bluuebunny/crossref_metadata_2025_split huggingface dataset:
Matrine treatment reduces retinal ganglion cell apoptosis in experimental optic neuritis
Authors: Kang Jian , Liu Shuqing , Song Yifan , Chu Yaojuan , Wang Mengru , Shi Yamin , Zhang Fengyan , Zhu Lin 
Published: May 2021

AbstractInflammatory demyelination and axonal injury of the optic nerve are hallmarks of optic neuritis (ON), which often occurs in multiple sclerosis and is a major cause of visual disturbance in young adults. Although a high dose of corticosteroids can promote visual recovery, it cannot prevent permanent neuronal damage. Novel and effective therapies are thus required. Given the recently defined capacity of matrine (MAT), a quinolizidine alkaloid derived from the herb Radix Sophorae flavescens, in immunomodulation and neuroprotection, we tested in this study the effect of matrine on rats with experimental autoimmune encephalomyelitis, an animal model of multiple sclerosis. MAT administration, started at disease onset, significantly suppressed optic nerve infiltration and demyelination, with reduced numbers of Iba1+ macrophages/microglia and CD4+ T cells, compared to those from vehicle-treated rats. Increased expression of neurofilaments, an axon marker, reduced numbers of apoptosis in retinal ganglion cells (RGCs). Moreover, MAT treatment promoted Akt phosphorylation and shifted the Bcl-2/Bax ratio back towards an antiapoptotic one, which could be a mechanism for its therapeutic effect in the ON model. Taken as a whole, our results demonstrate that MAT attenuated inflammation, demyelination and axonal loss in the optic nerve, and protected RGCs from inflammation-induced cell death. MAT may therefore have potential as a novel treatment for this disease that may result in blindness.
Supplemental state observer‐based sliding mode control for a dynamic system
Authors: Chen Kun‐Yung 
Published: May 2021

AbstractA supplemental state observer‐based sliding mode control (SSOBSMC) for a dynamic system is proposed in this paper. First, a supplemental state vector is formulated including system output and supplemental output. A supplemental state observer (SSOB) is proposed to estimate the unavailable states. Furthermore, the SSOBSMC consisting of SSOB and sliding mode control (SMC) is proposed to perform state estimation and robust tracking control for the dynamic system with some unavailable states. To demonstrate the application of the proposed methods, a mass‐spring‐damper (MSD) system is used as an application example to perform numerically. From the simulation results, the SSOB shows a good ability for state estimation, while the SSOBSMC simultaneously demonstrates accuracy in state estimation ability as well as a robust tracking control performance for the non‐linear MSD system. The major contributions of this paper are the proposed SSOB that can accurately estimate the unavailable states for the non‐linear dynamic system with time‐varying disturbances; and the SSOBSMC, which simultaneously displays accuracy in state estimation ability and robust tracking control performance.
Bayesian active learning for multi‐objective feasible region identification in microwave devices
Authors: Garbuglia Federico , Qing Jixiang , Knudde Nicolas , Spina Domenico , Couckuyt Ivo , Deschrijver Dirk , Dhaene Tom 
Published: May 2021

AbstractIn microwave device and circuit design, many simulations are often needed to find a set of designs that satisfy one or multiple specifications chosen by the designer upfront: the feasible region. A novel Bayesian active learning framework is presented to accurately identify the feasible region with a low number of simulations. The technique leverages on a stochastic model to obtain an efficient and automated procedure. A suitable application example validates the proposed technique and shows its effectiveness to rapidly obtain many suitable designs.
Research on graphene/silicon pressure sensor array based on backpropagation neural network
Authors: Li Fangqing , Fang Shuxing , Shen Yu , Wang Debo 
Published: May 2021

AbstractIn order to improve the recognition accuracy of graphene pressure sensors, a graphene/silicon pressure sensor array is studied based on backpropagation (BP) neural network. The principle of the pressure sensor array and the workflow of BP neural network are introduced. The 100 groups of training samples ranging from 0 to 1000 kPa are studied based on levenberg‐marquardt (L‐M) optimisation algorithm, and a multiple BP (M‐BP) neural network is designed to improve the recognition accuracy of the pressure sensor array. The training recognition accuracy and test recognition accuracy of M‐BP neural network are about 99.9%. This work plays an important role in the application of graphene pressure sensors in more fields, especially in the solutions for weak pressure detection in artificial intelligence and human‐computer interaction.
Novel 1064 nm DBR lasers combining active layer removal and surface gratings
Authors: Brox O. , Wenzel H. , Fricke J. , Della Casa P. , Maaßdorf A. , Matalla M. , Wenzel S. , Wicht A. , Knigge A. 
Published: May 2021

AbstractThe fabrication and characterisation details of novel distributed Bragg reflector (DBR) diode lasers emitting around 1064 nm are presented here. The AlGaAs epitaxial layer stack used here allows the removal of the active quantum wells in passive sections where no current is injected. The DBR lasers fabricated with a two‐step epitaxial approach without an active layer in the grating sections show improved performance in terms of power, efficiency, and linewidth in comparison to its all‐active DBR counterparts.
Robust active contours based on local threshold preprocessing fitting energies for fast segmentation of inhomogenous images
Authors: Guo Baojun , Cui Jinlong , Gao Beibei 
Published: May 2021

AbstractThis letter presents a robust active contour model driven by the local threshold preprocessing fitting energies to actualize fast segmentation of inhomogenous images. First, the local threshold preprocessing is carried out to compute two local intensity means. Second, these two means are sorted and serve as the local area fitting centres, which makes the contour move along the internal or external target boundaries. Finally, the above local area fitting centres are used to build the model's energy functional based on the symmetric cross entropy. Experimental results of the real inhomogenous images confirm that the presented model can segment inhomogenous images much faster and is robust to the setting up of the original contour.
Modified load‐modulation network with two π‐type high‐pass equivalent λ/4 lines for wideband compact GaN MMIC Doherty power amplifier design
Authors: Liu Rui‐Jia , Zhu Xiao‐Wei , Xia Jing , Jiang Yi‐Fan , Chen Peng , Yu Chao , Ge Chuan 
Published: May 2021

AbstractA modified load‐modulation network to design a fully integrated wideband Doherty power amplifier (DPA) from 4.6 to 5.5 GHz is presented in this letter. The modified load‐modulation network uses only two π‐type high‐pass equivalent quarter wavelength transmission lines (QWTLs). Unlike the existing methods, the bandwidth of the load‐modulation network composed of two merged π‐type equivalent QWTLs is broadened. Furthermore, a drain bypass capacitor compensation method is proposed to reduce the leakage of RF power after the drain supply inductors are merged. For verification, a wideband monolithic microwave integrated circuit DPA was designed and fabricated using a 0.25‐μm gallium nitride based high‐electron‐mobility transistor process. Measurement results show that a saturated power of 41.6–42.4 dBm, a saturated drain efficiency (DE) of 65.3–72%, and a 6‐dB back‐off DE of 45–55.4% are achieved from 4.6 to 5.5 GHz. Using a 160‐MHz orthogonal frequency‐division multiplexing signal with a peak‐to‐average power ratio of 6.2 dB, the high average efficiency of 49% with an adjacent channel leakage ratio of –47.2 dBc after linearisation was obtained at an average output power of 35 dBm at 4.9 GHz.
Spot electricity market design for a power system characterized by high penetration of renewable energy generation
Authors: Yang Jiajia , Dong Zhao Yang , Wen Fushuan , Chen Qixin , Liang Bomiao 
Published: May 2021

AbstractThe continuous growth of renewable generation in power systems brings serious challenges to electricity markets due to their characteristics different from conventional generation technologies. These challenges come from two dimensions, including short‐term (energy and ancillary service markets) and long‐term (long‐term bilateral and capacity markets) aspects. Under this background, the design of energy and ancillary service markets is studied for power systems with a high penetration level of variable renewable generation. In the proposed spot market mechanism, energy and frequency regulation service (FRS) bids are jointly cleared, where renewable generators are motivated to proactively manage the intermittency and uncertainty of their power outputs. The proposed market mechanism can also ensure the adequacy of FRS capacity for compensating variability of renewables. Besides, in order to ensure the execution of spot market clearing outcomes, this paper established a penalty scheme for mitigating the real‐time fluctuations of renewable generation outputs in the spot market. Differences between real‐time generation outputs and market clearing outcomes are managed within a certain limit by imposing the designed penalty prices on deviations. Finally, the feasibility and efficiency of the developed market mechanism and algorithms are manifested in the case studies.
Execution of advanced solar‐shunt active filter for renewable power application
Authors: Sahoo Buddhadeva , Routray Sangram Keshari , Rout Pravat Kumar 
Published: May 2021

AbstractThis manuscript proposes a novel Solar‐shunt active filter (solar‐SHAF) controller for improving the power flow in a larger power‐application. The proposed fault and non‐linear detection unit (FANDU) avails faster disturbance detection. A novel FAND regulator (FANDR) is suggested to disconnect the solar power generation from the considered system within 10–12 s and the total inverter capacity is used for providing reactive power support to compensate the power fluctuation. A solar active power regulator (SAPR) is proposed to restore the active power output of the solar plant in a ramp‐wise manner after the successful damping of the power fluctuations. The restoration of rated solar power results offers a faster settling time in comparison to the standard grid code limits. The proposed approach is applicable for light, dark, and partial shading conditions. During the dark period, the total inverter capacity is used to compensate the power fluctuations and in partial shading conditions, the inverter is used to balance the solar power at its rated limit. The overall system is designed and tested through MATLAB software by considering different test conditions. It is analyzed that the suggested approach facilitates a significant improvement in power exchange on a 24/7 basis during the local and inter‐area fluctuating mode.
Optimal high‐level control of building HVAC system under variable price framework using partially linear model
Authors: He Yiliu , Zhong Haiwang , Tan Zhenfei , Zhan Jiayue 
Published: May 2021

AbstractAs typical thermostatic loads, heating, ventilation, and air conditioning (HVAC) systems possess high flexibility due to buildings' thermal inertia. It is beneficial to coordinate HVAC loads with power grid operations. However, it is difficult to model the HVAC loads of realistic buildings. A partially linear model is proposed to capture the electrothermal characteristics of building HVAC loads. The proposed model combines the explainability of a physically based model and the fitting capability of a data‐driven model. The model retains linearity regarding mapping the control variables to the HVAC load and thus is compatible with existing studies based on the assumption of a succinct HVAC load model. Based on the proposed model, an optimal control strategy is developed to minimize the HVAC operational cost while considering comfort requirements of occupants. EnergyPlus is used as the building simulation engine to validate the proposed model and optimal control strategy.
Correlation analysis of factors affecting wind power based on machine learning and Shapley value
Authors: Pang Chuanjun , Yu Jianming , Liu Yan 
Published: May 2021

AbstractAn analysis of the impact of various factors on wind power can help grid dispatchers understand the characteristics of wind power output and improve the accuracy of wind power forecasting. A correlation analysis method of factors affecting wind power is proposed based on machine learning and the Shapley value. First, factors affecting wind power and the method of constructing wind power models based on machine learning are introduced. Then, to measure the influence of factors on wind power, the Shapley value is proposed based on the wind power model. In addition, calculation methods, properties, and application scenarios of the Shapley value are introduced. Finally, based on the actual data of a wind farm, the method is used to analyse environmental factors affecting wind power, and the main factors affecting the wind farm are determined. The experimental results show that the method can identify important factors affecting wind power and measure the complex non‐linear relation between each environmental factor and wind power.
A remote healthcare monitoring framework for diabetes prediction using machine learning
Authors: Ramesh Jayroop , Aburukba Raafat , Sagahyroon Assim 
Published: May 2021

AbstractDiabetes is a metabolic disease that affects millions of people each year. It is associated with an increased likelihood of vital organ failures and decreased quality of life. Early detection and regular monitoring are crucial for managing diabetes. Remote patient monitoring can facilitate effective intervention and treatment paradigms using current technology. This work proposes an end‐to‐end remote monitoring framework for automated diabetes risk prediction and management, using personal health devices, smart wearables and smartphones. A support vector machine was developed for diabetes risk prediction using the Pima Indian Diabetes Database, after feature scaling, imputation, selection and augmentation. This work achieved the performance metrics of accuracy, sensitivity and specificity scores at 83.20%, 87.20% and 79% respectively through the tenfold stratified cross validation method, which is competitive with existing methods. Patients can use multiple healthcare devices, smartphones and smartwatches to measure vital parameters, curb the progression of diabetes and close the communication loop with medical professionals. The proposed framework enables medical professionals to make informed decisions based on the latest diabetes risk predictions and lifestyle insights while attaining unobtrusiveness, reduced cost, and vendor interoperability.
High‐sensitivity synchronous image encryption based on improved one‐dimensional compound sine map
Authors: Wang Xingyuan , Zhang Maozhen 
Published: May 2021

AbstractAn improved one‐dimensional compound sine map is introduced. The evaluation of this chaotic system shows that it has high sensitivity and random chaotic behaviour. Based on this chaotic system, a new fast image encryption scheme is proposed. Through the cross‐processing of multiple chaotic sequences, two high‐sensitivity pseudo‐random sequences are generated, and the generated high‐sensitivity random sequence is used for synchronization cross‐processing in four directions. Using the symmetry of the image, the image is divided into four directions, and the processing of each direction is different based on the high‐sensitivity sequence and the chaotic value. Through one traversal, the entire image is processed four times differently. The encryption process is uncontrollable and invisible depending on the chaotic sequence. Simulation test results show that the algorithm has good encryption results, is sensitive to the initial secret key and provides satisfactory security capabilities compared with other algorithms.
Generation and prediction of flight delays in air transport
Authors: Li Qiang , Jing Ranzhe 
Published: May 2021

AbstractThis paper presents models for flight delay prediction by considering both the local effects and network effects for the individual airport. Following a complex network approach, the authors analyse the local and network effects separately. Results indicate that the long‐term flight delays are mainly caused by network effects, while the short‐term flight delays are strongly associated with local delays. Therefore, the existing factors such as temporal variables, weather condition and seasonal effects are replaced with specific novel factors (e.g. crowdedness degree of airport and air traffic system, demand‐capacity imbalance) for flight delay prediction. More specifically, this paper shows that the model prediction performance for both classification (predict whether the flight is delayed) and regression (predict the delay values) achieves higher accuracy when using the novel factors. Random Forest algorithms were trained and tested on the U.S. domestic flights in July 2018, and the results show that for classification model, the accuracy, precision and recall score reach 96.48%, 94.39% and 90.26% when classifying delays are within 15 min. Similarly, for regression model, 93.92% of the test errors are within 15 min.
Multi‐scale pedestrian detection based on self‐attention and adaptively spatial feature fusion
Authors: Wang Minjun , Chen Houjin , Li Yanfeng , You Yuhao , Zhu Jinlei 
Published: May 2021

AbstractPedestrian detection is a classic problem in computer vision, which has an essential impact on the safety of urban autonomous driving. Although significant improvement has been made in pedestrian detection recently, small‐scale pedestrian detection is still challenging. To effectively tackle this issue, a multi‐scale pedestrian detector based on self‐attention mechanism and adaptive spatial feature fusion is proposed in this paper. In order to better extract global information, the spatial attention mechanism asymmetric pyramid non‐local block (APNB) module is applied. To achieve scale‐invariance detection, multiple detection branches are designed, which include a high‐resolution detection branch and a low‐resolution detection branch. In integrating multi‐scale features, the adaptively spatial feature fusion (ASFF) method is employed, which can solve the problem of feature inconsistency across different scales. Experimental results show that the proposed method obtains competitive performance on Caltech and CityPersons datasets.
Polarised beampattern synthesis against array manifold mismatch
Authors: Shi Shuli , Xu Yougen , Zhao Kang , Liu Zhiwen 
Published: May 2021

AbstractThe problem of joint synthesis of spatial power pattern and mainlobe radiation polarisation using a vector antenna array is considered in the presence of array manifold errors. A general approach to the synthesis problem is to formulate the restriction on the power and radiation polarisation as a convex optimisation programing. However, for certain types of vector antennas like tripole and electromagnetic vector sensors, the optimal weights might be extremely large and the resultant beampattern could be very sensitive to the array manifold errors. An intuitive explanation on this phenomenon is given based on the subspace theory. A worst case performance optimisation technique‐ based scheme is presented to draw attention to the polarised beampattern synthesis against the large weights problem and array manifold errors. Numerical examples regarding different types of vector antennas used for one‐ and two‐dimensional polarised beampattern synthesis are provided to validate the efficacy of the scheme.
Biogenic synthesis of copper oxide nanoparticles using leaf extracts of Cissus quadrangularis and Piper betle and its antibacterial effects
Authors: Kasi Sofiya Dayana , Ramasamy Jothi Mani , Nagaraj Dharmalingam , Santiyagu Valanarasu , Ponraj Joice Sophia 
Published: May 2021

AbstractOwing to its environment‐friendly, biocompatible, non‐toxic nature of nanoparticles attained from green plant extract, it has remarkable significance in the field of nanotechnology towards effective biomedical application. This work reports the synthesis of copper oxide nanoparticles by means of Cissus quadrangularis and Piper betle via green‐synthesis route. The medicinal leaf extracts possessing phytochemicals such as alkaloids and polyphenols act as a reducing agent in the current synthesis. The characterization of functional groups of CuO NPs by two different leaf extracts was investigated by FTIR analysis which measures reduction and stabilization of CuO NPs. X‐ray diffraction confirms the orthorhombic structure with an average crystallite size of about 32.54 and 32.09 nm. The irregular spherical‐shaped morphology of the samples is evidenced from scanning electron microscopy analysis. The transmission electron microscopy images demonstrated 50 nm sized prepared CuO nanoparticles. The bioactivity of the prepared sample is confirmed from antibacterial studies and zeta potential.
The top performer: Towards optimized parameters for reduced graphene oxide uniformity by spin coating
Authors: Reiner‐Rozman Ciril , Hasler Roger , Andersson Jakob , Rodrigues Teresa , Bozdogan Anil , Bintinger Johannes , Aspermair Patrik 
Published: May 2021

AbstractReduced graphene oxide has shown great potential for the fabrication of different electronic devices, especially biosensors and electric circuits. It allows the usage of simple deposition techniques such as drop casting or spin coating of graphene oxide solutions with a subsequent chemical, electrochemical, and/or thermal reduction to reduced graphene oxide. However, the utilized deposition strategy requires a defined protocol that ensures reproducibility. A study on a range of concentrations of GO and spin coating parameters is presented, aiming at the most uniform layers consisting of only single and double sheets for the application in electronic devices. The effect of film deposition on morphological parameters like the number of layers, overlaps, and surface coverage as well as the influence on electronic properties such as baseline stability and the transfer characteristics for the application as an electrolyte gated field‐effect transistor (EG‐FET) are shown. Best film characteristics and electronic properties are achieved by using a GO concentration of 214 μg/mL applied via spin coating with 1800 rpm, after surface modification with adhesive layer.
Copper‐based materials derived from metal‐organic frameworks for electrochemical sensing of hydrazine
Authors: Li Shi , Feng Wenshuai , Gao Xiaohui , Guo Aimin , Li Hongjian 
Published: May 2021

AbstractAn effective and rapid method for the detection of hydrazine hydrate is urgently needed as a result of its high toxicity and extensive use. Here, copper nanoparticles supported on carbon (written as: Cu NP/C) and copper oxide polycrystalline solids (written as: CuO PS), synthesized by calcining the same metal–organic framework at high temperature, were respectively used as electrode materials for electrochemical sensing hydrazine. The results show that the prepared Cu NP/C exhibits a better performance with a sensitivity of 0.1726 mA (mmol/L)−1, a wider linear range of 0.1–2.4 mM and a lower detection limit of 0.0014 mmol·L−1, compared to the obtained CuO PS. This can be reasonably explained by the lower copper valence state and its interaction with the carbon supports in Cu NP/C. Furthermore, the good selectivity and anti‐interference ability from the Cu NP/C are also acquired for electrochemical detection of hydrazine.