Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Coupled Electroencephalography Signals in Electrooculography Signals on Sleep Staging Based on Deep Learning Methods

The influence of the coupled electroencephalography (EEG) signal in electrooculography (EOG) on EOG-based automatic sleep staging has been ignored. Since the EOG and prefrontal EEG are collected at close range, it is not clear whether EEG couples in EOG or not, and whether or not the EOG signal can achieve good sleep staging results due to its intrinsic characteristics. In this paper, the effect of a coupled EEG signal in an EOG signal on automatic sleep staging is explored. The blind source separation algorithm was used to extract a clean prefrontal EEG signal. Then the raw EOG signal and clean prefrontal EEG signal were processed to obtain EOG signals coupled with different EEG signal contents. Afterwards, the coupled EOG signals were fed into a hierarchical neural network, including a convolutional neural network and recurrent neural network for automatic sleep staging. Finally, an exploration was performed using two public datasets and one clinical dataset. The results showed that using a coupled EOG signal could achieve an accuracy of 80.4%, 81.1%, and 78.9% for the three datasets, slightly better than the accuracy of sleep staging using the EOG signal without coupled EEG. Thus, an appropriate content of coupled EEG signal in an EOG signal improved the sleep staging results. This paper provides an experimental basis for sleep staging with EOG signals.
Estimation of nonparametric dynamical models within Reproducing Kernel Hilbert Spaces for network inference

We consider the problem of network inference that occurs for instance in systems biology. A dynamical system (a gene regulatory network) is observed through time and the goal is to infer the dependence structure between state variables (mRNAs concentrations) from time series. Works concerning net- work inference usually rely on sparse linear models estimation or Granger causality tools. A very few address the issue in the nonlinear cases. In this work, we propose a nonparametric approach to dynamical system modeling that makes no assumption about the nature of the underlying nonlinear system. We develop a general framework based on Reproducing Kernel Hilbert Spaces based on matrix-valued kernels to identify the dynamical system and retrieve the target network. As in the linear case, the network inference task calls for sparsity control. We show very good results both in autoregressive models and differential equations estimation on DREAM benchmarks as well as on the IRMA datasets.
New conjugacy condition with pair-conjugate gradient methods for unconstrained optimization

Conjugate gradient methods are wildly used for unconstrained optimization especially when the dimension is large. In this paper we propose a new kind of nonlinear conjugate gradient methods which on the study of Dai and Liao (2001), the new idea is how to use the pair conjugate gradient method with this study (new cojugacy condition) which consider an inexact line search scheme but reduce to the old one if the line search is exact. Convergence analysis for this new method is provided. Our numerical results show that this new methods is very efficient for the given ten test function compared with other methods.
Analysis of the influence of optical phased array element duty cycle on beam control quality

Optical phased array is a method of beam control by controlling the wavefront distribution of the beam. It has the characteristics of all-solid state, low power consumption and no inertia. The realization of multi-beam control in free-space optical communication through optical phased array has obvious advantages in energy consumption and control accuracy. The optical phased array modulates the phase directly on the beam ideally, but in fact the optical phased array device parameters obviously have an effect on the complex amplitude of the beam. In this paper, the modeling of the optical phased array device is realized, and the influence of the duty cycle of the optical phased array element on the efficiency and efficiency of the multi-beam control is analyzed. The experimental results show that the optical phase control controls the energy of the multi-beam pointing. The efficiency gradually increased from 0.23 to 0.88 when the fill factor increased from 50% to 100%, and the control accuracy of the optical phase control to control the multi-beam pointing did not significantly correlate with the fill factor. The experimental results have a certain reference for the application of optical phased array in multi-beam control.
Heap Adjustment Algorithms Based on Complete Binary Tree Structure

In many applications, it is necessary to adjust the node position of complete binary tree, making the data set have the nature of heap. Because when binary tree node data volume is huge, it will be needed apparently for heap adjustment efficiency of repair algorithms which is based on heap linear list to be improved. Now an improved algorithm is proposed by experiments, which utilizes characteristics of priority queue (delete the earliest data) and stack (delete the latest data), and conducts stack pushing on binary tree node, then accesses node successively to call glide adjustment algorithm to improve the adjustment efficiency.
3-D Printable Synthetic Metasurface to Realize 2-D Beam-Steering Antenna

This article presents highly radio-frequency (RF) transparent phase gradient synthetic metasurfaces made of sub-wavelength-sized 3D printable meta-atoms with tailored permittivity that cannot be achieved with off-the-shelf, commercially available materials. The synthesized meta-atoms design uses one dielectric block of PREPERM ® ABS 1000 material with air and metallic inclusions to make low- and high-permittivity materials. The inclusions’ size and height are varied to achieve a complete phase range from 0 to 360°, while maintaining transmission magnitudes greater than −3.0 dB. A two-dimensional array of meta-atoms forms a metasurface, which can be used for phase-shifting structures. Such metasurfaces can manipulate RF waves by introducing progressive phase delay into array elements. The proposed meta-atoms are employed to create highly RF transparent phase-gradient metal-dielectric composite metasurfaces (MDCMs) operating at 11 GHz. These MDCMs can be implemented through 3D printed technology using low-cost thermoplastics or polymers with composite filaments and minimal human intervention. A pair of MDCMs are combined with an array of microstrip patches to demonstrate 2D beam steering functionality numerically. The antenna system provides a peak directivity of 19.9 dBi with a maximum conical scanning angle of 114° and a directivity variation of less than 3 dB.
A Novel Improved SMOTE Resampling Algorithm Based on Fractal

Synthetic Minority Over-sampling Technique (SMOTE) is an over sampling method used to deal with the class imbalance problem, but this method has limitations to some extent because the new synthesized examples is interpolated between the minority examples that lie together, so this method cannot show the distribution of the data completely. Inspired from the theory of fractal interpolation, we thought that all the minority examples obey the distribution of self-similarity and dilation symmetry in space, then the interpolated examples must obey the self-similarity and dilation symmetry distribution too. In this work we proposed a new SMOTE resampling algorithm (FSMOTE) to improve the classification performance of the minority class in imbalanced data sets. Our comparative experiments show that, the FS MOTE algorithm provides more accurate results considering the criterion of F-measure and area under the ROC curve (AUC) upon the UCI standard data sets.
Application of Bat Algorithm to Optimize Scaling Factors of Fuzzy Logic-Based Power System Stabilizer for Multimachine Power System

Abstract This article presents the design of optimized fuzzy logic-based power system stabilizer (FPSS) to enhance small signal stability using bat algorithm (BA). The proposed optimization of scaling factors of FPSS is considered with an objective function based on square error minimization to guarantee the stability of nonlinear models of test system using BA. The BA-optimized FPSS (BAFPSS) controller is applied to the standard IEEE ten-machine thirty-nine-bus test power system model in the decentralized manner, and the performance is compared with the robust fuzzy controller. The robustness is tested by considering four different models of the test power system with different fault locations to establish the superiority of the proposed BAFPSS over the FPSS.
Label-Free Direct Detection of Cylindrospermopsin via Graphene-Enhanced Surface Plasmon Resonance Aptasensor

In this work, we report a novel method for the label-free detection of cyanotoxin molecules based on a direct assay utilizing a graphene-modified surface plasmon resonance (SPR) aptasensor. Molecular dynamic simulation of the aptamer’s interaction with cylindrospermopsin (CYN) reveals the strongest binding sites between C18–C26 pairs. To modify the SPR sensor, the wet transfer method of CVD monolayer graphene was used. For the first time, we report the use of graphene functionalized by an aptamer as a bioreceptor in conjunction with SPR for the detection of CYN. In a direct assay with an anti-CYN aptamer, we demonstrated a noticeable change in the optical signal in response to the concentrations far below the maximum tolerable level of 1 µg/L and high specificity.
Spatial regularization based on support tensor machine for neuroimaging classification

Recently, a high dimensional classification framework has been proposed to introduce spatial and anatomical priors in support vector machine (SVM) optimization scheme for brain image analysis. However, classical SVM has to convert 3D discrete brain images naturally represented by higher-order tensors to one-dimensional vectors in order to meet the input requirements. This traditional method destroys the natural structure and correlation in the original data, and generates high dimensional vectors. In this manuscript, the method is improved by a modified support tensor machine (STM) algorithm to make full use of spatial prior and the inherent information of tensor. The new approach reduces memory requirements and computational complexity significantly, and it is comparably demonstrated by experimental results on classification of Alzheimer patients and elderly controls. Introduction Alzheimer disease (AD) is the most prevalent neurodegenerative dementia in worldwide, and thus prevention and early accurate diagnosis of AD is increasingly crucial. In the last few years, support vector machine (SVM) methods for AD subject classification have become an incredibly active research topic. In the related work, SVM approach, as same as feature selection and extraction, has been taking the specificity of neuroimaging data into account. As an example, a frame work is proposed in [1] to include spatial and anatomical priors into SVM by using regularization operators, and it indicates a flexible way to model various types of proximity. However, for the purpose of satisfying the input requirements of classical SVM, 3D discrete brain images naturally represented by higher-order tensors have to convert to vectors in advance. This conversion breaks the inherent structure and correlation in the original data, and easily produces high dimensional vectors and leads to a poor performance in time complexity and space complexity. In order to improve above method including spatial prior of 3D discrete brain images in SVM, a new method is proposed in this manuscript to maintain the natural structure in the original tensor data and include spatial prior of all frontal slices of the tensors in STM [2]. To illustrate the improvement, we apply the proposed method to classification of 299 subjects, 137 AD patients and 162 cognitively normal (CN) controls, as the same study population in [3]. It can be observed that the proposed method reduces size of memory requested and the time complexity significantly and produces better classification performances. Spatial prior in SVM SVM is a supervised learning algorithm introduced by Vapnik and performs classification by mapping the data into higher dimensional space [4]. Let (xs, ys), s∈1,. ,S be a training set of instance-labeled pairs, xs∈Ω, ys∈{-1,1} and Ω is the input space, then SVM optimization problem can be written as:
Linear Transformations for Multivariate Binary Data

The interpretatioii of statistical data may often be simplified by a preliminary transformatioii. In the conitext of contingency tables, one way of achieving this would be to relabel the possible outcomes, or in other words to permute the cells of the table. For a 2d table, certaiui permuitations have the property that a loglinear model for the cell probabilities transforms in a simple way. These are, in a sense, linear transformations of the original variables. The aim of making such a transformation is to fit the transformed data by a simple model, such as a low-order hierarchical model or one in which certain variables are inidependent of others. A 24 table has beeni analyzed with this end in view. All the models were fitted to the original data, anid to do this a computer program has been developed which will fit iionhierarchical models by iterative scalinig.
The Rehabilitation Program Improves Balance Control in Children with Excessive Body Weight and Flat Feet by Activating the Intrinsic Muscles of the Foot: A Preliminary Study

Background: determining the appropriate rehabilitation protocol is essential to influence the correction of flat feet, e.g., by activating the intrinsic muscles of the foot. Therefore, this study aimed to determine the impact of the exercises activating the intrinsic foot muscles for postural control in children with flat feet, with normal and excessive body weight. Methods: Fifty-four children aged 7 to 12 were enrolled in the research. Forty-five children were qualified for the final evaluation. Each child in the experimental group was demonstrated an appropriate technique for performing a short foot exercise without compensation by extrinsic muscle. The participants then performed a supervised short foot training session once a week and on other days of the week under the supervision of caregivers for 6 weeks. Flat feet were scored on the foot posture index scale. A postural test was evaluated with a Biodex balance system SD. Statistical significance in the foot posture index scale and postural test were evaluated using an analysis of variance (ANOVA) with Tukey’s post-hoc test. Results: according to the six indices of the foot posture index scale, five indicators showed statistically significant improvement after rehabilitation. At the 8–12 platform mobility level, it was revealed that the excessive body weight group had significant improvements in the overall stability index and medio-lateral stability index, with eyes closed. Conclusion: our results indicate that a 6-week rehabilitation program based on the activation of the intrinsic muscles of the foot resulted in an improvement in the foot position. This, in turn, affected balance control, especially in children with excess body weight in conditions of closed eyes.
Articulatory–Acoustical Relationship in Cantonese Vowels

This paper investigates the articulatory–acoustical relationship in the production of the Cantonese long point vowels [iː uː aː] in CV syllables and shortened equivalents [i u a] in checked CVC syllables, through an analysis of the tongue positions and corresponding formant frequencies for the vowels. The articulatory and acoustical data from four Cantonese speakers, two male and two female, show that: (i) the shortened high vowels [i] and [u], relative to the long [iː] and [uː], undergo formant undershoot in the acoustical vowel space and [u] also undergoes target reduction in the articulatory vowel space, and (ii) the effect of shortened vowel duration is not observed in the low [a]. The data on the articulatory–acoustical relationship show that: (i) the levels of sensitivity of F1 and F2 to the variations in tongue displacement are low in each of the vowels [iː uː aː] and [i u a], suggesting that the Cantonese point vowels are quantal, and (ii) the sensitivity of the formant frequencies is lower to the.
Single-Pilot Aircraft in Commercial Air Transport Operations: A Comparison of Potential Architectures

Single-pilot operations (SPOs) in commercial air transport present a range of benefits and challenges, but there is a need to define architectures and compare them in different operational contexts. Here, we identified various combinations of architectural decisions based on the literature, and we compared them to current operations (in different operating contexts) on a safety versus cost tradespace. Safety was defined as a function of the pilot nominal operations workload, handling of off-nominal situations, and pilot incapacitation; whereas the cost was defined as a combination of acquisition and operating costs. Our analysis suggests that different classes of aircraft (wide bodies, narrow bodies, and regional jets) have different levels of benefits and costs in moving to SPOs. The capabilities of automation need to improve drastically before the second human in the flight deck can be replaced, and this is borne out by the dominance of human-centered architectures in the tradespace. The analysis also reveals that regional aircraft may be prime candidates to move to SPOs first because most regional architectures generate positive savings.
The Analysis of Coffee Shop Customer Loyalty in Jakarta Metropolitan Area During the Covid-19 Pandemic

Since Starbucks Coffee first opened its stores in Indonesia, Indonesia’s coffee culture was significantly developed and the growth of local coffee shops greatly improved. It also transformed coffee into the symbol of social status and lifestyle for society. People then came to coffee shop to meet friends and family while enjoying coffee and other beverages. Furthermore, the value of Indonesia’s coffee shop business rose into Rp4.8 billion which indicates a huge business potential. Unfortunately, Covid-19 struck Indonesia and the coffee shop business was hampered. The purpose of this research is to determine the effect of service quality, food quality, price fairness, and store atmosphere on coffee shop customer loyalty in Jabodetabek during the Covid-19 pandemic with the mediation of customer satisfaction. A questionnaire and 6-point Likert scale was designed for this research and has collected 179 respondents who live in Jakarta Metropolitan Area (Jabodetabek) and have regular coffee shops to be visited during the Covid-19 pandemic. This research used the PLS-SEM approach to analyse the collected data. This research found that service quality, food quality, price fairness, and store atmosphere positively and significantly affect customer satisfaction. Customer satisfaction also has a positive and significant influence on customer loyalty.
Towards Trajectory Forecasting From Detection

Trajectory forecasting for traffic participants (e.g., vehicles) is critical for autonomous platforms to make safe plans. Currently, most trajectory forecasting methods assume that object trajectories have been extracted and directly develop trajectory predictors based on the ground truth trajectories. However, this assumption does not hold in practical situations. Trajectories obtained from object detection and tracking are inevitably noisy, which could cause serious forecasting errors to predictors built on ground truth trajectories. In this paper, we propose to predict trajectories directly based on detection results without relying on explicitly formed trajectories. Different from traditional methods which encode the motion cues of an agent based on its clearly defined trajectory, we extract the motion information only based on the affinity cues among detection results, in which an affinity-aware state update mechanism is designed to manage the state information. In addition, considering that there could be multiple plausible matching candidates, we aggregate the states of them. These designs take the uncertainty of association into account which relax the undesirable effect of noisy trajectory obtained from data association and improve the robustness of the predictor. Extensive experiments validate the effectiveness of our method and its generalization ability to different detectors or forecasting schemes.
Measurements of the H$sub 2$CO 6-centimeter excitation temperature. I. Dark dust clouds

Highly accurate H/sub 2/CO 6-cm line profiles were observed in 14 positions located in dark dust clouds. Analyses of the relative strengths of the hyperfine components showed no anomalies. Therefore, excitation temperatures were derived for 10 positions which exhibited measurable saturation due to optical-depth effects. Nine of these positions have nearly the same excitation temperature, about 1.6M. The other position definitely has a higher excitation temperature, about 2.2 deg K; this position is located in a cloud whose kinetic temperature is 18 deg K, substantially hotter and also probably denser than the usual'' dark dust cloud. (auth)
MENINGKATKAN KEMAMPUAN BELAJAR MEMBACA AL QURAN MELALUI METODE PREPIEW, QUESTION, READ, REFLECT, RECITE, REVIEW SEBUAH LITERASI SISWA MAN 1 KERINCI

Guru pada hakekatnya merupakan tenaga kependidikan yang memikul berat tanggung jawab kemanusian, khususnya berkaitan dengan proses pendidikan generasi penerus bangsa menuju gerbang pencerahan dalam melepaskan diri dari belenggu kebodohan. Betapa berat tugas dan kewajiban yang harus diemban oleh guru tersebut sehingga menuntut profesionalitas tinggi dalam proses pembelajaran. Melalui kompetensi profesionalnya, guru harus mampu mewujudkan langkah-langkah pembelajaran inovatif dan kreatif, sehingga proses belajar mengajar dapat bermakna serta tranfer of knowledge dan transfer of value dapat dengan mudah tersampaikan. Pembelajaran membaca Al Quran terdapat dalam pembelajaran kelas VII semester II. Pembelajaran Membaca Al Quran masih belum banyak mendapat perhatian dari guru. Siswa pun masih kurang terbiasa untuk membaca, apalagi jika pembelajaran membaca Al Quran dari refleksi kehidupan sehari-hari. Hal ini terjadi karena guru belum memberikan bimbingan kepada siswa secara maksimal. Akibatnya siswa mengalami kesulitan. Jika mendapat tugas membaca Al Quran dengan menggunakan metode PQ4R (Preview, Question, Read, Reflect, Recite, Review ) diharapkan siswa dapat termotivasi untuk Membaca Al Quran dari refleksi kehidupan sehari-hari.
A study of the characteristics of the southwestern China regional meteorological drought events during 1960-2010

An objective identification technique for the regional extreme events MITRED and the daily composite-drought index(CI)of the 101 stations in southwestern China including Sichuan,Yunnan,Guizhou and Chongqing were used to investigate the southwestern China regional meteorological drought events during 1960-2010.The values of the parameters of the OITREE method were determined.87 events were identified,including 9 extreme events in which the 2009/2010 severe drought in southwestern China is the most serious meteorological drought event for the past 50 years.Further analysis reveals that:The durations are generally between 10-80d,with the longest period being 231 d.November to April is the dry season in southwestern China.As far as the regional distribution is concerned,Yunnan and southern Sichuan have the highest drought frequency and intensity,and strong(extreme and severe)southwestern China regional meteorological drought events could be divided into five types,with the Southern type occurring more frequently.During the past 50 years,the southwestern China regional meteorological drought events show a significant increase trend in frequency and an obvious increase trend in intensity,for which the main reason may be the significant decrease of the annual precipitation in this region,with a contribution by the significant increase trend in temperature.