Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault diagnosis of rotating machinery via multi-structure fusion discriminative projection

Rotating machinery is one of the important parts of industrial production equipment, and it is of great practical significance for fault diagnosis. Aiming at the problem of classification difficulty caused by feature interleaving in complex working conditions and high-dimension of rotating machinery fault samples, a rotating machinery fault diagnosis method based on multi-structure fusion discriminative projection (MFDP) is proposed. MFDP constructed intraclass and interclass hypergraph structures with multivariate relationships, fully revealing the higher-order association information among multiple samples. Besides, a tangential graph structure of MFDP is further constructed by combining the tangential affine of local samples to preserve the local tangential information of the manifold space. In the method, a unified objective optimization model of the discriminative hypergraph structures and local tangential graph structures is developed, and by solving the model, we can obtain fault structure features with well intraclass compactness and interclass separability. Extensive experiments on the Case Western Reserve University bearing dataset and Connecticut gear dataset show that the method has a good diagnostic accuracy of rotating machinery in different working conditions.
Identification of Potential Phytochemical/Antimicrobial Agents against Pseudoperonospora cubensis Causing Downy Mildew in Cucumber through In-Silico Docking

Compatibility interactions between the host and the fungal proteins are necessary to successfully establish a disease in plants by fungi or other diseases. Photochemical and antimicrobial substances are generally known to increase plant resilience, which is essential for eradicating fungus infections. Through homology modeling and in silico docking analysis, we assessed 50 phytochemicals from cucumber (Cucumis sativus), 15 antimicrobial compounds from botanical sources, and six compounds from chemical sources against two proteins of Pseudoperonospora cubensis linked to cucumber downy mildew. Alpha and beta sheets made up the 3D structures of the two protein models. According to Ramachandran plot analysis, the QNE 4 effector protein model was considered high quality because it had 86.8% of its residues in the preferred region. The results of the molecular docking analysis showed that the QNE4 and cytochrome oxidase subunit 1 proteins of P. cubensis showed good binding affinities with glucosyl flavones, terpenoids and flavonoids from phytochemicals, antimicrobial compounds from botanicals (garlic and clove), and chemically synthesized compounds, indicating the potential for antifungal activity.
Improving the Precision of On-Wafer W-Band Scalar Load-Pull Measurements

This article presents an empirical investigation of calibration effects on load-pull measurements collected on wafer and at W-band frequencies. An analysis of scattering parameter (S-parameter) measurements provides insight into how small-signal metrics germane to load pull are affected by choice of the calibration technique. It is found that off-wafer line-reflect-reflect-match (LRRM) calibrated measurements of the same transistor with different probes exhibit drastically different maximum small-signal gains compared to equivalent on-wafer multiline thru-reflect-line (mTRL) calibrated measurements. Load-pull measurements are heavily influenced by choice of calibration algorithm, and LRRM calibrated large-signal measurements collected with different waveguide probes yield variations in large-signal gain of over 2 dB and variations in peak PAE of over 24 percentage points. The equivalent on-wafer mTRL calibrated load-pull measurements collected with different waveguide probes are consistent to within 0.1 dB for large-signal gain and 1 percentage point for peak PAE. This work provides quantitative evidence that on-wafer mTRL calibration with well-designed calibration structures is preferred for large-signal measurements collected at millimeter-wave frequencies. If utilization of on-wafer mTRL calibration is not possible, this work suggests using similar measurement setups, i.e., waveguide probes, calibration standards, etc., for evaluating on-wafer unmatched transistors in a consistent manner.
On the Bayesian estimation for Cronbach's alpha

ABSTRACT This article considers the problem of estimating Cronbach's alpha under a Bayeisan framework. Such Bayes estimator arrives through out approximating distribution of the maximum likelihood estimator for Cronbach's alpha by an F distribution. Then, employing a noninformative prior distribution, Bayes estimator under squared-error and LINEX loss functions have been evaluated. Simulation studies suggest that the Bayes estimator under LINEX loss function reduce biasness of the ordinary maximum likelihood estimator. Moreover, The LINEX Bayes estimator does not sensitive with respect to choice of hyperparameters of prior distribution. R codes for readers to calculate Bayesian Cronbach's alpha have been given.
APPLICATION OF THE SCHWARZ-CHRISTOFFEL TRANSFORMATION IN SOLVING TWO-DIMENSIONAL TURBULENT FLOWS IN COMPLEX GEOMETRIES

In this paper, two-dimensional turbulent flows in different and complex geometries are simulated by using an accurate grid generation method. In order to analyze the fluid flow, numerical solution of the continuity and Navier-Stokes equations are solved using CFD techniques. Considering the complexity of the physical geometry, conformal mapping is used to generate an orthogonal grid by means of the Schwarz-Christoffel transformation. The standard k-e turbulence model is employed to simulate the mean turbulent flow field, using a linear low-Re k-e model for near wall region. The governing equations are transformed in the computational domain and the discretized forms of these equations are obtained by the control volume method. Finite difference forms of the governing equations are solved in the computational plane and the SIMPLE algorithm is used for the pressure- velocity coupling. The important part of the present work is based on the numerical integration of Schwraz-Christoffel transformation in grid generation for simulating fluid flow in different complex geometries. To validate the computational results, the theoareticil data is compared with that of theoretical results achieved by other investigators, which are in reasonable agreement.
Convergence of modified S-iteration process for two asymptotically quasi-nonexpansive type mappings in CAT(0) spaces

Abstract The purpose of this paper is to study convergence of a newly defined modified S-iteration process to a common fixed point of two asymptotically quasi-nonexpansive type mappings in the setting of CAT(0) space. We give a suffcient condition for convergence to a common fixed point and establish some strong convergence theorems for the said iteration process and mappings under suitable conditions. Our results extend and improve many known results from the existing literature.
Performance of Quantized Massive MIMO With Fronthaul Rate Constraint Over Quasi-Static Channels

We provide a rigorous framework for characterizing and numerically evaluating the error probability achievable in the uplink and downlink of a fully digital quantized multiuser multiple-input multiple-output (MIMO) system. We assume that the system operates over a quasi-static channel that does not change across the finite-length transmitted codewords, and only imperfect channel state information (CSI) is available at the base station (BS) and at the user equipments. The need for the novel framework developed in this paper stems from the fact that, for the quasi-static scenario, commonly used signal-to-interference-and-distortion-ratio expressions that depend on the variance of the channel estimation error are not relatable to any rigorous information-theoretic achievable-rate bound. We use our framework to investigate how the performance of a fully digital massive MIMO system subject to a fronthaul rate constraint, which imposes a limit on the number of samples per second produced by the analog-to-digital and digital-to-analog converters (ADCs and DACs), depends on the number of BS antennas and on the precision of the ADCs and DACs. In particular, we characterize, for a given fronthaul constraint, the trade-off between the number of antennas and the resolution of the data converters, and discuss how this trade-off is influenced by the accuracy of the available CSI. Our framework captures explicitly the cost, in terms of spectral efficiency, of pilot transmission—an overhead that the outage capacity, the classic asymptotic metric used in this scenario, cannot capture. We present extensive numerical results that validate the accuracy of the proposed framework and allow us to characterize, for a given fronthaul constraint, the optimal number of antennas and the optimal resolution of the converters as a function of the transmitted power and of the available CSI.
Multimodel Inference: Understanding AIC relative variable importance values

The goal of this material is to present extended theory and interpretation for the variable importance weights in multimodel information theoretic (IT) inference. We show that these statistics are a valid measure of relative predictor variable importance under all subsets model selection. This became of concern to the author upon realizing that the validity and value of variable importance weights, given in Burnham and Anderson (2002), has been called into question recently. An aspect of the “validity” issue that seems to have been raised is that these importance weights did not relate to regression parameter estimates, which historically are the basis for inference about predictor importance given a single model. The material here answers these issues with theory that clarifies the relative variable importance values in question and shows they are valid.
An Adaptive Deep Learning Neural Network Model to Enhance Machine-Learning-Based Classifiers for Intrusion Detection in Smart Grids

Modern smart grids are built based on top of advanced computing and networking technologies, where condition monitoring relies on secure cyberphysical connectivity. Over the network infrastructure, transported data containing confidential information, must be protected as smart grids are vulnerable and subject to various cyberattacks. Various machine learning based classifiers were proposed for intrusion detection in smart grids. However, each of them has respective advantage and disadvantages. Aiming to improve the performance of existing machine learning based classifiers, this paper proposes an adaptive deep learning algorithm with a data pre-processing module, a neural network pre-training module and a classifier module, which work together classify intrusion data types using their high-dimensional data features. The proposed Adaptive Deep Learning (ADL) algorithm obtains the number of layers and the number of neurons per layer by determining the characteristic dimension of the network traffic. With transfer learning, the proposed ADL algorithm can extract the original data dimensions and obtain new abstract features. By combining deep learning models with traditional machine learning-based classification models, the performance of classification of network traffic data is significantly improved. By using the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset, experimental results show that the proposed ADL algorithm improves the effectiveness of existing intrusion detection methods and reduces the training time, indicating a promising candidate to enhance network security in smart grids.
Engaging with health programming in head start: Parents’ perspectives

The purpose of this study was to explore parents’ common experiences with healthy eating and physical activity-focused (HE-PA) programming in one Head Start (HS) program (two centers) in eastern North Carolina (NC). Researchers conducted 13 in-depth semi-structured telephone interviews November 2017–May 2018. Participants were parents of preschool children (4–5 years) enrolled in HS. Interviews were recorded and transcribed verbatim. Phenomenology was used to guide study design and analysis to identify themes. Researchers identified seven emergent themes within three categories: (1) strategies to facilitate engagement; (2) motivators for engagement; and (3) barriers to engagement. HS facilitated engagement through HE-PA focused programming. Parents reported they were motivated by self-interest and their children, but engagement was challenged by poor communication and socioeconomic barriers. HS programs may benefit from new strategies that help parents overcome barriers to making HE-PA a priority amongst competing priorities. Research is also needed to explore new methods of communication that align with parent-reported motivations.
Two-dimensional simulation of spatiotemporal generation of dielectric barrier columnar discharges in atmospheric helium

A two-dimensional (2D) fluid model is presented to investigate the spatiotemporal generation and dynamic mechanics of dielectric barrier columnar discharges in atmospheric helium. The model was examined with discharge currents measured in experiments and images taken by an intensified charge couple device camera. Based on the model, a columnar discharge was simulated for several cycles after being ignited. The discharge could be regarded as an initial unstable stage for the first three and a half cycles, then a steady state for the following cycles. In the initial stage, the discharge evolves from a uniform pattern into a columnar one. The calculated equipotential lines, 2D radial electric field, and electron density distributions at the edge of uniform discharges show the radial electric field accounts for the shrinking discharge area and the formation of discharge columns in the end. The columnar glow discharges and the Townsend discharges beyond the columns could coexist in the initial stage, and a Townsend discharge might develop into a new glow column in the next half-cycle. The radial electric field surrounding a glow discharge column has an inhibiting effect on the ionization in the peripheral area.
Bradykinin and Neurotensin Analogues as Potential Compounds in Colon Cancer Therapy

Colorectal cancer (CRC) is one of the most lethal malignancies worldwide, so the attempts to find novel therapeutic approaches are necessary. The aim of our study was to analyze how chemical modifications influence physical, chemical, and biological properties of the two peptides, namely, bradykinin (BK) and neurotensin (NT). For this purpose, we used fourteen modified peptides, and their anti-cancers features were analyzed on the HCT116 CRC cell line. Our results confirmed that the spherical mode of a CRC cell line culture better reflects the natural tumour microenvironment. We observed that the size of the colonospheres was markedly reduced following treatment with some BK and NT analogues. The proportion of CD133+ cancer stem cells (CSCs) in colonospheres decreased following incubation with the aforementioned peptides. In our research, we found two groups of these peptides. The first group influenced all the analyzed cellular features, while the second seemed to include the most promising peptides that lowered the count of CD133+ CSCs with parallel substantial reduction in CRC cells viability. These analogues need further analysis to uncover their overall anti-cancer potential.
Sex Differences in the Anti-Hypertensive Effect of Calcium-Channel Blockers: A Systematic Review and Meta-Analysis

Cardiovascular disease (CVD) is the number one cause of death worldwide, with hypertension as the leading risk factor for both sexes. As sex may affect responsiveness to antihypertensive compounds, guidelines for CVD prevention might necessitate divergence between females and males. To this end, we studied the effectiveness of calcium channel blockers (CCB) on blood pressure (BP), heart rate (HR) and cardiac function between sexes. We performed a systematic review and meta-analysis on studies on CCB from inception to May 2020. Studies had to present both baseline and follow-up measurements of the outcome variables of interest and present data in a sex-stratified manner. Mean differences were calculated using a random-effects model. In total, 38 studies with 8202 participants were used for this review. In females as compared to males, systolic BP decreased by −27.6 mmHg (95%CI −36.4; −18.8) (−17.1% (95%CI −22.5;−11.6)) versus −14.4 mmHg (95%CI −19.0; −9.9) (−9.8% (95%CI −12.9;−6.7)) (between-sex difference p < 0.01), diastolic BP decreased by −14.1 (95%CI −18.8; −9.3) (−15.2%(95%CI −20.3;−10.1)) versus −10.6 mmHg (95%CI −14.0; −7.3) (−11.2% (95%CI −14.8;−7.7)) (between-sex difference p = 0.24). HR decreased by −1.8 bpm (95%CI −2.5; −1.2) (−2.5% (95%CI −3.4; −1.6)) in females compared to no change in males (0.3 bpm (95% CI −1.2; 1.8)) (between-sex difference p = 0.01). In conclusion, CCB lowers BP in both sexes, but the observed effect is larger in females as compared to males.
Sustainable business value model in the ICT4D research agenda

ABSTRACT Information and Communication Technologies for Development (ICT4D) emerges positively as a sustainable practice since several studies are focusing on these positive aspects, such as digital financial inclusion and digital platforms for helping communities. This study intends to investigate how a sustainable business value model (SBVM) might be framed in ICT4D from the perspective of business practices. We analyzed the literature of 160 publications in the ICT4D research field from 2003 to 2022 using social network and content analysis to perform a systematic literature review. The findings reveal six different business value perspectives to establish an SBVM in ICT4D initiatives, suggesting a broader perspective for innovators and entrepreneurs to create new business formations for better development outcomes in their communities. This paper contributes to socioeconomic development by identifying ways in which the SBVM may contribute to different communities’ development in emergent countries.
Multimodality Video Acquisition System for the Assessment of Vital Distress in Children

In children, vital distress events, particularly respiratory, go unrecognized. To develop a standard model for automated assessment of vital distress in children, we aimed to construct a prospective high-quality video database for critically ill children in a pediatric intensive care unit (PICU) setting. The videos were acquired automatically through a secure web application with an application programming interface (API). The purpose of this article is to describe the data acquisition process from each PICU room to the research electronic database. Using an Azure Kinect DK and a Flir Lepton 3.5 LWIR attached to a Jetson Xavier NX board and the network architecture of our PICU, we have implemented an ongoing high-fidelity prospectively collected video database for research, monitoring, and diagnostic purposes. This infrastructure offers the opportunity to develop algorithms (including computational models) to quantify vital distress in order to evaluate vital distress events. More than 290 RGB, thermographic, and point cloud videos of each 30 s have been recorded in the database. Each recording is linked to the patient’s numerical phenotype, i.e., the electronic medical health record and high-resolution medical database of our research center. The ultimate goal is to develop and validate algorithms to detect vital distress in real time, both for inpatient care and outpatient management.
Novel Listen-Before-Talk Access Scheme With Adaptive Backoff Procedure for Uplink Centric Broadband Communication

To cater for the data-hungry Internet of Things (IoT) applications, uplink centric broadband communication (UCBC) has been identified as a new service class in the vision of 5.5G, where the unlicensed spectrum has been regarded as a promising solution to boost the uplink capacity. The new radio unlicensed (NR-U) network adopts category-4 (Cat4) listen before talk (LBT) access scheme to exploit the unlicensed spectrum and fairly coexist with the incumbent wireless fidelity (WiFi) network. However, the existing Cat4 LBT access scheme adopts single fixed energy detection (ED) threshold and backoff speed, which cannot adapt to the sophisticated interference and achieve the expected uplink system throughput. To tackle this issue, in this article, we develop a novel Cat4 LBT access scheme with adaptive backoff procedure for UCBC, which includes instantaneous interference level quantification, instantaneous interference level sharing, and backoff speed determination. The results have shown that our proposed adaptive Cat4 LBT scheme achieves over 70% uplink system throughput performance gain where cell throughput of NR-U network rises by over 100%, and cell throughput of WiFi network increases by 25%.
Stereological analysis of particles of varying ellipsoidal shape

Stereological analysis of d-dimensional particles of ellipsoidal shape based on lower-dimensional sections through the particles is discussed. It is proved that the non-void intersections between three parallel hyperplanes and an ellipsoid uniquely determine the ellipsoid, and based on this fact we may reconstruct ellipsoids from sectional information. Combining this reconstruction with a new sampling procedure we obtain a useful tool for non-parametric stereological analysis of particle aggregates of ellipsoids. Finally, parametric models for ellipsoids which are mathematically convenient for the present set up are introduced and discussed.
Oil spill preparedness: Modelling challenges and implications for decision-making

Oil spills can occur from petroleum activities in sensitive geographical regions, and such spills are of serious environmental concern. To reduce the consequence of such spills, measures can target the probability, rate, and duration of a well blowout, or attempt to limit the impact of any spills through oil spill response measures. This paper presents a simplified stochastic framework for representing realistic oil spill scenarios and possible oil volumes recovered. The framework is used as a basis for discussing relations between parameters in assumed spill scenarios and efficiency in oil spill recovery, and how this may impact decision-making related to oil spill preparedness planning and dimensioning. Crucial issues of response measures include type of technology, suitability, localization and mobilization capacity. The paper also presents specific challenges that need to be overcome in order to improve both modelling and decision-making relating to oil spill preparedness.
Data compression of digital images with grey level using triangular plane patches

This paper describes data compression of gray level digital images. A digital image is considered to be a curved surface in three-dimensional space. An image array is subdivided into non-uniform blocks according to the spatial frequency of the image. The original luminance values in a block are approximated by two or four triangular plane patches which are constructed by the luminance values at the four corners of the block. It is clarified that the approximation with two triangular plane patches is more efficient than the approximation with four in compression. When the image array is subdivided into non-uniform blocks, luminance discontinuity appears on the boundaries of blocks. The luminance discontinuity is corrected using the luminance value of small blocks