Original Components Research Articles

Ultrasonic measurement of axial preload in anchor rods

This paper reports on ultrasonic measurement of the axial preload of anchor rods loaded by a pulling machine. The axial preload of ultrasonic measurement was as high as 90 kN, limited by manual operation and the anchor protection structure. Ultrasonic response characterisation, high accuracy and effective ultrasonic measurement of axial preload in anchor rods are reported here and are fully demonstrated. By bonding lead zirconate titanate (PZT) piezoelectric ceramic sensors and self-designed ultrasonic probes on the anchor rods, A-scan ultrasonic signals were successfully measured and originated from the bottom of the hollow anchor rod. The temporal A-scan signal featured a pure and broad frequency spectrum that peaked at around 16 MHz, but no original frequency signal component of the sensors was found. This phenomenon is attributed to the reflective characteristics of the thread-like structure of the hollow anchor rod, potentially inducing a non-linear ultrasound effect, independent of the test instrumentation and coupling agent. When the loading tension of the calibrated pulling machine is lower than 30 kN, the axial preload of the anchor rods obtained from the ultrasonic measurement has an error as high as ±16%. By comparison, the error is less than ±5% for the tension increasing from 60 kN to 90 kN. The experimental results indicate that the proposed ultrasonic measurement method is a simple, accurate and effective way to obtain the axial preload of anchor rods.

The Effect of Additive “Depositional” Reprofiling of Compressor Blade Leading Edges on Engine Performance

Abstract A common problem for gas turbine engines after ingesting atmospheric dust is compressor fouling, where small particles adhere to component surfaces. By sampling components from both a test engine and a service engine, deposits that are hard and sintered were observed to have formed on the leading edges of compressor blades and stators reprofiling their leading-edge geometry. Sectioning of the components showed that the deposits consist of layers of different chemical compositions and that new minerals have crystallized within the deposits. The change in geometry caused by the deposits suggests that they negatively affect the operating incidence range, surface pressure distribution, and profile losses from the design intent of the original component, changing the compressor working line and reducing surge margin, efficiency, and pressure ratio.

Metamodeling of turbofan engine compressor characteristics using SVM-based improved Kriging method

Purpose The purpose of this paper is to overcome the inherent lack of precision in commonly used interpolation procedures when solving the mathematical model of turbofan engines, as well as to address the issue that the theoretical variogram model in traditional Kriging models is prone to subjective selection bias, which makes it impossible to accurately capture the inherent fluctuation patterns in compressor data. Design/methodology/approach To mitigate this challenge, based on the spatial distribution characteristics of the compressor characteristic data of a certain type of turbofan engine, the input and output dimensions of the model are defined. By determining the stable operating region from the original component data, the authors use the proposed Kriging method improved with a support vector machine model to reconstruct the characteristics at unknown speeds within this region. The effectiveness of the proposed method is evaluated using the established assessment metrics. Findings Experimental results demonstrate that the proposed method exhibits significant advantages over the conventional Kriging approach. Specifically, it leads to a substantial reduction in root mean square error and mean absolute error by 0.0153/0.0118 (low speed), 0.1306/0.0362 (medium speed) and −0.0066/0.2366 (high speed). Originality/value This refined approach not only offers notable engineering applicability but also contributes significantly to the enhancement of aerospace engine model solutions’ precision.

Orbital angular momentum at the tight focus of a circularly polarized Gaussian beam

When tightly focusing a circularly polarized optical vortex, a phenomenon of spin-to-orbit conversion has been known to occur. As a rule, the longitudinal component of the spin angular momentum (SAM) vector is assumed to be converted into the longitudinal component of the orbital angular momentum (OAM) vector. In this work, we show that, due to the focusing, the original longitudinal SAM component, averaged over the beam cross-section, is partly converted to the transverse SAM component. In a similar way, the original longitudinal energy flow is partly converted, upon focusing, to the azimuthal component. Meanwhile, the longitudinal component of the OAM vector, averaged over the entire beam cross-section at the focus, increases exactly by the magnitude of the (canonical) averaged azimuthal orbital energy flow at the focus. We show that, upon focusing, the azimuthal energy flow is formed at the focus due to the fact that a right-handed circularly polarized light wave generates two optical vortices: a transverse left-handed circularly polarized vortex with topological charge 2 and a longitudinal vortex with topological charge 1.

Mean time to failure of a k-out-of-n system with a single cold standby unit having dependent components

. In this article, we consider a k-out-of-n system which is equipped with a single cold standby component. We assume that the components within the k-out-of-n system are exchangeable. The standby component becomes active when the k-out-of-n system experiences a failure. The active cold standby component reboots the system in collaboration with the remaining original active components. The dependency between the residual lifetimes of the survival components and the cold standby component is assumed to be modeled by a copula function C *, while the dependency between the original components of the k-out-of-n system is assumed to be modeled by a symmetric copula function C. Initially, we derive the common cumulative distribution function of the survival components of the k-out-of-n system at the time of the failure of the k-out-of-n system. After that, we compute the mean time to failure of the considered system. We analyze how various dependence parameters among the components impact the system’s reliability.

Comparison of analysis methods for separating and recognizing multicomponent radar signals

This paper proposes a model for separating and recognizing a mixture of radar signals using combined multiresolution methods and convolution neural networks. The model involves three main steps: separating the signal into individual components using the Multiresolution analysis (MRA) methods: Empirical mode decomposition (EMD), Variational mode decomposition (VMD), and Maximal overlap discrete wavelet packet transform (MODWPT); transforming these components into the time-frequency domain using Wigner-Ville distribution (WVD) and storing them as images; and then feeding these images into the SqueezeNet for recognition. These multiresolution methods are then compared based on three criteria: The number of successful separations, the SNR ratio of the input signal, and the correlation between the separated signal components and the original signal components. Additionally, we evaluate the performance of the SqueezeNet with real-time signals.

A YOLO Network Based on Depthwise Convolution Attention, Feature Fusion, and KL Divergence (DFK-YOLO): A Deep Learning Method for Infrared Small Target Detection Based on YOLOv7

Infrared imaging technology has a wide range of applications across various fields, with one of its most critical uses being the detection of small infrared targets. However, model-driven approaches often lack robustness in identifying these small targets, while current deep learning-based methods face challenges in effectively extracting and integrating features. Additionally, appropriate labeling strategies for small infrared targets remain underdeveloped. To address these limitations, this paper proposes a novel detection method based on YOLOv7. Specifically, an attention module leveraging Depthwise Convolution is incorporated into the backbone of YOLOv7. Furthermore, a new Feature Fusion Neck is designed to replace the original neck component of YOLOv7. Lastly, a novel label assignment strategy is introduced. The proposed method achieves a mAP@0.5 of 99.5% and a mAP@0.75 of 71.6% on a public dataset, surpassing the baseline YOLOv7 by 1% and 4.6%, respectively. Compared to state-of-the-art deep learning object detection methods, the proposed approach demonstrates superior performance.

Untangling stellar components of galaxies: Evaluation of dynamical decomposition methods in simulated galaxies with GalaxyChop

Galaxy formation is intrinsically connected to the distinct evolutionary processes of disk and spheroidal systems, which are the fundamental stellar components of galaxies. Understanding the mutual, dynamical interplay and co-evolution of these components requires a detailed dynamical analysis to allow for disentanglement of these systems. We introduce JEHistogram, a new method for the dynamical decomposition of simulated galaxies into disk and spheroidal stellar components that utilizes the angular momentum and energy of star particles. We evaluate its performance against five previously established methods using a sample of equilibrium galaxies with stellar masses in the range 1010 ≤ Mgal/M⊙ ≤ 1012. Our assessment involves several metrics, including the completeness and purity of stellar particle classification, scale lengths, mass density profiles, velocity dispersion, and rotational velocity profiles. While all methods approximate the properties of the original components, such as mass fractions and density or velocity profiles, JEHistogram demonstrates a better accuracy, particularly in the inner regions of galaxies where component overlap complicates separation. Additionally, we apply JEHistogram to a Milky Way-like galaxy from the IllustrisTNG cosmological simulations, showcasing its capability to derive properties such as the size, mass, velocity, color, and age of dynamically defined disk and spheroidal components. All the dynamical decomposition methods we analyzed are publicly accessible through the Python package GalaxyChop.

The Possibility of Using Components of Plant Origin as Reinforcements in Composite Friction Materials-A Simulation-Based Braking Process Study.

An innovative prototype composition of a composite friction material was developed. The actual values of selected parameters were determined, as described in a previous paper. It was decided to verify whether the proposed material differs from conventional materials in terms of temperature characteristics, and if so, to what extent. For this purpose, numerical studies were performed using the problem of initially boundary thermal conductivity. The braking system of a popular passenger car was used as the object of the research. A mathematical model of the studied phenomenon was developed, which was implemented in a virtual environment. The results showed that changing the reinforcement method to a more ecological one than the conventional one does not cause significant changes in the temperature profiles obtained for the adopted braking scenario.

Погляд усередину: гніздові матеріали і деякі закономірності гніздової біології коноплянки Linaria cannabina (Linnaeus, C 1758) на Львівщині

The article deals with some aspects of the nesting biology of Linnaria cannabina (Linnaeus, C 1758) in Lviv region. The question of placement, the composition of the nests and the factors that affect this is revealed. The material for writing this article was the data of the Bank of bird nests of Western Ukrainian Ornithological Society (ZUOT) (n=181) and own data (n=18) for 40 years (from 1973 to 2012). Nests were collected in different biotopes and, depending on the distance to the human settlement, were divided into three groups (within settlements, on their outskirts and far beyond in natural biotopes). It was found that for the placement of nests, the common linnet often chooses coniferous (mainly young spruce, thuja or juniper). Among deciduous found trees, a significant variety of bushes and twisted vines (ivy). To choose a place for linnet nest, the architectonics of the aboveground part of the plant is important, so even trees were chosen that are similar to bushes. Very often common linnet places nests in various hedges (spirea, acacia yellow, etc.). Probably due to the fact that the nest is well disguised, its placement height is small (0.2–2.2 m, on average 1.10±0.03 m). The main materials that L. cannabina chooses to build nests are typical of birds of this species in Europe. Linnet nests consisted of plant (in 100 % of the studied nests), animal (98.4 %) and sometimes (23.2 %) materials of anthropogenic origin. From plant materials, linnet most often chose for the nest the roots and stems of herbaceous plants, less often the branches and leaves of trees, plant fluff and moss. Among the materials of animal origin, animal hair and bird feathers were often found in the lining of the nest. Some of the nests contained components of anthropogenic origin (СAО) in different quantities (most often cotton wool). The basic nesting materials of the linnet in Lviv region were small roots and stems of single- and dicotyledonous herbaceous plants, and in the inner part – hair of mammals, feathers of birds, cotton wool. The presence of CAO in bird nests depends on the distance to human settlements and the time of nest construction. In the last couple of decades, the frequency of use of CAO in the nests of the linnet has increased rapidly, which may indicate a growing environment pollution, as well as, perhaps, the habituation to use such materials by common linnet.

RSG-YOLOV8: Detection of rice seed germination rate based on enhanced YOLOv8 and multi-scale attention feature fusion.

The lack of obvious difference between germinated seeds and non-germinated seeds will cause the low accuracy of detecting rice seed germination rate, remains a challenging issue in the field. In view of this, a new model named Rice Seed Germination-YOLOV8 (RSG-YOLOV8) is proposed in this paper. This model initially incorporates CSPDenseNet to streamline computational processes while preserving accuracy. Furthermore, the BRA, a dynamic and sparse attention mechanism is integrated to highlight critical features while minimizing redundancy. The third advancement is the employment of a structured feature fusion network, based on GFPN, aiming to reconfigure the original Neck component of YOLOv8, thus enabling efficient feature fusion across varying levels. An additional detection head is introduced, improving detection performance through the integration of variable anchor box scales and the optimization of regression losses. This paper also explores the influence of various attention mechanisms, feature fusion techniques, and detection head architectures on the precision of rice seed germination rate detection. Experimental results indicate that RSG-YOLOV8 achieves a mAP50 of 0.981, marking a 4% enhancement over the mAP50 of YOLOv8 and setting a new benchmark on the RiceSeedGermination dataset for the detection of rice seed germination rate.

As virtual work becomes more prevalent, an important question arises: how do surface colors and light color temperature in a virtual environment impact creativity?

ABSTRACT Colors and light affect human emotions, and positive emotions encourage human creativity. To enhance student creativity, the appropriate surface colors and light color temperatures in a virtual environment must be determined. This study explores how color and light color temperature in a virtual space impact student creativity. In Experiment 1, color and light color temperature were chosen by surveying 189 architect students who answered questionnaires after watching 10 pictures of a workspace with five different surface colors and two light color temperatures. The workspace with white walls and daylight had the highest mode scores for positive emotion perception. Experiment 2 assessed the creativity of 46 participants using virtual reality (VR) and the alternative uses test (AUT). Participants were divided into two groups of 23. The first group was exposed to white surfaces under daylight. The second group interacted with red surfaces under warm light. The results indicate that a space with white surfaces and daylight significantly enhances creativity, specifically in originality component, compared to a space with red surfaces and warm light. Using light colors and daylight in VR workspace design may promote positive emotions and enhance creativity.

Antishock effect of soybean extract during sperm cryopreservation of different animal species

The results of the study of the fortification effect on the plasma membranes of sperm from bulls, rams, and boars treated with soybean seed hydrolysate after washing and after exposure to temperature changes on sperm are presented. It was found that the lipoprotein extract from soybeans has the ability to protect germ cells from temperature shock under conditions of instantaneous temperature drop from 28°C to 0°C at the same level as native yolk. The direct dependence of the osmotic pressure in the extracts on the exposure temperature and extraction conditions was established. The replacement of native yolk in cryoprotective media with anti-shock components of plant origin ensures the preservation of high biological parameters of sperm after thawing. The use of a plant-derived plasma membrane fortifier instead of native yolk makes it possible to apply simple and reliable methods of sterilization, prevent contamination of sperm and female genital tract with yolk-transmitted pathogens, and thus increase the sanitary and hygienic level of artificial insemination

Experimenting with a strong dual necessity approach to social progress

Different social progress indices assess well-being and development differently and thus reflect distinct conceptions of social progress. Broadly, these conceptions fall into two categories: ‘subjective’—the attitudes of individuals; and ‘objective’—external standards. Our starting point is a strong ‘dual necessity’ claim, namely that the two categories of conception have joint-special significance. Such a strong dual necessity conception, when operationalized, demands very low compensability/degree of substitution between the two independent indices, objective and subjective. Experimenting with this idea, we hypothesize that implementing low substitution empirically influences outcomes. Previous research presented this concept and exemplified initial empirical outcomes. The present work enhances and broadens this research by employing the constant elasticity of substitution (CES) function, which allows for the adjustment of substitution levels and comparison of resulting rankings. Furthermore, we demonstrate the results using two different well-known and commonly used indices: Human Development Index (HDI) and Sustainable Development Goals Index (SDGI). Our findings show that using a low substitution approach alters the social progress rankings of many countries in comparison to the original components’ rankings and to a high substitution combination of the two. This holds particularly true for countries in the middle of the original rankings. The paper’s original contribution is that it establishes the CES function as a useful device for implementing the idea of ‘dual necessity.’ Importantly, by experimenting with low substitution, and exemplifying its empirical significance with the CES function, the paper also contributes to further validating a strong dual necessity conception and justifies its own measurement. Such measurement should be of value to social scientists, practitioners and policymakers who care for balanced social progress: social progress that does not neglect either of the two aspects (subjective or objective).

Cuffless Wearable Sphygmomanometers Using Dual Digital Optical Frequency Combs in Chalcogenide Chips

AbstractSphygmomanometers typically use a rubber cuff around the upper arm to measure blood pressure, but this method can be uncomfortable. Thus, cuffless sphygmomanometers for continuous blood pressure monitoring in emergencies and healthcare are desirable. While electrical and optical blood pressure sensors offer high sensitivity, they often face issues like limited pressure range, temperature drift, slow response, and wearability problems. To tackle these limitations, the study designs a unique cuffless wearable blood pressure sensor (12 mm × 2 mm × 0.5 mm) featuring two original components: a GeSbS/AsS chalcogenide dual‐microring chip for high sensitivity, and a dual digital optical frequency comb (DDOFC) system for fast response and cost‐effective detection. The sensor is comparable to reported electrical and optical blood pressure sensors in its detection limit (23 Pa) but has a ten‐times faster response rate (2 kHz), a 1000‐times longer stable operation (>4.3 × 107 cycles), and a well‐compensated temperature drift (<0.012 pm/°C) that eliminates the need for temperature control. This innovation shows promise for wearable electronics and mobile healthcare, enabling the capture of critical cardiovascular details for applications like in‐surgery monitoring, orthostatic hypotension studies, and early detection of myocardial ischemia and strokes.

LC-MS/MS-based quantitative method and metrological traceability technology for measuring components of animal origin in beef and lamb and their products

Establishing a quantitative detection method for adulterated meat products is crucial for ensuring food safety. However, due to the lack of metrological traceability in current quantitative methods, poor reliability and comparability always occur when measuring adulterated meat. Therefore, this study established an accurate quantitative method with metrological traceability for the quantitative analysis of pork and duck meat added to beef, lamb, and their products. We took the amino acid certified reference materials (CRMs) as the source of traceability, and traced the quantitative peptide concentrations of adulterated beef, lamb and their products to SI. Finally, the quantitative method has high accuracy and high repeatability and we utilized it to achieve accurate quantification of adulterated meat products, with an uncertainty range of 10.7 %–18.5 %, k = 2.

Improvement method for sound quality of pharyngeal speech by using Bayes' theorem

In general, a speech signal can be measured by a microphone and a pharyngeal microphone etc.. However, speech signal measured by a microphone often contains the background noise. On the other hand, though a pharyngeal microphone is effective for background noise, it involves body conducted noise. In this study, we propose an improvement method for sound quality of speech signal measured trough a pharyngeal microphone to achieve well the speech recognition. The relationship between the original speech signal and the speech measured by the pharyngeal microphone is not clear. Therefore, we consider the relationship as multiplicative and additive models of the original speech signal and noise components with unknown parameters. An algorithm to estimate simultaneously the original speech signal and the unknown parameters is proposed by using Bayes' theorem based on the measured speech through the pharyngeal microphone. Finally, a speech recognition experiment is conducted to confirm the effectiveness of the proposed algorithm.

Preliminary beam formations after the upgrade of the TIARA two microbeam systems

This paper presents a comprehensive study on the enhancements made to the heavy-ion microbeam (H-MB) and light-ion microbeam (L-MB) systems at the Takasaki Institute for Advanced Quantum Science, part of the National Institutes for Quantum Science and Technology (QST), and their implications for future research in ion microbeam technology. Our work addresses the critical need for submicron beam sizes in advanced ion microbeam applications, a challenge previously unmet due to limitations in the original system designs and aging components. The manuscript details the extensive upgrades undertaken for both the H-MB and L-MB systems, including the implementation of new quadruple magnets, refined demagnification strategies, and advanced control systems, culminating in the successful formation of submicron ion beams. The meticulous alignment procedures for both the H-MB and L-MB have enabled precise ion beam focusing. The current beam sizes have been measured at 1.0 × 1.1 µm2 with a beam current of approximately 10 pA for the H-MB, and 500 × 600 nm2 with a beam current of approximately 50 pA for the L-MB at a 3 MeV H+ beam.

An evolutionary algorithm‐based classification method for high‐dimensional imbalanced mixed data with missing information

AbstractThe data scale keeps growing by leaps and the majority of it is high‐dimensional imbalanced data, which is hard to classify. Data missing often happens in software which further aggravates the difficulty of classifying the data. In order to resolve high‐dimensional imbalanced mixed‐variables missing data classification problem, a novel method based on particle swarm optimization is developed. It has three original components including multiple feature selection, mixed attribute imputation, and quantum oversampling. Multiple feature selection uses a two‐stage strategy to obtain stable relevant features. Mixed attribute imputation separates features into continuous and discrete features and fills missing values with different models. Quantum oversampling chooses instances to balance data based on the quantum operator. Furthermore, particle swarm optimization is employed to optimize the parameters of the components to obtain preferable classification results. Six representative classification datasets, six typical algorithms, and four measures are taken to conduct exhaust experiments, and results indicate that the proposed method is superior to the comparison algorithms.

Vibronic Splitting of the Electronic Origin in Two Conformers of the 3-Tolunitrile Dimer.

3-tolunitrile (3-TN) can form three different dimers, which differ in the relative orientation of the methyl groups. We determined the geometry changes of two of these conformers of 3-TN upon electronic excitation via a Franck-Condon fit of the vibronic intensities in the fluorescence emission spectra. Both aromatic rings expand upon electronic excitation, showing a delocalized one-photon excitation of the cluster. The conformer with the smaller center-of-mass distance shows an unusual order of the split components of the electronic origin. We attribute this changed order to the stronger charge transfer contributions to the splitting and a partial breakdown of the point dipole approximation, made in the Frenkel type interaction.