bluuebunny/crossref_metadata_2025_split huggingface dataset:
Simulation Module of Crank Rocker Mechanism Based on GTENG Engine
Authors: Chu Weijun , He Xiaohui , Liu Qing 
Published: November 2020

Abstract
GTENG engine technology is a complete set of real-time 3D engine that is mainly applied to the simulation field of engineering equipment. Crank rocker mechanism is common in engineering equipment. This paper analysed the evolution of crank slider mechanism, and two motion types and parameter compute of crank rocker mechanism. The design of CCrankRocker, associated mesh scene node and using coordinate system all were discussed. Further, compute way of motion simulation of the four components required for CCrankRocker was emphasized. The paper indicated that is needs set firm parent-child relationship between bar and slider when modeling in 3DS MAX, and enumerated some legends of the origin of each node and the directing of axis in coordinate system of crank rocker mechanism. Finally, the use of crank rocker mechanism module was illustrated by analysing the main function of Load function correlating model nodes with classes, and the motion simulation example of bucket rocker mechanism was given with partial program. The application shows that the simulation of crank rocker mechanism based on GTENG engine is smooth, practical and wide.
Muti-Dimensional Reservoir Simulation Platform
Authors: Huang Rui , Wang Ruihe , Yu Changhai , Jin Xiaohan , Zhao Changjie 
Published: November 2020

Abstract
The muti-dimensional reservoir simulation platform is a digital twin reservoir system that integrates seismic, geology, numerical simulation and production. Based on years of accumulation, we make full use of late-comer advantages and adapt to the tide of networking and cloudification to fully integrate with informatization. In the oil and gas industry applications, we regard integration and sharing as our goal. The muti-dimensional reservoir simulation platform will realize application integration on the basis of digital integration realized by informatization and maximize knowledge sharing on the basis of data sharing realized by informatization.
Study on the Convective Heat Transfer Characteristic of a Horizontal Piezoelectric Fan
Authors: Ding Peng , Liu Zhe 
Published: November 2020

Abstract
In this study, the horizontal cooling performance of piezoelectric fans was evaluated numerically. The flow field, average heat transfer coefficient and average temperature were estimated, and the best performance was found to be at (h/Lb=1/20).The experimental results showed that height reduction of the piezoelectric fan to a certain extent could improve the heat transfer performance. However, when the distance between the fan and the heating wall was too small (h/Lb<1/20), the heat transfer performance was negatively affected. The increase of vibration frequency and amplitude has a similar positive effect on the overall heat transfer capacity, and the increase of vibration frequency increases the heat dissipation capacity of the point heat source more significant. The increase of vibration amplitude has a greater effect on the heat dissipation range of the downstream area of the fan than the vibration frequency.
Performance Prediction Method of Prognostics and Health Management of Marine Diesel Engine
Authors: Lan Feng , Jiang Yong , Wang Haiyang 
Published: November 2020

Abstract
The development of intelligent ship puts forward more requirements for the maintenance and management of marine diesel engine. Prognostics and health management technology can realize the condition-based maintenance of the main engine through intelligent analysis, evaluation and prediction of the running parameters. This paper reviews the development and basic functions of health management technology, and analyzes the application and development of performance prediction technology in marine diesel engine. Through the analysis, it can be found that the prediction method based on deep learning neural network model has become the trend in prediction technology area. Deep learning neural network has the characteristics of deeper network hierarchy, stronger understanding ability, and good at dealing with the variability and uncertainty of data relationship. Therefore, it will become a powerful tool to solve the performance prediction problem of diesel engine as a whole under varying working conditions.
Control of Cricket System Using LQR Controller Optimized by Particle Swarm Optimization
Authors: Zhang Yongli , Li Yunfei , Liu Yu , Yi Guorong 
Published: November 2020

Abstract
The cricket system, as a strongly coupled, nonlinear and multivariable two dimensional cue system, is a typical representative of unstable and underactuated system. In this paper, the system is analyzed by modeling based on the cricket experimental platform, and while designing the LQR controller, the particle swarm algorithm is introduced to optimize the controller parameters to determine the optimal weight matrix Q in order to solve the difficult problem of controller parameter rectification. The simulation results show that the PSO-LQR can restore the balance of the system in a shorter period of time with enhanced stability compared to a single LQR controller, which exhibits good control performance.
Modelling and Optimizing for Distributed Systems with limited Resource Migration Capacity
Authors: Zhang Haipeng , Lu Ningning , Song Ruiliang , Li Jie 
Published: November 2020

Abstract
This paper discusses the resource scheduling of some distributed systems, such as logistics system and the mobile system. By analysing the characteristic of the resource allocation, we found the resource or load of the distributed systems has limited migration capacity, but the load can be transmitted between neighbour nodes. By diverting resource or load between neighbour nodes, the system can reach a more balanced state. Then a model, shown as a constrained optimization problem, is presented to describe the load balancing problem of the distributed systems. The constrained terms are processed by the penalty function, and then an algorithm based on penalty function is presented to obtain an optimal solution of the constrained optimization problem. Finally, we design some experiments to verify the load model and the optimization algorithm proposed in this paper.
Combined Forecasting Method of Ammunition Consumption Based on Wavelet Network
Authors: Li Kang , Shi Xianming , Li Yuan , Zhao Mei 
Published: November 2020

Abstract
In order to solve the problem that the result of a single algorithm for ammunition consumption prediction is not accurate enough, a combination forecasting method of ammunition consumption based on a wavelet network is proposed. This paper combines three classical methods, namely, task volume estimation method, battle case estimation method and ammunition consumption standard estimation method, which are most commonly used in ammunition consumption prediction, and takes the prediction result of a single method as the input of neural network. By replacing the transfer function of traditional neural network with wavelet basis function, a combined forecasting model of ammunition consumption based on wavelet network is established. The calculation of the example shows that the fitting speed of this method is fast and the accuracy of the result is higher.
Research on Construction and Application of Internationalization System of Scientific and Technological Innovation in Shandong Province
Authors: Li Sisi , Zhu Shiwei , Yu Junfeng , Wang Chao , Zhang Mingjun , Xu Beibei , An Xiu 
Published: November 2020

Abstract
At present, the wave of globalization has swept the world and penetrated into all aspects of social life, bringing opportunities and challenges. In the context of economic and technological globalization, the concept of internationalization of scientific and technological innovation has emerged as the times require. It has become an important strategic means for countries to adapt to globalization trends, respond to globalization challenges, and seize globalization opportunities. In recent years, the internationalization of scientific and technological innovation has increasingly become an important issue of concern to the provincial government. In order to further integrate deeply into the global innovation network, maximize the use of international scientific and technological resources, and strive to achieve new breakthroughs in becoming the forefront of innovative provinces and building an open innovation highland, this paper constructs the internationalization system of scientific and technological innovation in Shandong Province, and puts forward countermeasures and guarantee measures to promote the internationalization of scientific and technological innovation in Shandong Province.
Role of Additives for High Performance Polymer Electrolytes
Authors: Paul Sharma Jitender , Bharti Vikas 
Published: November 2020

AbstractToday, the progress of polymer electrolytes with high ambient ionic conductivity, better mechanical and thermal properties, has become the main objective in the field of polymer science because of their requirements in super capacitors, rechargeable batteries, fuel cells and other electrochemical devices. Many kinds of polymer electrolytes have been developed, however main drawback is the low ionic conductivity, which is inadequate for most of the device applications. Efforts have also been made to enhance the ionic conductivity of polymer electrolytes to meet the demand for different devices. Although polymer electrolytes with high value of ionic conductivity (∼10−3-10−4S/cm) have been developed but these also suffer from poor mechanical properties. Incorporation of plasticizers as well as nano fillers in polymer electrolytes, is of great importance for enhancing their performance in devices. In the recent years, researchers came up with an idea to make the use of ionic liquids in polymer electrolytes to address the problem of low ionic conductivity of polymer electrolytes at room temperatures as well as to keep good electrochemical and mechanical properties. The optimized ionic conductivity value of the order of ∼10−3S/cm has been observed by different workers at different concentrations of ionic liquids. Also, polymer gel electrolytes have been reported to possess high ionic conductivity (of the order of ∼ 10−2S/cm) at ambient temperature. However, despite possessing high value of ionic conductivity these gel electrolytes have poor mechanical and thermal properties. Hence, the modified polymer /gel electrolytes having optimized properties, are described in this review paper. An attempt has also been made to highlight the work of different researchers in the form of synthesis, characterization and analysis of different modified polymer electrolytes for their use in different ionic devices.
Formation of metal vacancy arrays in coalesced WS2 monolayer films
Authors: Reifsnyder Hickey Danielle , Yilmaz Dundar E , Chubarov Mikhail , Bachu Saiphaneendra , H Choudhury Tanushree , Miao Leixin , Qian Chenhao , Redwing Joan M , van Duin Adri C T , Alem Nasim 
Published: November 2020

Abstract
Defects have a profound impact on the electronic and physical properties of crystals. For two-dimensional (2D) materials, many intrinsic point defects have been reported, but much remains to be understood about their origin. Using scanning transmission electron microscopy imaging, this study discovers various linear arrays of W-vacancy defects that are explained in the context of the crystal growth of coalesced, monolayer WS2. Atomistic-scale simulations show that vacancy arrays can result from steric hindrance of bulky gas-phase precursors at narrowly separated growth edges, and that increasing the edge separation leads to various intact and defective growth modes, which are driven by competition between the catalytic effects of the sapphire substrate and neighboring growth edge. Therefore, we hypothesize that the arrays result from combined growth modes, which directly result from film coalescence. The connections drawn here will guide future synthetic and processing strategies to harness the engineering potential of defects in 2D monolayers.
PyXtal_FF: a python library for automated force field generation
Authors: Yanxon Howard , Zagaceta David , Tang Binh , Matteson David S , Zhu Qiang 
Published: November 2020

Abstract
We present PyXtal_FF—a package based on Python programming language—for developing machine learning potentials (MLPs). The aim of PyXtal_FF is to promote the application of atomistic simulations through providing several choices of atom-centered descriptors and machine learning regressions in one platform. Based on the given choice of descriptors (including the atom-centered symmetry functions, embedded atom density, SO4 bispectrum, and smooth SO3 power spectrum), PyXtal_FF can train MLPs with either generalized linear regression or neural network models, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from ab-initio simulations. The trained MLP model from PyXtal_FF is interfaced with the Atomic Simulation Environment (ASE) package, which allows different types of light-weight simulations such as geometry optimization, molecular dynamics simulation, and physical properties prediction. Finally, we will illustrate the performance of PyXtal_FF by applying it to investigate several material systems, including the bulk SiO2, high entropy alloy NbMoTaW, and elemental Pt for general purposes. Full documentation of PyXtal_FF is available at https://pyxtal-ff.readthedocs.io.
Satisfaction Survey of Women After Cosmetic Genital Procedures: A Cross-Sectional Study From Saudi Arabia
Authors: Al-Jumah Modhi M , Al-Wailiy Shorug K , Al-Badr Ahmed 
Published: November 2020

Abstract

Background
Female cosmetic genital surgery (FCGS) aims for better aesthetic genital appearance and improved functional aspects to enhance women’s self-esteem and satisfaction.

Objectives
This study aims to assess the satisfaction of women who have undergone FCGS and its impact on their sexual, psychological, and aesthetic aspects.

Methods
An observational cross-sectional study was conducted in private clinics in Riyadh, Kingdom of Saudi Arabia, between March and June 2019, in women who underwent FCGS. Phone interviews were conducted in the Arabic language. The survey comprised 4 sections: demographics, motives for FCGS, quality-of-life questionnaires about genital appearance satisfaction, and sexual function.

Results
Out of the 196 women undergoing FCGS during the study period, 11.7% refused to participate, and 37.2% did not answer phone calls; 51% of the women participated in the study. The women’s age ranged between 23 and 55 years; 64% underwent vaginoplasty, and 73% underwent other cosmetic procedures. Ninety-two percent of the women did not have any complications after these procedures.

Conclusions
In this group of women, FCGS was safe and effective, and the majority of participants reported overall satisfaction and improvement of sexual function, genital appearance, and self-esteem.

Level of Evidence: 4
Automation in signal management in pharmacovigilance—an insight
Authors: Wadhwa Diksha , Kumar Keshav , Batra Sonali , Sharma Sumit 
Published: November 2020

Abstract
Drugs are the imperial part of modern society, but along with their therapeutic effects, drugs can also cause adverse effects, which can be mild to morbid. Pharmacovigilance is the process of collection, detection, assessment, monitoring and prevention of adverse drug events in both clinical trials as well as in the post-marketing phase. The recent trends in increasing unknown adverse events, known as signals, have raised the need to develop an ideal system for monitoring and detecting the potential signals timely. The process of signal management comprises of techniques to identify individual case safety reports systematically. Automated signal detection is highly based upon the data mining of the spontaneous reporting system such as reports from health care professional, observational studies, medical literature or from social media. If a signal is not managed properly, it can become an identical risk associated with the drug which can be hazardous for the patient safety and may have fatal outcomes which may impact health care system adversely. Once a signal is detected quantitatively, it can be further processed by the signal management team for the qualitative analysis and further evaluations. The main components of automated signal detection are data extraction, data acquisition, data selection, and data analysis and data evaluation. This system must be developed in the correct format and context, which eventually emphasizes the quality of data collected and leads to the optimal decision-making based upon the scientific evaluation.
Automated model versus treating physician for predicting survival time of patients with metastatic cancer
Authors: Gensheimer Michael F , Aggarwal Sonya , Benson Kathryn R.K , Carter Justin N , Henry A. Solomon , Wood Douglas J , Soltys Scott G , Hancock Steven , Pollom Erqi , Shah Nigam H , Chang Daniel T 
Published: November 2020

Abstract

Objective
Being able to predict a patient’s life expectancy can help doctors and patients prioritize treatments and supportive care. For predicting life expectancy, physicians have been shown to outperform traditional models that use only a few predictor variables. It is possible that a machine learning model that uses many predictor variables and diverse data sources from the electronic medical record can improve on physicians’ performance. For patients with metastatic cancer, we compared accuracy of life expectancy predictions by the treating physician, a machine learning model, and a traditional model.

Materials and Methods
A machine learning model was trained using 14 600 metastatic cancer patients’ data to predict each patient’s distribution of survival time. Data sources included note text, laboratory values, and vital signs. From 2015–2016, 899 patients receiving radiotherapy for metastatic cancer were enrolled in a study in which their radiation oncologist estimated life expectancy. Survival predictions were also made by the machine learning model and a traditional model using only performance status. Performance was assessed with area under the curve for 1-year survival and calibration plots.

Results
The radiotherapy study included 1190 treatment courses in 899 patients. A total of 879 treatment courses in 685 patients were included in this analysis. Median overall survival was 11.7 months. Physicians, machine learning model, and traditional model had area under the curve for 1-year survival of 0.72 (95% CI 0.63–0.81), 0.77 (0.73–0.81), and 0.68 (0.65–0.71), respectively.

Conclusions
The machine learning model’s predictions were more accurate than those of the treating physician or a traditional model.
A primer on Bayesian estimation of prevalence of COVID-19 patient outcomes
Authors: Gao Xiang , Dong Qunfeng 
Published: November 2020

Abstract
A common research task in COVID-19 studies often involves the prevalence estimation of certain medical outcomes. Although point estimates with confidence intervals are typically obtained, a better approach is to estimate the entire posterior probability distribution of the prevalence, which can be easily accomplished with a standard Bayesian approach using binomial likelihood and its conjugate beta prior distribution. Using two recently published COVID-19 data sets, we performed Bayesian analysis to estimate the prevalence of infection fatality in Iceland and asymptomatic children in the United States.
Potential distribution of Guiana dolphin (Sotalia guianensis): a coastal-estuarine and tropical habitat specialist
Authors: de Jesus Lobo Aline , Wedekin Leonardo Liberali , Sobral-Souza Thadeu , Le Pendu Yvonnick 
Published: November 2020

Abstract
Ecological niche models (ENMs) predict where species can occur in accordance with environmental factors. Suitability maps are generated through models to identify habitats more or less adapted to the species. Published works on the distribution and habitat use of Guiana dolphin, Sotalia guianensis, are limited to fine spatial scales. Here, we aimed to predict the potential geographical distribution of Guiana dolphins through ENMs and generate a map of suitable habitats for the species. Data were collected between 1997 and 2015 in Brazil, French Guiana, and Colombia. The environmental data were obtained from MARSPEC database with a cell resolution of 10 × 10 km. For modeling, 99 of the 859 initial occurrence points of the species were considered after rarefaction. Seven environmental variables were selected through factorial analysis: bathymetry, distance to shore, bathymetric slope, sea surface salinity (minimum monthly and annual range), and sea surface temperature (mean annual and annual range). Results from five distinct algorithms were assembled to generate the distribution model. Our findings show potential areas in shallow platforms of the continental margin of South and Central America, including regions where the species has never been reported, such as the Pacific Ocean, the Gulf of California, the Gulf of Mexico, and the oceanic islands in the Caribbean Sea. The absence of Guiana dolphins in these regions may be due to geographical (linking of North and South America), physical (water temperature), and biological (competition, limited ability to dispersal) limiting factors. The models suggest that the presence of other species of coastal dolphin may be an important limiting factor for the Guiana dolphin at both extremes of its distribution. The Guiana dolphin is habitat specialist with a clinal potential geographic distribution concentrated in tropical and subtropical shallow and coastal waters of the continental shelf of the western Atlantic Ocean. This more restricted distribution than reported by IUCN and other studies suggests a cautionary approach to its conservation status due to limited dispersal abilities and high overlap with human activities.
Stimulants: From Coca to Caffeine
Authors: Winter Jerrold 
Published: November 2020

Abstract
Unlike the opiates, which are a rather homogeneous group, the drugs we call stimulants come in a variety of forms. In this chapter, we will devote most of our time to the classical stimulants, cocaine and the amphetamines, but will consider as well caffeine, nicotine, ephedrine, and modafinil. All are capable of enhancing mental and physical performance, and some produce distinctly pleasurable effects that sometimes lead to addiction. About the time that humans living in what is now South America started to draw on the walls of their caves, one among them discovered the unusual properties of the coca shrub. When the leaves were chewed, wondrous things happened to the chewer: Hunger and fatigue were replaced by feelings of strength and power; the world seemed not such a bad place to live. By the time Francisco Pizarro led his conquistadors into Peru early in the 16th century, coca leaf had found an exalted place in the Incan Empire. One legend has it that coca was brought from heaven to earth by Manco Capac, son of the Sun god and the Inca from whom the ruling class traced its lineage. (Interesting how often royalty has claimed divine origins.) The Spaniards developed no great respect for coca, regarding it as but another facet of a pagan people who had no claim on civilization. But the new rulers were nothing if not practical. Coca allowed native workers to be pushed beyond the normal bounds of physical endurance. More tin and silver could be brought from the mines with fewer workers fed less food. Coca leaf lost its status as a sacrament and a pleasure of the ruling class. It became a part of the internal economy of Spanish Peru, a means of enhancing productivity, and a contributor to the destruction of the Incan people and their civilization. It was inevitable that Europeans would become familiar with the effects of coca leaf both by their observation of native use and by personal experience. In 1859, an Italian physician named Paolo Mantegazza who had spent some time among the Peruvian natives put it this way.