Miracle-dz/UniFAR-FTUs huggingface dataset:
Turret-Electrode Antenna for UHF PD Measurement in Power Transformers - Part I: Introduction and Design

This contribution is the first part of a two-part series which deals with proposing a new type of UHF probe to overcome problems of UHF partial discharge (PD) measurement in power transformers. In the first part, the main obstacles of using the UHF technique in power transformers including problems of installation, unfeasibility of calibration, low sensitivity, etc. are addressed. Moreover, a new type of UHF sensor called the “Turret-Electrode” antenna is presented to deal with the above obstacles. In the new antenna, the transformer turrets employed as the housing of open waveguide antenna (horn antenna with zero flare angle), while an electrode inside of the turret is designed to capture the electromagnetic (EM) waves produced by the PD signals inside of the transformer tank. The significant characteristics of the proposed antenna are investigated in detail and a prototype of the antenna is investigated via practical measurements. The results show the antenna parameters are quite appropriate for online PD measurement. The second part of the paper shows how the proposed antenna can resolve the problems pertaining to the UHF PD measurement in power transformers.
Effects of different materials on dielectric barrier discharge characteristic

Dielectric barrier material is an important factor that affects dielectric barrier discharge. In this paper, a rod - plate electrode structure is used to compare the discharge characteristics of metal, PTFE, water and ethanol. Under the condition of low pressure, when the medium is metal, the discharge obviously shrinks into arc discharge. When the medium is PTFE, the discharge phenomenon is the coexistence of filamentous channel and diffuse discharge. When the medium is water, there are obviously two kinds of contraction discharge channels in the discharge area. In this paper, the cause of charge transport on the surface of different media is analyzed. In addition, this paper further explores the influence of the conductivity of water on the discharge characteristics. The research shows that the higher the conductivity of the hydroelectric pole is, the higher the discharge conduction current will be and the worse the discharge uniformity will be.
Design and Evaluation of a Wearable Lower Limb Robotic Exoskeleton for Power Assistance

Design, control and evaluation of a lower limb wearable robotic exoskeleton for power assistance are presented in the paper. The proposed four degree-of-freedom robotic exoskeleton, an active flexion/extension and a passive abduction/adduction rotation at each hip joint, is characterized with complying with the swinging motion of lower limbs as close as possible. To perform power assistance on walking, the linear extended state observer (LESO) based controllers were designed for the walking assistance. Finally, the experiments were conducted to validate the prototype of lower limb robotic exoskeleton. The associated evaluations for the walking assistance were also investigated using the motion captured system and EMG signal.
Review: Nucleation in solutions revisited

Existing and new results in nucleation in solutions are outlined from a unified point of view. The thermodynamics of the process is considered and expressions are given for the supersaturation, the nucleation work and the size of the nucleus in homogeneous or heterogeneous nucleation. It is shown how the nucleation theorem can be used for a model‐independent determination of the nucleus size from experimental data. The mechanism and kinetics of nucleation are also considered and formulae are presented for the supersaturation dependence of the monomer attachment frequency and the stationary rate of homogeneous or heterogeneous nucleation. General expressions for the induction time and the critical supersaturation ratio for crystallization are given. An approximate formula is derived for estimating the width of the metastable zone with the help of data for the solubility of the substance crystallized. Existing experimental data are used for verification of the validity of some of the presented theoretical dependences.
Research on Propagation Characteristics of Partial Discharge Signal in Cable Joint Based on CST

The purpose of this article is to study the propagation characteristics of cable joints. Using partial discharge signals to reliably locate the fault location is one of the popular cable fault detection methods. However, the partial discharge signal is prone to attenuation and distortion when it propagates in a power cable or a cable joint. From the perspective of scattering parameters, this paper first used the CST software to calculate the scattering parameters of the cable joint. Then, using the relationship between transmission line characteristic parameters and scattering parameters, the attenuation constant and phase velocity were obtained. Finally, in order to ensure the reliability of the simulation results, the vector network analyzer was used to measure the scattering parameters of the actual cable joint. From the analysis of the results, it could be seen that due to the complexity of the cable structure, the attenuation constant increases with frequency and is accompanied by spike-like jitter. The phase velocity has obvious fluctuation in the low frequency range, and basically remains unchanged in the high frequency range.
Application of Convolutional Neural Network Transfer Learning in Partial Discharge Pattern Recognition

Due to the lack of expert experience and the shortcomings of high blindness, the traditional partial discharge feature extraction method of gas insulated switchgear (GIS) has an impact on the accuracy of pattern recognition; convolutional neural network emerged in recent years has the ability to adaptively extract features, but training a network with better performance needs to increase the network depth on the one hand, and more supportive data on the other. Therefore, this paper propose a GIS partial discharge pattern recognition method based on transfer learning of three pre-trained network models (VGG, InceptionV3, and Resnet50) under the small data set. And the feature extracted by the network are applied to SVM classifier which performs well on a small data set. Realizing the combination of deep learning and traditional machine learning. Experimental result shows that this method can effectively improve the accuracy of GIS partial discharge pattern recognition.
Self-Recovery Tolerance Latch Design Based on the Radiation Mechanism

Digital latches are becoming more sensitive to Multiple-Node Upsets (MNUs) due to lower supply voltage and higher integration density of devices. The hardening technique based on using multiple C-Elements (CEs) (the CE acts as an inverter if its inputs are equal to each other, otherwise holds the high-impedance output) has been used to propose the MNU tolerance latches. However, it brings important hardware overheads. Based on the radiation mechanism, this paper proposes an MNU tolerance latch with self-recovery properties to prevent Singe Node Upset (SNU) and MNU propagation in the feedback loops, and providing the smallest overheads in terms of power, delay, rising time <inline-formula> <tex-math notation="LaTeX">$\text{t}_{\mathrm {r}}$ </tex-math></inline-formula>, falling time <inline-formula> <tex-math notation="LaTeX">$\text{t}_{\mathrm {f}}$ </tex-math></inline-formula> and Power-Delay-Area-Product (PDAP) metric compared with the existing MNU tolerance latches.
Feature extraction method of analog circuit based on deep learning

The signals of early fault analog circuits are weak, and it is difficult to extract the depth and essential features by using the traditional extraction method. In addition, the extracted features are easy to overlap. To solve these problems, a novel optimization deep belief network (DBN) for extracting circuit features is proposed in this paper. which uses particle swarm optimization to optimize the number of neurons in the hidden layer, so as to determine the optimal network structure. At the same time, it can overcome the deficiency that many attempts should be paid to set the number of neurons according to experience, and improve the effectiveness of feature extraction furtherly. This method digs the feature information from the original data directly, and it has strong characterization ability, which overcomes the dependence of the traditional feature extraction method on the knowledge of signal processing. So the project is easy to realize. The validity of this method was verified by using the simulation of the Sallen-Key filter circuit. The results confirm that the characteristics of the different fault categories extracted by this method have better separation. The redundancy of features was reduced, besides, the accuracy of subsequent health state monitoring and residual life prediction was improved.
Performance of an Axial Flux Hybrid Excitation Motor with SMC for HEVs

Axial flux permanent magnet (AFPM) motors are used as drive motors for hybrid vehicles due to their high torque/power density and high efficiency. This technology makes the use of soft magnetic composites (SMC) attractive for both manufacturing and performance. SMC has the characteristics of low eddy current loss, convenient processing, low magnetic permeability, low mechanical strength. An additional dc magnetic field excitation coil is added to the motor to overcome the shortcomings of the low torque density of the SMC, which ensures the high performance of the motor. The performance of axial flux hybrid excitation motor (AFHEM) is evaluated based on finite element analysis (FEA) method. The analysis results show that the proposed motor is able to achieve high torque density and wide speed range, which are suitable for hybrid electric vehicles.
PSPU-Net for Automatic Short Axis Cine MRI Segmentation of Left and Right Ventricles

Characterization of the heart anatomy and function is mostly done with magnetic resonance image cine series. To achieve a correct characterization, the volume of the right and left ventricle need to be segmented, which is a timeconsuming task. We propose a new convolutional neural network architecture that combines U-net with PSP modules (PSPU-net) for the segmentation of left and right ventricle cavities and left ventricle myocardium in the diastolic frame of short-axis cine MRI images and compare its results against a classic 3D U-net architecture. We used a dataset containing 399 cases in total. The results showed higher quality results in both segmentation and final volume estimation for a test set of 99 cases in the case of the PSPU-net, with global dice metrics of 0.910 and median absolute relative errors in volume estimations of 0.026 and 0.039 for the left ventricle cavity and myocardium and 0.051 for the right ventricles cavity.
Single-Modal Video Analysis of Personality Traits using Low-Level Visual Features

The ability to analyze the first impression of a person automatically enables novel applications in human-computer interaction and other areas. A person’s first impression can decide about a positive or negative outcome in different daily-life situations. The human brain is able to get a picture of the counterpart’s personality at short notice. The main aim of this paper is to show how a system based on a standard Convolutional Neural Network (CNN) as well as a 3D-CNN architecture, can be built to solve a multi-label regression task using only visual low-level features. This paper investigates how various pre-processing methods, such as face-extraction and data-augmentation, influence the predicted personality confidences. Furthermore, it explores different training strategies and optimization techniques e.g. regularization in order to improve the model performance. The results of this paper demonstrate an image-based as well as a time-sequence-based system to predict the Big-Five personality dimensions of humans in short video sequences by solving a multi-label regression task. The approaches reach an overall accuracy of 0.891 by using visual features only.
Machine Learning aided Fiber-Optical System for Liver Cancer Diagnosis in Minimally Invasive Surgical Interventions

A flexible fibre optical probe is implemented to record the parameters of the endogenous fluorescence during minimally invasive interventions in patients with cancers of hepatoduodenal area. Using machine learning techniques, the obtained spectra are classified to indicate cancerous or healthy tissue. For this, a set of different binary classifiers has been trained and tested. The classifiers showing best performance for this task are identified.
Solubility of Cellulose in NaOH/Urea Aqueous Solution

NaOH/urea aqueous solution as solvent of cellulose including cotton linter, bagasse, alkali-soluble cellulose and Bemliese® was studied by solubility analysis, viscometry and light scattering. The addition of 2—4 wt% urea significantly improved the solubility of cellulose in 6—8 wt% NaOH aqueous solutions, and moderate urea plays a role in improving solubility and avoiding the formation of cellulose gel. Celluloses I with viscosity-average molecular weight (Mη) of 6.7×104 were completely dissolved in 6 wt% NaOH/4 wt% urea aqueous solution. Cellulose dissolved in 6 wt% NaOH/4 wt% urea aqueous solution degraded slowly with storage time, and Mη of cellulose in the solution decreased 20% after storage for 100 days. The stability of cellulose solution was higher than cellulose cuoxam. A regenerated cellulose membrane having the tensile strength of 88.7MPa and breaking elongation of 11% was successfully prepared by coagulating 4 wt% Bemliese® solution in 6 wt% NaOH/4 wt% urea with 5 wt% CaCl2 then 1.3 wt% HCl aqueous solution as coagulate at 20°C.
A Novel Scheme for Classification of Motor Imagery Signal using Stockwell Transform of CSP and CNN Model

The classification of motor imagery (MI) task has gained lots of attention in brain-computer interface (BCI) field and numerous studies have proposed various methods for MI classification. In recent years, time-frequency analysis method and convolutional neural network (CNN) have been combined to extract more complex features and exhibit superiors performance in comparison with conventional methods. In this paper, we presented a new classification method based on Stockwell Transform of common spatial pattern and CNN. The proposed framework with two different activation functions (ReLU and ELU) was compared with CSP-SVM and CSP-CNN methods by validation on BCI competition IV dataset I calibration data to assess the performance of the proposed method. We achieved an average classification accuracy of 81.22% (ReLU) and 81.34% (ELU), which outperformed the conventional CSP-SVM (76.45%) and CSP-CNN methods (69.29%). Further interrogation showed that the higher accuracy was attributed to improved performance in the subjects with lower detection rate by CSP-SVM. In sum, our results showed that the proposed framework can be applied to MI-BCI systems with superior performance, leading new insights towards more practical MI-based BCI applications.
The Strategic Use of Nudging and Behavioural Approaches in Public Health Policy during the Coronavirus Crisis

Nudging and behavioural insights need to be taken into account in strategic action planning for suppression and reduction of pandemic effects. This paper focuses on a few selected areas of significant importance to getting SAR-CoV2 under control in both the non-pharmaceutical and the pharmaceutical phase, i.e. after the development of an effective vaccine.
Characterization of the interaction between RIN13 (RPM1-Interacting13) and SUMO proteins in Arabidopsis thaliana.

Small Ubiquitin-related MOdifier (SUMO) proteins can be found in many organisms, including A. thaliana, which possesses 9 SUMO genes. SUMO binds to various target proteins in a reversible reaction called SUMOylation. SUMOylation participates in transcription, chromosome organization, proteins localizations and stress responses. Our study showed that RIN13 (RPM1-Interacting13/At2g20310) is a target of SUMOylation, which was initially found by interaction between this protein and AtSCE1a (E2). Recent report showed that overexpression of RIN13 enhanced the resistance to pathogen without inducing hypersensitive response. However, the molecular interaction between RIN13 and SUMO proteins and its significance have not been studied yet. Thus, our study aimed to characterize the Interaction between RIN13 and SUMO proteins in A. thaliana. The result showed an isoform-specific SUMOylation between RIN13 and SUMO proteins. RIN13 is SUMOylated by SUMO1, 2, 3, and 5. Though expressed ubiquitously in A.thaliana, fluorescence microscopy showed that RIN13 localizes subcellularly in the nuclear body. Moreover, complete abolishment of SUMOylation with inactive E2 suggests the exclusion of RIN13 from nuclear body. These results showed that SUMOylation affected RIN13 localization, and indirectly influenced its interaction to other proteins and putative function. This paper presents evidence of RIN13 SUMOylation. Furthermore, RIN13 function in pathogenic resistance is shown to be supported by SUMOylation. Thus, this study enhanced the understanding of SUMO in plants and served as reference to molecular studies concerning post-translational modification of SUMO.
Asset Pricing Model without Consumption Data: An Empirical Study of Pacific Basin Equity Markets

This study examines whether the IAPM can explain the variation in Pacific Basin equity markets. We use an asset-pricing model without consumption data, which is developed by Campbell [5], to avoid the measurement errors of consumption data in these markets. The overidentifying restrictions of the IAPM are not rejected at a conventional significance level; however, the null hypothesis of the IAPM is marginally rejected against the model that include a constant term to each market to account for the cross-sectional variations. This result implies that Campbell’s IAPM may explain some behaviors of the Pacific Basin equity markets. However, there must exist some other risk factors omitted in our formulation, especially the news of state variables on future world wealth. There also exist other potential sources of misspecification that might explain our results, such as the omission of transaction cost, poor proxy of world market portfolio, heterogeneity of consumers, and invalid assumption of full market integration.
Implementing Enterprise VoIP Deployment

Voice over Internet Protocol (VoIP) is perceived as the best example of collaboration technology in today’s telecommunication space. There have being a paradigm shift from regular legacy telephone systems to VoIP over the last decade by most telecommunication and multinational companies globally. One of the huge benefit of VoIP is noticeable reduction in international call rates. In addition to the potential cost savings, VoIP offers the prospect of integrating voice with data and video applications, this goes a long way in increasing workers’ productivity within organizations. Many organizations are already embracing VoIP as replacement for their legacy PBX, others are integrating the existing PBX system with VoIP. This write up will focus on deployment of PBX-VoIP converged solution in enterprise organizations, where cost saving is one of the primary objectives.
An analysis of farmers’ perceptions of the impact of technology on farm

The study analyses Scottish livestock farmers’ perceptions of the impact of using smart technologies on their farms. The data used in this study were collected through a large scale survey of 441 Scottish agricultural holdings in 2016, which investigated farmers’ uptake of novel technologies. The analysis focusses on the factors influencing farmers’ perceptions of how useful to their businesses some of the technologies applied on farm are, in this case, electronic identification (EID) reading equipment for sheep or cattle management (e.g. handheld EID tag reader or EID enabled crates and pens) and precision agriculture (management tools aimed at continuous automatic monitoring of each animal in real time recording). We use structural equation modelling (SEM), a statistical method used to test hypotheses and assess the strength of the causal relationships between variables, i.e., how much these variables influence one another. In our case, we use SEM to test the effect of various factors on perceptions of the impact of using technologies on the farm business. We perform model estimation with the Diagonally Weighted Least Squares method using the statistical package Lisrel 8.80. The model has a good fit according to the measures of absolute, incremental and parsimonious fit. The model explains 62 per cent of the variance in perceptions of the impact of using technologies on the farm business. Results indicate that significant influences on the perceived impact of using technologies on the farm business include age, gender, education, profit orientation, farm labour, perceived usefulness of information on technologies, attitudes towards technological uptake, and perceived difficulty to uptake technologies.
Incorporating Uncertainty in Analyzing the Financial Feasibility of Solar Project in India*

The recent drops in tariffs and the prevalent construction delay become concerns harming the financial sustainability of India’s solar projects. However, the conventional financial assessment method does not adequately incorporate those risks. This paper suggests a way to incorporate construction delay risk of solar projects in the financial analysis. The paper identifies the key risk factors affecting the financial sustainability of the solar projects in India. To assess the identified risks, the paper builds a hypothetical solar project and runs Monte Carlo simulation by using @Risk Software. The financial and sensitivity analysis portray the impacts of construction delays on the key financial indicators, especially net present value and payback periods.
Effect of the chain mobility of polymeric reversed-phase stationary phases on polypeptide retention

The resolution of polypeptides in reversed-phase chromatography can be improved by inducing conformational changes in the solute. Protein unfolding can be achieved by an increased accessability of the hydrophobic stationary phase. We elucidated how far results from CP/MSS solid-state NMR relaxation measurements could be correlated with chromatographic data in terms of protein and peptide retention. The retentivity and denaturation potential of reversed stationary phases is controlled not only by their hydrophobicity but also by their ligand mobility and steric properties
Photocatalytic oxygen isotopic exchange between oxygen molecule and the lattice oxygen of titanium dioxide prepared from titanium hydroxide

Photoinduced isotopic exchange between /sup 18/O/sub 2/ and lattice oxygen was studied at room temperature on titanium hydroxide calcined at various temperatures. Although the sample calcined below 200/sup 0/C shows little activity for the oxygen isotope exchange, the sample calcined at around 500/sup 0/C shows much higher activity than any commercial TiO/sub 2/. The activity, however, declined sharply with increasing calcination temperature beyond 600/sup 0/C and drops to almost zero when the sample is calcined above 750/sup 0/C. No oxygen isotopic exchange occurs in the dark at room temperature. The photocatalytic activity of calcined titanium hydroxide for the oxidation of ethane correlates well with the activity for oxygen isotope exchange.