bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Contribution of Neighborhood Context to the Association of Race with Severe Maternal Morbidity
Authors: Hacker Francis M. , Phillips Jaclyn M. , Lemon Lara S. , Simhan Hyagriv N. 
Published: June 2023

Objective Severe maternal morbidity (SMM) has disproportionate frequencies among racial minorities and those of socioeconomic disadvantage, with people of Black race consistently having the highest proportion. Neighborhood level deprivation has been associated with maternal morbidity and mortality, including adverse pregnancy outcomes. We sought to explore the relationship between neighborhood socioeconomic disadvantage and SMM and describe how neighborhood context impacts the relationship between race and SMM.
Study Design We performed a retrospective cohort analysis of all delivery admissions in a single health care network from 2015 to 2019. Area deprivation index (ADI) was used to represent neighborhood socioeconomic disadvantage and is a composite index of neighborhood that spans income, education, household characteristics, and housing. The index ranges from 1 to 100 with higher values indicating higher disadvantage. Logistic regression assessed the relationship between ADI and SMM and estimated the effect that ADI has on the relationship between race and SMM.
Results Of the 63,208 birthing persons in our cohort, the unadjusted incidence of SMM was 2.2%. ADI was significantly associated with SMM, with higher values conferring higher risk for SMM (p < 0.001). The absolute risk of SMM increased roughly by 1.0% from the lowest to highest ADI value. Those of Black race had the highest unadjusted incidence of SMM compared with the referent group (3.4 vs. 2.0%) and highest median ADI (92; interquartile range [IQR]: 20). In the multivariable model, in which the primary exposure was race and ADI was adjusted for, Black race had a 1.7 times odds SMM when compared with White race (95% confidence interval [CI]: 1.5–1.9). This association was attenuated to 1.5 adjusted odds when controlling for ADI (95% CI: 1.3–1.7). Risk attenuation for SMM was not seen in other race categories.
Conclusion Neighborhood context contributes to SMM but does not explain the majority of racial disparities.
Key Points
Glacial changes in sea level modulated millennial-scale variability of Southeast Asian autumn monsoon rainfall
Authors: Patterson Elizabeth W. , Johnson Kathleen R. , Griffiths Michael L. , Kinsley Christopher W. , McGee David , Du Xiaojing , Pico Tamara , Wolf Annabel , Ersek Vasile , Mortlock Richard A. , Yamoah Kweku A. , Bùi Thành N. , Trần Mùi X. , Đỗ-Trọng Quốc , Võ Trí V. , Đinh Trí H. 
Published: June 2023

Most paleoclimate studies of Mainland Southeast Asia hydroclimate focus on the summer monsoon, with few studies investigating rainfall in other seasons. Here, we present a multiproxy stalagmite record (45,000 to 4,000 years) from central Vietnam, a region that receives most of its annual rainfall in autumn (September-November). We find evidence of a prolonged dry period spanning the last glacial maximum that is punctuated by an abrupt shift to wetter conditions during the deglaciation at ~14 ka. Paired with climate model simulations, we show that sea-level change drives autumn monsoon rainfall variability on glacial-orbital timescales. Consistent with the dry signal in the stalagmite record, climate model simulations reveal that lower glacial sea level exposes land in the Gulf of Tonkin and along the South China Shelf, reducing convection and moisture delivery to central Vietnam. When sea level rises and these landmasses flood at ~14 ka, moisture delivery to central Vietnam increases, causing an abrupt shift from dry to wet conditions. On millennial timescales, we find signatures of well-known Heinrich Stadials (HS) (dry conditions) and Dansgaard–Oeschger Events (wet conditions). Model simulations show that during the dry HS, changes in sea surface temperature related to meltwater forcing cause the formation of an anomalous anticyclone in the Western Pacific, which advects dry air across central Vietnam, decreasing autumn rainfall. Notably, sea level modulates the magnitude of millennial-scale dry and wet phases by muting dry events and enhancing wet events during periods of low sea level, highlighting the importance of this mechanism to autumn monsoon variability.
Disentangling the complexity of coupled vibrations by two-dimensional electronic-vibrational spectroscopy
Authors: Zhang Xuanchao , Liu Zihui , Jha Ajay , Liang Xian-Ting , Thorwart Michael , Dwayne Miller R J , Duan Hong-Guang 
Published: June 2023

Abstract
We employ two-dimensional electronic-vibrational (2DEV) spectroscopy to study the coherent dynamics of coupled vibrational modes in an excitonically-coupled dimer model. The advantage of separating excitation and detection in different frequency regimes allows us to directly probe the electronic and vibrational coherences in the time-evolved 2DEV spectra. The complexity of vibrational coherence of two coupled modes is directly revealed by cross peaks in the 2DEV spectra. With the help of the ensuing time traces, we can follow how the vibrational coherence changes over time in the monomer model and, subsequently, in the dimer model. We show that the complexity of two coupled vibrational modes and the interaction between electronic and vibrational coherences in molecular systems can be effectively disentangled using the 2DEV spectroscopy technique.
A new GNSS outlier mitigation method for GNSS/INS integrated system
Authors: Guo Jingwen , Zhou Yilan , Zhao Shuai , Hu Zhijian 
Published: June 2023

Abstract
High-precision positioning with global navigation satellite systems (GNSS) remains a significant challenge in urban environments, due to the outliers caused by the insufficient number of accessible satellites and environmental interference. A GNSS outlier mitigation algorithm with effective fault detection and exclusion (FDE) is required for high-precision positioning. The traditional methods are designed to deal with zero-mean noise in GNSS, which leads to instabilities under biased measurements. Considering that GNSS data are typical time series data, a dynamic FDE scheme is constructed by combining a prediction-model-based method and a dissimilarity-based method. First, a hybrid prediction model which combines autoregressive integrated moving average (ARIMA) model and multilayer perceptron (MLP) model is proposed to provide pseudo-GNSS series by predicting the vehicle’s location for several future steps. Then, a dissimilarity-based method of dynamic time warping measure is utilized to analyze the pairwise dis-similarity between the pseudo-GNSS series and the received GNSS series. The performance of the different models in forecasting is evaluated, and the results show that the positioning accuracy is significantly improved by applying the ARIMA-MLP. The effectiveness of the proposed FDE method is verified through simulation experiments and real experiments based on a typical urban canyon public dataset collected in Tokyo.
A novel method to repair missing vibration data in rolling bearing vibration signals based on improved optimized measurement matrix
Authors: Wang Haiming , Liu Yongqiang , Li Qiang , Yang Shaopu , Wang Yanli 
Published: June 2023

Abstract
Missing data, caused by many factors such as equipment short circuits or data cleaning, affect the accuracy of condition monitoring for rotating machinery. To improve the precision of missing data recovery, a compressed sensing-based vibration data repair method is developed. First, based on the Gaussian random matrix, an improved optimized measurement matrix (OMM) is proposed to accurately sample data. Then, a sparse representation of the vibration signal, through a discrete cosine transform, is utilized to make the noisy vibration signal sparse. Finally, the orthogonal matching pursuit algorithm is employed to reconstruct the missing signal. The effectiveness of the proposed method is verified by analyzing constant and variable speed time series of rolling bearings. Compared with other data repair methods, it is shown that the OMM has a higher repair precision at different loss rates.
Embedding TiO2 microspheres into the active layer as light scatterers in a perovskite photodetector to boost light harvesting
Authors: Feng Jianwen , Cui Xiangqian , Li Bobo , Qiu Mingxia , Liu Weifeng , Liu Xuguang 
Published: June 2023

Abstract
Here, TiO2 microspheres with particle sizes of 200–400 nm are embedded in p–i–n perovskite photodetectors, which are used as light scatterers. This approach was implemented to change the light transfer path in the perovskite layer, which gives the device higher photon-capture ability in a specific incident wavelength range. Compared with a pristine device, the photocurrent and responsivity of the device based on such a structure are obviously enhanced in the ranges of 560–610 nm and 730–790 nm. The photocurrent under 590 nm incident light wavelength illumination (light intensity P = 31.42 μW·cm−2) increases from 1.45 μA to 1.71 μA, with an increase of 17.93%, and the responsivity reaches 0.305 A·W−1. In addition, the introduction of TiO2 has no additional negative impact on the carrier extraction and the dark current. Also, the response time of the device did not deteriorate. Finally, the role of TiO2 as light scatterers is further verified by embedding microspheres into mixed-halide perovskite devices.
Improving breast tumor segmentation via shape-wise prior-guided information on cone-beam breast CT images
Authors: Lin Tongxu , Lin Junyu , Huang Guoheng , Yuan Xiaochen , Zhong Guo , Xie Fenfang , Li Jiao 
Published: June 2023

Abstract

Objective. Due to the blurry edges and uneven shape of breast tumors, breast tumor segmentation can be a challenging task. Recently, deep convolution networks based approaches achieve satisfying segmentation results. However, the learned shape information of breast tumors might be lost owing to the successive convolution and down-sampling operations, resulting in limited performance. Approach. To this end, we propose a novel shape-guided segmentation (SGS) framework that guides the segmentation networks to be shape-sensitive to breast tumors by prior shape information. Different from usual segmentation networks, we guide the networks to model shape-shared representation with the assumption that shape information of breast tumors can be shared among samples. Specifically, on the one hand, we propose a shape guiding block (SGB) to provide shape guidance through a superpixel pooling-unpooling operation and attention mechanism. On the other hand, we further introduce a shared classification layer (SCL) to avoid feature inconsistency and additional computational costs. As a result, the proposed SGB and SCL can be effortlessly incorporated into mainstream segmentation networks (e.g. UNet) to compose the SGS, facilitating compact shape-friendly representation learning. Main results. Experiments conducted on a private dataset and a public dataset demonstrate the effectiveness of the SGS compared to other advanced methods. Significance. We propose a united framework to encourage existing segmentation networks to improve breast tumor segmentation by prior shape information. The source code will be made available at https://github.com/TxLin7/Shape-Seg.
Proton irradiation influence on gate-channel low-field carrier mobility of AlGaN/GaN HEMTs
Authors: Ji Qizheng , Yang Ming , Liu Jun , Ma Guilei , Xie Xining , Hu Xiaofeng , Liu Shanghe 
Published: June 2023

Abstract
AlGaN/GaN high electron mobility transistors (HEMTs) with different device sizes were prepared and exposed to 0.4 MeV proton irradiation. The low-field carrier transport characteristics of the gate channel are obtained from the capacitance-voltage curves and current-voltage curves. For the device with a longer gate-drain distance (30 μm), after 0.4 MeV proton irradiation, the gate-channel low-field carrier mobility increases by 14.3% on average. For the device with a shorter gate-drain distance (15 μm), the carrier mobility decreases by 13.4% on average after proton irradiation. This phenomenon is studied with regard to the polarization scattering effect. It is found that the polarization distribution in the AlGaN/GaN HEMTs changes after proton irradiation and different gate-drain distances correspond to different polarization distributions.
A novel photodiode array structure with double-layer SiO2 isolation
Authors: Jiang Ningning , Zhang Shubin , Jiang Yanfeng 
Published: June 2023

Abstract
A novel photodiode array structure is proposed and implemented in the paper. Based on the silicon-on-insulator substrate, the structure adopts double SiO2 layers for isolation among PN junctions. Compared with incumbent photodiode arrays with a single isolation layer, the novel design shows less leakage current, less noise, high optical response sensitivity, and better compatible integration with other devices. The dark current of this structure is less than nanoamperes (10−9 A), with the benefits of 1000 times’ less leakage current than that of other structures, and a higher sensitivity with a response speed less than 200 ns. The device is fabricated and characterized. The doping concentration and structural profile are characterized. The fabricated device is packaged with a photo light-emitting diode and the MOS device to act as the MOS relay driver. The wavelength used in the photodiode is between 500 nm–560 nm, with peak wavelength 520 nm, which can be used to drive MOS device.
Multiparameter quantum metrology and mode entanglement with spatially split nonclassical spin ensembles
Authors: Fadel Matteo , Yadin Benjamin , Mao Yuping , Byrnes Tim , Gessner Manuel 
Published: June 2023

Abstract
We identify the multiparameter sensitivity of entangled spin states, such as spin-squeezed and Dicke states that are spatially distributed into several addressable spatial modes. Analytical expressions for the spin-squeezing matrix of families of states that are accessible by current atomic experiments reveal the quantum gain in multiparameter metrology, as well as the optimal strategies to maximize the sensitivity gain for the estimation of any linear combination of parameters. We further study the mode entanglement of these states by deriving a witness for genuine k-partite mode entanglement from the spin-squeezing matrix. Our results highlight the advantage of mode entanglement for distributed sensing, and outline optimal protocols for multiparameter estimation with nonclassical spatially-distributed spin ensembles. We illustrate our findings with the design of a protocol for gradient sensing with a Bose–Einstein condensate in an entangled spin state in two modes.
Hydrogen peroxide as an additive to curcumin on the photodynamic inactivation of bacteria: a preliminary study
Authors: Sammarro Silva Kamila Jessie , Lima Alessandra Ramos , Corrêa Thaila Quatrini , Dias Lucas Danilo , Bagnato Vanderlei Salvador 
Published: June 2023

Abstract
Photodynamic inactivation (PDI) of microorganisms has been shown as a promising alternative against the spread of resistant bacteria. It has been used not only in clinical applications, but also in the public health sector, for water and wastewater disinfection. In order to improve efficiency, recent research has focused on novel photosensitizing materials, as well as chemical additives. To our knowledge, however, hydrogen peroxide (a widely known standalone disinfectant) has not been employed combined with curcumin (a vegetal-based and environmental-friendly photosensitizer) for PDI seeking for future environmental applications. In this sense, this study aimed to assess whether there were any competitive or cooperative effects by the simultaneous use of curcumin and hydrogen peroxide on the PDI of Staphylococcus aureus, in in-vitro batch tests. Photobleaching for individual and combined factors was performed in order to compare results of inactivation with changes in chemical aspects in solution under blue light irradiation. One may think that the combination may add strength to PDI due to hydrogen peroxide being a natural supplier of oxygen. However, antagonistic effects were found in combined treatments, implying that such combination is non profitable. The results indicate that special care is necessary when considering combining techniques.
Rosa: Language Model for Landscape Text Assisted Writing
Authors: Chen Yijia , Zheng Lingna 
Published: June 2023

Abstract
In landscape design, the text of landscape design plays an important role. However, in practice, designers are often faced with an industry situation where they need to complete a large number of design tasks in a short period. If language models are used to assist in the writing of design texts, it will be of great help not only to designers but also to the landscape design industry. A pre-trained language model trained on collected landscape design texts and used for landscape text-assisted authoring. It is better than the existing models in terms of perplexity, readability and manual evaluation. It has great potential to be used to improve the landscape design industry.
A Closed Stockyard UAV Intelligent Inventory System
Authors: Qiu Ziyi , Lin Defu , Lv Junning , Sun Zhiwen , Zheng Zhangxiong 
Published: June 2023

Abstract
Strategic resources affect national development and security at all times. Actual utilization of strategic resources is crucial. In recent years, strategic resources such as coal and mineral powder usually need to be stored in closed stockyards after mining due to environmental protection, concealment, and other reasons. The existing stockpiling inventory system cannot autonomously complete the stockpiling inventory in the closed stockyard with low risk, low cost, and high precision. Therefore, this paper proposes a closed stockyard UAV intelligent inventory system. Our inventory system comprises a UAV system, a visual mapping system, and a classification and volume calculation system. In this paper, we focus on the navigation system belonging to the UAV system and propose a GNSS-denied environment navigation system based on lidar and ArUco markers. We conduct experiments in the dry coal shed of thermal power plants, one of the application scenarios. After analysis and comparison, it can be concluded that our intelligent UAV stockpiling inventory system can complete the task with high robustness and high precision with the assistance of our navigation system.
PyCaNet: Pose Estimation of Primary and Middle School Students Using Pyramid Convolutions and Coordinate Attention
Authors: Yi Weike , Chen Ling , Wu Liangdong 
Published: June 2023

Abstract
2D pose estimation task is the basics of action recognition, human-computer interaction and other applications, but there is few research on pose estimation of primary and middle school students. In this paper, we present APMSS: a new dataset based on primary and middle school scenarios. The dataset fills the gap of the current human pose estimation dataset with few campus scenes, and the human keypoints and object bounding boxes in the dataset are manually annotated. In addition, we propose PyCaNet: a new top-down human pose estimation neural network model that uses pyramid convolutions to give the network a larger receptive field for better results. Coordinate attention is also added to the network, which is a kind of channel attention and can significantly improve the network performance. Extensive experiments shows that PyCaNet outperforms the previous methods by at least 1.0 AP on APMSS val and at least 0.7 PCKh on MPII test.
A method of laser stripe centerline extraction based on deep learning for structured light 3D reconstruction
Authors: Huang Ming , Xu Xiao 
Published: June 2023

Abstract
To overcome the stray light noise in the centerline extraction method during line structured light 3D reconstruction process, an end-to-end trainable neural network for laser stripe centerline extraction based on Convolutional Neural Network and Multi-Layer Perception is proposed. The proposed network can self-adapt to a variety of lighting (brightness) conditions and overcome the interference of different stray lights. In addition, unlike prior work on enhancing the accuracy of centerline extraction using deep learning methods that only performs it for noise reduction in pre-processing, the proposed network unifies the noise reduction and prediction processes, so that it can be optimized end-to-end directly on centerline extraction performance. The network learns an intermediate feature representation of noise reduction, which requires less complexity for data annotation, reduces the training difficulty, and has more scalability. Experiments show that the proposed method can perform centerline extraction with relatively high accuracy for laser stripes of different widths, brightness, and inclination, thus obtaining a smooth and stable reconstructed surface in the structured light 3D reconstruction process.
Deep Wyner-Ziv Decoding Using Auxiliary Hierarchical Features
Authors: Zhou Junwei , Yi Xiangbo 
Published: June 2023

Abstract
Wyner-Ziv (WZ) video coding shifts the burden of complex calculations from the encoder to the decoder, making it suitable for video coding scenarios with limited resources. Motivated by deep learning has shown superior performance over traditional methods, this paper proposes deep WZ video coding with the help of auxiliary hierarchical features in the decoder. It uses an autoencoder to encode WZ frames. In the decoder side, an inter-frame correlation model called SI-Net is employed to enhance WZ frame quality with Key frames. The auxiliary hierarchical features are extracted from Key frames through multi-level downsampling and employed in autoencoder to optimize feature extraction, avoiding gradient disappearance caused by deep networks. Since the auxiliary hierarchical features of Key frames describe spatial information and expand the network’s perception of video frame features, a high-quality WZ frame reconstructed can be obtained. Compared with previous work, our method shows obvious superiority on four video datasets with different degrees of motion.
Global Path Link Prediction Method Based on Improved Resource Allocation
Authors: Wang Yu , Ming Liu 
Published: June 2023

Abstract
Link prediction is a basic method to study complex networks, which has important practical application value. This paper proposes a global path link prediction method based on resource allocation for complex network topology information. Firstly, we improve the current quantization method of node resource transmission capability, and then consider the global path information in the network structure comprehensively. Finally, the link prediction method is defined based on the quantized resource transmission capability and global path information. Compared with 11 classical similarity algorithms, the experimental results show that the link prediction accuracy of this method has been improved to some extent, among which the highest improvement is 22.85%.
Fault detection and fault tole rant control of interturn short circuit of double redundancy motor
Authors: Jin Tianbao , Tian Wei , Zhao Wenxiang 
Published: June 2023

Abstract
This paper presents the robust detection of interturn short circuit fault of double redundant permanent magnet synchronous motor. In the static coordinate system and double dq-axis system, the mathematical model of the fault state is established. Further, the residual signal of the dq-axis current caused by the turn-to-turn short circuit fault is predicted using the Luenberger observer. Next, the fault marker and the fault phase marker are obtained by processing the current residual signal. Finally, the processor performs the excision of the faulty phases and hardware reconfiguration, thus allowing fault-tolerant operation of the motor. The simulation results show the effectiveness of the detection and fault tolerance of double redundant permanent magnet synchronous motor.