bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical Application of Thyrotropin Receptor
Antibodies
Authors: Yang Yang , Chen Hui 
Published: December 2024

AbstractThyrotropin receptor antibodies (TRAb) are specific for Graves’ disease (GD) and
play a crucial role in the pathogenesis of GD. TRAb assays have recently been
greatly improved. This review discusses the clinical application of TRAb in the
differential diagnosis of hyperthyroidism, the prognosis of GD, GD in gestation
and pediatrics, and GD related ophthalmopathy (GO). In addition to the classical
competition and bioassays, a new bridging assay has emerged for TRAb assays.
TRAb is the main pathogenic mechanism of hyperthyroidism in GD. Treated GD still
has a high rate of recurrence and even a short-term surge of TRAb, leading to
rapid deterioration of GO. Fetal goiter may be associated with elevated maternal
TRAb during pregnancy, overtreatment may lead to fetal hypothyroidism. Pediatric
patients with GD have high TRAb, poor remission from treatment, and
insignificant manifestations of GO. TRAb is significantly correlated with GO
activity and severity. Currently, TRAb assay has high specificity and
sensitivity and can be used directly to identify the cause of hyperthyroidism.
TRAb can be used to predict recurrence of drug treated GD or progression of GO
after RAI therapy. TRAb should be measured regularly for GD in gestation to
guide anti-thyroid medication to avoid thyrotoxicosis or hypothyroidism in the
fetus. Monitoring TRAb in pediatric GD may help control the progression of GO.
TRAb assay is an important guide for the treatment of GO.
Direct Access to 5,6-Dihydropyrido[2′,1′:2,3]pyrido-Fused Imidazo [4,5-c]quinolines via Consecutive C–N and C–C Bond Formation in Deep Eutectic Solvent under Microwave Irradiation
Authors: Maiti Barnali , Chanda Kaushik , Dasmahapatra Upala , Choudhury Badruzzaman , Ahmad Md Gulzar 
Published: December 2024

AbstractDeep eutectic solvents (DESs) are a novel class of ionic liquids that have recently gained considerable attention as a sustainable, nontoxic substitute for traditional organic solvents; recent years have seen an increasing demand for DES systems to synthesize various heterocyclic moieties. Polyheterocycle-containing scaffolds are important as potential therapeutic agents. Herein, we reported a tandem methodology for the synthesis of rarely explored 5,6-dihydropyrido[2′,1′:2,3]imidazo[4,5-c]quinolines and pyrido[2′,1′:2,3]imidazo[4,5-c]quinolines in a choline chloride based DES medium under microwave irradiation. This sustainable approach offers a clean, straightforward synthetic strategy under mild reaction conditions for the synthesis of a wide range these compounds in excellent yields.
Bayesian inverse Navier–Stokes problems: joint flow field reconstruction and parameter learning
Authors: Kontogiannis Alexandros , Elgersma Scott V , Sederman Andrew J , Juniper Matthew P 
Published: December 2024

Abstract
We formulate and solve a Bayesian inverse Navier–Stokes (N–S) problem that assimilates velocimetry data in order to jointly reconstruct a 3D flow field and learn the unknown N–S parameters, including the boundary position. By hardwiring a generalised N–S problem, and regularising its unknown parameters using Gaussian prior distributions, we learn the most likely parameters in a collapsed search space. The most likely flow field reconstruction is then the N–S solution that corresponds to the learned parameters. We develop the method in the variational setting and use a stabilised Nitsche weak form of the N–S problem that permits the control of all N–S parameters. To regularise the inferred geometry, we use a viscous signed distance field as an auxiliary variable, which is given as the solution of a viscous Eikonal boundary value problem. We devise an algorithm that solves this inverse problem, and numerically implement it using an adjoint-consistent stabilised cut-cell finite element method. We then use this method to reconstruct magnetic resonance velocimetry (flow-MRI) data of a 3D steady laminar flow through a physical model of an aortic arch for two different Reynolds numbers and signal-to-noise ratio (SNR) levels (low/high). We find that the method can accurately (i) reconstruct the low SNR data by filtering out the noise/artefacts and recovering flow features that are obscured by noise, and (ii) reproduce the high SNR data without overfitting. Although the framework that we develop applies to 3D steady laminar flows in complex geometries, it readily extends to time-dependent laminar and Reynolds-averaged turbulent flows, as well as non-Newtonian (e.g. viscoelastic) fluids.
Materials and strategies for enhancing the reliability of AgNW-based flexible electronics: a review
Authors: Li Chenyuan , Chen Mingxiang , Xie Bin 
Published: December 2024

Abstract
Silver nanowires (AgNWs), which are one-dimensional metallic nanomaterials, have attracted a great deal of attention owing to their superior electrical conductivity, flexibility and transparency. AgNWs have found extensive applications in the realm of flexible and stretchable electronics, where they act as flexible conducting networks. However, AgNWs face severe reliability challenges, including mechanical fracture, chemical corrosion and thermal failures, when dispersed in a polymer matrix. To tackle this issue, numerous strategies involving surface modification and passivation have been proposed. This review discusses the underlying failure mechanisms of AgNWs, with a particular focus on the effective strategies for enhancing the reliability of AgNWs and their composite materials. Finally, insights into potential strategies for enhancing the reliability of AgNWs and boosting their industrial deployment are also described.
DC series arc fault detection method using LDA-CatBoost for PV system under shaded condition
Authors: Yan Jinjing , Tang Shengxue , Geng Likai , Geng Jiayu 
Published: December 2024

Abstract
Detecting DC series arc faults (SAFs) in photovoltaic (PV) systems is challenging due to shaded conditions. A detection method for SAFs in PV systems focusing on shaded conditions is proposed in this paper. By analyzing voltage–current characteristics under different shaded scenarios and the interaction of arc and PV sources to ignite arc faults, the DC SAFs are classified into three types: strong, weak, and intermittent arcs, and then the DC arc features under various shaded conditions are investigated from the current signals. Then, a linear discriminant analysis (LDA)-CatBoost method is proposed to detect and identify SAFs using the features from extracting both time- and frequency-domain current signals. Experimental results demonstrate that the detection and identification accuracy is 98% under various shaded conditions. It significantly improves SAF detection applicability compared with other methods.
Research on rolling bearing fault diagnosis method based on improved multi-source fusion convolutional neural network
Authors: Shi Huaitao , Sun Huayang , Bai Xiaotian , Song Zelong , Gao Tianhao 
Published: December 2024

Abstract
As sensor technology advances, the variety and number of sensors increase, leading to the capture of more signals. Existing multi-source fusion methods often face issues such as increased model complexity or the failure to fully utilize the potential correlations among multi-sensor data, thereby affecting the accuracy and reliability of fault diagnosis. To address this issue, this paper proposes a multi-source fusion convolutional neural network (MFCNN) that diagnoses bearing faults by integrating features from multi-source signals. Firstly, multiple convolution blocks with gradually increasing one-dimensional kernel sizes are utilized to extract features from the integrated multi-source data. This approach enhances feature extraction efficiency and simplifies the network architecture. Secondly, a feature fusion based on the convolutional block attention module attention mechanism is proposed, which refines feature representation through channel and spatial attention modules. This makes the model more focused on important information, thereby improving recognition accuracy. The diagnostic capabilities of the proposed MFCNN are evaluated utilizing two datasets.
Dual-path fusion network based on transformer and local binary patterns for compressive sensing reconstruction
Authors: Lin Leping , Mao Jiaqi , Ouyang Ning 
Published: December 2024

Abstract
In recent years, deep-learning-based methods have made significant progress in the field of compressed sensing. However, most existing deep-learning-based solutions commonly encounter issues in image reconstruction due to inadequate interaction with image textures during the reconstruction process. In this study, we developed a dual-path fusion network that combined structural and textural information for image reconstruction. Guided by structural priors, we designed a new focus linear cross window transformer network that computes attention through parallel cross-SWs of different sizes, integrating both local and global structural information to enhance feature interaction. Meanwhile, a submodule based on local binary pattern is used to leverage image texture priors for learning texture features, thus providing rich texture information for image reconstruction. The features from these two distinct paths are adaptively fused, and the reconstruction results are optimized using an iterative thresholding algorithm. This method effectively combines the advantages of traditional algorithms and deep neural networks, taking advantage of the structural and textural prior information. Experiments demonstrated that the proposed method significantly improves the quality of image reconstruction and robustness to noise.
Generalized cross-correlation based wavelength extraction for chromatic confocal microscopy
Authors: Zhao Wenhao , Yue Kun , Liang Yongkang , Guo Wenping , Xia Min 
Published: December 2024

Abstract
In this paper, we propose a peak extraction algorithm based on generalized cross-correlation method in chromatic confocal microscopy. In this approach, a high level of localization precision is achieved by utilizing the generalized cross-correlation function between the chromatic confocal signal and the template signal. Compared to the algorithms proposed in recent years, the proposed algorithm is found to have lower localization bias and standard deviation.Both the simulation and the experimental results confirm the significant accuracy advantage of the proposed algorithm.
An improved Patlak-based Ki parametric imaging approach for clinical 18F-FDG total-body PET
Authors: Gu Wenjian , Zhu Zhanshi , Liu Ze , Wang Yihan , Li Yanxiao , Xu Tianyi , Liu Weiping , Wang Kuanquan , Luo Gongning , Zhou Yun 
Published: December 2024

Abstract
Objective. The objective is to generate reliable K
i parametric images from 18F-fluorodeoxyglucose (18F-FDG) total-body positron emission tomography (PET) with clinically acceptable scan durations using Patlak and shallow machine learning algorithms, under conditions of limited computational and data resources. Approach. We proposed a robust and fast algorithm named Patlak-KXD to generate K
i images from dynamic PET images with shortened scan durations. In the training phase, K-means is employed to generate a K
i-balanced training dataset. Subsequently, XGBoost is utilized to learn the mapping relationship between the tissue-to-blood standardized uptake ratio (SUR) time curves and Patlak-based K
i values using this balanced dataset. In the prediction phase, the trained XGBoost can generate K
i images by calculating the K
i values from voxel-based SUR time curves obtained from the dynamic images. We compared the accuracy of K
i images generated by both the Patlak-KXD and the traditional Patlak methods across a range of shortened scan durations, and the differences in K
i images generated by the XGBoost model using static (Patlak-KXS) and dynamic PET inputs. Main results. The K
i images generated by the Patlak-KXD from just a 4 min (56–60 min) dynamic 18F-FDG total-body PET scan are comparable to those generated by the traditional Patlak method using 40 min (20–60 min) dynamic PET images, as demonstrated by a normalized mean square error of 0.13 and a Pearson’s correlation coefficient of 0.94 on average. The K
i images generated by the Patlak-KXD is robust to the scan duration, and the quality of K
i images generated from Patlak-KXD is superior to those from Patlak-KXS as scan duration >10 min. Significance. Reliable K
i images can be rapidly generated using shallow machine learning algorithms from dynamic 18F-FDG total-body PET scans with durations as short as four minutes. This total-body K
i parametric imaging method has potential to be used in clinical nuclear medicine and molecular imaging.
Estimating two-dimensional physical parameters of digital rocks using deep learning
Authors: Alexandra Aldenia , Akmal Fadhillah , Nurcahya Ardian , Yulita Intan Nurma , Dharmawan Irwan Ary 
Published: December 2024

Abstract
This research focused on estimating the physical parameters of porous rocks crucial in hydrocarbon exploration using deep learning algorithms. Laboratory measurements have limitations such as time, cost, and core sample limitations, so digital rock models have emerged as a powerful alternative. Digital rock technology involves creating high-resolution images of rock samples using techniques such as micro-CT scanning for the detailed analysis of rock structures and calculation of physical parameters through image processing and numerical simulations. In this work, the CNN architectures included custom-developed models, and transfer learning was applied using pre-trained models DenseNet201, ResNet152, MobileNetV2, InceptionV3, and Xception to estimate physical parameters such as permeability, absolute porosity, effective porosity, tortuosity, and average grain size. Both CNN A and CNN B were good models for estimating permeability with CNN B being the best model for estimating tortuosity, Xception the best model for estimating absolute porosity and effective porosity, and DenseNet201 the best model for estimating average grain size. These results underscore the potential of deep learning in enhancing the efficiency and accuracy of physical parameter estimation in digital rock analysis.
Gate-tunable high-responsivity photodiode based on 2D ambipolar semiconductor
Authors: Yu 于 Wentao 文韬 , Zhao 赵 Long 龙 , Gao 高 Yanfei 延飞 , Gao 高 Shiping 石平 , Yang 杨 Yuekun 悦昆 , Pan 潘 Chen 晨 , Liang 梁 Shi-Jun 世军 , Cheng 程 Bin 斌 
Published: December 2024

Abstract
Electrically tunable homojunctions based on ambipolar two-dimensional materials have attracted widespread attention in the field of intelligent vision. These devices exhibit inherent switchable positive and negative photovoltaic properties that effectively mimic the behavior of human retinal cells. However, the photovoltaic responsivity of most electrically tunable homojunctions remains significantly low due to the weak light absorption, making it challenging to meet the application requirements for high-sensitivity target detection in the field of intelligent vision. Here, we propose a gate-tunable photodiode based on two-dimensional ambipolar WSe2 with an asymmetric gate electrode, achieving high photovoltaic responsivity. By adjusting the gate voltage and keeping bias voltage zero, we can dynamically realize reconfigurable n−–p and n−–n homojunction states, as well as gate-tunable photovoltaic response characteristics that range from positive to negative. The maximum photovoltaic responsivity of the electrically tunable WSe2 homojunction is approximately 0.4 A/W, which is significantly larger than the previously reported value ∼0.1 A/W in homojunction devices. In addition, the responsivity can be further enhanced to approximately 1.0 A/W when the n−–p photodiode operates in reverse bias mode, enabling high-sensitivity detection of targets. Our work paves the way for developing gate-tunable photodiodes with high photovoltaic responsivity and advancing high-performance intelligent vision technology.
A novel method of cognitive overload assessment based on a fusion feature selection using EEG signals
Authors: Li Zhongrui , Tong Li , Zeng Ying , Gao Yuanlong , Gong Diankun , Yang Kai , Hu Yidong , Yan Bin 
Published: December 2024

Abstract

Objective. Cognitive overload, as an overload state of cognitive workload, negatively impacts individuals’ task performance and mental health. Cognitive overload assessment models based on electroencephalography (EEG) can effectively prevent the occurrence of overload through early warning, thereby enhancing task execution efficiency and safeguarding individuals’ mental health. Although existing EEG-based cognitive load assessment methods have achieved significant research outcomes, evaluating cognitive overload remains an ongoing challenge. Current research aims to develop an effective cognitive overload assessment model and enhance its efficacy through feature selection methods. Approach. In the cognitive overload assessment model, we firstly employ variational mode decomposition to adaptively decompose the signal from each channel into four sub-band signals to capture valuable time–frequency information. Subsequently, frequency domain features are extracted from each sub-band, and an effective feature selection method based on mutual information and neighborhood component analysis was applied for feature selection, which optimizes the distribution of the feature space while considering feature correlations, making the selected features more representative. Finally, traditional machine learning methods are utilized for classification, and the effectiveness of the proposed method is tested using both offline and online classification results. Main results. The average accuracy of offline cognitive overload assessment using the proposed method on local and open datasets is 83.44 ± 1.59% and 78.24 ± 1.43%, respectively. The average classification accuracy of its online cognitive overload assessment is about 79.90 ± 2.53%. This indicates that the proposed method can effectively assess cognitive overload under both offline and online conditions. Furthermore, we found that higher-frequency sub-bands are more advantageous for cognitive overload assessment. Significance. EEG signals can be used for effectively cognitive overload assessment, and the integration of feature selection methods enhances the accuracy of the evaluation, providing reliable methodological support for future cognitive overload monitoring in human–computer interaction systems.
Electrical Explosion-Proof design of Low-Temperature condensation oil and gas recovery machine
Authors: Wang Nana , Lin Ruobo , Bai Zhenwei , Shen Lifeng 
Published: December 2024

Abstract
The condensation oil and gas recovery machine is a highly efficient device specifically designed to recover oil and gas using condensation principles. This equipment is primarily used in flammable and explosive environments; thus, its electrical control system must meet explosion-proof requirements. This paper takes the oil and gas recovery machine as an example to introduce the design process of an explosion-proof electrical control system. The control system is composed of PLC and HMI as the core. The appropriate explosion-proof type is chosen based on the structural and performance characteristics of the recovery machine. The key points and considerations for designing the explosion-proof control box are elaborated on. The experimental results indicate that after undergoing triple-stage refrigeration, the oil and gas recovery machine can reduce the temperature of the oil and gas to −80°C. The electrical control system meets the explosion-proof safety and control function requirements of the oil and gas recovery machine.
Research on the drilling state intelligent sensing and adaptive optimization control of the fast drilling robot
Authors: Liu Xiaohua , Wang Qingfeng , Liu Yang 
Published: December 2024

Abstract
The fast drilling robot is the most intelligent mining drilling equipment at present. The drilling state intelligent sensing and adaptive control technology are developed to solve the problem of the intelligent control of the drilling process. According to the engineering practice, with drilling efficiency as the control target, the characteristics of the working process of the fast drilling robot and the influence of boundary conditions such as sticking probability, slag-discharge smoothness, and anchorage stability on the drilling process are analyzed. Corresponding mathematical formulas are derived, and the mathematical model of the drilling state intelligent sensing and adaptive control is constructed. The adaptive control technology based on the differential evolution algorithm is developed to realize the automatic optimization of control parameters. The ground drilling tests prove that intelligent sensing and adaptive control technology can adjust the control parameters quickly when the drilling rock changes so that the drilling robot can maintain an efficient and stable drilling state.
Intelligent control of extra-high arch dams
Authors: Yang Yunbao , Lv Runqi , Han Shenqiu , Li Yang , Yue Jiali , Wu Jianming 
Published: December 2024

Abstract
At this stage, the intelligent construction system integrating sensing technology, communication transmission, data processing, and intelligent decision-making is becoming increasingly mature. It is fully applied in the construction industry. As a large-volume concrete building with concrete temperature control, the extra-high arch dam faces challenges such as high construction difficulty, management difficulty, and other thorny issues. By introducing an intelligent control system, real-time sensing during the pouring process of the extra-high arch dam allows for autonomous analysis to be conducted in a timely manner. Anticipation of various risks in the construction process and autonomous problem-solving are achieved through intelligent decision-making within the system. Artificial assistance makes up for the shortcomings of the intelligent construction system. Through the system’s own independent learning ability and continuous optimization, the problem of intelligent construction can be solved. It also can solve most of the problems in the dam pouring process, accelerate the project progress, significantly reduce the construction difficulty, improve the quality of dam construction, and improve the overall safety performance of the dam.