Miracle-dz/UniFAR-FTUs huggingface dataset:
Bearing Fault-Detection Method Based on Improved Grey Wolf Algorithm to Optimize Parameters of Multistable Stochastic Resonance

In an effort to overcome the problem that the traditional stochastic resonance system cannot adjust the structural parameters adaptively in bearing fault-signal detection, this article proposes an adaptive-parameter bearing fault-detection method. First of all, the four strategies of Sobol sequence initialization, exponential convergence factor, adaptive position update, and Cauchy–Gaussian hybrid variation are used to improve the basic grey wolf optimization algorithm, which effectively improves the optimization performance of the algorithm. Then, based on the multistable stochastic resonance model, the structure parameters of the multistable stochastic resonance are optimized through improving the grey wolf algorithm, so as to enhance the fault signal and realize the effective detection of the bearing fault signal. Finally, the proposed bearing fault-detection method is used to analyze and diagnose two open-source bearing data sets, and comparative experiments are conducted with the optimization results of other improved algorithms. Meanwhile, the method proposed in this paper is used to diagnose the fault of the bearing in the lifting device of a single-crystal furnace. The experimental results show that the fault frequency of the inner ring of the first bearing data set diagnosed using the proposed method was 158 Hz, and the fault frequency of the outer ring of the second bearing data set diagnosed using the proposed method was 162 Hz. The fault-diagnosis results of the two bearings were equal to the results derived from the theory. Compared with the optimization results of other improved algorithms, the proposed method has a faster convergence speed and a higher output signal-to-noise ratio. At the same time, the fault frequency of the bearing of the lifting device of the single-crystal furnace was effectively diagnosed as 35 Hz, and the bearing fault signal was effectively detected.
Phenomenology and Models of Dark Matter and Neutrinos

The evasive nature of dark matter has been challenging experimentalists and theorists alike for decades. In order to bridge both approaches, we investigate the phenomenology that different simplified models would imprint on various experiments, stressing the importance of the complementarity that they offer. We also address the different roles that neutrinos can play in the dark matter problem. Finally, we consider a more complete theory able to explain neutrino masses and provide a dark matter candidate. We start by analysing the dark sequential Z ′ portal, where direct detection and collider searches put the strongest bounds to the Majorana fermion, which plays the role of dark matter. Then we consider a heavy right-handed neutrino as the mediator between the Standard Model and the dark sector, ruling out dark matter masses below 200 GeV through indirect detection. We also explore the possibility of a sterile neutrino in the keV mass range being the dark matter particle itself, assessing how current and future direct detection experiments can impose limits. We conclude by analysing the phenomenology of a Two Higgs-Doublet Model, with the addition of a U(1)X gauge symmetry in order to avoid flavour changing neutral interactions and produce neutrino masses at the same time. Further constraints are imposed when dark matter is added in this context.
Transcriptome analysis reveals key regulatory genes for root growth related to potassium utilization efficiency in rapeseed (Brassica napus L.)

Root system architecture (RSA) is the primary predictor of nutrient intake and significantly influences potassium utilization efficiency (KUE). Uncertainty persists regarding the genetic factors governing root growth in rapeseed. The root transcriptome analysis reveals the genetic basis driving crop root growth. In this study, RNA-seq was used to profile the overall transcriptome in the root tissue of 20 Brassica napus accessions with high and low KUE. 71,437 genes in the roots displayed variable expression profiles between the two contrasting genotype groups. The 212 genes that had varied expression levels between the high and low KUE lines were found using a pairwise comparison approach. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional classification analysis revealed that the DEGs implicated in hormone and signaling pathways, as well as glucose, lipid, and amino acid metabolism, were all differently regulated in the rapeseed root system. Additionally, we discovered 33 transcription factors (TFs) that control root development were differentially expressed. By combining differential expression analysis, weighted gene co-expression network analysis (WGCNA), and recent genome-wide association study (GWAS) results, four candidate genes were identified as essential hub genes. These potential genes were located fewer than 100 kb from the peak SNPs of QTL clusters, and it was hypothesized that they regulated the formation of the root system. Three of the four hub genes’ homologs—BnaC04G0560400ZS, BnaC04G0560400ZS, and BnaA03G0073500ZS—have been shown to control root development in earlier research. The information produced by our transcriptome profiling could be useful in revealing the molecular processes involved in the growth of rapeseed roots in response to KUE.
Optimal Ensemble Learning Based on Distinctive Feature Selection by Univariate ANOVA-F Statistics for IDS

—Cyber-attacks are increasing day by day. The generation of data by the population of the world is immensely escalated. The advancements in technology, are intern leading to more chances of vulnerabilities to individual’s personal data. Across the world it became a very big challenge to bring down the threats to data security. These threats are not only targeting the user data and also destroying the whole network infrastructure in the local or global level, the attacks could be hardware or software. Central objective of this paper is to design an intrusion detection system using ensemble learning specifically Decision Trees with distinctive feature selection univariate ANOVA-F test. Decision Trees has been the most popular among ensemble learning methods and it also outperforms among the other classification algorithm in various aspects. With the essence of different feature selection techniques, the performance found to be increased more, and the detection outcome will be less prone to false classification. Analysis of Variance (ANOVA) with F-statistics computations could be a reasonable criterion to choose distinctives features in the given network traffic data. The mentioned technique is applied and tested on NSL KDD network dataset. Various performance measures like accuracy, precision, F-score and Cross Validation curve have drawn to justify the ability of the method.
Fibrosis severity scoring on Sirius red histology with multiple-instance deep learning

Abstract Non-alcoholic fatty liver disease (NAFLD) is now the leading cause of chronic liver disease, affecting approximately 30% of people worldwide. Histopathology reading of fibrosis patterns is crucial to diagnosing NAFLD. In particular, separating mild from severe stages corresponds to a critical transition as it correlates with clinical outcomes. Deep Learning for digitized histopathology whole-slide images (WSIs) can reduce high inter- and intra-rater variability. We demonstrate a novel solution to score fibrosis severity on a retrospective cohort of 152 Sirius-Red WSIs, with fibrosis stage annotated at slide level by an expert pathologist. We exploit multiple instance learning and multiple-inferences to address the sparsity of pathological signs. We achieved an accuracy of $ 78.98\pm 5.86\% $ , an F1 score of $ 77.99\pm 5.64\%, $ and an AUC of $ 0.87\pm 0.06 $ . These results set new state-of-the-art benchmarks for this application.
Laser-based two-dimensional position measurement of a cylindrical object for precision alignment

Abstract. Changes in the position and alignment of cylindrical objects (shafts, pipes, etc.) in heavy machines from a particular coordinate system or from its aligning counterpart need to be measured accurately to prolong the life of machinery and to avoid large-scale accidents. A method for online measurement of the position of cylindrical objects in a two-dimensional (2-D) plane using linear optical fiber array-based laser triangulation sensors is proposed. A mathematical model was first developed to deduce the 2-D change in position of the target axis using two perpendicularly placed sensors and considering the effect of the curvature of the target surface on measurements. Then, using the formula derived and our recorded experimental data, we have demonstrated the measurement of the 2-D position of a cylindrical object with two developed sensors placed remotely at a 100-mm standoff distance.
ANALYSIS OF GENDER DIFFERENCES IN THE MAKING OF MATHEMATICAL CROSS-STITCH ART SYMBOLS AND LEARNING OUTCOMES OF HIGH SCHOOL STUDENTS

This study aims to determine the influence of gender differences on the making of mathematical cross-stitch art symbols and students’ learning outcomes. This study is descriptive qualitative research. The study used observation, interviews, documentation, and students’ learning outcomes tests as research instrument. This research has been conducted in the crafts subject at SMA Negeri 1 Pengasih, Kulon Progo, Yogyakarta class XI. Meanwhile, the results of midterm and final exam were used for all students of class XI. This study revealed a significant difference between male and female students when completing the cross art of mathematical symbols. Female students are found to be able to complete the art within 1 month, while male students have not been able to complete the cross stitch. Furthermore, the average scores obtained during midterm and final exam between male and female students also have quite significant differences, in which female students show higher learning outcomes than male students. It is in line with the completion of the cross-stitch assignment given in the craft subject where female students have better abilities in completing the cross-stitch art. AbstrakPenelitian ini bertujuan untuk mengetahui ada tidaknya pengaruh perbedaan gender terhadap pembuatan seni kristik symbol matematika dan hasil belajar siswa. Jenis penelitian yang digunakan dalam penelitian ini adalah penelitian deskriptif kualitatif. Adapun teknik pengumpulan data menggunakan observasi, wawancara, dokumentasi, dan tes hasil belajar siswa. Penelitian ini akan dilakukan pada mata pelajaran Prakarya di SMA Negeri 1 Pengasih, Kulon Progo, Yogyakarta kelas XI. Sedangkan secara kuantitatif digunakan hasil PTS dan PAS semua siswa kelas XI. Penelitian ini menghasilkan perbedaan yang cukup signifikan antara siswa laki-laki dan perempuan saat menyelesaikan seni kristik symbol matematik. Yang mana siswa perempuan mampu menyelesaikan seni tersebut dalam waktu 1 bulan, sedangkan siswa laki-laki belum mampu menyelesaikan seni kristik tersebut. Lebih lanjut, rata-rata skor yang diperoleh saat PTS dan PAS antara siswa laki-laki dan perempuan juga memiliki perbedaan yang cukup signfiikan, yang mana siswa perempuan memiliki hasil belajar yang lebih tinggi daripada siswa laki-laki. Hal tersebut selaras dengan penyelesaian tugas seni kristik yang diberikan pada mata pelajaran prakarya yang mana siswa perempuan memiliki kemampuan yang lebih baik dalam menyelesaikan seni kristik tersebut.
Experimental and Numerical Studies of Hydrodynamics of Gas Flow through Orthogonal Networks

Simulation programs contain Computational Fluid Dynamics - CFD codes and are a useful tool used for gas flow through porous materials. Conducting numerical simulations allows for detailed analysis of hydrodynamic phenomena. The results of numerical modeling should always be verifiable based on experimental data. Only their compliance with the results of experimental tests is a determinant of the correctness of the applied method. As part of the work, experimental studies of hydrodynamics of gas flow through an isotropic porous material were carried out and numerical simulation for material of the same shape was used. In the CFD modeling Kolmogorov's hypothesis for the transport of kinetic energy of turbulence k and transport of dissipation rate of kinetic energy of turbulence ε was used.
Simple Lawn Irrigation Measurement Training for Master Gardeners and Homeowners

For municipalities across Oklahoma and the southern United States, maximum domestic water usage occurs during July and August, and up to 50% of this water is used outdoors. There is a need to conserve water resources through reduced outdoor irrigation. The objective of the Extension project described here was to teach Oklahoma Master Gardeners a simple technique to effectively measure lawn irrigation output through simple irrigation audit workshops. Seventy-six Master Gardeners participated in three simple irrigation audit workshops across Oklahoma. The results suggest that simple irrigation workshops can significantly increase Master Gardener knowledge of home lawn irrigation system output.
Semidefinite Programming Approaches to Network Clustering and Smoothing

Community detection and link prediction are two important problems in network analysis. In this dissertation, we propose semidefinite programming approaches to network community detection and edge probabilities estimation. Interestingly, despite different motivations, it turns out that the proposed approaches to the two problems are based on the same semidefinite program (SDP) with appropriately chosen input matrices. Our SDP relies on a semidefinite relaxation of the set of normalized clustering matrices. We derive optimality conditions of the SDP from a geometric point of view and deal with a convex body, called the Fantope. This enables us to analyze the SDP by making use of the properties of the Fantope. We design an efficient alternating direction method of multipliers algorithm to solve the SDP, which only requires computation of a small number of leading eigenvectors of symmetric matrices. For community detection, the SDP is derived from the partition criterion of maximizing the sum of average intra-cluster similarities over all clusters. The feasible set of our SDP is contained in the Fantope, which enables us to connect our SDP to sparse PCA and spectral clustering. Unlike previously proposed SDPs for community detection which use the adjacency matrix as the input matrix, we also consider using the symmetric normalized graph Laplacian matrix and the squared adjacency matrix as the input matrix to the SDP. The former choice of the input matrix is motivated ii by the connection between our SDP and spectral clustering and the latter builds an interesting connection between network clustering and multivariate data clustering. Using optimality conditions, we analyze the population consistency of the SDP using different input matrices under various network models. We show the exact recovery property of our SDP under strongly assortative stochastic block models when the input matrix is chosen to be the adjacency matrix. For network edge probability estimation, the SDP is derived from a least squares criterion. Since the solution to our SDP is a symmetric doubly stochastic matrix, it can be used as a smoother matrix to smooth the adjacency matrix. The estimated edge probability matrix is the smoothed adjacency matrix. We also connect our SDP-based method of estimating edge probabilities to a recently proposed neighborhood smoothing method. Our numerical experiments show that the proposed SDP has good empirical performances for community detection, edge probability estimation, and link prediction and it outperforms other state-of-the-art methods on Facebook collegiate networks for both community detection and link prediction.
All-in-focus reconstruction of a dynamic scene from multiple-multifocus captures

Multifocus acquisition provides a way to reconstruct high resolution images from a 3D scene by capturing a sequence of multiple focal planes along the optical axis of the camera. Since the capture of multifocus images is not simultaneous but relies on a temporal sequence of captures while focusing at different focal planes the multifocus method is usually limited to static scenes. However since the development of high speed electrically focus tunable lenses it is also possible to reconstruct dynamic scenes from multifocus acquisition provided that the time to perform a complete sweep along z axis is much shorter than the time involved in the dynamic scene. In the present work we propose all-in-focus reconstruction of a 3D dynamic scene from multiple-multifocus captures.
Using the photoplethysmography method to monitor age-related changes in the cardiovascular system

Introduction: Aging is a physiological process characterized by progressive changes in all organ systems. In the last few decades, the elderly population has been growing, so the scientific community is focusing on the investigation of the aging process, all in order to improve the quality of life in elderly. One of the biggest challenges in studying the impact of the aging on the human body represents the monitoring of the changes that inevitably occur in arterial blood vessels. Therefore, the medical community has invested a great deal of effort in studying and discovering new methods and tools that could be used to monitor the changes in arterial blood vessels caused by the aging process. The goal of our research was to develop a new diagnostic method using a photoplethysmographic sensor and to examine the impact of the aging process on the cardiovascular system in adults. Long-term recorded arterial blood flow waveforms were analyzed using detrended fluctuation analysis. Materials and Methods: The study included 117 respondents, aged 20–70 years. The waveform of the arterial blood flow was recorded for 5 min, with an optical sensor placed above the left common carotid artery, simultaneously with a single-channel ECG. For each cardiac cycle, the blood flow amplitude was determined, and a new time series was formed, which was analyzed non-linearly (DFA method). The values of the scalar coefficients α 1 and α 2, particularly their ratio (α 1/α 2) were obtained, which were then monitored in relation to the age of the subjects. Result: The values of the scalar ratio (α 1/α 2) were significantly different between the subjects older and younger than 50 years. The value of the α 1/α 2 decreased exponentially with the aging. In the population of middle-aged adults, this ratio had a value around 1, in young adults the value was exclusively higher than 1 and in older adults the value was exclusively lower than 1. Conclusion: The results of this study indicated that the aging led to a decrease in the α 1/α 2 in the population of healthy subjects. With this non-invasive method, changes in the cardiovascular system due to aging can be detected and monitored.
An alternative method for evaluating stationarity in transition models

ABSTRACT Transition models are an important framework that can be used to model longitudinal categorical data. A relevant issue in applying these models is the condition of stationarity, or homogeneity of transition probabilities over time. We propose two tests to assess stationarity in transition models: Wald and likelihood-ratio tests, which do not make use of transition probabilities, using only the estimated parameters of the models in contrast to the classical test available in the literature. In this paper, we present two motivating studies, with ordinal longitudinal data, to which proportional odds transition models are fitted and the two proposed tests are applied as well as the classical test. Additionally, their performances are assessed through simulation studies. The results show that the proposed tests have good performance, being better for control of type-I error and they present equivalent power functions asymptotically. Also, the correlations between the Wald, likelihood-ratio and the classical test statistics are positive and large, an indicator of general concordance. Additionally, both of the proposed tests are more flexible and can be applied in studies with qualitative and quantitative covariates.
Uważność a radzenie sobie ze stresem u kobiet i mężczyzn

The study aimed to expand knowledge of the relationship between mindfulness trait and coping styles in stressful situations, taking into account gender differences. Based on previous research findings, it was expected that mindfulness would be positively associated with Active Coping and Seeking Support styles and negatively associated with Avoidant Coping and Helplessness styles. In addition, differences in levels of mindfulness and coping styles were expected between women and men, as well as a moderating effect of gender on the relationship between mindfulness and coping styles. The study included 259 participants. The Freiburg Mindfulness Inventory in the short version and the Mini-CO PE Inventory were used. A positive correlation was observed between the level of mindfulness and strategies from the Active coping group. A negative correlation between mindfulness and Helplessness strategies was also found. The results also showed that women had significantly lower levels of the mindfulness trait and a greater tendency to use the Support Seeking, Avoidant Behavior, and Helplessness styles than men. In addition, women with higher mindfulness had a lower tendency to use the Helplessness style, while this relationship was not significant for men. The study contributes to a better understanding of the relationship between mindfulness and stress coping by gender.
Cognitively Inspired Cyber Physical Systems Design through Brain Signal Analysis and Decoding

Cybernetics, or the study of control and communication, informs the design, modelling, and characterization of cyber-physical systems. In order to construct functional cyber-physical systems in the actual world, the thesis presents a unique method for analysing and decoding brain signals involved in cognitive processes. The goal of this research is to create and evaluate next-generation cyber-physical systems that, when connected to a human brain via a brain-computer (BCI) interface, can read the human brain's neural code and act as smart tools in fields like medicine, gaming, affective computing, and the study of subjective creativity. The non-invasive examination of human memory for the potential early prediction of memory-related disorders like dementia and prosopagnosia is one example of the healthcare application. The use of brain-computer interfaces to simulate the skills of human game trainers for the purpose of robotic game instruction is also discussed. Due to a lack of qualified individuals and the inherent monotony of the work, it is essential that human game trainers be replaced by robots. Using the predicted brain-connectivity (anticipated signalling routes) between active brain lobes/regions, affective computing apps attempt to identify human emotional states. A network study of human emotions has potential for use in psychotherapy. The implementation of a creativity test is crucial for solving issues related to scientific creativity by autonomously detecting subjective creativity from the brain's reaction to memory-generated stimuli.