Miracle-dz/UniFAR-FTUs huggingface dataset:
Integration of Prediction Scores From Various Automated Essay Scoring Models Using Item Response Theory

In automated essay scoring (AES), essays are automatically graded without human raters. Many AES models based on various manually designed features or various architectures of deep neural networks (DNNs) have been proposed over the past few decades. Each AES model has unique advantages and characteristics. Therefore, rather than using a single-AES model, appropriate integration of predictions from various AES models is expected to achieve higher scoring accuracy. In this article, we propose a method that uses item response theory to integrate prediction scores from various AES models while taking into account differences in the characteristics of scoring behavior among models. It is found that the proposed method achieves higher accuracy than that of individual AES models and conventional score-integration methods. Furthermore, the proposed method facilitates interpreting each AES model's scoring characteristics and score-integration mechanism.
Menopausal symptoms and quality of life in female survivors treated with hematopoietic stem cell transplantation

Objectives To assess the severity of menopausal symptoms and the correlation among different quality of life questionnaires and compare the quality of life of patients who underwent hematopoietic stem cell transplantation (HSCT) for hematological disorders with the norm group in order to facilitate personalized and directed therapeutic intervention for patients. Methods We recruited women who had premature ovarian failure (POF) after HSCT for hematologic diseases in the gynecological endocrinology outpatient clinic of Peking University People's Hospital. Women with HSCT were included in the study if they had 6 months of spontaneous amenorrhea with serum follicle-stimulating hormone levels greater than 40 mIU/mL taken 4 weeks apart. The patients who had other causes of POF were excluded. During the survey, all women were required to fill out the questionnaires [Quality of Life Questionnaire (MENQOL), Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and 36-item Short-Form (SF-36)] online. We analyzed the severity of menopausal symptoms, anxiety, and depression in Participants. In addition, differences on the SF-36 scale scores between the study group and norm groups were examined. Results In total, 227 (93.41%) patients completed the survey and were analyzed. The severity of all symptoms is “none and mild” in MRS, MENQOL, GAD-7, and PHQ-9. On the MRS, the most common symptoms were irritability, physical and mental exhaustion, and sleep problems. The severest symptoms were sexual problems (53, 73.82%), followed by sleep problems (44, 19.38%) and mental and physical exhaustion (39, 17.18%). In the MENQOL, the most common symptoms were psychosocial and physical symptoms. The severest symptoms were sexual symptoms (35, 48.75%) followed by psychosocial symptoms (23, 10.13%). Moderate-severe scores were shown in 11.89% (27) and 18.72% (42) cases in the GAD-7 and PHQ-9, respectively. Based on SF-36, in comparison with the norm group, the HSCT participants had higher vitality scores and lower role physical, physical functioning, and role emotional scores aged 18–45. In addition, the HSCT participants had lower mental health scores aged 18–25, and lower general health scores aged 25–45. No strong correlation was observed between questionnaires in our study. Conclusion Overall, menopausal symptoms are milder in female patients after HSCT. There is no single scale that comprehensively assesses the patient's quality of life after HSCT. We need to assess the severity of various symptoms in patients using different scales.
Preparation and Raman spectroscopy of low-angle twisted trilayer graphene

Graphene is the most studied material in the world in recent years, from two dimensions to three dimensions, through Bernal stack to multilayer, and finally into graphite, we can see obvious dimensional changes. Therefore, the interaction between layers is very important, and one of them, twisted graphene, which is different from natural ordered stack, has also appeared. If the rotation angle is regarded as a new degree of freedom, angle related properties can be introduced into graphene, and a series of new physical phenomena have become a new research interest. This article shows a transfer glass slide for picking up two-dimensional materials, which can effectively pick up hBN, graphene, etc. The low-angle twisted (1+2) trilayer graphene and hexagonal boron-nitride heterojunction was made by tear-rotate-pick, and their stacking rules were observed by Raman spectroscopy. We found that after covering a certain thickness of hBN, the Raman characteristic peak intensity of the trilayer graphene has a certain attenuation, and has a great impact on the G peak, while the G peak and 2D peak positions have a shift of 2 cm-1 and 5 cm-1 respectively. The Raman spectrum of the low-angle twisted trilayer graphene prepared by us also shows a different characteristic peak pattern from that of the common ABA and ABC stacked trilayer graphene. Among them, the G peak of the 0.1° sample becomes flat, and the 2D peak appears an obvious inflection point. While the 0.5° sample is similar to the ABA stacked trilayer graphene, but the 2D peak is more gentle.
Multitask Sparse Bayesian Channel Estimation for Turbo Equalization in Underwater Acoustic Communications

In this article, a multitask sparse Bayesian learning channel estimation based on factor graphs (MT-SBL-FG) for turbo equalization (TEQ) in underwater acoustic (UWA) communications is proposed. Based on the framework of multitask sparse Bayesian learning, the received data are divided into subblocks and the channel estimation (CE) of each subblock is regarded as a task. Considering the time-varying channel in UWA communications and the CE error in turbo equalization, a factor graph is proposed for the multitask CE of the subblocks. Based on the factor graph, a message-passing algorithm with weighting factor is derived. The performance of channel estimation is improved by utilizing the temporal correlation between the subblocks and the sparsity of the UWA channel. The proposed algorithm has been tested by the underwater trial data collected in an experiment conducted in Qiandao Lake, Hangzhou, China, in May 2016. We have verified that the proposed algorithm outperforms other CE algorithms in terms of the bit error rate (BER).
Evaluation of policy synergy in coastal ocean pollution prevention and control: The case from China

In recent years, the Chinese central government and coastal provincial governments have promulgated a series of Coastal Ocean Pollution Prevention and Control Programs (COPPCP). Whether the government’s COPPCP can achieve policy synergy will affect the level of marine pollution governance. This study constructs a two-dimensional assessment framework of policy “subject department”–”content theme” and conducts a comprehensive evolution of policy synergy from objective, process, and state perspectives. From the subject department dimension, the study used social network analysis to find that interdepartmental collaboration was not done well overall. The policy synergy process is difficult to be guaranteed. Meanwhile, the structure of departmental synergy networks in different provinces shows differences and can be divided into three types of governments with single-core, multi-core, and vertical synergy. In the content synergy dimension, it was found by the Chinese Bidirectional Encoder Representations from Transformers-Whole Word Masking (BERT-WWM) model that the policy content is mainly focused on four themes of marine ecological protection (MEP), marine pollution control (MPC), land-based pollution reduction (LPR), and safeguard measures (SAM). The three types of governments show different performances on the four theme synergies. The governments perform well in the objective synergy; however, it is difficult for them to agree on the policy tool synergy, and the status of policy synergy is difficult to maintain. This paper adopts the BERT-WWM model instead of the manual coding method in the previous policy content analysis, enhancing the evaluation’s objectivity. The study results will provide a reference for further improving marine pollution governance systems in developing countries.
Research on Dynamic Parameter Identification of Large Inertia Industrial Robot Based on RBFNNs

Aiming at the accuracy of the large inertia industrial robot dynamic model, a radial basis function neural networks (RBFNNs) weighted least square (WLS) identification scheme is proposed to further improve the accuracy of the dynamic model. Based on the dynamic linearization model of a large inertia industrial robot, the open-source toolbox Sympybotics is introduced to assist in obtaining the minimum inertia parameter set and observation matrix. The finite-term Fourier series is selected as the excitation trajectory while the condition number of the observation matrix is applied as the performance index for optimization. Its purpose is to ensure that the impact of external disturbances on the identification data is minimized while fully exciting the robot dynamics. Based on the actual operating data, the weighted least squares method is used to identify the kinetic parameters to obtain a rough solution of the kinetic parameters. Further, the accurate solution is obtained by nonlinear constraint function optimization and RBFNNs optimization. The experimental results show that the proposed method could guarantee the accuracy of the dynamic model of the large inertia industrial robot effectively, which provides an important technical support for its high-performance motion control.
Neural-network-based Algorithm for Cancelling Tremor in Surgical Robots

As a representation of teleoperated robots, surgical robots based on teleoperation are widely applied in the medical field. Generally, it can greatly guarantee the performance of microsurgery in terms of control and postoperative recovery by using a surgical robot in comparison with traditional surgical operation. However, the performance of surgical robots is greatly disturbed by the physiological tremor of surgeon in the process of operation. In order to cancel the impacts of tremor signal, a neural-network-based (NN-based) algorithm is developed in this paper. For the proposed NN-based approach, we develop a hybrid wavelet basis function to deal with the variable tremor signal. Additionally, the proposed method can cancel the tremor signals based on the excellent ability of nonlinear mapping and generalization and does not rely on a priori structural parameters. In order to evaluate the performance of the proposed method, comparative experiments of five different kinds of NN-based tremor filter are performed by using tremor signals with different frequencies and amplitudes. Experimental results validated that the proposed algorithm can achieve the performance of suppressing the tremor signal of the processing error. It is can be noted that the surgical robots can ensure the control performance of the surgical robots by using the developed NN-based filter. The developed method can also be applied as a filter for suppressing vibrations of processing operations in the future, such as chatter in micro-milling.
The stake of informing patients of the risk of hypofertility after chemotherapy for breast cancer

Introduction Too few women with invasive breast cancer are informed of the risk of hypofertility after chemotherapy. However, this risk can be prevented by offering gamete preservation by a specialized team. We believe that if more women were informed about gamete preservation, more of them would accept it. Objectives The primary objective is to describe each step of the oncofertility care pathway from provision of information to gamete preservation. The secondary objective is to estimate the impact of not receiving information by determining the proportion of women who would have undergone gamete preservation if they had been informed. Method 575 women aged 18–40 years treated with chemotherapy for breast cancer between 2012 and 2017 in the Ouest-Occitanie region (~3 million inhabitants) were included. We first constructed a multivariate predictive model to determine the parameters influencing the uptake of the offer of gamete preservation among women who were informed and then applied it to the population of uninformed women. Results Only 39% of women were informed of the risks of hypofertility related to chemotherapy and 11% ultimately received gamete preservation. If all had been informed of the risk, our model predicted an increase in gamete preservation of 15.35% in the youngest women (<30 years), 22.88% in women aged between 30 and 35 years and zero in those aged ≥36 years. We did not find any association with the European Deprivation Index (EDI). Conclusion Oncologists should be aware of the need to inform patients aged ≤ 35 years about gamete preservation. If all received such information, the impact in terms of gamete preservation would likely be major.
Optimal Operation of Energy Hub: An Integrated Model Combined Distributionally Robust Optimization Method With Stackelberg Game

This paper proposes a low-carbon operation model for an energy hub (EH) that combines the distributionally robust optimization (DRO) method with the Stackelberg game. Firstly, a bilevel single-leader-multi-follower Stackelberg game model is presented where the EH is the leader while users and electric vehicles (EVs) are regarded as two followers. Then, the Kullback-Leibler (KL) divergence-based DRO model is developed to deal with the uncertainty of renewable generation (RG) in the EH. Besides, Karush–Kuhn–Tucker (KKT) conditions, strong duality theory, and big-M approach are combined to transform the bilevel model into a single-level model. The reformulated single-level operation model is incorporated into the KL-based DRO approach. Furthermore, since the crafted column and constraint generation (C&CG) algorithm can prevent possible numerical problems caused by the exponential function and accelerate the solution speed, the crafted C&CG algorithm with linearization for the upper-level slave problem is proposed to iteratively solve the KL-based DRO integrated with Stackelberg game. Finally, numerical case studies are conducted with all simulation results confirming the effectiveness of the proposed model and method.
Tidal tailspin cold dark matter cosmologies

ABSTRA C T We study the formation of tidal tails in pairs of merging disc galaxies with structural properties motivated by current theories of cold dark matter (CDM) cosmologies. In a recent study, Dubinski, Mihos & Hernquist showed that the formation of prominent tidal tails can be strongly suppressed by massive and extended dark haloes. For the large halo-to-disc mass ratio expected in CDM cosmologies their sequence of models failed to produce strong tails like those observed in many well-known pairs of interacting galaxies. In order to test whether this effect can constrain the viability of CDM cosmologies, we construct N-body models of disc galaxies with structural properties derived in analogy to the recent analytical work of Mo, Mao & White. With a series of self-consistent collisionless simulations of galaxy‐galaxy mergers we demonstrate that even the discs of very massive dark haloes have no problems developing long tidal tails, provided the halo spin parameter is large enough. For our class of models, the halo-to-disc mass ratio is not a good indicator of the ability to produce tails. Instead, the relative size of disc and halo or, alternatively, the ratio of circular velocity to local escape speed at the half mass radius of the disc is a more useful criterion. This result holds in all CDM models. While tidal tails can provide useful information on the structure of galaxies, it thus appears unlikely that they are able to constrain the values of the cosmological parameters within these models.
Small-Scale Variability in Sea Surface Salinity and Implications for Satellite-Derived Measurements

AbstractCalibration and validation efforts of the Aquarius and Soil Moisture and Ocean Salinity (SMOS) satellite missions involve comparisons of satellite and in situ measurements of sea surface salinity (SSS). Such estimates of SSS can differ by the presence of small-scale variability, which can affect the in situ point measurement, but be averaged out in the satellite retrievals because of their large footprint. This study quantifies how much of a difference is expected between in situ and satellite SSS measurements on the basis of their different sampling of spatial variability. Maps of sampling error resulting from small-scale noise, defined here as the root-mean-square difference between “local” and footprint-averaged SSS estimates, are derived using a solution from a global high-resolution ocean data assimilation system. The errors are mostly 0.2 psu in several regions, particularly near strong currents and outflows of major rivers. To examine s.
Texture segmentation with tree-structured wavelet transform

A tree-structured wavelet transform has previously been developed and used for texture classification by the authors. The transform is able to zoom into dominant frequency channels containing significant texture information for further wavelet decomposition, and can be interpreted as the decomposition of a 2-D function with the wavelet packet basis function. The texture segmentation problem is addressed here. A texture segmentation algorithm based on the tree-structured wavelet transform and a hierarchical fuzzy clustering technique is proposed. Numerical experimental results are given. <<ETX>>
The role of plant diversity and facilitation during tropical dry forest restoration

Restoration programmes that promote the functioning of restored ecosystems are in urgent demand. Although several biodiversity and ecosystem functioning (BEF) experiments have demonstrated the importance of functional complementarity in enhancing plant community performance, no biodiversity experiment has yet manipulated facilitation to test its contribution to how the complementarity effect (CE) modulates community performance. We built a restoration experiment manipulating diversity and facilitation potential in a tropical semi‐arid forest. We planted 4704 seedlings of 16 native tree species to assemble 147 experimental communities with 45 different compositions comprising 1, 2, 4, 8 or 16 species. Facilitation potential was included in the experimental design by creating a gradient of communities from low to high facilitation potential (based on prior research). We measured functional dispersion and functional identity using species above‐ and below‐ground traits to investigate how they modulate the effects of species diversity and facilitation potential on leaf biomass production, using the additive partition biodiversity effects CE and selection effect (SE). The joint influence of diversity and facilitation potential was tested separately for leaf biomass production and net biodiversity effect using linear mixed models (LMMs). We subsequently ran LMMs including functional dispersion and functional identity. We hypothesised that facilitation potential would increase community productivity and functioning and that functional dispersion and functional identity related to above‐ and below‐ground traits would explain facilitation performance. Facilitation potential positively influenced leaf biomass production as predicted, but unexpectedly, neither of the functional traits were important for modulating the facilitation process. Positive values for CE showed that plants performed better in mixtures in comparison to monocultures. SE negative values, showed that species with below average performance in monocultures, performed better in mixtures. Unexpectedly, CE did not increase as species diversity or facilitation potential increased. The SE was influenced negatively by facilitation potential leading to a more equal distribution of biomass production between species in mixtures. Synthesis. Facilitation improves biomass production in restored communities and increases biomass equitability among plant species and thus ecosystem reliability. To improve restoration success, plant communities should be built using a combination of facilitative species.
Multi-Domain Resource Multiplexing Based Secure Transmission for Satellite-Assisted IoT: AO-SCA Approach

Due to the wireless broadcasting and broad coverage in satellite-supported Internet of things (IoT) networks, the IoT nodes are susceptible to eavesdropping threats. Considering the distance difference between satellite and nearby destinations is negligible, the main and wiretapping channels between satellite and IoT node are similar, it poses great challenges to reach physical layer security in satellite-assisted IoT networks. In this paper, to guarantee secure transmissions for satellite-assisted IoT downlink communications, the multi-domain resource multiplexing based secure approach is proposed. Particularly, the self-induced co-channel interference between adjacent nodes is leveraged to increase the difference of signal transmission quality over both main and wiretapping channels. By comprehensively optimizing multi-domain resources, i.e., frequency, power, and spatial domains, secure transmissions from satellite to IoT nodes are reached. Specifically, the problem to maximize the sum secrecy rate of IoT nodes is formulated with a constraint of common communication rate of IoT nodes. To solve this non-convex problem, an alternating optimization (AO) algorithm with two inner successive convex approximation (SCA) algorithms are executed to solve the power allocation, spectral multiplexing, and precoding. In addition, simulation results are carried out to evaluate the secrecy rate performance and verify the efficiency of our proposed approach.
SAW characteristics of AlN/SiO 2 /3C-SiC layered structure with embedded electrodes

A novel sandwich structure of AlN/SiO 2 /3C-SiC with embedded electrodes, which enables the growth of highly quality AlN thin films, is proposed and studied. The phase velocity, coupling factor, and temperature coefficient of frequency (TCF) of SAWs in the proposed structure have been investigated using the finite element method (FEM). The simulation results show that high velocity of 5,485m/s and large effective coupling factor of 1.45% can be simultaneously obtained, which is about 3.4 times larger than the value previously reported. The excellent zero TCF is also achieved without deteriorating the coupling factor by adding the SiO 2 overlay.
NAS: Neuron Analyzer Suite for Automatic Analysis of Neuronal Activities from Calcium Imaging Data

—Neuronal activities can be visualized through functional Multi-neuron Calcium Imaging (fMCI) using fluorescence microscopes. However, the necessary components of data processing pipelines have been separately implemented and not well integrated previously. To this end, this paper introduces a Neuron Analyzer Suite (NAS) that integrates several components, from image preprocessing to inferred functional connectivity, in order to establish a processing pipeline for calcium imaging data. NAS is composed of three main modules. First, a motion removal module employing image registration is applied to raw images to remove artifacts caused by the motion of model organisms. Second, a neuron segmentation module is utilized to segment neurons and to extract their time series signals. Third, a neuron connectivity module is applied to time series signals to bi/cluster neurons based on their activity patterns and to reverse engineer their putative functional connectivity using a Bayesian learning approach. The NAS suite was tested on zebrafish calcium images to infer functional connectivity that may change over time. 