved, are further partitioned between continued growth of existing trees and new afforested/reforested areas, and include time estimates for realization. They are also assessed for sensitivity to potential changes in vegetation productivity and disturbance rate in response to climate change. The results from this study are intended as input into regional, state, and local planning efforts that consider future climate mitigation in forests along with other land-use considerations.
New approach for predicting nitrification and its fraction of N2O emissions in global terrestrial ecosystems
Authors: Pan Baobao , Lam Shu Kee , Wang Enli , Mosier Arvin , Chen Deli 
Published: February 2021

Abstract
Nitrification is a major pathway of N2O production in aerobic soils. Measurements and model simulations of nitrification and associated N2O emission are challenging. Here we innovatively integrated data mining and machine learning to predict nitrification rate (

R

nit

) and the fraction of nitrification as N2O emissions (

f

N

2

O

Nit

). Using our global database on

R

nit

and

f

N

2

O

Nit

, we found that the machine-learning based stochastic gradient boosting (SGB) model outperformed three widely used process-based models in estimating

R

nit

and N2O emission from nitrification. We then applied the SGB technique for global prediction. The potential

R

nit

was driven by long-term mean annual temperature, soil C/N ratio and soil pH, whereas

f

N

2

O

Nit

by mean annual precipitation, soil clay content, soil pH, soil total N. The global

f

N

2

O

Nit

varied by over 200 times (0.006%–1.2%), which challenges the common practice of using a constant value in process-based models. This study provides insights into advancing process-based models for projecting N dynamics and greenhouse gas emissions using a machine learning approach.
Ground state of a cross-cavity quantum Rabi model
Authors: Shen Lituo , Yang Jingwen , Shi Zhicheng , Zhong Zhirong , Xu Canhua 
Published: February 2021

Abstract
We study a cross-cavity quantum Rabi model with ultrastrong coupling, and its analytical ground state is demonstrated to be approximately obtained by using the rotating-transformation method. Our method requires making three unitary transformations to the original Hamiltonian and ignoring the three-party cross interaction. In near-resonance and ultrastrong-coupling regimes, the ground-state fidelity is higher than 99% for a ±20% detuning range. We analytically demonstrate that the entanglement in ground state first quadratically increases with the qubit-oscillator coupling strength, then gets close to the maximum value 1. The oscillator’s nonclassical property in the ground state is well explained by analytically solving the Wigner function.
Affecting Factors for Implementing Performance-Based Maintenance Contracting: Perspective of Malaysian Road Contractors
Authors: Manogaran R , Rahman R A , Ismail A , Razelan I S M , Mandiartha P 
Published: February 2021

Abstract
Traditionally, road maintenance is conducted either using road authorities’ in-house resources or method-based contracting, where contractors do the work and are paid based on predefined volume and unit prices. Nowadays, Performance-based Maintenance Contracting (PBMC) is one of the most current approaches in road maintenance works. PBMC provides contractors with freedom in design, construction methods, materials, and innovation in performing the contract as long as the standard specified is fulfilled. While PBMC implementation is increasing around the world, it can be considered new in Malaysia. This research aims to investigate the viability and practicability of PBMC’s implementation for road maintenance in Malaysia. Specifically, the research’s objective is to identify factors that are affecting the adoption of PBMC for road maintenance works in Malaysia. To achieve that objective, interview data from industry practitioners that are involved in managing roads are analyzed. Factors affecting PBMC’s implementation for road maintenance in Malaysia can be categorized into two groups: PBMC (ex. awareness on PBMC) and Non-PBMC (ex. contractor’s integrity) related factors. The findings of this research would help policymakers, industry practitioners, and researchers improve the success of implementing PBMC in the facility management industry.
Online estimation of battery power state based on improved equivalent circuit model
Authors: Wang Yu 
Published: February 2021

Abstract
The state-of-power (SOP) of lithium-ion batteries is an important parameter for safety control and energy recovery of electric vehicles. In the battery management system (BMS), the Equivalent-Circuit Model (ECM) is commonly used to simulate battery dynamics. However, there is always a contradiction between the complexity and accuracy of the model. A simple model usually cannot reflect all the dynamic effects of the battery, which may bring error identification to the parameters. A complex model always has too many parameters that cannot be identified, and there may be parameter divergence problems. In order to solve this problem, this paper proposes a new equivalent circuit model, that is, the equivalent circuit model of the vehicle power battery based on the Auto Regression (AR) model. Based on this model, some inaccurate parameters were found in the parameter identification process, so a parameter identification method based on the extended Kalman filter algorithm and Recursive Least Squares (RLS) was used, and a composite model estimation method was used Comprehensive consideration of a series of restrictions such as State of Charge (SOC) limit and factory setting limit, combined with other methods such as equivalent circuit model, requires the estimated result to meet all the restrictions, to make up for some of the shortcomings of other methods, and make SOP estimation more accurate and reliable.
Defect analysis of the same batch of substation equipment based on big data analysis algorithm
Authors: Gao Qiang , Zhong Chuan , Wang Yong , Wang Peng , Yu Zaiming , Zhang Jiannan 
Published: February 2021

Abstract
High-quality power equipment is the basis for ensuring the safe operation of the power grid and improving the reliability of power supply. In actual operation, although some electrical equipment can continue to be used, abnormal operation or hidden dangers will affect the safety of people, equipment and power grid, reliable and economic operation of power grid and equipment, equipment output or life span, and power quality. Therefore, research on multi-dimensional analysis of the same batch of substation equipment, and identification of suspected family defects and frequent defects is of great significance for improving the company’s equipment management and defect management. The maturity and promotion of unstructured text data mining, graph computing technology, and semantic analysis have provided a wider dimensional space for the analysis of power equipment defect data. Aiming at the shortcomings of traditional defect data analysis, this article summarizes and analyzes the defects and hidden dangers found in the equipment, traces the distribution status of the same equipment through the physical “ID”, carries out multi-dimensional analysis of the same batch of equipment, and investigates the hidden dangers of equipment family defects. A big data analysis algorithm to build automatic identification models of suspected familial defects and frequent defects in main substation equipment such as transformers, disconnectors, circuit breakers, and use graph computing technology to quickly integrate analysis capabilities in multi-source heterogeneous data fusion with hidden relationship discovery capabilities, identify equipment defects with potential impact relationships. From historical data, discover which manufacturers or purchased batches of equipment are more likely to have the same defect, and which defect types have a higher frequency, improve the recognition model of family defects and frequent defects, and improve the accuracy and comprehensiveness of defect recognition.
Design and Implementation of a Capacitive Energy Storage Pulse Drive Source
Authors: Miao Weitong , Huang Jun , Chen Haitao , Wang Lei , Wang Yucheng 
Published: February 2021

Abstract
According to the requirement of driving power supply for pulsed semiconductor laser, a method of constant current output is proposed by combining large energy storage capacitance with MOS transistor working in linear region to solve the problem of generating high amplitude, short pulse width and high stability current pulse on low impedance load. The front stage uses the buck circuit to charge the energy storage capacitor, and through the hysteresis control of the buck circuit, the voltage of the energy storage capacitor is controlled. In the latter stage, the MOS transistor working in the linear region is used to realize the pulse output, and the PI module is used to adjust the output current to ensure the constant output current. Through PSPICE simulation, the key factors affecting the output pulse amplitude, pulse width and rise time are analyzed and verified by experiments. Using 155V DC power supply, the experimental results show that the capacitor energy storage pulse driver circuit can achieve a pulse constant current output with amplitude of 30A, pulse width of 300μs and rise time of less than 10μs on a 5 Ω low impedance load.
Quantitative Analysis of Generalized Suction on the Shear Strength of Unsaturated Soils
Authors: Sun Yu , Zhang Weichao 
Published: February 2021

Abstract
Taking the shear strength as the characteristic index to measure the strength of the soil, a characteristic parameter is established that can describe the contact distance between the surface of soil particles—the mean value of particle contact density. Using the indoor direct shear test of the preset scheme, the first-hand test data that three different factors such as moisture content, dry density and grain size quantitatively affect the shear strength of soil are obtained. Through the establishment of the quantitative relationship between the mean value of particle contact density and the shear strength, the theoretical description of the influence law that several key elements such as moisture content, void ratio, gradation, and particle size on the shear strength of unsaturated soil is realized, and the theoretical description and quantitative analysis of the influence of generalized suction on the shear strength of unsaturated soils are also completed.
Design of Coal Mine Power Grid Parameter Monitoring System Based on CAN Bus
Authors: Ma Yunlong 
Published: February 2021

Abstract
In order to ensure the safe production of coal mine enterprises, a real-time power grid parameter monitoring system is proposed by using modern computer and communication technology, so as to ensure the necessary power supply safety for the safe production of coal mines. Based on the analysis of coal mine safety production form and underground special operation environment, this paper introduces can communication technology and ECAN module in detail, designs the overall structure of coal mine power grid parameter monitoring system based on CAN bus, DSP minimum system, can interface circuit and software flow.
Study on the secondary plugging technology of drilling through the floor through drainage
Authors: Qin Muguang 
Published: February 2021

Abstract
According to the analysis and research results of the leakage channels of the boreholes in the coal mine face, the secondary plugging area of the leakage boreholes was determined, and the secondary plugging technology of “new extraction pipe + ‘one plug, one injection’” was formulated. And field test investigation was carried out. The research results show that after adopting the new secondary plugging technology for gas drainage, the concentration of gas drainage increases by 5~20 times, and the pure flow rate of gas drainage increases by 5~23 times, indicating that the drainage effect has improved significantly, which proves the feasibility of the secondary plugging technology.
Effects of Air Outlet Position and Air Speed on Airflow and Dust Field Distributions
Authors: Zhuang Xue’an 
Published: February 2021

Abstract
To meet the actual demands on ventilation and dust removal in mine heading faces and to reduce the potential safety threats and pollution risks of dust, this study investigates the migration and distribution trends of airflow and dust fields at different air outlet positions and air speeds under scenarios using forced ventilation and long-forced-short-exhaust ventilation techniques at the heading face under continuous mining operation, which is accomplished in the engineering context of Xiaobaodang Coal Mine by a combination of two-phase flow analysis and finite element simulation. As the results reveal, the return air speeds exhibit a “rising-falling-stable” trend at different air outlet positions and inlet air speeds. Appropriate increase of inlet air speed is effective in covering the front corner area and in improving the dust distribution in the roadway. Dust concentration on the return air side is markedly higher than on the air duct side, with distribution primarily in the front section. In the case of forced ventilation for the roadway having a large cross-sectional area of 22 m2, reasonable dust distribution on the return air side can be ensured when the air duct outlet is 10 m away from the front and the inlet air speed is 11 m/s. In the case of long-forced-short-exhaust ventilation, reasonable settings are a 15 m distance of air duct outlet from the front and a 9 m/s speed of inlet air.
Research on injection profile logging technology
Authors: Li Huiqiang 
Published: February 2021

Abstract
in this paper, through the analysis of the existing problems and influencing factors in the application of historical data of injection profile, continuous correlation logging technology of injection well, electromagnetic flow and tracer correlation logging technology are applied to solve the problems of overflow and contamination of isotope profile test in low injection wells. In general injection wells, the current collecting electromagnetic flow logging technology is applied; in stratified injection wells with different water injection intensity, oxygen activation logging, isotope water injection profile logging, tracer correlation logging and other logging technologies are used to combine logging according to the injection volume and string structure testing instrument scope, so as to eliminate the contamination effect and improve the accuracy of interval water injection profile test.
LUCC study under different geological conditions in Nanjiang district, Sichuan Province
Authors: Qu Shaodong , Li Yanan , Xiong Yufei 
Published: February 2021

Abstract
With the development of the times, more and more people begin to pay attention to land use change. This paper analyses the influence of different geological background conditions on the dynamic change of land use in Nanjiang County, Sichuan Province, and attempts to establish a land use model that meets the geological conditions of each region. According to different geological conditions, the purple soil of Cretaceous and Jurassic tilted single-sided low mountains and eroded terrace like narrow valley low mountains in the south is suitable for planting and breeding; the yellow soil of Jurassic and Triassic single-sided low and middle mountain purple soil in the middle is suitable for developing multi economic crops dominated by forest land; the limestone of yellow brown soil of Triassic, Sinian, Cambrian and middle mountain yellow soil in the north can be used for forestry and animal husbandry Industry.
Geological Recognition and Expansion Potential Analysis of the New 172-52 Drilling Area
Authors: Zheng Jianghua , Qiao Shasha , Zhao Yue , Yi Jun , Wang Chao 
Published: February 2021

Abstract
Fine reservoir characterization is the basis of tapping the potential of high-yield exploration and evaluation wells. In this paper, the comprehensive prediction of New 172-52 reservoir in high-yield exploration and evaluation well area is carried out by using seismic data combined with drilling data, the sand body in the study area is re-depicted, and the geological understanding after drilling is integrated. It is concluded that the effective thickness of the well area on the east side of the fault in this area changes rapidly and gradually becomes thicker to the southeast, and the sand body is developed and has good connectivity, and there is potential for edge expansion above the oil-water interface. In the well area on the west side of the fault, according to the seismic reservoir attribute, the abandoned well after drilling is at the edge of the sand body, and the sand body extends to the northeast around the well point, and meets the fault block at the high part of the structure, which has the potential of expanding the edge. Through fine reservoir characterization, the boundary and scale of sand bodies are explored, and the productivity potential of high-yield exploration and evaluation wells is tapped.
Reservoir prediction and application of multi-attribute fusion
Authors: Wang Xingye 
Published: February 2021

Abstract
Under the existing well pattern density conditions, there are still uncertainties in the understanding of reservoir distribution in XX Oilfield. The main reasons are that the different sedimentary environments and complex and changeable sedimentary models lead to uncertainties in reservoir prediction under the guidance of sedimentary models; Second, the sedimentary mechanism of different types of sand bodies is different, and there are multiple solutions in cross-well prediction. Therefore, other means must be used for auxiliary analysis in reservoir prediction, so as to improve the accuracy of sand cross-well prediction. This paper focuses on the basic method of reservoir prediction based on seismic multi-attribute fusion, which can further improve the accuracy of cross-well prediction in the cross-well prediction of thick oil layers In addition, the sand body distribution characteristics corrected by multi-attribute fusion slice are applied to measure well selection and layer selection, and comprehensive analysis of remaining oil is carried out by integrating various disciplines, and relatively high-quality potential tapping horizons are determined, which further improves the coincidence rate of potential tapping by measures.