Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis Of The K12 Education Of United States Using Machine Learning And Data Mining Techniques

Quality K-12 education is essential for a student’s success in college and future career. In this paper, we proposed a Random Forest-based algorithm to identify the principal factors affecting the academic success of students in K-12 education across various states in the United States. Our model outperforms other machine learning-based models like Lasso and ElasticNet, reaching an MSE of 0.910. Grid search is used to automatically search for a set of optimal hyperparameters and we concluded that enrollment number, the year when data is recorded and capital outlay expenditure per student have the strongest effect on student’s academic success.
Evaluation of Antioxidant Properties and Antimicrobial Activity of Garcinia cambogia Hort. Ex Boerl (Clusiaceae) fruit extracts.

Garcinia cambogia Hort. Ex Boerl (Clusiaceae) is a traditionally used medicinal plant claimed to possess antioxidant properties and antimicrobial activity. The present study was aimed to evaluate the in vitro antioxidant property and antimicrobial activity. The antioxidant property of the methanolic fruit extracts were evaluated using two methods. DPPH Radical Scavenging Assay, Total antioxidant phosphomolybdenum method and Total Reducing Capacity. The results revealed that free radical scavenging activity of methanolic fruits extracts of G. cambogia has significant radical scavenging ability on DPPH with IC50 value of 39. 45 µg/ml respectively. The positive control ascorbic acid showed the IC50 values of 32.42 µg/ml. The phytochemical screening showed the presence of Terpenoids, Tannins, Saponins, Phenols, Flavonoids and Steroids compounds. Methanol, Ethyl acetate and chloroform extracts of the plant was tested for antimicrobial activity against Gram positive bacteria such as Bacillus subtilis, Staphylococcus aureus, Streptococcus faecalis, Gram negative bacteria such as Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and a fungus Candida albicans using agar well plate method. The results obtained in the present study indicated that the fruit of G. cambogia showed the best antimicrobials activity against the C. albicans, S. aureus (G+) and K. pneumoniae (G-). Gentamicin is used as a positive control. In conclusion, the results indicate antimicrobial activity of the extract which could be further explored for purification of antioxidant compounds.
Practical Aspects of the Skin Effect in Low Frequencies in Rectangular Conductors

Although the skin effect has been widely studied over the years, many of its topics remain unclear for most electrical engineers, including undergraduate students and fresh graduates. Yet, in the cases of application of power frequency current, the knowledge gap to be bridged is more significant. Thus, for contributing to the widening in the knowledge of this effect, this work presents an analysis of the behavior of the current density distribution over the cross-section of differently combined rectangular conductors subject to power frequency current. In this case, since an analytical approach is not feasible, the numerical method of matrix solution of integral equations was adopted due to its simplicity, fast convergence, and its application is presented through some providentially chosen theoretical examples before a practical case may be analyzed. As the main results, the influence of a correlated effect that is the proximity effect shows itself as being predominant and the method presents itself as being very convenient for studying the current density distribution in conductors with a cross-section other than the circular, which allow bridging the knowledge gap in this theme. Moreover, the comparison of theoretical cases quite significant. Moreover, the method is adequate to planar configurations, by typical configurations, as in the case of bus bars of electrical panels.
Model and Analyze the Cascading Failure of Scale-Free Network Considering the Selective Forwarding Attack

Many real-world networks have scale-free characteristics and can be abstracted into scale-free networks. Aiming at the problem that scale-free networks have low fault tolerance in the face of malicious attacks, we focus on the selective forwarding attack behavior that exists widely in the networks, and build a selective forwarding attack model based on node importance. Moreover, according to the neighbor node’s malicious and non-malicious behavior, a load redistribution strategy for failed node is proposed. Then, the network’s damage degree is given to evaluate the comprehensive impact of cascading failure phenomenon on network connectivity and information integrity under selective forwarding attack. Finally, a cascading failure model of scale-free networks considering selective forwarding attack behavior is obtained. Based on this model, the propagation condition without triggering network cascading failure, the selective forwarding attack intensity threshold and the load loss ratio are obtained. By simulation on the classical BA scale-free network model, the results show that multiple nodes’ random failure occurs in scale-free networks, selective forwarding attack behavior is helpful to improve the connectivity of the network. Besides, the network exists selective forwarding attack intensity threshold, when attack intensity is greater than the intensity threshold, the malicious nodes will not fail because of the failure of the neighbor nodes. But, selective forwarding attack behavior can destroy the information integrity, and there is a negative correlation between the attack intensity and the information integrity. These results have certain guiding significance for cascade failure analysis and prevention method research and design in real life.
Proximity Moving Horizon Estimation for Discrete-Time Nonlinear Systems

In this letter, we address the moving horizon estimation (MHE) problem of constrained discrete-time nonlinear systems. We propose a proximity-based formulation of the underlying optimization problem in which the state estimate is designed to lie in proximity of a stabilizing a priori estimate that is based on the extended Kalman filter (EKF). Using Lyapunov’s direct method, we prove exponential stability of the estimation errors under suitable assumptions. For a benchmark example, an improved performance of the proposed estimator in comparison to the EKF and to a common MHE scheme with filtering update is showcased.
Ensemble Influence Nets for Equipment Health Status Classification

As a simplified model of Bayesian Network, Influence Nets (IN) has a wide range of applications in the description and modeling of uncertain causality. However, too many nodes in IN will increase the number of parameters in modeling and limit the applicability of IN model to high-dimensional problems. In this paper, based on the bagging framework, we propose an Ensemble-IN model to overcome this flaw. Firstly, several weak IN models are integrated coherently, each of which only consists of a subset of nodes. Then, difference evaluation algorithm (DE) is introduced to optimize the parameters in weak IN models. Finally, the combination methods is utilized to integrate the analysis results of weak IN models. The rationality and feasibility of the proposed Ensemble-IN model are verified by a practical case of health status classification of engine.
Research on Lithology Identification Method Based on Stacked Capsule Auto-Encoder Network

The capsule network is able to divide the data features into different capsules, which express their invariance and covariation by vectors. Aiming at the insufficiency of traditional machine learning methods for the ability in mapping logging parameter sequence structure and lithological feature diversity, a stacked capsule auto-encoder network (SCAE-Net) is proposed to improve the lithology identification in complex carbonate rocks. Firstly, multiple capsule auto-encoders are stacked and the capsules in the encoder can express the internal relationship of the object from part to whole. Secondly, SCAE-Net mines sample subspace by capsules, where sample representations are similar. Attention mechanisms are introduced into the model and renew the loss function to improve the convergence speed of the model. Finally, taking the carbonate reservoir in the Sulige gas field as an example, the accuracy of the SCAE-Net lithology identification model is up to 95.92%, which provides a new idea for complex carbonate lithology identification.
Analysis of Conventional Non-isolated Bidirectional Converters with Smooth Transient Operation

The objective of this work is to achieve a smooth transient operation of non-isolated bidirectional dc-dc converters (BDCs), by implementing two different PWM schemes for transient and steady-state operations, respectively. These two PWM schemes also facilitate the converter operation in continuous conduction mode (CCM) and discontinuous conduction mode (DCM). The CCM and DCM modes of converter operation enables efficient control over the bidirectional power flow, especially during low power operation. Apart from this, an equation for average inductor current is derived and validated with active sources on both input and output sides. The analysis of the BDCs has been included using the switch average modeling technique. The operation of the proposed PWM schemes and the expression for the steady-state average inductor current are validated in simulation and hardware. The hardware results are obtained at 1.5 kW output power, with an input voltage of 132 V and an output voltage of 330 V for validating the concept.
Numerical Modeling of AC Loss in HTS Coated Conductors and Roebel Cable Using T-A Formulation and Comparison With H Formulation

With recent advances in second-generation high temperature superconductors (2G HTS) and cable technologies, various numerical models based on finite-element method (FEM) have been proposed to help interpret measured AC loss and assist cable design. The T-A formulation, implemented in COMSOL, shows great potential for reducing the overall computation costs. In this paper, the performance of the T-A formulation for calculating the AC loss of coated superconductors and cables were assessed and compared against the widely accepted H formulation, with benchmark model of a single REBCO tape in 2D/3D and a 14-strand Roebel cable. Evaluation and comparison on key metrics including the computation time, the number of degrees of freedom and the numerical accuracy were presented, which could provide a reference for researchers in applying the T-A formulation for AC loss calculation.
A Comparative Evaluation of Probabilistic and Deep Learning Approaches for Vehicular Trajectory Prediction

This work compares two innovative methodologies to predict the future locations of moving vehicles when their current and previous locations are known. The two methodologies are based on: (a) a Bayesian network model used to infer the statistics of prior vehicles, trajectory data that is further adopted in the estimation process; (b) a deep learning approach based on recurrent neural networks (RNNs). We present experimental results obtained with both prediction methodologies. The results indicate that the prediction accuracy is improved in both methods as more information about prior vehicle mobility is available. The Bayesian network-based method is advantageous because the statistical inference can be updated in real-time as more trajectory data is known. On the contrary, the RNN-based method requires a time-consuming learning task every time new data is added to the inference dataset. However, the RNN achieves a higher prediction accuracy performance (3% to 5% higher). Additionally, we show that the computational cost to predict the next position a vehicle will move to can be substantially reduced when the Bayesian network is adopted, a scenario where the RNN method requires more computational time. But when the quantity of prior data used in the prediction increases, the computational time required by the RNN-based method can be two orders of magnitude lower, showing that the RNN method is advantageous in both accuracy and computational time. Both methods achieve a next position successful prediction rate higher than 90%, confirming the applicability and validity of the proposed methods.
Broadband and Wide-Angle Scanning Capability in Low-Coupled mm-Wave Phased-Arrays Incorporating ILA With HIS Fabricated on FR-4 PCB

This paper presents a compact millimeter-wave (mm-Wave) phased array incorporating inverted-L antenna (ILA) with high-impedance electromagnetic surfaces (HIS) based on single-layer FR-4 PCB for user equipment (UE). In order to realize mm-Wave UE in low production cost, the design methodology of phased array antenna in single-layer FR-4 PCB is proposed without any additional packaging process. The presented antenna consists of low-coupled antenna element and low loss feeding networks in compressed linear array configuration with 0. 39λ0 inter-element spacing. First, in order to realize broadband characteristics, the ILA is integrated with one-dimensional electromagnetic bandgap (1-D EBG) structures featuring HIS via slow-wave behavior. Due to high-impedance properties of the embedded via wall structures in common ground plane between each antenna element, the mutual coupling path through surface-current can be reduced. Second, in order to minimize leakage power caused by surface-waves, low loss coplanar waveguide ground (CPWG) feeding networks with via wall structures and island-shape pads are realized. The fabricated 4-elements and 8-elements phased array antennas including antenna element and feeding network based on FR-4 PCB achieve wide 3 dB beam coverage while featuring low beam squint in the Ka-band. In order to investigate over-the-air (OTA) system performance at three beam steering scenarios (boresight and ±60° scan angle), the measured maximum error vector magnitude (EVM) in QPSK modulation scheme is 16.6%. The coverage efficiency beyond 50% for the realized gain of −1.5 dBi from 26 to 36 GHz is demonstrated.
Temporal H/alpha Target Decomposition for Landslide Monitoring Using Ku-Band GB-SAR Time Series

We investigate the applicability of the entropy (<italic>H</italic>)/ alpha <inline-formula><tex-math notation="LaTeX">$(\bar{\alpha })$</tex-math></inline-formula> target decomposition realized by the temporally averaged coherency matrix, called temporal <inline-formula><tex-math notation="LaTeX">$H/(\bar{\alpha })$</tex-math></inline-formula>. We apply the temporal <inline-formula><tex-math notation="LaTeX">$H/\bar{\alpha }$</tex-math></inline-formula> to ground-based synthetic aperture radar (GB-SAR) continuous monitoring data to characterize the scattering mechanism temporal change. As a case study, this work demonstrates the application of the temporal <inline-formula><tex-math notation="LaTeX">$H/\bar{\alpha }$</tex-math></inline-formula> technique to landslide monitoring to detect and investigate the temporal scattering mechanism. The study acquired long-term GB-SAR polarimetric data over the postlandslide slope, Minami-Aso, Kumamoto, Japan. The study first investigated the property of the temporal <inline-formula><tex-math notation="LaTeX">$H/\bar{\alpha }$</tex-math></inline-formula> parameters over selected land cover types by comparing it with that derived by spatial averaging (spatial <inline-formula><tex-math notation="LaTeX">$H/\bar{\alpha }$</tex-math></inline-formula>) to explain the landslide monitoring results. Also, the rainfall effects on the temporal <italic>H</italic>/<inline-formula><tex-math notation="LaTeX">$\bar{\alpha }$</tex-math></inline-formula> parameters are demonstrated. The temporal <italic>H</italic> and <inline-formula><tex-math notation="LaTeX">$\bar{\alpha }$</tex-math></inline-formula> values increase up to 0.07<inline-formula><tex-math notation="LaTeX">$^\circ $</tex-math></inline-formula> and 13.54<inline-formula><tex-math notation="LaTeX">$^\circ $</tex-math></inline-formula>, respectively, when the rainfall rate is 52.5 mm/h. The time-series analysis of the temporal <inline-formula><tex-math notation="LaTeX">$H/\bar{\alpha }$</tex-math></inline-formula> indicates an obvious temporal transition of the scattering mechanism and a change of the backscattering stationarity when a landslide occurs. The applicability of the temporal <inline-formula><tex-math notation="LaTeX">$H/\bar{\alpha }$</tex-math></inline-formula> for the change-detection is discussed by comparing it with the classical spatial <inline-formula><tex-math notation="LaTeX">$H/\bar{\alpha }$</tex-math></inline-formula> parameters.
Camera-Based Surgical Navigation System: Evaluation of Classification and Object Detection CNN Models for X-markers Detection

In hip and spine surgery, accurate tracking of the medical instruments allows the surgeon to navigate the instruments accurately and safely within the operating field. X-markers based optical tracking are now applied in various fields owing to their simple shape and robustness against contamination and coverage. Nevertheless, they are not unique and easily distinguishable compared to other visual markers, e.g. ArUco, April Tags, QR codes, etc. Incorrect localization and classification of the X-markers hinder the pose estimation process. Recent studies have shown the great potential of deep learning approaches in solving visual marker detection problems often faced in conventional approaches. In this paper, we evaluate the performance of classification and object detection models for X-markers detection. To evaluate the models, we present our unique X-markers annotated dataset. The evaluation results show the high performance of classification and object detection models for X-markers detection, thereby outperforming the conventional methods. The models, however, have a higher computational cost in contrast to conventional methods.
The Development of Automation System of Frequency Responses Estimation for Ultrasonic Sounding

The paper describes a development of a automation system of frequency response estimation for ultrasonic sounding. The ultrasonic sounding is widely used in non-destructive testing. One of application is elastic moduli estimation in industrial and natural materials including rocks. Basic techniques are time-of-flight and resonance techniques. Both differ by frequency range, type of used electroacoustic transducer and measurement parameters. For widening of frequency range of elastic moduli estimation, wave packets may be used. It makes possible to vary spectral composition and time-dependent parameters of sounding pulses independently. The paper shows a practical realization of this technique includes a mathematical model, apparatus, a control flow, a software. The results of a test experiment are presented. In the limit, the sounding by wave packets may combine both basic techniques of ultrasonic sounding.
Security Enhancement With a Hybrid Cooperative NOMA Scheme for MEC System

This article investigates how to exploit the cooperative mechanism between non-orthogonal multiple access (NOMA) user pairs to enhance the security of the mobile-edge computing (MEC) system. Considering the different delay requirements of Internet of Things (IoT) users, we propose a two-slot hybrid cooperative NOMA security (THCNS) scheme that utilizes the cooperative interference between NOMA user pairs to enhance the security of offloading. In the first time slot, the interference to the eavesdropper comes from the task signals (TSs) transmitted by the two users, while in the second time slot comes from jamming signal (JS) transmitted by the user who has completed the offloading tasks. The weighted sum secrecy outage probability (wSOP) and secrecy computation probability (SCP) are utilized to measure the security performance of users in the proposed scheme. To confirm the feasibility and applicability of the proposed scheme, we analyze the impact of some key parameters on users’ security, including the users’ local computation time and offloading time, transmit power, task allocation coefficients of two time slots. The analysis results indicate that the proposed scheme is more suitable for the case where the wiretap channel outperforms the main channel, and it can enhance the system security when selecting the optimal parameters. Finally, the numerical simulation results reveal how the analysis parameters affect the security performance of users and validate the theoretical analysis, it provides good insights about how to design parameters of IoT users to enhance their security performance in MEC system.
Surface-Based Path Following Control: Application of Curved Tapes on 3-D Objects

In this article, a novel approach for the versatile wrinkle-free application of (curved) precut adhesive tapes on freeform 3-D surfaces is presented. Straight and curved tape application paths are mapped onto the 3-D object as geodesics and as lines with imposed geodesic curvature, respectively. The proposed surface-based path following control concept extends the classical path following control by a novel parallel contact frame and a parallel projection operator. Using a static state feedback, the robotic system is transformed into a system with linear input-output behavior in the path coordinates. This allows to traverse a path on a 3-D object with a draping roll without turning around the surface normal vector. The latter prevents distortions and wrinkles of the applied tape. Experimental results with a Kuka LBR iiwa 14 R820 demonstrate the feasibility of the proposed approach.
Usage of RMarkdown and Bookdown for Educational and Scientific Writing

in this article we discuss the usage of lightweight Markdown markup language for educational and scientific writing. We start with a brief analysis of current methods and tools for document editing and publishing. Then we switch to the simple Markdown markup language and describe its extensions – Markdown Extra, RMarkdown and bookdown. The use of the latter significantly facilitates the writing of various documents for educational and scientific purposes, such as materials on practical classes for students, lecture notes, teaching materials, scientific articles and books.
EfficientNet-Based Model With Test Time Augmentation for Cancer Detection

Skin cancer is the most common type of cancer and among various kinds of skin cancer, melanoma causes the most deaths. In clinical practice, contextual information from every one of a patient’s moles help dermatologists make better judgments about whether a particular one is a lesion. In this paper, we proposed an EfficientNet based deep learning method to identify melanoma in skin lesion images. Our method takes into consideration all skin lesion images from a patient and employs effective data augmentation during training and test time augmentation during inference to improve classification accuracy. On the SIIM-ISIC Melanoma Classification dataset, our method achieved 0.901 Auc-Roc scores, outperforming other deep learning models such as VGG16 or Resnet50.
A case study of industrial data analysis: Gearbox temperature prediction of wind turbines using ensemble deep learning regression

The damage and effectiveness of gearboxes may directly lead to the shutdown of wind turbines (WTs) and increase the unplanned maintenance cost. The variation and distribution of gearbox temperature can reflect its working condition. The predicting gearbox temperature has emerged as an essential method for guaranteeing the safety, availability, and efficiency of WTs. A method called ensemble deep learning regression (EDLR) is proposed for gearbox temperature prediction. Using random forest method, the collected features are then selected as main impacting features. Long short-term memory is sequentially employed for the temperature prediction. The proposed approach is investigated with real data from a wind farm. The experimental results reveal that EDLR has excellent regression performance in terms of the adopted criteria and that it can accurately predict the gearbox temperature.