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The unique Karst landforms in Southwest China led to fragile soil structures and erosion, compounded by the large-scale non-agricultural transfer of the rural labor force. This study employed a two-way fixed effects model to analyze the impact of rural labor mobility on the green utilization efficiency of cultivated land, controlling for unobservable individual and time-specific factors. Mediation analysis examined how changes in planting structure influenced this relationship, considering labor force structure heterogeneity. Results revealed a U-shaped relationship, with rural labor mobility initially causing a short-term decline in efficiency but leading to a long-term increase. The mechanism of action showed that rural labor mobility indirectly affected efficiency by altering planting structures; specifically, increased replanting frequency had a partial mediating effect (0.158), while a “grain-oriented” focus masked the effect (0.067). Additionally, an aging workforce and low education levels negatively impacted green utilization efficiency, complicating the situation in the Karst region. To enhance green utilization of cultivated land, it is essential to protect migrant farmers’ rights, improve agricultural labor quality, promote industrial upgrades, and adjust planting structures. These strategies will guide regional agriculture and ensure sustainable land resource use.

Background: The influence of serum uric acid (SUA) on bone metabolism, as suggested by previous studies, remains a contentious issue. SUA plays a complex role in bone health and hypertension, making it challenging to discern its impact on the skeletal status of middle-aged and elderly hypertensive patients. This study aims to elucidate the effects of SUA on bone health, with a particular focus on its association with osteoporosis and the risk of fractures. Methods: Multiple linear regression analyzed SUA levels against bone mineral density (BMD) and future fracture risk. Additionally, multivariate logistic regression was used to examine the association between SUA and osteoporosis. Dose–response relationship analysis was conducted using generalized smooth curve fitting (GSCF) and restricted cubic spline (RCS) methods. Results: With the exception of the total femur region, SUA and BMD showed a positive connection. GSCF analysis revealed an inverted U-shaped relationship between SUA and BMD, alongside a U-shaped trend with FRAX scores. Moreover, RCS analysis indicated a U-shaped relationship between osteoporosis risk and SUA levels, with higher risks identified in the first and third tertiles compared to the second tertile. Conclusions: In individuals with middle-aged and older hypertension, SUA is substantially linked to bone health. The identification of an inverted U-shaped relationship with BMD and U-shaped relationships with FRAX scores and osteoporosis risk highlights the nuanced influence of SUA. These findings suggest that both low and high SUA levels may adversely affect bone health, emphasizing the need for further research.

The relationship between the stability of tool materials and their cutting performance is a critical technical challenge for the manufacturing industry, which is essential for selecting appropriate treatment processes to achieve superior treatment tool performance. In this paper, a standard cutting tool experiment was used to study the sharpness of the knife with different residual austenite content. The cutting edges of the knife were characterized by an optical microscope (OM), scanning electron microscope (SEM), electron back scattering diffraction (EBSD), and transmission electron microscope (TEM), to analyze the relationship between tool edge hardness and microstructure. The microstructure stability of the material was analyzed by a separated Hopkinson pressure bar (SHPB) experiment. The results show that the hardness and cutting performance of the knives are affected by the joint action of carbide and residual austenite, with an initial increase followed by decreases as the heat treatment quenching temperature increases. After the knife material is treated by cryogenic process, the hardness of the knife is increased by 3.89 HRC, the initial sharpness by 15.3%, and the sharpness and durability by 18.8%. The residual austenite in the knives was found to be unstable and easy to transformation during high-rate deformation processes. This study elucidates the effect of residual austenite content on the sharpness of the knives, providing a foundation for the reasonable control of residual austenite content in the actual production settings.

Botrytis cinerea, the grey mould fungus affecting over 1400 plant species, employs infection cushion (IC), a branched and claw-like structure formed by mycelia, as a critical strategy to breach host surface barriers. However, the molecular mechanisms underlying IC formation remain largely unexplored. In this study, we utilized a forward genetics approach to establish a large T-DNA tagged population of B. cinerea, which contained 14,000 transformants. Through phenotype screening, we identified 161 mutants with defects in IC development. Detailed analyses revealed that these mutants exhibited various degrees of impairment in IC formation, ranging from complete failure to form ICs to a reduction in the number and maturity of ICs. Further genetic analysis of one of the mutants led to the identification of EXO70, a gene encoding a component of the exocyst complex, as a key regulatory factor in IC development. Mutants with deletion of EXO70 failed to form ICs, confirming its crucial role in the process. The mutant library reported here provides a rich resource for further large-scale identification of genes involved in IC development. Our findings provide valuable insights into the genetic and molecular basis of IC formation and offer new targets for controlling B. cinerea pathogenicity.

Agricultural green development plays a crucial role in addressing the conflict between agricultural production and ecological environmental protection. This study aims to analyze the impact and mechanisms through which digital inclusive finance influences agricultural green development, providing empirical evidence to support the role of digital inclusive finance in promoting sustainable agricultural practices. Using panel data from 30 provinces (cities and districts) in China spanning from 2011 to 2020, this study measures regional agricultural green development levels using the entropy power method. By clarifying the theoretical mechanisms behind the relationship between digital inclusive finance and agricultural green development, this research employs panel fixed effects, mediation effects, and threshold regression models to empirically examine the impact of digital inclusive finance, specifically its coverage breadth, usage depth, and digitization level, on agricultural green development. Furthermore, this study explores the threshold effects of digital inclusive finance on agricultural green development. The findings from this study indicate that digital inclusive finance significantly contributes to agricultural green development, with a double-threshold effect observed as follows: when digital inclusive finance surpasses critical thresholds, its impact on agricultural green development intensifies. This result remains robust after conducting a series of endogeneity and robustness tests. The mechanism analysis reveals that digital inclusive finance drives agricultural green development by fostering technological innovation and increasing farmers’ income. Heterogeneity analysis further demonstrates that digital inclusive finance has a substantial impact on agricultural green development in eastern China compared to the central and western regions. Consequently, this study suggests that digital inclusive financial services should be further expanded and deepened to foster the integration of financial services with agriculture, creating a sustainable development model that promotes shared prosperity and effectively supports the green development of agriculture.

For electrically driven commercial vehicles equipped with three-speed automatic mechanical transmission (AMT), the transmission control unit (TCU) without vehicle mass and road gradient estimation function will lead to frequent shifting and insufficient power during vehicle full-load or grade climbing. Therefore, it is necessary to estimate the mass and road gradient for the electrically driven commercial vehicles equipped with the three-speed AMT, and to adjust the shift rule according to the estimation results. Given the above problems, this paper focuses on the control and development of the electrically driven three-speed AMT and takes the shift controller with the vehicle mass and road gradient estimation as the research goal. The mathematical model and simulation model of vehicle dynamics are established to verify the shift function of TCU. The least squares method and calibration techniques are applied to estimate the vehicle mass and road gradient. According to the estimation results, the existing shift strategy is optimized, and the software-in-the-loop simulation of the transmission controller is carried out to verify the function of the control algorithm software. The hardware-in-the-loop test model is established to verify the shift strategy’s optimization effect, which shortens the controller’s forward development cycle. According to the estimation results of mass and gradient, the error result of the proposed method is controlled within 4.5% for mass and 8.6% for gradient. The experiment verifies that the optimized shift strategy can effectively improve the dynamic performance of the vehicle. The HIL experimental results show that the vehicle can maintain low gear while climbing the hill, and the vehicle speed does not decrease significantly.

In order to reduce CO₂ emissions from industrial processes, countries have commenced the vigorous development of CCUS (carbon capture, utilization and storage) technology. The high geographical overlap between China’s extensive coal mining regions and CO2-emitting industrial parks provides an opportunity for the more efficient reduction in CO2 emissions through the development of Enhanced Coal Bed Methane (ECBM) Recovery for use with CCUS technology. Furthermore, the high geographical overlap and proximity of these regions allows for a shift in the transportation mode from pipelines to tanker trucks, which are more cost-effective and logistically advantageous. The issue of transportation must also be considered in order to more accurately assess the constructed cost function and CCUS source–sink matching model for the implementation of ECBM. The constructed model, when considered in conjunction with the actual situation in Shanxi Province, enables the matching of emission sources and sequestration sinks in the province to be realized through the use of ArcGIS 10.8 software, and the actual transport routes are derived as a result. After analyzing the matching results, it is found that the transportation cost accounts for a relatively small proportion of the total cost. In fact, the CH4 price has a larger impact on the total cost, and a high replacement ratio is not conducive to profitability. When the proportion of CO2 replacing CH4 increases from 1 to 3, the price of CH4 needs to increase from $214.41/t to $643.23/t for sales to be profitable. In addition, electric vehicle transportation costs are lower compared to those of fuel and LNG vehicles, especially for high-mileage and frequent-use scenarios. In order to reduce the total cost, it is recommended to set aside the limitation of transportation distance when matching sources and sinks.

The prolonged practice of continuous potato cropping, coupled with inadequate field management, disrupts the soil bacterial community equilibrium. Such disturbances compromise the resilience of the soil ecosystem, predisposing it to an increased incidence of potato diseases. However, the effects of the phosphorus fertiliser application rate on the rhizosphere soil bacterial community composition of potatoes and the occurrence of potato common scab (CS) have not been adequately studied. Here, diseased field soils from Dingxi and Huidong Counties were collected for potting tests, and field tests were conducted in Huidong County for validation. An examination of the relationship between the bacterial community composition in the potato rhizosphere soil and potato CS under different phosphate fertiliser treatments was conducted using 16S rRNA high-throughput sequencing. The results show that a lower phosphorus fertiliser application rate was more conducive to maintaining soil bacterial community diversity under different phosphorus fertiliser treatments in different habitats. In addition, the relative abundance of the txtA gene increased significantly (p < 0.05) with the increase in the phosphate fertiliser application rate. Field trials conducted in Huidong revealed that treatments F1, F2, and F3 had respective CS incidence rates of 28.33%, 46.67%, and 59.44%, while their corresponding disease severity indices were 7.67, 17.33, and 29.44. Further analysis revealed that the relative abundance of antagonistic genera of pathogenic S. scabies decreased significantly (p < 0.05) with increases in the phosphorus fertiliser application rate. In summary, the correlation between potato CS and changes in the bacterial community of rhizosphere soil was used to determine the optimal phosphorus application rate during potato production, which can provide a scientific basis for the management of phosphorus fertiliser in potato farmland.

Accurate deblending techniques are essential for the successful application of blended seismic acquisition. Deep-learning-based deblending methods typically begin by performing a pseudo-deblending operation on blended data, followed by further processing in either the common-shot domain or a non-common-shot domain. In this study, we propose an iterative deblending framework based on deep learning, which directly addresses the blended data in the shot domain, eliminating the need for pseudo-deblending and domain transformation. This framework is built around a unique architecture, termed WNETR, which derives its name from its W-shaped network structure that combines U-Net and Transformer. During testing, the trained WNETR is incorporated into the iterative framework to extract useful signals iteratively. Tests on synthetic data validate the effectiveness of the proposed deblending iterative framework.

As unmanned aerial vehicles (UAVs) are widely used in various fields, there is an increasing demand for UAV anti-jamming, multipath mitigation, and covert secrecy. Frequency-hopping binary offset carrier (FH-BOC) signals possess higher anti-jamming and multipath mitigation capabilities than direct-sequence spread spectrum (DSSS) and binary offset carrier (BOC) signals. A prerequisite for constructing communication links between UAVs using FH-BOC signals is the design of efficient acquisition algorithms to capture the signals successfully. In this paper, the modulation and characteristics of the FH-BOC signal are introduced. The maximum relative velocity between UAVs is 5.5 km/s, the maximum acceleration is 50 g, and the maximum plus acceleration is 20 g/s. In this high dynamic environment, the parameters for the parallel code phase and Partial Matched Filter–Fast Fourier Transform (PMF-FFT) acquisition algorithms targeting FH-BOC(10,1) signals are designed, and the acquisition performance of these algorithms is comparatively analyzed. The acquisition time for the first and second algorithms is 4.3317 s and 6.137 s. The number of real additions required by the first and second algorithms is approximately 10.9×109 and 8.9×109, and the number of real multiplications is approximately 7.6×109 and 6.7×109. This helps in selecting the acquisition algorithm when FH-BOC signals are used to build inter-UAV communication links.

Since the goals of carbon peaking and carbon neutrality have been established, forest carbon sinks have garnered significant attention. As a fundamental component of forest carbon sinks, the quality of forest land significantly influences the carbon sink capacity of forests. This study utilized Kaizhou District, Chongqing City, a typical forest area as a case study, and used the correction method, factor method, CASA model, landscape ecology indexes, and canonical correlation analysis to evaluate the level of forest land quality and reveal the spatial distribution pattern and influencing mechanisms of forest land quality. The results showed that: (i) The quality index of public welfare forest land was distributed in [37.89, 148.15], and each quality level was diversified in space. The quality index of commodity forest land was distributed in [40.00, 92.67], and some high-quality forest land appeared in the transition zone of each region; (ii) The forest land quality index and the amount of net primary productivity passed the correlation test. Primary net productivity was higher on forest land with a high-quality index and lower on forest area with a low-quality index; (iii) public welfare forest land was mainly positively affected by community structure, average annual precipitation, average annual temperature, and soil moisture. Commodity forest land was mainly positively affected by average annual temperature, soil moisture, and slope aspect. However, landform had a significant negative impact on the two types of forest land. Given these findings, we also proposed a series of measures aimed at promoting the sustainable development of research on regional forest land.

Chinese Baijiu is a famous fermented alcoholic beverage in China. Interactions between key microorganisms, i.e., Saccharomyces cerevisiae and Lactiplantibacillus plantarum, have recently been reported at specific temperatures. However, empirical evidence of their interactions at various temperatures during fermentation is lacking. The results of this study demonstrated that S. cerevisiae significantly suppressed the viability and lactic acid yield of L. plantarum when they were cocultured above 15 °C. On the other hand, L. plantarum had no pronounced effect on the growth and ethanol yield of S. cerevisiae in coculture systems. S. cerevisiae was the main reducing sugar consumer. Inhibition of lactic acid production was also observed when elevated cell density of L. plantarum was introduced into the coculture system. A proteomic analysis indicated that the enzymes involved in glycolysis, lactate dehydrogenase, and proteins related to phosphoribosyl diphosphate, ribosome, and aminoacyl-tRNA biosynthesis in L. plantarum were less abundant in the coculture system. Collectively, our data demonstrated the antagonistic effect of S. cerevisiae on L. plantarum and provided insights for effective process management in light-flavor Baijiu fermentation.

Healthy community assessment standards significantly influence community design and planning and are an important measure of a community’s ability to support health and well-being. Previous studies have mostly focused on built-environment indicators. However, building a healthy community is a complex issue involving multiple dimensions and factors. The consideration of the full range of health promotion factors is the key to determining their potential impact on individuals’ health. It is necessary to consider multiple perspectives to deepen the understanding of community health influences and enhance the effectiveness of the implementation of the assessment standards. The socio-ecological model (SEM) provides a critical framework for understanding the multiple influences on individual health. In this study, a two-dimensional interdisciplinary analytical framework of “socio-ecological factors–development goals” is developed by integrating development goals that summarize building considerations in assessment standards. Contextual analysis is used to examine the provisions of the following assessment standards: Assessment Standard for Healthy Community (ASHC), Assessment Standard for Healthy Retrofitting of Existing Residential Area (ASHRERA), WELL Community Standard (WELL), and Fitwel Certification System of Community (Fitwel). The results show that community and organization factors are used more than interpersonal and policy factors among the four standards. Humanistic constructions lack attention in the ASHC and ASHRERA standards compared with the other standards. The differences between the four standards indicate that there is a need to focus on regional features and develop locally adapted interventions. This study offers a novel exploration of the potential effectiveness of healthy community assessment standards from a multidisciplinary perspective. The results of this study support standard-setters and planners in the development of interventions to improve building healthy communities using intersectionality frameworks.

Since FTIR is a sensitive micro-region measurement method, research on the impact of the heterogeneity of both aggregates and asphalt mortar is meaningful and comprehensive for accurate measurement with FTIR spectroscopy. In this paper, the impact of the heterogeneity of aggregates coated with asphalt mortar on their FTIR spectra and spectral reproducibility was creatively studied. The comparative analysis of the respective absorption peaks indicated that the characteristic absorption peaks of the aggregate coated with asphalt mortar were the superposition of the respective absorption peaks of its components. And research on the spectra of the coated aggregates obtained from the same batch of asphalt mixture fabricated at the same time showed that significantly different peak intensities could be affected by minor variations in their components due to the heterogeneity. Furthermore, statistical analysis suggested that the original spectral reproducibility of the coated aggregates was greatly affected by their heterogeneity, with a high coefficient of variation values. In conclusion, the heterogeneity of the coated aggregates could affect peak intensities and spectral reproducibility in micro-regions.

By studying the development law of urbanization, the problems of disorderly expansion and resource wastage in urban built-up areas can be effectively avoided, which is crucial for the long-term sustainable development of cities. This study proposes a high-precision urban built-up-area extraction method for county-level cities for small and medium-sized towns in county-level regions. Our process is based on the Defense Meteorological Satellite/Operational Linescan System (DMSP/OLS) and the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS), which develops long-term series of coordinated night-time light (NTL) datasets. We then combined this with the Normalized Vegetation Index (NDVI) to calculate the Vegetation-Adjusted NTL Urban Index (VANUI). We combine land use data and a support vector machine (SVM) for semi-supervised classification learning to propose a high-precision urban built-up-area extraction method for county-level cities. We achieved the following results: (1) we fit binary polynomials to the DMSP/OLS and VIIRS NTL datasets based on the correspondence of the mean values to construct a consistent time series of NTL data. (2) Our method effectively improves the accuracy of urban built-up-area extraction, especially for county-level cities, with an overall accuracy of 91.84% and a Kappa coefficient of 0.83. (3) Our method can perform a long-time series of urban built-up-area extraction, and, by studying the spatial and temporal changes in urban built-up areas, it can provide valuable information for sustainable urban development and urban planning.

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs’ localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement Units (IMUs), cameras, and high-definition (HD) maps. Firstly, we use a novel electronic horizon module to assess GNSS integrity and concurrently load the HD map data surrounding the AVs. This map data are then transformed into a visual space to match the corresponding lane lines captured by the on-board camera using an improved BiSeNet. Consequently, the matched HD map data are used to correct our localization algorithm, which is driven by an extended Kalman filter that integrates multiple sources of information, encompassing a GNSS, IMU, speedometer, camera, and HD maps. Our system is designed with redundancy to handle challenging city tunnel scenarios. To evaluate the proposed system, real-world experiments were conducted on a 36-kilometer city route that includes nine consecutive tunnels, totaling near 13 km and accounting for 35% of the entire route. The experimental results reveal that 99% of lateral localization errors are less than 0.29 m, and 90% of longitudinal localization errors are less than 3.25 m, ensuring reliable lane-level localization for AVs in challenging urban tunnel scenarios.

This paper presents a contraction-based learning control architecture that allows for using model learning tools to learn matched model uncertainties while guaranteeing trajectory tracking performance during the learning transients. The architecture relies on a disturbance estimator to estimate the pointwise value of the uncertainty, i.e., the discrepancy between a nominal model and the true dynamics, with pre-computable estimation error bounds, and a robust Riemannian energy condition for computing the control signal. Under certain conditions, the controller guarantees exponential trajectory convergence during the learning transients, while learning can improve robustness and facilitate better trajectory planning. Simulation results validate the efficacy of the proposed control architecture.

The aim of this paper is to prove a theorem for holomorphic twisted quiver bundles over a special non-compact Gauduchon manifold, connecting the existence of (σ,τ)-Hermite–Yang–Mills metric in differential geometry and the analytic (σ,τ)-stability in algebraic geometry. The proof of the theorem relies on the flow method and the Uhlenbeck–Yau’s continuity method.

This research investigates the impact of nonlinear clearance factors on position tracking accuracy in the servo drive system of a harmonic reducer. The study introduces a technique for modeling and compensating for transmission errors, thereby improving position tracking accuracy through online compensation combined with an auto-disturbance rejection controller. Initially, the mathematical model of the permanent magnet synchronous motor is outlined, and the current loop and speed loop control models are derived. Subsequently, an electromechanical actuator (EMA) simulation model with clearance is established, and detailed simulation analysis is conducted to verify the impact of clearance on tracking accuracy. A model for online compensation of transmission errors is then developed. Following the principles of active disturbance rejection control (ADRC), a second-order ADRC is formulated for real-time compensation of transmission errors in EMA position mode. Finally, through no-load and load experiments, the change in position tracking error with and without transmission error compensation is compared and analyzed. The results demonstrate that utilizing automatic disturbance rejection control with transmission error compensation achieves the highest position tracking accuracy. Compared to the proportion integration differentiation (PID) control method, the root mean square of position tracking error is reduced by approximately 12.8% and 17.3% under no-load and load conditions, respectively. By compensating for position errors online, the accuracy of the EMA position can be improved.

This research addresses the issues of weak anti-disturbance ability, fast response, and incompatibility of overshoot in the control process of brushless DC motors (BLDCs). A six-step commutation control method based on second-order active disturbance rejection control (ADRC) is derived following the analysis of the BLDC model and the mathematical model of ADRC. A control model of the BLDC using both PI and ADRC is constructed. Detailed comparative and quantitative analyses of the simulation results using PI and ADRC are conducted, focusing on the anti-load disturbance capabilities using the integrated square error (ISE), integrated time square error (ITSE), integrated absolute error (IAE), and integrated time absolute error (ITAE). Experimental testing on the STM32F4 controller is also carried out, analyzing four error integral criteria in depth. The results indicate that both the ADRC and PI control modes can track the target signal without overshooting, demonstrating strong anti-load disturbance ability and robustness at varying working speeds. In the BLDC control system, using the ADRC control method can achieve fast and non-overshoot tracking of target signals compared to the PI control method, and ADRC has stronger resistance to load disturbances.

Leveraging data from various modalities to enhance multimodal segmentation tasks is a well-regarded approach. Recently, efforts have been made to incorporate an array of modalities, including depth and thermal imaging. Nevertheless, the effective amalgamation of cross-modal interactions remains a challenge, given the unique traits each modality presents. In our current research, we introduce the semantic guidance fusion network (SGFN), which is an innovative cross-modal fusion network adept at integrating a diverse set of modalities. Particularly, the SGFN features a semantic guidance module (SGM) engineered to boost bi-modal feature extraction. It encompasses a learnable semantic guidance convolution (SGC) designed to merge intensity and gradient data from disparate modalities. Comprehensive experiments carried out on the NYU Depth V2, SUN-RGBD, Cityscapes, MFNet, and ZJU datasets underscore both the superior performance and generalization ability of the SGFN compared to the current leading models. Moreover, when tested on the DELIVER dataset, the efficiency of our bi-modal SGFN displayed a mIoU that is comparable to the hitherto leading model, CMNEXT.

In passive seismic exploration, the number and location of underground sources are very random, and there may be few passive sources or an uneven spatial distribution. The random distribution of seismic sources can cause the virtual shot recordings to produce artifacts and coherent noise. These artifacts and coherent noise interfere with the valid information in the virtual shot record, making the virtual shot record a poorer presentation of subsurface information. In this paper, we utilize the powerful learning and data processing abilities of convolutional neural networks to process virtual shot recordings of sources in undesirable situations. We add an adaptive attention mechanism to the network so that it can automatically lock the positions that need special attention and processing in the virtual shot records. After testing, the trained network can eliminate coherent noise and artifacts and restore real reflected waves. Protecting valid signals means restoring valid signals with waveform anomalies to a reasonable shape.

At present, the situation regarding heavy metal pollution in aquatic environments is becoming more and more serious. The bioaccumulation of heavy metals in aquatic plants causes obvious phytotoxicity, which can also induce secondary pollution in the aquatic environment. Zinc and copper, as indispensable elements for plant growth, are also prominent heavy metals in water pollution in China, and their concentrations play a crucial role in plant growth. In this study, we investigated the response of Pistia stratiotes (P. stratiotes) to different concentrations of Zn and Cu, and the results showed that plant growth and photosynthesis were inhibited under both Zn (1, 2, 4, and 8 mg/L) and Cu (0.2, 0.4, 0.8, and 1 mg/L) stresses. The relative growth rates of P. stratiotes under 8 mg/L Zn or 1 mg/L Cu stress were 6.33% and 6.90%, which were much lower than those in the control group (10.86%). Meanwhile, Zn and Cu stress caused insignificant change in the relative water contents of plants. The decrease in phlorophyll fluorescence parameters and chlorophyll contents suggested the significant photoinhibition of Zn and Cu stress. Chemical analysis of plant root exudates showed that the root secretion species obtained by gas chromatography–mass spectrometry (GC–MS) mainly included amino acids, alkanes, aldehydes, ketones, phenols, and more. Compared with the control group, the influence of Zn or Cu on the reduction in relative amounts of exudates was greater than that on the increase. The results of this study provide important data for the utilization of P. stratiotes in heavy metal-polluted water environments.

Carbon quantum dots (CQDs) have already demonstrated their utility as lubricant additives, and non-contact temperature sensing based on CQDs offers considerable potential for condition monitoring in mechanical, electrical, and other fields, as well as lubrication-temperature multifunctional applications in lubricants. In this paper, we have successfully synthesized and designed high-brightness carbon quantum dots/polyvinyl alcohol (PVA) temperature sensor thin film and dispersions of CQDs in a liquid paraffin lubrication system. Based on fluorescence intensity and the fluorescence intensity ratio, the carbon quantum dot/PVA film exhibited exponential temperature-dependent properties with a wide applicability range, a high goodness of fit (R2 > 0.99), and high relative thermal sensitivity (relative sensitivities of 1.74% K−1 and 1.39% K−1 for fluorescence intensity and fluorescence intensity ratio, respectively). In addition, based on the fluorescence intensity, the CQDs exhibited a wide temperature range (20–90 °C), a high goodness of fit (R2 > 0.99), and higher sensitivity (2.84% K−1) in a liquid paraffin lubrication system, which reflects the temperature responsive properties of carbon quantum dots as additives in lubrication systems. These findings provide convenient and effective possibilities for the sensing and monitoring of carbon quantum dots and their multifunctional applications under lubrication systems.

Micro-pore structures are an essential factor for the electrical properties of porous rock. Theoretical electrical conductivity models considering pore structure can highly improve the accuracy of reservoir estimation. In this study, a pore structure characterization method based on a multi-fractal theory using capillary pressure is developed. Next, a theoretical electrical conductivity equation is derived based on the new pore structure characterization method. Furthermore, a distinct interrelationship between fractal dimensions of capillary pressure curves (Dv) and of resistivity index curves (Dt and Dr) is obtained. The experimental data of 7 sandstone samples verify that the fitting result by the new pore structure characterization method is highly identical to the experimental capillary pressure curves, and the accuracy of the improved rock resistivity model is higher than the Archie model. In addition, capillary pressure curves can be directly converted to resistivity index curves according to the relationship model between fractal dimensions of capillary pressure curves (Dv) and resistivity index curves (Dt and Dr). This study provides new ideas to improve the accuracy of pore structure characterization and oil saturation calculation; it has good application prospects and guiding significance in reservoir evaluation and rock physical characteristics research.

Federated edge learning (FEL) has recently attracted great interest due to its real-time response and energy-efficient characteristics. Most existing work focuses on designing algorithms to improve model performance, ignoring the malicious behavior and personal decision-making of self-interested edge servers. Although some efforts have been devoted to incentivizing honest edge server engagement by compensating training costs, this rarely considers resource efficiency and often assumes that edge servers provide complete information to the platform, which may lead to the risk of private attribute leakage. Hence, we aim to achieve an incentive mechanism that promotes secure and efficient model training between the platform and edge servers. However, edge servers’ multi-dimensional private attributes and training strategies make the optimization problem nonconvex, and incomplete information further increases the complexity of the analysis. In order to address these challenges and by integrating contract theory and exponential mechanism, we propose an efficiency-aware incentive mechanism, FedNow, which enables edge servers to personally determine their local training rounds while motivating their participation without giving access to their true training strategies and private attributes. Specifically, we enabld edge servers to add noise to their submitted training strategy to hide their true training rounds; then, we carefully designed an efficiency score function to select honest and efficient edge servers without disclosing their private attributes. In order to demonstrate that FedNow strictly outperforms existing schemes in terms of total costs, we theoretically derived sufficient conditions for making the total costs of FedNow lower than existing schemes and designed a greedy algorithm that uses the Monte Carlo method to find feasible near-optimal solutions in polynomial time. Our extensive experimental assessment using synthetic and real datasets shows the superiority of FedNow.

A gear integrated error, a combination of individual and composite errors, carries richer information and has long been a key target of classic gear error measurement techniques. However, in the age of intelligent manufacturing, the classic methods for gear integrated error measurement are no longer able to meet the emerging requirements of large-scale gears and real-time online measurement. To address this gap, a novel approach to obtaining the gear integrated error based on GTC−Facet (Gaussian template convolution-Facet) is proposed. This method accurately pinpoints the sub-pixel contour of gears in images, enabling a quick derivation of the gear integrated error curve. From this curve, other individual and composite errors can be analyzed. The gear error information obtained through our method has higher measurement accuracy, achieving a positioning accuracy of 3.6 μm for the gear profile. Moreover, during the measurement process, the measured gear remains unclamped, and the entire measurement process can be completed within 0.35 s, which is much faster than classic methods. Our method meets the demands of online measurements and provides a new avenue for gear error measurement.

A two-layer control strategy for the participation of multiple battery energy storage systems in the secondary frequency regulation of the grid is proposed to address the frequency fluctuation problem caused by the power dynamic imbalance between the power system and load when a large number of new energy sources are connected to the grid. A comprehensive allocation model based on area regulation requirement (ARR) signals and area control error (ACE) signals is proposed to obtain the total output of the secondary frequency modulation (FM) demand with a higher degree of adaptation when the FM units respond to the automatic generation control command, and the total output is reasonably allocated to each FM unit by using the two-layer control. Considering the dynamic fluctuation of the grid frequency, the fluctuation is dynamically suppressed in real-time by applying model predictive control to successfully forecast the frequency deviation while realizing the deviation-free correction in the frequency dynamic correction layer. The optimal power distribution of FM units based on the distributed control concept, as well as the power depth of each unit, are coordinated in the equalization control layer while keeping a decent battery charge level. Finally, in Matlab/Simulink, the proposed control approach is simulated and validated. The findings show that the suggested control approach can suppress frequency difference fluctuation, keep the battery charged, and reduce the unit’s FM loss.

Similar to a Doherty power amplifier (DPA), a sequential power amplifier (SPA) is mainly composed of a main amplifier, an auxiliary amplifier and a combiner. However, SPA breaks the bandwidth limitation of the impedance inverter in the DPA, and also simplifies the design procedure. Since the main amplifier has no load modulation, it is easy for the SPA to realize broadband operation and improve the output back-off (OBO) power range. Therefore, SPA has great advantages and potential in expanding bandwidth, improving drain efficiency and expanding the back-off range of a power amplifier simultaneously. This paper describes the evolution and classification of the SPA. First, the basic theory of the SPA is reviewed. Then, some two-way SPAs using coupler and circulator as a power combiner are discussed. Thirdly, the latest popular sequential load modulated balanced amplifier is overviewed.

Vancomycin (VAN), a glycopeptide antibiotic, is the preferred therapeutic agent for treating Gram-positive bacteria. Rapid and precise quantification of VAN levels in cerebrospinal fluid (CSF) and plasma is crucial for optimized drug administration, particularly among elderly patients. Herein, we introduce a novel clinical test strip utilizing colloidal gold competitive immunoassay technology for the expedient detection of VAN. This test strip enables the detection of VAN concentrations in clinical samples such as plasma within 10 min and has a limit of detection of 10.3 ng/mL, with an inhibitory concentration 50% (IC50) value of 44.5 ng/mL. Furthermore, we used the test strip for pharmacokinetic analysis of VAN in the CSF and plasma of beagle dogs. Our results provide valuable insights into the fluctuations of the drug concentration in the CSF and plasma over a 24 h period after a single intravenous dose of 12 mg/kg. The test strip results were compared with the results obtained via liquid chromatography–mass spectrometry methods, and the measured VAN concentrations in the CSF and plasma via both of the methods showed excellent agreement.

Protein dephosphorylation is the process of removing phosphate groups from protein molecules, which plays a vital role in regulating various cellular processes and intricate protein signaling networks. The identification and prediction of dephosphorylation sites are crucial for this process. Previously, there was a lack of effective deep learning models for predicting these sites, often resulting in suboptimal outcomes. In this study, we introduce a deep learning framework known as “DephosNet”, which leverages transfer learning to enhance dephosphorylation site prediction. DephosNet employs dual-window sequential inputs that are embedded and subsequently processed through a series of network architectures, including ResBlock, Multi-Head Attention, and BiGRU layers. It generates predictions for both dephosphorylation and phosphorylation site probabilities. DephosNet is pre-trained on a phosphorylation dataset and then fine-tuned on the parameters with a dephosphorylation dataset. Notably, transfer learning significantly enhances DephosNet’s performance on the same dataset. Experimental results demonstrate that, when compared with other state-of-the-art models, DephosNet outperforms them on both the independent test sets for phosphorylation and dephosphorylation.

The friction factor of harmonic reducers affects the transmission accuracy in electromechanical actuators (EMAs). In this study, we proposed a friction feedforward compensation method based on improved active disturbance rejection control (IADRC). A mathematical model of EMA was developed. The relationship between friction torque and torque current was derived. Furthermore, the compound ADRC control method of second-order speed loop and position loop was studied, and an IADRC control method was proposed. A real EMA was developed, and the working principles of the EMA driving circuit and current sampling were analyzed. The three methods—PI, ADRC, and IADRC—were verified by conducting speed step experiments and sinusoidal tracking experiments. The integral values of time multiplied by the absolute error of the three control modes under the step speed mode were approximately 47.7, 32.1, and 15.5, respectively. Disregarding the inertia of the reducer and assuming that the torque during no-load operation equals the friction torque during constant motion, the findings indicate that, under a load purely driven by inertia, the IADRC control method enhances tracking accuracy.

In the current industrial revolution, advanced technologies and methods can be effectively utilized for the detection and verification of defects in high-speed steel filament production. This paper introduces an innovative methodology for the precise detection and verification of micro surface defects found in steel filaments through the application of the Eddy current principle. Permanent magnets are employed to generate a magnetic field with a high frequency surrounding a coil of sensors positioned at the filament’s output end. The sensor’s capacity to detect defects is validated through a meticulous rewinding process, followed by a thorough analysis involving scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). Artificial defects were intentionally introduced into a sample, and their amplitudes were monitored to establish a threshold value. The amplitude signal of these created defect was identified at approximately 10% FSH, which corresponds to a crack depth of about 20 µm. In the experimental production of 182 samples covering 38 km, the defect ratio was notably high, standing at 26.37%. These defects appeared randomly along the length of the samples. The verification results underscore the exceptional precision achieved in the detection of micro surface defects within steel filaments. These defects were primarily characterized by longitudinal scratches and inclusions containing physical tungsten carbide.

In microseismic monitoring, the reconstruction of a reliable velocity model is essential for precise seismic source localization and subsurface imaging. However, traditional methods for microseismic velocity inversion face challenges in terms of precision and computational efficiency. In this paper, we use deep learning (DL) algorithms to achieve precise and efficient real-time microseismic velocity modeling, which holds significant importance for ensuring engineering safety and preventing geological disasters in microseismic monitoring. Given that this task was approached as a non-linear regression problem, we adopted and modified the Attention U-Net network for inversion. Depending on the degree of coupling among microseismic events, we trained the network using both single-event and multi-event simulation records as feature datasets. This approach can achieve velocity modeling when dealing with inseparable microseismic records. Numerical tests demonstrate that the Attention U-Net can automatically uncover latent features and patterns between microseismic records and velocity models. It performs effectively in real time and achieves high precision in velocity modeling for Tilted Transverse Isotropy (TTI) velocity structures such as anticlines, synclines, and anomalous velocity models. Furthermore, it can provide reliable initial models for traditional methods.

A large-scale underwater volcanic eruption occurred at the volcano of Hunga Tonga-Hunga Ha’apai (HTHH) on 15 January 2022. At present, there is no consensus on the ionospheric response characteristics and interaction mechanism during volcanic eruptions. Based on the Global Navigation Satellite System (GNSS), AQUA satellite’s Atmospheric Infrared Sounder (AIRS), the experiment studies the response characteristics of the ionosphere and gravity waves during the eruption of the volcano and their interaction mechanisms and the International Real-Time Geomagnetic Observation Network (INTERMAGNET). First, a geomagnetic anomaly was detected before the eruption, which caused variations in the ionospheric VTEC (Vertical Total Electron Content) by about 15 TECU. Based on the IGS (International GNSS Service) observations, the VTEC distribution between 60° north and south latitudes was retrieved. The results show that before and after the eruption of Tonga Volcano, significant ionospheric anomalies were observed to the south, northwest and southwest of the volcano, with a maximum anomaly of 15 TECU. The study indicates that the geomagnetic anomaly disturbance is one of the precursors of volcanic eruption and has a certain degree of impact on the ionosphere. A correlation between geomagnetic anomalies and ionospheric anomalies was found to exist. The vast impact from the volcanic eruption excites gravity waves over the surface, which then propagate longitudinally, further perturbing the ionosphere. It is also detected that the ionospheric anomaly perturbation has a high coincidence effect with the gravity wave anomaly. Therefore, the gravity waves generated by atmospheric variations are used to explain the ionospheric perturbation phenomenon caused by volcanic eruptions.

Anaerobic digestion (AD) has the advantages of utilizing complex substrates and producing renewable energy and is currently one of the mainstream technologies for food waste (FW) resourcing. However, at high organic loads and low inoculum-to-substrate ratios (ISRs), AD with FW as substrate is prone to acid accumulation, resulting in a drastic decrease in gas production and system collapse. This study investigated the effect of the coupled addition of zero-valent iron (ZVI) and activated carbon (AC) on the AD of FW at three low ISRs of 0.715, 0.625, and 0.5. The results showed that the control group acidified and stopped producing biogas when the ISR decreased to 0.625 and 0.5, but ZVI coupled with AC alleviated the acidification and increased the cumulative biogas yield. Especially at ISR = 0.5, the cumulative biogas yield for the ZVI + AC group was 31.5%, 99.5%, and 11.43 times higher than that of the ZVI, AC, and control groups, respectively. ZVI coupled with AC also increased the degradation of volatile fatty acids (70.5–84.4%) and soluble chemical oxygen demand (50.0–72.9%) while decreasing propionate concentration and improving the stability of the AD system. COD mass balance analyses indicated that the coupled addition of ZVI and AC promoted the conversion of particulate organic matter to soluble organic matter and increased the conversion of carbon sources to methane.

Industries increasingly rely on intricate multi-component systems, necessitating efficient maintenance strategies to ensure system reliability and minimize downtime. Predictive maintenance, an emerging approach that utilizes data-driven techniques to forecast and prevent failures, holds significant potential in this regard. This paper presents a predictive maintenance strategy tailored specifically for multi-component systems. In order to accurately anticipate the remaining useful life (RUL) of components, we develop a method that combines data and model fusion based on a particle filtering approach and a degradation distribution model. By integrating degradation data with models, our method outperforms traditional model-based approaches in terms of prediction accuracy. Subsequently, we apply an optimized maintenance model to individual components based on the trigger threshold for RUL. This model determines the most optimal maintenance actions for each component, with the aim of minimizing maintenance costs. Furthermore, we introduce an optimized maintenance strategy that incorporates opportunistic maintenance to further reduce the overall maintenance cost of the system. This strategy leverages predicted RUL information to schedule proactive maintenance actions at the opportune moment, resulting in a significant cost reduction compared to traditional periodic maintenance approaches. To validate the feasibility and effectiveness of our proposed strategy, we utilize experimental data from open-source lithium-ion batteries at the NASA PCoE Center. Through this empirical validation, we provide real-world evidence showcasing the applicability and performance of our strategy in a multi-component system.

Daqu is a traditional starter for Baijiu fermentation and is produced by spontaneous fermentation of ground and moistened barley or wheat. The quality of Daqu is traditionally evaluated based on physicochemical and subjective sensory parameters without microbiological analysis. Here, we compared the physicochemical characteristics of qualified (QD) and inferior (ID) Daqu, their microbial communities based on plate counting and PacBio SMRT sequencing of rRNA gene libraries, and their impacts on Baijiu fermentation. The results showed that the glucoamylase and α-amylase activities of QD were significantly higher than those of ID. The counts of yeasts and relative abundances of functional microbes, especially the amylolytic bacterium Bacillus licheniformis and fungi Saccharomycopsis fibuligera and Lichtheimia ramosa, were significantly higher in QD than in ID. The laboratory-scale Baijiu fermentation tests showed that the relative abundances of the amylolytic microbes were higher in the QD than the ID fermentation set, resulting in more efficient fermentation, as indicated by more weight loss and higher moisture content in the former. Consequently, more glycerol, acetic acid, ethanol, and other volatile compounds were produced in the QD than in the ID fermentation set. The results suggest that Daqu quality is determined by, and can be evaluated based on, its microbial community.

The traditional electromagnetic–thermal bidirectional coupling model (EMTBCM) of permanent magnet synchronous motors (PMSMs) requires a long time to solve, and the temperature-induced torque change is not accounted for in the finite element (FE) numerical calculation of the EM field. This paper presents a precise and efficient EMTBC reduced-order solution model. The specific methods are as follows: First, a torque control technology based on the current injection method is proposed for determining the effect of temperature on the properties of EM materials and EM torque in an EM field, and the accuracy of the FE numerical calculation model is improved. Second, we use the improved EM field finite element numerical calculation model (FEMNCM) to analyze the correlation between the EM loss, the temperature, and the load, and we replace the FEMNCM with the EM field reduction model using the least-squares method. Then, we analyze the law of the PMSM’s internal temperature distribution. We choose the GA-BP algorithm with as few samples as possible and a high accuracy and stability to build the regression prediction model of the temperature field. We use this regression prediction model to replace the complex temperature field calculation. After analyzing the EMTBCM solution strategy, the original complex EMTBC numerical calculation model is substituted with iterations of the magnetic field reduction model and the temperature field regression prediction model. The FE numerical calculation is then used to validate the reduced-order model. The proposed model is validated through numerical simulations. The numerical results indicate that the proposed reduced-order EMTBC model in this paper is accurate and computationally efficient.

Control chart pattern recognition (CCPR) can quickly recognize anomalies in charts, making it an important tool for narrowing the search scope of abnormal causes. Most studies assume that the observations are normal, independent and identically distributed (NIID), while the assumption of independence cannot always be satisfied under continuous manufacturing processes. Recent research has considered the existence of autocorrelation, but the recognition rate is overestimated. In this paper, a novel scheme is proposed to recognize control chart patterns (CCPs) in which the inherent noise is autocorrelated. By assuming that the inherent noise follows a first-order autoregressive (AR (1)) process, the one-dimensional convolutional neural network (1DCNN) is applied for extracting features in the proposed scheme, while the grey-wolf-optimizer-based support vector machine (GWOSVM) is employed as a classifier. The simulation results reveal that the proposed scheme can effectively identify seven types of CCPs. The overall accuracy is 89.02% for all the autoregressive coefficients, and the highest accuracy is 99.43% when the autoregressive coefficient is on the interval (−0.3, 0]. Comparative experiments indicate that the proposed scheme has great potential to identify CCPs in autocorrelated processes.

Mental rotation tasks have been widely used to assess individuals’ spatial cognition and the ability to mentally manipulate objects. This study employed a computerized adaptive training method to investigate the behavioral performance of participants of different genders in mental rotation tasks with different rotation angles before and after training. A total of 44 Chinese university students participated in the experiment, with the experimental group undergoing a five-day mental rotation training program. During the training phase, a three-down/one-up staircase procedure was used to adjust the stimulus levels (response time) based on participants’ responses. The results showed that the training had a facilitative effect on the mental rotation ability of both male and female participants, and it was able to eliminate the gender differences in mental rotation performance. Regarding the angles, we observed that the improvement in the angles involved in the training was significantly higher compared to untrained angles. However, no significant differences in improvement were found among the three trained angles. In summary, these findings demonstrate the effectiveness of computerized adaptive training methods in improving mental rotation ability and highlight the influence of gender and angles on learning outcomes.

Two−dimensional (2D) materials with novel structures and electronic properties are promising candidates for the next generation of micro− and nano−electronic devices. Herein, inspired by the recent experimental synthesis of penta−NiN2 (ACS Nano, 2021, 15, 13539–13546), we propose for the first time a novel ternary penta−NiPN monolayer with high stability by partial element substitution. Our predicted penta−NiPN monolayer is a quasi−direct bandgap (1.237 eV) semiconductor with ultrahigh carrier mobilities (103–105 cm2V−1s−1). Furthermore, we systematically studied the adsorption properties of common gas molecules (CO, CO2, CH4, H2, H2O, H2S, N2, NO, NO2, NH3, and SO2) on the penta−NiPN monolayer and its effects on electronic properties. According to the energetic, geometric, and electronic analyses, the penta−NiPN monolayer is predicted to be a promising candidate for NO and NO2 molecules. The excellent electronic properties of and the unique selectivity of the penta−NiPN monolayer for NO and NO2 adsorption suggest that it has high potential in advanced electronics and gas sensing applications.

Watchman Nee (Ni Tuosheng, 1903–1972), one of the most prominent theologians in Republican China (1911–1949), developed a “spiritual theology” that is concentrated on the work of the Holy Spirit. This theological perspective shares parallels with Pentecostal-Charismatic theology. This paper examines Nee’s attitude toward Pentecostalism and the charismatic experience. First, based on his teaching of the three-stage work of the Holy Spirit, Nee argued that the inner guidance of the Holy Spirit was more important than any external manifestation and that the “baptism of the Holy Spirit” in the Pentecostal-Charismatic movement was not a personal experience that Christians should pursue. Second, in terms of subjective experience, Nee’s trichotomous view of human nature as consisting of spirit, soul, and body suggested that many charismatic experiences originate in the soul and need to be overcome. Third, concerning the spiritual world, Nee regarded as the work of evil spirits those charismatic experiences that occur in irrational and unconscious situations. Nee’s negative stance towards Pentecostalism and the charismatic experience serves as a means of distinguishing his spiritual theology from Pentecostalist-Charismatic movements. Both Nee’s theology and his church practices reveal a strong elitist element, in stark contrast to Pentecostalism.

Cyclophilin D (CypD) is regulated during the innate immune response of insects. However, the mechanism by which CypD is activated under innate immunosuppression is not understood. Microplitis bicoloratus bracovirus (MbBV), a symbiotic virus in the parasitoid wasp, Microplitis bicoloratus, suppresses innate immunity in parasitized Spodoptera litura. Here, we demonstrate that MbBV promotes the CypD acetylation of S. litura, resulting in an immunosuppressive phenotype characterized by increased apoptosis of hemocytes and MbBV-infected cells. Under MbBV infection, the inhibition of CypD acetylation significantly rescued the apoptotic cells induced by MbBV, and the point-mutant fusion proteins of CypDK125R-V5 were deacetylated. The CypD-V5 fusion proteins were acetylated in MbBV-infected cells. Deacetylation of CypDK125R-V5 can also suppress the MbBV-induced increase in apoptosis. These results indicate that CypD is involved in the MbBV-suppressed innate immune response via the CypD-acetylation pathway and S. litura CypD is acetylated on K125.

Both direct and indirect heating compression methods were proposed to satisfy the requirement of industrial steam compression and supply. The compression process in a single-screw compressor was analyzed with the determination of different inlet and outlet boundary conditions. Considering the misalignment of meshing pairs caused by the offset of the star wheel axis and screw origin, the differential expansion of the vertical meshing pair was further analyzed. The inner ring of the sealing end face, the wall of the inner ring of the bearing at the inlet and outlet ends, and the bottom surface of the bracket were analyzed by different coordinate systems and different constraints, thus facilitating the design and selection of the single screw. The critical point for the design of a steam-type single-screw compressor is an outlet temperature of 350 °C and an outlet volume flow rate of 130 m3/min. At a flow rate of 130 m3/min, when the temperature exceeds 350 °C, the reliability of the compressor is still limited, and the differential expansion of the meshing pair exceeds 0.3 mm. In addition, superheating of steam can be improved by applying a centrifugal compressor with cascade compression and centrifugal compression in conjunction with reflux adjustment at the compression outlet.

Serial monosomic alien addition lines (MAALs) provide an ideal system to elucidate the transcriptomic interactions between the alien chromosomes and recipient genome under aneuploidy. Herein, five available Brassica oleracea-nigra MAALs (CCB1, CCB4, CCB5, CCB6, CCB8), their derived B. oleracea plants (non-MAALs), and two parents were analyzed for their gene expressions by using high-throughput technology. Compared to parental B. oleracea, all MAALs showed various numbers of DEGs, but CCB8 gave much higher DEGs; the number of downregulated DEGs was slightly higher than the number of upregulated ones, except for in relation to CCB8. All derived B. oleracea plants also gave certain numbers of DEGs, despite these being much lower than in the respective MAALs. Compared to B. nigra, in all five MAALs more DEGs were downregulated than upregulated. Trans-effects were likely more prevailing than cis-effects, and these DEGs were predominantly associated with material transport by dysregulating the cellular component. Meanwhile, the orthologous genes on alien chromosomes could only play a feeble compensatory role for those gene pairs in C-subgenome, and different levels of the expressed genes had a greater tendency towards downregulation. These results revealed transcriptional aneuploidy response patterns between two genomes and suggested that cis- and trans-mechanisms synergistically regulated alien gene transcriptions after distant hybridization.

Selenium (Se) plays an important role in the growth of plants. Alfalfa (Medicago sativa L.) is a perennial legume forage crop with high nutritional value and Se-rich functions. Many studies have shown that selenium can promote alfalfa growth, but few have explored the molecular biology mechanisms behind this effect. In this study, alfalfa was divided into two groups. One group was sprayed with sodium selenite (Na2SeO3) and the other group was sprayed with distilled water as a control. This study determined the growth, reproductive traits, physiological changes, transcriptome and metabolome of both groups of alfalfa. We found that foliar spraying of 100 mg/L Na2SeO3 could significantly increase the growth rate, dry weight, total Se content, amount of pollen per flower, pollen viability, pod spirals, and seed number per pod of alfalfa plants. The level of chlorophyll, soluble protein, proline, and glutathione also increased dramatically in Na2SeO3-sprayed alfalfa seedlings. After transcriptome and metabolome analysis, a total of 614 differentially expressed genes (DEGs) and 1500 differentially expressed metabolites (DEMs), including 26 secondary differentially metabolites were identified. The DEGs were mainly enriched in MAPK signaling pathway, phenylpropanoid biosynthesis, isoflavonoid biosynthesis, cutin, suberine, and wax biosynthesis, and glycerolipid metabolism. The DEMs were mainly enriched in flavone and flavonol biosynthesis, carbon metabolism, glyoxylate and dicarboxylate metabolism, nitrogen metabolism, and phenylpropanoid biosynthesis. Integrative analysis of transcriptome and metabolome showed that the foliar spraying of Na2SeO3 mainly affects phenylpropanoid biosynthesis to promote alfalfa growth.

Establishing a database of building exposures is an important basic work in earthquake insurance research. How to efficiently, accurately, and scientifically construct the risk exposure database of buildings has become a hot topic these days. Based on multi-source data, a system for extracting seismic information from urban buildings was constructed in the Tangshan urban area, and a perfect earthquake insurance risk database was established in this study. In the extraction system, the U-net identification method, spatial overlay and kernel density estimation method, Kriging interpolation method, statistical analysis, and multi-temporal land cover data analysis were used, respectively, to extract the information of footprint areas, use function, story number, structure type, and construction age of the urban buildings. The extraction results are stratified and randomly sampled, and the confusion matrix is introduced to verify the extraction effect. The results show that the building covers an area of about 50 million square meters in the urban area of Tangshan City. With the training and validation of the U-net model, the global accuracy of the building footprint areas recognition model is 71%. By comparing the results of manually determined real data with the extraction results of this study for a sample of 660 buildings, it was found that the overall accuracy rates of the extraction results of building function, story number, structure type, and construction age were 88.62%, 86.65%, 86.49%, and 85.58%, respectively, and kappa coefficients were all over 0.8. These indicate that the information on buildings extracted by the method of this study is accurate and reliable. This study can provide data and methods for the establishment of the exposure database of earthquake insurance and provide strong data support for pre-earthquake disaster prevention, post-earthquake emergency rescue, and disaster loss assessment.