Miracle-dz/UniFAR-FTUs huggingface dataset:
Cortico-basal ganglia networks dysfunction associated with disease severity in patients with idiopathic blepharospasm

Background Structural changes occur in brain regions involved in cortico-basal ganglia networks in idiopathic blepharospasm (iBSP); whether these changes influence the function connectivity patterns of cortico-basal ganglia networks remains largely unknown. Therefore, we aimed to investigate the global integrative state and organization of functional connections of cortico-basal ganglia networks in patients with iBSP. Methods Resting-state functional magnetic resonance imaging data and clinical measurements were acquired from 62 patients with iBSP, 62 patients with hemifacial spasm (HFS), and 62 healthy controls (HCs). Topological parameters and functional connections of cortico-basal ganglia networks were evaluated and compared among the three groups. Correlation analyses were performed to explore the relationship between topological parameters and clinical measurements in patients with iBSP. Results We found significantly increased global efficiency and decreased shortest path length and clustering coefficient of cortico-basal ganglia networks in patients with iBSP compared with HCs, however, such differences were not observed between patients with HFS and HCs. Further correlation analyses revealed that these parameters were significantly correlated with the severity of iBSP. At the regional level, the functional connectivity between the left orbitofrontal area and left primary somatosensory cortex and between the right anterior part of pallidum and right anterior part of dorsal anterior cingulate cortex was significantly decreased in patients with iBSP and HFS compared with HCs. Conclusion Dysfunction of the cortico-basal ganglia networks occurs in patients with iBSP. The altered network metrics of cortico-basal ganglia networks might be served as quantitative markers for evaluation of the severity of iBSP.
Comprehensive comparison of three techniques for the treatment of adjacent segment degeneration after lumbar fusion

Purpose Adjacent segment degeneration (ASD) following lumbar fusion is technically challenging for spine surgeons. Posterolateral open fusion surgery with pedicle screw fixation is an effective way to treat symptomatic ASD with favorable clinical outcomes; however, it is associated with an increased morbidity rate. Therefore, minimally invasive spine surgery is advocated. This study was designed to compare clinical outcomes among patients with symptomatic ASD who underwent percutaneous transforaminal endoscopic discectomy (PTED) with the transforaminal approach, posterior lumbar interbody fusion (PLIF) with cortical bone trajectory screw fixation (CBT-PLIF), and PLIF with traditional trajectory screw fixation (TT-PLIF). Methods A retrospective study was conductedon 46 patients (26 men and 20 women; average age 60.8 ± 6.78 years) with symptomatic ASD. The patients were treated with three approaches. The operation time, incision length, time to return to work, complications, and the like were compared among three groups. Intervertebral disc (IVD) space height, angular motion, and vertebral slippage were obtained to assess spine biomechanical stability following surgery. The visual analog scale (VAS) score and Oswestry disability index were evaluated at preoperation and 1-week, 3-month, and the latest follow-ups. Clinical global outcomes were also estimated using modified MacNab criteria. Results The operation time, incision length, intraoperative blood loss, and time to return to work for the PTED group were significantly decreased compared with those for the other two groups (P < 0.05). The radiological indicators in the CBT-PLIF group and TT-PLIF group had better biomechanical stability compared with those in the PTED groups at the latest follow-up (P < 0.05). The back pain VAS score in the CBT-PLIF group was significantly decreased compared with those in the other two groups at the latest follow-up (P < 0.05). The good-to-excellent rate was 82.35% in the PTED group, 88.89% in the CBT-PLIF group, and 85.00% in the TT-PLIF group. No serious complications were encountered. Two patients experienced dysesthesia in the PTED group; screw malposition was found in one patient in the CBT-PLIF group. One case with a dural matter tear was observed in the TT-PLIF group. Conclusion All three approaches can treat patients with symptomatic ASD efficiently and safely. Functional recovery was more accelerated in the PTED group compared with the other approaches in the short term; CBT-PLIF and TT-PLIF can provide superior biomechanical stability to the lumbosacral spine following decompression compared with PTED; however, compared with TT-PLIF, CBT-PLIF can significantly reduce back pain caused by iatrogenic muscle injury and improve functional recovery. Therefore, superior clinical outcomes were achieved in the CBT-PLIF group compared with the PTED and TT-PLIF groups in the long term.
Cross-sectional determinants of lactose-free food awareness and purchase among adult consumers

Abstract This research was conducted to evaluate the knowledge and purchasing behaviour of consumers regarding lactose-free foods. Study data were collected from 1882 individuals with the help of a questionnaire form. Attitudes towards animal welfare, healthy nutrition and gastrointestinal symptoms were also evaluated. The majority (86.6%) of the participants reported that they had heard of the concept of lactose-free foods before, but more than half (67.0%) did not report buying them because of their higher costs compared to regular products and lack of sufficient information. Not causing gas/bloating (32.5%) and being healthier (28.3%) were shown to be the most significant motivational factors in purchasing lactose-free foods (P < 0.05). The factors affecting purchase of lactose-free products were education, employment status, history of having a food intolerance test and animal welfare perception together with higher scores from the gastrointestinal symptom rating scale, and attitude scale for healthy nutrition (P < 0.05). Body mass index was not found to be an effective factor determining purchase intention. The results provide a general overview of consumers, however, further studies on other demographic groups are needed.
MAGNETIC WELL AND INSTABILITY THRESHOLDS IN THE TJ-II STELLARATOR

Experimental evidence for the controlled onset of fluctuations by varying the magnetic well in the plasma edge region of the TJ-II stellarator (major radius R=1.5 m, minor radius a<0.22 m, magnetic field B=1 T) has been obtained. The level of fluctuation, the degree of intermittency and the radial correlation of relevant quantities increase as the magnetic well is reduced. TJ-II possesses a high degree of flexibility, making it an ideal device for investigating the characteristics of transport close to instability thresholds.
An Insight of Cryptocarya Secondary Metabolites as Anticancer P388: Study of Molecular Docking, ADMET Properties, and Molecular Dynamic Simulation

Secondary metabolites isolated from Cryptocarya was known to have various activity especially their cytotoxicity in P388 cell. There were two species of Cryptocarya studied in this research that were Cryptocarya konishii and Cryptocarya lucida . In both species, 8 isolate compounds had bioactivity as anticancer in P388 cells. This study aimed to know the binding affinity and ADMET properties of each isolated compound through P-glycoprotein substrate since this protein was reported to be responsible for the inhibition of P388 cells. Molecular docking was performed using AutoDock4 and AutoDockTools software to know the binding energy and interaction of isolate compounds against the P-glycoprotein substrate. ADMET properties calculation was done using the pkCSM web server for all compounds. Molecular docking results showed that Kurzichalcolactone B (7) isolated from C. lucida had the lowest binding energy. It resulted in the highest total intermolecular energy from the contribution of van der Waals and hydrogen bond energy. Binding energy calculation using MM-PBSA method revealed a similar result that ligand 7 had a lower binding energy than 5. Calculation of ADMET properties resulted that some of the isolate compounds fulfilling the minimum standard parameters in ADMET properties
Up-wind finite element solution of travelling magnetic field problems

The up-wind finite element solution is applied to the calculation of a magnetic field problem including moving parts. This method is useful for several problems, such as linear induction motors or linear actuators. The basic scheme is reviewed by using a 1-D model. A simple inductor model constructed from a DC magnet and moving conductor plate is used to validate this method. As a practical application, 2-D computation results for a linear induction motor are described. It is concluded that the proposed method can be used practically, due to its accuracy and cost effectiveness. >
How to Prevent Pressure Oscillations in Multicomponent Flow Calculations

A new numerical scheme for computing multicomponent flows is presented. It is able to handle strong shocks, is pressure oscillation free through the contact discontinuities, and guarantees the posi_tivity of mass fractions. Some numerical experiments on shock tubes indicate the convergence to the correct weak solution. Comparisons with classical finite volume schemes are also proposed.
Extension of Howard's Circle Theorem to Adiabatic Jets

L. N. Howard proved an inequality concerning the complex velocity of the propagation of waves disturbing the laminar, unidirectional flow of an incompressible stratified fluid. It is shown that this theorem is closely related to the virial theorem of hydrodynamics. It can be generalized for the steady motion of a compressible fluid, provided the streamlines are either parallel straight lines or coaxial circles. The connection of the circle and virial theorems provides an interpretation of the former in terms of the energy of the perturbation. A positive potential energy always exerts a stabilizing influence, as does the angular velocity in the case of circular streamlines. This latter is diminished for short waves; it is also complicated by the fact that radial variations of the angular velocity may contribute negative terms to the potential energy. Zonal flow, such as the jet stream of the Earth's atmosphere, is generally baroclinic. In the barotropic case, the potential energy of the perturbation is sim.
TESTING AGAINST GENERAL AUTOREGRESSIVE AND MOVING AVERAGE ERROR MODELS WHEN THE REGRESSORS INCLUDE LAGGED DEPENDENT VARIABLES

Since dynamic regression equations are often obtained from rational distributed lag models and include several lagged values of the dependent variable as regressors, high order serial correlation in the disturbances is frequently a more plausible alternative to the assumption of serial independence than the usual first order autoregressive error model. The purpose of this paper is to examine the problem of testing against general autoregressive and moving average error processes. The Lagrange multiplier approach is adopted and it is shown that the test against the nth order autoregressive error model is exactly the same as the test against the nth order moving average alternative. Some comments are made on the treatment of serial correlation.
Secure SVM Services with Multiple Service Providers and Subscribers in the Cloud

In recent years, cloud computing has emerged as a way to deliver high-quality and reliable services and the integration of cloud services offers many opportunities for broader business applications. Among them, cloud-based support vector machine services (SVMS) are widely used. However, the service suffers from model security and query privacy issues, and existing solutions are not suitable for practical SVMS with multiple service providers and multiple users. In this paper, we propose a secure SVM service (SSVMS) supporting multiple service providers and multiple users. Specifically, SSVMS has the following three key techniques: 1) we use vector homomorphic encryption (VHE) to ensure model security and query privacy; 2) we design a pair of invertible matrices to support multiple service providers and multiple users; 3) we construct a special matrix to hide multi-user queries to resist collusion attacks. The security analysis shows that SSVMS can guarantee model security and query privacy even for multi-service provider and multi-user scenarios. Performance evaluation shows that SSVMS has high accuracy and low communication and computational cost, which guarantees the realistic effectiveness of SSVMS.
A hybrid model based on deep convolutional neural network for medical named entity recognition

The typical pre-trained model's feature extraction capabilities are insufficient for medical named entity identification, and it is challenging to express word polysemy, which resulting in a low recognition accuracy for electronic medical records. To solve this problem, this paper proposes a new model that combines the BERT pre-training model and the BiConvLSTM-CRF model, namely BERT-BiLSTM-CRF. First, word embedding with semantic information is obtained by pre-training the corpus input to the BERT model. Next, the BiConvLSTM module is utilized to extract further features from the encoded outputs of BERT in order to account for context information and improve the accuracy of semantic coding; Then, CRF is used to modify the results of BiConvLSTM to screen out the annotation sequence with the largest score. Finally, extensive experimental results show that the performance of the proposed model is effectively improved compared with other models, and our F1-score value reaches 86.54%.
Evaluation of data poisoning attacks on federated learning-based network intrusion detection system

Recently, federated learning-based network intrusion detection system (FL-based NIDS) has been considered as an essential tool to protect network security. It enables learning an effective intrusion detection model collaboratively without data privacy leakage. However, FL- based NIDS has exhibited inherent vulnerabilities on the data poisoning attacks launched by malicious clients. In this paper, we conduct the first systematic robustness evaluations of FL-based NIDS under data poisoning attack. Firstly, in consideration of the traffic domain constraints, we design the clean-label data poisoning attack against FL-based NIDS. Specifically, we propose an improved poisoned sample generation model based on Generative Adversarial Network, called PT-GAN, which optimizes with a new loss function that incorporates the feedback of the target intrusion detection model. The minimally-perturbed and correctly-labeled traffic samples generated by PT-GAN are then injected in the local training dataset to corrupt the intrusion detection model. Then, we explore the potential defense mechanism for these data poisoning attacks against FL- based NIDS and propose a novel defense method based on poisoned sample detection. Concretely, we propose the important neuron activations extraction method based on the layerwise relevance propagation method and then apply Oneclass-SVM to detect the poisoned sample. Experiments show that the proposed PT-GAN can degrade the performance of FL-based NIDS up to 28% on UNSW-NB15 dataset. We also demonstrate the robustness of our proposed defense methods against data poisoning attacks.
High-Sensitivity Force Detection by Phase-Modulation Atomic Force Microscopy

Performance of phase-modulation atomic force microscopy (PM-AFM) is investigated to achieve high force sensitivity and high-resolution imaging. PM-AFM detects the phase change of the oscillating cantilever relative to the excitation signal and uses it as the feedback signal. We compare the force sensitivity of PM-AFM with that of amplitude-modulation AFM (AM-AFM) theoretically as well as experimentally. We show that PM-AFM has a better signal-to-noise ratio than AM-AFM. We demonstrate that the electrostatic force images obtained by PM-AFM have clearer contrast than those obtained by AM-AFM.
Smoothing Control Strategy of Hybrid Energy Storage System Based on Kalman Filter and Empirical Mode Decomposition

In view of the fluctuation of wind farm output power, this paper proposes a smoothing control method of hybrid energy storage system based on Kalman filter and empirical mode decomposition, which is based on the actual wind farm power data. Firstly, the Kalman filter is used to obtain the target grid-connected power and the total smoothing power control signal of the energy storage system. Then, considering the working characteristics of different energy storage media of Hybrid Energy Storage System (HESS), Empirical Mode Decomposition (EMD) is used to obtain each intrinsic mode component within the allowable range of charging and discharging frequency, and the Hilbert transform is used to obtain the main frequency of the energy storage power signal, so as to determine the filtering time constant and assign the high-frequency fluctuation to the super capacitor. The low frequency fluctuation is borne by the battery. Finally, the effectiveness and accuracy of the method are verified by simulation. The simulation results show that this method can effectively suppress the power fluctuation of wind farm, which is of great significance to improve the power quality of wind power integration and enhance the stability of power system.
Predicting parameter values of CT scanner using machine learning techniques

A computer axial tomography (CT) scan is a tool used in the medical field to diagnose health problems by allowing doctors to get a picture of internal organs, blood vessels, and tissues. It creates images of the internal structure of the body using special X-ray equipment. A CT scan exposes the patient’s body to radiation, this will have a small risk of developing tumors or cancers in the future. We can reduce this risk by reducing the dose level. But this involves the risk of losing some important details in the image of CT scan, and in some cases a user might need to do CT scan process again. This work aims to address these issues by optimizing the parameter values used to decide dose level and image quality. We are using a Philips Vertica database composed of the dose data used for patients. We are preprocessing data for removing outliers and further cleaning data to generate a regression model using machine learning techniques. This will help the radiologist to plan tests rapidly in some trauma cases. Also, this work will help inexperienced doctors to perform scans efficiently.
Comparative Analysis of Accuracy of Supervised Learning Classifier for breast cancer classification

Breast Cancer is widely spreading disease among women all over the world. This cancer is considered as one of the deadly disease among women. Data mining algorithms play a vital role to predict the early stage cancer . The research problem is that there are lots of classifier with different level of accuracy. An approach for improving the performance and accuracy of three different classifiers, Decision Tree, Logistic Regression, and SVM, is proposed in this study. We also compare the classifier on Wisconsin breast cancer dataset. Accuracy of classifier depends on the shape of data. Imbalanced data is a big problem for the classification phase. Each classifier's efficiency is accessed in terms of confusion matrix accuracy, AUC, and ROC.There are five sections to this study. First, we'll load the dataset and applied correlation to remove some features. For scaling and to filter data, preprocessing algorithm is applied then dataset is splited into training and testing data set and then applied CART algorithms for classification purpose. Then generate some graph to visualize the data. Based on the WBC dataset using Jupyter notebook software, the goal of this paper is to discover the most accurate classifier. The results have been compared with the results of previous papers and reveal that they are extremely precise. The SVM classifier model was discovered to be the best classifier of all the types. It also removes the over fitting of data.
Multi-Dimensional Intention Recognition for User-Generated Short Texts

Online social media platforms are becoming increasingly popular.A huge number of people are using such platforms to share various information. Supply and demand constitute two important kinds of information in social media platforms. Examples are sell and buy, positions and job seeking, houses for rent and needs of accommodation,and so on.Consequently, it is relevant to match such supply and demand information automatically in order to facilitate the trades online. However, the problem is challenging due to the high volumes of information expressed in short and vague texts. In this paper, we propose a complete set of techniques to resolve the problem. First, we design an intention recognition model to recognize a user’ s complete intention from her/his original text posts,The recognized intention includes expected behaviors, intention objects, user sentiments and other relevant constraints. Second, we define the similarity for two intentions that are recognized. Third, we devise an algorithm to match supply and demand intentions whose similarity is sufficiently high. We evaluate our proposal on real data. The results show that our approach returns accurate matching results efficiently.
Policy Optimization for Markovian Jump Linear Quadratic Control: Gradient Method and Global Convergence

Recently, policy optimization has received renewed attention from the control community due to various applications in reinforcement learning tasks. In this article, we investigate the global convergence of the gradient method for quadratic optimal control of discrete-time Markovian jump linear systems (MJLS). First, we study the optimization landscape of direct policy optimization for MJLS, with static-state feedback controllers and quadratic performance costs. Despite the nonconvexity of the resultant problem, we are still able to identify several useful properties such as coercivity, gradient dominance, and smoothness. Based on these properties, we prove that the gradient method converges to the optimal-state feedback controller for MJLS at a linear rate if initialized at a controller, which is mean-square stabilizing. This article brings new insights for understanding the performance of the policy gradient method on the Markovian jump linear quadratic control problem.
User-Perceived QoE Adaptation for Accelerated Playback in Mobile Video Streaming

User-perceived quality of experience (QoE) is critical as mobile video streaming experiences a substantial growth. User's demands are becoming diversified where accelerated play-back is the preference of a considerable part of users. However, the limited and fluctuate mobile bandwidth is often not capable of satisfying user's demand of watching video at 2x or higher speed because of consequential frequent rebuffering. Previous adaptive bitrate (ABR) algorithms hardly consider the variety of user playback rates. In this work, we fully exploit the relation between user-perceived, i.e., subjective video quality and the characteristic of video content. The result of our motivational experiments shows that viewers are less sensitive to the bitrate variation and playback rate alternation if there is higher degree of motion in the video. With above guidelines, we adaptively adjust the quality configuration and playback rate to significantly reduce the rebuffering while achieving similar or even higher subjective quality. Then we formulate subjective quality and playback rate adaption as a QoE maximization problem and propose the content based subjective quality and playback rate adaptation algorithm (CSP) utilizing Lyapunov optimization technique. Via rigorous proof, the time-average QoE achieved by CSP is in $O(1/V)$ gap compared to optimal value, where $V$ is the control parameter. Extensive evaluations confirm the superiority of our proposed algorithm over other state-of-the-art algorithms under both normal and accelerated playback rate.