bluuebunny/crossref_metadata_2025_split huggingface dataset:
Simulated ischaemia/reperfusion impairs trophoblast function through divergent oxidative stress- and MMP-9-dependent mechanisms
Authors: Barron Aaron , Tuulari Jetro J. , Karlsson Linnea , Karlsson Hasse , O'Keeffe Gerard W. , McCarthy Cathal M. 
Published: October 2024

Abstract
Early-onset pre-eclampsia is believed to arise from defective placentation in the first trimester, leading to placental ischaemia/reperfusion (I/R) and oxidative stress. However, our current understanding of the effects of I/R and oxidative stress on trophoblast function is ambiguous in part due to studies exposing trophoblasts to hypoxia instead of I/R, and which report conflicting results. Here, we present a model of simulated ischaemia/reperfusion (SI/R) to recapitulate the pathophysiological events of early-onset pre-eclampsia (PE), by exposing first trimester cytotrophoblast HTR-8/SVneo cells to a simulated ischaemia buffer followed by reperfusion. We examined different ischaemia and reperfusion times and observed that 1 h ischaemia and 24 h reperfusion induced an increase in reactive oxygen species (ROS) production (P&lt;0.0001) and oxygen consumption rate (P&lt;0.01). SI/R-exposed trophoblast cells exhibited deficits in migration, proliferation, and invasion (P&lt;0.01). While the deficits in migration and proliferation were rescued by antioxidants, suggesting an ROS-dependent mechanism, the loss of invasion was not affected by antioxidants, which suggests a divergent ROS-independent pathway. In line with this, we observed a decrease in MMP-9, the key regulatory enzyme necessary for trophoblast invasion (P&lt;0.01), which was similarly unaffected by antioxidants, and pharmacological inhibition of MMP-9 replicated the phenotype of deficient invasion (P&lt;0.01). Collectively, these data demonstrate that I/R impairs trophoblast migration and proliferation via a ROS-dependent mechanism, and invasion via an ROS-independent loss of MMP-9, disambiguating the role of oxidative stress and providing insights into the response of trophoblasts to I/R in the context of early-onset PE.
How to Efficiently Measure the Intelligibility of People With Parkinson's Disease
Authors: Dahl Kimberly L. , Balz Magdalen A. , Cádiz Manuel Díaz , Stepp Cara E. 
Published: October 2024

Purpose:
The purpose of this study was to determine the most efficient approaches to measuring the intelligibility of people with Parkinson's disease (PD) when considering the estimation method, listener experience, number of listeners, number of sentences, and the ways these factors may interact.

Method:
Speech-language pathologists (SLPs) and inexperienced listeners estimated the intelligibility of people with and without PD using orthographic transcription or a visual analog scale (VAS). Intelligibility estimates were based on 11 Speech Intelligibility Test sentences. We simulated all combinations of listeners and sentences to compare intelligibility estimates based on fewer listeners and sentences to a speaker-specific benchmark estimate based on the mean intelligibility across all sentences and listeners.

Results:
Intelligibility estimates were closer to the benchmark (i.e., more accurate) when more listeners and sentences were included in the estimation process for transcription- and VAS-based estimates and for SLPs and inexperienced listeners. Differences between the benchmark and subset-based intelligibility estimates were, in some cases, smaller than the minimally detectable change in intelligibility for people with PD.

Conclusions:
The intelligibility of people with PD can be measured more efficiently by reducing the number of listeners and/or sentences, up to a point, while maintaining the ability to detect change in this outcome. Clinicians and researchers may prioritize either fewer listeners or fewer sentences, depending on the specific constraints of their work setting. However, consideration must be given to listener experience and estimation method, as the effect of reducing the number of listeners and sentences varied with these factors.
Rehabilitation exoskeleton system with bidirectional virtual reality feedback training strategy
Authors: Gao Yongsheng , Lang Guodong , Zhang Chenxiao , Wu Rui , Zhu Yanhe , Zhao Yu , Zhao Jie 
Published: October 2024

AbstractVirtual reality (VR) technology revitalises rehabilitation training by creating rich, interactive virtual rehabilitation scenes and tasks that deeply engage patients. Robotics with immersive VR environments have the potential to significantly enhance the sense of immersion for patients during training. This paper proposes a rehabilitation robot system. The system integrates a VR environment, the exoskeleton entity, and research on rehabilitation assessment metrics derived from surface electromyographic signal (sEMG). Employing more realistic and engaging virtual stimuli, this method guides patients to actively participate, thereby enhancing the effectiveness of neural connection reconstruction—an essential aspect of rehabilitation. Furthermore, this study introduces a muscle activation model that merges linear and non‐linear states of muscle, avoiding the impact of non‐linear shape factors on model accuracy present in traditional models. A muscle strength assessment model based on optimised generalised regression (WOA‐GRNN) is also proposed, with a root mean square error of 0.017,347 and a mean absolute percentage error of 1.2461%, serving as critical assessment indicators for the effectiveness of rehabilitation. Finally, the system is preliminarily applied in human movement experiments, validating the practicality and potential effectiveness of VR‐centred rehabilitation strategies in medical recovery.
Research on construction method of command and control network model based on complex network theory
Authors: Wang Jianwei , Pan Chengsheng 
Published: October 2024

AbstractThe command and control (C2) network is a complete organizational system that connects operational units at all levels based on command relationships. Its purpose is to ensure that the C2 system can fully perform its command functions, achieving high precision and efficiency in decision‐making. As warfare models evolve rapidly from network‐centric warfare to multi‐domain operations, traditional C2 networks, which utilize a tree structure for connectivity, exhibit only a single hierarchical relationship, making it challenging for different operational units at the same level to interconnect. Furthermore, with the diversification of warfare, the three types of nodes in traditional C2 network models are insufficient to encompass all operational units. In response, this paper proposes a method for edge weighting in C2 networks based on a combination of node attributes and network attributes described by complex network theory. The node attributes mainly include node information transmission capacity, task coordination ability between nodes, node distance, and response time. The network attributes are primarily represented by hierarchy and betweenness centrality. Additionally, the traditional C2 network model's three types of nodes are expanded to five types of nodes. Based on the edge weighting method, internal command edges, inter‐network collaborative edges, and cross‐level command edges are generated within the C2 network. Simulation results demonstrate that the constructed C2 network model's characteristic parameters are superior to those of traditional C2 networks and collaborative C2 networks. This improvement enhances command and coordination abilities, aligns more closely with real‐world scenarios, and effectively improves network command and control efficiency.
Multi‐scale feature extraction for energy‐efficient object detection in remote sensing images
Authors: Wu Di , Liu Hongning , Xu Jiawei , Xie Fei 
Published: October 2024

AbstractObject detection in remote sensing images aims to interpret images to obtain information on the category and location of potential targets, which is of great importance in traffic detection, marine supervision, and space reconnaissance. However, the complex backgrounds and large scale variations in remote sensing images present significant challenges. Traditional methods relied mainly on image filtering or feature descriptor methods to extract features, resulting in underperformance. Deep learning methods, especially one‐stage detectors, for example, the Real‐Time Object Detector (RTMDet) offers advanced solutions with efficient network architectures. Nevertheless, difficulty in feature extraction from complex backgrounds and target localisation in scale variations images limits detection accuracy. In this paper, an improved detector based on RTMDet, called the Multi‐Scale Feature Extraction‐assist RTMDet (MRTMDet), is proposed which address limitations through enhancement feature extraction and fusion networks. At the core of MRTMDet is a new backbone network MobileViT++ and a feature fusion network SFC‐FPN, which enhances the model's ability to capture global and multi‐scale features by carefully designing a hybrid feature processing unit of CNN and a transformer based on vision transformer (ViT) and poly‐scale convolution (PSConv), respectively. The experiment in DIOR‐R demonstrated that MRTMDet achieves competitive performance of 62.2% mAP, balancing precision with a lightweight design.
Recursive dynamic state estimation for power systems with an incomplete nonlinear DAE model
Authors: Katanic Milos , Lygeros John , Hug Gabriela 
Published: October 2024

AbstractPower systems are highly complex, large‐scale engineering systems subject to many uncertainties, which makes accurate mathematical modeling challenging. This article introduces a novel centralized dynamic state estimator designed specifically for power systems where some component models are missing. Including the available dynamic evolution equations, algebraic network equations, and phasor measurements, the least squares criterion is applied to estimate all dynamic and algebraic states recursively. The approach generalizes the iterated extended Kalman filter and does not require static network observability, relying on the network topology and parameters. Furthermore, a topological criterion is established for placing phasor measurement units (PMUs), termed topological estimability, which guarantees the uniqueness of the solution. A numerical study evaluates the performance under short circuits in the network and load changes and shows superior tracking performance compared to robust procedures from the literature with computational times in accordance with the typical PMU sampling rates.
MRS‐Net: Brain tumour segmentation network based on feature fusion and attention mechanism
Authors: Shen Xiaoyan , Wang Ju , Zhao Yuhua , Zhou Rui , Gao Han , Zhang Jiakai , Shen Hongming 
Published: October 2024

AbstractAccurate segmentation of brain tumor magnetic resonance imaging (MRI) is crucial for treatment planning. Addressing the challenges of complex tumor structures and inadequate cross‐channel information utilization in Unet‐based segmentation, this paper proposes the multi‐scale residual brain tumor MRI segmentation network (MRS‐Net) incorporating an attention mechanism to enhance segmentation accuracy. First, the double residual feature fusion module is utilized to enhance the fusion of feature information between different levels. Second, the Atrous Spatial Pyramid Pooling is introduced as a bridging module of the network to capture the features at different scales of the image, so as to enhance the extraction capability of the network for detailed features. Finally, the inverted residual coordinate attention module replaces the direct splicing in Unet to fuse the large feature information at each level and scale, thus enhancing the model's ability to recognize the spatial location information of brain tumors. The Dice coefficients, positive predictive values (PPVs), sensitivities (Sensitivity) and Hausdorff distance (HD), which are the four evaluation indexes, reach 84.54%, 87.43%, 88.37% and 2.248, respectively, which are improved by 1.85%, 2.11%, 2.88% and 6.0%, respectively, compared with Unet. The experimental results show that MRS‐Net achieves better brain tumor image segmentation.
A hybrid equivalent circuit model and plane wave spectrum method for decoupling surface designs in multi‐band shared‐aperture antenna arrays
Authors: Deng Xuan , Chen Yikai , Yang Shiwen 
Published: October 2024

AbstractShared‐aperture antenna arrays are attractive for 5G base stations, owing to their compact size and multi‐band frequency coverage. Decoupling surfaces are effective for suppressing crossband mutual coupling. However, their designs typically rely on full‐wave simulations of scattering parameters, which may not ensure a good radiation performance and is time‐consuming. A systematic methodology based on the hybrid equivalent circuit model and plane wave spectrum method is proposed for decoupling surface designs. The decoupling surface is represented by its equivalent circuit and the spectral expression converts the electromagnetic field into a circuit problem. Both the scattering parameter and radiation pattern can be solved with the circuit, offering an efficient design method that ensures consistent radiation patterns between the model and full‐wave simulations. A double‐layer decoupling surface with wide stopbands, high transmission property, and sharp roll‐off transition was realised. Furthermore, to reduce the overall profile height, the reflection phase of the decoupling surface was tuned. A triple‐band shared‐aperture base station antenna array was developed. Simulated and measured results demonstrate the array works properly in the 0.69–0.96 GHz, 1.7–2.7 GHz, and 3.3–3.8 GHz frequency bands with stable radiation patterns, and crossband mutual coupling is well suppressed, validating the effectiveness of the proposed methodology.
Analysis of active impedance characteristics and harmonic deterioration of multiple grid connected inverters considering nonlinear factors
Authors: Li Jianwen , Song Shanshan , Li Rong , Sun Wei 
Published: October 2024

AbstractThe harmonic problems caused by non‐linear factors of the grid connected inverter (GCI) system are more complicated, including both non‐characteristic harmonics emitted by the dead‐time and the changes in harmonic impedance characteristics. Harmonics interact with the changing impedance, and even cause harmonic amplification and resonance. The harmonic deterioration of the multiple GCIs system is serious. To analyse the mechanism and way of harmonic deterioration in grid‐connected system caused by nonlinear factors, the active impedance models of single inverter and multiple GCIs system including dead‐time effect and digital control delay are established first. In view of this, the influence mechanism of non‐linear factors on system stability is explored. The improved modal analysis method is used to traverse the network series parallel resonance caused by nonlinear factors. The results show that both the dead‐time effect and digital control delay reduce phase margin of the system, resulting in the resonant frequency shift and the resonant peak increase. When the harmonic excitation source interacts with the complex network under the influence of nonlinear factors, it will lead to further deterioration of harmonics. Finally, based on the MATLAB and RT‐LAB hardware‐in‐the‐loop simulation platforms, a multiple GCIs system model is built to verify the correctness of the established active impedance model and harmonic deterioration analysis.
Subspace‐based distributed target detection method with small training data samples
Authors: Wei Guangfen , Zhou Zhan , Luo Yuan , Jian Tao , Tang Xiaoming 
Published: October 2024

AbstractDetecting distributed targets precisely in homogeneous environments has been a hot topic in radar signal processing. Generally, distributed targets are often modelled with subspace models of unknown coordinates, and clutter is modelled as the complex Gaussian distribution with zero mean and unknown covariance matrix, while covariance matrix is estimated with a set of training data without the target signal. However, in practice, the complexity of the external environment makes the training data that satisfy the condition of independent homogeneous distribution less available. Therefore, it is assumed that the covariance matrix of the clutter is persymmetric structure and the approach of dimensionality reduction using subspace transformations is introduced, two detectors based upon generalised likelihood ratio test criterion and Wald test criterion in homogeneous environments are proposed. Theoretical analyses indicate the constant false alarm rate characteristics of the two proposed detectors for unknown clutter covariance matrices. Simulation analyses indicate that the proposed detector works well even with fewer training data samples, and its detection performance outperforms that of existing contrast detectors.
Validity of Diagnosis of Disseminated Intravascular Coagulation Based on International Classification of Diseases Coding in a Claims Database
Authors: Umemura Yutaka , Yamakawa Kazuma , Mori Hirotaka , Okamoto Kohji , Oda Jun , Fujimi Satoshi 
Published: October 2024

Background Accuracy in diagnoses recorded using the International Classification of Diseases (ICD) coding is the most important element ensuring the foundation of research using real-world data analyses.
Objective To evaluate the validity of ICD coding for diagnoses of disseminated intravascular coagulation (DIC) using the International Society on Thrombosis and Haemostasis (ISTH) overt DIC criteria and the Japanese Association for Acute Medicine (JAAM) DIC criteria as reference standards.
Methods This retrospective observational study included adult hospitalized patients diagnosed as having diseases potentially causing DIC extracted from a part of a large-scale database in Japan. The index test was a diagnosis of DIC based on the ICD-10 codes. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated using ISTH overt DIC criteria and JAAM-2 DIC criteria as the reference standards. We also conducted subgroup analyses according to the underlying diseases.
Results We included 84,300 patients in this study. In the overall study population, sensitivity, specificity, PPV, and NPV of the ICD-based diagnosis for ISTH criteria were 26.28, 98.10, 35.12, and 97.14%, respectively. In subgroup analyses according to the underlying disease, sensitivity ranged from 9.48 to 52.08%, and specificity ranged from 96.94 to 99.47%. The accuracy of the ICD-based diagnosis for JAAM-2 criteria was similar to that for ISTH criteria.
Conclusion Identification of DIC patients using ICD-10 codes had relatively low sensitivity but very high specificity for DIC diagnostic criteria. Approximately 65% of patients identified by ICD coding are likely to meet the JAAM-2 DIC criteria.
Chronic psychosocial stress affects insulin-like growth factor 1 and its receptors in mouse ovaries
Authors: Igonina Tatyana , Lebedeva Daria , Tsybko Anton , Rozhkova Irina , Babochkina Tatyana , Levinson Alisa , Amstislavsky Sergei 
Published: October 2024

Context Chronic psychosocial stress negatively affects folliculogenesis and oogenesis. Intraovarian mechanisms mediating these effects are poorly understood. Aims This work aimed to find out how chronic psychosocial stress affects ovarian IGF1 and its receptor (IGF1R), as well as Igf1 and Igf1r gene expression in cumulus–oocyte complexes (COCs). It also aimed to address possible protective effects of gonadotropin stimulation on IGF1 ovarian signalling. Methods Female CD1 mice experienced chronic psychosocial stress of 11-day isolation followed by overcrowding for 10 days. To verify the model, blood corticosterone levels and the quality of oocytes were evaluated in stressed females. The levels of IGF1/IGF1R, blood IGF1 concentration, and expression of Igf1/Igf1r in the ovaries were compared in stressed and unstressed females. Key results Psychosocial stress caused an elevation of corticosterone level, which was alleviated by gonadotropin treatment. The stressed mice showed a decreased IGF1 level in the ovaries and a decreased expression of Igf1 and Igf1r in COCs. In the unstressed females, gonadotropin injection decreased the expression of Igf1 and Igf1r; in the stressed females, the same treatment increased Igf1r expression. Neither stress nor ovarian stimulation with gonadotropins affected the serum IGF1 level. Conclusions Psychosocial stress suppresses IGF1 signalling in the ovaries. Gonadotropin treatment modulates these effects differently in stressed and unstressed animals. Implications The results may have translational value for human reproduction. Ovarian IGF1 can be considered a candidate for further improvement of IVF results in women under conditions of chronic stress.
Multi-bolt looseness monitoring using guided waves: a cross-correlation approach of the wavelet energy envelope
Authors: Chen Dongdong , Li Wei , Dong Zhiqiang , Fu Ruili , Yu Qiao 
Published: October 2024

Abstract
This paper proposes a novel approach for monitoring multi-bolt looseness using guided waves and the cross-correlation of the wavelet energy envelope. By assessing variations in the wave packet, the looseness in multi-bolt assemblies can be estimated. First, the dispersion effects of Lamb waves were theoretically analyzed using the Rayleigh–Lamb equation. Next, the wavelet energy was derived through wavelet transform, and the Lamb wave envelope was obtained as a criterion for accurately separating the wave packet. Cross-correlation analysis was employed to quantitatively evaluate the dispersion of wave packets for varying levels of bolt looseness. A looseness index, termed the normalized decorrelation coefficient of wavelet energy (NDCWE), was defined. Then, validation experiments were conducted using a joint with five M8 bolts, each tightened to a standard torque of 42 N·m. Two piezoelectric transducers were attached to the periphery of the bolt group. Three preload conditions were tested for each bolt: fully tightened, 80% of the standard torque, and 10% of the standard torque, corresponding to no looseness, minor looseness, and significant looseness, respectively. Results showed that when significant looseness occurs, the NDCWE value exceeds 0.4, confirming the effectiveness of NDCWE in detecting substantial reductions in bolt preload. Experiments assessing the effect of temperature revealed that temperature has a negligible effect on the waveforms of the S
0 and A
0 mode waves. Finally, to quantitatively evaluate the efficiency of the ultrasonic transducers, the bolt-to-sensor ratio (BSR) was introduced. In this study, the BSR reached 2.5, indicating that a single piezoelectric transducer can monitor the preload of 2.5 bolts. The proposed approach shows great potential for multi-bolt looseness monitoring.