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The prevalence and impact of fear of childbirth (FOC) has not been sufficiently understood. We aimed to investigate the prevalence of FOC among Chinese population and its impact on mode of delivery, postpartum mental health and breastfeeding. We conducted a prospective cohort study, wherein pregnant women in their third trimester who underwent antenatal assessments at Shanghai Changning Maternity and Infant Health Hospital between September 2020 and March 2021 were recruited. Sociodemographic data of the participants were gathered by self-administered questionnaire, and their FOC was assessed using the Wijma Delivery Expectancy Questionnaire. Participants were followed up to 42 days postpartum. Information regarding their modes of delivery was retrieved from medical records, and data regarding postpartum mental health symptoms and one-month postpartum breastfeeding were obtained through self-administered questionnaires. Among 1287 participants, 461 (35.8 %) had high-level FOC (W-DEQ ≥ 66). Logistic regressions showed that women with high-level of FOC had higher rates of caesarean delivery on maternal request (CDMR) (aOR = 1.55, 95 % CI: 1.00–2.41, p = 0.049), a higher incidence of postpartum mental health symptoms (aOR = 1.68, 95 % CI: 1.09–2.59, p = 0.018), lower rates of one-month postpartum exclusive breastfeeding (aOR = 0.33, 95 % CI: 0.16–0.69, p = 0.003) and mixed feeding (aOR = 0.44, 95 % CI: 0.21–0.91, p = 0.028). The long-term implications of FOC beyond the immediate postpartum period were not explored in the study. High-level FOC during the third trimester was associated with increased CDMR and postpartum mental health symptoms and reduced breastfeeding establishment. These results underscore the significance of FOC screening and tailored interventions for affected women. • High-level fear of childbirth was associated with higher rates of cesarean delivery and postpartum mental health symptoms. • High-level fear of childbirth led to lower rates of breastfeeding. • This was among the first studies to explore the influence of fear of childbirth on breastfeeding. • This study addressed the inconsistencies in current research on fear of childbirth, by adding evidence of Chinese population. • This study suggests the integration of appropriate interventions on fear of childbirth into current perinatal care system. [ABSTRACT FROM AUTHOR]

Sentence coherence is an essential foundation for discourse coherence in natural language processing, as it plays a vital role in enhancing language expression, text readability, and improving the quality of written documents. With the development of e-government, automatic generation of official documents can significantly reduce the writing burden of government agencies. To ensure that the automatically generated official documents are coherent, we propose a sentence coherence evaluation model integrating repetitive words features, which introduces repetitive words features with neural network-based approach for the first time. Experiments were conducted on official documents dataset and THUCNews public dataset, our method has achieved an averaged 3.8% improvement in accuracy indicator compared to past research, reaching a 96.2% accuracy rate. This result is significantly better than the previous best method, proving the superiority of our approach in solving this problem. [ABSTRACT FROM AUTHOR]

(1) Background: To investigate the association between maximum standardized uptake value (SUVmax) based on 18F-FDG PET/CT and EGFR mutation status in lung adenocarcinoma. (2) Methods: A total of 366 patients were retrospectively collected and divided into the EGFR mutation group (n = 228) and EGFR wild-type group (n = 138) according to their EGFR mutation status. The two groups' general information and PET/CT imaging parameters were compared. A hierarchical binary logistic regression model was used to assess the interaction effect on the relationship between SUVmax and EGFR mutation in different subgroups. Univariate and multivariate logistic regression was used to analyze the association between SUVmax and EGFR mutation. After adjusting for confounding factors, a generalized additive model and smooth curve fitting were applied to address possible non-linearities. (3) Results: Smoking status significantly affected the relationship between SUVmax and EGFR mutation (p for interaction = 0.012), with an interaction effect. After adjusting for age, gender, nodule type, bronchial sign, and CEA grouping, in the smoking subgroup, curve fitting results showed that the relationship between SUVmax and EGFR mutation was approximately linear (df = 1.000, c2 = 3.897, p = 0.048); with the increase in SUVmax, the probability of EGFR mutation gradually decreased, and the OR value was 0.952 (95%CI: 0.908–0.999; p = 0.045). (4) Conclusions: Smoking status can affect the relationship between SUVmax and EGFR mutation status in lung adenocarcinoma, especially in the positive smoking history subgroup. Fully understanding the effect of smoking status will help to improve the accuracy of SUVmax in predicting EGFR mutations. [ABSTRACT FROM AUTHOR]

The evolution of defects and irradiation damage in Ti and Ti-Mo alloy under hydrogen ion irradiation were investigated by slow positron-beam doppler broadening spectroscopy (DBS) and coincidence doppler broadening spectroscopy (CDB), X-ray diffraction (XRD) and transmission electron microscopy (TEM). The XRD results indicate that the existence of the β phase enhances the solid solubility of hydrogen in Ti-Mo alloy. The metastable γ-TiH hydrides are formed in pure titanium after hydrogen ion irradiation, while no hydride is found in Ti-Mo alloy under the same irradiation conditions. Moreover, DBS and CDB results show that hydrogen vacancy-complexes are formed in Ti and Ti-Mo alloys after hydrogen ion irradiation. Calculated doppler curves trend qualitatively agrees well with the experimental results. The TEM results show lower defect concentration and smaller defect sizes in Ti-Mo alloy with (α + β) than pure titanium. The addition of molybdenum significantly refines the grains and increases the density of the interface, which plays an important role in the management of irradiation-induced defects. • Molybdenum alloying increases the solid solubility of titanium alloy to hydrogen. • Molybdenum alloying can reduce hydrogen-induced defects and irradiation damage. • The hydrogen-vacancy complexes were identified by calculated Doppler spectra. [ABSTRACT FROM AUTHOR]

Harmonic retrieval (HR) is a problem of significance with numerous applications. Many existing algorithms are explicitly or implicitly developed under Gaussian noise assumption, which, however, are not robust against non-Gaussian noise such as impulsive noise or outliers. In this paper, by employing the $\ell _p$ -fitting criterion and block successive upper-bound minimization (BSUM) technique, a variant of the classical RELAX algorithm named as BSUM-RELAX is devised for robust HR. It is revealed that the BSUM-RELAX successively performs alternating optimization along coordinate directions, i.e., it updates one harmonic by fixing the other $(K-1)$ components, such that the whole problem is split into $K$ single-tone HR problems, which are then solved by creating a surrogate function that majorizes the objective function of each subproblem. To further refine the frequency component, the Newton's method that takes linear complexity $\mathcal {O}(N)$ is derived for updating the frequency estimates. We prove that under the single-tone case, BSUM-RELAX converges to a Karush-Kuhn-Tucker point. Furthermore, the BSUM-RELAX is extended to the multidimensional HR case. Numerical results show that the proposed algorithm outperforms the state-of-the-art methods in heavy-tailed noise scenarios. [ABSTRACT FROM AUTHOR]

Acquiring channel state information (CSI) at the base station (BS) is a critical requirement for successfully employing transmit beamforming in multiantenna systems. In practice, channel estimation/quantization errors, feedback delays, and fast fading can make it difficult to obtain accurate CSI at the BS. In this paper, we consider an outage-based approach for transmit beamforming in order to deal with the channel uncertainty at the BS. Our formulation is applicable to both point-to-point transmit beamforming as well as single-group multicasting scenarios. A key difference from prior works is that we do not assume knowledge of the underlying channel distribution; instead, stochastic approximation is used for computing approximate solutions of a nonconvex stochastic optimization problem via simple first-order methods (FOMs). We evaluate the performance of our FOMs in two settings: First) where we design a beamformer at the BS based on historical channel realizations collected over a relatively long time window before deployment, and second) in a post-deployment phase where we perform incremental updates of our beamformer based on intermittent, delayed, or peer feedback. Simulation results reveal the effectiveness of FOMs for our problem compared to other alternatives. [ABSTRACT FROM AUTHOR]

Local overlapping community detection is a hot problem in the field of studying complex networks. It is the process of finding dense clusters based on local network information. This paper proposes a method called local greedy extended dynamic overlapping community detection (GLOD) to address the challenges of detecting high-quality overlapping communities in complex networks. The goal is to improve the accuracy of community detection by considering the dynamic nature of community boundaries and leveraging local network information. The GLOD method consists of several steps. First, a coupling seed is constructed by selecting nodes from blank communities (i.e., nodes not assigned to any community) and their similar neighboring nodes. This seed serves as the starting point for community detection. Next, the seed boundaries are extended by applying multiple community fitness functions. These fitness functions determine the likelihood of nodes belonging to a specific community based on various local network properties. By iteratively expanding the seed boundaries, communities with higher density and better internal structure are formed. Finally, the overlapping communities are merged using an improved version of the Jaccard coefficient, which is a measure of similarity between sets. This step ensures that overlapping nodes between communities are properly identified and accounted for in the final community structure. The proposed method is evaluated using real networks and three sets of LFR (Lancichinetti–Fortunato–Radicchi) networks, which are synthetic benchmark networks widely used in community detection research. The experimental results demonstrate that GLOD outperforms existing algorithms and achieves a 2.1% improvement in the F-score, a community quality evaluation metric, compared to the LOCD framework. It outperforms the best existing LOCD algorithm on the real provenance network. In summary, the GLOD method aims to overcome the limitations of existing community detection algorithms by incorporating local network information, considering overlapping communities, and dynamically adjusting community boundaries. The experimental results suggest that GLOD is effective in improving the quality of community detection in complex networks. [ABSTRACT FROM AUTHOR]

A structured least squares (SLS)?based ESPRIT algorithm for direction-of-arrival (DOA) estimation of strictly second-order noncircular signals using a crossed array is devised in this paper. Unlike the conventional ESPRIT for noncircular signals, which employs the least squares or total least squares, the proposed solution is able to exploit the overlapping structure of the subarray configurations to efficiently solve the shift-invariant equations (SIEs), ending up with the SLS solution for a three-axis crossed array. Moreover, an additional constraint requiring the SIEs of the three linear subarrays directed along the x-, y-, and z-axes to share the same set of eigenvectors is applied to solve the rank-deficiency problem. Consequently, the proposed approach provides more accurate DOA estimates, and its superiority over existing ESPRIT schemes is demonstrated via computer simulations. [ABSTRACT FROM AUTHOR]

Background: This study aims to construct radiomics models based on [18F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models. Methods: A total of 515 patients were retrospectively collected and divided into a training set (n = 404) and an independent testing set (n = 111) according to their examination time. After semi-automatic segmentation of PET/CT images, the radiomics features were extracted, and the best feature sets of CT, PET, and PET/CT modalities were screened out. Nine radiomics models were constructed using logistic regression (LR), random forest (RF), and support vector machine (SVM) methods. According to the performance in the testing set, the best model of the three modalities was kept, and its radiomics score (Rad-score) was calculated. Furthermore, combined with the valuable clinical parameters (gender, smoking history, nodule type, CEA, SCC-Ag), a joint radiomics model was built. Results: Compared with LR and SVM, the RF Rad-score showed the best performance among the three radiomics models of CT, PET, and PET/CT (training and testing sets AUC: 0.688, 0.666, and 0.698 vs. 0.726, 0.678, and 0.704). Among the three joint models, the PET/CT joint model performed the best (training and testing sets AUC: 0.760 vs. 0.730). The further stratified analysis found that CT_RF had the best prediction effect for stage I–II lesions (training set and testing set AUC: 0.791 vs. 0.797), while PET/CT joint model had the best prediction effect for stage III–IV lesions (training and testing sets AUC: 0.722 vs. 0.723). Conclusions: Combining with clinical parameters can improve the predictive performance of PET/CT radiomics model, especially for patients with advanced lung adenocarcinoma. [ABSTRACT FROM AUTHOR]

When drafting official government documents, it is necessary to firmly grasp the main idea and ensure that any positions stated within the text are consistent with those in previous documents. In combination with the field's demands, By taking advantage of suitable text-mining techniques to harvest opinions from sentences in official government documents, the efficiency of official government document writers can be significantly increased. Most existing opinion mining approaches employ text classification methods to directly mine the sentential text of official government documents while disregarding the influence of the objects described within the documents (i.e., the target entities) on the sentence opinion categories. To address these issues, this study proposes a sentence opinion mining model that fuses the target entities within documents. Based on the Bi-directional long short-term (BiLSTM) and attention mechanisms, the model fully considers the attention given by a official government document's target entity to different words within the corresponding sentence text, as well as the dependency between words of the sentence. The model subsequently fuses two by using feature vector fusion to obtain the final semantic representation of the text, which is then classified using a fully connected network and softmax function. Experimental results based on a dataset of official government documents show that the model significantly outperforms baseline models such as Text-convolutional neural network (TextCNN), recurrent neural network (RNN), and BiLSTM. [ABSTRACT FROM AUTHOR]

Hydrogen embrittlement (HE) can lead to the unintentional fracture of large engineering materials, resulting in severe economic losses and safety hazards. It is critical to explore economical and scalable solutions to overcome the challenges of HE. In this work, the HE resistances of four face-centered cubic (FCC) concentrated solid solution alloys (CSAs), including NiFe 20 , NiCoCr, NiCoCrFe, NiCoCrFeMn, and pure Ni were comprehensively investigated and compared by using TDS, EBSD and TEM. It was found that Fe and Cr element can effectively inhibit the solubility of deuterium in the CSAs and alleviate the HE behaviors. In contrast, Mn element significantly increases the solubility of deuterium and caused brittle fractures. Tensile experiments were performed to compare the HE sensitivity of CSAs. The tensile strength of pure Ni and NiFe 20 alloys decreased after deuterium charged, whereas the tensile strength of NiCoCr, NiCoCrFe and NiCoCrFeMn alloys increased. Based on the experimental results, it is deduced that deuterium atoms promote dislocation accumulation and twin growth by reducing the stacking fault energy, which eventually leads to an increase in yield strength and a decrease in plasticity. In addition, the accumulation of deuterium atoms near grain boundaries and cracks during deformation leads to dislocation pinning and stress concentration, which ultimately promotes crack nucleation and propagation. The HE resistances of CSAs are strongly influenced by the deuterium concentration, which depends on the effects of constituent elements and deuterium charging time on the deuterium concentration. Collectively, the specific alloying elements play more important role for the HE resistances of CSAs than the number of elements in the current study. These results provide valuable insights for further understanding and design higher HE resistance CSAs in the future. [Display omitted] ● The addition of Fe and Cr elements to CSAs effectively inhibits deuterium dissolution and enhances the resistance to HE. ● The addition of the Mn element to CSAs significantly promoted deuterium dissolution and caused HE fractures. ● Deuterium atoms reduce the SFE and promote dislocation propagation and twin growth. ● The accumulation of localized stresses induced by deuterium promote the initiation and propagation of cracks. ● Higher deuterium concentrations and deuterium charging times are more likely to trigger HE. [ABSTRACT FROM AUTHOR]

[Display omitted] • High density dislocations inhibit hydrogen atoms diffusion. • Different types of hydrides were identified by CDB spectrum and theoretical calculations. • Hydrogen concentration decreases with increasing dislocation density. This work aims to elucidate the interaction between hydrogen atoms and pre-introduced dislocations in commercially pure titanium (CP-Ti). Positron annihilation spectroscopy supplemented by the first-principle calculations was utilized to reveal the effects of dislocation densities on the formation of hydride types and the concentration of hydrogen-induced defects in CP-Ti. Results show that, in the hydrogen-charged sample with 40 % deformation, hydrogen-induced damage gradually decreases when the hydrogen enters the sample at a depth of about 350 nm. This finding suggests that the high-density dislocations present in deformed specimens effectively trap hydrogen atoms and inhibit their diffusion, which ultimately mitigate damage within the samples. γ-TiH and δ-TiH 2 hydrides are observed in the hydrogen-charged sample. The thermal desorption spectroscopy results show that the desorption amount of deuterium decreases from 1.14 × 1018 D/cm2 to 5.10 × 1017 D/cm2 with an increase in dislocation density because dislocations inhibit the diffusion of deuterium. In samples with higher deformation, the high dislocation density inhibits the formation of δ-TiH 2 hydrides and promotes the formation of γ-TiH hydrides, which significantly reduce the hardening of samples. These results provide insight into the interaction of hydrogen with pre-introduced defects and provide strong theoretical support for the development and improvement of more damage-resistant materials. [ABSTRACT FROM AUTHOR]

Purpose: This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (18F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs). Methods: We retrospectively analyzed patients with suspicious GGNs who underwent 18F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks). Results: A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372–1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73]. Conclusion: The 3D-CNN based on 18F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together. [ABSTRACT FROM AUTHOR]