Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Bearing fault detection using Intelligent Algorithm combined with Wavelet Transform

The monitoring of bearings in electromechanical induction machines has become in the last decades a field where a lot of research is invested. We can understand this because of their responsibility for the defects of these machines and the enormous losses and expenses that they generate. However, if it is true that many methods have been proposed, it must be said that a large part of them, although based on the stator current, does not focus on the only defect of the bearing as we propose through this work. In this study, our goal is to analyze the current induced in a copper coil placed side by side with magnets placed in the inner ring of the bearing, by the Machine Current Signature Analysis method in order to detect from the genesis any defect of the bearing. To do this we will apply algorithms such as Cuckoo Search-Support Vector machine, Convolutional Neural Network, Kernel-Principal Component Analysis, Recurrent Neural Network, and Support Vector Machine on the data extracted from this induced current using the Wavelet Transform technique in order to determine among these algorithms, which one would allow to detect in real time and with a good precision the defect of bearing when it occurs during the operation of the machine that incorporates it. The whole with an aim of developing, in combination with the embedded electronics an autonomous electronic system of detection of the defect of bearing in real time in an effective way.
Q-learning for the speed trajectory optimization of the freight train

The train speed trajectory optimization (TSTO) aims at finding the optimal speed trajectory considering the safety, energy efficiency, punctuality and stopping accuracy. From the perspective of mitigating the greenhouse effect, it’s quite significant to study the TSTO problem. This paper proposed an optimization algorithm based on Reinforcement Learning (RL). Firstly, a global optimization model using RL was established. In the model, the control sequence including the control regimes and their switching points was taken as the state. The optimization objectives were taken as the reward function. The adjustment of the position of the switching points in the control sequence was taken as the decision space of the agent. Secondly, an adjustment method of the control sequence based on the deep Q-learning and embedding matrix was proposed. The training data was sampled using the experience replay. The optimal control sequence was obtained through the iterative training of the neural network. Finally, the optimization algorithm based on RL was compared with the driving strategies based on the Pontryagin’s Maximum Principle (PMP) and the field test data. The results show that the energy consumption of the proposed algorithm is reduced by 0.16% in comparison with that of the PMP, which proves that the proposed method can be applied to the multi-objective optimization of the train operation. Comparing with the field test data, the energy consumption of the optimization algorithm is reduced by 4.89%, which demonstrates that the proposed method can be used to guide the drivers to drive the freight train energy-efficiently.
Deep Models with Differential Privacy for Distributed Web Attack Detection

The complexity of today’s web applications entails many security risks, mainly targeted attacks on zero-day vulnerabilities. New attack types often disable the detection capabilities of intrusion detection systems (IDS) and web application firewalls (WAFs) based on traditional pattern matching rules. Therefore, the need for new generation WAF systems using machine learning and deep learning technologies is urgent today. Deep learning models require an enormous amount of input data to be able to train the models accurately, leading to the very resource-intensive problem of collecting and labeling data. In addition, web request data is often sensitive or private and should not be disclosed, imposing a challenge to develop high-accuracy deep learning and machine learning models. This paper proposes a privacy-preserving distributed training process for the web attack detection deep learning model. The proposed model allows the participants to share the training process to improve the accuracy of the deep model for web attack detection while preserving the privacy of the local data and local model parameters. The proposed model uses the technique of adding noise to the shared parameter to ensure differential privacy. The participants will train the local detection model and share intermediate training parameters with some noise that increases the privacy of the training process. The results evaluated on the CSIC 2010 benchmark dataset show that the detection accuracy is more than 98%, which is close to the model that does not guarantee privacy and is much higher than the maximum accuracy of all non-data-sharing local models.
Review of Intraoperative Adjuncts for Maximal Safe Resection of Gliomas and Its Impact on Outcomes

Simple Summary Understanding the impact of intraoperative modalities in glioma surgery on the extent of resection (EOR), survival, and complications is vital to maximizing safe resection while preserving neurological function. A systematic literature search was performed to assess the impact of intraoperative modalities of glioma surgery, including one or a combination of the following: intraoperative magnetic resonance imaging (iMRI), awake/general anesthesia craniotomy mapping (AC/GA), fluorescence-guided imaging, or combined modalities. The heterogeneity in reporting the amount of surgical resection prevented further analysis. The studies reviewed indicated that these modalities significantly improved EOR but most often underreported Progression-free survival/overall survival (PFS/OS). Combining intraoperative modalities during the same brain glioma operation seems to have the highest effect compared to each modality alone. Abstract Maximal safe resection is the mainstay of treatment in the neurosurgical management of gliomas, and preserving functional integrity is linked to favorable outcomes. How these modalities differ in their effectiveness on the extent of resection (EOR), survival, and complications remains unknown. A systematic literature search was performed with the following inclusion criteria: published between 2005 and 2022, involving brain glioma surgery, and including one or a combination of intraoperative modalities: intraoperative magnetic resonance imaging (iMRI), awake/general anesthesia craniotomy mapping (AC/GA), fluorescence-guided imaging, or combined modalities. Of 525 articles, 464 were excluded and 61 articles were included, involving 5221 glioma patients, 7(11.4%) articles used iMRI, 21(36.8%) used cortical mapping, 15(24.5%) used 5-aminolevulinic acid (5-ALA) or fluorescein sodium, and 18(29.5%) used combined modalities. The heterogeneity in reporting the amount of surgical resection prevented further analysis. Progression-free survival/overall survival (PFS/OS) were reported in 18/61(29.5%) articles, while complications and permanent disability were reported in 38/61(62.2%) articles. The reviewed studies demonstrate that intraoperative adjuncts such as iMRI, AC/GA mapping, fluorescence-guided imaging, and a combination of these modalities improve EOR. However, PFS/OS were underreported. Combining multiple intraoperative modalities seems to have the highest effect compared to each adjunct alone.
High Frequency Response of Non-Volatile Memristors

This paper presents an analytical investigation of the transient and steady-state response of non-volatile memristors to high frequency periodic inputs, using as a case study a $\textrm {TaO}_{\textrm {x}}$ -based nano-scale memristor model derived at HP Labs. For the first time, we provide a mathematical proof for the fading memory phenomenon in memristors stimulated by periodic inputs in the high frequency limit. Specifically, we demonstrate that the steady-state response of a non-volatile memristor, exhibiting asymmetric switching kinetics with respect to the polarity of the input, depends only on the amplitude of the testing signal and not on the device initial conditions. Based on the results of our analyses, we provide an alternative method for tuning the memristor state by using high-frequency AC inputs, and introduce a new system-theoretic visualization tool, namely the input-referred High-Frequency Dynamic Route Map (HF-DRM), that allows the reproduction of the memristor time-response to any high-frequency periodic input from each admissible initial condition. The purely theoretical results introduced in this paper could inspire new approaches for modulating the memory states of practical non-volatile memristors.
A Multi-Channel Descriptor for LiDAR-Based Loop Closure Detection and Its Application

Simultaneous localization and mapping (SLAM) algorithm is a prerequisite for unmanned ground vehicle (UGV) localization, path planning, and navigation, which includes two essential components: frontend odometry and backend optimization. Frontend odometry tends to amplify the cumulative error continuously, leading to ghosting and drifting on the mapping results. However, loop closure detection (LCD) can be used to address this technical issue by significantly eliminating the cumulative error. The existing LCD methods decide whether a loop exists by constructing local or global descriptors and calculating the similarity between descriptors, which attaches great importance to the design of discriminative descriptors and effective similarity measurement mechanisms. In this paper, we first propose novel multi-channel descriptors (CMCD) to alleviate the lack of point cloud single information in the discriminative power of scene description. The distance, height, and intensity information of the point cloud is encoded into three independent channels of the shadow-casting region (bin) and then compressed it into a two-dimensional global descriptor. Next, an ORB-based dynamic threshold feature extraction algorithm (DTORB) is designed using objective 2D descriptors to describe the distributions of global and local point clouds. Then, a DTORB-based similarity measurement method is designed using the rotation-invariance and visualization characteristic of descriptor features to overcome the subjective tendency of the constant threshold ORB algorithm in descriptor feature extraction. Finally, verification is performed over KITTI odometry sequences and the campus datasets of Jilin University collected by us. The experimental results demonstrate the superior performance of our method to the state-of-the-art approaches.
Deveplopment of the Wiring Diagram of the Device Based on the LoRa Module RAK3172

The paper presents the results of the development of circuitry of electronic device for the acquisition and transmission of telemetry data. The purpose of this work is to design and develop circuitry of the device for data acquisition using LoRa technology. The integrated end-to-end design environment Altium Designer was used to develop the electronic part of the device. The STM32F407VGT6 series microcontroller was used as the central control unit. A LoRa RAK3172 radio module is provided in the circuit for receiving and transmitting data. Unlike previous series, this module is equipped with a UART interface, which greatly simplifies the process of designing and writing a program for control. In this article the relevance of developing device with the use of LoRa - technology is considered. Briefly considered the theoretical basis of LoRa-modulation. In the section, the results and their discussion, the results of the circuit design of the device are presented. The information presented in the paper reflects only that part of the developed device, which is related to LoRa technology. In general, this work is part of a much larger project, which involves the use of GSM, Wi-Fi, RFID technology. Circuit of power supply is designed for 220 volt AC voltage with further transformation to +12 V. In case of electric power supply absence, the circuit provides automatic switching to emergency power supply from 12 - 24 Volt battery pack.
Sudden Disappearance of a Small Sunspot Associated with the 2002 February 20 M2.4 Flare

In this Letter, we present the evidence of rapid disappearance of a small sunspot associated with a M2.4 flare, which occurred in NOAA Active Region 9830, at 2106 UT, on 2002 February 20. The sunspot, in the size of about 45 arcsec2, completely disappeared in about 60 minutes, coinciding with the onset of the flare. The magnetic flux of this sunspot disappeared in the similar timescale. Furthermore, we found that the disappearing sunspot is located near the edge of the main hard X-ray source observed by RHESSI. This hard X-ray source is located in the magnetic neutral line dividing the disappearing spot and the opposite polarity flux. Two possible explanations are offered: (1) the disappearing sunspot is due to the rapid magnetic reconnection and subsequent submergence; (2) the disappearing sunspot represents impulsive emergence and detachment of a magnetic "bubble. "
Effect of vocal fold radii on pressure distributions in the glottis

Modeling laryngeal aerodynamics requires specification of glottal geometry. This study examines primary effects of glottal inlet and outlet radii. A velocity‐pressure integrated finite‐element method produced glottal pressure profiles for three radius conditions: completely rounded vocal folds (the edematous condition}; vocal folds with sharp corners; and vocal folds with realistic radii. Converging, uniform, and diverging glottal shapes were used with a Reynolds number of 800 and a minimum glottal diameter of 0.02 cm. For the diverging glottis, sharp corners produce unrealistically large local negative pressure spikes due to convective acceleration. For convergence, the expansion curvature of realistic radii creates pressure recovery hear the glottal exit, producing a location of negative pressure. This may reinforce the motion phase difference between the upper and lower glottal regions due to a net positive pressure upstream and a net negative pressure downstream. For the completely rounded vocal fold,.
Impact damage model for ceramic materials

The fracture process in ceramic materials upon impact loading is complex in nature. Most often, the brittle ceramic deforms inelastically due to microcracking under shock (compression) loading. At high‐velocity impact, the shock generated microcracks rapidly open and extend under subsequent tension (due to the release waves from the stress‐free boundaries) leading to complete pulverization of the ceramic materials. The main objective of this paper is to model the damage process in ceramics due to impact loading. A computationally oriented continuum damage‐based constitutive model is considered. Several modifications incorporating strain rate and damage effects on the compressive strength have been introduced into the model. Results are presented in terms of numerical simulations of a plate‐impact test configuration. Effects of the model parameters on the compressive strength and spall strength are described. The proposed damage model has been used successfully to match the measured stress history from a p.
Body-worn cameras as a research tool in early years settings: Strengths and weaknesses

This paper discusses the innovative research method of using body-worn cameras for capturing speech and experiences of 3- to 5-year olds in English early years settings. The strengths and weaknesses will be discussed in this multiple case study approach to capturing the quality of speech from young children (n = 43). Adopting an interactionist theoretical framework and viewing the project through an interpretive paradigm, it is asserted there is the necessity to capture data in a way that is naturalistic and ethical at all times. It is argued that that in order to gain a full and deep understanding of young children’s lives, the use of body-worn cameras is pivotal in gaining the data that may not exist otherwise. It is therefore argued throughout this paper that although both strengths and weaknesses exist that weaknesses should be overcome and accommodated in order to enhance future research.
A New Mixed-Gas-Detection Method Based on a Support Vector Machine Optimized by a Sparrow Search Algorithm

To solve the problem of the low recognition rate of mixed gases and consider the phenomenon of low prediction accuracy when traditional gas-concentration-prediction methods deal with nonlinear data, this paper proposes a mixed-gas identification and gas-concentration-prediction method based on a support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Principal component analysis (PCA) is applied to perform data dimensionality reduction on the input data, and SSA is adopted to optimize the SVM hyperparameters to improve the recognition rate and gas-concentration-prediction accuracy of mixed gases. For the mixed-gas identification, the classification accuracy is significantly improved under the proposed SSA optimization SVM method (SSA-SVM), compared with random forest (RF), extreme-learning machine (ELM), and BP neural network methods. With respect to gas-concentration prediction, the maximum fitting degrees reached 99.34% for single gas-concentration prediction and 97.55% for mixed-gas-concentration prediction. The experimental results show that the SSA-SVM method had a high recognition rate and high concentration-prediction accuracy in gas-mixture detection.
Hate Speech in a Telegram Conspiracy Channel During the First Year of the COVID-19 Pandemic

Research has explored how the COVID-19 pandemic triggered a wave of conspiratorial thinking and online hate speech, but little is empirically known about how different phases of the pandemic are associated with hate speech against adversaries identified by online conspiracy communities. This study addresses this gap by combining observational methods with exploratory automated text analysis of content from an Italian-themed conspiracy channel on Telegram during the first year of the pandemic. We found that, before the first lockdown in early 2020, the primary target of hate was China, which was blamed for a new bioweapon. Yet over the course of 2020 and particularly after the beginning of the second lockdown, the primary targets became journalists and healthcare workers, who were blamed for exaggerating the threat of COVID-19. This study advances our understanding of the association between hate speech and a complex and protracted event like the COVID-19 pandemic, and it suggests that country-specific responses to the virus (e.g., lockdowns and re-openings) are associated with online hate speech against different adversaries depending on the social and political context.
Impact of innovation characteristics of airport self-bag-drop service on attitude, trust, and behavioural intention: using trust transfer theory

ABSTRACT Although self-bag-drop (SBD) kiosks provide a fast and convenient way to check in bags at airports, many airline passengers are reluctant to use and accept this innovative technology. Using innovation diffusion theory, this study employs three characteristics of innovation (relative advantage, trialability, and compatibility) to examine which constructs influence airline passenger attitude toward, trust in, and intention to use SBD kiosks. Results of an online survey of 330 Koreans reveal that among the three characteristics of innovation, relative advantage and trialability positively affect attitude toward and trust in SBD service providers and kiosks, and compatibility affects only passenger attitude toward and trust in SBD kiosks. Results also show that trust in SBD service providers is positively related to trust in SBD kiosks, which in turn influences passenger intention to utilise SBD services. Several theoretical and practical implications are suggested.
Performance Analysis and Optimization for Layer-Based Scalable Video Caching in 6G Networks

Scalable video caching is a promising technique to alleviate backbone traffic in sixth generation (6G) networks, and to serve users with video quality that adapts to varying channel conditions. In this paper, we develop a layer-based scalable video caching technique with non-orthogonal transmission by taking advantage of the layer feature in the scalable video. In addition, the impact of different serving base station selection algorithms is investigated. Our results indicate that both the caching placement design and transmission scheme design dominate the caching performance. To evaluate the interplay of these two policies, a tractable metric of Caching Aided Data Rate (CADR) is characterized and maximized by jointly optimizing the aforementioned two policies. Together with extensive Monte Carlo simulations, numerical results are also evaluated in this paper, demonstrating that the proposed Layer-based video Caching scheme with Non-Orthogonal Transmission (LCNOT) can achieve higher CADR performance than other baseline schemes.
Blocked natural ventilation: the effect of a source mass flux

We analyse the density evolution of fluid within a confined ventilated space resulting from the action of a dense turbulent plume originating at the top of the space with finite source volume flux, $Q_0$, and initial source buoyancy flux, $B_0$. The space is ventilated through upper and lower openings of areas $A_u$ and $A_l$ respectively, which are separated by a vertical distance $H$. We show that if $Q_0^3\,{<}\, 2 B_0 H c_l^2 A_l^2$ (where $c_l$ is an empirically determined discharge coefficient) then a two-layer steady stratification becomes established in the room, with outflow through the lower opening and inflow through the upper opening. The interface location depends not only on the geometry of the openings, but also the source conditions. We show that as $Q_0$ increases for fixed $B_0$, the height of the interface, which equals the depth of the lower layer of relatively dense fluid, increases. Eventually, when the source volume flux has a value greater than $Q_m\,{=}\,(c_l A_l)^{2/3}(2B_0 H)^{1/3}$, the natural exchange flow becomes blocked and a steady outflow through both of the openings develops. As a result, the density of the fluid throughout the room gradually evolves towards the density of the incoming dense fluid. We compare our theoretical predictions with a series of laboratory experiments, and discuss the implications of our model for the design of ventilation systems.