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To systematically analyze the damage caused by bedrock and overburden layer slope under seismic action, a set of large-scale shaking table test was designed and completed. Interpolation of the acceleration amplification coefficient, Hilbert–Huang transform and transfer function was adopted. The damage mechanisms of the bedrock and overburden layer slopes under seismic action are systematically summarized in terms of slope displacement, acceleration field, vibration amplitude, energy, vibration frequency, and damage level. The results show a significant acceleration amplification effect within the slope under seismic action and a localized amplification effect at the top and trailing edges of the slope. With an increase in the input seismic intensity, the difference in the vibration amplitude between the overburden layer and bedrock increased, low-frequency energy of the overburden layer was higher than that of the bedrock, and the vibration frequency of the overburden layer was smaller than that of the bedrock. These differences cause the interface to experience cyclic loading continuously, resulting in the damage degree of the overburden layer at the interface being larger than that of the bedrock, reduction of the shear strength, and eventual formation of landslides. The displacement in the middle of the overburden is always greater than that at the top. Therefore, under the action of an earthquake and gravity, the damage mode of the bedrock and overburden layer slope is such that the leading edge of the critical part pulls and slides at the trailing edge, and multiple tensile cracks are formed on the slope surface. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the well-posedness of stochastic variational inequalities on ℝd with discontinuous drift coefficients. First, a case that drift coefficients are only integrable on (0,T) × ℝd is considered. Utilizing Girsanov’s theorem, Zvonkin’s transform and Yamada–Watanabe’s principle, we derive the weak well-posedness if d > 1 and strong well-posedness if d = 1. Second, if the drift coefficients are piecewise Lipschitz continuous and d = 1, strong well-posedness is also obtained by virtue of a transform which regularizes the discontinuous drifts. [ABSTRACT FROM AUTHOR]

Polyaniline (PANi) has high theoretical specific capacitance by storing energy through redox reactions and thus is widely used in supercapacitors. However, the skeleton of PANi expands and contracts during the process of ion inhalation and exfoliation, which leads to a decrease in electrochemical performance. To solve this problem, we graft the sulfonic acid group onto the PANi backbone via a borate ester bond, which serves as a proton reservoir to provide protons for PANi to undergo redox reactions and provide pseudocapacitance. SPANi is then polymerized in situ on graphene oxide (GO), and its comprehensive performance of the electrode is further enhanced by designing ordered structures and synergistic interactions. The SPANi 5 /GO 5 composite that are created in-situ have a fantastic specific capacitance of 460.56 F g−1 at 1 A g−1 and maintained 85.19% of the primary capacitance after 10,000 cycles. The device is assembled with PAAM/H 2 SO 4 /GO as the gel electrolyte, which achieves a specific capacitance of 137.75 F g−1, and the capacitance is retains at 90.74% after 10,000 cycles. The supercapacitor exhibits a large energy density of 13.76 Wh kg−1 when the power density is 200.27 W kg−1. The present study presents a novel and efficient approach for the fabrication of high-performance supercapacitor electrodes. [Display omitted] • Grafting the sulfonic acid group onto the PANi backbone via borate ester bond. • The in-situ polymerization of SPANi on GO formed a composite electrode. • SPANi 5 /GO 5 composite electrode with specific capacitance up to 460.56 F g−1. • SPANi/GO as electrode and PAAM/H 2 SO 4 /GO as gel electrolyte form a supercapacitor. • The device has a capacitance retention rate of 90.74% after 10,000 cycles. [ABSTRACT FROM AUTHOR]

Aims Design Methods Results Conclusion Impact Reporting Method Patient or Public Contribution This study aims to investigate the mediating role of regulatory emotional self‐efficacy and self‐compassion in the relationship among anxiety, depression, body image distress and subjective well‐being among women with polycystic ovary syndrome.A cross‐sectional study.The study recruited 510 women with polycystic ovary syndrome from a tertiary hospital affiliated with a university in Hunan Province, China. The study employed several tools to collect data, including the Generalized Anxiety Scale, the Patient Health Questionnaire‐9, the Body Image States Scale, the Self‐Compassion Scale, the Regulatory Emotional Self‐Efficacy Scale and the Index of Well‐being questionnaire. Data analysis was carried out using descriptive analysis, spearman correlation analysis, ordinary least squares regression and bootstrapping.The study's findings indicate that regulatory emotional self‐efficacy and self‐compassion both act as mediators in the connection between anxiety, depression, body image distress and subjective well‐being among women with polycystic ovary syndrome.The study emphasizes the significance of regulatory emotional self‐efficacy and self‐compassion in promoting well‐being among women with polycystic ovary syndrome. It also implies that interventions targeted at enhancing these factors could potentially enhance the subjective well‐being of women affected by PCOS.Our study's primary contribution is to underscore the crucial mediating roles of regulatory emotional self‐efficacy and self‐compassion in the relationship among anxiety, depression, body image distress and subjective well‐being. Our study indicates that clinical practitioners should prioritize improving the regulatory emotional self‐efficacy and self‐compassion of women with polycystic ovary syndrome, reducing their anxiety, depression and body image distress and improving their subjective well‐being.This study was reported according to the Standards for Reporting Qualitative Research (SRQR).No patient or public contribution outside of participation in the actual study for purposes of data collection. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to evaluate the effects of a new treatment—conditioned medium from human orbital adipose-derived stem cells (OASC-CM)—on corneal recovery after alkali burns in a rabbit model. Methods: The corneal alkali burn rabbit model was established and treated with OASC-CM, conditioned medium from human abdominal subcutaneous adipose-derived stem cells (ABASC-CM), and fresh control culture medium (con-CM) three times a day for 7 days, respectively. Subsequently, the treatment effects were evaluated and compared through clinical, histological, immunohistochemical, and cytokine evaluations. Results: Clinically, OASC-CM alleviated corneal opacity and edema and promoted recovery of corneal epithelium defect. Histologically and immunohistochemically, OASC-CM inhibited neovascularization, conjunctivalization, and immuno-inflammatory reaction, while promoting corneal regeneration and rearrangement. Increased secretion of interleukin-10 and inhibited protein levels of cluster of differentiation 45, interferon-γ, and tumor necrosis factor-α were observed in the alkali-burned cornea after OASC-CM treatment, which might be the relevant molecular mechanism. Conclusions: OASC-CM showed significant effects on the recovery of rabbit corneal alkali burns and eliminated immunological and ethical limitations, representing a new option for corneal wound treatment. [ABSTRACT FROM AUTHOR]

This study investigates the representation of ocean convection in the Labrador Sea in seven Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 5 and 6 datasets. The relative role of the oceanic and atmospheric biases in the subpolar North Atlantic gyre are explored using regional ocean simulations where the atmospheric forcing or the ocean initial and boundary conditions are replaced by reanalysis data in the absence of interactive air-sea coupling. Commonalities and differences among model behaviors are discussed with the objective of finding a pathway forward to improve the representation of the ocean mean state and variability in a region of fundamental importance for climate variability and change. Results highlight that an improved representation of ocean stratification in the North Atlantic subpolar gyre is urgently needed to constrain future climate change projections. While improving the ocean model resolution in the North Atlantic alone may contribute a better representation of both boundary currents and propagation of heat and freshwater anomalies into the Labrador Sea, it may not be sufficient. Addressing the atmospheric heat flux bias with better resolution in the atmosphere and land topography may allow for deep convection to occur in the Labrador Sea in some of the models that miss it entirely, but the greatest priority remains improving the representation of ocean stratification. [ABSTRACT FROM AUTHOR]

Simple Summary: Procambarus clarkii is typically viewed as a predator rather than prey, due to its strong anti-predatory capabilities. However, during its juvenile stage, it remains vulnerable to predators. Nonetheless, complex outdoor habitats can offer some protection. By examining the effects of three different types of shelters with various coverages and combinations on the predation rhythm, behavior, and efficiency of Silurus asotus on juvenile Procambarus clarkii, we found that the presence and complexity of shelters in the environment have a limited impact on the predatory activities of Silurus asotus. Among the shelter types, PVC pipes offered the best protection, followed by stone, with water grass providing the least protection. Furthermore, the protective effect of shelters increased with coverage rate. Therefore, we suggest that the type and abundance of shelters significantly influence the predation rhythm and behavior of Silurus asotus on Procambarus clarkii in outdoor environments. Procambarus clarkii is adept at using natural shelters and caves to evade attacks from predators. However, the concealment abilities and mechanisms of P. clarkii for different types of shelters under predation pressure have not yet been reported. In this study, laboratory experiments were carried out to determine the effects of different coverages (25%, 50%, and 75%) and different combinations (I–VII) of three types of shelters (PVC pipes, water grass, and stone) on the predation rhythm, behavior, and abilities of Silurus asotus on P. clarkii. The results indicated that the predation of S. asotus on P. clarkii exhibited significant rhythmicity under shelter conditions, excluding PVC pipes, 75% stone, and combination VI. Among the three types of shelters, PVC pipes provided the strongest concealment, followed by stone and water grass. With the increase in shelter coverage, the anti-predation ability of P. clarkii continued to increase, and the optimal shade rate for water grass was 50%. In the different shelter combinations, the environmental complexity had little effect on the predation activity of S. asotus on P. clarkii. These findings demonstrated that the type and abundance of shelters in the wild environment can affect the predation rhythm and activities of S. asotus on P. clarkii. [ABSTRACT FROM AUTHOR]

In the field of robotic automation, achieving high position accuracy in robotic vision systems (RVSs) is a pivotal challenge that directly impacts the efficiency and effectiveness of industrial applications. This study introduces a comprehensive modeling approach that integrates kinematic and joint compliance factors to significantly enhance the position accuracy of a system. In the first place, we develop a unified kinematic model that effectively reduces the complexity and error accumulation associated with the calibration of robotic systems. At the heart of our approach is the formulation of a joint compliance model that meticulously accounts for the intricacies of the joint connector, the external load, and the self-weight of robotic links. By employing a novel 3D rotary laser sensor for precise error measurement and model calibration, our method offers a streamlined and efficient solution for the accurate integration of vision systems into robotic operations. The efficacy of our proposed models is validated through experiments conducted on a FANUC LR Mate 200iD robot, showcasing notable improvements in the position accuracy of robotic vision system. Our findings contribute a framework for the calibration and error compensation of RVS, holding significant potential for advancements in automated tasks requiring high precision. [ABSTRACT FROM AUTHOR]

Optical processing technology can improve the accuracy and efficiency of image processing. Visual packaging art design simulation is to quickly generate realistic packaging effects in the early stage of packaging design, so as to make better design decisions. Traditional simulation methods often require a lot of time and human resources, and the results lack realism and detail. Machine vision technology based on optical processing can make full use of image processing algorithms and computer vision technology to achieve efficient and accurate packaging design simulation. In this paper, a machine vision technology framework based on image optical processing is proposed. In the image acquisition stage, high-resolution cameras are used to acquire real packaging images, and in the optical processing stage, algorithms are used to enhance, reduce noise and correct images to improve image quality and clarity. In the image analysis stage, machine learning algorithms are used to extract features and detect and identify packaging elements, providing data support for subsequent simulation rendering. The results show that the machine vision technology based on image optical processing can generate high quality and realistic simulation effect of packaging design. Compared with traditional methods, this technique has achieved significant improvements in terms of time cost and design quality. [ABSTRACT FROM AUTHOR]

Cold stress is a serious threat to global crop production and food security, but plant cold resistance is accompanied by reductions in growth and yield. In this study, we determined that the novel gene BcGSTF10 in non-heading Chinese cabbage [NHCC; Brassica campestris (syn. Brassica rapa) ssp. chinensis ] is implicated in resistance to cold stress. Biochemical and genetic analyses demonstrated that BcGSTF10 interacts with BcICE1 to induce C-REPEAT BINDING FACTOR (CBF) genes that enhance freezing tolerance in NHCC and in Arabidopsis. However, BcCBF2 represses BcGSTF10 and the latter promotes growth in NHCC and Arabidopsis. This dual function of BcGSTF10 indicates its pivotal role in balancing cold stress and growth, and this important understanding has the potential to inform the future development of strategies to breed crops that are both climate-resilient and high-yielding. [ABSTRACT FROM AUTHOR]

Anxiety and depressive symptoms are prevalent in women with polycystic ovary syndrome (PCOS), and there is a great need for culturally relevant interventions to improve anxiety and depressive symptoms in women with PCOS. The current study aimed to analyze the factors influencing body-image satisfaction, anxiety, and depressive symptoms in Chinese women with PCOS. It also aimed to clarify the association between body-image satisfaction and anxiety and depressive symptoms in women with PCOS, and whether this association was mediated by cognitive reappraisal and expressive suppression. A total of 477 women with PCOS were recruited from the Department of Gynecology and Reproductive Medicine Center of one university-affiliated tertiary hospital in China. They completed the Generalized Anxiety Disorder-7 scale, the Patient Health Questionnaire-9, the Body Image States Scale, and the Emotion Regulation Questionnaire. The study found body-image satisfaction showed significant associations with anxiety and depressive symptoms. This association was mediated by cognitive reappraisal and expressive suppression. The findings of this study offer new insights into the relationship between body image and anxiety and depressive symptoms in women with PCOS. They also emphasize the potential value of promoting body-image satisfaction and enhancing cognitive reappraisal abilities to improve the mental health of women with PCOS. [ABSTRACT FROM AUTHOR]

When photographing objects underwater, it is important to utilize an optical window to isolate the imaging device from the water. The properties of the entire imaging system will change, and the imaging quality will decrease due to the refraction impact of the water and the window. The theoretical calculation method for air imaging is no longer relevant in this context. To analyze the unique rule, this research derives the formulas for key parameters of underwater imaging systems under paraxial circumstances. First, the optical window is modeled, then the formula for the optical window's focal length in the underwater environment is derived, and the change rule for the focal length of various window forms underwater is condensed. For the ideal imaging system using a domed optical window, the equivalent two-optical group model of the imaging system is established, and the formula for calculating the focal length, working distance, and depth of field of the underwater imaging system is derived through paraxial ray tracing. The accuracy of the formula is verified through the comparative analysis of the formula calculation results and the Zemax modeling simulation results. It provides an important theoretical basis for the in-depth study of underwater imaging technology. [ABSTRACT FROM AUTHOR]

Objective To investigate the factors affecting the number of lymph nodes obtained by unilateral lobectomy and isthmus combined with prophylactic ipsilateral central lymph node dissection for papillary thyroid cancer, and to analyze the pattern of change so as to provide an important basis for clinical treatment of papillary thyroid cancer. Methods Retrospective analysis was performed on the clinical and pathological data of 193 patients admitted to the department of Thyroid Surgery of the First Affiliated Hospital of Jinzhou Medical University from January 2019 to January 2022 for papillary thyroid cancer who underwent unilateral glandular lobe and isthmus resection combined with prophylactic ipsilateral central lymph node dissection. The number of obtained lymph nodes was divided into a group with high number of obtained lymph nodes (n > 6) and a group with low number of obtained lymph node((n ^ 6). Univariate analysis and multivariate analysis were used to explore the factors affecting the number of obtained lymph nodes. The influence of the number of lymph nodes on the central lymph node metastasis was evaluated by correlation analysis, and the influence of the number of lymph nodes on the quality of life of patients by postoperative complications analysis. Results The number of lymph nodes obtained was positively correlated with the number of lymph node metastases in the central region (r = 0.240, P < 0.05) and the rate of lymph node metastasis (r = 0.161,P < 0.05), respectively. The number of lymph node metastases controlled remained unchanged,and the rate of lymph node metastasis decreased with the increase of the number of obtained lymph nodes (r = -0.444, P < 0.05). Univariate analysis of the number of lymph nodes between the two groups showed no significant differences in terms of sex, age, operation duration, BMI, maximum tumor diameter, multifocality, Hashimoto's thyroiditis, focal location, capsular invasion, contralateral thyroid nodule, parathyroid transplantation and BRAF gene V600E mutation (P > 0.05). There was statistical significance in the application of nano carbon between the two groups (P < 0.05). Logistic binary regression analysis showed that the combination of Hashimoto's thyroiditis and the application of carbon nanoparticles were independent influencing factors for the high number of lymph nodes obtained (P < 0.05). ROC curve analysis suggeste- 0.735, P < 0.05), and the area under the curve occupied by Hashimoto's thyroiditis was 0.584 (95%C7: 0.504 - 0.665, P < 0.05 ). In addition, there was no significant difference in the incidence of vocal cord paralysis and lymphatic leakage between the two groups (P > 0.05). Conclusions When the number of lymph nodes in the central region remained unchanged, the higher the number of lymph nodes obtained, the lower the rate of lymph node metastasis. The application of nano-carbon and patients with Hashimoto's thyroiditis could increase the number of lymph nodes obtained, and there is no significant difference in the incidence of postoperative complications between high and low number of lymph nodes obtained. [ABSTRACT FROM AUTHOR]

Simple Summary: The objective of this study was to investigate the correlation between juvenile Acipenser sinensis density and the eDNA content in a controlled laboratory aquarium environment. The results obtained from fluorescent quantitative PCR indicated an overall increasing trend in the eDNA content, which tended to stabilize after three days. Furthermore, a significant positive linear correlation was observed between the density of juvenile A. sinensis and eDNA content at different time points (R2 = 0.768~0.986). When the juvenile A. sinensis were removed at a density of 0.114 ind./L, there was a negative correlation between eDNA content and time. Based on AIC and BIC analyses, a power function model was employed to illustrate the degradation relationship between eDNA content and time. Since the construction of the Gezhouba Dam, Chinese sturgeon (Acipenser sinensis) numbers have gradually declined, rendering this species critically endangered according to the International Union for the Conservation of Nature. Environmental DNA (eDNA) technology plays an important role in monitoring the abundance of aquatic organisms. Species density and biomass have been proven to be estimable by researchers, but the level of accuracy depends on the specific species and ecosystem. In this study, juvenile A. sinensis, an endangered fish, were selected as the research target. Under controlled laboratory conditions in an aquarium, one, two, four, six, and eight juvenile A. sinensis were cultured in five fish tanks, respectively. Water samples were filtered at eight different time points for eDNA content analysis. Additionally, eDNA yield was tested at six different time points after a 0.114 ind./L density of A. sinensis was removed, and the employed degradation model was screened using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). The results showed that eDNA content remained stable after 3 days and exhibited a significant positive linear correlation with the density of A. sinensis (R2 = 0.768~0.986). Furthermore, eDNA content was negatively correlated with the 3-day period after the removal of A. sinensis. The power function had the smallest AIC and BIC values, indicating better fitting performance. This study lays a momentous foundation for the application of eDNA for monitoring juvenile A. sinensis in the Yangtze Estuary and reveals the applicability of eDNA as a useful tool for assessing fish density/biomass in natural environments. [ABSTRACT FROM AUTHOR]

An oxidant‐free photocatalytic [2+2+m] cyclization of 2‐cyanoaryl acrylamides with 2‐bromocarbonyls is reported, enabling the assembly of biologically important N‐heterocycle‐fused quinolinones including benzo[1,6]naphthyridinones and pyrrolo[3,2‐c]‐quinolines. This photocatalytic protocol proceeds through a radical relay pathway including alkene difunctionalization along with an intramolecular cyano insertion and subsequent site‐specific functionalization of inert C(sp3)−H bond enabled by cyano‐derived iminyl radical‐mediated 1,n‐hydrogen atom transfer. Notably, this transformation selectively formed two distinct C−C bonds, one C−N bond, and one quaternary carbon center in a one‐pot procedure. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the experiences, emotional coping strategies, and help-seeking needs of women with PCOS from their perspective, considering common psychological issues such as stress, anxiety, and depression that are prevalent among individuals with PCOS. Materials and Methods: The study recruited 14 women with PCOS for semi-structured interviews between October and November 2022, using a descriptive phenomenology method design. The interviews were analyzed using NVivo 12 software. Results: Four themes and eleven subthemes were derived from the semi-structured interviews: (1) Negative Mental Health Status; (2) Four Patterns of Emotion Regulation; (3) The Psychological Double-Edged Sword: Family Social Network; (4) Strong Demands for Psychological Counseling and Lifestyle Guidance. Conclusion: The study suggests that interventions should focus on fostering internalized self-efficacy and emotional expression, promoting constructive familial support, and providing psychological counseling and lifestyle recommendations to alleviate psychological distress experienced by women with PCOS. [ABSTRACT FROM AUTHOR]

The ocean circulation is modulated by meandering currents and eddies. Forecasting their evolution is a key target of operational models, but their forecast skill remains limited. We propose a machine learning approach that improves the output of an ocean circulation model by learning and predicting its systematic biases. This method can be applied a priori to any region, and is tested in the Gulf of Mexico, where the Loop Current (LC) and the large anticyclonic eddies that detach from it are major forecasting targets. The LC dynamics are recurrent and lie on a low‐dimensional dynamical attractor. Building upon the information gained analyzing this low dimensional attractor, we improve the representation of sea surface anomalies in model outputs through information from satellite altimeter data using a Sequence‐to‐Sequence model, which is a special class of Recurrent Neural Network. Building upon the HYCOM‐NCODA analysis system, we deliver a correction to the forecast at the observation resolution. For at least 15 days the proposed method learns to forecast the systematic bias in the HYCOM‐NCODA, outperforming persistence, and improving the forecast. This data‐driven approach is fast and can be implemented as an added step to any dynamical hindcasting or forecasting model. It offers an interesting avenue for further developing hybrid modeling tools. In these tools, fundamental physical conservations are preserved through the integration of partial differential equations which obey them. In addition, the method highlights specific deficiencies of the hindcast system that deserve further investigation in the future. Plain Language Summary: Predicting the evolution of ocean circulation using a physically constrained models is critical for weather forecasting and for quantifying nutrient transport and water mass exchanges, but models are far from perfect. We propose a generalized data‐driven method to improve a circulation model performance by identifying and predicting its systematic biases, and we test it in the Gulf of Mexico. We use a recurrent neural network to improve and predict the evolution of sea surface anomalies over 7–15 days using information obtained from satellite altimetry data. The results show that our proposed data‐driven method is fast and accurate and can be easily implemented as an additional step to other dynamic models, providing an interesting avenue for further development of hybrid forecasting tools. Key Points: Machine learning tools are used to improve model predictive skill using observationally based data setsOur method has great potential to extend the current prediction length from about 7 days to 11–15 days without significantly compromising accuracy [ABSTRACT FROM AUTHOR]

Eucalyptus plantation forests in southern China provide not only the economic value of producing timber, but also the ecological value service of absorbing carbon dioxide and releasing oxygen. Based on the theory of spatial colonial modeling, this paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus monocotyledons, which consists of the main steps of tree branch and trunk separation, skeleton extraction and optimization, 3D reconstruction, and carbon stock calculation. The main trunk and branches of the tree point clouds are separated using a layer-by-layer judgment and clustering method, which avoids errors in judgment caused by sagging branches. By optimizing and adjusting the skeleton to remove small redundant branches, the near-parallel branches belonging to the same tree branch are fused. The missing parts of the skeleton point clouds were complemented using the cardinal curve interpolation algorithm, and finally a real 3D structural model was generated based on the complemented and smoothed tree skeleton expansion. The bidirectional Hausdoff distance, average Hausdoff distance, and F distance were used as evaluation indexes, which were reduced by 0.7453 m, 0.0028 m, and 0.0011 m, respectively, and the improved spatial colonization algorithm enhanced the accuracy of the reconstructed tree 3D structural model. To verify the accuracy of our method to determine the carbon stock and its related parameters, we cut down 41 eucalyptus trees and destructively sampled the measurement data as reference values. The R2 of the linear fit between the reconstructed single-tree aboveground carbon stock estimates and the reference values was 0.96 with a CV(RMSE) of 16.23%, the R2 of the linear fit between the trunk volume estimates and the reference values was 0.94 with a CV(RMSE) of 19.00%, and the R2 of the linear fit between the branch volume estimates and the reference values was 0.95 with a CV(RMSE) of 38.84%. In this paper, a new method for reconstructing eucalyptus carbon stocks based on TLS point clouds is proposed, which can provide decision support for forest management and administration, forest carbon sink trading, and emission reduction policy formulation. [ABSTRACT FROM AUTHOR]

This cross-sectional study aimed to identify latent mental health subgroups of reproductive-aged women with polycystic ovary syndrome (PCOS) and examine differences in emotion regulation and social support. A total of 477 samples (age = 27.22 ± 5.37) diagnosed with PCOS were included in this study, all of whom were of the same ethnicity. Latent profile analysis (LPA) was conducted in Mplus version 8.3 to identify mental health latent profiles in women with PCOS based on the dual-factor approach. The current study identified three distinct mental health profiles within women with PCOS: High-Symptom and High-Well-being Profile, Low-Symptom and High-Well-being Profile, and High-Symptom and Low-Well-being Profile, with group proportions of 52.3%, 35.7%, and 11.1%, respectively. This study revealed that cognitive reappraisal and social support are important protective factors for the mental health of women with PCOS, whereas expressive suppression is a negative factor for their mental health. [ABSTRACT FROM AUTHOR]

In this paper, the linear low-density polyethylene (LLDPE) fibers were used as the precursors to prepare low-cost carbon fibers (CFs) via ultraviolet crosslinking-vulcanized stabilization-carbonization processing. The spinning particles were obtained by mixing linear low-density polyethylene with photoinitiator 4-chlorobenzophenone (4-CBP), crosslinkers triallyl isocyanurate (TAIC), and thermal stabilizer antioxidant 1010, and then the ultraviolet (UV) photosensitive LLDPE fibers were fabricated by traditional melt spinning. Then, the LLDPE primary fibers were treated by UV irradiation, vulcanized stabilization and carbonization to obtain the low-cost CFs. to characterize the structure and properties of ultraviolet crosslinked polyethylene (UV-XLPE) fibers and CFs, the Fourier transform infrared spectrometer, differential scanning calorimeter, thermogravimetric analyzer, X-ray diffractometer and scanning electron microscope were adopted. The results showed that, UV irradiation introduced a crosslinked structure of LLDPE fibers, and the degree of gelation of XLPE fibers reached a maximum of 48.6% when UV irradiation was carried out for 15 min; with the extension of UV irradiation time, the melting temperature and crystallization temperature of UV-XLPE fibers move to the direction of low temperature, and the crystal size of the fiber decreases; after vulcanization, the carbon yield of the fiber is significantly improved, reaching up to 62.9%, and the surface and section of CFs are dense. [ABSTRACT FROM AUTHOR]

This review aimed to examine the effects of mindfulness-based interventions on physical and psychological outcomes in women with infertility. Studies were included if participants were women diagnosed with infertility, were over the age of 18, and the design was a randomized controlled trial of a mindfulness-based intervention program. Two independent reviewers undertook eligibility screening, data extraction, and methodological quality assessment. We used RevMan software version 5.4 (The Cochrane Collaboration) to conduct the meta-analyses. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was followed. Ten articles were finally included in the current meta-analysis. The results of the meta-analysis revealed large, significant effects of mindfulness-based interventions that can effectively reduce depression[SMD = −1.28, 95% CI (−1.95, −0.60), P < 0.0001], anxiety [SMD = −0.89, 95% CI (−1.26, −0.51), P < 0.00001] symptoms, and improve five domains of health-related quality of life (physical function [MD = 9.47, 95% CI (4.33, 14.61), P = 0.0003], general health [MD = 15.77, 95% CI (7.62, 23.92), P = 0.0002], vitality [MD = 14.85, 95% CI (4.95, 24.74), P = 0.003], role-physical [MD = 22.44, 95% CI (14.97, 29.91), P < 0.00001] and social function [MD = 8.27, 95% CI (3.56, 12.97), P = 0.0006)] in women with infertility. The current meta-analysis results revealed that mindfulness-based interventions could effectively reduce depression and anxiety symptoms and improve health-related quality of life in women with infertility. Future rigorously designed, high-quality research is required to demonstrate whether mindfulness-based intervention programs can effectively reduce perceived stress and BMI. [ABSTRACT FROM AUTHOR]

Organic semiconductor nanocrystals are important building blocks for miniaturized room‐temperature optoelectronic and excitonic devices, benefitting from their large oscillator strength, high quantum yield, and large exciton binding energy. Specifically, 1D organic semiconductor nanowires have been widely studied with promising applications such as nanoscale light sources and optical waveguides. Although both amplified spontaneous emission and lasing have been well demonstrated in organic semiconductor nanowires under pulsed laser excitation, amplification of guided photons during propagation along the nanowire has been rarely investigated. Here, a highly polarized luminescent organic semiconductor nanowire with self‐amplified photon guiding capability under continuous‐wave laser excitation is reported. The nanowire waveguide itself functions as a gain medium to provide optical gain during its photoluminescence propagation, with the value of gain depending on temperature and excitation laser power. This research reveals the optical gain effect in the organic semiconductor waveguide system, which paves the way for on‐chip energy‐efficient photonic communications. [ABSTRACT FROM AUTHOR]

Few-shot learning is a challenging task because it focuses on classifying new object categories given only limited labeled samples and often results in poor generalization. Most of existing methods based on metric learning are not very simple for the networks. Moreover, these methods cannot solve low-data problem and are unable to extract discriminative features. This paper presents a simple, effective and general framework for few-shot image classification. Specifically, we first exploit data augmentation technique to alleviate overfitting problem. We propose a self-attention position attention module (PAM) which is utilized to extract discriminative features for constructing a few-shot representation model. Furthermore, we design a novel nearest-neighbor learner with feature transformation to obtain the appealing accuracy in few-shot learning (FSL). Our network is comprised of backbone and attention module and trained from scratch in an end-to-end manner. The backbone module is to extract multi-level features. The self-attention PAM is used to discover non-local information and allow long-range dependency. Excellent performance on benchmark demonstrates that our work provides a unified and effective approach for few-shot image classification. [ABSTRACT FROM AUTHOR]

Manganese-based mullite-type and perovskite-type mixed oxides were prepared by sol–gel method, and then palladium was loaded onto the mixed oxides by wet impregnation method. The catalytic performance for methane combustion, the influence of the supports, and the interaction between noble metal and the mixed oxides were investigated via experiments and theoretical calculations. The Pd/SmMn2O5 catalyst exhibited superior low-temperature catalytic activity for methane combustion (T50 = 328 °C), which was superior to the Pd/perovskites catalysts and even better than Pd/Al2O3 catalyst. It was ascribed to the abundant labile surface lattice oxygen species with high mobility and the uniformly dispersed Pd species on SmMn2O5, as revealed by TEM, CO-chemisorption, H2-TPR and XPS. DFT calculations verified the significant metal-support interaction via electron transfer between Pd and SmMn2O5, which further activated the lattice oxygen and improved the catalytic performance. It demonstrates the prospect of utilizing SmMn2O5 mullite to optimize the typical noble metal catalysts. [ABSTRACT FROM AUTHOR]

During the manual grinding of blades, the workers can estimate the material removal rate based on their experiences from observing the characteristics of the grinding sparks, leading to low grinding accuracy and low efficiency and affecting the processing quality of the blades. As an alternative to the recognition of spark images by the human eye, we used the deep learning algorithm YOLO5 to perform target detection on spark images and obtain spark image regions. First the spark images generated during one turbine blade-grinding process were collected, and some of the images were selected as training samples, with the remaining images used as test samples, which were labelled with LabelImg. Afterwards, the selected images were trained with YOLO5 to obtain an optimisation model. In the end, the trained optimisation model was used to predict the images of the test set. The proposed method was able to detect spark image regions quickly and accurately, with an average accuracy of 0.995. YOLO4 was also used to train and predict spark images, and the two methods were compared. Our findings show that YOLO5 is faster and more accurate than the YOLO4 target detection algorithm and can replace manual observation, laying a specific foundation for the automatic segmentation of spark images and the study of the relationship between the material removal rate and spark images at a later stage, which has some practical value. [ABSTRACT FROM AUTHOR]

Double-plane grinding plays an important role in the processing of high-precision flat parts. A double-side autonomous grinding (DSAG) method was proposed and investigated to improve the uniformity of the abrasive grains of the grinding disc on the surface of the workpiece by releasing the constraints on the workpiece. In DSAG, the main driving force of workpiece movement comes from the friction force generated by the relative movement of the upper and lower grinding pads. Experiment of dynamic friction coefficient measurement between workpiece and grinding plates is carried out to take into account the differences of dynamic friction factor under different speed and force conditions. A mathematical model of the relative motion trajectory between the particles on the upper and lower plate and the workpiece is established based on kinematics and dynamics theories. Then, the effects of the linkage mode of the lower plate, grinding pressure, and speed ratio of the upper and lower plates on the frequency and direction uniformity of the grinding trajectory of the abrasive particles on the workpiece surface are simulated and analyzed. Aiming at the trajectory uniformity, the grinding parameters are optimized through orthogonal experiment and verified on the independently developed experimental platform. The flatness and roughness of the workpiece are detected. The results show that the DSAG performs well in obtaining the better roughness and flatness of the workpiece surface than that of planetary grinding. [ABSTRACT FROM AUTHOR]

Cognitive behavioural therapy (CBT) has been used for women with infertility. However, the efficacy of CBT among the infertile female population remains inconclusive. We performed a systematic literature search using the Cochrane Library, MEDLINE (OVID), EMBASE (OVID), CINAHL, PsycINFO (OVID), Web of Science, and ProQuest databases from the inception to May 2022. The Cochrane risk of bias tool for randomized trials was used to assess the methodological quality of the included studies. Revman 5.4 was conducted for statistical analysis, and meta‐analyses were performed to calculate the pooled effects of CBT. Sixteen articles were finally included in the meta‐analysis. The results of the meta‐analysis revealed large, significant effects of cognitive‐behavioural interventions that can effectively reduce depression, anxiety symptoms, psychological distress, perceived stress, infertility‐specific stress, and improve the quality of life in women with infertility. However, the pooled MD using a fixed‐effects model was 0.25, 95% CI [−0.33, 0.84], P = 0.40, and no significant differences in BMI were observed among those who received CBT therapy compared with those who did not. CBT effectively alleviates depression, anxiety, psychological distress, and infertility‐specific stress levels and improves the quality of life among women with infertility. Future studies should explore the sustainability and long‐term effect of cognitive‐behavioural interventions in women with infertility. [ABSTRACT FROM AUTHOR]

Iron nanoparticles (NPs) priming is known to affect the seed germination and seedling growth in many plants. However, whether it has an important role in stimulating the growth of perennial Qinghai-Tibet Plateau plants remains unclear. In this study, the effects of seed priming with different concentrations of nFe 2 O 3 and FeCl 3 (10, 50, 100, 500, and 1000 mg L−1) on seed germination, plant growth, photosystem, antioxidant enzyme activities, root morphology, and biomass distribution of Kobresia capillifolia were evaluated under laboratory conditions. The results showed that compared with treatment materials, concentration had more significant effects on K. capillifolia development. There was no significant impact on germination rate were discovered under all treatments, but decreased the seed mildew rate at 100 mg L−1 nFe 2 O 3. Compare with control, Fe-based priming significantly decreased root biomass. All Fe-based treatments increased rubisco activity of leaves, and significantly enhanced Pn at ranged from 10 to 100 mg L−1. Meanwhile, chlorophyll contents were decreased, the chloroplasts were swollen, and thylakoids were disorganized under all Fe treatments. Iron-based priming significantly enhanced SOD, POD, and CAT activities in Kobresia roots. In conclusion, the thick cuticle-covered seed coat of K. capillifolia postponed the penetration of FeNPs into seeds, so FeNPs priming had a weak impact on seed germination. The sustainable release of Fe ions from FeNPs and the uptake of Fe ions by roots affected the physiology, biochemistry and morphology of K. capillifolia. The findings of this study provide an in-depth understanding of how FeNPs impact the alpine meadow plant, K. capillifolia. [Display omitted] • Fe 2 O 3 NPs had no significant directly effect on Kobresia capillifolia seed germination. • High concentration Fe 2 O 3 NPs disrupted chloroplast structural and reduced photosynthesis. • Fe 2 O 3 NPs induced oxidative stress response in Kobresia capillifolia. • Fe 2 O 3 NPs had negatively affected roots more than leaves in Kobresia capillifolia. • Fe 2 O 3 NPs reduced biomass via inhibiting roots development and substance translocation. [ABSTRACT FROM AUTHOR]

The wear state of an abrasive belt is one of the important factors affecting the grinding precision of belt grinding processes. At present, there are two problems associated with the monitoring method of the wear condition of abrasive belts: (1) there are no uniform wear criteria to classify the wear condition of abrasive belts, and the segmentation threshold of the wear condition is affected by the change in the grinding parameters; and (2) an abrasive belt wear model based on indirect sensor monitoring of signals is affected by the change in the grinding parameters of abrasive belts; therefore, it is only suitable for abrasive belt wear monitoring under specific grinding parameters. This paper introduces a method of belt wear state monitoring based on machine vision and image processing. Surface images of an abrasive belt during its entire life are captured using a noncontact electron microscope. Three image features related to the wear state are selected: first-order distance of color component R, entropy of the horizontal subgraph, and vertical subgraph of the texture feature. Moreover, the wear state is classified into three categories based on the selected features. Using the selected features and the random forest classification algorithm, an abrasive belt wear state classifier is established. The performance of the classifier is verified and evaluated using a data subset of different images. The results show that the proposed method has high recognition accuracy for belt wear state, which can reach 99% in the accelerated wear stage. The proposed method solves the problem of the dependence and sensitivity of the monitoring model on the variation in the grinding parameters in the process of abrasive belt wear monitoring, and it improves the adaptability and versatility of the monitoring method. [ABSTRACT FROM AUTHOR]

Wind regime is one of the main natural factors controlling the evolution and distribution of aeolian sand landforms, and sand drift potential (DP) is usually used to study the capacity of aeolian sand transport. The Badain Jaran Desert (BJD) is located where polar cold air frequently enters China. Based on wind data of eight nearby meteorological stations, this research is intended to explore the temporal variation and spatial distribution features of wind speed and DP using linear regression and cumulative anomaly method, and reveal the relationship between atmospheric circulation and wind speed with correlation analysis. We found that the wind speed and frequency of sand-blowing wind in the BJD decreased significantly during 1971–2016, and the wind speed obviously mutated in 1987. The regional wind speed change was affected by the Asian polar vortex, the northern hemisphere polar vortex and the Tibet Plateau circulation. The wind rose of the annual sand-blowing wind in this region was the "acute bimodal" type. Most of the annual wind directions clustered into the W-NW, and the prevailing wind direction was WNW. During 1971–2016, the annual DP, the resultant drift potential (RDP) and the directional variability (PDP/DP) in the desert showed an obvious downtrend, with a "cliff-like" decline in the 1980s and relative stable fluctuation thereafter. The BJD was under a low-energy wind environment with the acute bimodal wind regime. Wind speed, sand-blowing wind frequency and DP were high in the northeast and low in the southwest. [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) have been the subject of an exponentially growing number of studies covering their biogenesis mechanisms, isolation and analysis techniques, physiological and pathological roles, and clinical applications, such as biomarker and therapeutic uses. Nevertheless, the heterogeneity of EVs both challenges our understanding of them and presents new opportunities for their potential application. Recently, the EV field experienced a wide range of advances. However, the challenges also remain huge. This review focuses on the recent progress and difficulties encountered in the practical use of EVs in clinical settings. In addition, we also explored the concept of EV heterogeneity to acquire a more thorough understanding of EVs and their involvement in cancer, specifically focusing on the fundamental nature of EVs. • Known and unknown understanding of EV heterogeneity and homogeneity, especially at a single-vesicle level. • The natural role, that EVs themselves are neither beneficial nor harmful, of EVs is to transmit bioinformation. • A comprehensive review of advances and challenges in therapeutic applications and biomarker functions of EVs. [ABSTRACT FROM AUTHOR]

Most few-shot learning(FSL) methods rely on a pre-trained network on a large annotated base dataset with a feature distribution similar to that of the target domain. Conventional transfer learning and traditional few-shot learning methods are ineffective when there is a large gap between the source and target domain. We propose a simple teacher-student network solution to facilitate unlabeled images from the target domain to alleviate domain gap. We impose a self-supervised loss by calculating predictions from large crops of the unannotated samples of target domain using a teacher network and matching them with small crops of the same images from a student network. Furthermore, we design a novel contrastive loss for large crops to sufficiently utilize the self-supervised information of unlabeled images on target domain for the model training. The feature representation can be easily generalized to the target domain without the pretraining phase on target-specific classes. The accuracies of our model are 23. 61 ± 0. 42 , 33. 87 ± 0. 59 , 63. 21 ± 0. 88 , 74. 36 ± 0. 88 on ChestX, ISIC, EuroSAT, and CropDisease datasets for the 1-shot scenario respectively. Extensive experiments show that the proposed method achieves competitive performance on the challenging cross-domain FSL image classification. [ABSTRACT FROM AUTHOR]

[Objectives] The quality of pear wine is directly affected by parameter control in the production process, especially in the fermentation stage. The fermentation parameters of pear wine were controlled by studying the effects of fermentation parameters on the product, in order to optimize the fermentation parameters. [Methods] The fermentation conditions of pear wine were optimized by single factor experiments and an orthogonal experiment. [Results] The optimum fermentation conditions of pear wine were nitrogen source 1.0 g/L, fermentation temperature 20 °C, and pH of fermentation liquid 4. 3. The pear wine brewed was full, mellow and harmonious, and had a unique style. The ethanol content of the pear wine was 12. 33 through physical and chemical analysis. [Conclusions] This study provides theoretical basis and technical support for the industrial production and promotion of pear wine. [ABSTRACT FROM AUTHOR]

Beauvericin (BEA) is a well-known mycotoxin produced by many fungi, including Beaveria bassiana. The purpose of this study was to evaluate the in vitro distribution and metabolism characteristics as well as the in vivo pharmacokinetic (PK) profile of BEA. The in vitro metabolism studies of BEA were performed using rat, dog, mouse, monkey and human liver microsomes, cryopreserved hepatocytes and plasma under conditions of linear kinetics to estimate the respective elimination rates. Additionally, LC-UV-MSn (n = 1~2) was used to identify metabolites in human, rat, mouse, dog and monkey liver microsomes. Furthermore, cytochrome P450 (CYP) reaction phenotyping was carried out. Finally, the absolute bioavailability of BEA was evaluated by intravenous and oral administration in rats. BEA was metabolically stable in the liver microsomes and hepatocytes of humans and rats; however, it was a strong inhibitor of midazolam 1′-hydroxylase (CYP3A4) and mephenytoin 4′-hydroxylase (CYP2C19) activities in human liver microsomes. The protein binding fraction values of BEA were >90% and the half-life (T1/2) values of BEA were approximately 5 h in the plasma of the five species. The absolute bioavailability was calculated to be 29.5%. Altogether, these data indicate that BEA has great potential for further development as a drug candidate. Metabolic studies of different species can provide important reference values for further safety evaluation. [ABSTRACT FROM AUTHOR]

When a temporary support is used to control new surrounding rock in a deep mining roadway, the new surrounding rock is supported by the working resistance of the temporary support. In this study, the influence of deep well boring roadway deformation and rock failure characteristics under different surrounding pressure was investigated. In this paper, for each confining pressure, we experimentally identified the stress-strain, strength, and acoustic emission characteristics of the rocks. The results show that: the surrounding pressure has a significant effect on the damage deformation characteristics of the rock, and the change of the surrounding pressure directly affects the strength, damage form and elastic modulus of the rock; the strength limit of the rock increases with the surrounding pressure, and the damage form of the rock gradually changes to ductile damage with increase of the surrounding pressure; the elastic modulus of the rock increases non-linearly with the increase of the surrounding pressure. The acoustic emission signal of a rock can be divided into three stages: calm, sudden increase, and destruction. The acoustic emission ringing count rate increases suddenly and reaches a peak before the main fracture. Therefore, a sudden increase in the acoustic emission value can be considered a precursor to rock destruction. [ABSTRACT FROM AUTHOR]

Evolutionary multi-objective optimization problems have attracted increasingly attention in the evolutionary computing community. Now a lot of efforts have been devoted to this direction. For example, the proposed Pareto-dominated, decomposition-based and indicator-based methods have improved the scalability of multi-objective evolutionary algorithms (MOEAs). However, with the increase of the number of objectives, the portion of non-dominated individuals in the population is too large, and the distinguishability of individuals in the objective space decreases, which will affect the selection of elite solutions, thereby losing the balance of convergence and diversity. In this paper, a self-adaptive stochastic ranking method (SSR) is proposed to adaptively balance the convergence and diversity in high-dimensional space according to the state of the population. It has been embedded into the MOEA/D framework to form a novel multi-objective optimization algorithm, named MOEA/D-SSR. In addition, an improved shift-based density estimation strategy (ISDE) is adopted to enhance the convergence and diversity. Compared with the existing MOEAs on benchmark suite DTLZ and WFG with up to 10 objectives, the performance of the our algorithm has been verified. Experimental results show that the proposed algorithm is competitive compared with the most advanced MOEAs. [ABSTRACT FROM AUTHOR]

Deep underground engineering often utilizes cyclic loading. To understand the deformation and damage characteristics of rock under cyclic loading conditions, cyclic loading tests of three different specimens with varying lithology were performed. The dissipated energy method was used to analyze the magnitude of damage and rock failure characteristics during the energy evolution process of cube specimens. The results indicated that the modulus of elasticity of three lithologies were stable prior and subsequent to cyclic loading. While the cyclic loading profile improved the rock's resistance to deformation, it increased the internal mesostructure defects. Rock damage caused by the cyclic loading reduced the uniaxial compression strength. This was especially true for coal samples. For coal samples, these observations were consistent with internal coal damage calculated by analysis. Under the same lithology and different loading modes, rock damage was caused by cyclic action, and the elastic strain energy released by instantaneous unloading of rock samples with significant damage was reduced. The rupture magnitude for cyclic loading was observed to be less than that of uniaxial compression. Under cyclic loading and varying different lithologies, sandstone absorbed the most energy, resulting in a larger final fracture magnitude compared to other lithologies. [ABSTRACT FROM AUTHOR]

In the abrasive belt grinding process, actual material removal is an important parameter that affects its accuracy. At present, for obtaining the actual material removal, offline measurements are required to establish the mathematical prediction model. To improve the accuracy and efficiency of abrasive belt machine grinding, this paper proposes a novel method for monitoring material removal using multiple sensors and a two-dimensional (2D) convolutional neural network (2D-CNN) learning algorithm. In this method, features of multiple types (color, texture, and shape) are extracted from vision signals, and that of multiple domains (time, frequency, and time–frequency domain) are extracted from sound and tactile signals. These features are constructed into a 2D feature matrix as the input model, and the 2D-CNN prediction model is established between the multisensor features and the material removal rate of the abrasive belt grinding process. An experimental dataset is used to train and verify the established model. The results show that the proposed method can identify that sensor signals are sensitive to the material removal rate. After optimizing and tuning the model parameters, the coefficient of determination of the prediction results is as high as 94.5% and the root mean square error is 0.017. Therefore, the proposed method can be employed for the prediction of material removal rate for different belt specifications and different grinding parameters. Compared to traditional machine learning methods, this method can yield better training results without feature selection and optimization. [ABSTRACT FROM AUTHOR]

Radiotherapy is one of the major approaches to cancer treatment. Artificial intelligence in radiotherapy (shortly, Intelligent radiotherapy) mainly involves big data, deep learning, extended reality, digital twin, radiomics, Internet plus and Internet of Things (IoT), which establish an automatic and intelligent network platform consisting of radiotherapy preparation, target volume delineation, treatment planning, radiation delivery, quality assurance (QA) and quality control (QC), prognosis judgment and post-treatment follow-up. Intelligent radiotherapy is an interdisciplinary frontier discipline in infancy. The review aims to summary the important implements of intelligent radiotherapy in various areas and put forward the future of unmanned radiotherapy center. [ABSTRACT FROM AUTHOR]

Kobresia plants are important forage resources on the Qinghai-Tibet Plateau and are essential in maintaining the ecological balance of grasslands. Therefore, it is beneficial to obtain Kobresia genome resources and study the adaptive characteristics of Kobresia plants on the Qinghai-Tibetan Plateau. Previously, we have assembled the genome of Carex littledalei (Kobresia littledalei), which is a diploid with 29 chromosomes. In this study, we assembled genomes of Carex parvula (Kobresia pygmaea) and Carex kokanica (Kobresia royleana) via using Illumina and PacBio sequencing data, which were about 783.49 Mb and 673.40 Mb in size, respectively. And 45,002 or 36,709 protein-coding genes were further annotated in the genome of C. parvula or C. kokanica. Phylogenetic analysis indicated that Kobresia in Cyperaceae separated from Poaceae about 101.5 million years ago after separated from Ananas comosus in Bromeliaceae about 117.2 million years ago. C. littledalei and C. parvula separated about 5.0 million years ago, after separated from C. kokanica about 6.2 million years ago. In this study, transcriptome data of C. parvula at three different altitudes were also measured and analyzed. Kobresia plants genomes assembly and transcriptome analysis will assist research into mechanisms of plant adaptation to environments with high altitude and cold weather. [ABSTRACT FROM AUTHOR]

As contemporary star materials, 2D monolayer semiconductors have drawn huge research interests owing to their striking electrical and optical properties, rendering them ideal candidates as building blocks for novel optoelectronic devices. Towards light emitting devices with extended functions, it is necessary to manipulate the polarization of light emission from monolayer semiconductors. However, most of these monolayer semiconductors exhibit no or very limited polarization sensitivity inherited from their structural anisotropy, making it challenging to develop highly polarized light sources. Herein, by embedding monolayer tungsten diselenide (WSe2) in a nanowire‐on‐film nanocavity, highly polarized light emission is demonstrated, featuring degree of polarization (DoP) of ≈99%. The highly anisotropic light emission originates from the near‐field coupling between the WSe2 monolayer with the polarization‐dependent gap plasmons of the nanowire‐on‐film nanocavity. Moreover, as the width of the nanowire essentially defines the gap‐plasmon resonance of the nanowire‐on‐film nanocavity, the anisotropy of the highly polarized light emission is systematically tuned by changing the nanowire width. The findings pave the way for engineering anisotropic optical properties of monolayer semiconductors and will boost the development of functional polarization‐sensitive 2D optoelectronic devices. [ABSTRACT FROM AUTHOR]

Today, with the rapid development of virtual reality technology, 3D printing technology has gradually gained people's attention. Copper alloy parts have a wide range of uses in our lives, but due to the high technology required, production is more difficult. In order to make copper alloy parts more widely used, this paper uses 3D printing technology to print copper alloy parts, and through comparative experiments, the printed copper alloy parts are analyzed in detail. In the printing process, by adjusting the printing parameters, different parts are printed, and the printing results are analyzed for different parameters. The experimental results show that the experimental 3D printing technology can successfully print copper alloy parts. During the printing process, the input parameters have an extremely important effect on the results. The experimental results show that there are related parameters in the printing process, such as layer height, length, density, etc., which will cause deviations in the printing results. Among them, the layer height and length have the most important effect on the results, accounting for more than 50% of the deviation value. This shows that based on virtual reality technology, the use of 3D printing copper alloy parts is achievable. [ABSTRACT FROM AUTHOR]

• Migration characteristics of radionuclide in 3D rough shear fractures are systematically investigated. • The influence of fracture roughness, aperture distribution, contact rates, and adsorption on radionuclide migration is considered. • A novel equivalent model of radionuclide migration in 3D rough shear fractures is proposed. Accurate prediction of radionuclide distribution in fractured rock mass is a key problem in high-level radioactive waste disposal engineering. However, it is inappropriate to directly regard rough fractures as smooth fractures when predicting radionuclide migration in real fractured rock masses. In this study, the characteristics of radionuclide migration in three-dimensional rough shear fractures are numerically analyzed considering the influence of fracture roughness, aperture distribution, contact ratios, and adsorption. Different fracture contact ratios and aperture distributions are realized by shearing two rough fracture surfaces. Based on the study, we found that the influence of surface contact is greater than that of surface roughness and aperture distribution on radionuclide migration in rough fractures with the same initial fracture aperture under laminar flow. The phenomena of dominant migration channels and low-velocity dispersion zones are observed for radionuclide dispersion in rough fractures containing contact areas. The higher the contact ratio, the more obvious this phenomenon. The breakthrough and steady state times of radionuclide are also significantly affected by the contact ratio. The higher the contact ratio, the shorter the breakthrough time, and the longer the duration from the breakthrough time to the steady state time. Adsorption delays the overall process of the radionuclide migration and prolongs the duration between the breakthrough time and the steady state time. A novel equivalent method of the radionuclide migration in rough shear fractures is proposed based on the study of radionuclide transport in rough fractures. In the proposed equivalent method, an adjustment coefficient for the transport velocity and a longitudinal dispersion coefficient for the rough fracture are defined to connect rough fractures and the corresponding smooth fractures with the same fracture aperture. The influence regularity of fracture contact and adsorption on the two defined coefficients is obtained based on over 400 numerical simulations. The specific formulas of the two coefficients are given based on the form of the analytical solution for the radionuclide transport in a smooth fracture. The dispersion coefficient and transport velocity in a rough fracture are equivalent to the dispersion coefficient and transport velocity in a smooth fracture and the fluctuation term affected by fracture surface contact and adsorption, respectively. It was verified that the equivalent model has higher accuracy, which can help characterize radionuclide migration in 3D rough fractures efficiently and precisely. [ABSTRACT FROM AUTHOR]

• Leguminous shrub encroachment enhanced ecosystem multifunctionality in alpine grasslands. • Facilitative effects of leguminous shrub on EMF-diversity and productivity in alpine grasslands. • Leguminous shrub encroachment significantly increased soil nutrient contents in alpine grasslands. • Increasing of ecosystem multifunctionality resulted from higher soil nutrients by shrub encroachment. Shrub encroachment in grassland ecosystems is projected to intensify worldwide because of rapid climate change and anthropogenic activity, amplifying the encroachment of woody shrubs and strongly altering the ecosystem function. The effects of leguminous shrub encroachment on multiple dimensions of ecosystem functioning, however, remains poorly understood. This study experimentally quantified the effects of leguminous shrub encroachment (primarily Caragana spinifera Kom.) on various ecosystem functionality relevant to plant diversity, productivity, and storage of soil carbon (C) and nitrogen (N) in alpine meadows and steppes. Further, the study assessed the potential influences of shrub encroachment on ecosystem multifunctionality (EMF, EMF-diversity, EMF-productivity, EMF-C, and EMF-N). Our findings suggest that leguminous shrub encroachment enhanced ecosystem multifunctionality in alpine meadows and steppes. Plant diversity and aboveground biomass of herbaceous species increased after leguminous shrub encroachment. Additionally, the impacts of leguminous shrub encroachment on belowground biomass differed with grassland types, increasing in alpine steppes but decreasing in alpine meadows. The shrub encroachment resulted in significant increases in soil nitrogen and overall nutrient contents in both forms of alpine grasslands. Our findings highlight that shrub encroachment's increasing of ecosystem multifunctionality was closely related to the promotion of plant diversity, biomass of herbaceous plants, and soil nutrients. Overall, our study demonstrates that the encroachment of leguminous shrubs can promote ecosystem multifunctionality in both alpine meadows and alpine steppes. These findings expand our understanding of the ecosystem functioning in leguminous shrub‐encroached alpine meadows and steppes. [ABSTRACT FROM AUTHOR]

• Optimal design of various periodic unit cells aiming at high thermal conductivity. • Schemes to modulate porous microstructure based on topology optimization. • Computational and experimental validation of effective thermal conductivity with error less than 10 %. Porous structures are lightweight and thus possess tailorable thermophysical properties through topological design. A multi-constraint topology optimization scheme is developed for designing 2D periodic lattice material with controllable porosity and optimal thermal conductivity. The porosity, pore size and specific surface area are modulated by imposing local density constraints, and thus the structure-determined thermal conductivity can be systematically investigated. The isotropic porous structures after optimization show effective thermal conductivity close to the Hashin-Shtrikman theoretical bound. The specific surface area of base cell is enlarged with imposing local density constraint. In addition, microstructure with anisotropic thermal conductivity can be also obtained. The samples of lattice structure are additively manufactured via selective laser melting, and the thermal conductivities are experimentally validated with deviation within 10 %. The proposed porous structures with targeted porosity have potential application in skeleton embedded with phase change materials. [ABSTRACT FROM AUTHOR]

• A non-empirical method for predicting the mechanical properties of metallic materials is developed. • The explicit microstructural evolution mechanisms of metallic materials are described. • The strengthening mechanisms are unified through dislocation theory and thermal activation model. • A thermal-mechanical coupling activation model is established. A mechanical constitutive relationship is the basis for the design and application of structural materials, and the establishment of a mechanical constitutive relationship generally relies on experimental data. Herein a computational mechanical constitutive modeling method is proposed, in which all required parameters possess unambiguous physical interpretations and/or can be obtained from accurate numerical simulations. With the inclusion of interaction between dislocations and crystal defects, the dislocation density and grain size evolution with strain for polycrystalline metallic materials are mathematically modeled, and the corresponding contribution to strength is analyzed, further a computational mechanical constitutive relationship can be obtained. Its effectiveness is verified by comparing it with experimental stress-strain relationships for Cu, Al-Mg, and Cu-W alloys. This method might be a powerful tool for the design of structural materials with desirable mechanical properties. [ABSTRACT FROM AUTHOR]

Hypoxia is a remarkable trait of the tumor microenvironment (TME). When facing selective pressure, tumor cells show various adaptive characteristics, such as changes in the expression of cancer hallmarks (increased proliferation, suppressed apoptosis, immune evasion, and so on) and more frequent cell communication. Because of the adaptation of cancer cells to hypoxia, exploring the association between cell communication mediators and hypoxia has become increasingly important. Exosomes are important information carriers in cell-to-cell communication. Abundant evidence has proven that hypoxia effects in the TME are mediated by exosomes, with the occasional formation of feedback loops. In this review, we equally focus on the biogenesis and heterogeneity of cancer-derived exosomes and their functions under hypoxia and describe the known and potential mechanism ascribed to exosomes and hypoxia. Notably, we call attention to the size change of hypoxic cancer cell-derived exosomes, a characteristic long neglected, and propose some possible effects of this size change. Finally, jointly considering recent developments in the understanding of exosomes and tumors, we describe noteworthy problems in this field that urgently need to be solved for better research and clinical application. [ABSTRACT FROM AUTHOR]

Summary: Achnatherum splendens Trin. (Gramineae) is a constructive species of the arid grassland ecosystem in Northwest China and is a major forage grass. It has good tolerance of salt and drought stress in alkaline habitats. Here, we report its chromosome‐level genome, determined through a combination of Illumina HiSeq sequencing, PacBio sequencing and Hi‐C technology. The final assembly of the ~1.17 Gb genome sequence had a super‐scaffold N50 of 40.3 Mb. A total of 57 374 protein‐coding genes were annotated, of which 54 426 (94.5%) genes have functional protein annotations. Approximately 735 Mb (62.37%) of the assembly were identified as repetitive elements, and among these, LTRs (40.53%) constitute the highest proportion, having made a major contribution to the expansion of genome size in A. splendens. Phylogenetic analysis revealed that A. splendens diverged from the Brachypodium distachyon–Hordeum vulgare–Aegilops tauschii subclade around 37 million years ago (Ma) and that a clade comprising these four species diverged from the Phyllostachys edulis clade ~47 Ma. Genomic synteny indicates that A. splendens underwent an additional species‐specific whole‐genome duplication (WGD) 18–20 Ma, which further promoted an increase in copies of numerous saline–alkali‐related gene families in the A. splendens genome. By transcriptomic analysis, we further found that many of these duplicated genes from this extra WGD exhibited distinct functional divergence in response to salt stress. This WGD, therefore, contributed to the strong resistance to salt stress and widespread arid adaptation of A. splendens. [ABSTRACT FROM AUTHOR]

Most activity-based molecular probes are designed to target enzymes that catalyze the breaking of chemical bonds and the conversion of a unimolecular substrate into bimolecular products. However, DNA topoisomerases are a class of enzymes that alter DNA topology without producing any molecular segments during catalysis, which hinders the development of practical methods for diagnosing these key biomarkers in living cells. Here, we established a new strategy for the effective sensing of the expression levels and catalytic activities of topoisomerases in cell-free systems and human cells. Using our newly designed biosensors, we tricked DNA topoisomerases within their catalytic cycles to switch on fluorescence and resume new rounds of catalysis. Considering that human topoisomerases have been widely recognized as biomarkers for multiple cancers and identified as promising targets for several anticancer drugs, we believe that these DNA-based biosensors and our design strategy would greatly benefit the future development of clinical tools for cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]