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Three‐dimensional displacement monitoring over long distances has been a long‐standing concern in the structural health monitoring industry. In this study, a multi‐degree‐of‐freedom slope displacement monitoring method is developed by fusing computer vision and the 3D point triangulation method. Attributed to this method, the problems of outdoor binocular camera calibration, multi‐target mismatching, and outdoor illumination effects were solved. First, a two‐stage camera calibration method is proposed to accurately calibrate intrinsic and extrinsic camera parameters under a large field of view and long working distance conditions. Second, the adaptive spatial‐frequency method is proposed to calculate the coding and pixel coordinates of the monitored target. In this step, to solve the problem of mismatching monitored points in different camera frames, the Augmented Reality University of Cordoba code is introduced to provide a unique identity code for each monitored point. To mitigate the impact of illumination and other factors on pixel coordinate calculation, an adaptive pixel coordinate calculation method that combines information from the spatial and frequency domains is proposed., Third, based on the intrinsic and extrinsic parameters of the stereo camera and the pixel coordinates of the monitored points, the 3D coordinates of the monitored points are obtained through triangulation. Finally, the accuracy experiments and stability experiments are conducted. According to the results of the experiments, the measurement distance is positively correlated with the measurement error. And the baseline length is negatively correlated with the measurement error in the z‐direction. Ultimately, we suggest that the ratio of baseline length to measurement distance should be greater than 40%. When the recommended value is satisfied, the measurement error is less than 1 mm when the measurement distance is less than 40 m. When the measurement distance is equal to 90 m, the measurement error is less than 5 mm. Meanwhile, stability experiments of the algorithm were carried out, and in a period of outdoor validation experiments, the fluctuations were only sub‐millimeter, demonstrating good anti‐interference performance. Moreover, the method proposed in this study successfully monitored a landslide disaster in Guangxi, which demonstrated its outstanding practical application capabilities. [ABSTRACT FROM AUTHOR]

The Space-Air-Ground Information Network (SAGIN) provides extensive coverage, enabling global connectivity across a diverse array of sensors, devices, and objects. These devices generate large amounts of data that require advanced analytics and decision making using artificial intelligence techniques. However, traditional deep learning approaches encounter drawbacks, primarily, the requirement to transmit substantial volumes of raw data to central servers, which raises concerns about user privacy breaches during transmission. Federated learning (FL) has emerged as a viable solution to these challenges, addressing both data volume and privacy issues effectively. Nonetheless, the deployment of FL faces its own set of obstacles, notably the excessive delay and energy consumption caused by the vast number of devices and fluctuating channel conditions. In this paper, by considering the heterogeneity of devices and the instability of the network state, the delay and energy consumption models of each round of federated training are established. Subsequently, we introduce a strategic node selection approach aimed at minimizing training costs. Building upon this, we propose an innovative, empirically driven Double Deep Q Network (DDQN)-based algorithm called low-cost node selection in federated learning (LCNSFL). The LCNSFL algorithm can assist edge servers in selecting the optimal set of devices to participate in federated training before the start of each round, based on the collected system state information. This paper culminates with a simulation-based comparison, showcasing the superior performance of LCNSFL against existing algorithms, thus underscoring its efficacy in practical applications. [ABSTRACT FROM AUTHOR]

Crack detection at the pixel level across complex scenarios (structural interference and adverse working conditions) is a critical consideration in the maintenance of ballastless track slab (BTS). Although existing deep learning models achieve acceptable accuracy on cracks with a monotonous background, the ground truth with high labor cost is inevitable and their performance in complex scenarios may fall far below their theoretical bounds or even cause "all-black images." A hybrid algorithm based on synthetic data from digital twin model and weakly supervised style transfer is proposed in this paper for addressing the above challenges. The algorithm uses a region-attention strategy to enable the uncontrolled generative adversarial network (GAN) focusing its attention on weak labels containing crack regions, directly obtaining segmentation results with the same style as the ground truth of the crack forest dataset. In addition, a digital twin model that can simulate the real inspection working conditions is established to generate a synthetic crack dataset, enabling the hybrid algorithm to extract the most discriminative features. The results show that the performance of the hybrid algorithm on inspection images across complex scenarios is nearly 25% higher than that of the DeepLabv3+ network, while the time cost consumed is only 0.5% of the latter. The deployment of the region attention strategy also enables the hybrid algorithm to achieve a mean intersection of union (MIoU) of 79.38%, which is nearly twice as much as that of GAN. It not only eliminates the oversegmentation caused by structures such as rails and fastener systems but also overcomes "all-black images." In addition, synthetic data can greatly enlarge the range, type, and number of discriminative crack features compared with data augmentation based on limited real data, thus enhancing the performance of the hybrid algorithm for uncertain inspection data. Particularly, the fully trained hybrid algorithm based on the synthetic dataset shows good adaptability and generalization to adverse working conditions such as uneven lighting, noise, and blur. [ABSTRACT FROM AUTHOR]

Objective: This study investigates the efficacy and safety of sublingual immunotherapy (SLIT) with A. annua allergens in patients with seasonal allergic rhinoconjunctivitis over two pollen seasons. Methods: Seventy patients with moderate-severe seasonal allergic rhinoconjunctivitis were divided evenly into the SLIT and control groups. The SLIT last from 3 months before the summer-autumn pollen season in 2021 till the end of the summer-autumn pollen season in 2022. The daily individual symptom score, total rhinoconjunctivitis symptom score (dTRSS), total medication score (dTMS), combined score of medication and rhinoconjunctivitis symptom (dCSMRS), visual analog scale (VAS) score, and adverse events (AEs) were evaluated. Results: The average pollen concentration in 2022 was twice that previous two-year during the pollen season. Fifty-six patients completed treatments (SLIT group: 29, control group: 27). Compared with baseline, the individual symptoms, dTRSS, dTMS, dCSMRS, and VAS scores of SLIT group declined in 2021. After 16 months of SLIT, all efficacy indexes in 2022 were still lower than baseline and equivalent to those in 2021. In control group, the efficacy indexes in 2022 were higher than that in 2020 and 2021. The efficacy indexes of SLIT group were lower than those of control group in 2021 and 2022. SLIT is effective for both mono- and poly-sensitized patients. AEs incidence in SLIT group was 82.7% without severe AEs. Conclusions: The A. annua-SLIT can obtain efficacy and safety over two pollen seasons for patients with moderate-severe seasonal allergic rhinoconjunctivitis. [ABSTRACT FROM AUTHOR]

This work is mainly intended to investigate the flysch landslide reinforcement measures used in the Smokovac-Mateševo section of the North-South Expressway project in Montenegro. Bentley's Plaxis software is used for a numerical analysis of sliding surface parameters of flysch strata in the limit equilibrium state. This study analyzes the slope safety factor for rreinforcement measures such as rock bolts, retaining walls, anti-sliding piles, slope unloading and bolt anchoring and obtains an optimal combination of reinforcement application for the flysch landslide. The effects of seismic action on complex stress and the discontinuous stress boundary conditions arising from various reinforcement measures on landslide stability are also examined. The measures applied in this paper can be used as a reference for flysch landslide reinforcement or other similar slope engineering measures. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment (LSA) is an essential method to prevent and mitigate landslides. Many studies thus involve the LSA and improve the accuracy of LSA using various methods; however, most of the studies optimize the LSA by improving the evaluation models and neglect the influence of factors. The present study initially selects fifteen factors and combines the optimization algorithms, namely, the genetic algorithm (GA), with entropy theory (ET) to divide the factors. The division results are compared with that of the natural breakpoint method (NBM) and equidistant empirical method (EEM) which are the most commonly used methods. The division results are employed to select factors based on the chi-square test and multicollinearity test, and the optimal number of factors involved in the evaluation and the factor systems are determined by the recursive feature elimination method based on the out-of-bag error. The LSAs of ET-GA, NBM, and EEM are then obtained using improved deep belief networks (DBN), and the distribution patterns of the landslide susceptibility maps are similar. The landslide density of very high susceptibility using the three methods is more than three times that of low susceptibility, which illustrates that the three methods are applicable to LSA in the study area. The assessment results are evaluated by the area under the receiver operating characteristic curve (AUC). The ET-GA showed the highest AUC, proving that the optimization algorithms can improve the accuracy of LSA and the ET-GA is more applicable for LSA in the study area. [ABSTRACT FROM AUTHOR]

Extreme-scale computing involves hundreds of millions of threads with multi-level parallelism running on large-scale hierarchical and heterogeneous hardware. Some OpenMP multi-threaded applications increasingly suffer from runtime hidden behaviors owning to shared resource contention as well as software- and hardware-related problems. Such hidden behaviors can result in failure and inefficiencies and are among the main challenges in system resiliency. To minimize the impact of hidden behaviors, one must quickly and accurately detect and diagnose the hidden behaviors that cause the failures. However, it is difficult to identify hidden behaviors in the dynamic and noisy data collected by OpenMP multi-threaded monitoring infrastructures. This paper presents an anthropomorphic diagnosis framework for hidden behaviors of OpenMP multi-threaded applications. In the framework, we first design injected heartbeat functions for OpenMP multi-threaded applications. Then, we leverage the heartbeat sequences to extract features of hidden behaviors. Finally, we develop a feature learning-based algorithm using heartbeat analysis, namely HSA, to diagnose hidden behaviors. To evaluate our framework, the NAS Parallel NPB benchmark, EPCC OpenMP micro-benchmark suite, and Jacobi benchmark are used to test the performance of our proposed framework. The experimental results demonstrate that our framework successfully identifies 90.3% of the injected hidden behaviors of OpenMP multi-threaded applications while acquiring low overhead. • The anthropomorphic framework is proposed for diagnosing hidden behaviors. • The framework based on heartbeats supports multi-threaded OpenMP applications. • Small-size samples also demonstrate the high efficiency of this framework. • The framework is lower overhead and is more accurate than the state-of-the-art. • The framework supports extension and specificity in most of our experiments. [ABSTRACT FROM AUTHOR]

Parental work stress was associated with parenting irritability and use of harsh disciplinary practices, but little is known about how daily fluctuations in work stress proliferate to everyday parenting practices in the context of multigenerational living. Using a quasi-experimental design, this study examined how grandparental coresidence moderates the link between daily work stress and parent-to-child aggression (PCA). Using propensity scores, we matched 129 three-generation households with 133 two-generation households in Zhengzhou, China, based on multiple family characteristics. Parents in both groups completed daily questionnaires for 15 consecutive days. Fixed effects models estimated the within-person associations between parent daily work stress and PCA among the two comparison groups. We found substantial daily fluctuations in parent work stress and PCA. The association between parent daily work stress and PCA significantly varied between two- and three-generation families and was only significant among the former. We conclude that co-residing grandparent(s) could be an important resource that buffers the work-to-PCA stress proliferation. Child abuse prevention programs would likely benefit from concerting with policies supporting the wellbeing of older adults and programs promoting work-life balance. [ABSTRACT FROM AUTHOR]

In this paper, we generalize the notions of quermassintegrals, harmonic quermassintegrals, and affine quermassintegrals to -quermassintegrals so that the cases of -quermassintegrals are quermassintegrals, harmonic quermassintegrals, and affine quermassintegrals, respectively. Further, we obtain some inequalities associated with -quermassintegrals, including Brunn–Minkowski-type inequalities, a monotonic inequality, and a Bourgain–Milman-type inequality. [ABSTRACT FROM AUTHOR]

The availability of glucose transporter in the small intestine critically determines the capacity for glucose uptake and consequently systemic glucose homeostasis. Hence a better understanding of the physiological regulation of intestinal glucose transporter is pertinent. However, the molecular mechanisms that regulate sodium-glucose linked transporter 1 (SGLT1), the primary glucose transporter in the small intestine, remain incompletely understood. Recently, the Drosophila SLC5A5 (dSLC5A5) has been found to exhibit properties consistent with a dietary glucose transporter in the Drosophila midgut, the equivalence of the mammalian small intestine. Hence, the fly midgut could serve as a suitable model system for the study of the in vivo molecular underpinnings of SGLT1 function. Here, we report the identification, through a genetic screen, of Drosophila transmembrane protein 214 (dTMEM214) that acts in the midgut enterocytes to regulate systemic glucose homeostasis and glucose uptake. We show that dTMEM214 resides in the apical membrane and cytoplasm of the midgut enterocytes, and that the proper subcellular distribution of dTMEM214 in the enterocytes is regulated by the Rab4 GTPase. As a corollary, Rab4 loss-of-function phenocopies dTMEM214 loss-of-function in the midgut as shown by a decrease in enterocyte glucose uptake and an alteration in systemic glucose homeostasis. We further show that dTMEM214 regulates the apical membrane localization of dSLC5A5 in the enterocytes, thereby revealing dTMEM214 as a molecular regulator of glucose transporter in the midgut. [Display omitted] • dTMEM214 acts in the midgut enterocytes to regulate systemic glucose homeostasis. • The subcellular distribution of dTMEM214 in the enterocytes is regulated by Rab4. • dTMEM214 controls the apical membrane localization of dSLC5A5 in the enterocytes. [ABSTRACT FROM AUTHOR]

Large‐scale rural‐to‐urban migration has shaped the socialization contexts of rural adolescents in China and can potentially impact their developmental outcomes. In this study, using data from the first wave of the China Education Panel Study collected in 2013, we focused on self‐efficacy, an important but under‐studied facet of noncognitive development, and assessed how it was influenced by family migration status. We also explored the mediating role of family and school resources. We compared three groups of rural‐origin adolescents with different family migration statuses: rural left‐behind children (LBC), rural not‐left‐behind children (NLBC), and rural‐to‐urban migrant children (MC). Structural equation modeling was performed to estimate the main effects of rural‐origin groups on self‐efficacy and the mediating effects of family income, family social capital, and school social capital for the significant group effects on self‐efficacy. We found similar levels of self‐efficacy among MC and NLBC, who in turn, exhibited greater self‐efficacy than LBC. Discrepancies in family and school resources mediated the self‐efficacy gaps between LBC and their MC and NLBC counterparts. Notably, when their disadvantages in family and school resources were controlled for, LBC were more efficacious than MC and NLBC, indicating LBC's resilience and the potential for promoting self‐efficacy in LBC by providing adequate resources and support. [ABSTRACT FROM AUTHOR]

This study aimed to explore the effect of the Wuhan lockdown on body mass index (BMI) among Wuhan residents as well as factors including food habits affected and changes in physical activity mediating the association. As a survey of the Wuhan lockdown, a random digit dialing method was used to sample residents aged 18 years and older. A computer-aided telephone interview was applied to survey 11,223 residents in Wuhan City in July 2020. Ordinary least squares (OLS) regression and mediation analysis were used to analyze the influence of the Wuhan lockdown on BMI. Demographic characteristics, lifestyle characteristics, and health status differed statistically significantly between residents living in and those who left Wuhan during the lockdown (mean BMI, 23.05 ± 0.04 kg/m2 and 22.34 ± 0.06 kg/m2, respectively). The mean BMI among residents living in Wuhan was 0.42 kg/m2 higher (P < 0.001) than that of residents who left Wuhan during the lockdown, as determined by multivariate OLS regression analysis. Residents living in Wuhan during the lockdown had higher BMI among those aged 18–29 and 30–44 years (P < 0.001), with no difference among those aged 45+ years. The association between living in Wuhan and increased BMI was the strongest among residents who were overweight/obese. The mediation analysis results showed an indirect effect path with living in Wuhan (through decreased physical activity and increasing BMI) and the direct effect of living in Wuhan during the lockdown. The lack of association between living in Wuhan and increased BMI among residents aged 45+ years was due to the indirect influence of living in Wuhan affecting food habits. The findings indicated that the lockdown in Wuhan city increased residents' BMI, especially among those who were young or overweight/obese, partly because of decreased physical activity. [ABSTRACT FROM AUTHOR]

In order to improve the economy of the multi energy system and the efficiency of energy utilization, the research adopts the non dominated sorting genetic algorithms II (NSGA-II) to expand the population space. The elite strategy is introduced to improve the intelligent algorithm, and then the diversity of the population is retained to improve the optimization accuracy of the algorithm. In addition, the adaptive operator is introduced to improve the NSGA-II algorithm to improve the global search efficiency. The performance test of fast non dominated sorting genetic algorithm shows that the improved algorithm using elite strategy has better performance in coverage index, diversity index and convergence index. For example, in terms of convergence index, the improved NSGA-II algorithm has improved 0.0159, 0.822, 0.0243 and 0.0171 in four ZDT test functions. On the energy optimization operation for the integration of wind and hydrogen, the improved NSGA-II algorithm has obtained lower cost, with a total configuration cost of 606 million yuan, while the total system configuration cost corresponding to the unimproved NSGA-II algorithm is 624 million yuan, so the total system cost after the algorithm improvement has decreased by 18 million yuan. Therefore, this method has a better economy and higher energy efficiency. [ABSTRACT FROM AUTHOR]

The K homology (KH) repeat is an RNA‐binding motif that exists in various proteins, some of which participate in plant growth. However, the function of KH domain‐containing proteins in plant defence is still unclear. In this study, we found that a KH domain‐containing protein in apple (Malus domestica), HEN4‐like (MdKRBP4), is involved in the plant immune response. Silencing of MdKRBP4 compromised reactive oxygen species (ROS) production and enhanced the susceptibility of apple to Valsa mali, whereas transient overexpression of MdKRBP4 stimulated ROS accumulation in apple leaves, indicating that MdKRBP4 is a positive immune regulator. Additionally, MdKRBP4 was proven to interact with the VmEP1 effector secreted by V. mali, which led to decreased accumulation of MdKRBP4. Coexpression of MdKRBP4 with VmEP1 inhibited cell death and ROS production induced by MdKRBP4 in Nicotiana benthamiana. These results indicate that MdKRBP4 functions as a novel positive regulatory factor in plant immunity in M. domestica and is a virulence target of the V. mali effector VmEP1. [ABSTRACT FROM AUTHOR]

Ferroptosis is a new form of programmed cell death, which achieved great breakthroughs in cell biology during past decade. However, the regulation of ferroptosis is yet to be identified thoroughly. The latest study published on Nature cell biology by Nguyen and colleagues found a new NADPH sensor, MARCHF6 an E3 ubiquitin ligase, mediates ferroptosis in tumor growth and animal development. This finding provides a novel insight into ubiquitin system and energy metabolism in regulation of ferroptosis, which may open up new avenues for tumor treatment. [ABSTRACT FROM AUTHOR]

Blockchain has attracted tremendous attention from both the academia and the capital market. The insertion of data in a blockchain known as anchoring makes data verifiable and independently auditable and has many potential applications. The objective of this paper is to introduce a dynamic cross-chain anchoring framework for snapshotting users' data into multiple public blockchains with the help of smart contracts. This paper starts by proposing the framework and depicting the components and workflow. Second, we design a target chain selection algorithm that combines the states of different blockchains. Using the algorithm, users can dynamically select a suitable public blockchain for anchoring tasks. Third, the virtual chain protocol is presented and achieves data anchoring orderly and integrally. Analyzation and evaluation show that the framework can enhance user experience, robustness and performance. [ABSTRACT FROM AUTHOR]

• This study examines the placement of a controlled current source on the secondary side of a current limiting inductor and the enhancement of clamping voltage stability on the primary side of a current limiting inductor from the standpoint of controlling flexible current limiting technology. • This study combines two-degree-of-freedom algorithm and model predictive control, which is appropriate for flexible current limiting devices to address the issue of inadequate response time and control accuracy under standard control. The issue of DC fault ride-through can now be resolved in a novel way thanks to flexible current limiting technology, but its practical worth cannot be realized due to flaws in the technology. This study examines the placement of a controlled current source on the secondary side of a current limiting inductor and the enhancement of clamping voltage stability on the primary side of a current limiting inductor from the standpoint of controlling flexible current limiting technology. It offers a theoretical foundation for the specified secondary-side controlled source through an analysis of the iron core's inductance properties. Two-degree-of-freedom algorithm and model predictive control are combined in a control strategy that is appropriate for flexible current limiting devices to address the issue of inadequate response time and control accuracy under standard control. In MATLAB/Simulink simulation software, the presented theory's soundness is lastly confirmed. [ABSTRACT FROM AUTHOR]

• Deep learning mdoels explore pricise and efficient ash content estimation. • Scrutinizing color and texture features in neural network-based estimation. • Feature disentangled pipline and interpretable decision module were proposed. • CNN, ViT, hybrid model, and GCN were employed for feature representation. • Feature contribution analysis guides accuracy-driven model design. Determining coal ash content is paramount when evaluating coal quality and optimizing industrial processes. Conventional methods reliant on manual analysis prove exceedingly time-consuming and labor-intensive. The advent of deep learning has galvanized researchers to explore various models aimed at precision and efficiency in the coal industry. However, the selection of appropriate features assumes a pivotal role in achieving accurate estimation. This study meticulously scrutinizes the importance of color and texture features in estimating coal ash content using neural networks. The proposed framework for elucidating feature contributions encompasses the following steps: (1) A feature disentangled pipeline was employed to generate a color and texture dataset from the original dataset; (2) Harnessing convolutional neural network (CNN), vision Transformers(ViT), a hybrid model combing CNN and ViT, and graph convolution network (GCN) to learning feature representation for texture and color. 3) An interpretable decision module was employed to aggregate these two feature representations, thereby achieving an interpretable estimation of ash content. Experimental results and visualizations demonstrated the substantial importance of color in CNN, accounting for an impressive 64.77%, whereas the texture feature modestly contributed at 35.23%. The analysis of feature contributions assumes a crucial role in guiding the design of accuracy-driven models and in comprehending the inherent contributions or biases within deep learning architectures. [ABSTRACT FROM AUTHOR]

Coal-gangue sorting robots have been established as a ground-breaking technology in mineral processing, particularly for mitigating labor intensity and increasing production efficiency. Existing coal-gangue sorting robots tend to employ object detection algorithms as the framework, but the crude bounding boxes provided by this approach is insufficient to support the robot in all sorting scenarios. The instance boundary enables the robot to dexterously grab the target amid numerous interfering objects, which is essential for enhancing the capabilities of the coal-gangue sorting robots. Currently, there is an increasing focus on the performance of instance segmentation algorithms at boundaries, but their mindset is still to extract the outer edge based on the segmentation instance mask, which is unable to determine whether the output boundary is reliable. In this study, we proposed a framework for generating high-confidence instance boundary and named it BGF. The overall idea is to crop each instance into patch based on output of object detection model; the boundary regression network then directly generated the boundaries of the salient target in the patch; all results were finally reassembled to get a global instance boundary with detailed semantic information. In parallel, we introduced an attention mechanism and auxiliary branch to enhance the model's ability on extracting boundary features. The rationality of various designing in the BGF has also been demonstrated through a serious of experiments on varies dataset. Compared with conventional segmentation networks, BGF has more credible capability in boundary extraction and generalization. • A new framework (BGF) is proposed for generating high-confidence instance boundary. • A boundary confidence-map is treated as a label for supervising boundary generation. • The attention mechanism enhanced the ability of model to perceive boundaries. • BGF can obtain global instance boundary with detailed semantic information. • The great performance of BGF is verified on the coal-gangue datasets. [ABSTRACT FROM AUTHOR]

• GR-3 increased the availability of bioactive compounds in the fermented lily bulbs. • LFB+GR-3 fraction restricted deposition of CBNPs and tissue damages in mice lungs. • Lung microbiota was restored in LFB+GR-3 fraction group than solely CBNP exposure. Limited application in protecting lung health is attributed to the low levels of active compounds in lily plant bulbs. This study focused on enhancing the active compounds by fermenting Lilium davidii (Lanzhou Lily) bulbs with Limosilactobacillus fermentum GR-3, isolated from Jiangshui. Lily fermented bulbs with strain GR-3 (LFB+GR-3) increased the bioavailability of hexadecanoic acid methyl ester, 22-tetrahydroxy-5alpha-cholestan-6-one-3-O-beta- d -allopyranoside, 22-O-(6-deoxy-Alpha- l -mannopyranosyl)-3-O-beta- d -glucopyranosyl-pregn-5-en-20-one, 1-O- trans -feruloylglycerol, and 3,4 dihydroxybenzoic acid. LFB+GR-3 fraction was employed to treat the mice model exposed to the carbon black nanoparticles (CBNPs). Immunohistochemical analysis revealed that the deposition of CBNPs and damages in lung tissues were limited in the LFB+GR-3 treatment group, while TNF-α, IL-10, and IL-6 were elevated by 6.9, 4.3, and 7 folds in the CBNP exposure group. In addition, Lactobacillus , Escherichia , Lactococcus , and Muribacter were dominant in the lung microbiota of LFB+GR-3 than the CBNP group. The use of probiotic fermented lily bulbs might be helpful in lung infection treatment. [ABSTRACT FROM AUTHOR]

Centroid bodies are a continuous and G L (n) -contravariant valuation and play critical roles in the solution to the Busemann–Petty problem. In this paper, we introduce the notion of harmonic Blaschke–Minkowski homomorphism and show that such a map is represented by a spherical convolution operator. Furthermore, we consider the Shephard-type problem of whether Φ K ⊆ Φ L implies V (K) ≤ V (L) , where Φ is a harmonic Blaschke–Minkowski homomorphism. Some important results for centroid bodies are extended to a large class of valuations. Finally, we give two interesting results for even and odd harmonic Blaschke–Minkowski homomorphisms, separately. [ABSTRACT FROM AUTHOR]

Very volatile organic compounds (VVOCs) are a group of important odor pollutants affecting indoor air quality that have been shown to be harmful to human health. A 15 L environmental chamber, combined with multi-bed tube was used to collect gases. Fifteen very volatile organic compounds (VVOCs), including 12 odor compounds, were identified from veneered medium density fiberboard coated with water-based lacquer (WB-MDF) using gas chromatography–mass spectrometry/olfactometry (GC-MS/O). The total very volatile organic compound (TVVOC) and total odor intensity (TOI) showed a decreasing trend over time, reaching equilibrium on day 28. TVVOC showed an overall slow-fast-slow emission profile, from day 3 to day 7, with a maximum decay rate of 29.7%. TOI showed the greatest rate of decline from day 1 to day 3, at approximately 12%. Alkane and alcohol VVOCs were the more abundant compounds, accounting for at least 60% and even up to 80% of the total. The major odor impression was fruity, with a highest odor rating of 6.6, followed by sweet, with an odor rating of 6.1. Although the odor impression changed from sweet to fruity over time, it seemed pleasant overall. The odor contributors were mainly alkanes, alcohols, esters, and ethers, which had relatively high odor intensities. The main odor-contributing substances were dichloromethane, ethanol, ethyl acetate, 2-methylacrylic acid methyl ester, and tetrahydrofuran. When WB-MDF is used for furniture or other decorative materials, it is strongly recommended that it be stored under ventilation for at least 28 days and the adoption of substitute solvents of lacquers, modified adhesives, and low-odor wood raw materials is recommended. These possible initiatives would contribute to the aim of building an environmentally friendly indoor environment. [ABSTRACT FROM AUTHOR]

Breaking internal tides and induced mixing are critical to shelf dynamics, including heat and mass exchanges. Spatiotemporal variability of internal tides and modulation factors for the southern East China Sea shelf were examined based on a combination of a three-month mooring velocity and satellite altimeter data. Semidiurnal and diurnal internal tides exhibited distinct temporal trends, with the semidiurnal internal tides enhanced by an order of magnitude during the latter half of the record, while the diurnal internal tides followed quasi spring-neap cycles with a generally stable intensity except for two specific periods of strengthening. These internal tides probably originated remotely over the shelf-slope area northeast of Taiwan. Time-varying stratification was the most important factor for the internal tidal magnitude. In addition, varying background currents influenced the diurnal critical latitude band, which explains the slightly enhanced diurnal internal tides during the two periods. Although both semidiurnal and diurnal internal tides were mode-1 dominated, the semidiurnal internal tides were surface intensified while the diurnal tides were bottom intensified. The proportion of higher mode internal tides increased during robust eddy activities. Stronger background vertical shear corresponded to high-frequency events and energy transfers from tidal frequencies to high frequencies associated with turbulent mixing. [ABSTRACT FROM AUTHOR]

• DBN models are improved using SA, PSO and SSA for spatial landslide susceptibility assessment (LSA). • LSA obtained using SA-DBN, PSO-DBN, and SSA-DBN are compared with that of the DBN model using ROC curve and SCAI. • SSA-DBN model has the highest accuracy in spatial LSA. • Temporal LSA is analyzed using the effective rainfall model (ERM) and peak ground acceleration (PGA). • Spatiotemporal LSA map is generated by coupling the optimal spatial LSA map and temporal LSA map. Landslide is one of the most serious geo-hazards, and the landslide susceptibility assessment (LSA) is an existing effective method to efficiently mitigate the loss caused by landslides. This study develops the improved deep belief network (DBN) models by selecting the optimal model hyperparameter to improve the accuracy of LSA. Evaluation factors of the LSA are selected from fifteen influencing factors by chi-square test, multicollinearity test and out-of-bag error. 30% data of the study area are selected randomly as the training data to assess the landslide susceptibility of each grid in the study area. The spatial LSA is then obtained by integrating the DBN models with three optimization algorithms, namely the simulated annealing (SA), particle swarm optimization (PSO) and sparrow search algorithm (SSA). The assessment results obtained using DBN and improved DBN models are thus compared and verified using the receiver operating characteristic (ROC) curve and seed cell area index. It shows that the three improved DBN models outperform the DBN model, which demonstrates the ability of optimization algorithms to improve model performance, and the SSA-DBN model achieves the highest assessment accuracy, followed by the PSO-DBN and SA-DBN models. Meanwhile, the effective rainfall model and peak ground acceleration are respectively employed to evaluate the impact of two inducing factors, namely the rainfall and earthquake, and the temporal LSA is thus obtained. The spatiotemporal LSA map is then generated by coupling the optimal spatial LSA map and temporal LSA map. Therefore, the present study further explores the proposed improved methods and offers instructions for spatiotemporal LSA. [ABSTRACT FROM AUTHOR]

Key message: A major quantitative trait locus (QTL) modulating soybean (Glycine max) branch angle was identified by linkage analysis using two bi-parental mapping populations with and without pedigree from wild soybean (Glycine soja). Soybean branch angle is a critical architectural trait that affects many other traits of agronomic importance associated with the plant's productivity and grain yield and is thus a vital consideration in soybean breeding. However, the genetic basis for modulating this important trait in soybean and many other crops remain unknown. Previously, we developed a recombinant inbred line (RIL) population derived from a cross between a domesticated soybean (Glycine max) variety, Williams 82, and a wild soybean (Glycine soja) accession, PI 479,752, and observed drastic variation in plant architecture including branch angle among individual RILs. In this study, one of the RILs possessing extremely wide branch angle (WBA) was crossed with an elite soybean cultivar (LD00-3309) possessing narrow branch angle (NBA) to produce an F2 population composed of 147 plants and F2-derived F3 families for inheritance analysis and QTL mapping. We found that branch angle is controlled by a major QTL located on chromosome 19, designated qGmBa1 and that WBA—derived from the wild soybean accession—is dominant over NBA. This locus was also detected as a major one underlying branch angle by QTL mapping using a subset of the soybean nested association mapping (SoyNAM) population composed of 140 RILs, which were derived from a cross between a landrace, PI 437169B, possessing WBA and an elite variety, IA3023, possessing NBA. Molecular markers located in the QTL region defined by both mapping populations can be used for marker-assisted selection of branch angle in soybean breeding. [ABSTRACT FROM AUTHOR]

Damage detection plays an important role in pavement health monitoring and inspection. Unfortunately, research about damage detection of the special component of pavement structures, such as pavement manhole covers, is relatively few. A new pipeline for the detection and quantification of damage around the pavement manhole covers is proposed in this research. In this pipeline, the Attention-enhanced Manhole Detection Model (AMDM) is proposed to detect manhole covers. AMDM achieves an ideal balance between accuracy and speed by eliminating redundant structures and incorporating an attention mechanism. The BCSM (Boundary-enhanced Crack Segmentation Model) is proposed to segment the damage around the manhole cover, and the boundary loss function is used to enhance the fine segmentation ability of the model on the boundary. The MAP (Mean Average Precision) of the manhole covers detection model is 96.68%, and the MIOU (Mean Intersection Over Union) of the crack segmentation model is 89.73%. Attributing to this efficient and accurate pipeline, a reasonable damage evaluation method is proposed in the end, which is based on statistical data and engineering experience. Overall, not only this research will contribute an automatic and cost-effective method to the detection and evaluation of damage around the manhole cover, but also inspire the detection and evaluation of other special components of civil engineering structures. [ABSTRACT FROM AUTHOR]

Recently, it was found that deep neural networks (DNNs) are susceptible to adversarial input perturbations. Most defense strategies adopt the denoising method based on preprocessing, which mitigates the impacts of adversarial perturbations on DNNs by learning the distributions of nonadversarial datasets and projecting adversarial inputs into the learned nonadversarial manifolds. However, existing defense strategies commonly focus on reconstructing clean images while ignoring the role of adversarial perturbations, which results in the reconstructed images failing to achieve the visual quality and classification accuracy of the original clean images, and the induced adversarial robustness improvement is limited. This paper proposes a feature decoupling-interaction network (FDIN), which introduces the concepts of clean features and adversarial features to separate the two kinds of features from the input adversarial examples (AEs) in a feature decoupling-interaction manner. The clean features are used to reconstruct the input image so that it is infinitely close to the original clean image, and the adversarial features are used to reconstruct the adversarial perturbations. Adversarial perturbations are removed from the adversarial examples across multiple cross cycles to improve further the reconstructed image's visual quality and classification accuracy. The features of the original clean image are used as prior knowledge to guide the network to learn the clean features of the adversarial examples and improve the classification accuracy of the model on the clean examples. In addition, a classification loss function based on the Carlini & Wagner (CW) attack algorithm is used instead of the conventional cross-entropy loss function to improve the adversarial robustness of the FDIN. The experimental results show that the proposed method achieves better defense performance than the current state-of-the-art methods on both standard tests and various attack tests and even exceeds the test accuracy of the target classifier on the original test set. • This paper proposes a feature decoupling-interaction network (FDIN). • We introduces the concepts of clean features and adversarial features. • The FDIN separates the clean features and adversarial features. • We use the CW loss instead of the CE loss to improve the adversarial robustness. • The experimental results show that FDIN achieves better defense performance. [ABSTRACT FROM AUTHOR]

Key message: A soybean landrace carries broad-spectrum resistance to Phytophthora sojae, which is conferred by a single gene, designated Rps14, on the short arm of chromosome 3. Phytophthora sojae is the causative agent for Phytophthora root and stem rot in soybean [Glycine max (L.) Merr.] and can be managed by deployment of resistance to P. sojae (Rps) genes. PI 340,029 is a soybean landrace carrying broad-spectrum resistance to the pathogen. Analysis of an F2 population derived from a cross between PI 340,029 and a susceptible cultivar 'Williams' reveals that the resistance to P. sojae race 1 is conferred by a single gene, designated Rps14, which was initially mapped to a 4.5-cM region on the short arm of chromosome 3 by bulked segregant analysis (BSA), and subsequently narrowed to a 1.48 cM region corresponding to 229-kb in the Williams 82 reference genome (Wm82 v2.a1), using F3:4 families derived from the F2 population. Further analysis indicates that the broad-spectrum resistance carried by PI 340,029 is fully attributable to Rps14. The genomic sequences corresponding to the defined Rps14 region from a set of diverse soybean varieties exhibit drastic NBS-LRR gene copy number variation, ranging from 3 to 17 copies. Ultimate isolation of Rps14 would be critical for precise selection and deployment of the gene for soybean protection. [ABSTRACT FROM AUTHOR]

• Ecological wetland is key to multi-barrier of water source security. • Most active plant-bed/ditch system especially fringes harbor hot zones. • Pond-wetland complex with constructed root channels becomes a paradigm. • Human-body wetland model based on bionics or biomimetics was established. • Upgraded wetland performance provide reference for developing countries. Jiaxing created a precedent using bypass riparian marshes to purify micro-polluted water sources in China. Pond-wetland complex with constructed root channel technology becomes a paradigm which can be analogized as "human-body wetland model" based on bionics or biomimetics. Heterogeneous plant-bed/ditch system with highly active land/water ecotone interfaces, especially meandering boundaries, breeds many biochemical reactions "living areas". Optimization of hydraulic regulation promotes redox environment alternations and wetland treatment efficiency. Here we reported a series of upgrades and performances in Guanjinggang wetland after the Shijiuyang prototype. Morphological reform of plant-bed/ditch system played a vital role. Spatially root channel zone was main force of wetland purification, and temporally the treatment effect was higher in low-temperature seasons indicating non-temperature dependent mechanisms worked. Water pollution comprehensive index improved steadily from IV to III, and comprehensive pollution load was reduced by ca. 40%–60%. Comprehensive evaluation function value further showed the gradients purification effect of the upgraded wetland. Ecological wetlands ameliorated source water quality, and reduced drinking water treatment reagents, thereby bringing about economic benefits. Through wetlands operation, people can see how the micro-polluted surface water becomes clear and clean, so promoting a significant social benefit. As a viable component of urban green space, wetlands could beautify regional eco-environment, freshen the air, increase urban ecological taste, and enhance the eco-environmental protection publicity. Thus, the multifunctional service values and indirect benefits are substantial. Jiaxing ecological wetlands provide a typical paradigm for water pollution remediation in developing countries and plays a leading role in technology engineering radiation effect. [Display omitted] [ABSTRACT FROM AUTHOR]

Summary: Large structural variations frequently occur in higher plants; however, the impact of such variations on plant diversification, adaptation and domestication remains elusive.Here, we mapped and characterised a reciprocal chromosomal translocation in soybeans and assessed its effects on diversification and adaptation of wild (Glycine soja) and semiwild (Glycine gracilis) soybeans, and domestication of cultivated soybean (Glycine max), by tracing the distribution of the translocation in the USDA Soybean Germplasm Collection and population genetics analysis.We demonstrate that the translocation occurred through CACTA transposon‐mediated chromosomal breakage in wild soybean c. 0.34 Ma and is responsible for semisterility in translocation heterozygotes and reduces their reproductive fitness. The translocation has differentiated Continental (i.e. China and Russia) populations from Maritime (i.e. Korea and Japan) populations of G. soja and predominately adapted to cold and dry climates. Further analysis revealed that the divergence of G. max from G. soja predates the translocation event and that G. gracilis is an evolutionary intermediate between G. soja and G. max.Our results highlight the effects of a chromosome rearrangement on the processes leading to plant divergence and adaptation, and provides evidence that suggests G. gracilis, rather than G. soja, as the ancestor of cultivated soybean. [ABSTRACT FROM AUTHOR]

The high‐polar compounds from natural products are often used as medicines due to their good bioactivities. However, owing to the complexity and diversity of their structure, the separation of high‐polar compounds is still a challenging work. For this, an efficient method for enrichment and separation of the high‐polar compounds from Saussurea obvallata was developed. First, the target compounds were enriched from the total extract using a solid‐phase extraction method. An offline two‐dimensional liquid chromatography method was used for the separation of pure compounds from the enriched sample. After optimization of chromatographic conditions, high separation selectivity of target compounds was obtained on a polar‐modified C18 column and a HILIC XAmide column. Hence, a two‐dimensional reversed‐phase × hydrophilic interaction liquid chromatography system was constructed and enlarged from the analytical level to the preparative level. In the first dimension, four fractions were obtained on the XCharge C18 column with a recovery rate of 71.2%. In the second‐dimension preparation on the XAmide column, eight high‐polar compounds with more than 96% purity were isolated. All compounds were isolated from Saussurea obvallata for the first time. The results demonstrated that this developed strategy is effective for preparative‐scale isolation of high‐polar compounds from natural products. [ABSTRACT FROM AUTHOR]

In 2006, Schuster introduced the concept of Blaschke-Minkowski homo-morphism of convex bodies. In this paper, we introduce the Lp-mixed Blaschke-Minkowski homomorphism in Lp-Brunn-Minkowski theory. We then further study the Shephard type problem involving an affirmative answer and two negative answers for Lp-mixed Blaschke-Minkowski homomorphism. [ABSTRACT FROM AUTHOR]

This article proposes a method that can be used to improve the differentiation of coal and gangue via image processing and use of a support vector machine (SVM). Images of coal and gangue were converted to grayscale in this approach, the background was segmented, and the contrast was stretched. A basic eigenvalue was then determined based on the contrast between the grayscale mean and the gray-level co-occurrence matrix in each image. The biorthogonal wavelet was then used to expand coal and gangue images based on discrete wavelet transforms in two dimensions (2-D), while the supplementary eigenvalue is comprised of the mean variance of the wavelet coefficient at different scales. The eigenvalue of coal was then contrasted with each gangue eigenvalue, as well as the basic and the supplementary eigenvalue to construct a mathematical recognition model based on image processing and use of a SVM. At the same time, the penalty factor and kernel function coefficient of the mathematical model were optimized using K-fold cross validation. Experimental results indicate that the method proposed in this article can be used to recognize coal and gangue more effectively (at a rate up to 95.12%), compared to the conventional image processing recognition method. [ABSTRACT FROM AUTHOR]

The notion of (p, q)-mixed volume was first introduced by Lutwak, Yang and Zhang in 2018. According to this concept, Li, Wang and Zhou defined the (p, q)- mixed affine surface areas in 2019. In this paper, we establish some inequalities including cyclic inequality, monotonous inequality and product inequality for the (p, q)-mixed affine surface areas. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the L p q-torsional measure for p ∈ ℝ and q > 1 by the L p variational formula for the q-torsional rigidity of convex bodies without smoothness conditions. Moreover, we achieve the existence of solutions to the L p Minkowski problem with respect to the q-torsional rigidity for discrete measures and general measures when 0 < p < 1 and q > 1 . [ABSTRACT FROM AUTHOR]

Flexible pressure sensors play a significant role in wearable devices and electronic skin. Iontronic pressure sensors with high sensitivity, wide measurement range, and high resolution can meet requirements. Based on the significant deformation characteristics of alveoli to improve compressibility, and the ability of the arch to disperse vertical pressure into horizontal thrust to increase contact area, a graded hollow ball arch (GHBA) microstructure is proposed, greatly improving sensitivity. The fabrication of GHBA ingeniously employs a double‐sided structure. One side uses mold casting to create convex structures, while the other utilizes the evaporation of moisture during the curing process to form concave structures. At the same time, a novel side‐by‐side package structure is proposed, ensuring pressure on flexible substrate is maximally transferred to the GHBA microstructure. Within the range of 0.2 Pa–300 kPa, the iontronic pressure sensor achieves a maximum sensitivity of 10 420.8 kPa−1, pressure resolution of 0.1% under the pressure of 100 kPa, and rapid response/recovery time of 40/35 ms. In wearable devices, it is capable of monitoring dumbbell curl exercises and wirelessly correcting sitting positions. In electronic skin, it can non‐contactly detect the location of the wind source and achieve object classification prediction when combined with the CNN model. [ABSTRACT FROM AUTHOR]

Piezo1 is an essential mechanosensitive transduction ion channel in mammals. Its unique structure makes it capable of converting mechanical cues into electrical and biological signals, modulating biological and (patho)physiological processes in a wide variety of cells. There is increasing evidence demonstrating that the piezo1 channel plays a vital role in renal physiology and disease conditions. This review summarizes the current evidence on the structure and properties of Piezo1, gating modulation, and pharmacological characteristics, with special focus on the distribution and (patho)physiological significance of Piezo1 in the kidney, which may provide insights into potential treatment targets for renal diseases involving this ion channel. [ABSTRACT FROM AUTHOR]

The pore-water pressure response during vibratory pile driving considerably affects the piling process and environment. With the applications of high-frequency technology in upgrading the vibratory hammer, the pore-water pressure variation under high-frequency driving conditions becomes more complicated. However, there is a lack of relevant detailed investigations. This paper conducts an in situ experiment with a comprehensive monitoring and measuring system to examine the pore-water pressure response in the complete process of high-frequency vibratory pile driving. The real-time variations of pore-water pressure at the pile-soil interface and surrounding soil are evaluated, respectively. Furthermore, the excess pore-water pressure distributions and evolutions are analyzed in-depth. Then, the mechanism analysis and affected zone range determination are performed based on the development of excess pore-water pressure. The analysis results indicate that vibratory pile driving with high frequency leads to an increase of excess pore-water pressure and consequently soil quasi-liquefaction at the pile-soil interface. The apparent accumulation of excess pore-water pressure concentrates in the local range of soil around the pile tip and decays rapidly in the radial direction. According to the field observations, the increase of the pore-water pressure in the surrounding soil is attributed to the frictional penetration of the pile tip and the frictional vibration of the pile shaft. Furthermore, the maximum radial radii of the fully and partially quasi-liquefied zones are approximately 2.9 and 3.6 times the pile diameter, respectively. [ABSTRACT FROM AUTHOR]

To predict the volume change of stage III NSCLC after radiotherapy with 60 Gy. This retrospective study included two independent cohorts, a train cohort of 192 patients, and a test cohort of 31 patients. We developed a radiomics model based on radiomics features and clinical variables. LIFEx package was used to extract radiomics texture features from CT images. The classification method was logistic regression analysis and feature selection was performed by correlation coefficients. Performance metrics of logistic regression include accuracy, precision, the receiver operating characteristic curves, and recall. The combination features of clinical variables and radiomics can predict the tumor volume change after radiotherapy with 88.7% accuracy (88.6% precision, 88.7% recall, and 88.7% ROC area). Radiomics features combined with medical knowledge have a great potential to predict accurately tumor volume change of stage III NSCLC after radiotherapy with 60 Gy. [ABSTRACT FROM AUTHOR]

Gaussian process (GP) regression is widely used to find the extreme of a black-box function by iteratively approximating an objective function when new evaluation obtained. Such evaluation is usually made by optimising a given acquisition function. However, for non-parametric Bayesian optimisation, the extreme of the objective function is not a deterministic value, but a random variant with distribution. We call such distribution the maximum distribution which is generally non-analytical. To construct such maximum distribution, traditional GP regression method by optimising an acquisition function is computational cost as the GP model has a cubic computation of training data. Moreover, the introduction of acquisition function brings extra hyperparameters which made the optimisation more complicated. Recently, inspired by the idea of Sequential Monte Carlo method and its application in Bayesian optimisation, a Monte Carlo alike method is proposed to approximate the maximum distribution with weighted samples. Alternative to the method on GP model, we construct the maximum distribution within the framework of Student-t process (TP) which considers more uncertainties from the training data. Toy examples and real data experiment show the TP-based Monte Carlo maximum distribution has a competitive performance to the GP-based method. [ABSTRACT FROM AUTHOR]

Accurate determination of ash content in coal is paramount for quality control in the coal industry. However, conventional methods are time-consuming and ill-suited for real-time process control. To address this challenge, our study proposed a novel approach, GFNet, which integrated graph convolution and a feedforward network for ash content estimation. The image was divided into patches and transformed into a graph structure using a k-nearest Neighbor (k-NN) function. We introduced relative position encoding to each node feature to enhance feature representation. The GFNet architecture consisted of a stack of GF (graph convolution and FFN), facilitating feature extraction from the graph representation of image patches. Graph convolution captured intricate spatial relationships and interdependencies among patches, enabling effective information aggregation and updating within the graph. The FFN module, featuring two linear layers, facilitated node feature transformation. We developed pyramid (PGFNet) and isotropic (IGFNet) architectures by stacking GF blocks. Extensive experiments substantiated the superior performance of the proposed approach compared to traditional methods, emphasizing accuracy and efficiency. The GFNet achieved remarkable results, with PGFNet-S attaining a 96.36 accuracy and a mean absolute error (MAE) of 0.145%. IGFNet-Ti demonstrated a higher accuracy of 98.30% with an MAE of 0.093%. Visualization of the graph structure elucidated the model's proficiency in processing graph structure and node features, illustrating its capability to capture content-relevant nodes and gradually connect nodes with similar semantics. This pioneering work presented a transformative approach to ash content estimation in coal quality analysis, offering an invaluable tool for the coal industry's quality control needs. [ABSTRACT FROM AUTHOR]

Online detection of coal and gangue is the key to intelligent separation of coal and gangue which is significant for improving coal mining efficiency and deducing environmental pollution. To improve the detection of coal and gangue under complex conditions, we proposed a cascade network that consists of detector and discriminator. Employing the idea of combining traditional computer vision with deep learning, the multi-channel feature fusion layer was designed in the discriminator. The convolutional neural network (CNN) in the discriminator was optimized from the perspective of loss function and classifier, and a decision function was designed to unify the results of the detector and the discriminator. Finally, we used the visualization to analyze the classification basis during the production by CNN. Our results showed that the cascade network proposed in this study took the characteristics of material truncation as the classification basis, and the detection accuracy reached up to 91.375%. Unlabelled Image • Generalization of CNN is strengthened by combining traditional computer vision. • SVM as classifier improves accuracy of binary classification of coal and gangue. • Cascade improves the performance of coal and gangue detection network. • Difference at truncated surface is the basis of classification coal and gangue. [ABSTRACT FROM AUTHOR]

Flavonoid glycosides exist widely in medicine herbs and often used as nutraceuticals because of their excellent bioactivity and low toxicity. For accurate quality control and bioactivity assessment of Sphaerophysa salsula, a rapid and productive method to isolate flavonoid glycosides is needed. Therefore, this work reports the development of a novel comprehensive strategy based on an online middle‐pressure chromatography and preparative high‐performance liquid chromatography for rapid enrichment and separation of flavonoid glycosides from S. salsula. First, the flavonoid glycosides were enriched using an online middle‐pressure chromatographic column containing stationary middle chromatogram isolated phase. During this process, the high‐volume injection of the extracting solution was realized by an empty precolumn positioned before the main chromatographic tower. Then, the compounds were separated through preparative high‐performance liquid chromatography with Megress C18. As a result, one new flavonol 3‐O‐glycoside (2) and two known flavonol 3‐O‐glycosides (1, 3) were targetedly isolated from S. salsula. The content of compounds 1–3 in S. salsula was 0.09, 0.11, and 0.18 wt%, respectively. Comparing to traditional enrichment and separation methods, our technique offers significantly shorter sample pretreatment time as well as high reproducibility. We believe that our separation method has a strong potential to be used for the processing of other medicinal plants. [ABSTRACT FROM AUTHOR]

In this paper, an alternating iterative weighted least squares method is proposed to handle the off-grid issue in sparsity-based direction of arrival (DOA) estimation for acoustic vector hydrophone (AVH) array. Firstly, the off-grid model via AVH array is formulated by introducing a bias parameter into the signal model. Secondly, the reconstructed interference plus noise covariance matrix is calculated as the weighting term. Then, a novel objective function with respect to the sparse signal and the unknown bias parameter is developed based on weighted least squares. Finally, the closed-form solutions of the sparse signal and the unknown bias parameter are deduced. Simulation results reveal that compared with the state-of-the-art algorithms, the proposed method improves the DOA estimation accuracy in the presence of a coarse sample grid and has a faster convergence speed. Furthermore, the effectiveness and robustness of the proposed method are verified by the underwater experimental results. [ABSTRACT FROM AUTHOR]

Gender inequality has long been an important topic of concern. This article empirically measures whether there exists son preference and eldest son preference in China, from the perspective of an individual's educational attainment, by using the data set of China Family Panel Studies in 2010. We find that (a) sons receive more education than daughters, and that the gender education gap for rural residents is greater than that of residents from urban areas; (b) regardless of the eldest or noneldest sons, the education received by sons is significantly higher than that of daughters, and there is no significant difference between the eldest and noneldest son's education; (c) the gender education gap narrows over time, and expands as the number of sibling increases. Finally, we explore the multiple effect mechanisms. [ABSTRACT FROM AUTHOR]

β-Thalassemia is a genetic disease characterized by the defective synthesis of the hemoglobin tetramer β-globin chains. So far, a number of mutations have been identified and associated with this genetic disease. A high incidence of thalassemia has been found in Guangxi (China). Herein, we report a case of a patient with slightly increased HbA2 levels (4.6%). Based on the clinical data and laboratory findings, the patient was diagnosed with β-thalassemia. Routine genetic screening tests were negative. Sequencing revealed an A deletion at codon 30 (HBB: c.91delA) and the genotype of this patient was βCD30M/β. This mutation changes the splice receptor site of intron 1 from AG to GG, which likely abolishes splicing at the normal 5′ splicing site and may cause β0-thalassemia. Based on hematological and clinical evaluations, this novel mutation was regarded as a β0-thalassemia allele. A homozygosity or compound heterozygosity of this mutation and other β0-thalassemia alleles can lead to severe thalassemia disease. [ABSTRACT FROM AUTHOR]

Fertilized agroecosystems may show considerable leaching of the mobile nitrogen (N) compound NO 3 −, which pollutes groundwater and causes eutrophication of downstream waterbodies. Riparian buffer zones, positioned between terrestrial and aquatic environments, effectively remove NO 3 − and serve as a hotspot for N 2 O emissions. However, microbial processes governing NO 3 − reduction in riparian zones still remain largely unclear. This study explored the underlying mechanisms of various N-loss processes in riparian soil horizons using isotopic tracing techniques, molecular assays, and high-throughput sequencing. Both anaerobic ammonium oxidation (anammox) and denitrification activity were maximized in the riparian fringe rather than in the central zones. Denitrifying anaerobic methane oxidation (damo) process was not detected. Interestingly, both contrasting microbial habitats were separated by a groundwater table, which forms an important biogeochemical interface. Denitrification dominated cumulative N-losses in the upper unsaturated soil, while anammox dominated the lower oxic saturated soil horizons. Archaeal and bacterial ammonium oxidation that couple dissimilatory nitrate reduction to ammonium (DNRA) with a high cell-specific rate promoted anammox even further in oxic subsurface horizons. High-throughput sequencing and network analysis showed that the anammox rate positively correlated with Candidatus 'Kuenenia' (4%), rather than with the dominant Candidatus 'Brocadia'. The contribution to N-loss via anammox increased significantly with the water level, which was accompanied by a significant reduction of N 2 O emission (∼39.3 ± 10.6%) since N-loss by anammox does not cause N 2 O emissions. Hence, water table management in riparian ecotones can be optimized to reduce NO 3 − pollution by shifting from denitrification to the environmentally friendly anammox pathway to mitigate greenhouse gas emissions. Image 1 • Horizontally anammox and denitrification rate maximized in riparian fringe zones. • Denitrification and anammox separately dominate N-loss in upper unsaturated and lower saturated soil horizons. • Anammox contribution increased with water-level rising accompanied with N 2 O reduction. • Optimizing water-table management can reduce NO 3 − moreover mitigating N 2 O emission. • Ammonium oxidation coupling DNRA promote anammox in riparian zones. [ABSTRACT FROM AUTHOR]

Ammonia-oxidizing archaea (AOA) microorganisms have been increasingly found in aquatic and terrestrial environments. These microorganisms make vital contributions to ammonia oxidation in such systems. However, their community succession characteristics in man-made wetland ecosystems have scarcely been reported. We assessed the AOA's spatiotemporal shifts in the sediments of a constructed wetland (CW) — the Shijiuyang constructed wetland (SJY-CW) — in China from the third year (2011) to the fifth year (2013) of the CW operation. The SJY-CW is composed of a pretreatment pond, a multiple plant-bed/ditch system, and a post-treatment pond. Results showed that AOA abundance in the pre- and post-treatment ponds remained invariant through 2011–2012 and decreased in 2013, while the abundance in the plant-bed/ditch system decreased gradually with wetland operation. The AOA abundance in 2013 was one order of magnitude lower than that through 2011–2012, and the AOA abundance in the plant-bed/ditch system was generally higher than that in the pre- and post-treatment ponds from 2011 to 2013. AOA diversity showed little temporal differentiation with a slightly decreasing trend for community richness index Chao1 and diversity index Shannon H' from 2011 to 2013. The AOA community was dominated by the Nitrososphaera cluster accompanied by an increasing Nitrosopumilus cluster and Nitrososphaera sister cluster within the wetland operation. Hierarchical clustering and redundancy analysis verified the horizontal shifts of AOA communities. The shifts occurred preferentially in the central plant-bed/ditch system. The operational duration of the wetland became a key factor influencing AOA abundance and community shift in SJY-CW sediments. Unlabelled Image • AOA community dynamics in a constructed wetland were tested. • Abundance and diversity of AOA decreased during restoration. • AOA genes horizontally shifted in the different cells of wetland. • AOA shifts mainly occurred in the plant-bed/ditch system. • Succession time dominated the spatiotemporal differentiation of AOA. [ABSTRACT FROM AUTHOR]

• An integrated model for investigating the train-induced cumulative deformation of subgrade is established. • The subgrade deformation is regarded as the time-varying excitation of train-track-subgrade interaction system. • The time-varying dynamic response of train-track-subgrade interaction system is described by this integrated model. This work focuses on two research projects. The first project deals with the prediction of the train-induced cumulative deformation of the high-speed railway subgrade based on the train-track-subgrade dynamic model, and the second project deals with the long-term performance of the train-track-subgrade interaction system subject to the permanent deformation of the subgrade. The train-track-subgrade interaction is established as the model fundament, and the empirical model of soil cumulative deformation prediction is introduced to realize the joint analysis of the train-induced cumulative deformation of the subgrade and its effect on the performance of the time-varying dynamic system. From the numerical results, the train-induced cumulative deformation of subgrade shows a fast and then slow trend, and the deformation has periodic distribution in the longitudinal direction. Although the cumulative deformation of the subgrade for the ballastless track is small compared with that of ballasted track, its effect on the vertical response of the train-track-subgrade interaction system is obvious. With the development of cumulative deformation of subgrade, the vibrations of the car body are excited in the frequency band of 0∼100 Hz. Furthermore, the maximum car body vertical accelerations, the wheel-rail vertical forces, and the vertical accelerations of track and subgrade increase by about 34∼48.4%, 125 %, 400 %, and 66.7 %, respectively. [ABSTRACT FROM AUTHOR]

Enhanced Loran (eLoran) is the most important backup system for Global Navigation Satellite System (GNSS). However, most current eLoran demodulation methods are based on digital signal processing, which may produce large errors under the influence of strong interference or noise. The authors propose an eLoran signal message prediction algorithm based on deep learning. Using time‐frequency (TF) analysis convert one‐dimensional signal into two‐dimensional high‐resolution image. It improves Alexnet by adjusting the convolutional layer structure, adding an attention module, and reducing the pooling layer window to construct the Alexnet‐ECA network. The network is used to identify TF image features and predict signal message. The experimental results show that novel method for eLoran signal message prediction based on Alexnet‐ECA can achieve high accuracy and robustness in various SNR scenarios. The accuracy is more than 90% at SNR greater than 10db and not less than 80% even in the complex environments. Compared with Resnet34 and VGG16 networks, Alexnet‐ECA demonstrates its efficiency and effectiveness using real measurement data. This method can enhance the performance and reliability of eLoran receivers and applications. [ABSTRACT FROM AUTHOR]