Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault Diagnosis Based on Space Mapping and Deformable Convolution Networks

The data-driven method based on deep learning is one of the popular issues in the field of fault diagnosis. The completeness and representativeness of the feature matrix from massive and high-dimensional fault data have a great impact on fault diagnosis performance. In addition, the ability of deep networks to extract the spatial characteristics between fault data is especially important for the accuracy of fault diagnosis. Therefore, we propose a method based on space mapping and deformable convolution networks (DCN) to ensure diagnostic accuracy by improving the spatial resolution and spatial constraint characteristics, and both the size and shape of the convolution kernel, one of the key steps in DCN, are adjusted adaptively according to the input of different sizes. Original data are projected into a more discriminative space by the combination of CN and PCA (i.e., space mapping). Then, DCN extract spatial constraints between fault data by training. The Case Western Reserve University (CWRU) bearing dataset and Xi’an Jiaotong University and Changxing Sumyoung Technology Co., Ltd. (XJTU-SY) datasets are used as benchmarks to perform experiments. The results demonstrate that the fault diagnosis method proposed in this paper performs well and can achieve 100% accuracy in the first several epochs. Comparative experiments based on 3 deep learning methods that combine preprocessed and unprocessed data with a convolutional neural network (CNN), residual networks (ResNets) and DCN are carried out to further show the advantages of the fault diagnosis method based on space mapping and DCN.
Signal Fusion-based Detection with an Intuitive Weighting Method

In a distributed multiple-input multiple-output (MIMO) radar, signal fusion based detection can achieve the best detection performance but the global test may be a complicated combination of local observations, such that a closed-form expression of the false alarm rate may be hard to develop, especially for heterogeneous local observations. In this paper, we study how to impose better linear weights on local test statistics for a global test resulting in a better detection performance. Since the weights of the generalized likelihood ratio test (GLRT) have been proved to be suboptimal, an intuitive and tractable weighting method is presented that weights the local test statistics according to the estimated channel signal-to-noise ratios (SNRs) and the power of the local test statistics measured by the detection probability curve. With a linear combination of local test statistics, the false alarm rates can be computed conveniently. This method is also applicable to heterogeneous local observations. Numerical results for the performance of the distributed cell-averaging constant false alarm rate (CA-CFAR) algorithm and the distributed GLRT algorithm indicate that the proposed weighting method can outperform conventional algorithms.
Laterally substituted dimeric liquid crystals

Abstract Ten members of the homologous series α,ω-bis(2,4-dimethylanilinebenzylidine-4′-oxy)alkanes and four members of the α-(4-cyanobiphenyl-4′-oxy)-ω-(2,4-dimethylanilinebenzylidine-4′-oxy)alkanes have been synthesized. Their transitional properties are compared to those of the analogous compounds not possessing the 2-methyl substituent. Lateral substitution results in a large decrease in the nematic-isotropic transition temperatures. In contrast, the reduction in the entropy change associated with the nematic-isotropic transition is only slight.
Attention Guided Encoder-Decoder Network With Multi-Scale Context Aggregation for Land Cover Segmentation

Land cover segmentation is an important and challenging task in the field of remote sensing. Even though convolutional neural networks (CNNs) provide great support for semantic segmentation, standard models are still difficult to capture global information and long-range dependencies in remote sensing images. To overcome these limitations, we proposed an attention guided encoder-decoder network with multi-scale context aggregation to achieve more accurate segmentation of land cover. Based on the structure of the encoder-decoder network, we introduce a multi-scale feature fusion module with two attention modules to the top of the encoder. The multi-scale feature fusion module is employed to aggregate multi-scale features and capture global correlations. The attention modules are used to exploit the long-range dependencies and the interdependence between channels from the perspective of space and channel respectively. The experimental results on the GF-2 images show that our proposed method achieves state-of-the-art performance, with an OA of 84.1% and the mIoU of 62.3%. Compared with the baseline network, our method improves the OA by 3.3% and the mIoU by 4.4%. The comparative experiments also demonstrate that the proposed approach can significantly improve the accuracy of land cover segmentation than other compared methods.
Exploring English Teachers’ TPACK Level and the Impacts on Strategies of Assessment Practices

This study was aimed to investigate 30 English teachers’ technological pedagogical and content knowledge (TPACK) level and its impact on strategies of assessment practice. This quantitative descriptive study used a questionnaire adapted from Schmidt and semi-structured interviews in collecting the data. After analyzing the data, more than 50% of the participants had a high level and the rest of less than 50% of the participants had a fair and low level for the seven domains knowledge. Each teacher had strengths and weaknesses in applying the seven domains knowledge. Teachers who had a high TPACK level tend to have confidence and any solution while assessing the students. Therefore, this study is recommended for English teachers and curriculum developers to improve their TPACK level and use the variance of assessment strategies.
On performance analysis of extended state observer for a class of systems with multiple uncertainties and biased measurement

This paper studies the state estimation problem of a class of systems with multiple uncertainties and biased measurement. Since the observability of such system is not satisfied, the estimations for states and uncertainties definitely have biases. For the extended state observer which aims to estimate the system states and the dominant term of uncertainties, the quantitative estimating performance is studied in the paper. Furthermore, the biased estimation error is explicitly shown, which can be derived from the following three parts: 1) the biased measurement; 2) the unobservable dominant uncertainty; 3) the non-dominant term of multiple uncertainties. The theoretical analysis in the paper can help practitioners quantitatively evaluate the effectiveness of the designed extended state observer.
Modeling Temporal Visual Salience for Human Action Recognition Enabled Visual Anonymity Preservation

This paper proposes a novel approach for visually anonymizing video clips while retaining the ability to machine-based analysis of the video clip, such as, human action recognition. The visual anonymization is achieved by proposing a novel method for generating the anonymization silhouette by modeling the frame-wise temporal visual salience. This is followed by analysing these temporal salience-based silhouettes by extracting the proposed histograms of gradients in salience (HOG-S) for learning the action representation in the visually anonymized domain. Since the anonymization maps are based on the temporal salience maps represented in gray scale, only the moving body parts related to the motion of the action are represented in larger gray values forming highly anonymized silhouettes, resulting in the highest mean anonymity score (MAS), the least identifiable visual appearance attributes and a high utility of human-perceived utility in action recognition. In terms of machine-based human action recognition, using the proposed HOG-S features has resulted in the highest accuracy rate in the anonymized domain compared to those achieved from the existing anonymization methods. Overall, the proposed holistic human action recognition method, i.e., the temporal salience modeling followed by the HOG-S feature extraction, has resulted in the best human action recognition accuracy rates for datasets DHA, KTH, UIUC1, UCF Sports and HMDB51 with improvements of 3%, 1.6%, 0.8%, 1.3% and 16.7%, respectively. The proposed method outperforms both feature-based and deep learning based existing approaches.
Highly Nonlinear Organic-Silicon Slot Waveguide for Ultrafast Multimode All-Optical Logic Operations

Highly nonlinear multimode optical integrated devices hold great promise for the emerging large-capacity mode division multiplexing (MDM) technology, and they correspondingly enable multimode optical signal processing at nodes of the MDM network. Here, we propose a highly nonlinear multimode organic-silicon slot waveguide (HN-OSSW) whose nonlinearity is greatly improved by the intensive optical field confinement in the nano-gaps filled with highly nonlinear organic material. Specifically, the achieved nonlinear coefficients of the HN-OSSW under TE0 and TE1 modes are estimated higher than 7000 W−1m−1 and 5800 W−1m−1, respectively. Also, by phase mismatch optimization, this nanostructure exhibits superior intramode four-wave mixing conversion efficiency but ignorable intermode crosstalk. Coupling efficiency of the mode multiplexer and the optimal nonlinear interaction length are discussed in detail. Based on the HN-OSSW, we then numerically demonstrate a multimode all-optical hexadecimal addition and subtraction for sixteen phase-shift keying (16PSK) signals with the operation rate of 1.28Tb/s. Six different logic operations including A+B-C, A+C-B, B+C-A, A+B, A-B and B-A are simultaneously obtained with good performance evaluations, indicating the promising prospect of the HN-OSSW applied in ultrafast multimode signal processing.
MirGAN: Medical Image Reconstruction using Generative Adversarial Networks

Computerized tomography scan (or CT scan) is one of the most prominent diagnostic tools used in medical imaging which provides anatomical information about the human body. It uses X-ray machines that rotate and produce 2-dimensional images of the sections of the body under observation and can be visualized easily on the computing devices. There are various deep learning approaches used to reconstruct the medical images by the use of neural networks. In this paper, deep learning based Generative Adversarial Networks (or GAN’s) methods have been proposed to reconstruct images from a given randomized vector and trained data. The main goal of the proposed model is to train a better generator than a discriminator in such a way that the reconstructed images are as real as possibly present in the training data.
A 3-D Printed Bandpass Filter Using TM₂₁₁-Mode Slotted Spherical Resonators With Enhanced Spurious Suppression

This article presents a <italic>Ka</italic>-band fourth-order slotted spherical resonator waveguide bandpass filter (BPF) with a wide spurious suppression stopband. It uses the first higher-order TM<sub>211</sub> mode rather than the fundamental TM<sub>101</sub> mode of the spherical resonator in order to obtain a higher unload quality factor (<inline-formula> <tex-math notation="LaTeX">$Q_{\mathrm {u}}$ </tex-math></inline-formula>) for smaller in-band insertion loss, as well as a larger filter volume that gives better tolerance to fabrication errors in high frequency applications. By introducing slots that interrupt surface currents, the fundamental TM<sub>101</sub> and two higher spurious modes (TE<sub>101</sub> and TM<sub>311</sub>) can be suppressed without compromising the unloaded quality factor of the TM<sub>211</sub> mode. In addition, the filter topology is optimized to further enhance the suppression level of the spurious passbands. A Z-shaped topology has been found effective in decreasing the coupling strength of the spurious TM<sub>311</sub> mode. An analysis is performed to demonstrate the better tolerance of the Z-shaped filter over the conventional TM<sub>101</sub>-mode spherical resonator filter. For verification, a fourth-order slotted spherical resonator waveguide BPF with a center frequency of 31 GHz and bandwidth of 880 MHz is designed and manufactured using a selective laser melting (SLM) 3-D printing process. The measured results show an average in-band insertion loss of 1.53 dB, and a passband return loss better than 13.6 dB. The stopband of the filter is extended up to 40.9 GHz with a rejection level greater than 20 dB.
L-EDGE X-RAY ABSORPTION-SPECTROSCOPY OF PYROCOCCUS-FURIOSUS RUBREDOXIN

X-ray absorption spectroscopy has been used to probe the frozen solution structure of the metal site in Pyrococcus furiosus rubredoxin in the native, iron-containing protein and in zinc- and mercury-substituted proteins. For all samples studied, the spectra have been interpreted in terms of a single shell of coordinated sulfur, with approximately tetrahedral coordination. For the native protein we obtain Fe-S bond-lengths of 2.29 and 2.33 A for oxidized and reduced proteins, respectively. These values are in excellent agreement with those previously obtained from X-ray crystallography. The metal-substituted rubredoxins possess metal-sulfur bond lengths of 2.34 and 2.54 A for the zinc- and mercury-substituted proteins, respectively.
Using deep-learning to automatically detect epileptic seizure in a wearable device

Epilepsy is a chronic disease which affects over 40 million people worldwide. The majority of these people are able to keep the symptoms under control; however, some still suffer with frequent seizures. As such, methods for detecting the occurrences of these episodes are required. The main source of data for detecting seizures is the electroencephalogram (EEG) signal. Machine Learning algorithms, for instance, can be applied to these signals to perform such a task. Moreover, Deep Learning techniques, such as Convolutional Neural Networks, have been proven to be effective to the problem with some results achieving an accuracy of 93% or more. Nevertheless, there is little exploration towards applying these types of networks in an embedded system environment and even less with the aim of building a wearable device, which could forewarn a patient of impeding episodes. This article presents the steps taken to evaluate an architecture that can be applied to intra-patient data considering a low resource environment. We consider the application of said architecture in an embedded platform with estimates for time and memory consumption to build a wearable device. An architectural overview of such a device is also discussed.
Multimodal Deep Fusion Network for Visibility Assessment With a Small Training Dataset

Visibility is a measure of the transparency of the atmosphere, which is an important factor for road, air, and water transportation safety. Recently, features extracted from convolutional neural networks (CNNs) have obtained state-of-the-art results for the estimation of the visibility range for images of foggy weather. However, existing CNN-based approaches have only adopted visible images as observational data. Unlike these previous studies, in this paper, visible–infrared image pairs are used to estimate the visibility range. A novel multimodal deep fusion architecture based on a CNN is then proposed to learn the robust joint features of the two sensor modalities. Our network architecture is composed of two integrated residual network processing streams and one CNN stream, which are connected in parallel. In addition, we construct a visible–infrared multimodal dataset for various fog densities and label the visibility range. We then compare our proposed method with conventional deep-learning-based approaches and analyze the contributions of various observational and classical deep fusion models to the classification of the visibility range. The experimental results demonstrate that both accuracy and robustness can be strongly enhanced using the proposed method, especially for small training datasets.
COVID-19: Accelerating Digital Transformation for University's Research Administration

This study examined how COVID-19 has accelerated the digital transformation of research administration at the State Islamic University of Syarif Hidayatullah (UIN Jakarta). A mixed-method embedded approach was used in this study, with data obtained from a web-based survey and also focus group discussions (FGDs). The survey involved 137 grant recipients from UIN Jakarta. The FGDs included eight research administrators from the Center of Research and Publication (Puslitpen) UIN Jakarta, who six of them are also the researchers for this report. The data revealed: 1) COVID-19 has accelerated the use of digital technology in research administration 2) 67.9 percent of grant recipients stated that it was easy to use the national integrated research management system for Islamic Higher Education (Litapdimas app), but there are still some issues that need to be improved; 3) 94.4 percent of the grant recipients found that the online service provided by the center was very helpful and they suggested to continue its use. As COVID-19 is a worldwide issue, these findings are also useful for other research administration offices to learn how different forms of digital technology can improve the delivery of research administration services.
Convolutional Neural Networks for Detection of Storage Disorders on ‘Abbé Fétel’ pears

Image processing has recently been adopted for fruit damage detection in post-harvest operations. Through the implementation of hard-coded feature extraction algorithms, high accuracy has been found. The present study tested the fast and operational convolution neural networks with “YOLO v3” architecture using the online platform Supervise.ly to detect on pear fruit ‘Abbé Fétel’ physiological disorders such as superficial scald. Two different models were trained: I) one to detect the individual pear fruits within the batches; II) one to detect superficial scald or senescence scald on pear skin. Preliminary statistics show that the model to count the fruit inside the batches reaches an accuracy of 64.70% with a 0.5 of Intersection of Units. The second one has less accuracy (up to 20% of true positive) but maintains a good level of average precision (0.6) with different confidence thresholds (0.4 and 0.2). Further research is needed to improve the accuracy of both models and to map quality pre- and post-harvest. These results will help the packing house to manage fruit batches and to ensure good fruit quality for consumers.
Tuning Thermoelectric Properties of Misfit Layered Cobaltites by Chemically Induced Strain

We have applied density functional theory and high-resolution transmission electron microscopy to investigate the relationship between chemically induced strain and charge transfer on the structural, electronic, vibrational, and thermoelectric properties of misfit layered cobaltites (M2CoO3)0.6CoO2 (M = Mg, Ca, Sr, Ba). The electron and phonon density of states are analyzed and rationalized by accounting for the effects of internal strain and charge transfer, and lay the foundations to disentangle these effects on a promising thermoelectric oxide material. We found that the choice of different interlayer cations has little effect on the magnetic properties, but it generates internal strain between the rock-salt M2CoO3 and hexagonal CoO2 subsystems, changing the hybridization of the cations with the environment. Increasing the mass of the cation leads to decoupling of the vibrations between the rock-salt and CoO2 subsystems so that heavier cations are predicted to enhance phonon scattering. On the other ha.
Abnormal glow discharges in Ar: experiments and models

We review measured and calculated voltages and cathode-fall thicknesses against current and electrode separation for parallel-plane Ar glow discharges in the above-normal, or abnormal, current range. We consider dc and pulsed discharges, but not radio-frequency discharges. Reasons for the very high degree of variability among experiments and models are reviewed and suggestions are made for improving the reliability of both. Variations of a factor of two in experimental cathode-fall voltages at a fixed normalized current density are attributed to differences in the cathode emission properties, varying electrode separation, and effects of discharge tube walls. We explore a wide range of normalized current densities, models of the ionization coefficient against E/n, and the effective electron yields per positive ion reaching the cathode using simple scaling laws. Ionization source terms for local-field and non-local-field models are compared and used as the basis for a simplified non-local model. We discuss the present author's recommendation that, because of current large uncertainties in the electron yield per ion for practical cathodes, the yield used in cathode-fall models should be treated as an unknown. Recent models successfully predict the behaviour of transient abnormal discharges on the microsecond time scale.
HealthSOS: Real-Time Health Monitoring System for Stroke Prognostics

Electroencephalography (EEG) is immediate and sensitive to cortical impairment resulting from ischemic stroke and is considered as the potential predictive tool of stroke onset, and post-stroke clinical management. Brainwave monitoring outside the heavily equipped clinical environment demands a low-cost, portable, and wearable EEG system. This study aims to assess the feasibility of using an ambulatory EEG system to classify the stroke patient group with neurological changes due to ischemic stroke and the control healthy adult group. HealthSOS, a real-time health monitoring system for stroke prognostics, is proposed here, which consists of an eye-mask embedded portable EEG device, data analytics, and medical ontology based health advisor service. This system was investigated with 37 stroke patients (mean age 71.6 years, 61% male) admitted in the emergency unit of a hospital and 36 healthy elderly volunteers (mean age 76 years, 28% male). EEG was recorded in resting-state using the portable device with frontal cortical electrodes (Fp1, Fp2) embedded in an eye-mask within 120 h after the onset of symptoms of ischemic stroke (confirmed clinically). The EEG data acquisition of the left and right brain hemispheres was done for at least 15 minutes in the awake resting state while subjects laid down on the bed. The statistical result shows that the revised brain symmetry index (rsBSI), the delta-alpha ratio, and the delta-theta ratio of the stroke group differ significantly from those of the healthy control group. In the machine learning analysis, the support vector machine (SVM) model shows the highest accuracy (Overall accuracy: 92%) and the highest Gini coefficient (95%) in classification performance. This study will be useful for early stroke prognostics and the management of post-stroke treatment.
On the Reliability and Validity of Digital Competence Evaluation in Higher Education

This paper aims to provide an overview of the reliability and validity of the instruments used to measure digital competence in higher education. Three research questions were addressed about: 1) the demographic and methodological characteristics of the literature, 2) typical assessments and reporting practices, and 3) the association between studies’ characteristics and their reporting practices. Through a systematic mapping study, 60 primary studies published in the period 2018-2020 were analyzed and classified. The results indicated that most of the studies originated in Europe and Asia; they focused on university students from Social Sciences; they administered instruments composed of cognitive items for diagnosing digital competence; they were published in journals not indexed by the Journal Citation Report; and about 50% did not report on the reliability and validity of the instrument used. Studies published in low-quality journals or focused on administration only are more susceptible to not reporting these psychometric assessments.