bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between serum oestradiol level on the hCG administration day and neonatal birthweight after IVF-ET among 3659 singleton live births
Authors: Liu Yu , Li Jing , Zhang Wanyu , Guo Yihong 
Published: March 2021

AbstractOestradiol, an important hormone in follicular development and endometrial receptivity, is closely related to clinical outcomes of fresh in vitro fertilization-embryo transfer (IVF-ET) cycles. A supraphysiologic E2 level is inevitable during controlled ovarian hyper-stimulation (COH), and its effect on the outcome of IVF-ET is controversial. The aim of this retrospective study is to evaluate the association between elevated serum oestradiol (E2) levels on the day of human chorionic gonadotrophin (hCG) administration and neonatal birthweight after IVF-ET cycles. The data of 3659 infertile patients with fresh IVF-ET cycles were analysed retrospectively between August 2009 and February 2017 in First Hospital of Zhengzhou University. Patients were categorized by serum E2 levels on the day of hCG administration into six groups: group 1 (serum E2 levels ≤ 1000 pg/mL, n = 230), group 2 (serum E2 levels between 1001 and 2000 pg/mL, n = 524), group 3 (serum E2 levels between 2001 and 3000 pg/mL, n = 783), group 4 (serum E2 levels between 3001 and 4000 pg/mL, n = 721), group 5 (serum E2 levels between 4001 and 5000 pg/mL, n = 548 ), and group 6 (serum E2 levels > 5000 pg/mL, n = 852). Univariate linear regression was used to evaluate the independent correlation between each factor and outcome index. Multiple logistic regression was used to adjust for confounding factors. The LBW rates were as follows: 3.0% (group 1), 2.9% (group 2), 1.9% (group 3), 2.9% (group 4), 2.9% (group 5), and 2.0% (group 6) (P = 0.629), respectively. There were no statistically significant differences in the incidences of neonatal LBW among the six groups. We did not detect an association between peak serum E2 level during ovarian stimulation and neonatal birthweight after IVF-ET. The results of this retrospective cohort study showed that serum E2 peak levels during ovarian stimulation were not associated with birth weight during IVF cycles. In addition, no association was found between higher E2 levels and increased LBW risk. Our observations suggest that the hyper-oestrogenic milieu during COS does not seem to have adverse effects on the birthweight of offspring after IVF. Although this study provides some reference, the obstetric-related factors were not included due to historical reasons. The impact of the high estrogen environment during COS on the birth weight of IVF offspring still needs future research.
Bicyclic RGD peptides enhance nerve growth in synthetic PEG-based Anisogels
Authors: Vedaraman Sitara , Bernhagen Dominik , Haraszti Tamas , Licht Christopher , Castro Nava Arturo , Omidinia Anarkoli Abdolrahman , Timmerman Peter , De Laporte Laura 
Published: March 2021

aECMs imparting integrin selectivity (α5β1 and αvβ3) and directional guidance cues are developed using MMP degradable PEG hydrogels, functionalized with bicyclic RGD peptides, and including aligned magnetic, short fibers for 3D oriented nerve growth.
Borane-catalysed dinitrogen borylation by 1,3-B–H bond addition
Authors: Coffinet Anaïs , Zhang Dan , Vendier Laure , Bontemps Sébastien , Simonneau Antoine 
Published: March 2021

1,3-B–H addition of various boranes over a W–NN motif has been explored to probe its scope and limitations as a method for N2 borylation. The reaction can be catalysed or not, and boranes that undergo retrohydroboration react as monoalkylboranes.
Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm
Authors: Xiao Xiangjie , Pan Yaohui , Lv Lingling , Shi Yanjun 
Published: March 2021

AbstractA modified particle swarm optimization (PSO) approach is presented for the multi‐mode resource‐constrained scheduling problem of automated guided vehicle (AGV) tasks. Various constraints in the scheduling process of the AGV system are analysed, and the types and quantities of AGVs as allocable resources are considered. The multiple‐AGV combined distribution mode and its impact on distribution tasks is also considered. Finally, a multi‐mode resource‐constrained task scheduling model is established for which the object is to minimise material delivery time. Based on the above model, the discrete particle swarm optimization algorithm that improved the basic PSO was proposed. The simulation results with the test set in PSPLIB standard library showed the effectiveness of the improved PSO algorithm.
Low‐rank constrained weighted discriminative regression for multi‐view feature learning
Authors: Zhang Chao , Li Huaxiong 
Published: March 2021

AbstractIn recent years, multi‐view learning has attracted much attention in the fields of data mining, knowledge discovery and machine learning, and been widely used in classification, clustering and information retrieval, and so forth. A new supervised feature learning method for multi‐view data, called low‐rank constrained weighted discriminative regression (LWDR), is proposed. Different from previous methods handling each view separately, LWDR learns a discriminative projection matrix by fully exploiting the complementary information among all views from a unified perspective. Based on least squares regression model, the high‐dimensional multi‐view data is mapped into a common subspace, in which different views have different weights in projection. The weights are adaptively updated to estimate the roles of all views. To improve the intra‐class similarity of learned features, a low‐rank constraint is designed and imposed on the multi‐view features of each class, which improves the feature discrimination. An iterative optimization algorithm is designed to solve the LWDR model efficiently. Experiments on four popular datasets, including Handwritten, Caltech101, PIE and AwA, demonstrate the effectiveness of the proposed method.
Design and analysis of recurrent neural network models with non‐linear activation functions for solving time‐varying quadratic programming problems
Authors: Zhang Xiaoyan , Chen Liangming , Li Shuai , Stanimirović Predrag , Zhang Jiliang , Jin Long 
Published: March 2021

AbstractA special recurrent neural network (RNN), that is the zeroing neural network (ZNN), is adopted to find solutions to time‐varying quadratic programming (TVQP) problems with equality and inequality constraints. However, there are some weaknesses in activation functions of traditional ZNN models, including convex restriction and redundant formulation. With the aid of different activation functions, modified ZNN models are obtained to overcome the drawbacks for solving TVQP problems. Theoretical and experimental research indicate that the proposed models are better and more effective at solving such TVQP problems.
Using the zeroth order resonance of an inter‐digital capacitive unit‐cell to design antennas for passive UHF RFID tags
Authors: Masud Muhammad Mubeen , Mitra Dipankar , Ijaz Bilal , Ullah Irfan , Braaten Benjamin D. 
Published: March 2021

AbstractMeander‐line and multi‐layer antennas have been used extensively to design compact UHF radio‐frequency identification (RFID) tags; however the overall size reduction of meander‐line antennas is limited by the amount of parasitic inductance that can be introduced by each meander‐line segment and multi‐layer antennas can be too costly. In this paper, a new compact antenna topology for passive UHF RFID tags based on zeroth order resonant (ZOR) design techniques is presented. The antenna consists of lossy coplanar conductors and inter‐connected inter‐digital capacitor (IDC) unit‐cells with a ZOR frequency near the operating frequency, which is a key component in the design process because the unit‐cells chosen for the design are inductive at the operating frequency. This makes the unit‐cells very useful for antenna miniaturization. The new design in this work has several benefits; namely the coplanar layout can be printed on a single layer, matching inductive loops that reduce antenna efficiency are not required and ZOR analysis can be used for the design. Finally, for validation, a prototype antenna was designed with a unit‐cell ZOR resonant frequency of 900 MHz, fabricated and characterized. A maximum read‐range of 7.6 m was determined.
Modelling and stability assessment of the MMC‐HVDC energy interconnected system with the cyber delay of communication network
Authors: Cao Jingming , Dong Chaoyu , Yu Xiaodan , Wang Rui , Xiao Qian , Jia Hongjie 
Published: March 2021

AbstractModular multilevel converters (MMCs) are favoured in high voltage direct current (HVDC) systems because of huge energy transmission capacity and few harmonics. However, the complicated control and the intricate communication network introduce cyber delay that affects the system dynamics. The communication structure is discussed and delay components are studied. The authors derive the MMC‐HVDC system model considering the cyber delay effect. Padé approximation is utilised to transform the transcendental characteristic equation of the delay model into an algebraic equation so that the dynamics can be observed through the eigenvalues. The comparison between the established model and the simulation in Simulink validates the proposed model's accuracy. The exact delay margin and the root cause of losing the stability are also discussed. The results show that the model is able to obtain the delay margin with a tiny error caused by the omitted high‐order harmonics and the cyber delay destabilises the system by lowering the controller performances.
Intelligent diagnosis of cascaded H‐bridge multilevel inverter combining sparse representation and deep convolutional neural networks
Authors: Du Bolun , He Yigang , Zhang Chaolong 
Published: March 2021

AbstractEffective fault diagnosis for cascaded H‐bridge multilevel inverter (CHMLI) can reduce failure rate and prevent the unscheduled shutdown. Nevertheless, traditional signal‐based feature extraction and feature selection methods show poor distinguishability for insufficient fault features in a one‐dimensional space. The shallow learning models are prone to fall into local extremum, slow convergence speed and overfitting. To cope with these problems, a novel image‐oriented fault diagnosis strategy based on sparse representation (SR) and deep convolutional neural network (DCNN) is proposed for CHMLI. Initially, Hilbert–Huang transform (HHT) is applied to obtain the HHT spectral images of original monitoring signals, where these images comprehensively represent the features with detailed information of multiple domains on the time‐frequency plane. Furthermore, an image fusion method based on the SR algorithm is employed on these spectral images of the same fault category to construct fused feature images, which effectively reflects the complicated relationships between the measured signals and fault features. Ultimately, the DCNN models can not only mine the relationship between the various fault categories and the different fused feature images but also can alleviate the problem of overfitting that is caused by the limited availability of training samples.
Short circuit detection and driving control with no blanking time for high voltage high power insulated gate bipolar transistors
Authors: Huang Xianjin , Li Xin , Wang Fengchuan , Liu Yixin , Sun Hu 
Published: March 2021

AbstractInsulated gate bipolar transistors (IGBT) short‐circuit (SC) protection is one of the most important protection methods for IGBT converter equipment. The fast detection and protection response time could reduce the permanent damage of devices, and extend their use life cycle. At present, the collector emitter voltage vce desaturation method is widely used in the commercial IGBT drive circuit, which has blanking time of fault detection and protection. In this paper, an improved adaptive vce SC detection and protection is proposed to realize the blanking time adjustment under different SC conditions. By the determination of the di/dt characteristics of the current variation on the parasitic inductance between the power emitter and the Kelvin emitter of IGBT modules, the execution time of the two detection methods can be set. According to the switch process and the SC type, the corresponding logic processing will be carried out to realize the fast detection of different SC conditions. The circuit scheme without blanking time detection and protection is designed, and the circuit is modelled and simulated by Pspice. The IGBT driving circuit SC test without blanking time detection and soft turn‐off protection has been carried out. Simulation and test results can verify the feasibility of the proposed circuit.
Open‐switch fault diagnosis in voltage source inverters of PMSM drives using predictive current errors and fuzzy logic approach
Authors: Gmati Badii , Jlassi Imed , Khojet El Khil Sejir , Marques Cardoso Antonio J. 
Published: March 2021

AbstractIn critical industrial applications fault diagnosis and fault tolerance are considered key features, in order to ensure the required reliability and availability levels. In this context, this paper proposes a new and effective diagnostic algorithm for power semiconductors open‐circuit faults, in three‐phase, two‐level, voltage‐source inverter‐fed permanent magnet synchronous machine (PMSM). The proposed method is based on the analysis of the errors between the reference currents and the PMSM stator predictive currents. Hence, for each phase of the PMSM two fault diagnostic variables have been defined, which allow the diagnosis of both single and multiple open‐circuit faults. The fuzzy logic approach is applied to the fault diagnostic variables in order to identify the faulty power switches. The method is experimentally validated on a model predictive controlled (MPC) permanent magnet synchronous motor (PMSM) drive, which shows the effectiveness of the fault diagnosis algorithm with a high robustness regarding the operating point and parameter variations of the PMSM drive system.
LVRT capability based on P‐V curve fitting under partial shading conditions
Authors: Jervekani Mehdi Kazemi , Niroomand Mehdi , Madani Seyed Mohammad 
Published: March 2021

AbstractThe new generation of photovoltaic (PV) systems represents higher sustainability during grid faults thanks to increased ancillary services, such as low voltage ride‐through (LVRT) capability used when the PV system is subjected to voltage sag. Unlike previously presented strategies that just dealt with voltage sag problem under uniform radiation conditions, in this study, a new control strategy implementing LVRT capability during low‐voltage faults under partial shading conditions is proposed. First, radiation levels are estimated by using the least‐squares curve fitting (LSCF) algorithm. Second, the voltage/current of maximum power points (MPPs) and minimum power points are calculated. Also, the corresponding algebraic function for the (Power ‐ Voltage) P‐V curve is extracted using only PV voltage and power vectors. Finally, under partial shading conditions, the moving operating point to the right side of MPP is well‐achieved through a power proportional‐integral controller. To validate the effectiveness of the proposed control strategy, simulations and experiments are conducted on PV systems. The simulation and experimental results and the comparison made between this algorithm's performance and other methods confirm that the proposed algorithm outperforms other methods in terms of high accuracy, fast dynamic and low oscillations in different partial shading conditions and with different radiations.
Evaluation method for moisture content of oil‐paper insulation based on segmented frequency domain spectroscopy: From curve fitting to machine learning
Authors: Yao Huanmin , Mu Haibao , Ding Ning , Zhang Daning , Liang ZhaoJie , Tian Jie , Zhang Guanjun 
Published: March 2021

AbstractIn recent years, frequency domain spectroscopy (FDS) is often used to evaluate oil paper insulation state in power transformer bushing. But it is still very difficult to evaluate the moisture content accurately and quickly. In order to solve this problem, this paper proposes an intelligent algorithm based on random forest regression (RFR) to construct an efficient evaluation method through segmented FDS curves. Furthermore, the characteristics of FDS curves were studied and the intelligent method was compared with support vector regression (SVR) and deep neural networks (DNN). The results show that the dielectric loss, the real part and imaginary part of complex capacitance all move upward with the moisture increasing, so they can be used as the input feature of the evaluation model; The moisture content evaluation accuracy of the RFR model in the whole frequency band is higher than that of SVR and DNN models; With the increase of lower cut off frequency (FDS test stop frequency), the FDS test time is greatly shortened, and the accuracy of the RFR model can still meet the evaluation requirements. Therefore, the data in a compromise frequency band can be used to evaluate the moisture content of oil paper insulation accurately and quickly.
Analysis of the influence of device‐to‐device communication on the access terminal of electric power data communication network
Authors: Cui Mingshi , Liu Shimin , Sun Tianzi , Du Na 
Published: March 2021

AbstractThe expansion of the electric power communication network year by year has enabled a large number of intelligent terminals to be accessed exponentially. Massive intelligent terminals connected to the power communication network have a huge processing demand, which causes a heavy maintenance burden on the equipment of communication networks. In order to solve this problem, this study proposes a flexible communication mechanism based on the device‐to‐device (D2D) technique for the intelligent terminals to deal with the processing pressure of massive information in the network. This proposed mechanism introduces where and how to use the D2D technique. In this study, this communication mechanism makes terminals give a high priority to transferring the information of important services. The D2D communication model is formulated for this communication mechanism, and the influence of different conditions on the D2D communication is analysed. The D2D communication mechanism is simulated to optimise the existing network indexes, such as traffic and sum of service level.
Image semantic segmentation method based on GAN network and ERFNet model
Authors: Dong Chaoxian 
Published: March 2021

AbstractThis article addresses the problems of traditional methods in image semantic segmentation, such as insufficient segmentation of small‐scale targets and weak anti‐noise ability. A method of image semantic segmentation using a generative adversarial network (GAN) combined with ERFNet model is proposed. First, the asymmetric residual module (ARM) and weak bottleneck module are used to improve the ERFNet network model. Moreover, dilated convolution is used to reduce information loss and improve the performance of small target image semantic segmentation. Then, a U‐shaped network is used to improve the generator of GAN to avoid low‐level information sharing. In addition, the residual module is introduced into convolution layer to realise the dynamic adjustment of generator weight. Finally, the improved ERFNet model is used as the input of the generator to output the segmented image. It is input to the discriminator together with the label for judgment, which further improves the performance of image semantic segmentation. The proposed method is demonstrated experimentally based on the PyTorch platform. The results show that mean pixel accuracy and mean intersection over the union of the proposed method on the CamVid and Cityscapes datasets are higher than those of other comparisons. In addition, the execution time is short, and the overall image semantic segmentation performance is relatively ideal.
Study on the drying characteristics of rice by microwave and hot air drying
Authors: Ling Fangqing , Sun Tongsheng 
Published: March 2021

AbstractThis study presents the experimental methods of microwave drying and hot air drying of rice to simultaneously improve the dry storage effect of rice, reduce the mouldy and spoilage of rice, and measure the moisture content, water activity and temperature changes. A thermal imaging camera was used to photograph the temperature distribution of rice at different times to study the changing law of rice drying characteristics and analyse the relationship between rice water activity and drying speed. Results show that the changes in the moisture content and water activity of rice are inversely proportional with time. A 400 W microwave drying has a more pronounced heating effect on rice than 850 W hot air drying. The efficiency of the first 20 min of rice drying is better than that of the last 20 min. The highest temperature of 40°C was reached after microwave drying for 8 min, while nearly 40°C was reached after hot air drying for 56 min. These results can be applied to further analyse the influence of different drying methods on rice water activity and guide the reduction of bacterial propagation in rice and the improvement of the quality of rice. This work provides a theoretical basis for choosing the optimal drying method.
On IP2 impact to nonlinear distortion of RF amplifiers
Authors: Yang Xianzhen , Li Shiyu , Yan Siyuan , Li Fu 
Published: March 2021

AbstractThe second‐order distortion, often using second‐order intercept point (IP2) as an indicator, has been considered easy to be filtered in narrowband radio frequency (RF) communications because it is located far away from the passband of transmitted signals. However, with rapidly increased applications in wideband RF communications, the second‐order intermodulation (IM) might become important, partially evidenced by the increased appearances of IP2 in the amplifier datasheets in recent years. In this article, a power spectrum model of the second‐order IM is derived to quantify the second‐order distortion using the IP2. This model could be used to determine under what conditions, in terms of the relation between the carrier frequency and bandwidth, the second‐order IM will affect the passband or the adjacent bands or other applications, using three types of frequency scenarios. These discussions are beneficial for RF engineers and spectrum planners to predict interferences from second‐order IM. The experimental measurement at the end of the article validates the spectrum model.
Perioperatives Management: vom OP in den Aufwachraum/auf die Station
Authors: Martin Christoph 
Published: March 2021

ZusammenfassungDie postoperative Phase hat in der modernen operativen Medizin bei zunehmendem operativem Spektrum an komplex vorerkrankten Patienten eine wachsende Bedeutung. Das räumliche Korrelat ist der Aufwachraum – seine personelle und räumliche Struktur muss ein Mindestmaß erfüllen und den vorliegenden operativen Strukturen gerecht werden. Gute interdisziplinäre und interprofessionelle Kommunikation sowie eine gesunde Fehlermeldekultur sind essenziell.