bluuebunny/crossref_metadata_2025 huggingface dataset:
Glioma segmentation based on dense contrastive learning and multimodal features recalibration
Authors: Hu Xubin , Wang Lihui , Wang Li , Chen Qijian , Zheng Licheng , Zhu Yuemin
Published: March 2024

Accurate segmentation of different regions of gliomas from multimodal magnetic resonance (MR) images is crucial for glioma grading and precise diagnosis, but many existing segmentation methods are difficult to effectively utilize multimodal MR image information to recognize accurately the lesion regions with small size, low contrast and irregular shape. To address this issue, this work proposes a novel 3D glioma segmentation model DCL-MANet. DCL-MANet has an architecture of multiple encoders and one single decoder. Each encoder is used to extract MR image features of a given modality. To overcome the entangle problems of multimodal semantic features, a dense contrastive learning (DCL) strategy is presented to extract the modality-specific and common features. Following that, feature recalibration block (RFB) based on modality-wise attention is used to recalibrate the semantic features of each modality, enabling the model to focus on the features that are beneficial for glioma segmentation. These recalibrated features are input into the decoder to obtain the segmentation results. To verify the superiority of the proposed method, we compare it with several state-of-the-art (SOTA) methods in terms of Dice, average symmetric surface distance (ASSD), HD95 and volumetric similarity (Vs). The comparison results show that the average Dice, ASSD, HD95 and Vs of DCL-MANet on all tumor regions are improved at least by 0.66%, 3.47%, 8.94% and 1.07% respectively. For small enhance tumor (ET) region, the corresponding improvement can be up to 0.37%, 7.83%, 11.32%, and 1.35%, respectively. In addition, the ablation results demonstrate the effectiveness of the proposed DCL and RFB, and combining them can significantly increase Dice (1.59%) and Vs (1.54%) while decreasing ASSD (40.51%) and HD95 (45.16%) on ET region. The proposed DCL-MANet could disentangle multimodal features and enhance the semantics of modality-dependent features, providing a potential means to accurately segment small lesion regions in gliomas.
A photon source model for alpha-emitter radionuclides
Authors: Sarrut D , Etxebeste A , Létang J M
Published: March 2024

Objective. A Monte Carlo virtual source model named PHID (photon from Ion decay) that generates photons emitted in the complex decay chain process of alpha-emitter radionuclides is proposed, typically for use during the simulation of SPECT image acquisition. Approach. Given an alpha-emitter radionuclide, the PHID model extracts from Geant4 databases the photon emission lines from all decaying daughters for both isometric transition and atomic relaxation processes. According to a given time range, abundances and activities in the decay chain are considered thanks to the Bateman equations, taking into account the decay rates and the initial abundances. Main results. PHID is evaluated by comparison with analog Monte Carlo simulation. It generates photons with the correct energy and temporal distribution, avoiding the costly simulation of the complete decay chain thus decreasing the computation time. The exact time gain depends on the simulation setup. As an example, it is 30× faster for simulating 1 MBq of 225Ac in water for 1 section Moreover, for 225Ac, PHID was also compared to a simplified source model with the two main photon emission lines (218 and 440 keV). PHID shows that 2 times more particles are simulated and 60% more counts are detected in the images. Significance. PHID can simulate any alpha-emitter radionuclide available in the Geant4 database. As a limitation, photons emitted from Bremsstrahlung are ignored, but they represent only 0.7% of the photons above 30 keV and are not significant for SPECT imaging. PHID is open-source, available in GATE 10, and eases the investigation of imaging photon emission from alpha emitters.
An automated technique for global noise level measurement in CT image with a conjunction of image gradient
Authors: Kuo Hsiang-Chi , Mahmood Usman , Kirov Assen S , Mechalakos James , Della Biancia Cesar , Cerviño Laura I , Lim Seng Boh
Published: March 2024

Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10 × 10 pixel for calculating Hounsfield unit (HU) standard deviation and the noise map is further combined with the gradient magnitude map. By employing Boolean operation, pixels with high gradients are excluded from the noise histogram generated with the noise map. By comparing the shape of the noise histogram from this method with Christianson’s tissue-type global noise measurement algorithm, it was observed that the noise histogram computed in anthropomorphic phantoms had a similar shape with a close GNI value. In patient CT images, excluding the HU deviation due the structure change demonstrated to have consistent GNI values across the entire CT scan range with high heterogeneous tissue compared to the GNI values using Christianson’s tissue-type method. The proposed GNI was evaluated in phantom scans and was found to be capable of comparing scan protocols between different scanners. The variation of GNI when using different reconstruction kernels in clinical CT images demonstrated a similar relationship between noise level and kernel sharpness as observed in uniform phantom: sharper kernel resulted in noisier images. This indicated that GNI was a suitable index for estimating the noise level in clinical CT images with either a smooth or grainy appearance. The study’s results suggested that the algorithm can be effectively utilized to screen the noise level for a better CT image quality control.
Implementation of a new EGSnrc particle source class for computed tomography: validation and uncertainty quantification
Authors: Kuhlmann Marie-Luise , Pojtinger Stefan
Published: March 2024

Objective. Personalized dose monitoring and risk management are of increasing significance with the growing number of computer tomography (CT) examinations. These require high-quality Monte Carlo (MC) simulations that are of the utmost importance for the new developments in personalized CT dosimetry. This work aims to extend the MC framework EGSnrc source code with a new particle source. This, in turn, allows CT-scanner-specific dose and image calculations for any CT scanner. The novel method can be used with all modern EGSnrc user codes, particularly for the simulation of the effective dose based on DICOM images and the calculation of CT images. Approach. The new particle source can be used with input data derived by the user. The input data can be generated by the user based on a previously developed method for the experimental characterization of any CT scanner (doi.org/10.1016/j.ejmp.2015.09.006). Furthermore, the new particle source was benchmarked by air kerma measurements in an ionization chamber at a clinical CT scanner. For this, the simulated angular distribution and attenuation characteristics were compared to measurements to verify the source output free in air. In a second validation step, simulations of air kerma in a homogenous cylindrical and an anthropomorphic thorax phantom were performed and validated against experimentally determined results. A detailed uncertainty evaluation of the simulated air kerma values was developed. Main results. We successfully implemented a new particle source class for the simulation of realistic CT scans. This method can be adapted to any CT scanner. For the attenuation characteristics, there was a maximal deviation of 6.86% between the measurement and the simulation. The mean deviation for all tube voltages was 2.36% (σ = 1.6%). For the phantom measurements and simulations, all the values agreed within 5.0%. The uncertainty evaluation resulted in an uncertainty of 5.5% (

k
=
1

).
Generative adversarial network-based synthesis of contrast-enhanced MR images from precontrast images for predicting histological characteristics in breast cancer
Authors: Fan Ming , Cao Xuan , Lü Fuqing , Xie Sangma , Yu Zhou , Chen Yuanlin , Lü Zhong , Li Lihua
Published: March 2024

Objective. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive tool for assessing breast cancer by analyzing tumor blood flow, but it requires gadolinium-based contrast agents, which carry risks such as brain retention and astrocyte migration. Contrast-free MRI is thus preferable for patients with renal impairment or who are pregnant. This study aimed to investigate the feasibility of generating contrast-enhanced MR images from precontrast images and to evaluate the potential use of synthetic images in diagnosing breast cancer. Approach. This retrospective study included 322 women with invasive breast cancer who underwent preoperative DCE-MRI. A generative adversarial network (GAN) based postcontrast image synthesis (GANPIS) model with perceptual loss was proposed to generate contrast-enhanced MR images from precontrast images. The quality of the synthesized images was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The diagnostic performance of the generated images was assessed using a convolutional neural network to predict Ki-67, luminal A and histological grade with the area under the receiver operating characteristic curve (AUC). The patients were divided into training (n = 200), validation (n = 60), and testing sets (n = 62). Main results. Quantitative analysis revealed strong agreement between the generated and real postcontrast images in the test set, with PSNR and SSIM values of 36.210 ± 2.670 and 0.988 ± 0.006, respectively. The generated postcontrast images achieved AUCs of 0.918 ± 0.018, 0.842 ± 0.028 and 0.815 ± 0.019 for predicting the Ki-67 expression level, histological grade, and luminal A subtype, respectively. These results showed a significant improvement compared to the use of precontrast images alone, which achieved AUCs of 0.764 ± 0.031, 0.741 ± 0.035, and 0.797 ± 0.021, respectively. Significance. This study proposed a GAN-based MR image synthesis method for breast cancer that aims to generate postcontrast images from precontrast images, allowing the use of contrast-free images to simulate kinetic features for improved diagnosis.
CT respiratory motion synthesis using joint supervised and adversarial learning
Authors: Cao Y-H , Bourbonne V , Lucia F , Schick U , Bert J , Jaouen V , Visvikis D
Published: March 2024

Objective. Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery. Approach. In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware treatment planning. The model produces patient-specific deformation vector fields (DVFs) by conditioning synthesis on external patient surface-based estimation, mimicking respiratory monitoring devices. A key methodological contribution is to encourage DVF realism through supervised DVF training while using an adversarial term jointly not only on the warped image but also on the magnitude of the DVF itself. This way, we avoid excessive smoothness typically obtained through deep unsupervised learning, and encourage correlations with the respiratory amplitude. Main results. Performance is evaluated using real 4DCT acquisitions with smaller tumor volumes than previously reported. Results demonstrate for the first time that the generated pseudo-respiratory CT phases can capture organ and tumor motion with similar accuracy to repeated 4DCT scans of the same patient. Mean inter-scans tumor center-of-mass distances and Dice similarity coefficients were 1.97 mm and 0.63, respectively, for real 4DCT phases and 2.35 mm and 0.71 for synthetic phases, and compares favorably to a state-of-the-art technique (RMSim). Significance. This study presents a deep image synthesis method that addresses the limitations of conventional 4DCT by generating pseudo-respiratory CT phases from static images. Although further studies are needed to assess the dosimetric impact of the proposed method, this approach has the potential to reduce radiation exposure in radiotherapy treatment planning while maintaining accurate motion representation. Our training and testing code can be found at https://github.com/cyiheng/Dynagan.
Self-healing properties of mortar with crystalline admixture: experimental investigation and parameter optimization
Authors: Liu Baoju , Liang Yajun , Jiang Junyi , Shi Jinyan , Yalçınkaya Çağlar , Ashour Ashraf
Published: March 2024

The inclusion of crystalline admixture (CA) is a highly effective method for enhancing the self-healing properties of mortar. This study examined the complexing abilities of different complexing agents under varying temperatures, pH levels, and ion species in order to select effective complexing agents for diverse environments, as complexing agents play a crucial role in CAs. After determining the type of complexing agent, an orthogonal array design was used to optimize the components of CA, and the strengthening mechanism of CA for mortar was discussed through microstructure analysis. The results showed that the complexation behavior of triethanolamine (TEA) and glycine performed better than sodium citrate for different pH levels, temperatures, and ion species. Meanwhile, TEA and glycine showed complementarity at different stages, so TEA and glycine were used as complexing agents in this study. Based on the orthogonal experiment, the optimal contents of Na2SiO3, Na2CO3, TEA, glycine, Ca(COOH)2, and nano-SiO2 in CA were determined to be 1.0%, 1.0%, 0.04%, 1.0%, 0.5%, and 1.0%, respectively. Under the synergistic effect of TEA and glycine, the hydration of aluminate and ferrialuminate was accelerated, and the hydration degree of cement paste was increased. At 28 d, the contents of CaCO3, calcium-silicate-hydrate (C-S-H) gel, and ettringite of cement paste with CA were higher than these of plain paste, but its Ca(OH)2 content was lower. Although the Ca(OH)2 content in the cement paste with CA was lower, the Ca(OH)2 crystal structure filled in the pores was larger. Therefore, the mortar mixed with CA exhibited higher compressive strength, water impermeability, and self-healing ability.
Thermal stress estimation of a constrained metallic plate using symmetric and antisymmetric Lamb wave group velocities
Authors: Azad Ali , Kang Myung Soo , Lee Changgil , An Yun-Kyu
Published: March 2024

This paper presents a technique for estimating thermal-induced stress in constrained metallic plates using the group velocity of Lamb waves, the accuracy of which is crucial for assessing the structural integrity and serviceability of metallic structures. However, without the ability to gauge the current stress levels, obtaining such measurements is technically challenging. To overcome this, we propose a thermal stress estimation technique that uses changes in the group velocities of the fundamental symmetric (S0) and antisymmetric (A0) Lamb wave modes caused by thermal and stress variations. First, this study introduces a theoretical-based zero-crossing algorithm to measure the group velocities of S0 and A0 Lamb wave modes. Next, leveraging the acoustoelastic coefficients corresponding to the S0 and A0 modes, which are determined before the plate’s installation, this study generates the lines depicting the changes in group velocity induced by temperature variations (CT) for both the S0 and A0 modes. These CT lines are derived from the lines illustrating changes in group velocity due to thermal stress variations (CTS), which are obtained after plate installation. Ultimately, the generated CT lines can be used to estimate thermal stress throughout the entirety of the plate’s operational life span by isolating the distinct stress variation effects from the CTS lines. The numerical validation results show favorable accuracy in thermal stress estimation in a constrained plate subjected to temperature variation using both S0 and A0 Lamb wave modes, with average errors of 0.63 % and 0.91 %, respectively.
Thermo-rheological improvement of magnetorheological foam with the addition of silica nanoparticles
Authors: Mohamed Khaidir Rahayu Emilia , Nordin Nur Azmah , Mazlan Saiful Amri , Ubaidillah Ubaidillah , Rahman Hamimah Abd , Marzuki Ainaa Amirah , Wahab Siti Aisyah Abdul
Published: March 2024

Magnetorheological (MR) foam has become a potential soft robotic gripper-based material that can provide a better grasping force and handling objects due to its ability in varying stiffness in correspond to applied magnetic fields. However, MR foams are facing degradation issue that may reduce the storage modulus when often exposed to thermal exposure from the operating system of a device. Therefore, this study focuses on improving the storage modulus and simultaneously enhancing the thermal properties of MR foam. Hence, silica nanoparticles were introduced as an additive to achieve the improvement target. MR foams were embedded with different concentrations of silica nanoparticles from 0 to 5 wt.%, and the corresponding rheological properties was examined under different temperature conditions from 25 °C to 65 °C. The results revealed that increasing temperatures have reduced the storage modulus of MR foams, however, the embedded silica has countered the drawbacks by strengthening the interfacial interactions between CIP-polyurethane foam matrix. In addition, the morphological characteristics of MR foams also showed less debris or peel-off PU foam with silica nanoparticles. Besides, the silica nanoparticles have delayed the thermal degradation of MR foam for approximately 30 °C.
Enhanced photocatalytic efficiency of sol-gel derived ZnS-rGO binary nanocomposite
Authors: Kumari Priyanka , Sharma Aashish , Mishra Saurav , Surolia Praveen Kumar , Mukherjee Sanat Kumar , Misra Kamakhya Prakash , Samanta Susruta , Kabi Sanjib , Chattopadhyay Saikat
Published: March 2024

A facile chemical route of synthesis of ZnS-rGO binary nanocomposites is reported here. The efficacy of such nanocomposites as a photocatalyst in degrading the common pollutant dye such as Methylene blue (MB), has been thoroughly investigated and the underlying mechanism is also presented. The standard characterization methods were applied to understand the structure, bonding, morphology, optical and elemental compositions. The results indicated that the ZnS nanoparticles were well dispersed into the rGO nanosheets which due to their 2D sheet structure, served as a favourable template for growth and control of morphology. Increase in rGO amount showed a direct impact on particle size confirmed by XRD and Raman both. The synthesized nanocomposites were utilized as photocatalyst for the degradation of MB dyes under UV irradiation. The optimal combination of ZnS and rGO (in the ratio of 3:2) exhibited enhanced photocatalytic activity. A higher rate constant of 7.01×10−3 min−1, and an approximate degradation efficiency of 75% were obtained after 90 min of degradation. The improvement in photocatalytic activity can be attributed to the enhancement in charge separation, suppressed recombination of electron–hole (e−–h+) carriers, and a possible longer electron lifetime due to the presence of higher amount of rGO. Here, rGO assisted the suppression of charge recombination process in ZnS-rGO and ignited hydroxyl radicals and super-oxide ions which further accelerated the degradation rate of dye. Based on the nature of the dye and its concentration, a significant amount of rGO was needed to maximize the photocatalytic efficiency of ZnS-rGO binary nanocomposites. In addition, the dark current variation with applied bias was explored and it depicted a reduction in dark current with optimized amount of rGO in nanocomposite. The nanocomposites have a strong potential to be utilized in water purification and nano-detectors.
Fabrication of thin film solar cell based on CoO-CoS/P2-Amino-1-mercaptobenzene/polypyrrole with additional promising sensitivities to photons in a broad optical region
Authors: Madkhali Nawal , Rabia Mohamed
Published: March 2024

The production of a thin-film solar cell device, employing CoO-CoS/P2-Amino-1-mercaptobenzene/polypyrrole (CoO-CoS-P2AMB/Ppy), showcases enhanced sensitivity across a broad optical spectrum. This achievement is realized through a one-pot polymerization process involving 2-Amino-1-mercaptobenzene, Co(NO3)2, and K2S2O8. Notably, the polymerization reaction incorporates the inclusion of a Ppy seeding layer into the solution. To comprehensively characterize the synthesized materials, an array of analytical tools is employed. Subsequently, the CoO-CoS-P2AMB/Ppy device undergoes rigorous testing under diverse light conditions, including darkness, white light, and monochromatic light. These conditions aim to assess the device’s responsiveness to photons of varying energies, where the resulting current density (Jph) serves as an indicator of generated hot electrons and the overall efficiency of the fabricated device. At a potential of 2.0 V, the measured values for Jph and dark current (Jo) stand at 0.11 and 0.04 mA.cm−2, respectively. The solar cell characteristics are further evaluated through measured values for JSC (−0.01 μA.cm−2) and VOC (0.038 V), reflecting the device’s performance under solar testing conditions. In addition to its solar cell attributes, the device exhibits light-sensing behavior, quantified through calculated photoresponsivity (R) and detectivity (D) values. These values, obtained under various monochromatic lights, amount to 0.49 mA.W−1 and 1.1 × 108 Jones, respectively. The exceptional performance of the CoO-CoS-P2AMB/Ppy thin-film solar cell device, coupled with its light-sensing capabilities, positions the device as a promising candidate for commercial applications. Its potential extends to the fabrication of cost-effective thin-film sensing devices, presenting opportunities for widespread utilization in various industries.
Direct detection of nitrite and nitrate in water on three-dimensional composite surface-enhanced raman scattering substrate
Authors: Lai Chunhong , Wen Jing , Zhang Xiaolei , Chen Xianqin , Tang Hao , Xiang Jinhong
Published: March 2024

To solve the problem of the low sensitivity of the direct detection of nitrite and nitrate in water using Raman spectroscopy, we propose a three-dimensional composite surface-enhanced Raman spectroscopy (SERS) substrate. In addition, this cysteamine/gold/cysteamine composite structure is self-assembled onto anodic aluminum oxide and has a three-dimensional porous structure. Experimental results determined that the substrate’s detection limits for nitrite and nitrate are 0.1 mg l−1 and 1 mg l−1, respectively. Moreover, these meet environmental groundwater quality class II (nitrite, ≤0.1 mg l−1) and class I (nitrate, ≤1 mg l−1), respectively. The relative standard deviations calculated for nitrite and nitrate were 6.19% and 4.93%, respectively. Furthermore, the SERS substrate achieved equivalent detection limits for mixed nitrite and nitrate solution compared to separate nitrite and nitrate solutions, albeit with a weaker Raman signal. Therefore, this SERS substrate is capable of the sensitive and rapid determination of nitrite and nitrate at low concentrations without any treatment.
Design of three-dimensional encryption algorithm for image based on improved 6th-order cellular neural network
Authors: Song Xiaoming , Li Guodong , He Ping
Published: March 2024

The chaotic trajectory of the traditional natural chaotic system and the single chaotic system is relatively simple, and the keyspace is small, resulting in low security, low complexity, and poor resistance to attacks based on traditional chaotic design encryption algorithms. This article first constructed a 6th-order cellular neural network hyperchaotic system based on the theory of a 6th-order cellular neural network. Then the Chaotic Sequence Enhancer (CSE) is constructed by using infinite folding mapping. We use CSE to improve our cellular neural network. Compared with the old system, the new system has a larger Lyapunov exponent, higher PE complexity values, and 0–1 test results. Then based on the new chaotic system, a three-dimensional encryption algorithm was designed. The algorithm rearranges the pixels of the image into cubes and performs scrambling and diffusion operations based on the cubes. While making the encryption effect better, the information entropy of the ciphertext image is also above 7.99, and the correlation between adjacent pixels is less than 0.1. At the same time, the encryption algorithm can better resist various corrosion attacks, and the original image can still be better decrypted even when the ciphertext image has received a 25% loss. The result of the NPCR and UACI test is close to the expected values of 99.61% and 33.46%. The ciphertext image produced by the algorithm can pass the NIST SP800-22 statistical tests. The results of various tests and experiments show that our proposed encryption algorithm has high initial value sensitivity, resistance to differential attacks, and resistance to cropping attacks, and has good application prospects in the field of image security.
Design of flexural wave bessel metasurface with resonant pillar-type metamaterials
Authors: Liu Yining , Dou Shuihai , Du Yanping , Zhao Liuxian , Sun Zhaoyong
Published: March 2024

In this paper, we design a flexural wave Bessel metasurface with resonant pillars, which converts the flexural wave produced by a point into a Bessel beam. The refractive index is determined through the application of the generalized Snell’s law, subsequently discretized into pixel blocks. These blocks facilitate implementation via the use of metamaterial unit cells. The metasurface is realized by resonant pillar-type metamaterials, and composed of 41 different independent unit cells obtained by retrieving the energy bands. Simulation results demonstrate that the designed metasurface exhibits effective focusing for flexural wave. Additionally, the self-reconstruction effect of the Bessel metasurface is verified through the introduction of obstacles. This research provides a new perspective for the application of Bessel beam in the domain of flexural wave.
A compact circularly polarized dual port MIMO antenna with DGS and parasitic patch for modern wireless communication and IOT applications
Authors: Roges Rashmi , Sharma Sandeep , Malik Praveen Kumar , Islam Tanvir , Asha Sivaji , Das Sudipta
Published: March 2024

The article proposes a broadband dual-port planar multiple-input-multiple-output (MIMO) antenna for the lower sub-6 GHz band (1.67 GHz to 3.15 GHz) applications. The simple structure of the MIMO antenna system consists of a semi-annular ring-shaped patch with a covering cap-like structure connected with two symmetrical L-shaped structures. A partial ground with a rectangular mesh-like structure at the center is designed and the same rectangular mesh has been added as the parasitic element with the driven patch to attain circular polarization and enhanced isolation. The miniaturized dimensions of the two-port MIMO antenna system are 0.28 λ
0 × 0.54 λ
0. The suggested two-port MIMO antenna offers a wider impedance bandwidth (IBW) of 61.41% around the versatile resonant frequency of 2.4 GHz. The proposed MIMO also depicts a minimum isolation of 26 dB with an edge-to-edge spacing of 0.19 λ
0. An axial ratio bandwidth of 280 MHz is attained between 2.36 GHz and 2.64 GHz. The proposed antenna shows good IBW with excellent isolation, along with good radiation and MIMO diversity characteristics to operate at 2.4 GHz with circular polarization. The designed MIMO antenna can be utilized for IoT and modern mobile wireless applications as the broad bandwidth covers various IoT communication protocol frequencies such as 1.8 GHz (NB-IoT) and 2.4 GHz (Wireless LAN).
Controlled commutation modes in a switch based on HTS composites
Authors: Martirosian I V , Malyavina A Y , Pokrovskii S V , Rudnev I A
Published: March 2024

This paper presents the results of experimental studies of switching processes of high-temperature superconducting tapes under the action of a pulse current load of microsecond duration. The reversible, stable and irreversible switching of the superconductor to the normal state has been realized. To demonstrate the opportunity of controlled generation of transients in superconducting composites, the characteristics of a superconducting switch capable of cyclic switching between flux flow and ohmic modes with preservation of switching parameters were investigated. The characteristics of a superconducting switch have been calculated based on experimental results.
Construction of new wave structures and stability analysis for the nonlinear Klein-Gordon equation
Authors: Tariq Kalim U , Bekir Ahmet , Nisar Sana , Alp Murat
Published: March 2024

In this study, the nonlinear Klein–Gordon equation the relativistic equivalents of the nonlinear Schrödinger equations is presented which characterizes the connection between relativistic energy-momentum in a quantized manner. A variety of exact solutions are developed by employing the extended Fan sub-equation approach and the Sardar sub-equation method. The study includes three dimensional surface plots of some derived solutions like dark and bright solitary waves, kink solitary waves, anti-kink solitary waves, periodic solitary waves and hyperbolic functions, with several solutions being novel. Furthermore, the stability analysis of the observed solutions is also established to validate the scientific computations. The 3D, 2D and contour visualizations of the wave dynamics are demonstrated using Mathematica for a suitable choice of parameters. The applied strategies are recognized as a significant mathematical tool for obtaining analytical solutions to partial differential equations in mathematical physics.
Early Evolution of Spin Direction in Dark Matter Halos and the Effect of the Surrounding Large-scale Tidal Field*
†
Authors: López Pablo
Published: March 2024

It is usually assumed that the angular momentum (AM) of dark matter halos arises during the linear stages of structure formation, as a consequence of the coupling between the proto-haloes’ shape and the tidal field produced by their surrounding density perturbations. This approach, known as linear tidal torque theory (TTT), has been shown to make fairly good predictions about the mean evolution of both the AM amplitude and orientation up to approximately the time when the proto-haloes collapse. After this point, proto-haloes are increasingly affected by nonlinear processes that are not taken into account by the model. However, it has been seen in numerical simulations that, even at very early stages, the AM of proto-haloes is systematically reoriented toward perpendicularity with respect to the forming cosmic filaments, in contradiction with the fixed direction expected from the TTT. In this work we present a novel analytical approach that introduces an anisotropic scaling factor to the standard TTT equations, which allows the AM orientation to change in time, even during the linear regime. The amplitude and direction of this shift depend on the large scale tidal field around the forming proto-haloes. Our results significantly improve the predictions for the AM direction up to the time of protohalo collapse and, in some cases, even further in time.
Electric field dependence of spin qubit in a Si-MOS quantum dot
Authors: Ma 马 Rong-Long 荣龙 , Ni 倪 Ming 铭 , Zhou 周 Yu-Chen 雨晨 , Kong 孔 Zhen-Zhen 真真 , Wang 王 Gui-Lei 桂磊 , Liu 刘 Di 頔 , Luo 罗 Gang 刚 , Cao 曹 Gang 刚 , Li 李 Hai-Ou 海欧 , Guo 郭 Guo-Ping 国平
Published: March 2024

Valley, the intrinsic feature of silicon, is an inescapable subject in silicon-based quantum computing. At the spin–valley hotspot, both Rabi frequency and state relaxation rate are significantly enhanced. With protection against charge noise, the valley degree of freedom is also conceived to encode a qubit to realize noise-resistant quantum computing. Here, based on the spin qubit composed of one or three electrons, we characterize the intrinsic properties of valley in an isotopically enriched silicon quantum dot (QD) device. For one-electron qubit, we measure two electric-dipole spin resonance (EDSR) signals which are attributed to partial occupation of two valley states. The resonance frequencies of two EDSR signals have opposite electric field dependences. Moreover, we characterize the electric field dependence of the upper valley state based on three-electron qubit experiments. The difference of electric field dependences of the two valleys is 52.02 MHz/V, which is beneficial for tuning qubit frequency to meet different experimental requirements. As an extension of electrical control spin qubits, the opposite electric field dependence is crucial for qubit addressability, individual single-qubit control and two-qubit gate approaches in scalable quantum computing.
First Digit Distributions of Gamma-Ray Bursts
Authors: Lai Hou-Yu , Wei Jun-Jie
Published: March 2024

The occurrence of the first significant digits from real world sources is usually not equally distributed, but is consistent with a logarithmic distribution instead, known as Benford’s law. In this work, we perform a comprehensive investigation on the first digit distributions of the duration, fluence, and energy flux of gamma-ray bursts (GRBs) for the first time. For a complete GRB sample detected by the Fermi satellite, we find that the first digits of the duration and fluence adhere to Benford’s law. However, the energy flux shows a significant departure from this law, which may be due to the fact that a considerable part of the energy flux measurements is restricted by lack of spectral information. Based on the conventional duration classification scheme, we also check if the durations and fluences of long and short GRBs (with duration T
90 > 2 s and T
90 ≤ 2 s, respectively) obey Benford’s law. We find that the fluences of both long and short GRBs still agree with the Benford distribution, but their durations do not follow Benford’s law. Our results hint that the long–short GRB classification scheme does not directly represent the intrinsic physical classification scheme.
Comparison of the activation level in the sensorimotor cortex between motor point and proximal nerve bundle electrical stimulation
Authors: Yuan Rui , Peng Yu , Ji Run , Zheng Yang
Published: March 2024

Objective. Neuromuscular electrical stimulation (NMES) is widely used for motor function rehabilitation in stroke survivors. Compared with the conventional motor point (MP) stimulation, the stimulation at the proximal segment of the peripheral nerve (PN) bundles has been demonstrated to have multiple advantages. However, it is not known yet whether the PN stimulation can increase the cortical activation level, which is crucial for motor function rehabilitation. Approach. The current stimuli were delivered transcutaneously at the muscle belly of the finger flexors and the proximal segment of the median and ulnar nerves, respectively for the MP and PN stimulation. The stimulation intensity was determined to elicit the same contraction levels between the two stimulation methods in 18 healthy individuals and a stroke patient. The functional near-infrared spectroscopy and the electromyogram were recorded to compare the activation pattern of the sensorimotor regions and the target muscles. Main Results. For the healthy subjects, the PN stimulation induced significantly increased concentration of the oxygenated hemoglobin in the contralateral sensorimotor areas, and enhanced the functional connectivity between brain regions compared with the MP stimulation. Meanwhile, the compound action potentials had a smaller amplitude and the H-reflex became stronger under the PN stimulation, indicating that more sensory axons were activated in the PN stimulation. For the stroke patient, the PN stimulation can elicit finger forces and induce activation of both the contralateral and ipsilateral motor cortex. Conclusions. Compared with the MP stimulation, the PN stimulation can induce more cortical activation in the contralateral sensorimotor areas possibly via involving more activities in the central pathway. Significance. This study demonstrated the potential of the PN stimulation to facilitate functional recovery via increasing the cortical activation level, which may help to improve the outcome of the NMES-based rehabilitation for motor function recovery after stroke.
Machine learning-enhanced model-based scenario optimization for DIII-D
Authors: Morosohk S. , Leard B. , Rafiq T. , Schuster E.
Published: March 2024

Scenario development in tokamaks is an open area of investigation that can be approached in a variety of different ways. Experimental trial and error has been the traditional method, but this required a massive amount of experimental time and resources. As high fidelity predictive models have become available, offline development and testing of proposed scenarios has become an option to reduce the required experimental resources. The use of predictive models also offers the possibility of using a numerical optimization process to find the controllable inputs that most closely achieve the desired plasma state. However, this type of optimization can require as many as hundreds or thousands of predictive simulation cases to converge to a solution; many of the commonly used high fidelity models have high computational burdens, so it is only reasonable to run a handful of predictive simulations. In order to make use of numerical optimization approaches, a compromise needs to be found between model fidelity and computational burden. This compromise can be achieved using neural networks surrogates of high fidelity models that retain nearly the same level of accuracy as the models they are trained to replicate while reducing the computation time by orders of magnitude. In this work, a model-based numerical optimization tool for scenario development is described. The predictive model used by the optimizer includes neural network surrogate models integrated into the fast Control-Oriented Transport simulation framework. This optimization scheme is able to converge to the optimal values of the controllable inputs that produce the target plasma scenario by running thousands of predictive simulations in under an hour without sacrificing too much prediction accuracy.
Dynamics of inertial particles under velocity resetting
Authors: Olsen Kristian Stølevik , Löwen Hartmut
Published: March 2024

We investigate stochastic resetting in coupled systems involving two degrees of freedom, where only one variable is reset. The resetting variable, which we think of as hidden, indirectly affects the remaining observable variable via correlations. We derive the Fourier–Laplace transforms of the observable variable’s propagator and provide a recursive relation for all the moments, facilitating a comprehensive examination of the process. We apply this framework to inertial transport processes where we observe the particle position while the velocity is hidden and is being reset at a constant rate. We show that velocity resetting results in a linearly growing spatial mean squared displacement at later times, independently of reset-free dynamics, due to resetting-induced tempering of velocity correlations. General expressions for the effective diffusion and drift coefficients are derived as a function of the resetting rate. A non-trivial dependence on the rate may appear due to multiple timescales and crossovers in the reset-free dynamics. An extension that incorporates refractory periods after each reset is considered, where post-resetting pauses can lead to anomalous diffusive behavior. Our results are of relevance to a wide range of systems, such as inertial transport where the mechanical momentum is lost in collisions with the environment or the behavior of living organisms where stop-and-go locomotion with inertia is ubiquitous. Numerical simulations for underdamped Brownian motion and the random acceleration process confirm our findings.
Ground fault detection in substation DC system based on improved differential current method
Authors: Li Zhanghongtai , Zhang Lutong , Shen Chuanqi , Yue Luansong
Published: March 2024

DC system is the main electrical equipment and control signal system in the substation, and its stability and reliability are directly related to whether the power system can operate safely. This paper firstly introduces the common methods of insulation detection of DC power system in substation and its advantages and disadvantages, and proposes the differential current method based on unbalanced bridge, i.e., using the combination of unbalanced bridge and leakage current sensor to detect the insulation condition of DC system, which makes up for the defects of the traditional differential current method that can only detect the faults of simple branch circuits, and realizes the detection of multi-branch grounding under complex situations. Finally, the feasibility of this method is verified by building a simulation model through simulink.
GPS/Beidou fusion positioning technology for transmission line grounding line
Authors: Wang Wei , Gao Bo , Li Gang , Li Guang , Zhao Xu , Shi Yuxin , Wang Ziming , Yang Yang
Published: March 2024

The grounding wire plays a role in buffering high-voltage electricity in power transmission lines. However, during operation, it is difficult for personnel to obtain real-time information about the location of the grounding wire. The existing positioning technology for grounding wires in power transmission lines can prevent accidents caused by missing grounding wires or transmitting electricity through grounding wires. However, due to the vast scale of power transmission lines, there are often limited visible satellites and poor satellite signal quality, resulting in insufficient accuracy of satellite positioning and navigation. This paper first discusses the necessity of online monitoring of grounding wires in power transmission lines. Then, based on this, it introduces the positioning methods of GPS and Beidou positioning systems. Finally, it focuses on the GPS/Beidou fusion positioning technology. Through experimental verification, the GPS/Beidou fusion positioning technology is shown to greatly enhance the reliability of positioning by coordinating and complementing various navigation systems.
Research on Convolutional Neural Network-Based Compression Methods for Multispectral Images
Authors: Wei Kegang , Liu Haiyang , Yu Peixu , Ren Yushuai , Gao Guang Min , Wen Changzhe
Published: March 2024

Considering the traits of multispectral images, which include numerous bands, high spatial and spectral redundancy, and a large data volume, there is a proposed investigation on compression methods based on convolutional neural networks to reduce the storage space consumed by an individual image and enhance compression effectiveness. This paper first introduces the development of image compression algorithms and deep learning in recent years. Based on these two structures, a framework for lossy compression of multispectral images utilizing an end-to-end convolutional neural network is proposed. A self-encoding structure is used to process three-dimensional hyperspectral images, extracting local spectral features and fusing spectral information using large convolutional kernels. Residual layers are employed to preserve spectral information. Rate-distortion optimization is performed to jointly optimize image distortion and compression bitrate. Finally, a comparison with the traditional JPEG method is conducted experiments to assess the efficacy of the proposed algorithm. The MS-SSIM is improved by nearly 0.08, and the compressed images exhibit no noticeable distortion.
Research on prediction method of tool wear degree based on SVM
Authors: Han Peng-hui , Zhu Can-shi
Published: March 2024

Tool wear of machine tools not only directly affects the processing quality, but also leads to the life of processing equipment and production costs. It is of great significance to correctly identify, classify and predict the state of tool wear. In this paper, through the collection of machine tool operation data, using machine learning modeling, using the model to identify the tool wear state, and then predict and classify the tool wear state, using the classification results to determine whether the tool can continue to use. After simulation verification, the results show that the model can identify and predict the wear state more realistically, and has strong practicability.
Thermal Energy-Kinetic Energy Conversion Efficiency Analysis Method Based on Grey Cluster Analysis
Authors: Min Hao , Zhang Yalin , Xu Juntao , Wang Xiaochen
Published: March 2024

At this stage, although thermal power generation is still the main method of power generation in my country’s power plants, due to more and more energy consumption, the reserves of resources are gradually beginning to be stretched. Application in the power generation and production process of thermal power plants has become a road that more and more power plants must explore for development. In this paper, through the grey cluster analysis, the power plant can fully improve the power generation efficiency of the power plant in the process of utilizing the existing energy, so as to achieve the purpose of energy saving and environmental protection. Combined with the connotation of thermal energy-kinetic energy conversion, this paper establishes a theoretical framework for the analysis method of thermal energy-kinetic energy conversion efficiency based on grey cluster analysis. On the basis of theoretical research, using descriptive statistical tools, the main performance of thermal energy-kinetic energy conversion to promote economic growth is summarized. The realization method of gray cluster analysis theory is to identify and process the external features of gray information, capture effective information, and quantify the features (ie, whitening process), so as to complete the evaluation or decision-making, and verify the effect of these five mechanisms on economic growth. The final results of the study show that when K=1, 2, 3 and 4, the proportion of heat transfer is 41.63%, 42.19%, 49.63% and 52.13%, and the proportion of heat convection is 37.56% and 41.33%, respectively., 29.69% and 26.78%, the heat transfer and heat convection have the greatest influence on the thermal energy-kinetic energy conversion efficiency.