bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of COVID-19 Severity at Delivery after Asymptomatic or Mild COVID-19 during Pregnancy
Authors: Sandoval Grecio J. , Metz Torri D. , Grobman William A. , Manuck Tracy A. , Hughes Brenna L. , Saade George R. , Longo Monica , Simhan Hyagriv N. , Rouse Dwight J. , Mendez-Figueroa Hector , Gyamfi-Bannerman Cynthia , Ranzini Angela C. , Costantine Maged M. , Sehdev Harish M. , Tita Alan T.N. ,   for the Eunice Kennedy Shriver National Institute of Child Health Human Development Maternal-Fetal Medicine Units Network, Bethesda, MD
Published: May 2024

Objective This study aimed to develop a prediction model that estimates the probability that a pregnant person who has had asymptomatic or mild coronavirus disease 2019 (COVID-19) prior to delivery admission will progress in severity to moderate, severe, or critical COVID-19.
Study Design This was a secondary analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-positive patients who delivered from March through December 2020 at hospitals across the United States. Those eligible for this analysis presented for delivery with a current or previous asymptomatic or mild SARS-CoV-2 infection. The primary outcome was moderate, severe, or critical COVID-19 during the delivery admission through 42 days postpartum. The prediction model was developed and internally validated using stratified cross-validation with stepwise backward elimination, incorporating only variables that were known on the day of hospital admission.
Results Of the 2,818 patients included, 26 (0.9%; 95% confidence interval [CI], 0.6–1.3%) developed moderate–severe–critical COVID-19 during the study period. Variables in the prediction model were gestational age at delivery admission (adjusted odds ratio [aOR], 1.15; 95% CI, 1.08–1.22 per 1-week decrease), a hypertensive disorder in a prior pregnancy (aOR 3.05; 95% CI, 1.25–7.46), and systolic blood pressure at admission (aOR, 1.04; 95% CI, 1.02–1.05 per mm Hg increase). This model yielded an area under the receiver operating characteristic curve of 0.82 (95% CI, 0.72–0.91).
Conclusion Among individuals presenting for delivery who had asymptomatic–mild COVID-19, gestational age at delivery admission, a hypertensive disorder in a prior pregnancy, and systolic blood pressure at admission were predictive of delivering with moderate, severe, or critical COVID-19. This prediction model may be a useful tool to optimize resources for SARS-CoV-2-infected pregnant individuals admitted for delivery.
Key Points
Keep the (social) distance! Turnout and risk perception during health crisis
Authors: Stancea Andreea , Muntean Aurelian 
Published: May 2024

AbstractThis article investigates the relationship between risk perception and electoral participation of citizens. To assess this, we use the case study of the novel coronavirus and its relationship with voter turnout during the 2020 Romanian elections. We assess the relationship between COVID-19 and citizens’ intention to cast a vote by employing an individual model. Additionally, using the share rate of the infected population with COVID-19, we examine the association between the intensity of the outbreak across counties and electoral participation. Either though our research is cross-sectional and focuses on covariation rather than causal relationships, provides insightful results. The individual model shows that the higher the risk perception of infection is, the lower the intention to cast a vote. The aggregated exploratory model employed shows that an increase in the percentage of the shared infected population decreases the chance of electoral participation.
On dynamical system modeling of learned primal-dual with a linear operator

K

: stability and convergence properties
Authors: Huang Jinshu , Gao Yiming , Wu Chunlin 
Published: May 2024

Abstract
Learned Primal-Dual (LPD) is a deep learning based method for composite optimization problems that is based on unrolling/unfolding the primal-dual hybrid gradient algorithm. While achieving great successes in applications, the mathematical interpretation of LPD as a truncated iterative scheme is not necessarily sufficient to fully understand its properties. In this paper, we study the LPD with a general linear operator. We model the forward propagation of LPD as a system of difference equations and a system of differential equations in discrete- and continuous-time settings (for primal and dual variables/trajectories), which are named discrete-time LPD and continuous-time LPD, respectively. Forward analyses such as stabilities and the convergence of the state variables of the discrete-time LPD to the solution of continuous-time LPD are given. Moreover, we analyze the learning problems with/without regularization terms of both discrete-time and continuous-time LPD from the optimal control viewpoint. We prove convergence results of their optimal solutions with respect to the network state initialization and training data, showing in some sense the topological stability of the learning problems. We also establish convergence from the solution of the discrete-time LPD learning problem to that of the continuous-time LPD learning problem through a piecewise linear extension, under some appropriate assumptions on the space of learnable parameters. This study demonstrates theoretically the robustness of the LPD structure and the associated training process, and can induce some future research and applications.
Uniqueness and numerical inversion in bioluminescence tomography with time-dependent boundary measurement
Authors: Gong Rongfang , Liu Xinran , Shen Jun , Huang Qin , Sun Chunlong , Zhang Ye 
Published: May 2024

Abstract
In the paper, an inverse source problem in bioluminescence tomography (BLT) is investigated. BLT is a method of light imaging and offers many advantages such as sensitivity, cost-effectiveness, high signal-to-noise ratio and non-destructivity. It thus has promising prospects for many applications such as cancer diagnosis, drug discovery and development as well as gene therapies. In the literature, BLT is extensively studied based on the (stationary) diffusion approximation (DA) equation, where the distribution of peak sources is reconstructed and no solution uniqueness is guaranteed without proper a priori information. In this work, motivated by solution uniqueness, a novel dynamic coupled DA model is proposed. Theoretical analysis including the well-posedness of the forward problem and the solution uniqueness of the inverse problem are given. Based on the new model, iterative inversion algorithms under the framework of regularizing schemes are introduced and applied to reconstruct the smooth and non-smooth sources. We discretize the regularization functional with the finite element method and give the convergence rate of numerical solutions. Several numerical examples are implemented to validate the effectiveness of the new model and the proposed algorithms.
Role of Dy 4f electrons on magnetic coupling and reorientation in DyFeO3
Authors: Cui Rui , Jiang Hongping , Du Yixuan , Xu Yuanhui , Jia Yongchao , Sun Keju , Hao Xianfeng 
Published: May 2024

Abstract
Spin reorientation transition is an ubiquitous phenomenon observed in magnetic rare earth orthferrites RFeO3, which has garnered significant attention in recent years due to its potential applications in spintronics or magnetoelectric devices. Although a plenty of experimental works suggest that the magnetic interaction between R3+ and Fe3+ spins is at the heart of the spin reorientation, but a direct and conclusive theoretical support has been lacking thus far, primarily due to the challenging nature of handling R 4f electrons. In this paper, we explored DyFeO3 as an example by means of comprehensive first principles calculations, and compared two different approaches, where the Dy 4f electrons were treated separately as core or valence states, aiming to elucidate the role of Dy 4f electrons, particularly in the context of the spin reorientation transition. The comparison provides a solid piece of evidence for the experimental argument that the Dy3+−Fe3+ magnetic interactions play a vital role in triggering spin reorientation of Fe3+ moments at low temperatures. The findings revealed here not only extend our understanding on the underlying mechanism for spin reorientation transition in RFeO3, but also highlight the importance of explicit description of R 4f electrons in rationally reproducing their structural, electronic and magnetic properties.
Order-N calculations for thermoelectric power factor based on linear response theory
Authors: Ishii Hiroyuki , Kobayashi Nobuhiko , Hirose Kenji 
Published: May 2024

Abstract
We present an order-N quantum transport calculation methodology to evaluate thermoelectric transport coefficients, such as electric conductivity and Seebeck coefficient. Different from a conventional method using the electric conductivity spectrum, it obtains the coefficients directly from the correlation function between heat and electric current based on linear response theory. As an example, we apply the methodology to a two-dimensional square-lattice model with static disorder and confirm that the calculated results are consistent with those obtained by the conventional method. The proposed methodology provides an effective approach to evaluate the thermoelectric performance of micron-scale materials based on quantum mechanics from an atomistic viewpoint.
A method for determining the actual coordinate system of spiral bevel gears by the measured positions of eight tooth surfaces
Authors: Zhou Lihua , Fang Suping , Liu Yongsheng , Li Yuan , Taguchi Tetsuya , Takeda Ryohei 
Published: May 2024

Abstract
The coordinate system for the measurement of spiral bevel gear (SBG) by gear measuring center (GMC) is discussed and the misalignment between the actual coordinate system and the theoretical coordinate system of the SBG is analyzed. The misalignment can cause measurement errors and cannot be neglected in SBG precision measurements. To calculate the misalignment and accurately establish the actual coordinate system, a novel re-fitting method based on the measured positions of eight tooth surfaces is proposed. First, eight sets of gear axis direction vectors and original points can be obtained from the measurement of eight tooth surfaces, by the single tooth surface matching method. Second, the actual axis vector and the original point of the gear can be obtained, by re-fitting those eight sets of gear axis vectors and original points. Then, the actual coordinate system can be established by the actual gear axis direction vector and the original point. The least square method is used to calculate the original point, and a basis vector fitting method is proposed to solve the gear axis direction vector. Finally, two groups of preset coordinate system deviation tests are carried out to verify the accuracy and feasibility of the fitting algorithm. The results show that the fitting error of the original point is far less than

1
×

10

−
1

μ

m

and the fitting error of the angle is far less than

1
×

10

−
7

degree, which are superior to the design accuracy of the GMC. Therefore, the proposed algorithm can be applied to improve the measurement accuracy of current GMC.
A novel decoupling method of compound faults with incomplete dataset of rotating machinery
Authors: Hu Aijun , Yang Bowen , Xing Lei , Yu Tianxiao , Xiang Ling 
Published: May 2024

Abstract
The occurrence of fault in rotating machinery is random and complex, and the diagnosis of the compound faults has been a challenge in industrial production. Accurate diagnosis of the compound faults can be of significant help to practical maintenance and management. However, most existing intelligent diagnostic methods typically require abundant data for training, which is often difficult to collect for compound faults. In this paper, a novel method called impact feature-based decoupling capsule network (IFDCN) is proposed for diagnosing compound faults with incomplete datasets. In this model, an improved Laplace wavelet kernel capsule neural network is proposed to extract and enhance the impact features of vibration signal. A decoupling classifier is designed to decouple the compound faults in the diagnostic process so as to identify the sub-faults contained in the compound faults. In using this proposed model for incomplete datasets, the compound fault data is not trained and is not labeled. Through training on single-fault data, the proposed model is capable of classifying and decoupling the fault types. The feature extraction capability of the network is visualized by heat maps, and the physical significance of feature extraction is explained by deep learning network. The effectiveness of IFDCN is verified through different experimental of gear and bearing and the experiment results indicate that the proposed model has higher identifying precision and can accurately decouple the compound faults without compound fault samples.
Research on indoor and outdoor positioning switching algorithm based on improved PSO-BP
Authors: Zhao Yang , Wang Tianhu , Miao Qiannian , Yan Jin , Wang Rong 
Published: May 2024

Abstract
Aiming at the problems of poor positioning accuracy in the indoor and outdoor junction areas and the loss of positioning signals and discontinuous positioning results during the transition from the indoor area to the outdoor area, this paper proposes a machine learning method to solve the positioning problem in the indoor and outdoor junction areas and the switching problem of positioning methods. An indoor and outdoor positioning switching algorithm based on Particle Swarm Optimization-Back Propagation (PSO-BP) and BP neural network is designed. Through this algorithm, the position of the positioning tag can be judged independently and the coordinates of the positioning tag in the indoor and outdoor junction area can be predicted independently. The experimental results show that the accuracy of positioning area judgment based on PSO-BP neural network can reach 99.91%. The indoor and outdoor boundary area coordinates obtained by BP neural network prediction method are lower than the root mean square error of ultra wide band indoor positioning method and BDS positioning method. The algorithm model proposed in this paper effectively improves the positioning problem of the indoor and outdoor junction area and the positioning problem of the transition from the indoor area to the outdoor area, improves the positioning accuracy and positioning stability of the indoor and outdoor junction area, reduces the positioning cost, and has strong practicability.
Measurement insights and error analysis of electronic parameters for ultrasonic transducers
Authors: Zhou Yuhua , Fan Wei , Tang Hui , Meng Xiankai , Han Liling , Wu Jiang , Xu Xiaojing , Zhang Yanhu 
Published: May 2024

Abstract
Piezoelectric ultrasonic transducers with the function of energy conversation, as well as their numerous advantages in high-power density, quick response, flexible design, and service reliability, are involved in wide applications of industrial processing, precision driving, smart sensing, and medical services. The electromechanical equivalent circuit and Kirchhoff’s law indicate that mechatronics parameters are essential for performance evaluation, reliability analysis, and fault diagnosis of ultrasonic transducers. Importantly, the ultrasonic transducer is a time-variant system, data of one single parameter collected from a certain test cannot match with the data of another single parameter acquired from a different test. So, a synchronous and precise online measurement of electronic parameters is encouraged for performance evaluation and optimization design of ultrasonic transducers. With the combination of virtual instrument technology, an asynchronous measurement system of electrical excitation parameters for the ultrasonic transducers of linear driving motors was established in this study. Furthermore, the systematic measurement methods and error theory were illustrated, including the calculation methods and measuring circuits of electric signals. Experimental results proved that the proposed system and methods for measuring the input electronic power of ultrasonic transducer (e.g. effective value method for voltage and current, energy moment method for frequency, and spectrum analysis method for phase difference) are highly precise, quickly responsive, robust, and reliable for ultrasonic transducers. The findings of this study provide valuable references and suggestions for efficient, accurate, and online performance evaluation of ultrasonic transducers, particularly for piezoelectric transducers utilizing ultrasonic high-voltage exciting signals.
Cation-polymerized artificial SEI layer modified Li metal applied in soft-matter polymer electrolyte
Authors: Tang Siming , Mei Qingyang , Zhai Yutong , Liu Yulong 
Published: May 2024

Abstract
Li metal batteries with polymer electrolyte are of great interest for next-generation batteries for high safety and high energy density. However, uneven deposition on the lithium metal surface can greatly affect battery life. Therefore, surface modification on the Li metal become necessary to achieve good performance. Herein, an artificial solid electrolyte interface (SEI) modified lithium metal anode is prepared using cation-polymerization process, as triggered by PF5 generated from CsPF6. As a result, the polarization voltage of Li||Li symmetric battery assembled with artificial SEI-modified Li metal anode was stable with a small over-potential of 25 mV after 3000 h at current density of 1.5 mA cm−2. Electrochemical performance of Li||NCM 622 (LiNi0.6Co0.2Mn0.2O2) full cell with soft-matter polymer electrolyte is significantly improved than bare Li-metal, the capacity retention is 75% after 120 cycles with N/P = 3:1 at a cut-off voltage of 4.3 V. Our work has shed lights on the commercialization of Li metal battery with polymer electrolyte.
Prompt-gamma track-length estimator with time tagging from proton tracking
Authors: Létang Jean M , Allegrini Oreste , Testa Étienne 
Published: May 2024

Abstract
The design of prompt-gamma detectors necessitates numerous Monte Carlo simulations to precisely develop and optimize the detection stages in proton therapy. Alongside the advancement of MC simulations, various variance reduction methods have been explored to speed-up calculations. Among these techniques, track-length estimators are interesting scoring methods for achieving both speed and accuracy in Monte Carlo simulations of rare events. This paper introduces an extension of the GATE vpgTLE module that incorporates the prompt-gamma emission time, which is tagged from the proton tracking, enhancing its utility for studies focused on detector design and optimization that rely on time measurements. The results obtained from a clinical radiotherapy plan are presented. We demonstrate that the new vpgTLE tally with time tagging is accurate, except for certain prompt-gamma lines corresponding to long mean-life nuclei.
Prediction of ovarian cancer prognosis using statistical radiomic features of ultrasound images
Authors: Zuo Ruochen , Li Xiuru , Hu Jiaqi , Wang Wenqian , Lu Bingjian , Zhang Honghe , Cheng Xiaodong , Lu Weiguo , Qin Jiale , Liu Pengyuan , Lu Yan 
Published: May 2024

Abstract

Objective. Ovarian cancer is the deadliest gynecologic malignancy worldwide. Ultrasound is the most useful non-invasive test for preoperative diagnosis of ovarian cancer. In this study, by leveraging multiple ultrasound images from the same patient to generate personalized, informative statistical radiomic features, we aimed to develop improved ultrasound image-based prognostic models for ovarian cancer. Approach. A total of 2057 ultrasound images from 514 ovarian cancer patients, including 355 patients with epithelial ovarian cancer, from two hospitals in China were collected for this study. The models were constructed using our recently developed Frequency Appearance in Multiple Univariate pre-Screening feature selection algorithm and Cox proportional hazards model. Main results. The models showed high predictive performance for overall survival (OS) and recurrence-free survival (RFS) in both epithelial and nonepithelial ovarian cancer, with concordance indices ranging from 0.773 to 0.794. Radiomic scores predicted 2 year OS and RFS risk groups with significant survival differences (log-rank test, P < 1.0 × 10−4 for both validation cohorts). OS and RFS hazard ratios between low- and high-risk groups were 15.994 and 30.692 (internal cohort) and 19.339 and 19.760 (external cohort), respectively. The improved performance of these newly developed prognostic models was mainly attributed to the use of multiple preoperative ultrasound images from the same patient to generate statistical radiomic features, rather than simply using the largest tumor region of interest among them. The models also revealed that the roundness of tumor lesion shape was positively correlated with prognosis for ovarian cancer. Significance. The newly developed prognostic models based on statistical radiomic features from ultrasound images were highly predictive of the risk of cancer-related death and possible recurrence not only for patients with epithelial ovarian cancer but also for those with nonepithelial ovarian cancer. They thereby provide reliable, non-invasive markers for individualized prognosis evaluation and clinical decision-making for patients with ovarian cancer.
Unlocking the adaptive advantage: correlation and machine learning classification to identify optimal online adaptive stereotactic partial breast candidates
Authors: Pogue Joel A , Harms Joseph , Cardenas Carlos E , Ray Xenia , Viscariello Natalie , Popple Richard A , Stanley Dennis N , Boggs D Hunter 
Published: May 2024

Abstract

Objective. Online adaptive radiotherapy (OART) is a promising technique for delivering stereotactic accelerated partial breast irradiation (APBI), as lumpectomy cavities vary in location and size between simulation and treatment. However, OART is resource-intensive, increasing planning and treatment times and decreasing machine throughput compared to the standard of care (SOC). Thus, it is pertinent to identify high-yield OART candidates to best allocate resources. Approach. Reference plans (plans based on simulation anatomy), SOC plans (reference plans recalculated onto daily anatomy), and daily adaptive plans were analyzed for 31 sequential APBI targets, resulting in the analysis of 333 treatment plans. Spearman correlations between 22 reference plan metrics and 10 adaptive benefits, defined as the difference between mean SOC and delivered metrics, were analyzed to select a univariate predictor of OART benefit. A multivariate logistic regression model was then trained to stratify high- and low-benefit candidates. Main results. Adaptively delivered plans showed dosimetric benefit as compared to SOC plans for most plan metrics, although the degree of adaptive benefit varied per patient. The univariate model showed high likelihood for dosimetric adaptive benefit when the reference plan ipsilateral breast V15Gy exceeds 23.5%. Recursive feature elimination identified 5 metrics that predict high-dosimetric-benefit adaptive patients. Using leave-one-out cross validation, the univariate and multivariate models classified targets with 74.2% and 83.9% accuracy, resulting in improvement in per-fraction adaptive benefit between targets identified as high- and low-yield for 7/10 and 8/10 plan metrics, respectively. Significance. This retrospective, exploratory study demonstrated that dosimetric benefit can be predicted using only ipsilateral breast V15Gy on the reference treatment plan, allowing for a simple, interpretable model. Using multivariate logistic regression for adaptive benefit prediction led to increased accuracy at the cost of a more complicated model. This work presents a methodology for clinics wishing to triage OART resource allocation.