Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Phenological Camera Photos and MODIS Reflectance Data to Predict GPP Daily Dynamics for Alpine Meadows on the Tibetan Plateau

Gross primary production (GPP) is the overall photosynthetic fixation of carbon per unit space and time. Due to uncertainties resulting from clouds, snow, aerosol, and topography, it is a challenging task to accurately estimate daily GPP. Daily digital photos from a phenological camera record vegetation daily greenness dynamics with little cloud or aerosol disturbance. It can be fused with satellite remote sensing data to improve daily GPP prediction accuracy. In this study, we combine the two types of datasets to improve the estimation accuracy of GPP for alpine meadow on the Tibetan Plateau. To examine the performance of different methods and vegetation indices (VIs), three experiments were designed. First, GPP was estimated with the light use efficiency (LUE) model with the green chromatic coordinate (GCC) from the phenological camera and vegetation index from MODIS, respectively. Second, GPP was estimated with the Backpropagation neural network machine learning algorithm (BNNA) method with GCC from the phenological camera and vegetation index from MODIS, respectively. Finally, GPP was estimated with the BNNA method using GCC and vegetation index as inputs at the same time. Compared with eddy covariance GPP, GPP predicted by the BNNA method with GCC and vegetation indices as inputs at the same time showed the highest accuracy of all the experiments. The results indicated that GCC had a higher accuracy than NDVI and EVI when only one vegetation index data was used in the LUE model or the BNNA method. The R2 of GPP estimated by BNNA and GPP from eddy covariance increased by 0.12 on average, RMSE decreased by 1.13 g C·m−2·day−1 on average, and MAD decreased by 0.87 g C·m−2·day−1 on average compared with GPP estimated by the traditional LUE model and GPP from eddy covariance. This study puts forth a new way to improve the estimation accuracy of GPP on the Tibetan Plateau. With the emergence of a large number of phenological cameras, this method has great potential for use on the Tibetan Plateau, which is heavily affected by clouds and snow.
Selected poster presentations from the American Association of Applied Linguistics conference, Denver, USA, March 2020

Introduction Although task-based language teaching (TBLT) and its companion, task-based language assessment (TBLA), have gained in prominence in research and teaching, research projects have been limited due to small sample sizes and a paucity of sharable data. In addition, although comparisons of learner development over time provide critical information that can inform second language acquisition (SLA), such longitudinal data are scarce, both in general and in TBLA/T. Moreover, data in languages other than English are equally difficult to access. Thus, this resource development project focuses on oral proficiency assessment data and reports on the design and plans for implementing an internationally available, multilingual corpus of speaking test data. The potential for the corpus is reviewed in light of preliminary research that explored lexical complexity and lexical diversity that employed parts of the corpus as a resource in language assessment research. The project will eventually result in a multilingual, sharable database of student oral language samples pre and post study abroad (SA) on the same tasks in each language that will allow for a variety of projects including but not limited to longitudinal comparisons of proficiency gains, task analyses, and complexity, accuracy and fluency (CAF) analyses.
Compressed-Sensing-Based Time–Frequency Representation for Disturbance Characterization of Maglev On-Board Distribution Systems

The frequency variating source, linear generator, and switching devices lead to dynamic characteristics of the low-frequency conducted emissions within maglev on-board distribution systems. To track the time-varying feature of these disturbances, a joint time–frequency representation combined adaptive optimal kernel with compressed sensing technique is proposed in this paper. The joint representation is based on Wigner–Ville distribution, and employs adaptive optimal kernel to remove undesirable cross terms. The compressed sensing technique is introduced to deal with the tradeoff between cross-component reduction and auto-component smearing faced by kernel-function-based bilinear time–frequency representation. The time–frequency aggregation and accuracy performance of joint time–frequency representation is quantified using Rényi entropy and l1-norm. To verify its performance in disturbance signature analysis for distribution systems and primarily characterize the low-frequency conducted emissions of maglev, a maglev on-board distribution system experimental platform is employed to extract the low-frequency disturbances which pose threats to the controlling system. Comparison with Wigner–Ville distribution demonstrates the joint time–frequency representation method outperforms in tracking time-varying and transient disturbances of maglev on-board distribution systems.
Detection and attribution of wildfire pollution in the Arctic and northern midlatitudes using a network of Fourier-transform infrared spectrometers and GEOS-Chem

Abstract. We present a multiyear time series of column abundances of carbon monoxide (CO), hydrogen cyanide (HCN), and ethane (C2H6) measured using Fourier-transform infrared (FTIR) spectrometers at 10 sites affiliated with the Network for the Detection of Atmospheric Composition Change (NDACC). Six are high-latitude sites: Eureka, Ny-Ålesund, Thule, Kiruna, Poker Flat, and St. Petersburg, and four are midlatitude sites: Zugspitze, Jungfraujoch, Toronto, and Rikubetsu. For each site, the interannual trends and seasonal variabilities of the CO time series are accounted for, allowing background column amounts to be determined. Enhancements above the seasonal background were used to identify possible wildfire pollution events. Since the abundance of each trace gas emitted in a wildfire event is specific to the type of vegetation burned and the burning phase, correlations of CO to the long-lived wildfire tracers HCN and C2H6 allow for further confirmation of the detection of wildfire pollution. A GEOS-Chem tagged CO simulation with Global Fire Assimilation System (GFASv1.2) biomass burning emissions was used to determine the source attribution of CO concentrations at each site from 2003 to 2018. For each detected wildfire pollution event, FLEXPART back-trajectory simulations were performed to determine the transport times of the smoke plume. Accounting for the loss of each species during transport, the enhancement ratios of HCN and C2H6 with respect to CO were converted to emission ratios. We report mean emission ratios with respect to CO for HCN and C2H6 of 0.0047 and 0.0092, respectively, with a standard deviation of 0.0014 and 0.0046, respectively, determined from 23 boreal North American wildfire events. Similarly, we report mean emission ratios for HCN and C2H6 of 0.0049 and 0.0100, respectively, with a standard deviation of 0.0025 and 0.0042, respectively, determined from 39 boreal Asian wildfire events. The agreement of our emission ratios with literature values illustrates the capability of ground-based FTIR measurements to quantify biomass burning emissions. We provide a comprehensive dataset that quantifies HCN and C2H6 emission ratios from 62 wildfire pollution events. Our dataset provides novel emission ratio estimates, which are sparsely available in the published literature, particularly for boreal Asian sources.
Managing differentiated disintegration: Insights from comparative federalism on post-Brexit EU–UK relations

This article applies insights from comparative federalism to analyse different models for managing future EU–UK relations. The argument is that the stability of the EU–UK relationship before as well as after Brexit is best understood by examining the presence of federal safeguards. Drawing on Kelemen, four types of safeguards are identified as the means for balancing centrifugal and centripetal forces. During the United Kingdom’s European Union membership, the strong glue provided by structural and judicial safeguards was undone by the weakness of partisan and socio-cultural ones. However, each post-Brexit scenario is characterised by weaker structural and judicial safeguards. The most stable outcome is an indeterminate Brexit that limits the incentive to politicise sovereignty and identity concerns by ending free movement of people and reducing the saliency of European Union rules. Such stability is nevertheless relative in that, from a comparative perspective, federal-type safeguards were stronger when the United Kingdom was still in the European Union.
Structural Changes in Ba(Sr1/3Ta2/3)O3-Type Perovskite Compounds upon Tilting of Oxygen Octahedra.

Since the early study by Galasso and Pyle, the structure of complex perovskite compounds with 1:2 ordering of B-site ions, ordered perovskite compounds, has been thought to be Ba(Sr1/3Ta2/3)O3-type hexagonal. Recently, a structural phase transformation has been found in A-site substituted ordered perovskite compounds, (Ba,Sr)(B'1/3B"2/3)O3. The structural change due to the phase transformation has been connected with the tilting of oxygen octahedra in the perovskite cell. However, the structure of the transformed phase has not been clarified because there has not been any model to treat the crystal structure of ordered perovskite compounds having tilted oxygen octahedra. In this paper, Glazer's model for structural changes in cubic perovskite due to the tilting of oxygen octahedra has been expanded to the 1:2 ordered perovskite. Based on the model, electron diffraction and X-ray diffraction of (Ba1-xSrx)(Mg1/3Ta2/3)O3 are discussed. A monoclinic unit cell in space group C2h6 with an angle γ > 90 °, which is derived from antiphase tilting of oxygen octahedra, successfully explains the experimental results.
Large-power synchronous motor braking by field current

The paper presents the braking process of a large-power salient-pole synchronous motor forced by field current. The motor control was carried out using a microprocessor block for the excitation of large-power synchronous motors. Cases of free retardation and braking resulting from eddy currents generated in the machine body by the magnetic field of the rotating winding of the rotor with open stator windings were considered. The method proposed was to determine the dependence of braking time on the value of the field current on the basis of the observation of changes in the rotational speed without the knowledge of the parameters of the engine and braking moments of the drive system. The measurement verification of the braking time calculated for the assumed field current has been presented. Streszczenie. W artykule przedstawiono proces hamowania jawnobiegunowego silnika synchronicznego dużej mocy za pomocą prądu wzbudzenia. Sterowanie silnika zrealizowano za pomocą mikroprocesorowego bloku zasilania wzbudzenia silników synchronicznych dużej mocy. Rozpatrzono przypadek wybiegu swobodnego oraz hamowania na skutek prądów wirowych generowanych w korpusie maszyny przez pole magnetyczne wirującego uzwojenia wirnika przy rozwartych uzwojeniach stojana. Zaproponowano metodę wyznaczania zależności czasu hamowania od wartości prądu wzbudzenia bazującą na obserwacji zmian prędkości obrotowej bez konieczności znajomości parametrów silnika i momentów hamujących układu napędowego. Zaprezentowano weryfikację pomiarową czasu hamowania obliczonego dla założonego prądu wzbudzenia. (Hamowanie silnika synchronicznego dużej mocy prądem wzbudzenia)
Word retrieval improves following a mindful breathing exercise but is unrelated to dispositional mindfulness

ABSTRACT Mindfulness can reduce anxiety and improve attentional control, so we tested for relationships between mindfulness and performance on word retrieval tasks. In Study 1, college students experienced a 10-min mindful breathing exercise or control condition, and participants in the mindful breathing condition correctly named more pictures than participants in the control condition. Participants also completed a self-report measure of dispositional mindfulness, but scores were unrelated to picture naming performance. In Study 2, online participants completed the mindfulness measure and attempted to produce target words that fit definitions and reported their tip-of-the-tongue (TOT) states. Neither TOT rate nor correct responding correlated with mindfulness scores, replicating Study 1 results with a larger, more varied sample. Results indicate that a mindful breathing exercise can facilitate word retrieval but that self-reported dispositional mindfulness does not positively affect word retrieval.
Unpaid Caregiving and Labour Force Participation among Chinese Middle-aged Adults

Unpaid family caregivers must consider the economic trade-off between caregiving and paid employment. Prior literature has suggested labour force participation (LFP) to decline with caregiving intensity, but no study has evaluated this relationship by accounting for the presence of both kinks and discontinuities. Here we used respondents of the China Health and Retirement Longitudinal Study baseline survey who were non-farming, of working-age (aged 45-60) and had a young grandchild and/or a parent/parent-in-law. For women and men separately, a caregiving threshold-adjusted probit model was used to assess the association between LFP and weekly unpaid caregiving hours. Instrumental variables were used to rule out the endogeneity of caregiving hours. Of the 3,718 respondents in the analysis, for men, LFP was significantly and inversely associated with caregiving that involved neither discontinuities nor kinks. For women, a kink was identified at the caregiving threshold of 8 hours per week such that before 8 hours, each caregiving hour was associated with an increase of 0.0257 in the marginal probability of LFP, but each hour thereafter was associated with a reduction of 0.0014 in the marginal probability of LFP. These results have implications for interventions that simultaneously advance policies of health, social care and labour force.
Perceived overparenting and developmental outcomes among Chinese adolescents: Do family structure and conflicts matter?

“Overparenting” refers to an inappropriate parenting style in which parents intervene intrusively in their children’s development and overprotect them from difficulties and challenges. However, there is scant research on the relationship between overparenting and developmental outcomes in early adolescence. Moreover, the moderating roles of parent–child conflict, family intactness, and the adolescent’s gender on the relationship are undetermined. This study examined the associations between overparenting and developmental outcomes (indexed by egocentrism and positive youth development) of a sample of 1,735 Grade 7 students in Hong Kong (mean age = 12.63; 47.4% females). The moderating effects of parent–child conflict, family intactness, and the adolescent’s gender on these relationships were also examined. The results indicated that paternal overparenting and maternal overparenting were positively related to egocentrism and positive developmental outcomes among young Chinese adolescents. Furthermore, father–child conflict moderated the associations of maternal overparenting with egocentrism and positive youth development. At higher levels of father–child conflict, egocentrism and positive youth development increased at higher levels of maternal overparenting. Family intactness and the adolescent’s gender were also found to have moderating effects. The results provide insights into how familial conditions alter the relationship between overparenting and adolescent development. As related studies using adolescent samples are sparse, this study is pioneering in examining the impacts of overparenting on adolescents.
A Low-Cost Electronic System for Human-Body Communication

Human-body communication (HBC) has increasingly gained attention from academia and industry. Most current works focus on characterizing the use of human-body tissues as a physical medium to enable reliable communication. However, designing coupling hardware and communication circuits for reliable data transmission (e.g., high throughput and low latency) is a demanding task, especially for achieving a compact full electronic implementation. For this purpose, there are few commercial devices, mainly differential probes and balun transformers, employed with electrical analysis instruments such as oscilloscopes and vector network analyzers. Although these devices are widely used, they are expensive and are difficult to miniaturize and integrate into real-world HBC-specific applications (e.g., data security). This article presents a low-cost electronic system that transfers collected data using a secondary channel: the ionic environment (the primary channel would be the wireless environment). We design an electronic system as an experimental setup for studying HBC, allowing the communication between instruments, sensors, and actuators by human-body tissues. The experimental evaluation of the proposed system follows (i) a phantom composed of saline (0.9%) and (ii) a real human forearm through adhesive surface electrodes.
An Explicit Algebraic Closure for Passive Scalar-Flux: Applications in Channel Flows at a Wide Range of Reynolds Numbers

In this paper, we propose an algebraic model for turbulent scalar-flux vector that stems from tensor representation theory. The resulting closure contains direct dependence on mean velocity gradients and quadratic products of the Reynolds stress tensor. Model coefficients are determined from Direct Numerical Simulations (DNS) data of homogeneous shear flows subjected to arbitrary mean scalar gradient orientations, while a correction function was applied at one model coefficient based on a turbulent channel flow case. Model performance is evaluated in Poiseuille and Couette flows at several Reynolds numbers for Pr=0.7, along with a case at a higher Prandtl number (Pr=7.0) that typically occurs in water–boundary interaction applications. Overall, the proposed model provides promising results for wide near-wall interaction applications. To put the performance of the proposed model into context, we compare with Younis algebraic model, which is known to provide reasonable predictions for several engineering flows.
Detection of Tuberculosis Bacilli Using Deep Learning

A harmful bacteria known as MT is the primary cause of the communicable disease tuberculosis (TB) (Mycobacterium Tuberculosis). This disease can be treated but because of its contagious nature, it comes under the category of the world's scariest as well as the deadliest disease. From this point of view, the detection of Tuberculosis (TB) at a very early stage is mandatory to provide successful treatment and then prevent it from spreading to other people also. Due to their efficiency and inexpensive cost, microscopy tests using ZN-stained images are typically favored in underdeveloped nations. However, manual detection of TB bacilli under a microscope is a very time-consuming and tedious task. In the proposed work patch wise detection strategy using CNN is done for which a set of image patches are extracted from the supplied list of annotated images. The patches were then categorized into negative and positive patches. Data augmentation is used to balance out the quantity of positive and negative patches.
Ultrafast Laser Interaction Processes for Libs and Other Sensing Technologies

Abstract : The overall objective of this MURI program is to obtain a comprehensive understanding of the interaction of ultrafast femtosecond laser pulses with matter, with specific reference to the detection of trace elements of materials by techniques such as LIBS and Raman spectroscopy. The UCF team forms the core effort for this MURI, and is concentrating on femtosecond (and nanosecond) LIBS interaction science, self-channeling of femtosecond laser radiation and standoff detection techniques, trace explosives detection, chemometrics and plasma modeling. The other members of the MURI bring key areas of expertise to the program. University of Nebraska, Lincoln (UNL-Dennis Alexander) in femtosecond LIBS, nanoparticles and fundamental interactions, University of California, Berkeley (UCB-Samuel Mao) in theory and experiments in the early stages of femtosecond laser materials interaction, Johns Hopkins University (JHU-Paul Dagdigian) in kinetics theory and LIBS spectroscopy, and Florida A&M University (FAMU-Lewis Johnson) in femtosecond and ns LIBS.
Discrimination and prediction of the chemical composition and the phytotoxic activity of Wedelia trilobata essential oil (EO) using metabolomics and chemometrics

Abstract Understanding metabolic variations of plant essential oils (EOs) in response to different growth conditions is of great importance for bioprospecting and standardizing purposes. In this study, combined gas chromatography-mass spectrometry (GC-MS) based metabolomics and bioassay experiments were carried out to identify chemical differences in W. trilobata EO grown in field and those grown in controlled conditions. Subsequently, the chemical profiles were correlated with their ability to inhibit germination, shoot and root growths of weedy rice and lettuce. Results showed that EO grown in controlled condition was abundant in sesquiterpene hydrocarbons whereas field-grown EO accumulated large amounts of monoterpenes. Principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA) indicated the chemical composition of isolated EOs varied considerably. PLS regression revealed that seed germinations of weedy rice and lettuce were susceptible to W. trilobata EO grown in controlled condition and nonanal may contributed to the phytotoxic activity of the EO. The field-grown EO exhibited strong inhibitory activity against shoot and root growths of weedy rice and lettuce, while α-phellandrene, terpinen-4-ol, trans-pinocarveol and pinocarvone might be responsible for the inhibitory activity. The findings highlight the combination of metabolomics and bioassays to provide a metabolic understanding and bioprospecting potential of W. trilobata EO as bio-herbicides.
Research on Name Entity Recognition Method in Civil Aviation Text

Named entity recognition technology has been applied to various fields in recent years, obtaining business entities in civil aviation passenger reviews can quickly locate pain points in the service process. The benchmark BiLSTM model performs well for general text, but there are a large number of composite entities in civil aviation public opinion, which needs to improve the model’s ability to recognize local text. The paper propose an ODCNN-BiLSTM-CRF model integrated with convolutional neural network. We obtain the standard data set through manual annotation and semisupervised learning firstly. Then learn the word embedding vector from the dataset. We use one-dimensional convolutional neural network (ODCNN) to obtain local context features of text data, and use long and short-term (LSTM) neural network to obtain context features that depend on long text, the two features were fused to obtain the predictive labels through conditional random field (CRF). Experimental results show that our three proposed models improved the recognition performance compared with the previous benchmark model, with the F value reaching 74.01%, 73.72% and 68.06%.