Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Visible-Light Photoactivity of CuWO4 through a Surface-Deposited CuO

Several papers have shown that CuWO4 is active under visible light for water oxidation at an applied potential bias and for organic degradation in an aerated aqueous suspension. In this work, we report that the observed reduction of O2 on the irradiated CuWO4 is a multielectron transfer process with the formation of H2O2. More importantly, the surface modification of CuWO4 with 1.8 wt % of CuO can increase the activity by approximately 9 times under UV light and by 5 times under visible light, for phenol degradation in aerated aqueous suspension. The catalyst was prepared by a hydrothermal reaction between Cu(NO3)2 and Na2WO4, followed by thermal treatment at 773 K. High-resolution transmission electron microscopy revealed that triclinic CuWO4 (40 nm) was covered by monoclinic CuO (4 nm). Through a combination of photo- and electrochemical measurement, a plausible mechanism responsible for the activity enhancement is proposed, involving an interfacial electron transfer from CuO to CuWO4 and an interfacial.
Vulnerable Code Detection Using Software Metrics and Machine Learning

Software metrics are widely-used indicators of software quality and several studies have shown that such metrics can be used to estimate the presence of vulnerabilities in the code. In this paper, we present a comprehensive experiment to study how effective software metrics can be to distinguish the vulnerable code units from the non-vulnerable ones. To this end, we use several machine learning algorithms (Random Forest, Extreme Boosting, Decision Tree, SVM Linear, and SVM Radial) to extract vulnerability-related knowledge from software metrics collected from the source code of several representative software projects developed in C/C++ (Mozilla Firefox, Linux Kernel, Apache HTTPd, Xen, and Glibc). We consider different combinations of software metrics and diverse application scenarios with different security concerns (e.g., highly critical or non-critical systems). This experiment contributes to understanding whether software metrics can effectively be used to distinguish vulnerable code units in different application scenarios, and how can machine learning algorithms help in this regard. The main observation is that using machine learning algorithms on top of software metrics helps to indicate vulnerable code units with a relatively high level of confidence for security-critical software systems (where the focus is on detecting the maximum number of vulnerabilities, even if false positives are reported), but they are not helpful for low-critical or non-critical systems due to the high number of false positives (that bring an additional development cost frequently not affordable).
Evaluating Entrepreneurial Perceptions on Blended Learning

Context: The blended learning (or b-learning), has grown over the years. However, few studies have been carried out on the effectiveness of entrepreneurial training in the blended modality. Objective: The general objective of the study is to evaluate the effectiveness of the learning experience in the blended modality experienced by adults for the development of entrepreneurial skills. Method: This work conducted a Digital Ethnography study by combining qualitative research techniques to capture the evidence of the participants’ perceptions in the blended modality. The survey was conducted with adult entrepreneurs. Results: At the end of the study, it was possible to identify aspects of improvements in the usability of the platform, reflections on the teaching process, identification of points that reinforce the positive experience for the participants and a reduction in dropout from 40% of face-to-face education to 6.67% (1st class) and 4.35% (2nd class), and 0% (3rd class) in blended education. Conclusions: If proper engagement mechanisms are used, blended learning is an effective and efficient educational instrument for adult entrepreneurs, which usually have busy daily routines and need flexibility in their study schedule.
CVT: A Crowdsourcing Video Transcoding Scheme Based on Blockchain Smart Contracts

Streaming media has been largely used by millions of users every day. The number of customers and programs, e.g., TV series, movies, and various shows, are still growing fast. However, the demand for video transcoding for various personal terminal devices results in the shortage of computing resources and the prolongation of processing delay in centralized video transcoding systems. To solve this issue, we propose a blockchain, especially, smart contract based scheme that can achieve decentralized and on-demand crowdsourcing for video transcoding, which remarkably mitigates the transcoding overhead. Specifically, our scheme consists of four key components such as employers, workers, task allocation, and payment. An employer initializes the smart contract, releases the task, and initiates the smart contract. Workers bid for the task, and the successful bidder will obtain the task and execute the task. The task allocation mechanism and the payment mechanism can guarantee the profits of both and encourage both as well. Moreover, the smart contract consists of the bidding contract and the task execution contract. The extensive analysis of our proposed scheme justified the feasibility, security for defending against typical threats, applicability in realistic situations, and portability for most multimedia such as videos and audios.
Induction Machines Efficiency Mapping

The paper presents a simple yet powerful method of efficiency mapping of induction machines. The whole calculation process is based on knowledge of induction machine equivalent circuit parameters and approximation of losses based on known dependencies on speed, current and magnetic flux linkage. The proposed method is then verified on two samples of electric machines – large industrial induction machine and a sample of a traction induction machine.
Comparisons Between Text-Only and Multimedia Tweets on User Engagement

Having highly engaged followers on social media allows us to spread information, seek feedback, and promote a sense of community efficiently. Crafting engaging posts, however, requires careful thoughts, creativity, and communication skills. This research studied tweets and explored the effect of content types on user engagement. More specifically, we compared the number of likes and retweets between text-only and multimedia tweets. We analyzed four Twitter accounts relevant to the City of Edmonton, Canada, and performed negative binomial regressions to model the expected count of likes and retweets based on accounts, content types, and their interaction. The results showed that multimedia content increased engagement in two of the four accounts but did not change engagement significantly in the other two. In other words, multimedia content had a positive or neutral effect on user engagement, depending on accounts. Our analysis also showed the effectiveness of well-written texts in attracting the attention of users. Tweets, by design, are text-oriented, and posting multimedia content may help, but is not a necessary condition to engage with followers effectively on Twitter.
Evaluation of Remote Crane Operation with an Intuitive Tablet Interface and Boom Tip Control

Remote crane operation has been a subject of research for several years. So far, there is a lack of intuitive human-machine interfaces that resolve present incompatibilities between operator orientation, the crane’s joint movements, and the resulting load movement. This results in poor interface quality and thus in unnecessary time losses, errors, and mental stress. To overcome this gap, an intuitive app interface is developed and applied on a loader crane with boom tip control. An evaluation study with 56 test persons in two experience groups is used to assess the interface’s usability in terms of effectiveness, efficiency, and satisfaction. Results show that the human-machine interface presented here reduces errors and increases the operator’s satisfaction in both user groups. Furthermore, unexperienced crane operators in particular profit from better load placement accuracy and greater time efficiency. The intuitive app-based human-machine interface can therefore improve remote crane operation significantly.
Numerical Analysis on Leakage Current of Arrester in Distribution Switchgear Under Multi-interference Conditions

It is usually difficult to accurately measure the resistive current of zinc oxide distribution arrester due to the influence factors such as interphase interference, voltage fluctuation and power grid harmonic. In this paper, the leakage current of zinc oxide arrester in 10 kV switchgear under multi-interference condition is numerically investigated. Firstly, the partial capacitances are calculated by three-dimensional finite element method and substituted into the circuit of resistance-capacitance coupled model to clarify the influence of the interphase interference. Numerical results of the leakage current extracted from each phase of arresters show that the interphase interference is low compared with the natural capacitance of zinc oxide arrester. Then, by varying the power grid voltage and power harmonic, the influence rules on the leakage current are obtained. Finally, the simulation of steep wave model at microsecond level for zinc oxide arrester is performed, and the results show that the terminal voltage and leakage current under lightning surge current return to the stable states after 0.2 milliseconds. Therefore, a delay function can be used to filter out the false alarm caused by lightning stroke. The results can provide a reference for the leakage current monitoring of medium-voltage zinc oxide arresters.
Methods of smoothing active power fluctuations of photovoltaic plants using the full size converter based variable speed pumped storage unit

This paper proposes a method of smoothing photovoltaic (PV) power fluctuations by using the full size converter based variable speed pumped storage unit (FSCVSPSU). The system models and control strategies of the FSCVSPSU are briefly discussed before analyzing its fast power response characteristics. The empirical mode decomposition (EMD) algorithm is used to decompose the PV output power to quickly and accurately determine the real-time active power compensation of the FSC-VSPSU. Simulation results indicate that the proposed methods can effectively reduce the power fluctuations of PV power by dynamically and quickly adjusting the FSC-VSPSU output power, ensuring that the final power fluctuations can meet the grid code.
Hybrid Short-Term Load Forecasting using the Hadoop MapReduce Framework

With the wide deployment of communication networks and advanced metering infrastructures (AMI) in smart grids, data from the electric demand side is growing exponentially and becomes more complex. To meet the high precision and high efficiency requirements of short-term load forecasting (STLF) in the big data era, this paper describes how an efficient hybrid STLF model can be realized through the utilization of Hadoop MapReduce framework. First, the variational mode decomposition (VMD) is used to decompose the original historical load data into a series of modal function components with different characteristics. Then, leveraging the Hadoop MapReduce architecture, distributed deep belief networks (DBNs) are built to predict each load component generated by the VMD. To further improve the prediction accuracy, the mutual information theory is introduced to select matching factors from the historical load, temperature and date type as the input variables. Test results demonstrate the efficiency of the proposed approach.
A Deep Bayesian Ensembling Framework for COVID-19 Detection using Chest CT Images

The chest computed tomography (CT) images have been used for COVID-19 detection. Automating the process of analyzing can save great amount of time and energy. In this paper a deep bayesian ensembling framework is proposed for automatic detection of COVID-19 cases using the chest CT scans. Data augmentation is applied to increase the size and quality of training data available. Transfer learning is utilized to extract informative features. The extracted features are used to train the three different bayesian classifiers. The uncertainty of the neural network predictions is estimated by anchored, unconstrained and regularized bayesian ensembling methods. The reliability of predictions is then delineated. The epistemic and aleatoric uncertainties are estimated and different bayesian classifiers are compared from different perspectives. We use a small dataset containing only 275 CT images of positive COVID-19 cases. The results sounds promising and they can be improved in the future, as the performance of deep neural networks is reliant to big datasets. Prediction accuracy and predictive uncertainty estimates for unseen chest CT images indicate that the deep bayesian ensembling is a promising framework for COVID-19 detection.
A Study on Human-Machine Cooperation on Decision Level

In the past decade, remarkable research has been done on human-machine cooperation to generate synergies and mutual benefits. However, most research so far only considers the control level of interaction with concepts like haptic shared control. This paper focuses on the emerging research on human-machine cooperation on higher levels of interaction to tackle more complex challenges. Therefore, we first introduce a generalized level model based on established models to define our research emphasis on emancipated human-machine cooperation on all levels. Second, the design and results of a study on human-machine cooperation on decision level are presented. We examine the negotiation behavior of humans in a scenario with discrete decision options and a deadline. The results indicate the validity of a previously proposed model based on negotiation theory to describe the observed human behavior. Additionally, the observed influencing factors on the negotiation behavior are crucial for a proper automation design: adaptation and identification methods are required to enable the automation to take part in an emancipated negotiation with a human.
A Hierarchical State of Charge Estimation Method for Lithium-ion Batteries via XGBoost and Kalman Filter

Different from previous data-driven methods for lithium-ion battery State-of-Charge (SoC) estimation, this paper aims to develop a hierarchical SoC estimation method to address the data dependency issue and measurement noise interferences. In the off-line training layer, aging-aware features are extracted to improve SoC estimation accuracy throughout the entire battery life cycle. Extreme gradient boosting (XGBoost) is introduced to map the relationship between the extracted features and SoC for its strong nonlinear fitting ability. In the on-line estimation layer, Ampere-hour integral method is utilized to provide SoC reference to guarantee the stability of the proposed method. Meanwhile, to suppress the measurement noise, we adopt Kalman filter to correct the SoC value estimated by XGBoost. The superiority of the proposed method is proved under the random walk discharging experiment by comparing with the results of XGBoost, i.e., without Kalman filter. The proposed method improved the accuracy of lithium-ion battery SoC by 4% to 10%.
Field Measurement and Analysis on The Characteristics of Transient Enclosure Voltage and Transient Ground Potential Rise in UHV GIS Substation

During the switching operation of GIS Substation, the Transient Enclosure Voltage (TEV)and the Transient Ground Potential Rise (TGPR)will occur, which will have a negative impact on the safety of substation. In this paper, TEV and TGPR are measured and analyzed in a 750kV substation at different locations of GIS, under three types of operation of disconnect switch and circuit breaker, include: single closing of disconnect switch, single closing of circuit breaker, three times opening and closing of circuit breaker. From the results, the maximum value of TEV and TGPR is 22.8 kV and 19.6kV, respectively, and the TGPR is higher than the TEV at many measuring points; Compared with the circuit breaker closing operation, the disconnect switch closing operation produces less electromagnetic oscillation energy, but it have longer macro-pulse duration and more breakdown times, which explained that the failure of secondary system caused by the operation of disconnect switch is more than caused by the operation of circuit breaker; Three times opening and closing operation of the circuit breaker produces stronger electromagnetic energy and more breakdown than single closing operation of the circuit breaker.
Spatial Crowdsourcing: Opportunities and Challenges in Motivating Malaysian Women’s participation in Gig Economy

Recent growth in the gig economy system has triggered Malaysia to venture into crowdsourcing initiatives including spatial crowdsourcing platforms that required specific-location of tasks to be conducted physically. This new phenomenon offered opportunities to crowd workers to improve their economy and livelihood by generating income through physical tasks such as e-hailing, food delivery, and household chores. Despite the opportunities, there are also challenges faced by crowd workers, who can be anybody including women. Women are one of the specific targeted groups identified to support Malaysia’s economic growth. To fully harness the benefits of spatial crowdsourcing trends for women, it is necessary to understand the factors that motivate them to participate in spatial crowdsourcing platform since it is still in the infancy state. This paper presents the preliminary exploratory study using semi-structured interviews to investigate the women’s motivational factors to participate in spatial crowdsourcing platforms. Thematic analysis was performed to identify the pattern of themes from the data gathered. Several intrinsic and extrinsic factors were identified, and trust emerged as one of the primary concerns among women to participate in this new phenomenon.
An Improved PEA System for Space Charge Measurement under AC/DC Voltage superimposed Impulse Voltage

As an excellent insulating material, XLPE is widely used in DC cables. AC/DC cables may withstand impulse voltages such as lightning or operating overvoltage, which can cause insulation damage. The aging of insulating materials is closely related to space charge. Therefore, it is necessary to study the space charge characteristics of polymers under AC/DC superimposed impulse voltage. In this paper, through the Pspice simulation software for calculation and analysis, a space charge measurement system under AC/DC superimposed impulse voltage is designed, which is based on the pulsed electroacoustic (PEA) method, a traditional space charge measurement technology. It is of great significance for future research on the aging of composite voltage polymers.
A Multilevel Edge Computing Architecture and Edge Generation Method of Distribution Networks

With the development of ubiquitous power Internet of things in the power system, in order to satisfy the intelligent and refined requirements of management in distribution networks and alleviate the increased communication and calculation burden of master stations, a multilevel edge computing architecture and edge generation method of distribution networks are proposed. First, a multilevel edge computing architecture of distribution networks based on edge computing and hierarchical distributed control is proposed. Then, the principles of edge generation are explained from three aspects: size, communication burden, and autonomous ability. Finally, based on community detection, a distribution network is abstracted into a weighted undirected graph model and divided into edges by the Louvain algorithm. The case analysis shows the validity and rationality of the proposed edge generation method.
Numerical Analysis of Screening Current in Noninsulated REBCO Insert Coil

In this paper, a model based on the T-A formulation and continuous approximation method have been developed. With this model, the current distribution at energized state and the central field error of the 11 T REBCO insert coil (without background field) at different ramping rates was numerically simulated and analyzed. Based on the numerical results, it can be inferred that the screening current effect and magnetic field penetration depth on each single pancake are related to its position in the coil, and the current density on the single pancake near the middle of the insert coil is more uniform. In addition, the central field error is not affected by the ramping rate of the insert coil.
Iron Losses Calculation in Non-Harmonic Power Supply

The non-harmonic input voltage waveform becomes more frequent with the increasing interest in the multiphase machines; the injecting of higher harmonics is used to the decrease of the saturation of the machine. The non-harmonic flux linkage waveform affects the total iron losses. Presented paper deals with the usage of the modified Steinmetz equation (MSE) used to the calculation of the iron losses. The analytical-numerical tool including the nonlinearity of the magnetic circuit and the dynamic BH curve is presented. Different configurations of input voltage and magnetic material properties are analyzed and the results are compared with the analytical calculation and finite element analysis.
Dual Path Nets with A New Multi-scale Backbone Architecture For Pulmonary Nodule Detection

This article mainly completed the detection of lung nodules using deep neural network and built a complete end-to-end detection system. U-net structure has significant advantages in image segmentation. We propose a dual-path network with a U-shaped structure that combines the advantages of residual learning and intensive learning for the detection of lung nodules. And on this basis, the residual module of Resnet was modified. The feature image fed into the residual module is divided into multiple blocks for convolution. Smaller residual blocks are added to each part to broaden the receptive field of each layer. So, it has the ability of extracting multi-scale feature. This network structure is called Res2net. The processed feature images are fused. We also added the SE module to focus on the relationship between feature information channels. It can strengthen the role of useful channels and suppress the output of useless channels. These changes significantly improve the detection effect of lung nodules. We used the position regression of lung nodules to return the predicted position of lung nodules and achieved good results. The detection indicator FROC is significantly better than other detection projects.