bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis on fracturing effect of polymer flooding oil layer II1-9 in North Region I
Authors: Junhua Kang 
Published: May 2021

Abstract
Fracturing, as an important means to tap potential remaining oil and alleviate three contradictions, has a great contribution to the completion of production. Since the implementation of measures to increase oil and create efficiency project in 2017, the fracturing effect has been greatly improved. This paper analyzes the development characteristics and production status of the reservoir, and expounds the necessity of fracturing in this block. Taking a single well as an example, this paper analyzes two types of Wells with the purpose of low-efficiency well treatment and thick-layer well fracturing effect from the aspects of well selection conditions, development status, measure technology, measure effect and post-pressure protection, etc. providing reference for the implementation of the whole process fracturing measures in the block of the second-class reservoir.
Feature Extraction of Down-hole Drilling-plug Impact Signal Based on Empirical Mode Decomposition
Authors: Xu Feiyan , Yang Quanzhi , Wu Bin , Zhang Xiaobin , Yu Xiaolong , Wan Shoubo 
Published: May 2021

Abstract
For analyzing the non-stationary drilling-plug impact signal under the complicated drilling environment, use of conventional discrete Fourier Transform (DFT) has been known to be less efficient. To extract useful information contained in the drilling-plug impact signal, a new extraction method based on empirical mode decomposition (EMD) is presented. In the signal processing, EMD is used to decompose the original drilling-plug impact signal into the intrinsic mode functions (IMFs). By analyzing kurtosis and energy spectrum of the IMFs, the first three IMFs are found that contain the characteristic of the drilling-plug, and the specific frequency band of the drilling-plug impact signal is determined after power spectrum analyzing. The reconstruction drilling-plug impact signal after band-pass filter can clearly show the detailed process of the drilling-plug such as drilling the rubber plug, drilling the float collars and drilling the formation.
Ultrasonic Guided Wave Damage Detection Method for Stiffened Plates Based on Deep Learning
Authors: Shen Qing , Yue Shengyao , Lu Wei , Xu Guidong , Xu Chenguang , Xu BaiQiang 
Published: May 2021

Abstract
In order to solve the problem of debonding damage detection commonly exists in stiffened plates, we propose an ultrasonic guided wave damage detection method based on deep learning and conduct a numerical study of the method. The guided wave signal get from a Finite-Element-Model (FEM) is pre-processed through wavelet transform to obtain the wavelet coefficient matrix (WCM), which is input into Convolutional Neural Network (CNN) in the form of gray image to obtain the neural weights. The detection accuracy of debonding damage in the stiffened plate has reached nearly 99%.
Design of arm layered oil recovery data monitoring system
Authors: Huiqin Jia , Cheng Cheng , Ruirong Dang , Gan Li 
Published: May 2021

Abstract
Aiming at the different characteristics of different reservoirs between oil wells, this paper studied and designed a layered underground oil recovery data acquisition system based on ARM Cortex-A7 architecture. This system mainly consists of three parts, namely sensor, measurement circuit and data acquisition and processing. I.mx6ull is selected as the main control chip, and a data acquisition platform is built with the peripheral device AD8229 instrument amplifier. The SPI protocol and state machine are used to communicate with the downhole multi-reservoir data, and ESP8266 module is used to transmit the downhole multi-parameter data to the Alibaba Cloud server. Through the software and hardware experiment system test, it shows that the designed system has the characteristics of good real-time performance, high precision and low cost.
Simulation and Implementation of NPC three-level inverter SVPWM algorithm
Authors: Zhang Tao , Lu Xiuhe , Shi Yuanlin 
Published: May 2021

Abstract
Taking diode clamp type three-level inverter as the research object, the principle of SVPWM algorithm is introduced, and the relationship between SVPWM algorithm and two-level algorithm is described. On this basis, the simulation principle of two-level SVPWM algorithm is used for reference, and the three-level space vector algorithm is simulated according to MATLAB/Simulink simulation environment. Finally, the simulation results under three-level and two-level modulation show that the three-level inverter SVPWM algorithm can reduce harmonics, and the simulation experiment verifies the correctness and feasibility of the control method.
Research of Incremental Learning Algorithm for SVM Based on Class Center Diameter
Authors: Li Jinfeng , Xie Wenhao 
Published: May 2021

Abstract
In the learning process based on ISVM, how to effectively retain the history information and selectively discard some new training data, so as to maintain the classification accuracy and save the storage space after adding new samples every time, which is the key of the current ISVM classification algorithm. This thesis proposes a new incremental learning algorithm, that is, SVM incremental learning algorithm based on cluster diameter (CD-ISVM). This algorithm firstly calculates the two centers of the positive and negative samples, and then to build the boundary vector set by the coordinates of the two class centers. Moreover, KKT conditions are combined to filter incremental data and boundary vector set, and feedback information is provided to adjust the position of the boundary vector set. Then, the union of SV set and boundary vector set is taken as the increment set. After several increments, a classifier with strong generalization is finally trained.
A New Method of LCL Filter Parameter Design Based on Fish Swarm Algorithm
Authors: Liu Sining , Wang Wei , Zheng Ruize 
Published: May 2021

Abstract
Aiming at the problem that LCL filter is better than L, LC filter in filtering performance. However, the selection of parameters is more complicated, and it is prone to resonant spikes in the filtering process, this study introduces passive damping to measure its resonant spikes suppression, and combined with the attenuation of the grid-connected harmonic current of the LCL filter to measure its excellent filtering performance. Based on this, the objective function model is established, the fish swarm algorithm is adopted to optimize its parameters, and the grid-connected simulation model is found in MATLAB/Simulink. Its filtering effect is compared with the traditional method. The simulation proves that the filter after parameter optimization can effectively suppress and filter the harmonic components of the inverter output voltage compared with the conventional filtering method, and provide positive theoretical guidance for the stable grid connection of new energy power generation.
A Prediction Model for Water Absorption Profile Based on IDW-DTW-RNN Method
Authors: Zhao Xu , Fu Ju Yao , Xiong Hong Li , Yu Gao Ming 
Published: May 2021

Abstract
At present, many low-permeability oil fields are entering the later stages of development, and there will be problems of difficulty in water injection and poor water injection effects. Obtaining water absorption profiles and their changes can more effectively help formulate layered water injection strategies. Generally, the isotope water absorption profile method is often used to obtain the water absorption profile. This method is accurate, but the test cost is higher and the actual oil field measurement data is less. Based on the measured water absorption profile data, this paper selects 8 factors including effective thickness of oil layer, effective permeability, measure coefficient, crude oil viscosity, crude oil volume coefficient, injection-production pressure difference, well spacing, and connection coefficient between oil and water wells as the main influencing factors of the profile prediction model. Taking water injection wells data with 8 factors as input, a water injection profile prediction neural network model based on inverse distance weighting method and dynamic time warping, namely called IDW-DTW-RNN, is trained in this study. This model is applied in oil field, and the accuracy is obviously improved compared with the conventional method, which is in line with the actual development situation. It provides a scientific basis for the adjustment and optimization of the later injection-production structure of the oilfield.
Research Status and Development Trend of Water Cut Detection Methods for Crude Oil
Authors: Guowang Gao , Dasen Hou , Hua Le , Wang Fei , Li Yuanbo 
Published: May 2021

Abstract
Water cut detection of crude oil plays a very important role in the petroleum and petrochemical industry. At present, the existing crude oil water cut detection methods include capacitance method, conductance method, radio frequency method, Ray method, spectrum method. Firstly, the measuring principle, application conditions, advantages and disadvantages of the existing methods are summarized, and the factors affecting the measuring accuracy of the existing methods are analyzed. Afterwards, the latest development of water cut measurement technology of crude oil is mentioned, such as multi-sensor information fusion technology, tomography technology, neural network technology. With the emergence of new technology and new method, the development trend of crude oil water cut measurement technology is towards full range, high precision, intelligence, multi-parameter fusion, safety, energy saving, environmental protection and so on.
Water Body Extraction from High-resolution Remote Sensing images Based on Scaling EfficientNets
Authors: Lu Li , Qi Wen , Bing Wang , Shichao Fan , Li Lingling , Qingjie Liu 
Published: May 2021

Abstract
Effectively and accurately extracting water bodies from high-resolution remote sensing images is an important yet extremely challenging task due to ambiguities raised by spectral and texture similarities between water bodies and distractors such as shadows buildings, mountains and vegetations. Recent developed deep learning techniques have provided researchers powerful tools to defeat traditional hand-engineered features by learning powerful hierarchical representations, however, with a cost of very high model complexity and computational resource requirement. To conquer this issue, in this paper, we propose to build a deep learning model for water extraction based on the EfficientNet-B5. We evaluate possible variations of such design and compare them with several widely used approaches on GF-2 and Sentinel-2 satellites. Experimental results demonstrate that our model obtains much better performance than SVM and U-Net, which clearly shows the effectiveness and robustness. We hope this work will facilitate further research in this field and inspire researchers in the community to develop better models.
Adaptive Weight Vector Solving Based on the Summation of Partial Eigenbeams in STAP
Authors: Gong Ziyi , Wang Wanlin , He Yinguang 
Published: May 2021

Abstract
For the side-looking airborne or spaceborne early warning radar, Space-time adaptive processing (STAP) is a well-established technique. The adaptive weight vector can be written in the form of summation that equivalents to the inversion of covariance matrix. In order to reduce the computational complexity of the adaptive weight vector, this paper proposes a modified calculation method based on the weighted sum of partial eigenbeams according to the array model of the radar system. Both the loss of signal-to-interference and noise ratio (LSINR) and adapted patterns are used as performance metrics. Computer simulations verified the effectiveness of the method and its adaptability in traditional full-dimension, fixed-structure reduced dimension STAP.
Electrostatic Signal analysis of rotor UAV
Authors: Xi Chen , Runsheng Wang , Pengfei Li , Wei Wang , Liwei Shao 
Published: May 2021

Abstract
With the rapid growth of unmanned aerial vehicle(UAV) market, incidents of small UAV flying without license or without permission frequently occur, which bring serious hidden dangers to the security of civil airports, sensitive targets and major activities. Rapid and accurate detection and recognition of small UAVs is the basis and premise of countermeasures against small UAVs. However, due to the fact that the production materials of small UAVs are mostly composite materials with good wave-absorbing and wave-penetrating properties and small size, the existing detection technologies such as radar and laser cannot effectively detect the signals of UAVs, so the detection accuracy is very low and the amount of information that can be obtained is small. In this paper, the most typical type of small UAV, rotor UAV, is taken as the research object to study the electrostatic signal of rotor UAV, which provides a theoretical basis for the electrostatic detection of rotor UAV.
Experimental Study on Compressive and Splitting Tensile Strength of Layer Hybrid Steel Fiber Recycled Aggregate Concrete
Authors: Wang Yuying , Tang Jiajun , Hu Jie , An Silong 
Published: May 2021

Abstract
In order to improve the mechanical properties of recycled aggregate concrete, hybrid steel fiber layers were added to recycled aggregate concrete. By changing the number of layers and contents of hybrid steel fibers, compressive and splitting tensile strength tests were carried out on 10 groups of 60 recycled aggregate concrete blocks. The results show that with the increase of the number of layers of hybrid steel fibers, the overall change of compressive strength is not significant, the splitting tensile strength gradually shows an upward trend, and the change of tension-compression ratio is not obvious, but the tension-compression ratio generally increases compared with group RC. With the increase of hybrid steel fiber contents, the compressive strength first increases and then decreases, the splitting tensile strength gradually increases, and the tension-compression ratio first decreases and then increases. After adding hybrid steel fiber layers, the failure of recycled aggregate concrete changes from brittleness to plasticity.
The ‘Pombalino Frontal’ design and its structural efficiency
Authors: Morais António , Fialho Carmo , Santos Lam dos 
Published: May 2021

Abstract
After November 1st, 1755, earthquake which hit and destroyed Lisbon, the Portuguese military architects innovated the constructive techniques and conceived a ‘new construction system’ with anti-seismic characteristics, the so called ‘Pombalino building’, often improperly named ‘pombalino cage’. Inserting in the ‘Pombalino cage’ a vertical ‘frontal’ element, made up of Santo André Crosse’s lattices, impresses the intellectual boldness, at that time, demonstrated by the military architects of the Kingdom, concerning the effectiveness and efficiency of the structural performance of the ‘Pombalino building’. It should be noted that we were in 1755, so the concept of stress and deformation had not yet appeared, and, therefore, mankind did not yet discover the structural calculus theory, so the concepts of bending moment and normal effort were not known at that time. Although they did not know it, the frontal element design suggests their engineers’ designers had an accurate knowledge about the phenomenology involved in their behavior, and, above all, they knew the optimal geometry to achieve structural efficiency to resist rupture of the ‘Pombalino frontal’ when an earthquake occurs. In this article we present structural calculations performed on a ‘frontal pombalino’ standard, used in the ‘Pombalino cage’, which demonstrate that the military architects, who developed the Pombalino system, had not only a perfect understanding of the St. André cross potential, but they also experimented with models to scale, because the ‘pombalino frontal’ geometric dimension is optimized in the plane of structural efficiency.
Impact on patients’ compliance with medication using prepacked blisters for multidrug medical therapy: I-COMPLY Study
Authors: Shah Gautam , Erickson Jessica L , Luxenburg Jennifer , Hu Bo , Reali-Sorrell Michele , Lovelace Rita , Pfoh Elizabeth , Kobaivanova Nana , Brateanu Andrei 
Published: May 2021

Abstract

Purpose
Studies have supported the use of packaging interventions such as pillboxes or blister packs to improve medication adherence but have not evaluated the efficacy of these interventions in a population of low socioeconomic status. The aim of this study was to assess the effect of home-delivered pill packs on medication adherence in a low-income Black American population with Medicaid insurance.

Methods
This study was an open-label, randomized, controlled trial. The patient population studied included 80 patients followed by primary care physicians at the Cleveland Clinic. Patients were randomized to a study group who received delivery of their multidrug medical therapy, defined as a minimum of 4 medications daily, in prepackaged blisters or a control group who obtained their prescriptions from their routine pharmacy.

Results
The primary analysis compared the mean percentage of missed pills between the 2 groups using t-test analysis. The percentage of missed pills in the study group was significantly lower than in the control group (mean [SD]: 3.7% [6.0%] vs 17.4% [16.6%] missed daily pills; P &lt; 0.001). The number of daily missed doses was also significantly lower in the study group (0.3 [0.5] vs 0.7 [0.6]; P = 0.002). Patients were on a mean of 8.1 (SD, 2.3) and 8.1 (SD, 2.6) medications in the study and control groups, respectively (P = 0.96).

Conclusion
Delivery of prepackaged medications in a low-income Black American community was demonstrated to improve medication adherence. The use of prepackaged blisters for medication home delivery is a model that can be utilized on a larger scale for patients on multidrug medical therapy.
Formation and validation of an acute care clinical pharmacist productivity model: Part 2
Authors: Simmons Adrienne , Vest Tyler A , Cicci Jonathan , Clarke Megan , Morbitzer Kathryn A , Valgus John M , Falato Chris , Colmenares Evan W , Vest Mary-Haston , Waldron Kayla M 
Published: May 2021

Abstract

Purpose
The purpose of the project described here was to use the work outputs identified in part 1 of a 2-part research initiative to build and validate an acute care clinical pharmacist productivity model.

Methods
Following the identification of work outputs in part 1 of the project, relative weighting was assigned to all outputs based on the time intensity and complexity of each task. The number of pharmacists verifying an inpatient medication order each day was selected to represent the labor input. A multivariable linear regression was performed to determine the final work outputs for inclusion in the model. Productivity and productivity index values were calculated for each day from July 1, 2018, through June 30, 2019.

Results
Of the 27 work outputs identified via consensus by the clinical pharmacist working team, 17 work outputs were ultimately included in the productivity model. The average productivity during the period July 2018 through June 2019 was derived from the model and will serve as the baseline productivity for acute care clinical pharmacists.

Conclusion
Validated consensus methodology can be useful for engaging clinical pharmacist in decision-making and developing a clinical productivity model. When thoughtfully designed, the model can replace obsolete measures of productivity that do not account for the responsibilities of clinical pharmacists.
A Content Analysis and Population Exposure Estimate Of Guinness Branded Alcohol Marketing During the 2019 Guinness Six Nations
Authors: Barker Alexander B , Bal Jaspreet , Murray Rachael L 
Published: May 2021

Abstract

Aims
To quantify Guinness-related branding in the 2019 Guinness Six Nations Championship.

Methods
Content analysis of Guinness-related branding (‘Guinness’ and the alibi brand ‘Greatness’) was shown during active play throughout all 15 games of the 2019 Guinness Six Nations Championship. The duration of each appearance was timed to the nearest second to provide information on the amount of time that Guinness-related branding was shown on screen. Census data and viewing figures were used to estimate gross and per capita alcohol impressions.

Results
Our coding identified a total of 3719 appearances of two logos of which 3415 (92%) were for ‘Guinness’ and 304 (8%) were for ‘Greatness’. ‘Guinness’ imagery was present for 13,640 s (227.3 min or 3.8 h, 16% of total active play time), ‘Greatness’ was present for 944 s (15.7 min, 1% of total active play time), with a combined total of 14,584 s across all games (243 min or 4.05 h, 17% of active play time). The 15 games delivered an estimated 122.4 billion Guinness-related branded impressions to the UK population, including 758 million to children aged under 16.

Conclusions
Alcohol marketing was highly prevalent during the 2019 Guinness Six Nations Championship and was a significant source of exposure to alcohol marketing and advertising for children, likely influencing youth alcohol experimentation and uptake.
pyconsFold: a fast and easy tool for modeling and docking using distance predictions
Authors: Lamb J , Elofsson A 
Published: May 2021

Abstract

Motivation
Contact predictions within a protein have recently become a viable method for accurate prediction of protein structure. Using predicted distance distributions has been shown in many cases to be superior to only using a binary contact annotation. Using predicted interprotein distances has also been shown to be able to dock some protein dimers.

Results
Here, we present pyconsFold. Using CNS as its underlying folding mechanism and predicted contact distance it outperforms regular contact prediction-based modeling on our dataset of 210 proteins. It performs marginally worse than the state-of-the-art pyRosetta folding pipeline but is on average about 20 times faster per model. More importantly pyconsFold can also be used as a fold-and-dock protocol by using predicted interprotein contacts/distances to simultaneously fold and dock two protein chains.

Availability and implementation
pyconsFold is implemented in Python 3 with a strong focus on using as few dependencies as possible for longevity. It is available both as a pip package in Python 3 and as source code on GitHub and is published under the GPLv3 license. The data underlying this article together with source code are available on github, at https://github.com/johnlamb/pyconsfold.

Supplementary information
Supplementary data are available at Bioinformatics online.