bluuebunny/crossref_metadata_2025_split huggingface dataset:
Links between soilborne pathogens, plant parasitic nematodes, farm management and biophysical constraints in a southern Australian rainfed cropping system
Authors: Harries Martin , Flower Ken C. , Renton Michael , Collins Sarah J. , Hüberli Daniel 
Published: June 2022

Context Rotations in rainfed farming systems of southwest Australia have shifted towards intensified cropping and it is necessary to reassess soilborne pathogens and plant parasitic nematodes within this context. Aims We tested the hypothesis that these recent changes in rotations and agronomy have altered the efficacy with which rotations reduce the incidence of common root pathogens and plant parasitic nematodes. Methods We tracked changes in common pathogen DNA in soil and the incidence and severity of crop root damage in 184 paddocks, over 6 years from 2010 to 2015, and related this to farmer practices. Key results Overall, severe root damage was rare, with 72% of plant samples showing no damage or only a trace and only 1% severely damaged. We found that the reduction of paddocks in pasture and resultant very low weed populations, combined with early sowing, reduced persistence of pathogens and nematode pests. But some aspects of crop management had the opposite effect: high rates of herbicide, increased frequency of cereals and canola at the expense of lupin and increased N fertiliser use. Conclusions Current agronomic practices and the frequency of non-host crops in rotations appear to be effective in controlling common root pathogens and plant parasitic nematodes. But the aspects of agronomic management that increased populations of pathogens should be applied cautiously. Implications Studies such as this that link multiple productivity constraints, such as pathogens and nematode pests, weeds and nutrients, to management practices are important to understand the sustainability of current or proposed production methods.
100 GSM paper as an SERS substrate for trace detection of pharmaceutical drugs in an aqueous medium
Authors: Sarma Dipjyoti , Biswas Sritam , Hatiboruah Diganta , Chamuah Nabadweep , Nath Pabitra 
Published: June 2022

Abstract
Surface-enhanced Raman spectroscopy (SERS) is a unique technique that allows us to detect samples in trace quantities. The spectral intensities of the characteristic Raman peaks of the analyte molecule are enhanced manifold in the presence of noble metal nanoparticles (NPs). The existence of NPs is necessary to couple the incident electromagnetic field with NPs through the localized surface plasmon resonance phenomenon, which primarily contributes to the enhancement of an SERS signal. The present work demonstrates the working of a paper-based SERS substrate to detect and quantify two pharmaceutical drugs—paracetamol and aspirin—in water. The proposed SERS substrate was obtained by drop-casting silver NPs over printing grade 100 grams per square meter (GSM) paper. 100 GSM denotes the class of paper where 100 grams of raw materials (cellulose) is used per square meter to manufacture the paper. The performance of the designed SERS substrate was initially evaluated with two Raman active samples—malachite green and rhodamine-6G. The applicability of the proposed SERS substrate was evaluated further through monitoring the Raman spectra of the two aforementioned pharmaceutical drugs in different field-collected water samples, thus establishing the reliability of the scheme in a real field environment.
A novel random subspace method considering complementarity between unsupervised and supervised deep representation features for soft sensors
Authors: Wang Gang , Zhu Hegong , Wu Zhangjun , Yang Min 
Published: June 2022

Abstract
Unsupervised and supervised deep learning extract effective and abstract features from different perspectives, which have been successfully applied in soft sensors. However, few studies have fused them and explored the complementary effect between the two kinds of features, which limits the utilization of comprehensive prediction information. To address the problem, a novel random subspace method with stacked auto-encoder (SAE) and bidirectional long short-term memory (Bi-LSTM), named RS-SBL, is proposed for soft sensors. Firstly, unsupervised and supervised deep representation features are extracted by SAE and Bi-LSTM, respectively. Secondly, to leverage the complementarity of the fusion features, an improved random subspcae (RS) method with a structure sparsity learning model is designed to discriminate the relative importance of different features and generate ensemble prediction results. Finally, the experiments on two real-world industrial nonlinear processes demonstrate that the proposed RS-SBL with the feature fusion strategy improves the prediction performance, and outperforms the other comparison soft sensor models.
Towards scalable plasmonic Fano-resonant metasurfaces for colorimetric sensing
Authors: Cerjan Benjamin , Gerislioglu Burak , Link Stephan , Nordlander Peter , Halas Naomi J , Griep Mark H 
Published: June 2022

Abstract
Transitioning plasmonic metasurfaces into practical, low-cost applications requires meta-atom designs that focus on ease of manufacturability and a robustness with respect to structural imperfections and nonideal substrates. It also requires the use of inexpensive, earth-abundant metals such as Al for plasmonic properties. In this study, we focus on combining two aspects of plasmonic metasurfaces—visible coloration and Fano resonances—in a morphology amenable to scalable manufacturing. The resulting plasmonic metasurface is a candidate for reflective colorimetric sensing. We examine the potential of this metasurface for reflective strain sensing, where the periodicity of the meta-atoms could ultimately be modified by a potential flexion, and for localized surface plasmon resonance refractive index sensing. This study evaluates the potential of streamlined meta-atom design combined with low-cost metallization for inexpensive sensor readout based on human optical perception.
Self-activatable carbon nanotube@ruthenium-catechol coordination complex for hydrogen evolution reaction
Authors: Wu Chenzhi , Wu Tong , Tang Zhenbin , Dai Juguo , Zeng Birong , Xu Yiting , Chen Guorong , Luo Weiang , Yuan Conghui , Dai Lizong 
Published: June 2022

Abstract
We report a simple metal ion-catechol coordination strategy to coat ruthenium-catechol polymer complex (TAC-Ru) on the surface of carbon nanotubes (CNT) to form a core–shell structure (abbreviated as CNT@TAC-Ru). This is achieved by firstly polymerizing catechol and boronic acid monomers on the surface of CNT to form a boronate ester polymer (BP) shell. Then, Ru3+ is used to etch the BP shell, and cleave the dynamic boronate ester bond, leading to the formation of a CNT@ruthenium-catechol coordination complex based on the coordinative efficiency of the catechol group. The electrocatalytic property of the CNT@TAC-Ru composite can be activated through electrochemical cycling treatment. The as-activated CNT@TAC-Ru exhibits evidently improved hydrogen evolution reaction (HER) performance with an overpotential of 10 mV in 1.0 M KOH at a current density of 10 mA cm−2 , which is better than that of commercial Pt/C (32 mV). And the long-term stability is also desirable. This work provides a pyrolysis-free method to form metal-polymer-carbon composite with high HER performance under the alkaline condition.
Centerline extraction by neighborhood-statistics thinning for quantitative analysis of corneal nerve fibers
Authors: Chen Ziye , Yin Xue , Lin Lin , Shi Guohua , Mo Jianhua 
Published: June 2022

Abstract

Objective. Corneal nerve fiber (CNF) has been found to exhibit morphological changes associated with various diseases, which can therefore be utilized to aid in the early diagnosis of those diseases. CNF is usually visualized under corneal confocal microscopy (CCM) in clinic. To obtain the diagnostic biomarkers from CNF image produced from CCM, image processing and quantitative analysis are needed. Usually, CNF is segmented first and then CNF’s centerline is extracted, allowing for measuring geometrical and topological biomarkers of CNF, such as density, tortuosity, and length. Consequently, the accuracy of the segmentation and centerline extraction can make a big impact on the biomarker measurement. Thus, this study is aimed to improve the accuracy and universality of centerline extraction. Approach. We developed a new thinning algorithm based on neighborhood statistics, called neighborhood-statistics thinning (NST), to extract the centerline of CNF. Compared with traditional thinning and skeletonization techniques, NST exhibits a better capability to preserve the fine structure of CNF which can effectively benefit the biomarkers measurement above. Moreover, NST incorporates a fitting process, which can make centerline extraction be less influenced by image segmentation. Main results. This new method is evaluated on three datasets which are segmented with five different deep learning networks. The results show that NST is superior to thinning and skeletonization on all the CNF-segmented datasets with a precision rate above 0.82. Last, NST is attempted to be applied for the diagnosis of keratitis with the quantitative biomarkers measured from the extracted centerlines. Longer length and higher density but lower tortuosity were found on the CNF of keratitis patients as compared to healthy patients. Significance. This demonstrates that NST has a good potential to aid in the diagnostics of eye diseases in clinic.
Constraining dark matter microphysics with the annihilation signal from subhalos
Authors: Runburg Jack , Baxter Eric J. , Kumar Jason 
Published: June 2022

AbstractIn the cold dark matter scenario, galactic dark matter halos are populated with a large number of smaller subhalos. Previous work has shown that dark matter annihilations in subhalos can generate a distinctive, non-Poisson signal in the gamma-ray photon counts probability distribution function (PDF). Here we show that the gamma-ray PDF also carries information about the velocity dependence of the dark matter annihilation cross section. After calculating the PDF assuming s-wave and Sommerfeld-enhanced annihilation, we perform a mock data analysis to illustrate how current and future observations can constrain the microphysics of the dark matter annihilation. We find that, with current Fermi data, and assuming a dark matter annihilation cross section roughly at the limit of current bounds from annihilation in dwarf spheroidal galaxies, one can potentially distinguish the non-Poissonian fluctuations expected from dark matter annihilation in subhalos from Poisson sources, as well as from dark matter models with an incorrect velocity-dependence. We explore how robust these results are to assumptions about the modeling of the galactic gamma-ray background, but further work is needed to determine the impact of realistic astrophysical source populations on our results. We also point out a four-parameter degeneracy between the velocity dependence of the dark matter annihilation, the minimum subhalo mass, the power law index of the subhalo mass function, and the normalization of the dark matter signal. This degeneracy can be broken with priors from N-body simulations or from observational constraints on the subhalo mass function.
Ionization and Star Formation in the Giant H ii Region SMC-N66
Authors: Geist E. , Gallagher J. S. , Kotulla R. , Oskinova L. , Hamann W.-R. , Ramachandran V. , Sabbi E. , Smith L. , Kniazev A. , Nota A. , Rickard M. J. 
Published: June 2022

Abstract
The NGC 346 young stellar system and associated N66 giant H ii region in the Small Magellanic Cloud are the nearest example of a massive star-forming event in a low metallicity (Z ≈ 0.2Z
⊙) galaxy. With an age of ≲3 Myr this system provides a unique opportunity to study relationships between massive stars and their associated H ii region. Using archival data, we derive a total Hα luminosity of L(Hα) = 4.1 × 1038 erg s−1 corresponding to an H-photoionization rate of 3 × 1050 s−1. A comparison with a predicted stellar ionization rate derived from the more than 50 known O-stars in NGC 346, including massive stars recently classified from Hubble Space Telescope far-ultraviolet (FUV) spectra, indicates an approximate ionization balance. Spectra obtained with SALT suggest the ionization structure of N66 could be consistent with some leakage of ionizing photons. Due to the low metallicity, the FUV luminosity from NGC 346 is not confined to the interstellar cloud associated with N66. Ionization extends through much of the spatial extent of the N66 cloud complex, and most of the cloud mass is not ionized. The stellar mass estimated from nebular L(Hα) appears to be lower than masses derived from the census of resolved stars which may indicate a disconnect between the formation of high and low mass stars in this region. We briefly discuss implications of the properties of N66 for studies of star formation and stellar feedback in low metallicity environments.
Solar Filament Detection Based on Improved DeepLab V3+
Authors: You Jiangchuan , Shang Zhenhong 
Published: June 2022

Abstract
A novel solar filament detection method based on an improved DeepLab V3+ is proposed to address the low detection accuracy of small solar filaments in Hα full-disk solar images. First, the Xception structure of the backbone network is fine-tuned, and the low-level feature information of the filaments is added to the decoder module of the network to improve the utilization of the solar filament features. Second, the receptive field of dilated convolution is expanded, and the information utilization rate is increased via cascaded dilated convolution to improve the detection accuracy of the small solar filaments. In the decoder module, two depthwise separable convolutions are used instead of ordinary convolutions to reduce incomplete detections. Finally, a dense conditional random field is added to optimize the edge of the detection results. Experiments on a public data set comprising full-disk Hα images show that compared with the original Deeplab V3+ algorithm, the proposed method improves the mean pixel accuracy, mean intersection over union, and F1-score by 1.86%, 1.95%, and 2.18%, respectively, which also demonstrates its superiority over other existing solar filament detection algorithms.
Effects of transcutaneous spinal stimulation on spatiotemporal cortical activation patterns: a proof-of-concept EEG study
Authors: Steele Alexander G , Manson Gerome A , Horner Philip J , Sayenko Dimitry G , Contreras-Vidal Jose L 
Published: June 2022

Abstract

Objective. Transcutaneous spinal cord stimulation (TSS) has been shown to be a promising non-invasive alternative to epidural spinal cord stimulation for improving outcomes of people with spinal cord injury (SCI). However, studies on the effects of TSS on cortical activation are limited. Our objectives were to evaluate the spatiotemporal effects of TSS on brain activity, and determine changes in functional connectivity under several different stimulation conditions. As a control, we also assessed the effects of functional electrical stimulation (FES) on cortical activity. Approach. Non-invasive scalp electroencephalography (EEG) was recorded during TSS or FES while five neurologically intact participants performed one of three lower-limb tasks while in the supine position: (1) A no contraction control task, (2) a rhythmic contraction task, or (3) a tonic contraction task. After EEG denoising and segmentation, independent components (ICs) were clustered across subjects to characterize sensorimotor networks in the time and frequency domains. ICs of the event related potentials (ERPs) were calculated for each cluster and condition. Next, a Generalized Partial Directed Coherence (gPDC) analysis was performed on each cluster to compare the functional connectivity between conditions and tasks. Main results. IC analysis of EEG during TSS resulted in three clusters identified at Brodmann areas (BA) 9, BA 6, and BA 4, which are areas associated with working memory, planning, and movement control. Lastly, we found significant (p < 0.05, adjusted for multiple comparisons) increases and decreases in functional connectivity of clusters during TSS, but not during FES when compared to the no stimulation conditions. Significance. The findings from this study provide evidence of how TSS recruits cortical networks during tonic and rhythmic lower limb movements. These results have implications for the development of spinal cord-based computer interfaces, and the design of neural stimulation devices for the treatment of pain and sensorimotor deficit.
Application of Fuzzy Sliding Mode Control in Voice Coil Motor Control System
Authors: Zhao Bingjie , Liu Yang , Jia Danping , Zhang Tao , Zhang Hualiang , Yan Hongyu , Lu Xu 
Published: June 2022

Abstract
The requirements of modern industry on the response speed and accuracy of voice coil motors has been the gradual growth, and PID control algorithms have become increasingly unable to meet their requirements. The use of sliding mode control can speed up the response time of the motor, and it also can improve the response speed, overshoot and instability problems in some degree. However, the sliding mode control algorithm will bring jitter to the entire system. This paper combines fuzzy control and sliding mode control to adjust the parameters of the sliding mode control algorithm in real time, which greatly reduces the jitter problem caused by the sliding mode. According to the actual parameters of the voice coil motor, the transfer function of the voice coil motor is deduced, a simulation model of fuzzy sliding mode control is built, and the sliding mode control and fuzzy sliding mode control algorithms are compared. Finally, two signals of step and sine are used as inputs for simulation model. It can be seen through the simulation waveform that the fuzzy sliding mode control algorithm can improve the response speed of the control system and reduce the tracking error.
PFCS Four-redundant Sidestick Randomly Dynamically Grouping Vote Technology
Authors: Wang Xiao-Hui , Yu Wei-Qian , Gu Chen-Bo , Jiang Yi 
Published: June 2022

Abstract
For civil aircraft, the reliability of control device sensors in flight control system is very important for aircraft control and safe flight. Redundancy signal voting and monitoring technology can improve the availability and integrity of sensor signals in fly by wire primary flight control system of civil aircraft. In this paper, a simplified four-redundant signal voting and monitoring method is proposed which randomly and dynamically divides the four redundant signals into two groups for comparison. The safety analysis and simulation results reveal that this simplified method meets the safety requirements and has advantages in robustness and availability.
Research on Transformer Fault Prediction Based on Graph Neural Network
Authors: Fang Mu , Jiang Yun , Li Longtan , Liu Yimin , Zhang Yuqi , Lv Fenfei , Liu Zhigang 
Published: June 2022

Abstract
Power transformer is an important part of the power generation system, as the bottom of the power network, directly connected with the user, only the normal work state of the transformer can ensure the safety and smooth operation of the power system. If the transformer breaks down, it will directly not only affect social production, can timely find power transformer latent fault, accurate judgment fault types and characteristics, to maintenance personnel to formulate appropriate repair plan provides guidance significance, at the same time to improve the power grid reliability and reduce the economic consequences of the transformer faults has important practical significance
Visualizing the school organic chemistry course with augmented reality
Authors: Ya Midak L , Pahomov Ju D , Kuzyshyn O V , Lutsyshyn V M , Kravets I V , Buzhdyhan Kh V , Baziuk L V 
Published: June 2022

Abstract
Nowadays, studying natural sciences, as well as chemistry, is impossible without good-quality visualization of the theoretic data. Supplying mobile apps with augmented reality give the opportunity to visualize the study information for the students and make its perception and learning easier. The paper is dedicated to developing a lap book and mobile app LiCo.STEAM Sugar with augmented reality and studying the “Carbohydrates” topic according to the 10
th
grade chemistry program, and also to investigate it’s effciency within the chemistry lessons. The developed lap book includes the theories with carbohydrate molecules’ images, an experimental part designed for performing chemical experiments and studying properties of organic compounds, and also tasks of different levels. Molecules of carbohydrates, their structure can be visualized with AR, and also video-experiments on this subjets can be played. Using the lap book “Carbohydrates” with augmented reality together with LiCo.STEAM Sugar mobile app allows to upgrade the content and the volumes of the theories, apply modern ICT within the study in order to build students’ skills of a new level. Applying educational data with augmented reality give students the ability to memorize the theories in a better way, which is shown with the increased results of educational achievements of students in chemistry.
Improving the quality of mathematical education of pupils: diagnostics and analytics
Authors: Hrynevych L M , Khoruzha L L , Proshkin V V 
Published: June 2022

Abstract
The article considers the problem of the quality of mathematical school education in the context of pupils’ ability to apply the acquired knowledge of mathematics to solve practical problems. It is established that this ability is a clear understanding and awareness of the role of mathematical knowledge in the modern world. It is manifested in the ability to explain natural and scientific phenomena, to draw sound conclusions, to understand the impact of science on human life. The results of a study in which 1849 pupils of Kyiv schools took part are presented. Their ability to apply mathematical knowledge to solve life problems has been established. Examples of mathematical problems developed for testing by specialists of the Ukrainian Center for Educational Quality Assessment are given. The typical pupils’ mistakes, as well as the most successful solutions are analyzed. Pedagogical recommendations for improving the quality of mathematics education of pupils in the context of the reform of the “New Ukrainian School” are given.
Scientific concepts related to physics from the perspective of students of biology
Authors: Stojanović M. , Maksimović B. 
Published: June 2022

Abstract
In many studies, special attention is paid to examining how students acquire scientific concepts and use scientific terminology. Proper understanding of scientific concept is in direct correlation with the quality of students‘ knowledge. The knowledge composed of irregularly adopted concept leads to misconceptions. The aim of the research was to examine how students of biology understand certain scientific concepts in physics that are relevant to their profession. Also, it is examined their intrinsic motivation for physics using a standardized questionnaire. This research was conducted in February 2016. The research results pointed that students do not understand some basic concepts related to physics, but students’ motivation for learning physics is on a medium level.