Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-field coupled sensing network for health monitoring of composite bolted joint

Advanced fiber reinforced composite materials are becoming the main structural materials of next generation of aircraft because of their high strength and stiffness to weight ratios, and excellent designability. As key components of large composite structures, joints play important roles to ensure the integrity of the composite structures. However, it is very difficult to analyze the strength and failure modes of composite joints due to their complex nonlinear coupling factors. Therefore, there is a need to monitor, diagnose, evaluate and predict the structure state of composite joints. This paper proposes a multi-field coupled sensing network for health monitoring of composite bolted joints. Major work of this paper includes: 1) The concept of multifunctional sensor layer integrated with eddy current sensors, Rogowski coil and arrayed piezoelectric sensors; 2) Development of the process for integrating the eddy current sensor foil, Rogowski coil and piezoelectric sensor array in multifunctional sensor layer; 3) A new concept of smart composite joint with multifunctional sensing capability. The challenges for building such a structural state sensing system and some solutions to address the challenges are also discussed in the study.
Squat Detection of Railway Switches and Crossings Using Point Machine Vibration Measurements

Railway switches and crossings (S&C) are among the most important high-value components in a railway network and a failure of such an asset could result in severe network disturbance. Therefore, potential defects need to be detected at an early stage to prevent traffic-disturbing downtime or even severe accidents. A squat is a common defect of S&Cs that has to be monitored and repaired to reduce such risks. In this study, a testbed including a full-scale S&C and a bogie wagon was developed. Vibrations were measured for different squat sizes by an accelerometer mounted at the point machine. A method of processing the vibration data and the speed data is proposed to investigate the possibility of detecting and quantifying the severity of a squat. One key technology used is wavelet denoising. The study shows that it is possible to monitor the development of the squat size on the rail up to around 13 m from the point machine. The relationships between the normalised peak-to-peak amplitude of the vibration signal and the squat depth were also estimated.
Wind-induced chaotic advection in shallow flow geometries. Part I: Circular basins

A numerical study is presented of horizontal wind-induced mixing in shallow water basins. The shallow water equations are solved on a dynamically adaptive quadtree grid using a Godunov-type finite volume scheme to predict the wind-induced flow field. Passive tracer dynamics are then simulated by time-integration of the advection equations using a Runge–Kutta Cash–Karp algorithm, with the flow velocities interpolated from the grid-based flow field. In a circular basin suggested by Kranenburg [J Hydraul Res 30 (1992) 29], numerical predictions of particle motions and mixing processes are in close qualitative agreement with Kranenburg's analytical solutions for the case of a wind of constant intensity abruptly changing direction between north-west and north-east. New results are presented for the case of wind changing direction between west and east. Further investigations are carried out for circular basins with conical and more complicated bed topographies. It is observed that the particle motions invariably alter from regular to chaotic as the storm duration increases, in agreement with Kranenburg's findings. The effect of basin plan-form shape on mixing is considered in the companion paper.
Green Hydrogen Energy Storage Sizing Method Based on Adversarial Learning Technology Under Inaccurate Supervision

Weakly supervised adversarial learning represented by conditional generative adversarial network (CGAN) can account for power fluctuations and generate scenario sets to characterize wind uncertainty in energy storage stochastic planning. Therefore, this paper first designs a corrected-conditional generative adversarial network (CCGAN) and constructs a label corrector to identify and correct the label information of the input generator under the prediction misaim and wind power ramping events. The multi-objective capacity sizing optimization of the green hydrogen energy storage system (GH-ESS) is constructed. The mathematical concept of knee region based on Chebyshev distance is introduced to guide the design of a multi-objective optimization algorithm. Finally, a New England 39-bus system is taken as a case. The results show that the inaccurate supervision will lead to the deviation of planning decisions from reality, while CCGAN can generate a high confidence output scenario considering wind uncertainty, and the planning results can balance robustness and economy.
DISCRETIZATION OF CONTINUOUS ATTRIBUTES USING GENETIC AND ENTROPY BASED CONCEPT LEARNER

Genetic Algorithm (GA) based concept learner is widely used in supervised learning sys- tem for attribute based spaces. The conventional GA based operators can not directly deal with continuous attributes. We have used two separate approaches one is pure Genetic Algorithm and another is Entropy based approach coupled with Genetic Algorithm for converting continuous at- tributes into discrete ones. Later on these con- verted discrete attributes have been used in tradi- tional GA based concept learner to enhance its performance. In our experiments with benchmark data set it has been revealed that statistically sig- nificant improvement has been achieved with the proposed technique.
Indirect Adaptive Control for Systems with Unknown or Varying Dynamics

An indirect adaptive control algorithm is presented that combines a Modified-Gain Extended Kalman Filter (MGEKF) and an Extended Linear Quadratic Gaussian (ELQG) controller to synthesize feedback control laws that are robust to large uncertainties in the system model. The MGEKF estimates the system dynamical parameters and their associated variances, which are used to synthesize the control gains. The gains are determined from the solution of the stochastic optimal control problem where the cost criterion to be minimized is the expected value of a quadratic function of the state and control variables, subject to linear dynamics, with White Gaussian stateand control-dependent noise. The ELQG finds the feedback control laws by solving a nonlinear matrix differential equation that takes in account both the mean and variance of the estimated parameters. This matrix equation reduces to a Riccati equation when the uncertainties go to zero. The controller gains are obtained by solving the algebraic form of this nonlinear matrix equation. Since the covariance of the estimated system parameters from the MGEKF dictate the controller gains, the resulting controller does not obey the Certainty Equivalence Principle. This indirect adaptive controller appears to be robust to large changes in system parameters. In comparison to a standard linear-quadratic-regulator, this controller demonstrates the ability to handle large changes in the system dynamics. The tracking performance of the controller is evaluated through a numerical example.
Pengaruh Niat Beli Pelanggan Uniqlo di Jakarta

Penelitian ini bertujuan untuk mengetahui pengaruh brand credibility, perceived value, dan brand identification pada purchase intention Uniqlo di Jakarta yang dimediasi oleh customer satisfaction. Penelitian ini menggunakan non-probability sampling melalui convenience sampling untuk memperoleh 200 responden yang pernah berbelanja di Uniqlo. Pengumpulan data dilakukan secara online dengan menggunakan kuesioner melalui google form. Data dalam penelitian ini dianalisis menggunakan PLS-SEM dengan aplikasi pengolahan data SmartPLS 4.0. Hasil penelitian ini menunjukkan bahwa brand credibility dapat mempengaruhi customer satisfaction, perceived value dapat mempengaruhi customer satisfaction, brand identification dapat mempengaruhi customer satisfaction. brand credibility dapat mempengaruhi purchase intention, perceived value dapat mempengaruhi purchase intention, brand identification dapat mempengaruhi purchase intention. Kemudian brand credibility, dapat mempengaruhi purchase intention melalui customer satisfaction, perceived value dapat mempengaruhi purchase intention melalui customer satisfaction, brand identification dapat mempengaruhi purchase intention melalui customer satisfaction. This research is conducted to know the effect of brand credibility, perceived value, and brand identification on purchase intention mediating effect of customer satisfaction. This research used non probability sampling through convenience sampling to obtain 200 respondent which are Uniqlo customers. Data are collected online through a questionnaire that was distributed by google form. The data in this study were analyzed using PLS-SEM with the SmartPLS 4.0 data processing application. The result of this research are brand credibility can effect customer satisfaction, perceived value can effect customer satisfaction, brand identification can effect customer satisfaction, brand credibility can effect purchase intention, perceived value can effect purchase intention, brand identification can effect purchase intention. And then brand credibility can effect purchase intention through customer satisfaction, perceived value can effect purchase intention through customer satisfaction, brand identification can effect purchase intention through customer satisfaction.
Throughput compensation through optical proximity correction for realization of an extreme-ultraviolet pellicle

The absorption of extreme-ultraviolet (EUV) pellicle could be the most critical problem because the EUV source power is still not good enough for achieving mass production. We found that the transmission loss due to the EUV pellicle could be compensated through proper optical proximity correction (OPC) of a pellicled mask. Patterning results of OPCed masks with different transmission pellicles are shown for various 1D and 2D patterns. From the results, it is clearly shown that we do not need to increase the dose to avoid the throughput loss even if a pellicle which has 80 % one-pass transmission is used. Therefore, the EUV pellicle manufacturing would be much easier because we can use much thicker film with higher absorption.
The Role of Genetic Polymorphisms in Differentiated Thyroid Cancer: A 2023 Update

Thyroid cancer is the most common endocrine malignancy, with an increasing trend in the past decades. It has a variety of different histological subtypes, the most frequent one being differentiated thyroid cancer, which refers to papillary carcinoma, the most common histological type, followed by follicular carcinoma. Associations between genetic polymorphisms and thyroid cancer have been investigated over the years and are an intriguing topic for the scientific world. To date, the results of associations of single nucleotide polymorphisms, the most common genetic variations in the genome, with thyroid cancer have been inconsistent, but many promising results could potentially influence future research toward developing new targeted therapies and new prognostic biomarkers, thus consolidating a more personalized management for these patients. This review focuses on emphasizing the existing literature data regarding genetic polymorphisms investigated for their potential association with differentiated thyroid cancer and highlights the opportunity of using genetic variations as biomarkers of diagnosis and prognosis for thyroid cancer patients.
Real-Time Data Sensing for Microseismic Monitoring via Adaptive Compressed Sampling

The large amount of monitoring data has posed enormous challenges to the quick response and accurate analysis of microseismic events. Compressed sensing (CS) has the advantages of low resource cost, high efficiency, and excellent data compression ratio (CR), over conventional sensing methods. However, there are still issues to be addressed for its applications: 1) the poor quality and complex signal structure significantly increased the difficulty of keeping satisfactory efficiency; 2) the systematic design of the sparse dictionary, and the measurement matrix for microseismic signal CS are still poor; and 3) the conventional recovery algorithms also require prior knowledge of signal sparsity, which is hardly possible to know or estimate in practice. Therefore, an adaptive real-time sensing method for microseismic monitoring from the perspective of systematic design was proposed in this work. We first analyzed noise and signal structure characteristics to construct an over-complete learning dictionary. Second, according to the learned dictionary, we analyzed the key performance factors of random projection through comparison between different matrices. Third, we explored the relationship between the signal sparsity and the residual energy decay during data recovery with the greedy pursuit algorithms and then presented an energy-ratio-based sparsity adaptive matching algorithm. Finally, we carried out the performance evaluation of the proposed real-time sensing method through synthetic signals and field monitoring data.
Energy Consumption Saving in 5G Network Based on Artificial Intelligence

Fifth generation (5G) technology is tasked with tackling the challenge of energy efficiency. As a result, the problem of selecting the optimal set of cells to be turned OFF is nondeterministic polynomial time hard. 5G is more than just a generational step; it opens a new world of possibilities. There are several ways to develop a nation, and Information and Communication Technology will be the best enabler toward realizing this challenge. The exponential increase in network traffic and the number of connected devices make energy efficiency an increasingly important concern for the entire technological ecosystem. With that in mind, we shall discuss some of the proven works related to the topic, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithms, controlling PDCCH channel skipping in sectors of IIoT (3GPP of 5G), and many other related solutions to the most vital element of concern.
Implementation of 28GHz Four Port MIMO Antenna with Funnel-Shaped Slot On Elliptical Patch And Five-Circular Slotted Common Shared Ground

This research article reports the design and analysis of single-port and four-port MIMO antenna designed to radiate at 28.0GHz for future 5G wireless applications access. The 5G Energy antenna is printed on Rogers Substrate with permittivity 2.20 and compact dimension of 14×14 mm2. The elliptical-patch radiates electromagnetic energy is etched with funnel-shaped slot on one plane and five-circular slots are etched on the square-common ground. The minimum inter-spacing between the radiating elements is fixed to 2.68mm resulting in isolation of more than 27.30dB. The proposed MIMO antenna offers maximum peak gain of 7.67dBi and radiation efficiency of 0.9912 at 28.0GHz. The antenna also maintains expected omni-directional and di-pole like 2-D radiation pattern at 28.0GHz with MIMO diversity parameters corresponding to ECC4Port=0.00125, DG4Port≈10.0dB, TARC4Port=-52.30dB and CCL4Port=0.02b/s/Hz at 28.0 is evaluated. The 5G infrastructure designed is well suited for electromagnetic wave transmission and is useful for information and communication technology as well as mobile users.
A Recent Prevalence of Hepatitis B Virus (HBV) Genotypes and Subtypes in Asia: A Systematic Review and Meta-Analysis

Background and Aim: Despite introducing the hepatitis B virus (HBV) vaccine, the incidence of the Hepatitis B virus globally is still a major health concern. This systematic review and meta-analysis were conducted to provide detailed information on the prevalence of HBV genotypes and subtypes in circulation in Asia. Methods: A systematic search for articles describing the prevalence of HBV genotypes and subtypes in Asia was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Results: Our search returned 207 eligible articles involving 49,279 genotypes and 7457 subtypes representing 28 Asian countries. A meta-analysis was performed on our eligible studies using the Random effect Model. The pooled prevalence of HBV genotypes showed that genotype C (30.9%) (95% CI, 27.5–34.5%; I2 = 97.57%; p < 0.001) was the most common HBV genotype in Asia, followed by genotype B (17.8%) (95% CI, 15.5–20.4%; I2 = 97.26%; p < 0.001) and genotype D (15.4%) (95% CI, 11.8–19.8%). Vietnam had the highest prevalence of genotype B, Lebanon had the highest prevalence of genotypes C, and Jordan had the highest prevalence of genotype D. There was variation in genotypic prevalence with respect to the target genes for HBV genotyping. Reverse dot blot hybridization had the highest estimate of genotypes B and C. HBV subtype C2 (40.0%) (95% CI, 33.3–47.0) is the most prevalent HBV subtype. Conclusion: Evidence from this study reveals that HBV genotypes C and B are the most dominant HBV genotypes in Asia, and HBV subtype C2 is more endemic in Asia.
A Stretched Deramping Radar Technique for High-Resolution SAR Processing in Ka-Band Using the Extended Integration Time

To increase the image quality of synthetic aperture radar (SAR) is one of the hot issues for high-performance detection/reconnaissance. With this interest in the SAR technique, it generates various techniques according to the purposed platforms (Auto-SAR, ViSAR, InSAR, and PolSAR). In this article, we introduce high-efficiency and useful techniques, which can be applied to various SAR applications for generating high-quality images in the Ka -band. We proposed the stretched deramping hardware system applicable in the Ka -band, and design the Range-Doppler algorithm which well matches the proposed radar system with the modified signal parameters. Thus, we can extend the integration time of the convolution process in slow time and it leads to the improvement of the SAR image quality. To verify the performance of our proposed technique, we process the practical experiments in conventional outdoor fields. Furthermore, the proposed method is operated on the range Doppler algorithm (RDA), which is mostly used for standard SAR.
Cyber Complaint Automation System

Due to the Internet's quick spread and the digitali-sation of commercial activity, cybercrime has risen dramatically. Computers or data are the intended victim of cybercrime. This paper is about “Cyber Complaint Automation System”. The current cyber cell of Kerala police has a time comsuming approach. Complaint can be given in two ways. A written complaint can be given to cyber police station. Second option is to use cyber crime reporting portal. In this portal user has to undergo four different steps to sucessfully register the complaint. These complaints are stored in a database and manually classified into respective departments. This methodology of a cyber complaint takes a great deal of effort and time. The time wasted on current system helps the criminal in covering his track. As a result, the victim's time and money are squandered, and police receive criticism. We are working to create a software that will drastically shorten the time it takes to handle a complaint in an effort to remedy this scenario. Our system automates the classification process and prioritize emergency complaints. Thus helps the police to apprehend the criminal faster.
UTD-Based Ray-Tracing MIMO Channel Modeling for the Next-Generation Communications Within Underground Tunnels

Limited work has previously been conducted into deterministic modeling of the fifth generation (5G) millimeter (mm)-wave wireless communications within geometrically nonuniform tunnels. This study predicts and analyzes broadband multiple–input multiple–output (MIMO) propagation in underground mine tunnels for 5G communications and beyond. At extremely short wavelengths, each rugged and uneven surface of the tunnel is modeled as a group of diffracting rectangular prism wedges. A deterministic ray-tracing (RT) MIMO model is developed based on the Uniform Theory of Diffraction (UTD). All predicted MIMO subchannels have been successfully fitted with the experimental measurements. MIMO multipath fading of underground tunnels is found to be lognormally distributed and strongly depends on the tunnel’s size and shape.
Production of a Series of Long-Chain Isomaltooligosaccharides from Maltose by Bacillus subtilis AP-1 and Associated Prebiotic Properties

Bacillus subtilis strain AP-1, which produces α-glucosidase with transglucosidase activity, was used to produce a series of long-chain isomaltooligosaccharides (IMOs) with degree of polymerization (DP) ranging from 2 to 14 by direct fermentation of maltose. A total IMOs yield of 36.33 g/L without contabacillusmination from glucose and maltose was achieved at 36 h of cultivation using 50 g/L of maltose, with a yield of 72.7%. IMOs were purified by size exclusion chromatography with a Superdex 30 Increase column. The molecular mass and DP of IMOs were analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS). Subsequently, linkages in produced oligosaccharides were verified by enzymatic hydrolysis with α-amylase and oligo-α-1,6-glucosidase. These IMOs showed prebiotic properties, namely tolerance to acidic conditions and digestive enzymes of the gastrointestinal tract, stimulation of probiotic bacteria growth to produce short-chain fatty acids and no stimulating effect on pathogenic bacteria growth. Moreover, these IMOs were not toxic to mammalian cells at up to 5 mg/mL, indicating their biocompatibility. Therefore, this research demonstrated a simple and economical method for producing IMOs with DP2–14 without additional operations; moreover, the excellent prebiotic properties of the IMOs offer great prospects for their application in functional foods.
Response Errors in the Measurement of Time Use

Abstract The relation between (a) length of the recall period and (b) validity and quality of data on the use of time is examined. All of the data are based on 24-hour diary constructions or reconstructions. We find that recall periods of one day are essentially equivalent, in terms of validity and quality, to recall periods of up to one week for time diaries of Fridays, Saturdays, or Sundays, but that one-day recall is significantly better than more extended recall for Monday—Thursday diaries. These findings suggest that a sample design using randomly designated rather than convenient diary dates would be of acceptable quality in the United States. We also find that technical change in telephone interviewing techniques significantly improves the quality of current data over data collected in the mid-1970s, and that longitudinal panel data have significantly higher quality than cross-sectional data.
Surface Electromyography based Hand Gesture Signal Classification using 1D CNN

Surface electromyography-based hand gesture recognition for human-machine interfacing has captivated the interest of researchers in recent years. Machine learning techniques are highly dependent on the features selected for classification. A poor choice of features can deteriorate the classification ability of the system. In order to overcome this issue, deep learning techniques which are capable of automatic feature extraction are employed these days. For any ideal classifying machine, it is required to have a short training period and an ability to produce classification results within a short interval of applying an input. This work proposes a 1D CNN model which is able to classify all the 52 gestures of the NinaPro DB1 database which can be applied in real time applications with fast response. The developed model achieves an accuracy of 78.95% with just 351,532 trainable parameters and an average inference time of 0.258 ms.