Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduced graphene oxide-polyaniline composites—synthesis, characterization and optimization for thermoelectric applications

Reduced graphene oxide (rGO) can improve the thermoelectric properties of polyaniline (PANI) by varying its concentration in composites of rGO nanosheets and PANI. The figure of merit (ZT) of rGO–PANI composites is increased with an increasing percentage of rGO (up to 50%), which is 7.5 times higher as compared to pure PANI. High resolution transmission electron microscopy (HRTEM), field emission scanning electron microscopy (FESEM) and X-ray diffraction (XRD) analyses show a uniform growth of PANI over the surface of rGO as a template, leading to a more ordered structure with high crystallinity during polymerization. Compared to pure PANI, both the electrical conductivity and thermoelectric power of the rGO–PANI composite is higher due to the increased carrier mobility as confirmed by a Hall effect measurement. Fourier transform infrared spectroscopy (FTIR), ultra-violet visible range spectroscopy (UV-Vis) and Raman spectroscopy analyses reveal that strong π–π interactions assisted the uniform distribution of PANI on the rGO nanosheets. Other strong interactions include electrostatic forces and hydrogen bonding between rGO and PANI, which provide a route for constructing highly ordered chain structures with improved thermoelectric performance of PANI. There is no significant change in the thermal conductivity of the rGO–PANI composite as compared to pure PANI, which improves the thermoelectric performance of composite.
Accurate Decentralized Application Identification via Encrypted Traffic Analysis Using Graph Neural Networks

Decentralized Applications (DApps) are increasingly developed and deployed on blockchain platforms such as Ethereum. DApp fingerprinting can identify users’ visits to specific DApps by analyzing the resulting network traffic, revealing much sensitive information about the users, such as their real identities, financial conditions and religious or political preferences. DApps deployed on the same platform usually adopt the same communication interface and similar traffic encryption settings, making the resulting traffic less discriminative. Existing encrypted traffic classification methods either require hand-crafted and fine-tuning features or suffer from low accuracy. It remains a challenging task to conduct DApp fingerprinting in an accurate and efficient way. In this paper, we present GraphDApp, a novel DApp fingerprinting method using Graph Neural Networks (GNNs). We propose a graph structure named Traffic Interaction Graph (TIG) as an information-rich representation of encrypted DApp flows, which implicitly reserves multiple dimensional features in bidirectional client-server interactions. Using TIG, we turn DApp fingerprinting into a graph classification problem and design a powerful GNN-based classifier. We collect real-world traffic datasets from 1,300 DApps with more than 169,000 flows. The experimental results show that GraphDApp is superior to the other state-of-the-art methods in terms of classification accuracy in both closed- and open-world scenarios. In addition, GraphDApp maintains its high accuracy when being applied to the traditional mobile application classification.
Uniform Nonlinear Error Model Based on Gibbs Parameter for the INS

In this paper, a uniform form for the nonlinear error model is proposed for the inertial navigation system (INS). A modified quaternion-based nonlinear error model is derived using the uniform coordinate frame for the INS. Based on the modified quaternion-based nonlinear error model, a uniform form of the nonlinear error model is proposed based on the Gibbs parameter. The uniform form can present the nonlinear error model based on low-order Rodrigues parameters or Rodrigues-like parameters for the INS. Field tests are carried out to assess the performance of the existing typical nonlinear error models.
Monitoring of Membrane Integrity Based on Electrical Measurement and Deep Learning

Membrane module integrity monitoring is essential in the water treatment process. Problems such as high cost and low sensitivity limit the development of existing detection methods. An intelligent detection method for membrane integrity based on array impedance measurement is proposed in this paper. The boundary voltage data are collected in real time through the designed electrical sensor array. A deep learning algorithm is used to analyze the degree of damage of the membrane based on the collected voltage data. Membrane integrity testing experiments are conducted for different water qualities (lake water and domestic sewage) under different aeration intensities and membrane fluxes. Detection models based on a convolutional neural network (CNN) and deep neural network (DNN) are built, and the identification results are compared with those of the average voltage method. The experiments show the following: 1) For the tests based on random samples of membranes under detection, the overall sensitivities of the CNN in the lake water experiment and domestic sewage experiment reach 97.3% and 98.5%, respectively, which are significantly higher than those of the DNN method (94.1% and 93.6%) and the average voltage method (87.4% and 77.4%). 2) When the membrane process is affected by the variation in the aeration intensity and membrane flux, the CNN still has the best robustness. Hence, the new method could stably and accurately reflect the level of membrane breakage, even mild damage to the membrane, under flow disturbance.
EFFECT OF WATER CONTENT, TEMPERATURE AND AVERAGE DROPLET SIZE ON THE SETTLING VELOCITY OF WATER-IN-OIL EMULSIONS

Water-in-oil (W/O) emulsions are complex mixtures generally found in crude oil production in reservoirs and processing equipment. Sedimentation studies of water-oil emulsions enable the analysis of the fluid dynamic behavior concerning separation of this system composed of two immiscible liquids. Gravitational settling was evaluated in this article for a model emulsion system consisting of water and a Brazilian crude oil diluted in a clear mineral oil as organic phase. The effects of water content and temperature were considered in the study of sedimentation velocity of water-oil emulsions. Water contents between 10% and 50 % and temperatures of 25, 40 and 60 oC were evaluated, and a Richardson-Zaki type correlation was obtained to calculate settling velocities as a function of the process variables investigated. Water contents and average droplet sizes were monitored at different levels in the settling equipment, thus enabling identification of the effect of these variables on the phenomena of sedimentation and coalescence of the emulsions studied. The results showed that the emulsion stability during sedimentation was governed by the emulsion water content, which yielded high settling velocities at low water contents, even when very small droplets were present. A quantitative analysis of the combined effects of drop size and droplet concentration supports the conclusion that a stronger effect is produced by the higher concentration of particles, compared with the relatively smaller effect of increasing the size of the droplets.
Composing an Assistive Control Strategy Based on Linear Bellman Combination From Estimated User's Motor Goal

In assistive control strategies, we must estimate the user's movement intentions. In previous studies, such intended motions were inferred by linearly converting muscle activities to the joint torques of an assistive robot or classifying muscle activities to identify the most likely movement from pre-designed robot motion classes. However, the assistive performances of these approaches are limited in terms of accuracy and flexibility. In this study, we propose an optimal assistive control strategy that uses estimated user movement intentions as the terminal cost function not only for generating movements for different task goals but to precisely enhance the motion with an exoskeleton robot. The optimal assistive policy is derived by blending the pre-computed optimal control laws based on the linear Bellman combination method. Coefficients that determine how to blend the control laws are derived based on low-dimensional feature values that represent the user's movement intention. To validate our proposed method, we conducted an assisted basketball-throwing task and showed that the performances of our subjects significantly improved.
Correlation Analysis and Augmentation of Samples for a Bidirectional Gate Recurrent Unit Network for the Remaining Useful Life Prediction of Bearings

The remaining useful life (RUL) prediction of bearings plays a crucial role in ensuring the safe operation of machinery and reducing costly maintenance. Sufficient degradation information contributes to the accuracy of RUL prediction, but the effective retention of degradation information is still a difficult task, especially for small samples. Thus, a novel RUL prediction method was proposed using data augmentation (DA) and a deep bidirectional gate recurrent unit (DBGRU). Initially, a feature selection method without consideration of the operating time that exploited the mutual information of different units, namely, the main factor correlation index (MFCI), was proposed to construct suitable feature input. In addition, a higher volume of training samples was generated with an effective DA technique, Mixup, and a bidirectional gate recurrent unit (BGRU) layer was utilized to further explore the subtle degradation information with the mixed data and the raw data, which also improved the robustness and generalization of the model. Ultimately, the advantage of the proposed method was demonstrated by a comparison with several state-of-the-art prediction models for the same circumstance. The test results showed that the proposed method was promising and that it had good prediction accuracy and robustness for different working conditions.
Research on Fire Detection and Image Information Processing System Based on Image Processing

In this paper, based on digital image processing technology, a series of preprocessing operations such as graying based on color components, filtering denoising, histogram equalization and so on are carried out to enhance the image effect. Then, the suspicious fire area obtained based on color component difference image is processed by threshold segmentation and edge detection. In the processing of flame image, pattern recognition technology is of great significance to the extraction of flame image. Computer vision theory is the key to fire location. With this scheme, the system can effectively eliminate the interference of distance and light intensity in fire detection, and improve the recognition accuracy.
Multimodal Hybrid Pedestrian: A Hybrid Automaton Model of Urban Pedestrian Behavior for Automated Driving Applications

For automated vehicles (AVs) to navigate safely, they must be able to anticipate and predict the behavior of pedestrians. This is particularly critical in urban driving environments where risks of collisions are high. However, a major challenge is that pedestrian behavior is inherently multimodal in nature, i.e., pedestrians can plausibly take multiple paths. This is because, in large part, pedestrian behaviors are driven by unique intentions and decisions made by each pedestrian walking along a particular sidewalk or crosswalk. As described in this paper, we developed a hybrid automaton model of multimodal pedestrian behavior called Multimodal Hybrid Pedestrian (MHP). We account for multimodal pedestrian behavior by identifying pedestrian decision-making points and developing decision-making models to predict pedestrian behaviors in a probabilistic hybrid automaton framework. The resulting MHP model is more likely to predict the ground truth trajectory compared to two baseline models—a baseline hybrid automaton model and a constant velocity model. The MHP model is applicable to a wide variety of urban scenarios—midblocks, intersections, one-way, and two-way streets, etc., and the probabilistic predictions from the model can be utilized for AV motion planning.
Research on Integrated Model and Interactive Influence of Energy Internet Cyber Physical System

Energy Internet is a typical cyber-physical system (CPS), in which the disturbance on cyber part may result in the operation risks on the physical part. In order to perform CPS assessment and research the interactive influence between cyber part and physical part, an integrated energy internet CPS model which adopts information flow matrix, energy control flow matrix and information energy hybrid flow matrix is proposed in this paper. The proposed model has a higher computational efficacy compared with simulation based approaches. Then, based on the proposed model, the influence of cyber disturbances such as data dislocation, data delay and data error on the physical part are studied. Finally, a 3 MW PET based energy internet CPS is built using PSCAD/EMTDC software. The simulation results prove the validity of the proposed model and the correctness of the interactive influence analysis.
Power Control for Body Area Networks: Accurate Channel Prediction by Lightweight Deep Learning

Recent advances in the Internet of Things (IoT) are reforming the health care industry by providing higher communication efficiency, lower costs, and higher mobility. Among the many IoT applications, wireless body area networks (BANs) are a remarkable solution caring for a rapidly growing aged population. Predictive transmit power control schemes improve BAN communications’ reliability and energy efficiency through long-term optimal radio resources allocation that supports consistent pervasive healthcare services. Here, we propose LSTM-based neural network (NN) prediction methods that provide long-term accurate channel gain prediction of up to 2 s over nonstationary BAN on-body channels. An incremental learning scheme, which enables the LSTM predictor to operate online, is also developed for dynamic scenarios. Our main contribution is a lightweight NN predictor, “LiteLSTM,” that has a compact structure and higher computational efficiency than other variants. We show that LiteLSTM remains functional under an incremental learning scheme, with only marginal performance degradation when implemented on hand-held devices. For optimal power allocation, we develop an interquartile range (IQR)-based power control for our channel prediction. When extensively tested using empirical channel measurements at different sampling rates, our proposed methods outperform the existing state-of-the-art methods in terms of prediction accuracy, power consumption, level crossing rate (LCR), and outage probability and duration.
Crowdsourcing Answer Integration Algorithm For Big Data Environment

Crowdsourcing is an emerging distributed computing model that is widely used. Aiming at the uneven quality of crowdsourcing answers due to different workers' capabilities and attitudes, it is necessary to effectively study the hotspot issue of crowdsourcing answer integration. A crowdsourced answer integration algorithm based on “filter-evaluate-vote” is proposed. This algorithm is implemented using MapReduce parallel programming model in the Hadoop platform, and experiments are performed on multiple data sets. The results show that the proposed algorithm can be effective. It improves the accuracy of crowdsourced answers, and has high computing performance and horizontal scalability, which is suitable for answer integration in a big data environment.
Modeling and Performance Analysis for Communication Network in Smart Power Distribution System

As the integrated energy system (IES) develops rapidly, multiple advanced components has been put into use in the smart power distribution system (SPDS). Facing challenges brought by massive heterogeneous data, complex information interaction and usage of diversified intelligent devices, how to allocate the limited resources of the communication network efficiently to fulfill the application demand is becoming an urgent issue. In this paper, the performance of the communication network during the SPDS operation is analyzed. Firstly, a communication network architecture based on the internet of things (IoT) technology was introduced and its applications and quality of service (QoS) requirements in SPDS are characterized. Followed by a proposed bandwidth model based on queueing theory, through which the predicted bandwidth scheme for data acquisition application considering QoS is obtained. Finally, a smart distribution station is taken as an example, where the network performance is analyzed considering three typical scenarios. The OPNET-based simulations are conducted to verify the rationality and effectiveness of the proposed method.
MIM Structure With Inverted M-Type Cavity for Sensing Applications

Herein, a high-sensitivity multifunctional nanosensor comprising a metal-insulator-metal (MIM) waveguide and an inverted M-type cavity (IMTC) is presented. Fano resonances are generated owing to the interactions between different light pathways. Two contrastive structures are proposed to illustrate that a proper interference area leads to better propagation characteristics. The influence of geometric parameters on sensing properties is studied. Four derived structures are investigated to discuss the main factor for improving sensitivity. The designed structure can detect temperature and NaCl solution concentration with sensitivities reaching 1.444 nm/°C and 6.6 nm/%, respectively. The designed structures can also potentially be applied to other nanoscale devices.
Prospects and Challenges of the Hyperloop Transportation System: A Systematic Technology Review

The present article outlines the core technologies needed to realize the Hyperloop transportation system (HTS). Currently, the HTS vacuum tube train concept is viewed as the fastest way to cross the earth’s surface. However, the concept has not yet been demonstrated for subsonic or near-sonic speeds in large-scale implementations. Among the challenging technical areas are the tube’s depressurization, the capsule’s air resistance, and choked flows occurring around the capsule. There is also a need for effective levitation and propulsion solutions compatible with the velocities being proposed. Moreover, several conflicting objectives have been identified in the HTS design. It is highlighted that the tubes should be wide enough to lower the capsule’s drag forces but that a large-sized tube comes at the expense of higher operational energy costs and infrastructure investments. Another struggle is aiming at a low-cost passive track design and, and at the same time, a lightweight vehicle. One technical path is to turn the whole guideway into an electric propulsor, arriving at a ‘lightweight capsule solution’ (LCS). Alternatively, the vehicle could be transformed into a ‘modified airplane’ that stores massive amounts of onboard energy, yielding an energy-autonomous ‘low infrastructure solution’ (LIS). In a case study, it is shown that even LIS technology is compatible with short-haul flights (1500 km) but that it requires an energy reservoir of about 30% of the capsule’s overall mass. Results and discussions presented in this paper are supported by analytical predictions with parameters or input data either supported by referred simulations or experiments. In general, the paper aims to open up the discussion, provide a sufficient understanding of the Hyperloop field’s multidisciplinary aspects, and establish a foundation for further investigations. In the end, new research tasks have been defined, which have the potential to go beyond the state of current knowledge.
Novel stereoselective alkenyl–aryl coupling via nickel-catalysed reaction of alkenylanes with aryl halides

trans-Alkenylalanes, readily obtainable via hydroaluminiation of acetylenes, react readily with aryl bromides and iodides in the presence of catalytic amounts of nickel complexes, such as tetrakis(triphenylphosphine)- nickel, to produce arylated alkenes in high yields, the stereochemistry of the products being >99%trans.
Deep Neural Network Pruning Using Persistent Homology

Deep neural networks (DNNs) have improved the performance of artificial intelligence systems in various fields including image analysis, speech recognition, and text classification. However, the consumption of enormous computation resources prevents DNNs from operating on small computers such as edge sensors and handheld devices. Network pruning (NP), which removes parameters from trained DNNs, is one of the prominent methods of reducing the resource consumption of DNNs. In this paper, we propose a novel method of NP, hereafter referred to as PHPM, using persistent homology (PH). PH investigates the inner representation of knowledge in DNNs, and PHPM utilizes the investigation in NP to improve the efficiency of pruning. PHPM prunes DNNs in ascending order of magnitudes of the combinational effects among neurons, which are calculated using the one-dimensional PH, to prevent the deterioration of the accuracy. We compared PHPM with global magnitude pruning method (GMP), which is one of the common baselines to evaluate pruning methods. Evaluation results show that the classification accuracy of DNNs pruned by PHPM outperforms that pruned by GMP.
Android Malware Detection Based on Call Graph via Graph Neural Network

With the widespread usage of Android smart-phones in our daily lives, Android platform has become an attractive target for malware authors. There is an urgent need for developing automatic malware detection approach to prevent the spread of malware. Traditional signature-based detection methods cannot handle the rapid evolution of complex malware or the emerging of new types of malware. Due to the limitation on code coverage and poor efficiency of the dynamic analysis, in this paper, we propose a new Android malware detection approach based on static analysis via graph neural network. Instead of extracting Application Programming Interface (API) call information, we further analyze the source code of Android applications to extract high -level semantic information, which increases the barrier of evading detection. Particularly, we construct approximate call graph from function invocation relationships within an Android application to represent this application, and further extract intra-function attributes, including required permission, security level and statistical instructions information, to form the node attributes within graph structures. Then, we use graph neural network (GNN) to generate a vector representation of the application, and then malware classification is performed on this representation. We conduct experiments on real-world application samples. The experimental results demonstrate that our approach implements high effective malware detection and outperforms state-of-the-art detection approaches.