bluuebunny/crossref_metadata_2025 huggingface dataset:
Study and Application of Wind Turbine Power Loss Assessment based on GD-BIN Algorithm
Authors: Zhan Yangyan , Zhou Minqiang , Ren Danyuan
Published: April 2020

In order to accurately assess the lost production of each wind turbine, a fusion method, which is the combination of Gaussian Distribution and bins of method (GD-BIN), was established. Firstly, Complete the first cleaning of operation data according to the operation characteristics and status logs of the unit. Further, GD-BIN algorithm is employed to establish the Wind Power Curve Model (WPCM), which can reflect the real performance of each unit. Thus, the operation data of the unit is delivered to WPCM to calculate theoretical power and lost power at each statistical moment. To verify the accuracy, it is applied in a wind farm in Northeast China and is compared to the other three current methods in the industry. Result shows that the proposed algorithm could effectively estimate lost production with a high accuracy.
Research and Development of 1200V 4H-SiC Trench JBS Diode
Authors: Li Jialin , Tian Liang , Li Ling , Zhang Hongdan , Zhu Tao , Jiao Qianqian , Wu Hao , Liu Rui , Jiang Chunyan , Sun Junmin
Published: April 2020

This paper introduces the design and fabrication process of 1200V SiC trench Junction Barrier Schottky (JBS) diode, and tests the performance of the diode. The cell and terminal of diode structure both adopt trench structure, by simulation and early stage, the structural parameters of device cell and terminal structure are determined, and the fabrication of 1200V SiC trench JBS diode is completed through an intact technological process. The test results show that the leak current of the device under 1200V at room temperature is about 0.36uA, when the current is 20A, the forward voltage drop is 1.53V.
Health State Prediction of Lithium Ion Battery Based On Deep Learning Method
Authors: Lu Sheng , Wang Fei , Piao Changhao , Ma Yiwei
Published: April 2020

To predict the health status of lithium-ion batteries, long and short-term memory (LSTM) recurrent neural networks are used to build two types of battery SOH evaluation models. The discharge capacity is as input to a single feature model. While, the charge capacity, the charge time, the average charge temperature, the charge average voltage, the discharge temperature, and the discharge average voltage are as input to a multiple feature input model. The data samples are separated to training and test dataset. The test results show that the maximum absolute error of the LSTM-based model is less than 2%, which satisfies the industry standard (less than 5%). Meanwhile, this study transfers the NASA-based lithium-ion battery model to the University of Maryland lithium-ion battery data, which can reduce model training iterations and get good performance as well. The experimental results validate the effectiveness of transfer learning in the field of lithium-ion battery SOH prediction and provide a reference for future work in this field.
Cluster Head Monitoring Nodes Secret Selection Protocol in Sensor Networks
Authors: Yuan Jinhui , Zhou Hongwei
Published: April 2020

In sensor networks, as the data collection point of nodes in the cluster, cluster head is easy to be the key target of attack. Once the cluster head node is controlled by the attacker, it will have a fatal impact on the security of the network. In order to solve the above problem, this paper proposes a cluster head monitoring nodes secret selection protocol. It uses secret selection algorithm to select the cluster head monitoring node from the neighbor nodes of the cluster head node. Thus, the attacker can not confirm the monitoring node, it results that the cluster head node can not be attacked. The protocol uses the spatio-temporal correlation between cluster head and cluster head monitoring nodes. Once it finds that cluster head node is suspected anomaly, it reports immediately to sink node. The analysis of protocol security shows that the protocol can meet the energy efficiency of sensor network and ensure the security of the system.
Power Quality Control Method of Distributed Generation Based on Improved PI and Repetitive Control
Authors: Ke Qingpai , Shi Xuntao , Yuan Zhiyong , Lei Jinyong
Published: April 2020

In view of the similar structure between the distributed generation (DG) inverter and the active power filter (APF), it is of great significance to develop the DG inverters with harmonic suppression functions. The accurate tracking and rapid response to the phase and amplitude of the harmonic components is the guarantee of the effective use of the DG inverter to suppress the harmonic. In this paper, a harmonic suppression control method of DG inverter based on improved PI and repetitive control is proposed. This method can make the DG inverter output the active power of the fundamental wave by using the PI controller, and output the high precision harmonic current compensation by the repetitive controller. While considering the existence of a periodic delay in the repetitive controller, the feedforward control of harmonic components is added to speed up the dynamic response of harmonic suppression. Finally, the simulation results show that, compared with the traditional harmonic suppression method, the proposed control strategy can ensure the steady-state accuracy and dynamic response speed of the active output and harmonic suppression of the DG inverter while reducing the computational complexity.
Research on Steam Cloud Explosion Overpressure Prediction Model Based on Improved BP Neural Network
Authors: Ma Shengxiang , Shi Xunxian , Yu Chenglong , Chen Bing
Published: April 2020

In order to improve the feasibility and accuracy of steam cloud explosion overpressure prediction, a combination of factor analysis method and BP neural network method is proposed to propose an improved BP neural network prediction method. According to the original data of the main influencing factors of steam cloud explosion overpressure, the factor analysis method is used to reduce the dimensional data of the five steam cloud explosion overpressure factors, and obtain a common factor; replace the original with a common factor. As an input layer parameter of BP neural network, a steam cloud explosion overpressure prediction model is established by combining the factor analysis method with BP neural network method to predict the overpressure of steam cloud explosion. The improved BP neural network prediction method is verified by the example data. The final verification result is that the relative average error between the predicted and actual values of the 15 training samples is 2.51%, which proves that the improved BP neural network model with training has a good fitting effect. The relative errors of the five predicted samples were 0.77%, 1.34%, 2.07%, 3.96%, and 6.27%, both of which were less than 10%, which proved that the improved BP neural network prediction model has better prediction accuracy.
Numerical Simulation of Flow and Heat Transfer in LPD Type Misaligned Fins
Authors: Liu Fangming
Published: April 2020

With the rapid development of all walks of life, heat exchangers have been used all over the various fields. Plate fin heat exchanger was concerned, because of its high heat transfer rate and the small size. Among them, the offset strip fin is called “high efficiency fin” because its special staggered arrangement structure can increase the turbulent flow of fluid and enhance heat transfer. In order to study the influence of flow and heat transfer in dislocation fins, the three-dimensional numerical simulation of LPD dislocation fins is carried out by numerical simulation. Firstly, the accuracy of the standard laminar flow model is verified by using the dislocation fins and experimental data provided by the literature. The average error between the calculated value of the heat transfer factor and the experimental value is 5.95%, and the average error between the calculated value and the experimental value of the friction factor is 2.02%. On this basis, the influence of flow parameters and structural parameters on the internal flow and heat transfer in the dislocation fins is analyzed.The results show that: (1) the smaller the fluid velocity, the lower the height of the dislocation fin; the smaller the tooth angle, the shorter the cut length; the greater the heat transfer factor j, the better the heat transfer performance;(2) The higher the fluid velocity, the higher the height of the dislocation fin, the smaller the tooth angle; the shorter the cut length, the smaller the friction factor f, the better the resistance characteristic;(3) the lower the fin height, the smaller the tooth angle; the shorter the cut length, the greater the temperature difference and the pressure drop with the length of the heat transfer section; the fin height has little effect on the velocity field, The flow distribution of the fluid flow in the flow channel is affected, the shorter the cutting length is, the faster the velocity field changes.
Human Motion Recognition Based On Inertial Sensor
Authors: Xing Mengmeng , Wei Guohui , Cao Hui , Yang Feng , Nie Yongqi , Liu Jing
Published: April 2020

Human activity recognition (HAR) has attracted considerable research attentions from all walks of life due to its application in human-computer interaction, smart medical treatment and smart home health care. There are various ways of using different motion capture methods for HAR. Among which, HAR based on inertial sensor signals has recently emerged as a challenging and hot research topic. In this paper, we presented a inertial sensor-based method for HAR. Firstly, the variance, mean, information entropy, peak, etc. were extracted from the inertial data as robust features of the actions. Then, the robust features were further processed by Principal Component Analysis (PCA) to reduce the dimensions of features. Finally, the features with different actions label were used to train the Extreme Learning Machine (ELM) and Support Vector Machine (SVM) model. The result can achieve an mean accuracy of 83.49%, it also verified the effectiveness of our method.
Research on Error Correction Coding Closed Set Recognition Technology Based on Deep Learning Method
Authors: Chen Jianjun
Published: April 2020

It is very difficult to obtain information in non-cooperative areas. Error correction coding analysis is one of the key and difficult links. The general analysis is highly demanding and highly dependent on the level of personal analysis. For this reason, we mainly study various deep learning methods, which are applied to scrambling codes, short constraint length convolutional codes, and short constraint length block codes and we also I want to rely on the development of deep learning now, to study and realize intelligence and automated analysis.
Correlation Analysis of Faults and Operating Environment of Electric Energy Meter
Authors: Peng Xinxia , Liu Ying , Du Jie , Liang Qilin , Pang Fukuan , Tan Panpan
Published: April 2020

The use of the re-entry energy meter is related to the on-site operating environment. First, the five-level quantitative standard is determined. Establish the correlation between the fault phenomenon and the operating environment, and use the software SPSS to analyze the correlation between the fault phenomenon and the operating environment. According to the size of the correlation, analyze the cause of the correlation. It provides a theoretical basis for the subsequent strengthening of quality supervision, management and production of electric energy meters.
Design and Implementation of Intelligent Storage System for Change Back Meter
Authors: Liu Ying , Peng Xinxia , Lu Guanna , Guo Lei , Zhao Kaiwen , Li Liang
Published: April 2020

Designed and developed a set of intelligent storage system for the change back meter. The storage system supports various types of inbound and outbound tasks in the MDS system and the four-line one-library interface specification, including the transfer of the inbound and outbound warehouses, the returning of the warehouses, the returning of the warehouses, and the returning of the warehouses. “Device outbound interface”. The warehousing system can receive multiple tasks simultaneously by receiving the MDS system, and has a queuing processing mechanism in the case of multi-task concurrency. The system has a complete operation and maintenance log, and automatically records information such as warehouse system inventory maintenance, manual operation, and software upgrade. Fully realize the process of asset management, back office management, and plan management management to achieve comprehensive measurement process management. The realization of process is more conducive to the orderly planning of measurement management, which is conducive to the assessment of work quality, reducing work handover and work errors, and improving work efficiency.
Design of Negative Pressure Climbing Robot Based on Numerical Simulation
Authors: Hao Guangfa , Zhang Chengliang , Liu Hao , Su Jingyi , Sun Kang
Published: April 2020

Aiming at the problem of low wall adsorption capacity of the wall-climbing robot, this paper designs a negative-pressure climbing wall tracing robot; the structure of the robot is designed, and a double-wheel differential control driving mode is determined; The key technical parameters are analyzed theoretically, and the calculation method of the minimum pressure difference and the minimum distance of the chassis from the ground is obtained. The numerical simulation of the negative pressure generator based on Fluent verified the rationality of the structure of the negative pressure generator. The distribution characteristics of the pressure in the negative pressure generator are displayed and analyzed. Finally, the physical model of the robot is built and the feasibility of the design is verified.
Key Technologies of Time-Sensitive Networking and Its Application
Authors: Li Qing , Li Dong , Sun Xinbo
Published: April 2020

Network is the core element of many industrial and automation systems. In the future, more attention will be paid to convergent and unified network. Time-critical traffic and non-time-critical traffic need to share communication channels. The goal of the IEEE TSN task group is to extend the existing Ethernet standard to achieve a degree of certainty that meets the hard-real-time requirements of modern control networks in the industrial automation and automotive industries. Time sensitive network (TSN) is characterized by low jitter, low delay and deterministic transmission. It can transmit critical and non-critical traffic in the same network, which is very suitable for time- critical applications with high requirements on transmission delay. This paper briefly summarizes the key components of TSN, studies the key technologies, and carries out a simple traffic scheduling experiment on the switch based on 802.1Qbv function, and finally analyzes the application of TSN in industrial automation and automobile industry.
Stability Analysis of Minimum Broken Top Layer Thickness of Rock Drilling Chamber in a Mining Site Based on Midas/GTS
Authors: Yang Jun , Zhu Zhaoxia , Chen Xiang , Zeng Weiwei
Published: April 2020

The determination of the reasonable thickness of the top layer of the large- diameter deep-hole stope is one of the important process parameters that must be considered in the high-stage mining recovery. The thickness of the top layer can ensure that the large-diameter deep-hole stope is not easy to collapse after the formation of the empty zone. To ensure the stability of the rock drilling chamber. According to the similar mine experience and mechanical model, the scientific and reasonable top-level safety thickness range is calculated. The three-dimensional numerical simulation of MIDAS/GTS is used to verify and optimize the stability of different top layer thicknesses. From the perspective of displacement and stress, comprehensive simulation analysis is carried out. After comprehensive comparison of angles, the optimal thickness of the top layer is determined to be 9 m.
Mineralogy Study on a Fine-Grained Cu-Mo Sulphide Ore in Jiudingshan China
Authors: Nie Qi , Cheng Yong , Liu Cong , Zhang Hanping , Peng Fenlan
Published: April 2020

This study researched mineralogy characteristics of Jiudingshan Cu-Mo sulphide ore by modern testing instruments, such as scanning electron microscope (SEM), reflected light microscope (RLM), X-ray diffractometer (XRD), and mineral liberation analyzer (MLA). The occurrence of Cu, Mo and other valuable minerals were mainly investigated. Results show that: it contain 0.50% Cu, 0.15% Mo and 7.04% Fe. Forty individual minerals are involved in the ore. Main valuable minerals are molybdenite, chalcopyrite, magnetite, pyrite; main gangue minerals are quartz, feldspar, garnet, calcite, dolomite.Molybdenite, chalcopyrite are finely disseminated in the magnetite,quartz, feldspar, garnet; some are inlayed along other minerals such as idocrase, actynolin.
Analysis of Track Bend Limit and Settlement Deformation Affecting the Transition Section of Highway and Bridge of High-speed Railway
Authors: Ling Pingping , Chen Zeshi
Published: April 2020

In recent years, with the rapid development of high-speed railways, the problem of differential settlement of road-bridge transition sections has become increasingly prominent. The uneven settlement of the road-bridge transition has a great impact on the driving safety and comfort of high-speed vehicles, especially for high-speed railway projects under special ground conditions such as soft stop. And with the continuous increase in train speed and axle weight, the differential settlement requirements at the over-bands are becoming more stringent. The problem of “jump at the bridgehead” has become one of the important factors affecting the safety of highspeed trains. In view of the current situation, theoretical analysis and numerical calculation are used as research methods in this paper. The factors of uneven settlement and deformation of the road-bridge transition section are systematically analyzed and corresponding methods for foundation treatment are proposed.
An Accelerated Convergence Algorithm for Sparse-View CT Image Reconstruction
Authors: Wang Zhao , Liu Zhiguo , Zhang Shuang , Pan Kai , Zhou Peng , Wang Xin , Wang Haoran , Sun Tianxi
Published: April 2020

In order to reduce radiation dose during CT scanning, sparse sampling is an effective way. Although the TV-based iterative reconstruction algorithm is a breakthrough to solve the problem of sparse-view CT image reconstruction, its applicability is still limited by huge computational burden. It is necessary to study the acceleration method of TV-based iterative algorithm. This paper show that the FISTA acceleration method is not suitable for POCS-TV algorithm, meanwhile, an improved acceleration method, IFISTA, is proposed to accelerate the convergence rate of POCS-TV. Numerical experiments show that the convergence rate of POCS-TV-IFISTA is about 35% faster than POCS-TV.
A Analysis and Calculation Method for Reasonable Decline of Fluid Production in Affected Wells in Transfer Polymerization Flooding
Authors: Zhang Li , Shu Zheng
Published: April 2020

When the J oilfield is switched from water flooding to polymer flooding, the fluid supply capacity of the effective well will decrease sharply, and it is often judged that the effective well is blocked. In fact, in addition to the blockage that occurs after the polymer flooding, some of the decline in the fluid production is caused by the nature of the polymer flooding. Therefore, it is necessary to analyze the characteristics of water flooding and polymer flooding, and compare the similarities and differences between water flooding and polymer flooding, so as to find the factors that will inevitably lead to the decline of production fluid after water flooding to polymer flooding. The range of decline in fluid production caused by these factors is a reasonable range of decline in the produced fluids of the affected wells, while the decline in fluid production caused by other factors is the blockage.
A mini-review of distribution, growth environment and nutrient composition from Scorias spongiosa (Bamboo Bird’s Nest)
Authors: Zhong Shengnan , Yu Hua , Nong Xiang , Liu Chao
Published: April 2020

Scorias spongiosa is an edible fungus. The fruiting bodies of Scorias spongiosa contain variouss chemical composition such as polysaccharides, steroids which have many pharmacological effects, such as anti-oxidation, antitumor and bacteriostatic. In this review, the distribution, growth environment and nutrient composition of Scorias spongiosa were summarized systematically, and the development prospect of Scorias spongiosa (Bamboo Bird’s Nest) was prospected.
Simulation and Performance Study of Quadrature Amplitude Modulation and Demodulation System
Authors: Zhang Bin , Qu Yuanhang , Chen Min
Published: April 2020

Both traditional frequency modulation and phase modulation digital modulation methods have low spectral utilization, poor multipath fading resistance, slow power spectrum attenuation, and severe out-of-band radiation. Quadrature Amplitude Modulation (QAM) is a digital modulation technique that combines phase and amplitude control. It not only achieves higher spectral efficiency, but also transmits higher rate data in a defined frequency band. In this paper, the basic principle, system structure and performance parameters of QAM modulation and demodulation are studied in depth to realize simulink simulation and performance analysis of QAM modulation and demodulation system. The basic theory and implementation method of analog signal digitization are analyzed in detail to realize simimlink of differential pulse code modulation. Based on the above theory, the analog source QAM transmission system is constructed, and simulink is used for modeling and simulation and performance verification.The simulation results show that the constructed QAM digital transmission system can achieve good transmission of analog signals.
A Quantile K-means Bayes Approach to Classification for Imbalanced Data
Authors: Hu Yanzhu , Zhao Xinghao , Wang Song
Published: April 2020

This paper focuses on the classification of imbalance data. In Machine Learning, a data set is imbalanced when the class proportions are highly skewed. A natural way of handling imbalanced data is to attempt to equalise the class frequencies and train the classifier of choice on balanced data. A new approach called Quantile K-means Bayes was proposed to solve the problem. The first focus is on a modified q-classifier. The second focus is on combine the k-means and Bayes algorithm using the data density. The proposed approach is evaluated by 101 benchmark data sets from KEEL collection. A comparison of the proposed approach and other conventional approaches is presented in terms of the G-mean. It can be seen that the proposed approach is able to acquire good performance among the other conventional approaches do. Therefore, this novel approach is an added value for the classification problem for imbalance data.
Research on New Mining Technology Based on Oilfield Reservoir Water Injection
Authors: Song Penghui
Published: April 2020

During the development of oilfields, we usually study reservoir water injection and water injection well fracturing as two important measures to improve the effect of water flooding. These two technologies play a big role in oilfield development, but there are also some problems in the application process. In this paper, through the analysis of the fracturing mechanism and problems of reservoir water injection and water injection wells, the adjustment method of reservoir water injection and water well fracturing is proposed, which reduces the water injection pressure technology of high pressure water injection well and improves the oilfield development effect.
Study on Land Use Structure of Hexi Corridor Based on Lorenz Curve and Gini Coefficient
Authors: Geng Yonghao , Pan Qiao , Xiao Bingyu
Published: April 2020

The development of the Hexi Corridor area has been attached great importance due to the “Belt and Road” international cooperation initiative. Meanwhile, the optimization of land use structure has played a major role in the development of this area. In order to promote healthier and more efficient development of the Hexi Corridor area, this paper collects and reviews recent multidimensional data on the development of the area. In addition, by applying the Lorentz curve and Gini coefficient method, this paper also concentrates on several aspects in the development of the Hexi Corridor area: (a) quantifying the land use balance based on data from various prefecture-level Hexi cities; (b) revealing developmental experiences and efficiency; (c) exploring overall urban land use status; (d) and proposing targeting urban planning and development strategies.
Research on Hi-C data enhancement technology based on generative adversarial networks
Authors: Bai Qian , Cheng Zhe , Wang Shipu , Zhou Wei
Published: April 2020

Hi-C technology is one of the most popular tools for studying three-dimensional(3D) genome organization. Due to the high cost of sequencing, most Hi-C data have low resolution and cannot be used to connect distal regulatory elements to their target genes. To solve the problem that hi-c data of high resolution are not easy to obtain, this paper proposes a Hi-C enhancement method (HiCGAN) based on generative adversarial networks, Taking the down-sampling interaction matrix which is highly similar to the original matrix as input, only 1/16 of the original sequencing reading can be used to generate the Hi-C interaction matrix of high resolution. In the experiment, Pearson correlation coefficient was used to measure the similarity between the generated high-resolution matrix and the real high-resolution hi-c matrix in numerical distribution. the apparent interaction pairs were analyzed by Fit-Hi-C, and calling ChromHMM annotates state of 12 kinds of chromatin. Experimental results show that HiCGAN models learned in one cell type can predict high-resolution Hi-C matrices for other cell types. This study proposes a computational framework (HiCGAN) for accurately predicting Hi-C data improving the resolution of Hi-C data.
Classification and Recognition of Turtle Images Based on Convolutional Neural Network
Authors: Liu Jiangchuan , Wang Mantao , Bao Lie , Li Xiaofan , Sun Jun , Ming Yue
Published: April 2020

The identification of turtle species mainly depends on the recognition of turtle head and shell, but there is no relevant study on turtle image. In this paper, a turtle image recognition and classification system based on transfer learning is introduced. The system consists of four phases. First, turtle images need to be collected for data enhancement and dataset production. Secondly, the Inception-v3 network model is used to train and save parameters and model structure on the ImageNet dataset. In addition, the network model needs to be modified to change Softmax classifier into 5 categories. Finally, the tortoise dataset was used for training and saving the model, and the classification accuracy of five representative turtles was verified. The experiment proves that the network model of migration learning adopted in this paper is faster and more accurate than the one not adopted.
Nonlinear Correction and Temperature Compensation Method of Turbine Flowmeter Based on Neural Network
Authors: He Wang , Yi Jianjun , Zhou Bo , Chen Bin , He Liang
Published: April 2020

For the nonlinear characteristics of turbine flow sensor and the influence of medium temperature, the traditional compensation method is difficult to apply. This paper uses BP neural network based method to improve measurement accuracy. Firstly, the reason of nonlinearity and the influence of temperature on flow measurement are analyzed theoretically. Then the nonlinear correction and temperature compensation scheme based on neural network is proposed, and different optimization algorithms are used for training. After simulation experiments and analysis, the results show that neural network the maximum reference error of the sensor after compensation is 0.683%. Compared with the traditional least square fitting, the accuracy is greatly improved, which can effectively solve the influence of nonlinearity and temperature change on flow measurement, expand the measurement range of turbine flow meter, and improve the measurement accuracy so as to meet the requirements of industrial use.
Volterra integral equations of the first kind and applications to linear diffusions
Authors: Jakubowski Jacek , Wiśniewolski Maciej
Published: April 2020

An algebraic formula for the solution of a Volterra integral equation of the first kind is given in the topological algebra of locally integrable functions using the notions of convolution triple and

ϕ
\phi

-deconvolution. Then, the formula is applied to problems from the theory of linear diffusions. In particular, the distributions of first hitting times, killed processes, and bridges are described.
Characterization of rectifiable measures in terms of 𝛼-numbers
Authors: Azzam Jonas , Tolsa Xavier , Toro Tatiana
Published: April 2020

We characterize Radon measures

μ
\mu

in

R

n

\mathbb {R}^{n}

that are

d
d

-rectifiable in the sense that their supports are covered up to

μ
\mu

-measure zero by countably many

d
d

-dimensional Lipschitz images and

μ
≪

H

d

\mu \ll \mathcal {H}^{d}

. The characterization is in terms of a Jones function involving the so-called

α
\alpha

-numbers. This answers a question left open in a former work by Azzam, David, and Toro.
The Importance of Tumor Length in Needle Biopsies of the Prostate
Authors: Vollmer Robin T
Published: April 2020

Objectives

To form a composite predictor variable that combines the effects of tumor length, serum prostate-specific antigen (PSA), and International Society of Urologic Pathologists (ISUP) grade on the observation of adverse prostatectomy pathology.

Methods

Logistic regression analysis was used to demonstrate how tumor length, serum PSA, and ISUP grade related to adverse prostatectomy results and to derive weighting factors for a composite variable, cx.

Results

The composite variable, cx, relates closely to adverse prostatectomy results as well as to observed PSA failure.

Conclusions

The composite variable cx uses preoperative information that may allow the sorting of patients into low, intermediate, and high risk for adverse outcomes after prostatectomy.
How to Manage Hypertension in People With Diabetes
Authors: Ali Waleed , Bakris George L
Published: April 2020

Hypertension is a common condition that is often seen in patients with diabetes. Both diseases increase the risk of morbidity and mortality from CV events and kidney disease progression. Factors that influence blood pressure (BP) control in diabetes include the persons’ genetic background for hypertension and kidney disease, level of obesity and insulin resistance, the magnitude of preexisting kidney disease, and lifestyle factors, such as level of sodium and potassium intake, sleep quality and exercise effort all of which can affect levels of sympathetic nerve activity and contribute to increased BP variability. Lifestyle intervention is a key component to the effective management of diabetes and hypertension and can markedly reduce event rates of both heart and kidney outcomes. The approach to pharmacologic treatment of BP in diabetes is crucial since certain classes of agents for both BP and diabetes confer significant benefits to reduce cardiorenal outcomes.
Vargas: heuristic-free alignment for assessing linear and graph read aligners
Authors: Darby Charlotte A , Gaddipati Ravi , Schatz Michael C , Langmead Ben
Published: April 2020

Motivation

Read alignment is central to many aspects of modern genomics. Most aligners use heuristics to accelerate processing, but these heuristics can fail to find the optimal alignments of reads. Alignment accuracy is typically measured through simulated reads; however, the simulated location may not be the (only) location with the optimal alignment score.

Results

Vargas implements a heuristic-free algorithm guaranteed to find the highest-scoring alignment for real sequencing reads to a linear or graph genome. With semiglobal and local alignment modes and affine gap and quality-scaled mismatch penalties, it can implement the scoring functions of commonly used aligners to calculate optimal alignments. While this is computationally intensive, Vargas uses multi-core parallelization and vectorized (SIMD) instructions to make it practical to optimally align large numbers of reads, achieving a maximum speed of 456 billion cell updates per second. We demonstrate how these ‘gold standard’ Vargas alignments can be used to improve heuristic alignment accuracy by optimizing command-line parameters in Bowtie 2, BWA-maximal exact match and vg to align more reads correctly.

Availability and implementation

Source code implemented in C++ and compiled binary releases are available at https://github.com/langmead-lab/vargas under the MIT license.

Supplementary information

Supplementary data are available at Bioinformatics online.
General regression model for the subdistribution of a competing risk under left-truncation and right-censoring
Authors: Bellach A , Kosorok M R , Gilbert P B , Fine J P
Published: April 2020

Left-truncation poses extra challenges for the analysis of complex time-to-event data. We propose a general semiparametric regression model for left-truncated and right-censored competing risks data that is based on a novel weighted conditional likelihood function. Targeting the subdistribution hazard, our parameter estimates are directly interpretable with regard to the cumulative incidence function. We compare different weights from recent literature and develop a heuristic interpretation from a cure model perspective that is based on pseudo risk sets. Our approach accommodates external time-dependent covariate effects on the subdistribution hazard. We establish consistency and asymptotic normality of the estimators and propose a sandwich estimator of the variance. In comprehensive simulation studies we demonstrate solid performance of the proposed method. Comparing the sandwich estimator with the inverse Fisher information matrix, we observe a bias for the inverse Fisher information matrix and diminished coverage probabilities in settings with a higher percentage of left-truncation. To illustrate the practical utility of the proposed method, we study its application to a large HIV vaccine efficacy trial dataset.
Marijuana Use in Inflammatory Bowel Disease: Understanding the Prevalence and the Potential Pitfalls
Authors: Scott Frank I
Published: April 2020

Lay Summary

Many patients with inflammatory bowel disease experiment with marijuana for symptom control. Marijuana may help reduce abdominal pain but does not control inflammatory bowel disease related inflammation. Patients should discuss their marijuana use with their providers and continue appropriate medications if considering marijuana.
Testing identification via heteroskedasticity in structural vector autoregressive models
Authors: Lütkepohl Helmut , Meitz Mika , Netšunajev Aleksei , Saikkonen Pentti
Published: April 2020

Tests for identification through heteroskedasticity in structural vector autoregressive analysis are developed for models with two volatility states where the time point of volatility change is known. The tests are Wald-type tests for which only the unrestricted model, including the covariance matrices of the two volatility states, has to be estimated. The residuals of the model are assumed to be from the class of elliptical distributions, which includes Gaussian models. The asymptotic null distributions of the test statistics are derived, and simulations are used to explore their small-sample properties. Two empirical examples illustrate the usefulness of the tests in applied work.
A test of oscillation in the human secondary sex ratio
Authors: Catalano Ralph , Casey Joan A , Bruckner Tim A
Published: April 2020

Background and objectives

The sex ratio of human birth cohorts predicts the health and longevity of their members. Most literature invokes natural selection in support of the argument that heritable tendencies to produce male or female offspring induce oscillation in the sex ratio and its sequelae. Tests of the argument remain exceedingly rare because they require vital statistics describing many generations of a population both unaffected by migration and exposed to an exogenous stressor virulent enough to change the sex ratio at birth. We contribute to the literature by using time-series modeling to detect oscillation in the best data currently available for such a test.

Methodology

We apply rigorous time-series methods to data describing Sweden from 1751 through 1830, a period when the population not only aged in place without migration, but also exhibited the effects of an Icelandic volcanic eruption including a historically low secondary sex ratio. That very low sex ratio should have induced oscillation if heritable mechanisms appear in humans.

Results

We detected oscillation in the ratio but not that predicted by heritable tendencies to produce males or females. We found peak-to-trough oscillation at 14 rather than the approximately 32 years expected from the heritable tendencies argument.

Conclusions and implications

Our findings suggest that mechanisms other than perturbation of heritable tendencies to produce males or females induce oscillation in the human secondary sex ratio. These other mechanisms may include reproductive suppression and selection in utero.

LAY SUMMARY

The male to female ratio in human birth cohorts predicts longevity but its variation over time remains unexplained. We test the long-held theory that the ratio oscillates due to heritable tendencies to produce males or females. We find oscillation, but it appears due to social processes rather than heritable mechanisms.
Hope and trust in times of Zika: the views of caregivers and healthcare workers at the forefront of the epidemic in Brazil
Authors: Simas Clarissa , Penn-Kekana Loveday , Kuper Hannah , Lyra Tereza Maciel , Moreira Maria Elisabeth Lopes , de Albuquerque Maria do Socorro Veloso , de Araújo Thália Velho Barreto , de Melo Ana Paula Lopes , Figueira Mendes Corina Helena , Nunes Moreira Martha Cristina , Ferreira do Nascimento Marcos Antonio , Pimentel Camila , Pinto Marcia , Valongueiro Sandra , Larson Heidi
Published: April 2020

This article investigates how hope and trust played out for two groups at the forefront of the Zika epidemic: caregivers of children with congenital Zika syndrome and healthcare workers. We conducted 76 in-depth interviews with members of both groups to examine hope and trust in clinical settings, as well as trust in public institutions, in the health system and in the government of Brazil. During and after the Zika epidemic, hope and trust were important to manage uncertainty and risk, given the lack of scientific evidence about the neurological consequences of Zika virus infection. The capacity of healthcare workers and caregivers to trust and to co-create hope seems to have allowed relationships to develop that cushioned social impacts, reinforced adherence to therapeutics and enabled information flow. Hope facilitated parents to trust healthcare workers and interventions. Hope and trust appeared to be central in the establishment of support networks for caregivers. At the same time, mistrust in the government and state institutions may have allowed rumours and alternative explanations about Zika to spread. It may also have strengthened activism in mother’s associations, which seemed to have both positive and negative implications for healthcare service delivery. The findings also point to distrust in international health actors and global health agenda, which can impact community engagement in future outbreak responses in Brazil and other countries in Latin America.
Wild Atlantic salmon exposed to sea lice from aquaculture show reduced marine survival and modified response to ocean climate
Authors: Shephard Samuel , Gargan Patrick
Published: April 2020

Impacts on marine survival of Atlantic salmon Salmo salar include sea lice Lepeophtheirus salmonis infestation from coastal aquaculture, and ocean climate forcing. These effects may interact because infestation compromises smolt growth and body condition, and thus response to environmental conditions. We hypothesized that migrating smolts exposed to lice from salmon farms would show (i) reduced one sea-winter (1SW) returns to natal rivers and (ii) a shift in relationships between ocean climate and returns. Annual counts of 1SW fish were studied from ten rivers in Ireland, including five “control” systems without salmon aquaculture. Most counts showed a downward trend, consistent with declines in Atlantic salmon populations. Rivers with aquaculture showed lesser returns (mean 33%, range 19–46%) in years following high lice levels on nearby salmon farms. The level of likely lice pressure also modified how annual 1SW returns varied with ocean conditions. Returns to control rivers showed a weak negative relationship with the Atlantic Multi-decadal Oscillation (AMO) in the late summer (September) of the out-migrating year. This negative AMO effect became much stronger for fish migrating in low lice years but was not evident for high lice years. Smolts experiencing mild-to-moderate lice infestation may show greater sensitivity to ocean warming.
Some Deficits and Some Misperceptions: Linking Partisanship With Climate Change Cognitions
Authors: Chinn Sedona , Pasek Josh
Published: April 2020

Partisans appear to hold very different information about climate change, with Republicans more likely to respond to knowledge questions incorrectly than Democrats or Independents. As polarization undermines the ability of science to inform policy, clarifying why partisans report different science knowledge is vital. This study uses novel measurement to differentiate among knowledge, misinformation, guessing, and rejection while identifying covariates of these cognitions. First, we find that Republicans’ incorrect beliefs about climate change stem from a mix of guessing, misperceptions, and rejection. Second, multiple cognitions appear closely linked to partisanship, suggesting politically motivated reasoning as likely driving knowledge differences. Finally, partisan knowledge differences about climate change do not reflect broader polarization of science knowledge but are limited to select issues.
A forum on batteries: from lithium-ion to the next generation
Authors: Zhao Weijie
Published: April 2020

The 2019 Nobel Prize in Chemistry was awarded to three pioneers of lithium-ion batteries (LIBs)—Prof. John B. Goodenough at the University of Texas, Prof. M. Stanley Whittingham at the State University of New York and Mr. Akira Yoshino at the Asahi Corporation of Japan, which is a great encouragement to the whole field.

LIBs have been developed for several decades with the progress slowing down and their performances approaching some theoretical limits. On the other hand, new types of batteries or power systems, including solid-state batteries, sodium-ion batteries, lithium-sulfur batteries and fuel cells, are being steadily developed, offering new choices for divergent applications. In this panel discussion chaired by NSR editorial board member Huiming Cheng, battery experts gather to discuss the challenges and trends of LIBs, the developments and applications of next-generation batteries, as well as the status quo of the battery research and industry in China.

Jun Chen

Professor of the College of Chemistry, Nankai University, Tianjin, China

Yunhui Huang

Professor of the School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

Hong Li

Professor of the Institute of Physics, Chinese Academy of Sciences, Beijing, China

Shigang Sun

Professor of the College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China

Hongli Zhang

Director of the R&D Institute of Battery, Gotion High-Tech Power Energy Co., Ltd., Hefei, China

Huiming Cheng (Chair)

Professor of the Shenzhen Geim Graphene Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China; Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, China
The value of lost productivity from workplace bullying in Ireland
Authors: Cullinan J , Hodgins M , Hogan V , Pursell L
Published: April 2020

Background

Workplace bullying is a pervasive problem with significant personal, social and economic costs. Estimates of the resulting lost productivity provide an important societal perspective on the impact of the problem. Understanding where these economic costs fall is relevant for policy.

Aims

We estimated the value of lost productivity to the economy from workplace bullying in the public and private sectors in Ireland.

Methods

We used nationally representative survey data and multivariable negative binomial regression to estimate the independent effect of workplace bullying on days absent from work. We applied the human capital approach to derive an estimate of the annual value of lost productivity due to bullying by sector and overall, in 2017.

Results

Bullying was independently associated with an extra 1.00 (95% CI: 0.38–1.62) days absent from work over a 4-week period. This differed for public and private sector employees: 0.69 (95% CI: −0.12 to 1.50) versus 1.45 (95% CI: 0.50–2.40) days respectively. Applying official data, we estimated the associated annual value of lost productivity to be €51.8 million in the public sector, €187.6 million in the private sector and €239.3 million overall.

Conclusions

The economic value of lost productivity from workplace bullying in Ireland is significant. Although bullying is more prevalent in the public sector, it has a larger effect on absence in the private sector. Given this, along with the greater overall share of employees, productivity losses from bullying are considerably larger in the private sector in Ireland.
Application of social marketing to recruitment for a digital weight management intervention for young adults
Authors: Whiteley Jessica A , Faro Jamie M , Mavredes Meghan , Hayman Laura L , Napolitano Melissa A
Published: April 2020

Recruiting young adults into weight loss interventions poses challenges that may be mitigated by the use of novel social marketing strategies. The purpose of this study is to describe how social marketing principles were applied to recruitment for a digitally delivered randomized controlled trial for weight management among young adults and report recruitment data and demographics on those who enrolled and did not enroll in the study. The marketing mix of the 7Ps (i.e., product, price, place, promotion, packaging, positioning, and people) was applied to intervention recruitment. Prior to enrollment, respondents completed a screening survey, which was examined to determine optimal strategies for study awareness and enrollment. Of the initial 5,731 who initiated a screener, 3,059 provided data on the source of where they heard about the study. Subsequently, 460 (12.5%) were enrolled in the study, 409 (51.3% non-White; 78.7% female; body mass index: 30.6 ± 4.3) provided data on recruitment source, with emails (72.5%), cited most often followed by flyers/posters (8.8%), “other” (6.7%), and multiple sources (6.6%). Although email remained the most frequently cited promotion source, Pearson’s chi-squared tests revealed that, compared to those not enrolled in the study, those who enrolled were more likely to hear about the study via flyers/posters (enrolled = 14.4%; not enrolled = 7.9%; p < .001) and multiple sources (enrolled = 11.7%; not enrolled 5.85%; p < .01) and less likely to hear via email (enrolled = 62.1%; not enrolled = 74.2%; p < .01). This study applied social marketing principles to successfully recruit a large and diverse group of young adults. While email emerged as the most effective source of study awareness, multiple channels and a mix of marketing principles are recommended for recruiting in university settings.