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Amomum villosum Lour. (A. villosum) is the original plant of the medicinal and culinary spice Amomi Fructus (Sharen) and is an important economic crop in the Lingnan region of China. During the cultivation and production of A. villosum, prolonged reliance on single asexual reproduction has exacerbated the degradation of its varieties, leading to inconsistent yields and quality. Building upon earlier cultivar selection efforts, this study provides a comprehensive evaluation of two newly bred A. villosum varieties (A11 and A12) from perspectives including plant traits, product characteristics, active ingredients, and multi-omics analysis. It was found that A12 plants display enhanced robustness, more aromatic fruits, higher yields, and elevated levels of bornyl acetate, A11 shows the advantage of a high camphor content, and the different metabolites and differentially expressed genes of the two varieties were significantly enriched in multiple metabolic pathways. Additionally, A12 contained more terpenoids and substances with aromatic odors such as sweet, fruity, floral, and green. Furthermore, a key gene (Wv_032842) regulating the acetylation of bornyl was discovered, and its significantly higher expression, in A12. In conclusion, this study has a guiding significance for the evaluation of germplasm resources and the breeding of excellent varieties of A. villosum. [ABSTRACT FROM AUTHOR]

Sweet basil (Ocimum basilicum L.) is an important aromatic plant with high edibility and economic value, widely distributed in many regions of the tropics including the south of China. In recent years, environmental problems, especially soil salinization, have seriously restricted the planting and spread of sweet basil. However, the molecular mechanism of the salt stress response in sweet basil is still largely unknown. In this study, seed germination, seedling growth, and chlorophyll synthesis in sweet basil were inhibited under salt stress conditions. Through comparative transcriptome analysis, the gene modules involved in the metabolic processes, oxidative response, phytohormone signaling, cytoskeleton, and photosynthesis were screened out. In addition, the landscape of transcription factors during salt treatment in sweet basil was displayed as well. Moreover, the overexpression of the WRKY transcription factor-encoding gene, ObWRKY16, and the phenylalanine ammonia-lyase-encoding gene, ObPAL2, enhanced the seed germination, seedling growth, and survival rate, respectively, of transgenic Arabidopsis, suggesting that they might be important candidates for the creation of salt-tolerant sweet basil cultivars. Our data enrich the study on salt responses in sweet basil and provide essential gene resources for genetic improvements in sweet basil in the future. [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]

Landslides represent a significant global natural hazard, threatening human settlements and the natural environment. The primary objective of the study was to develop a landslide susceptibility modeling approach that enhances prediction accuracy and informs land-use planning decisions. The study utilized a hybrid ensemble-based methodology to improve prediction accuracy and effectively capture the complexity of landslide susceptibility patterns. This approach harnessed the power of ensemble models, employing a bagging algorithm with base learners, including the reduced error pruning decision tree (REPTree) and functional tree (FT) models. Ensemble models are particularly valuable because they combine the strengths of multiple models, enhancing the overall performance and robustness of the landslide susceptibility prediction. The study focused on Yanchuan County, situated within the hilly and gully region of China's Loess Plateau, known for its susceptibility to landslides, using sixteen critical landslide conditioning factors, encompassing topographic, environmental, and geospatial variables, namely elevation, slope, aspect, proximity to rivers and roads, rainfall, the normalized difference vegetation index, soil composition, land use, and more. Model performances were evaluated and verified using a range of metrics, including receiver operating characteristic (ROC) curves, trade-off statistical metrics, and chi-square analysis. The results demonstrated the superiority of the integrated models, particularly the bagging FT (BFT) model, in accurately predicting landslide susceptibility, as evidenced by its high area under the curve area (AUC) value (0.895), compared to the other models. The model excelled in both positive predictive rate (0.847) and negative predictive rate (0.886), indicating its efficacy in identifying landslide and non-landslide areas and also in the F-score metric with a value of 0.869. The study contributes to the field of landslide risk assessment, offering a significant investigation tool for managing and mitigating landslide hazards in Yanchuan County and similar regions worldwide. [ABSTRACT FROM AUTHOR]

Change detection (CD) stands out as a pivotal yet challenging task in the interpretation of remote sensing images. Significant developments have been witnessed, particularly with the rapid advancements in deep learning techniques. Nevertheless, challenges such as incomplete detection targets and unsmooth boundaries remain as most CD methods suffer from ineffective feature fusion. Therefore, this paper presents a multi-scale gated fusion network (MSGFNet) to improve the accuracy of CD results. To effectively extract bi-temporal features, the EfficientNetB4 model based on a Siamese network is employed. Subsequently, we propose a multi-scale gated fusion module (MSGFM) that comprises a multi-scale progressive fusion (MSPF) unit and a gated weight adaptive fusion (GWAF) unit, aimed at fusing bi-temporal multi-scale features to maintain boundary details and detect completely changed targets. Finally, we use the simple yet efficient UNet structure to recover the feature maps and predict results. To demonstrate the effectiveness of the MSGFNet, the LEVIR-CD, WHU-CD, and SYSU-CD datasets were utilized, and the MSGFNet achieved F1 scores of 90.86%, 92.46%, and 80.39% on the three datasets, respectively. Furthermore, the low computational costs and small model size have validated the superior performance of the MSGFNet. [ABSTRACT FROM AUTHOR]

Defoliation and accelerating ripening are important measures for cotton mechanization, and judging the time of defoliation and accelerating the ripening and harvest of cotton relies heavily on the boll opening rate, making it a crucial factor to consider. The traditional methods of cotton opening rate determination are time-consuming, labor-intensive, destructive, and not suitable for a wide range of applications. In this study, the relationship between the change rate of the vegetation index obtained by the unmanned aerial vehicle multi-spectrum and the ground boll opening rate was established to realize rapid non-destructive testing of the boll opening rate. The normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) had good prediction ability for the boll opening rate. NDVI in the training set had an R2 of 0.912 and rRMSE of 15.387%, and the validation set performance had an R2 of 0.929 and rRMSE of 13.414%. GNDVI in the training set had an R2 of 0.901 and rRMSE of 16.318%, and the validation set performance had an R2 of 0.909 and rRMSE of 15.225%. The accuracies of the models based on GNDVI and NDVI were within the acceptable range. In terms of predictive models, random forests achieve the highest accuracy in predictions. Accurately predicting the cotton boll opening rate can support decision-making for harvest and harvest aid spray timing, as well as provide technical support for crop growth monitoring and precision agriculture. [ABSTRACT FROM AUTHOR]

The CDPK family genes play crucial roles in signal transduction pathways during plant development and stress response. In this study, we comprehensively analyzed the CDPK family genes in mustard (Brassica juncea L.), resulting in the identification of 101 genes (BjuCDPK1–101) located on chromosomes AA_Chr01 to BB_Chr08. RNA-seq analysis showed that most BjuCDPK genes were predominantly expressed in the root and flower bud, suggesting their organ-specific expression patterns. Furthermore, the expression levels of most BjuCDPK genes were significantly altered by cold stress. The promoter regions of BjuCDPK genes had abundant cis-acting elements related to hormones and stress. The interaction network analysis predicted that BjuCDPK proteins respond to abiotic stress probably via interaction with ABF1, ABF4, and/or DI19. Our findings provide valuable information for further the investigation of the cold stress adaption of mustard via the CDPK signaling pathway. [ABSTRACT FROM AUTHOR]

The transport and prediction of the concentration of particles in confined spaces are crucial for human well-being; this has become particularly evident during the current worldwide pandemic. Computational fluid dynamics (CFD) has been widely used for such predictions, relying on Eulerian–Eulerian (EE) and Eulerian–Lagrangian (EL) models to study particle flow. However, there is a lack of research on industrial factories. In this study, a scaled laboratory in an industrial factory was established for oil mist particles in a machining factory, and oil mist dispersion experiments were conducted under roof exhaust and mixed ventilation conditions. After that, the oil mist concentration distribution in the factory under the same working conditions was calculated by Eulerian and Lagrangian methods, and the corresponding calculation errors and resource consumption were compared. It was found that the simulation results of both methods are acceptable for mixed ventilation and roof exhaust ventilation systems. When there are more vortices in the factory, the Lagrangian method increases the computation time by more than 53% to satisfy the computational accuracy, and the computational error between the Eulerian and Lagrangian methods becomes about 10% larger. For oil mist particles with an aerodynamic diameter of 0.5 μm, both Eulerian and Lagrangian methods have reliable accuracy. Based on the same flow field, the Lagrangian method consumes more than 400 times more computational resources than the Eulerian method. This study can provide a reference for the simulation of indoor particulate transport in industrial factories. [ABSTRACT FROM AUTHOR]

Featured Application: Air supply design for indoor air. When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating a subgeneration, the utilization of existing samples is low and the number of individuals is high. Therefore, surrogate-based optimization has been developed, which greatly reduces the number of individuals in the subgeneration and the cost of optimization. In complex air supply scenarios, single-objective optimization results may not be comprehensive; therefore, this paper developed a double-objective air supply optimization method based on the Kriging surrogate model and Non-dominated Sorting Genetic Algorithms-II. And it proposed the infill criteria based on clustering to advance the Pareto Frontier. The method was validated with an inverse prediction case, and in particular, the problems when based on 3D steady-state simulations were analyzed. The results showed that the method can quickly achieve an approximate prediction of the boundary conditions (when predictions were made based on experimental data, the number of simulations was 82 and the average error was 6.8%). Finally, the method was used to optimize the air supply parameters of a dual-aisle, single-row cabin, with only 118 samples used in the optimization process. The Pareto set suggested that an airflow organization with dual circulation may be optimal. [ABSTRACT FROM AUTHOR]

Energy sustainability and the establishment of the 'national water network' are all inseparable from the construction of underground engineering. Monitoring indices for the surrounding rock are vital for the safety management of underground engineering construction, which determines the actual state of the surrounding rock. The complexity of deep underground engineering construction leads to many situations that cannot be predicted and explained by existing experience. Therefore, it is necessary to identify which monitoring index best represents the surrounding rock damage. Currently, there are no advanced and convenient effectiveness evaluation schemes for surrounding rock monitoring information. To fill the technical gap, this study introduces the volume expansion rate (VER) index for surrounding rock and proposes a machine learning (ML)-based evaluation scheme for the effectiveness of monitoring indices. First, six conditions with different in situ stresses are designed, and tunnel excavation monitoring tests are conducted. Second, the surrounding rock damage determination experiments using the ML classification algorithm are performed, and the accuracy matrix and index significance scores are obtained. The evaluation results show that: (1) The multi-class logistic regression algorithm is more suitable for determining surrounding rock damage with high accuracy and more appropriate significance evaluation outcomes. (2) Under the higher in situ stress condition, the tangential stress is more sensitive to the surrounding rock damage. (3) As the in situ stress increases, the significant monitoring indices demonstrate a transition 'from shallow to deep, from regional damage to point failure', describing the instability of the surrounding rock and inspiring a new instability criterion for surrounding rock. [ABSTRACT FROM AUTHOR]

In the context of the rapid development of green finance, this paper examines the impact of green insurance on green innovation. Based on panel data of listed firms from 2008 to 2020, we find that green insurance significantly increased firms' green patent applications. The mechanisms driving this positive relationship between green insurance and green innovation include that the insured firms are able to obtain more resources, are more willing to take risks, and are more likely to have a long-term vision. Further analysis shows that green insurance can enhance a firm's environmental performance by promoting green innovation. This study deepens our understanding of green insurance and enriches the research related to green finance. [ABSTRACT FROM AUTHOR]

The main focus of this article is on the jump-takeoff method for autogyros. On the basis of a high-confidence autogyro model, we design a jump-takeoff simulation experiment to study and optimize jump-takeoff performance. Using a simplified version of blade element theory, we conduct secondary development on the YASim dynamics library in FlightGear software and construct a highly accurate auto-rotation rotor model. The implementation of jump-takeoff requires appropriate control parameters for collective angle and pre-rotation speed. We explore the minimum collective angle condition and minimum pre-rotation speed condition to obtain the jump-takeoff envelope, and we investigate the effect of changes in control parameters within the jump envelope on jump-takeoff performance. Furthermore, we optimize the jump-takeoff performance by varying the rotor diameter and blade tip weighting. Through this study of jump-takeoff performance, we are able to determine appropriate control parameters and rotor parameters for jump-takeoff schemes, establish parameter settings for simulations of jump-takeoff tests, and thereby lay the foundation for future experimental investigations of jump-takeoff of actual autogyros. [ABSTRACT FROM AUTHOR]

Concerning the vision of achieving carbon neutral and peak carbon goals, wind energy is extremely important as a renewable and clean energy source. However, existing research ignores the implicit features of the data preprocessing technique and the role of the internal mechanism of the optimization algorithm, making it difficult to achieve high-accuracy prediction. To fill this gap, this study proposes a wind speed forecasting model that combines data denoising techniques, optimization algorithms, and machine learning algorithms. The model discusses the important parameters in the data decomposition technique, determines the best parameter values by comparing the model's performance, and then decomposes and reconstructs the wind speed time series. In addition, a novel optimization algorithm is used to optimize the parameters of the machine learning algorithm using a waiting strategy and an aggressive strategy to improve the effectiveness of the model. Several control experiments were designed and implemented using 10-min wind speed data from three sites in Penglai, Shandong Province. Based on the numerical comparison results and the discussion of the proposed model, it is concluded that the developed model can obtain high accuracy and reliability of wind speed prediction in the short term relative to other comparative models and can have further applications in wind power plants. [ABSTRACT FROM AUTHOR]

The structural specificity and hydrological sensitivity of loess have a strong impact on its long-term stability and safety. This topic is being actively researched and focuses on the macromechanical behavior of the shear strength of loess disturbed and its micromechanisms from the perspective of the dry–wet cycle (especially involving soluble salt erosion). In this paper, the correlation between micro-structural shear functional units and macroscopic degradation behavior was established by combining the changes in physicochemical properties of mass loss, surface cracking, strength deterioration, and structural disturbance of the loess with scanning electron microscopy (SEM) microscopic images in different dry–wet cycles and different salt contents. Results revealed that with the increase in dry–wet cycles and salt content, the mass loss of soil deteriorated and the surface crack rate increased. The cohesion of soil showed an overall decreasing trend, which decreased more obviously in the early stage of the dry–wet cycle, followed by a slow decrease, and tended to be constant after nine dry–wet cycles. However, the internal friction angle increased and then decreased during the whole cycle, and its value generally changed little. According to the deterioration and decay of shear strength, it can be concluded that the structural disturbance of loess increased with the increase in dry–wet cycles and salt content. At the same time, further linear quantization fitting of the structural disturbance parameters showed that the structural parameters had a positive correlation with salt content and a power function with dry–wet cycles, where dry–wet cycles seemed to play a dominant role in the loess structural deterioration rather than salt content. The microscopic study demonstrates that the dry–wet cycles and salt content do not directly affect the cohesion and internal friction angle of soil but change the basic shear structural unit of aggregate and then cause an essential impact on c and φ, which in turn have an essential impact on soil strength attenuation. This paper not only helps to elucidate the essence of water–soil–salt structural interactions but also provides theoretical references for sustainable development research in environmental engineering, geological engineering, and other related fields. [ABSTRACT FROM AUTHOR]

The integrated system with the strapdown inertial navigation system (SINS) and the global positioning system (GPS) is the most popular navigation mode. It has been used in many navigation fields. Before the integrated system works properly, it must determine the initial attitude for SINS. In SINS/GPS-integrated systems, the navigational velocity can be used to carry out the initial alignment when the system is installed in the in-motion vehicle. However, the initial velocity errors are not considered in the current popular in-motion alignment methods for SINS/GPS integration. It is well-known that the initial velocity errors must exist when the initial velocity is obtained from the GPS outputs. In this paper, an improved method was proposed to solve this problem. By analyzing the original observation vectors in the in-motion coarse alignment method, an average operation was used to construct the intermediate vectors, and the new observation vector can be calculated by subtracting the intermediate vector from the original observation vector. Then, the initial velocity errors can be eliminated from the newly constructed observation vector. Thus, the interferences of the initial velocity errors for the initial alignment process can be suppressed. The simulation and field tests are designed to verify the performance of the proposed method. The tests results showed that the proposed method can obtain the higher accurate results than the current methods when the initial velocity is considered. Additionally, the results of the proposed method were similar to the current methods when the initial velocity errors were not considered. This shows that the initial velocity errors were eliminated effectively by the proposed method, and the alignment accuracy were not decreased. [ABSTRACT FROM AUTHOR]

China's aging population has deeply affected the sustainable development of the Chinese economy. Based on the provincial panel data of China's population and economic indicators from 2000 to 2020, this paper develops a panel vector autoregressive model to analyze the effect of China's aging population on economic growth under the paths of household consumption and national savings, respectively. The results show that an aging population inhibits household consumption and promotes national saving, which has both direct and indirect effects on economic growth. In particular, an aging population is not conducive to sustainable economic development in the context of China's slow population growth over the past three years and the contraction of the global economy due to the COVID-19 pandemic. In accordance with the empirical results, this paper puts forward corresponding policy recommendations, as follows: improve the pension security system; develop the silver-hair industry; expand domestic demand in China; encourage fertility; and increase human capital investment to provide an impetus for sustainable economic development. [ABSTRACT FROM AUTHOR]

Based on panel data of Chinese provinces from 2011 to 2020, this paper presents empirical research on the quantitative relationship between China's national quality and sustainable development. Moreover, this research is implemented under the index system of national quality competitiveness. Firstly, a system for assessing national quality level is established, and the factor analysis method is applied to comprehensively analyze each province's national quality; this produces a national quality score for each province. Secondly, an Index of Sustainable Economic Welfare (ISEW) and calculation methods are created. Thirdly, the Pearson Correlation Coefficient is employed to explore the relationship between the national quality of Chinese provinces and the sustainability of their economic development. Finally, the test of Granger causality (panel data) is applied to further analyze the interplay between national quality indexes and sustainable economic development. As the research results indicate, national quality can comprehensively impact sustainable economic development at any economic level, as well as its sustainable capability. National quality can still comprehensively affect the sustainable development of the economy. Nevertheless, sustainable economic growth does not affect all elements of national quality, especially at the level of morality. Additionally, the positive influence of sustainable economic development on national quality is limited and lags behind. Based on the results of our empirical analysis, several policy recommendations are given to improve China's national quality and sustainable economic development. [ABSTRACT FROM AUTHOR]

To investigate the failure effects of critical fissures in rock specimens subjected to plane strain compression (PSC), five types of internal fissures in rock specimens were designed and twelve PSC tests were conducted for two lithologies based on the discrete element method (DEM). The results were analyzed in terms of the fracture mode, data characteristics, and crack evolution. The results indicated the following. (1) The rock samples with a critical fissure under PSC showed a weak face shear fracture mode, which was influenced by lithology, fissure angle, and fissure surface direction. (2) There were four critical expansion points (CEPs) of axial stress of the rocks under PSC, which were the stage signs of rock materials from local damage to complete fracture. The rock-bearing capacity index (RockBCI) was further proposed. (3) The bearing capacity of rock samples with horizontal fissures, fissures whose angles coincided with the fracture surface, and fissures whose surface was perpendicular to the lateral confine direction was the worst; their BCI2 values were found to be 80.6%, 70.8%, and 56.9% of the rock samples without any fissures, respectively. The delayed fracture situation under PSC was identified and analyzed. (4) The crack evolution followed the unified law of localization, and the fissures in the rocks changed the mode of crack development and the path of the deepening and connecting of crack clusters, as well as affecting the time process from damage to collapse. This research innovatively investigated the behavior characteristics of rock samples with a fissure under PSC, and it qualitatively and quantitatively analyzed the bearing capacity of rock mass from local damage to fracture. [ABSTRACT FROM AUTHOR]

Considering the deployment characteristics of the folding wing, this paper proposed three deployment modes, synchronous deployment, fixed-axis–non-fixed-axis stepwise deployment, and non-fixed-axis–fixed-axis stepwise deployment, to obtain the optimal deployment scheme of the orthogonal biaxial folding wing of Unmanned Aerial Vehicles (UAVs) at different airspeeds. On this basis, combined with the folding wing deployment action, the Lagrange method was used to establish the aerodynamic model of the folding wing, and the Fluent simulation software was used to simulate the aerodynamic simulation of multiple deployment modes of the orthogonal biaxial folding wing, which analyze the influence of the UAV deployment mode and airspeed towards the driving torque of the folding wing. Based on the driving moment of the folding wing, the optimal deployment mode at different airspeeds was obtained. The comparison of simulation results shows that when the airspeed is less than 40 m/s, the optimal deployment mode is synchronous deployment. When the airspeed is greater than or equal to 40 m/s, the optimal deployment mode is non-fixed-axis–fixed-axis stepwise deployment. The accuracy of the folding wing aerodynamics model can be proven according to the comparison of the simulation results with the theoretical results. [ABSTRACT FROM AUTHOR]

The high crystal quality and low dislocation densities of aluminum nitride (AlN) grown on flat and nano-patterned sapphire substrate that are synthesized by the metal-organic chemical vapor deposition (MOCVD) method are essential for the realization of high-efficiency deep ultraviolet light-emitting diodes. The micro-strains of 0.18 × 10−3 cm−2 for flat substrate AlN and 0.11 × 10−3 cm−2 for nano-patterned substrate AlN are obtained by X-ray diffractometer (XRD). The screw and edge dislocation densities of samples are determined by XRD and transmission electron microscope (TEM), and the results indicate that the nano-patterned substrates are effective in reducing the threading dislocation density. The mechanism of the variation of the threading dislocation in AlN films grown on flat and nano-patterned substrates is investigated comparatively. The etch pit density (EPD) determined by preferential chemical etching is about 1.04 × 108 cm−2 for AlN grown on a nano-patterned substrate, which is slightly smaller than the results obtained by XRD and TEM investigation. Three types of etch pits with different sizes are all revealed on the AlN surface using the hot KOH etching method. [ABSTRACT FROM AUTHOR]

Laser multilateration is a measurement method based on the distance intersection of multiple laser trackers which has been widely used in large-scale measurements. However, the layout of laser trackers has a great impact on the final measurement accuracy. In order to improve the overall measurement accuracy, firstly, a measurement uncertainty model based on laser multilateration is established. Secondly, a fast laser intersection detection constraint algorithm based on a k-DOPS bounding box and an adaptive target ball incident angle constraint detection algorithm are established for large-scale measurement scenes. Finally, the constrained layout optimization of the laser trackers is realized by using an improved cellular genetic algorithm. The results show that the optimized system layout can achieve the full coverage of measurement points and has higher measurement accuracy. Compared with the traditional genetic algorithm, the improved cellular genetic algorithm converges faster and obtains a better position layout. [ABSTRACT FROM AUTHOR]

Developing localized climate mitigation strategies requires understanding how national consumption drives local carbon dioxide (CO2) emissions from a sectoral perspective. Exploring the carbon footprint considering inter-provincial trade is vitally important; however, few studies have explored the production side of embodied carbon emissions and the drivers of embodied carbon. Here, we use the multi-regional input–output model to calculate the flow of embodied carbon between provincial departments on Shanghai's production side in 2012, 2015, and 2017. We also establish a decomposition method for the embodied carbon index with small data demand. Our results show that from 2012 to 2017, the outflow increased and went to tertiary industries in neighboring and economically developed provinces. Among them, the activity effect drove the increase in carbon emissions, and the technique effect drove the reduction in embodied carbon. Surprisingly, we found that the low efficiency of the energy utilization of metal products and the unreasonable structure of other services increased embodied carbon emissions from 2012 to 2015. Sectors with high exogenous embodied carbon emissions are critical areas in which collaborative mitigation efforts between Shanghai and downstream provinces drive these emissions. Shanghai should avoid falling into the "low-carbon trap" of developing countries. It should continue to adjust its industrial structure and increase the use of low-carbon energy to achieve carbon reduction. [ABSTRACT FROM AUTHOR]

Simple Summary: As a member of the bone morphogenetic protein families (BMPs), BMP6 is a key regulatory factor in the ovaries. It is expressed in the ovarian granulosa cells (GCs) of different species and plays an important regulatory role in follicular growth and development. Moreover, the steroid hormones secreted by ovarian granulosa cells are involved in important physiological processes such as follicle development, ovulation, and embryo implantation. However, the role of BMP6 in steroid hormone synthesis in goat ovarian GCs remains unclear. We aimed to examine the effects of BMP6 on the function of goat ovarian GCs. The results showed that BMP6 did not significantly affect the proliferation, cell cycle, and apoptosis of ovarian GCs but it up-regulated the expression of the steroid hormone synthesis rate-limiting enzymes CYP19A1 and CYP11A1 to promote the production of 17 beta-estradiol (E2) and progesterone (P4). This finding provides a new idea for the study of steroid hormone synthesis in follicles. The purpose of this study was to investigate the effects of BMP6 on the function of goat ovarian granulosa cells (GCs). The results showed that the exogenous addition of BMP6 did not affect the EdU-positive ratio of ovarian GCs and had no significant effect on the mRNA and protein expression levels of the proliferation-related gene PCNA (p > 0.05). Meanwhile, BMP6 had no significant effect on the cycle phase distribution of GCs but increased the mRNA expression of CDK4 (p < 0.05) and CCND1 (p < 0.01) and decreased the mRNA expression of CCNE1 (p < 0.01). Moreover, BMP6 had no significant effect on the apoptosis rate of GCs and did not affect the mRNA expression levels of apoptosis-related genes BAX, BCL2, and Caspase3 (p > 0.05). Importantly, BMP6 upregulated the secretion of 17 beta-estradiol (E2) and progesterone (P4) in ovarian GCs (p < 0.01). Further studies found that BMP6 inhibited the mRNA expression of 3β-HSD and steroid synthesis acute regulator (StAR) but significantly promoted the mRNA expression of the E2 synthesis rate-limiting enzyme CYP19A1 and the P4 synthesis rate-limiting enzyme CYP11A1 (p < 0.01). Taken together, these results showed that the exogenous addition of BMP6 did not affect the proliferation, cell cycle, and apoptosis of goat ovarian GCs but promoted the secretion of E2 and progesterone P4 in ovarian GCs by upregulating the mRNA expressions of CYP19A1 and CYP11A1. [ABSTRACT FROM AUTHOR]

Featured Application: Adaptive optics in large aperture telescopes for wide band imaging. To realize the decoupling control of the dual corrector of liquid crystal (LC)–deformable mirror (DM) adaptive optics (AO) systems and prevent the loss of correction ability induced by coupling aberrations, a wavefront decoupling method based on DM eigenmodes was proposed. The coupling relationship of two correctors was studied, and the distribution of aberrations corrected using the DM and LC was investigated. First, a DM eigenmode matrix was derived according to the DM influence function. Then, aberrations were decomposed according to the Strehl ratio, and the projection of the LC response matrix on the DM eigenmode matrix, that is, the coupling term, was derived. Before using the LC corrector, the coupling item was eliminated for the decoupling of the two correctors, and the decoupling effect was verified through simulations. Finally, a 2 m telescope LC–DM AO system was developed to validate the proposed method. The simulation and experimental results showed that the root mean square of the DM-corrected residual after 55 eigenmodes' correction was 0.09 μm. After decoupling correction, the resolution of the 700–1700 nm wide band improved from 1.1 to 1 times the diffraction limit, which is 1.2 times better than that obtained using the traditional Zernike mode correction. [ABSTRACT FROM AUTHOR]

A large number of metalworking fluids in industrial manufacturing processes generate high-concentrations of oil mist pollution, which is a typical semi-volatile aerosol and is generally composed of liquid particles and volatile gas components. Long-term exposure to oil mist pollution brings a series of occupational diseases to workers. For the semi-volatile aerosol, the traditional filter sampling method will lead to particle volatilization, which underestimates the concentration of particles and overestimates the concentration of gas. Therefore, this study combined the advantages of the electrostatic method and the Tenax tube adsorption method, to develop a more accurate measurement technology. First, a dichotomous sampler that could efficiently separate the gas and liquid phases of aerosols was optimized through a numerical model, which was validated by literature results. Next, a test table for oil mist sampling was built with a sampler which was fabricated by 3D printing, and the performance of the sampler was evaluated. The results show that the sampling technique can separate the gas and particulate phases of the oil mist efficiently and accurately. Compared with the traditional single sampling methods, the new sampler can better determine the true concentration of oil mist. [ABSTRACT FROM AUTHOR]

Simple Summary: Liver receptor homolog-1 (LRH-1) is highly observed in tissues with secretory function, such as the ovaries, suggesting that LRH-1 may play an essential role in the ovarian endocrine. In particular, ovarian granulosa cells (GCs) are the functional cells that produce steroid hormones. The fate of GCs directly affects follicular development or atresia. These effects were lost in granulosa-specific LRH-1-knockout mice, showing that LRH-1 is a central modulator of ovarian function. However, the underlying mechanism LRH-1 in the bovine ovaries remains unclear. We aimed to detect the effect of LRH-1 on steroid hormones in bovine GCs and explore the potential relationship between LRH-1 and the fate of GCs. The results show that LRH-1 was specifically highly expressed in GCs of atretic follicles. Mechanistically, LRH-1 induced the apoptosis of bovine GCs by the progestogen receptor signaling pathway. While this finding provided new ideas for the study of follicular atresia, it also provided a theoretical basis for the clinical diagnosis and treatment for infertility in cow. The purpose of the present investigation was to assess the function of LRH-1 on GCs and the mechanisms involved. Here, LRH- was highly expressed in the bovine GCs of atretic follicles. Treatment with 50 μM of LRH-1 agonist (DLPC) significantly induced the expression of LRH-1 (p < 0.05). In particular, LRH-1 activation blocked the progestogen receptor signaling pathway via downregulating progesterone production and progestogen receptor levels (p < 0.05), but had no effect on 17 beta-estradiol synthesis. Meanwhile, LRH-1 activation promoted the apoptosis of GCs and increased the activity of caspase 3 (p < 0.05). Importantly, upregulating the progestogen receptor signaling pathway with progestogen could attenuate the LRH-1-induced proapoptotic effect. Moreover, treatment with progestogen decreased the activity of the proapoptotic gene caspase 3 and increased the expression of antiapoptotic gene Bcl2 in LRH-1 activated GCs (p < 0.05). Taken together, these results demonstrate that LRH-1 might be dependent on the progestogen receptor signaling pathway to modulate bovine follicular atresia. [ABSTRACT FROM AUTHOR]

The alignment precision of manual brush assembly for a precision conductive slip ring is critical to its performance of reliability and service lifetime. Currently, the alignment precision cannot be guaranteed since it largely depends on the operator's experiences and skill level. In this paper, a machine vision-aided method is proposed to measure the ring groove positions as the brush alignment objective, and track the relative brush position deviation during the manual brush alignment assembly. A vision-aided brush alignment assembly system is also developed to provide quantitative position deviation for the precise alignment of the brush and the ring groove, ensuring higher alignment accuracy and efficiency. The experimental results indicate that, with the developed system, the brush alignment assembly accuracy can be controlled within ±0.02 mm. [ABSTRACT FROM AUTHOR]

High light intensity as one of the stresses could lead to generation of large amounts of reactive oxygen species (ROS) in plants, resulting in severe plant growth retardation. The photorespiration metabolism plays an important role in producing and removing a variety of ROS, maintaining the dynamic balance of the redox reaction, and preventing photoinhibition. Arabidopsis hydroxypyruvate reductase 1 (HPR1) is a primary metabolic enzyme in the photorespiration cycle. However, the role of HPR1 in plants response to high light is not clear. Here, we found that the expression of HPR1 could be induced by high light intensity. The growth and photosynthetic capacity of hpr1 mutants are seriously affected under high light intensity. The absence of HPR1 suppresses the rates of photorepair of Photosystem II (PSII), aggravates the production of ROS, and accelerates photorespiration rates. Moreover, the activity of ROS scavenging enzymes in the hpr1 mutants is significantly higher. These results indicate that HPR1 is involved in plant response to high light intensity and is essential for maintaining the dynamic balance of ROS and photorespiration. [ABSTRACT FROM AUTHOR]

In monocarpic plants, stem cells are fated to die. However, the potential mechanism of stem cell death has remained elusive. Here, we reveal that the levels of two forms of reactive oxygen species (ROS), superoxide anion free radical ( O 2 · − ) and hydrogen peroxide (H2O2), show dynamic changes in the shoot apex during the plant life cycle of Arabidopsis thaliana. We found that the level of O 2 · − decreased and disappeared at four weeks after bolting (WAB), while H2O2 appeared at 3 WAB and showed a burst at 5 WAB. The timing of dynamic changes in O 2 · − and H2O2 was delayed for approximately three weeks in clv3-2, which has a longer lifespan. Moreover, exogenous application of H2O2 inhibited the expression of the stem cell determinant WUSCHEL (WUS) and promoted the expression of the developmentally programmed cell death (dPCD) marker gene ORESARA 1 (ORE1). These results indicate that H2O2 triggers an important signal inducing dPCD in stem cells. Given that O 2 · − plays roles in maintaining WUS expression and stem cell activity, we speculate that the dynamic shift from O 2 · − to H2O2 in the shoot apex results in stem cell death. Our findings provide novel insights for understanding ROS-mediated regulation during plant stem cell death. [ABSTRACT FROM AUTHOR]

AP2/ERF (APETALA2/ethylene responsive factor) is a family of plant-specific transcription factors whose members are widely involved in many biological processes, such as growth, development, and biotic and abiotic stress responses. Here, 20 AP2/ERF genes were identified based on wheat RNA-seq data before and after drought stress, and classified as AP2, ERF, DREB, and RAV. The analysis of gene structure revealed that about 85% of AP2/ERF family members had lost introns, which are presumed to have been lost during the formation and evolution of the wheat genome. The expression of 20 AP2/ERF family genes could be verified by qRT-PCR, which further supported the validity of the RNA-seq data. Subsequently, subcellular localization and transcriptional activity experiments showed that the ERF proteins were mainly located in the nucleus and were self-activating, which further supports their functions as transcription factors. Furthermore, we isolated a novel ERF gene induced by drought, salt, and cold stresses and named it TaERF-6-3A. TaERF-6-3A overexpression increased sensitivity to drought and salt stresses in Arabidopsis, which was supported by physiological and biochemical indices. Moreover, the expression of stress- and antioxidant-related genes was downregulated in TaERF-6-3A-overexpressing plants. Overall, these results contribute to the further understanding of the TaERF-6-3A gene function in wheat. [ABSTRACT FROM AUTHOR]

The inertial sensor is the key measurement payload of the technology verification satellite of China's space gravitational wave detection mission-Taiji Project, which uses capacitive sensors to sense the acceleration disturbance of the test mass under the influence of non-conservative forces in the frequency range of 10 mHz~1 Hz. It is necessary to perform a ground performance evaluation and estimate the working state of the payload in orbit. However, due to the influence of the earth's gravity and seismic noise, it is impossible to directly evaluate the resolution level of the non-sensitive axis when testing with high-voltage levitation, which leads to incomplete evaluation of the performance of the inertial sensor. In order to implement this part of the test, the sensitive structure is designed and a torsion pendulum facility for performance testing is developed. The experimental results show that the measurement resolution of the non-sensitive axis of the inertial sensor can reach 9.5 × 10−7 m/s2/Hz1/2 under the existing ground environmental conditions and is mainly influenced by the seismic noise during the system measurement. If the inertial sensor enters orbit, the measurement resolution can achieve 3.96 × 10−9 m/s2/Hz1/2, which meets the requirements of the technology verification satellite for a non-sensitive axis. This proposed system also provides a reasonable method for the comprehensive evaluation of inertial sensors in the future. [ABSTRACT FROM AUTHOR]

A small Boron ion (B-ion) dose of 5 × 1014 cm−2 was implanted in a GaN epilayer at an energy of 50 keV, and the sample was subjected to high-temperature rapid thermal annealing (RTA). The resonant Raman spectrum (RRS) showed a strong characteristic of a photoluminescence (PL) emission peak associated with GaN before B-ion implantation and RTA treatment. The PL signal decreased significantly after the B-ion implantation and RTA treatment. The analysis of temperature-dependent Raman spectroscopy data indicated the activation of two transitions in B-ion-implanted GaN in different temperature ranges with activation energies of 66 and 116 meV. The transition energies were estimated in the range of 3.357–3.449 eV through calculations. This paper introduces a calculation method that can be used to calculate the activation and transition energies, and it further highlights the strong influence of B-ion implantation on the luminesce of GaN. [ABSTRACT FROM AUTHOR]

Surface plasmon (SP) enhancement of photoluminescence (PL) from a green-emitting InGaN/GaN quantum well (QW) using nanoparticles (NPs) made of different metals and their combinations was investigated. The NPs were formed by annealing the metal films in N2 followed by rapid cooling. Four-fold enhancement in PL intensity was achieved using random metal NPs made of Cu on Mg (Cu-Mg) double metal film that was more than two folds of the enhancement observed by AgNPs. Reversing the order of metal film deposition (Mg on Cu) resulted in much lower PL intensity due to significantly different NPs size distribution as the given annealing conditions did not cause homogeneous alloying of the two metals. The results pave the way for the application of NPs of relatively low-cost unconventional metals and their combinations in the SP enhancement of LEDs. [ABSTRACT FROM AUTHOR]

We investigated the functions of microRNAs in the therapeutic effects of noni (Morinda citrifolia L.) fruit juice on mouse models of acute gouty arthritis induced with monosodium urate (MSU). Compared with the model group (treated with MSU), mice in both the positive control group (treated with both MSU and colchicine) and noni fruit juice group (treated with MSU and noni fruit juice) showed a significantly decreased degree of paw swelling in 5 days, as well as the contents of two types of proinflammatory cytokines (i.e., NALP3 and TNF-α). Based on the next-generation sequencing technology, a total of 3896 microRNAs (234 known and 3662 novel) were identified in mice treated with noni fruit juice. A large amount of differentially expressed miRNAs were identified in the noni fruit juice group, suggesting the significant effects of noni fruit juice on the mice with acute gouty arthritis, while the different patterns of change in the numbers of both upregulated and downregulated miRNAs in both noni fruit juice and positive control groups indicated that the mice of acute gouty arthritis may be regulated by differential mechanisms between the treatments of noni fruit juice and colchicine. The target genes of microRNAs involved in the pathogenesis and pathology of acute gouty arthritis in mice were identified and further annotated by both Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Our results revealed the therapeutic effects of noni fruit juice on acute gouty arthritis in mice with a group of microRNAs involved in the pharmacological mechanisms of noni fruit juice, providing scientific evidence to support both the agricultural cultivation and pharmacological significance of noni plants. [ABSTRACT FROM AUTHOR]

Surface plasmon (SP)-enhanced quantum-well (QW) LEDs have proved their potential in replacing conventional lighting devices for their high-performance capabilities in ultraviolet (UV), blue and green spectral ranges. The SP-enhanced QW-LEDs have applications in light emission enhancement, light polarization, color conversion, and speed modulation. The electric field of the plasmonic mode of a metal couples with the exciton energy of QWs in resonance results in efficiency enhancement to several folds. The strength of the SP–QW coupling is mainly influenced by the type of metal used for SP enhancement, the metal nanostructure geometry, and the penetration depth of the SP fringing field in the p-GaN. The use of an appropriate dielectric interlayer between the metal and the p-GaN allows further control over SP resonance with QW emission wavelength. The penetration depth defines the p-GaN thickness and the QW period number for effective SP–QW coupling. The optimization of these parameters is key to achieve high efficiencies in SP-enhanced QW-LEDs for various applications. This review explains the SP enhancement mechanism and the key challenges facing the SP enhancement of QW-LEDs. The main factors that affect the SP–QW coupling have been explained in detail based on recent reports devoted to this field. [ABSTRACT FROM AUTHOR]

AlN epilayers were grown on a 2-inch [0001] conventional flat sapphire substrate (CSS) and a nano-patterned sapphire substrate (NPSS) by metalorganic chemical vapor deposition. In this work, the effect of the substrate template and temperature on stress and optical properties of AlN films has been studied by using Raman spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM), UV-visible spectrophotometer and spectroscopic ellipsometry (SE). The AlN on NPSS exhibits lower compressive stress and strain values. The biaxial stress decreases from 1.59 to 0.60 GPa for AlN on CSS and from 0.90 to 0.38 GPa for AlN on NPSS sample in the temperature range 80–300 K, which shows compressive stress. According to the TEM data, the stress varies from tensile on the interface to compressive on the surface. It can be deduced that the nano-holes provide more channels for stress relaxation. Nano-patterning leads to a lower degree of disorder and stress/strain relaxes by the formation of the nano-hole structure between the interface of AlN epilayers and the substrate. The low crystal disorder and defects in the AlN on NPSS is confirmed by the small Urbach energy values. The variation in bandgap (Eg) and optical constants (n, k) with temperature are discussed in detail. Nano-patterning leads to poor light transmission due to light scattering, coupling, and trapping in nano-holes. [ABSTRACT FROM AUTHOR]

Group III–V semiconductors with direct band gaps have become crucial for optoelectronic and microelectronic applications. Exploring these materials for spintronic applications is an important direction for many research groups. In this study, pure and cobalt doped GaN nanowires were grown on the Si substrate by the chemical vapor deposition (CVD) method. Sophisticated characterization techniques such as X-ray diffraction (XRD), Scanning Electron Microscope (SEM), Energy Dispersive X-Ray Spectroscopy (EDS), Transmission Electron Microscopy (TEM), High-Resolution Transmission Electron Microscopy (HRTEM) and photoluminescence (PL) were used to characterize the structure, morphology, composition and optical properties of the nanowires. The doped nanowires have diameters ranging from 60–200 nm and lengths were found to be in microns. By optimizing the synthesis process, pure, smooth, single crystalline and highly dense nanowires have been grown on the Si substrate which possess better magnetic and optical properties. No any secondary phases were observed even with 8% cobalt doping. The magnetic properties of cobalt doped GaN showed a ferromagnetic response at room temperature. The value of saturation magnetization is found to be increased with increasing doping concentration and magnetic saturation was found to be 792.4 µemu for 8% cobalt doping. It was also depicted that the Co atoms are substituted at Ga sites in the GaN lattice. Furthermore N vacancies are also observed in the Co-doped GaN nanowires which was confirmed by the PL graph exhibiting nitrogen vacancy defects and strain related peaks at 455 nm (blue emission). PL and magnetic properties show their potential applications in spintronics. [ABSTRACT FROM AUTHOR]

Wide bandgap III-V compounds are the key materials for the fabrication of short-wavelength optical devices and have important applications in optical displays, optical storage devices and optical communication systems. Herein, the variable-angle spectroscopic ellipsometry (SE) measurements are performed to investigate the thickness and optical properties of beryllium-implanted gallium nitride thin films that have been deposited on (0001) sapphire substrates by using low-pressure metalorganic chemical vapor deposition (LPMOCVD). The film layer details are described by using Parametric Semiconductor oscillators and Gaussian oscillators in the wavelength range of 200–1600 nm. The thickness, refractive indices and extinction coefficients of the Be-implanted films are determined at room temperature. Analysis of the absorption coefficient shows that the optical absorption edge of Be-implanted films changes from 3.328 eV to 3.083 eV in the temperature range of 300–850 K. With the variable temperature, Eg is demonstrated to follow the formula of Varshni. A dual-beam ultraviolet–visible spectrophotometer (UV–VIS) is used to study the crystal quality of samples, indicating that the quality of rapid thermal annealing (RTA) sample is better than that unannealed sample. By transport of ions in matter (TRIM) simulation and SE fitting the depths of Be implanted gallium nitride (GaN) films are estimated and in good agreement. The surface and cross-section morphologies are characterized by atomic force microscopy (AFM) and scanning electron microscope (SEM), respectively. The surface morphologies and thickness measurements of the samples show that RTA can improve crystal quality, while increasing the thickness of the surface roughness layer due to partial surface decomposition in the process of thermal annealing. [ABSTRACT FROM AUTHOR]

Although the bubble contacting a uniformly superaerophilic surface has caused concern due to its application potential in various engineering equipment, such as mineral flotation, very little is known about the mechanism of how the bubble spreads on a surface with anisotropic superaerophilicity. To unveil this mystery, we experimentally studied the anisotropic behavior of a bubble (2 mm in diameter) spreading on the superaerophilic straight trajectories (SALTs) of different widths (0.5 mm–5 mm) in water using a high-speed shadowgraphy system. The 1–3 bounces mostly happened as the bubble approached the SALTs before its spreading. It is first observed that the bubble would be split into two highly symmetrical sub-bubbles with similar migration velocity in opposite directions during the anisotropic spreading. Two essential mechanisms are found to be responsible for the anisotropic spreading on the narrow SALTs (W ≤ 2 mm with two subregimes) and the wide SALTs (W ≥ 3 mm with four subregimes). Considering the combined effect of the surface tension effect of SALT and Laplace pressure, a novel model has been developed to predict the contact size r(t) as a function of time. The nice agreement between this model and our experiments reconfirms that the surface tension effect and Laplace pressure prevail over the hydrostatic pressure. [ABSTRACT FROM AUTHOR]

Psoriasis is a skin disease that is characterized by a high degree of inflammation caused by immune dysfunction. (R)-salbutamol is a bronchodilator for asthma and was reported to alleviate immune system reactions in several diseases. In this study, using imiquimod (IMQ)-induced mouse psoriasis-like dermatitis model, we evaluated the therapeutic effects of (R)-salbutamol in psoriasis in vivo, and explored the metabolic pathway involved. The results showed that, compared with IMQ group, (R)-salbutamol treatment significantly ameliorated psoriasis, reversed the suppressive effects of IMQ on differentiation, excessive keratinocyte proliferation, and infiltration of inflammatory cells. Enzyme-linked immunosorbent assays (ELISA) showed that (R)-salbutamol markedly reduced the plasma levels of IL-17. Cell analysis using flow cytometry showed that (R)-salbutamol decreased the proportion of CD4+ Th17+ T cells (Th17), whereas it increased the percentage of CD25+ Foxp3+ regulatory T cells (Tregs) in the spleens. (R)-salbutamol also reduced the increased weight ratio of spleen to body. Furthermore, untargeted metabolomics showed that (R)-salbutamol affected three metabolic pathways, including (i) arachidonic acid metabolism, (ii) sphingolipid metabolism, and (iii) glycerophospholipid metabolism. These results demonstrated that (R)-salbutamol can alleviate IMQ-induced psoriasis through regulating Th17/Tregs cell response and glycerophospholipid metabolism. It may provide a new use of (R)-salbutamol in the management of psoriasis. [ABSTRACT FROM AUTHOR]

Soft sensing technology has been proved to be an effective tool for the online estimation of unmeasured or variables that are difficult to directly measure. The performance of a soft sensor depends heavily on its convergence speed and generalization ability to a great extent. Based on this idea, we propose a new soft sensor model, Isomap-SVR. First, the sample data set is divided into training set and testing set by using self-organizing map (SOM) neural network to ensure the fairness and symmetry of data segmentation. Isometric feature mapping (Isomap) method is used for dimensionality reduction of the model input data, which could not only reduce the structure complexity of the proposed model but speed up learning speed, and then the Support Vector Machine Regression (SVR) is applied to the regression model. A novel bat algorithm based on Cauchy mutation and Lévy flight strategy is used to optimize parameters of Isomap and SVR to improve the accuracy of the proposed model. Finally, the model is applied to the prediction of the temperature of rotary kiln calcination zone, which is difficult to measure directly. The simulation results show that the proposed soft sensor modeling method has higher learning speed and better generalization ability. Compared with other algorithms, this algorithm has obvious advantages and is an effective modeling method. [ABSTRACT FROM AUTHOR]