Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Personality Traits on Facebook Using Key Phrase Extraction, Language Models and Machine Learning

The study of automated personality trait detection has become increasingly prevalent in the field of affective computing and sentiment analysis. Psycholinguistic databases and linguistic styles are found to have correlations with personality traits. Natural language processing has improved thanks to transfer learning. With pre-trained language models as features, this paper presents a machine learning model that benefits from transfer learning. The proposed method using key phrase extraction and SentenceTransformer through decision tree classifier surpasses the XGBoost by 26.79% in terms of accuracy and exhibits 97.79% on myPersonality dataset.
Fault Detection For Dynamic System Based on Generative Adversarial Networks

To solve the problem of multivariable time series fault detection, a Long Short Term Memory Generative Adversarial network (LSTM-GAN) model was proposed in this paper. The contributions are as follows: 1. generators and discriminators were constructed from LSTM Network to capture the dynamic times series related characteristics; 2. then Generative Adversarial Network was introduced to generate pseudo-samples from a latent space to capture the real data distribution; 3. an inverter was proposed to map the time series data back to latent space and the status indicator based on reconstruction error was developed for fault detection. A big normal training dataset was obtained using a Simulink version Tennessee Eastman process simulation and experimental results show that the proposed method can capture dynamic characteristics from big data set and reduces the false alarm rate compared with the traditional method, which verifies the feasibility of the proposed method.
Prescribe performance dynamic surface control of a flapping-wing aerial vehicle with unknown input dead-zone

To perform the position and attitude control of the flapping wing aerial vehicle (FWAV), this article proposes a prescribed performance function based dynamic surface controller (PPDSC) with adaptive neural network (ANN) estimator in presence of unknown input dead-zone. A special radical Lyapunov function consists of a L function is chosen to guarantee the prescribed performance (PP) of the position and attitude trajectory tracking errors for the FWAV. The dynamic surface control (DSC) is a method that utilizing a first-order low pass filter to avoid the phenomenon named explosion of complex of the backstepping method. Meanwhile, the ANN estimator is designed to pay the effect of unknown input dead-zone and disturbance. Then, the stability of the closed-loop FWAV system is proved by utilizing Lyapunov’s direct method. Finally, numerical simulations are given to demonstrate the effectiveness of the above method.
Integrated Multi-Objective Optimization for Reheating Furnace Scheduling and Rolling Plan in Hot Rolling Process of Steel Industry

As one of the most important process, hot rolling that consists of several procedures exhibits as both a primary production period and an intensive energy-consuming consumer, of which the production efficiency and energy consumption is of great significance for the enterprises. Although existed methods have proposed some solutions, most of them are mainly based on single-objective optimization toward separate procedure rather than multi-objective alternative considering the practical connections between the consecutive stages. For this purpose, an integrated optimization method based on unified modeling and multi-stage evolutionary algorithm is proposed in this study. More specifically, a mathematical model for this Constrained Multi-Objective Problem (CMOP) is formulated at first, where the objectives and constraints for the scheduling of reheating furnace and rolling is ensemble considered. Furthermore, an improved Multi-Stage Multi-Objective Evolutionary Algorithm framework (MSMOEA) is proposed, which performs a sequential Pareto Front based optimization according to the flow of production procedures, so that a scheduling plan for both the reheating furnace and rolling is finally obtained. To evaluate the performance for both the modeling and optimization, the proposed unified model and MSMOEA framework are compared with single-objective model and commonly deployed optimization methods using real data from a steel plant in China, respectively. The results demonstrate the superiority of the proposed method, which can be beneficial for efficiency increasement and energy costs reduction on site.
Robust Low-Rank Matrix Factorization via Block Iteratively Reweighted Least-Squares

In this paper, we concentrate on the problem of low-rank matrix factorization (LRMF) in the presence of outliers and missing observations. This problem is paramount in practice as a variety of applications, such as signal processing, wireless communications, and machine learning, fit such an LRMF paradigm. We introduces a general LRMF formulation of minimizing the outlier robust $\ell_{p}$ -based fitting term plus structure-promoting constraints, including nonnegativity and/or sparsity. To address this computationally intractable problem, we develop an algorithmic framework, named block iteratively reweighted least-squares (BIRLS). Each iteration of BIRLS is obtained by minimizing a regularized reweighted $\ell_{2}$ -based problem with a closed-form solution in an interweaved block update fashion. Meanwhile, we prove that the whole iterative sequence of BIRLS is convergent and converges to a stationary point of the original problem. Numerical experiments demonstrate the superiority of the proposed algorithm.
Generated Tabular Data with Multi-GANs for Arrhythmia Classification Based on DNN Models

Globally, cardiovascular diseases (CVD) cause sudden cardiac death. According to the WHO, cardiovascular disease killed more than 32% of the world’s population in 2019. Arrhythmia is one of the symptoms of CVD that can be identified as a pattern of abnormal electrical impulses to the myocardium that can cause the heart to beat too quickly or too slowly, reducing its ability to pump blood properly and increasing mortality risk. In particular, AI, which includes Deep Learning (DL) or Machine Learning (ML), is contributing to the real-time expansion and complexity of big data in the healthcare industry. AI will make prediction easier for doctors to diagnose the symptoms of arrhythmia based on its signals. In this research, Multi-GANs give the best-generated data in 3000 epochs with the minimum percentage of loss discriminator system to identify if the data is fake or not, and also with the maximum error in loss generator system to create more variance in the generated data, including noise feature. To train the data, we use DNN models i.e. CNN, Resnet-101, and Densenet-121, which produce substantial training and validating outcomes. By testing the purpose of DNN’s accuracy, precision, recall, and Fl-score, it brings the best results with correct prediction of classes in normal, atrial premature, premature ventricular contraction, a fusion of ventricular and normal, and a fusion of paced and normal, respectively. For 20 epochs and 100 batches, we get an accuracy of 99.7609 percent for the CNN model and also 100 percent in precision, recall, and Fl-score, which is the best DNN model so far in this paper.
Usability Evaluation of a Muscle Strength Training System for Older Adults

In recent years, the medical care industry has gradually developed toward slowing the decline of older people. It aims to use related products, services, and appropriate training to enhance older peoples' mobile intensity and maintain their mobility and independence. This research integrates an augmented reality serious game with NimBO, a portable single-joint exoskeleton, to assist older people in lower extremity muscle strength training. It is hoped that a user-centered rehabilitation system can be proposed through iterative improvement. This study uses interactive technology to improve the experience of training. During the prototyping stage, we invited nine experts to assist in evaluating the usability of this lower limb muscle strength training system. We quantified subjective evaluation through System Usability Scale scores and identified the shortcomings of this serious game through two focus group interviews. The compiled expert advice can be used as a reference for developing similar systems for older people in the future.
A core temperature estimation method of lithium-ion battery based on electrothermal model

With the wide application of lithium-ion batteries (LIBs), its safety has attracted much attention. Due to the limitations of existing sensors and measurement technologies, the internal temperature of LIBs cannot be measured directly, which makes it impossible to monitor the core temperature of LIBs in real time. This will cause hidden hazards to the safe use of LIBs. In this paper, the accurate battery heat generation model is established by combining the heat generation model proposed by Bernardi with the heat transfer and heat dissipation model. Particle swarm optimization (PSO) algorithm is used to identify the parameters of the lumped parameter two-state thermal model of LIBs proposed in this paper. In the experiment, after drilling the battery, the built-in temperature sensor is utilized to measure the real core temperature, and then the unscented Kalman filter (UKF) algorithm is employed to estimate the core temperature. The experimental results show that the estimation errors by the method used in this paper are small and have good adaptability and robustness, which provides a reliable method for core temperature monitoring of LIBs.
Detecting COVID-19 on CT Images with Impulsive-Backpropagation Neural Networks

This paper presents an impulsive-backpropagation neural network (IBNN) based learning algorithm for detecting Coronavirus Disease 2019 (COVID-19), by classifying chest computed tomography (CT) images. Inspired by the nerve impulses in brain networks, the IBNN algorithm consists of two parts: a multi-layered network of impulsive neurons and a gradient decent backpropagation mechanism. The effectiveness of the IBNN algorithm is validated on clinical COVID-19 database, and a classification accuracy of 98.19% is achieved. It is further demonstrated by comparative studies that the IBNN may outperform some other learning algorithms through the integration of nerve impulses and backpropagation. Considering the intricate attributes of the chest CT scan images, the IBNN algorithm also exhibits a potential capacity of pattern recognition on complicated samples.
Level set methods : evolving interfaces in geometry, fluid mechanics, computer vision, and materials science

Part I. The Theory of Moving Interfaces: 1. Theory of front evolution 2. Level set formulation Part II. Algorithms, Analysis, and Implementations of Level Set Methods: 3. Traditional techniques for tracking interfaces 4. Hyperbolic conservation laws 5. Approximating the level set equation 6. Viscosity solutions and Hamilton-Jacobi equations 7. A hierarchy of fast level set methods 8. A fast marching level set method 9. Extensions to the basic method Part III. Applications: 10. Geometry: curve/surface shrinkage and self-similar surfaces 11. Grid generation 12. Image enhancement and noise removal 13. Minimal surfaces and surfaces of prescribed curvature 14. Combustion, crystal growth, and two-fluid flow 15. Computer vision: shape detection and recognition 16. Applications of the fast marching level set method 17. Etching and deposition in microchip fabrication 18. New areas.
Control methods and compensation characteristics of a series active filter for the neutral conductor

Due to the advance of information technologies, a large number of electronic products such as personal computers have been connected to power distribution systems in commercial buildings. Hence, voltage distortion on utility outlets and excessive neutral current on distribution lines have arisen, and those lead to a number of serious problems in the distribution system. Two control methods and the related compensation characteristics of a series active filter connected to the neutral conductor are presented in this paper. The distinct functions of the proposed active filter are mitigation of 3rd-harmonic voltage and neutral current in a three-phase four-wire distribution system in a building. The required power of the proposed active filter is less than 10% of that of the harmonic producing loads. A control method of the dc capacitor voltage on the active filter circuit is also described. it is clarified through experiments that one those two functions of the active filter can be realized selectively and the dc capacitor voltage of the active filter can be regulated to a desired value.
Simulations of electron elastic diffuse scattering patterns from individual nanometre-sized dislocation loops. I. Kinematical methodology

A new experimental technique has recently been developed for characterizing small dislocation loops in the electron microscope by collecting elastic diffuse scattering patterns in the vicinity of Bragg reflections. In this paper we develop the theory and computational approaches for simulating such patterns. We apply the anisotropic elastic theory of displacement fields of planar dislocation loops of arbitrary shape to compute the distortion diffuse scattering amplitudes. To do this we extend methods based on kinematical scattering theory, previously used to simulate X-ray and neutron diffuse scattering patterns. We demonstrate significant differences in the diffuse scattering patterns for the electron scattering case that arise from the small width of the incident electron beam, and under weak-beam conditions. We show that the method may be used to distinguish between individual, very small, vacancy or interstitial loops. We also show that elastic anisotropy may have an influence on elastic diffuse scattering patterns from loops, to an extent that varies with the magnitude of the deviation parameter.
Using spontaneous eye blink-related brain activity to investigate cognitive load during mobile map-assisted navigation

The continuous assessment of pedestrians’ cognitive load during a naturalistic mobile map-assisted navigation task is challenging because of limited experimental control over stimulus presentation, human-map-interactions, and other participant responses. To overcome this challenge, the present study takes advantage of navigators’ spontaneous eye blinks during navigation to serve as event markers in continuously recorded electroencephalography (EEG) data to assess cognitive load in a mobile map-assisted navigation task. We examined if and how displaying different numbers of landmarks (3 vs. 5 vs. 7) on mobile maps along a given route would influence navigators’ cognitive load during navigation in virtual urban environments. Cognitive load was assessed by the peak amplitudes of the blink-related fronto-central N2 and parieto-occipital P3. Our results show increased parieto-occipital P3 amplitude indicating higher cognitive load in the 7-landmark condition, compared to showing 3 or 5 landmarks. Our prior research already demonstrated that participants acquire more spatial knowledge in the 5- and 7-landmark conditions compared to the 3-landmark condition. Together with the current study, we find that showing 5 landmarks, compared to 3 or 7 landmarks, improved spatial learning without overtaxing cognitive load during navigation in different urban environments. Our findings also indicate a possible cognitive load spillover effect during map-assisted wayfinding whereby cognitive load during map viewing might have affected cognitive load during goal-directed locomotion in the environment or vice versa. Our research demonstrates that users’ cognitive load and spatial learning should be considered together when designing the display of future navigation aids and that navigators’ eye blinks can serve as useful event makers to parse continuous human brain dynamics reflecting cognitive load in naturalistic settings.
Wavelet-based point feature extractor for multisensor image restoration

Multi-sensor data such as visual/IR imagery are difficult to register due to the discrepancy in their grayscale characteristics. This paper presents a wavelet-based point feature extraction algorithm to select distinct and consistent point features across images in order to overcome this difficulty. It incorporates local statistical information of the image intensity to locate point features in multi-resolution contour maps generated using the wavelet transform. Experimental results using real multi-sensor data are presented.
Depression and HIV risks: Engaging older African American women in HIV prevention education through the church

This report describes the recruitment of a sample of older African American women to test the effectiveness of an educational HIV prevention intervention that sought to reduce depressive symptoms and thereby HIV risks in this population. The outreach venue is the Black church. A framework for maximizing response is suggested. Of 62 women who participated in two arms of the intervention, 29 were assigned randomly to a four-session discussion group (experimental condition) and 33 were assigned to a one-session informational group (control condition) focused on HIV prevention education. Between-within subjects analyses of variance showed that participation in the study was associated with a significant improvement in the women's psychological status, i.e., decreased depressive symptoms. This change in depressive symptoms was due in part to the experimental condition assignment. Implications for future HIV prevention interventions, research, and methods used to maximize the probability of response among older African American women are discussed.
The role of the gas in propagating star formation

The two-component gas model of stochastic self-propagating star formation is able to give a good description of the behavior of the gas in galaxies if we adopt a reinterpretation of the propagating star formation process. Previous work has concentrated on the stars; however, star formation is at least a two-step process; the H i aggregates into molecular clouds, and stars are then created in these clouds. Following Elmegreen, the rate determining process is taken as the first step, the creation of the clouds. With this choice the propagation star formation model prevides a natural explanation for the observed radial distribution of H i and H/sub 2/ in galaxies. In addition, in agreement with observations, it predicts that the optical edge of a galaxy should be sharp and that the radial dependence of the luminosity and H/sub 2/ should be the same. Last, the model predicts that the lifetime of molecular clouds should be long, of the order of 10/sup 8/ years at least.
Enhancing MTInSAR Phase Unwrapping in Decorrelating Environments by Spatiotemporal Observation Optimization

In multitemporal interferometric synthetic aperture radar (MTInSAR) processing, phase unwrapping (PhU) is a vital procedure, which affects the accurate deformation retrieval, especially in environments suffering decorrelation. Although a great surge of work focuses on solving the integers of $2\pi $ associated with the wrapped observations (i.e., the inputs of unwrapping algorithms), the quality of inputs deserves an equal attention, as it is a basis for a reliable unwrapping. In this letter, we seek to improve the PhU accuracy by enhancing the entire quality of differential phase observations. To select an optimal interferogram stack efficiently, we propose a quadtree-based coherence estimation method for fast evaluating the interferogram quality and then develop a strategy that constructs redundant networks connecting synthetic aperture radar (SAR) images and points, respectively. The proposed strategy utilizes the combination of minimum spanning tree (MST) and triangular closure to determine the pair of image/point included in the network. We validate the effectiveness of our method by Sentine-1 SAR data over Shenzhen airport, where decorrelated scatterers with weak backscattering energy in runways challenge a reliable subsidence extraction. The cross-comparison indicates the importance of the quality of inputs that affects the point connectivity in both temporal and spatial dimensions.
Multiple-model GWAS identifies optimal allelic combinations of quantitative trait loci for malic acid in tomato

Abstract Malic acid (MA) is an important flavor acid in fruits and acts as a mediator in a series of metabolic pathways. It is important to understand the factors affecting MA metabolism for fruit flavor improvement and to understand MA-mediated biological processes. However, the metabolic accumulation of MA is controlled by complex heredity and environmental factors, making it difficult to predict and regulate the metabolism of MA. In this study, we carried out a genome-wide association study (GWAS) on MA using eight milestone models with two-environment repeats. A series of associated SNP variations were identified from the GWAS, and 15 high-confidence annotated genes were further predicted based on linkage disequilibrium and lead SNPs. The transcriptome data of candidate genes were explored within different tomato organs as well as various fruit tissues, and suggested specific expression patterns in fruit pericarp. Based on the genetic parameters of population differentiation and SNP distribution, tomato MA content has been more influenced by domestication sweeps and less affected by improvement sweeps in the long-term history of tomato breeding. In addition, genotype × environment interaction might contribute to the difference in domestication phenotypic data under different environments. This study provides new genetic insights into how tomato changed its MA content during breeding and makes available function-based markers for breeding by marker-assisted selection.
Wave interactions - the evolution of an idea

This essay gives a personal and possibly incomplete history of the way in which the simple idea of weak resonant wave interactions grew to find application to a variety of phenomena in several contexts. The development involved incremental steps by many people in the past twenty years, gaining simplicity with maturity. The final stage seems to be approaching when the limits of usefulness of the idea are beginning to become apparent.