The exceptional performance advantages of the fiber optic gyroscope (FOG) position it as a dominant player in middle and high-end inertial navigation systems. To prevent the loss of sensor precision caused by algorithm design and simplify the complex modeling strategy in traditional methods. We gradually demonstrate the significant role of Convolutional Neural Network (CNN) in the navigation system based on FOG, and utilize the particle swarm optimization algorithm (PSO) to expedite the convergence of the network. The experimental results demonstrate that the initial alignment method based on deep learning is more accurate than the traditional method. The attitude angle error is reduced by 81.25%, 92.54% and 36.53% respectively. The research provides support for the future application of deep learning in optical navigation systems.

We investigate on the plasmons of monolayer MoS₂ in the presence of spin-orbit interactions (SOIs) under the random phase approximation. The theoretical study shows that two new and novel plasmonic modes can be achieved via inter spin sub-band transitions around the Fermi level duo to the SOIs. The plasmon modes are optic-like, which are very different from the plasmon modes reported recently in monolayer MoS₂, and the other two-dimensional systems. The frequency of such plasmons increases with the increasing of the electron density or the spin polarizability, and decreases with the increasing of the wave vectors q. Our results exhibit some interesting features which can be utilized to the plasmonic and terahertz devices based on monolayer MoS₂.

Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and Marine resources survey. However, the visibility of the underwater environment and the accuracy of target detection can be affected by complex underwater light changes and unpredictable background noise. To address these issues, we propose an improved underwater target detection algorithm based on YOLOv8n. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region's features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIOU loss function with the Wise-IOU loss function. To conduct our experiments, we create our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The [email protected] and [email protected]:0.95 of the original YOLOv8n algorithm on the underwater target detection were 88.6% and 51.8%, respectively, and the improved algorithm [email protected] and [email protected]:0.95 can reach 91.8% and 55.9%. The original YOLOV8n algorithm was 62.2% and 45.9% [email protected] and [email protected]:0.95 on the Pascal VOC dataset, respectively. The improved YOLOV8n algorithm [email protected] and [email protected]:0.95 were 65.7% and 48.3%, respectively. The floating-point computation volume of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method.

The paper examines the various uncertainties encountered in high-frequency trading (HFT) environments and delves into the multiple challenges faced by HFT firms in navigating the Dodd-Frank Wall Street Reform and Consumer Protection Act (referred to as the "Dodd-Frank Act"), particularly during the initial stages of its enactment. These challenges include the ambiguity surrounding the definition of HFT, the lack of clarity regarding regulatory requirements and boundaries, inconsistencies in enforcement resulting from deviations in understanding the content, and the absence of detailed descriptions of the Act’s provisions. These hurdles significantly impact not only the daily operations of HFT firms but also pose higher demands on their long-term strategic planning and risk management. Drawing upon the Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) model, this study employs an innovative analytical framework. Through the comprehensive application of conceptual space, cognitive space, and semantic space, it provides a systematic methodology for identifying and analyzing the aforementioned issues. This approach not only aids firms in better comprehending and adhering to complex regulatory requirements but also enables them to explore new business opportunities and competitive advantages while ensuring compliance.

The rock strata movement deformation generated by the mining process of the mine will inevitably lead to surface subsidence and a series of environmental hazards, so it is particularly important to monitor the rock strata movement deformation and surface subsidence in the goaf area. With the development of surface subsidence monitoring technology, InSAR technology has been widely used in the research of surface subsidence in mining areas with the advantages of high precision and wide monitoring. In this paper, by obtaining the image data of 59 Sentinel-1A scenes in the mining area, the obtained PS points were analyzed and processed by ArcGIS difference analysis by PS-InSAR technology, and the surface deformation cloud map and settlement curve of the mining area in various periods were obtained, and the rock strata movement deformation and surface subsidence law of the goaf before treatment and after different treatment methods were studied by influencing factors such as stope layout and mining depth. The results show that the rock strata movement deformation and surface subsidence degree of the goaf under different treatment methods are different due to different influencing factors, and their positions also change dynamically with time. Applying InSAR technology to study rock strata movement deformation and surface subsidence can provide an important basis for mine safety mining, goaf treatment, rock strata movement deformation and surface subsidence monitoring.

Rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can obtain images with centimeter or even subcentimeter ground resolution, which provides a new efficient and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we propose a real-time edge reconstruction crack detection network (ERNet) which adopts multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and background. To capture global dependencies across spatial and channel levels, we propose an efficient bilateral decomposed convolutional attention module (BDAM) that combines depth separable convolution and dilated convolution to capture global dependencies across spatial and channel levels. To enhance the accuracy of crack detection, we use a coordinate-based fusion module that integrates spatial, semantic, and edge reconstruction information. In addition, we propose an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. Experimental results demonstrate that our network achieves the best balance between accuracy and inference speed.

Build a special identical equation, use its calculation characters to prove and search for solution of any odd converging to 1 equation through (*3+1)/2^k operation, change the operation to (*3+2^m-1)/2^k, and get a solution for this equation, give a specific example to verify. Thus prove the Collatz Conjecture is true. Furthermore, analysis the sequences produced by iteration calculation during the procedure of searching for solution, build a weight function model, prove it decrease progressively to 0, build a complement weight function model, prove it increase to its convergence state. Build a (*3+2^m-1)/2^k odd tree, prove if odd in (*3+2^m-1)/2^k long huge odd sequence can not converge, the sequence must outstep the boundary of the tree after infinite steps of (*3+2^m-1)/2^k operation.

Wind loads have become one of the key influence factors for the running safety of vehicles and comfort of passengers. Investigation on the wind speed spectrum characteristics of a moving vehicle under turbulent crosswinds is of great influence. Expressions of the wind speed spectrum of a moving vehicle was obtained from the von Kármán spectrum based on Taylor’s frozen flow hypothesis. The influence factors, including the ratio of the vehicle speed to the wind speed and the wind yaw angle from 15° to 175°, were analyzed. The maximum value of the wind speed spectrum and the corresponding frequency were studied as well. The results show that the maximum values of the wind speed spectrum of the moving vehicle were larger than those of the static vehicle. The maximum value of the wind speed spectrum corresponding to the moving vehicle first increased and then decreased as the wind yaw angle increased. Some of the frequencies corresponding to the longitudinal wind speed spectrum values of the moving vehicles were smaller than those of the static vehicle. For moving vehicles, the frequency values corresponding to the maximum values of the longitudinal wind speed spectrum first increased and then decreased as the ratio of the vehicle speed to the wind speed and the wind yaw angle increased.

The Equator Principle is the goal of promoting the harmonious development of my country's economy and society. It promotes the green transformation and upgrading of the industrial structure by strictly controlling the flow of commercial bank funds, so as to achieve the goal of environmental protection and the coordinated development of the national economy. This paper uses factor analysis to reduce the dimensions of 10 financial indicators and non-financial indicators that measure the operating performance and development potential of joint-stock commercial banks, and then compares the comprehensive operating performance, liquidity, and safety of Industrial Bank and comparable banks. And the level of profitability, analyze the difference in comprehensive operating performance of joint-stock commercial banks joining the Equator Principles compared with commercial banks that have not joined the Equator Principles. The results of the study found that joining the Equator Principles of joint-stock commercial banks can improve comprehensive operating performance in the short term. In the long run, the development trend of Industrial Bank will be similar to that of comparable banks in the same category. Joining the Equator Principles of Industrial Bank can improve its liquidity and profitability, but it will not in the long run. Conducive to the improvement of the asset quality of joint-stock commercial banks. In this regard, the government, enterprises and financial institutions should work together to help commercial banks achieve an effective balance between operating performance and social responsibility, so as to achieve the goals of "carbon peak" and "carbon neutral".

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

The objective of this research is to establish a better understanding of the current landscape of digital learning research and sustainable development by using CiteSpace. First, we retrieved published publications from the Web of Science (2004–2022). Following that, we examined the primary research strengths and important subjects of digital learning from two perspectives: collaboration networks (including collaborative networks across countries, institutions, and authors) and co-citation networks. We examined the co-citation network from three perspectives: cluster analysis, the most active citers, top references. Furthermore, referenced journals, popular themes, and rising trends were examined. These findings indicate the primary study subjects in the field of digital learning, the most intriguing research literature, and each period's emerging research hotspots. Finally, we proposed further study ideas for future paths.

High salinity in soil affects the normal growth and development of crop. MicroRNA171 (miR171) plays a momentous role in plant resistance to adversity stress. The functions of Sly-miR171d and Sly-miR171e on growth of tomato and fruit nutritional quality under salt stress were studied. The results showed that the seed germination and seedling growth of tomato that silencing of Sly-miR171d/e by short tandem target mimic (STTM) were better than those of wild-type (WT). Silencing of Sly-miR171d/e seeds maintained a high germination rate, while WT tomato seeds barely germinated under 50 mM NaCl stress. Under 100 mM NaCl stress, silencing of Sly-miR171d/e increased the expression of their target gene SlGRAS24 with increasing duration of salt stress, while they enhanced the antioxidant activity, reduced reactive oxygen species (ROS) accumulation, and enhanced the salt tolerance of tomato plant by regulating gibberellin (GA) level. Moreover, the preharvest and postharvest fruit nutritional quality under salt stress was also studied, and the results showed that silencing of Sly-miR171d/e increased soluble solids, lycopene, total carotenoids, Vitamin C, organic acids and phenolic substances in tomato fruit. The present study demonstrated that silencing of Sly-miR171d/e enhanced plant salt tolerance through the GA pathway, and improved fruit nutritional quality under salt stress.

In this paper, the performance and emission characteristics of the engine were investigated with varying ratios of tung oil-based biodiesel blends (B10, B20, and B50) and 0# diesel under different operating conditions. The experimental results indicated that both the power and torque of B10 increased compared with 0# diesel, which increased by 1.9% and 6.6%. But the power and torque of B20 and B50 decreased slightly. The fuel consumption rate increased slightly with an increasing percentage of biodiesel added. In general, the overall emissions of tung oil-based biodiesel blends were lower compared to 0# diesel. Compared to 0# diesel, the CO-specific emissions of B10 decreased by 42.86% at medium and large load, and NOX-specific emissions of tung oil-based biodiesel blends were reduced at all load conditions, except for B50. In addition, HC-specific emissions were all reduced, especially for B20 decreased by 27.54% at 50% load. With the increase of the biodiesel blend ratio, the smoke decreased significantly. Among the blends tested, B50 showed the greatest reduction of 38.05% at 2000 rpm. Overall, it can be asserted that using biodiesel presents a favorable alternative fuel option that can lead to a more environmentally friendly exhaust output.

End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational complexity, learned image compression methods inherently align with learning-based data processing and analytic pipelines due to their well-suited internal representations. The concept of Video Coding for Machines has garnered significant attention from both academic researchers and industry practitioners. This concept reflects the growing need to integrate data compression with computer vision applications. In light of these developments, we present a comprehensive survey and review of lossy image compression methods. Additionally, we provide a concise overview of two prominent international standards, MPEG Video Coding for Machines and JPEG AI. These standards are designed to bridge the gap between data compression and computer vision, catering to practical industry use cases.

The Yixian Formation (Lower Cretaceous) of China is famous worldwide for its fossils of early angio-sperms, but there is only one record of flower bud hitherto. A previous fossil record of flower bud just showed the appearance and lacked any internal details. Such a lacuna in knowledge of flower buds hin-ders our understanding of the evolution of flowers. Our new specimen was collected from an outcrop of the Yixian Formation (the Barremian-Aptian, Lower Cretaceous) near Dawangzhangzi, Lingyuan, Liao-ning, China. The specimen was observed and photographed using a Sony ILCE-7 digital camera and a Nikon SMZ1500 stereomicroscope, and details were observed using a TESCAN MAIA3 SEM. Our ob-servations document a fossil flower bud, Archaebuda cretaca sp. nov, from the Lower Cretaceous of China. The occurrence of two species of Archaebuda in the Yixian Formation not only confirms the ex-istence of the expected gynoecium (and possible androecium) in a flower bud, but also underscores the occurrence of typical flowers in the Early Cretaceous.

Microporous organic polymers (MOPs) are promising materials for gas sorption because of the intrinsic and permanent porosity, designable framework and low density. The introduction of nitrogen-rich building block in MOPs will greatly enhance the gas sorption capacity. Here, we report the synthesis of MOPs from the 2,4,6-tris(4-ethynylphenyl)-1,3,5-triazine unit and aromatic azides linkers via click polymerization reaction. FTIR and solid state 13C CP-MAS NMR confirm the formation of the polymers. CMOP-1 and CMOP-2 exhibit microporous networks with BET surface area of 431 and 406 m2 g-1 and narrow pore size distribution under 1.2 nm. Gas sorption isotherms including CO2 and H2 were measured. CMOP-1 stores superior CO2 level of 8.2 wt% (1.88 mmol g-1) at 273 K/1.0 bar and H2 uptake up to 0.6 wt% at 77 K/1.0 bar, while CMOP-2 with smaller surface area shows lower CO2 adsorption capacity of 7.3 wt% (1.66 mmol g-1) and H2 uptake (0.5 wt%). In addition, I2 vapor adsorption was tested at 353 K. CMOP-1 shows higher gravimetric load of 160 wt%. Despite of the moderate surface area, the CMOPs display excellent sorption ability for CO2 and I2 due to the nitrogen-rich content in the polymers.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

In this paper, aiming at the safety evaluation of ultimate bearing safety capacity of ultra-high-performance-concrete (UHPC) composite pile bridge foundation, the symmetrical reliability method with forward and inverse analysis considering the randomness of parameters is proposed to evaluate the safety performance of composite pile foundation. The proposed method, consid-ering the randomness of parameters, was used to calculate the safety factor of ultimate bearing capacity of composite pile foundation to meet the prescribed target reliability requirements. An example was used to verify the correctness of the proposed method. The proposed method was applied to an actual engineering example and the sensitivity of the parameters were analyzed, the calculation results show that: ignoring the randomness of the parameters will overestimate the safety of the composite pile foundation; the target reliability index has a great influence on the safety of composite pile foundation; the influence of statistical parameters of random variables on the safety of UHPC composite pile foundation cannot be neglected; the initial iteration value of the safety factor has no effect on the final calculation result. The proposed method has high ac-curacy and applicability, and provides an effective way for the safety factor estimation of ultimate carrying capacity of UHPC composite pile foundation.

This research focuses on the Hopf bifurcation occurring at a singular point with high-order degeneracy in a class of three-dimensional systems. Based on center manifold theorem, by expanding the technique for determining singular point values of degenerate singularities in two-dimensional systems, the formal series method to determine the singular point values at the high-order degenerate critical point is discussed. Furthermore, a class of specific three-dimensional differential systems with high-order degenerate singularities is explored. By computing the singular point values and determining center conditions, we proved that a minimum of 5 small-amplitude limit cycles in the vicinity of the origin. This research provides new perspectives on the center problem and limit cycle bifurcation in high-dimensional systems with high-order degenerate singular points.

This study investigates the effects of Zinc (4wt.%) and severe plastic deformation on the mechanical properties of AZ61 magnesium alloy through the stir-casting process. The severe plastic deformation (equal channel angular pressing (ECAP)) has been done followed by T4 heat treatment. The microstructural examinations revealed that the addition of 4wt.%Zn enhances the uniform distribution of β-phase, contributing to a more uniformly corroded surface in corrosive environments. Additionally, dynamic recrystallization (DRX) significantly reduces the grain size of as-cast alloys after undergoing ECAP. The attained mechanical properties demonstrate that after a single ECAP pass, AZ61 + 4wt.%Zn alloy exhibits optimal yield strength (YS), ultimate compression strength (UCS), and hardness. This research highlights the promising potential of AZ61 + 4wt.%Zn alloy for enhanced mechanical and corrosion-resistant properties, offering valuable insights for applications in diverse engineering fields.

Higher education informatization (HEI) is an interdisciplinary field that examines the use and integration of information and communication technologies (ICTs) in higher education. This paper provides a bibliometric and visual analysis of the research trends, patterns, and topics in this field. Using the Web of Science database, we selected and analyzed 199 SCI and SSCI papers on HEI published from 2000 to 2023 by VOSviewer and CiteSpace software. The results indicate that the publication volume of HEI research has grown significantly in recent years, with China and the US being the most productive and influential countries. The author network shows the collaboration and contribution of different researchers and institutions, while the journal network reveals the multidisciplinary nature and scope of the field. The keyword network and the burst keyword analysis identify the main research themes and the emerging hot topics in HEI, such as online learning, big data, and artificial intelligence. The co-citation network of sources illustrates the theoretical and methodological foundations and influences of the field. The paper concludes with some implications and suggestions for future HEI research.

(1) Background: After the discovery and application of Chlamydomonas reinhardtii channelrhodopsins, the optogenetic toolbox has been greatly expanded with engineered and newly discovered natural channelrhodopsins. However, channelrhodopsins of higher Ca2+ conductance or more specific ion permeability are still in demand. (2) Methods: In this study, we mutated the conserved aspartate of the transmembrane helix 4 (TM4) within Chronos (Stigeoclonium helveticum channelrhodopsin = ShChR) and PsChR (Platymonas subcordiformis channelrhodopsin) and compared them with published ChR2 (C. reinhardtii channelrhodopsin-2) aspartate mutants. (3) Results: We found that the ChR2 D156H mutant (XXM) showed enhanced Na+ and Ca2+ conductance, which was not noticed before, while the D156C mutation (XXL) influenced the Na+ and Ca2+ conductance only slightly. Furthermore, the D173H mutant of PsChR showed a much improved photocurrent, compared to wildtype, and even higher Na+ selectivity to H+ than XXM. PsChR D173H also showed a strongly enhanced Ca2+ conductance, more than 2-fold that of the calcium translocating L132C of ChR2 (CatCh). (4) Conclusions: We found that mutating the aspartate of the TM4 to histidine influences the ion selectivity of channelrhodopsins. With the large photocurrent and enhanced Na+ selectivity and Ca2+ conductance, XXM and PsChR D139H are promising powerful optogenetic tools, especially for Ca2+ manipulation.

This paper discovers that current variational principle and Noether theorem for different physics systems with (in)finite freedom systems have missed the double extremum processes of the general extremum functional that both is deduced by variational principle and is necessarily taken in deducing all the physics laws, but these have not been corrected for over a century since Noether's proposing her famous theorem, which result in the crisis deducing relevant mathematical laws and all physics laws. This paper discovers there is the hidden logic cycle that one assumes Euler-Lagrange equations, and then he finally deduces Euler-Lagrange equations via the equivalent relation in the whole processes in all relevant current references. This paper corrects the current key mistakes that when physics systems choose the variational extreme values, the appearing processes of the physics systems are real physics processes, otherwise, are virtual processes in all current articles, reviews and (text)books. The real physics should be after choosing the variational extreme values of physics systems, the general extremum functional of the physics systems needs to further choose the minimum absolute extremum zero of the general extremum functional, otherwise, the appearing processes of physics systems are still virtual processes. Using the double extremum processes of the general extremum functionals, the crisis and the hidden logic cycle in current variational principle and current Noether theorem are solved. Furthermore, the new mathematical and physical double extremum processes and their new mathematical pictures and physics for (in)finite freedom systems are discovered. This paper gives both general variational principle and general Noether theorem as well as their classical and quantum new physics, which would rewrite all relevant current different branches of science, as key tools of studying and processing them.

This paper discovers that current canonical variational principle and canonical Noether theorem of (in)finite freedom systems for different physics systems have neglected doublet extreme value processes of the general extreme value functional that both is derived by variational principle and is necessarily be taken in deriving all ( quantum ) physics laws in phase space, but which have not been done for over one century since Noether's showing her distinguished theorem, which lead to the crisis deriving all (quantum) physics laws (necessary) in phase space. We discover there is the hidden logic cycle that people assume canonical equations, and then they finally deduce canonical equations by the equivalent relation in the whole processes in all current references. We correct the current key mistake concepts that when physics systems take the variational extreme values, the appearing processes of the physics systems are real physics processes, otherwise, are virtual processes in all current references. The real physics should be what after taking the physics systems' variational extreme values, the physics systems' general extremum functional needs to further take the general extremum functional's minimum absolute extremum zero, otherwise, the appearing processes of physics systems still are virtual processes. Conservation current equations and conservation currents, in phase space, of general canonical variational principle and general canonical Noether theorem are, respectively, deduced for the first time. Using the general extremum functionals' doublet extreme value processes, the hidden logic cycle and the crisis in current canonical variational principle and current canonical Noether theorem are solved. Consequently, the new mathematical pictures, classical and quantum new physics in phase space and the new mathematical and physical doublet extremum processes for (in)finite freedom systems are discovered. General canonical variational principle and general canonical Noether theorem naturally are given, which would rewrite all the different sciences in phase space, as key tools of studying and dealing with them.

RFID based technology innovated a new field of wireless sensing, which has been applied in posture recognition, object localization and the other sensing fields, the existing sensing methods often require tags to be attached to dynamic targets (because RFID signals are more sensitive to moving target), significantly limiting their applications. It is a challenging task to sensing a static target without tag attached in NLOS (Non-Line-of-Sight). We utilized RFID technologies to sense the static foreign objects in agricultural products, and taken metal, rock, rubber and clod as sensing targets that are common in agricultural. By deploying a tag matrices to create a sensing region, we observed the signal variations before and after the appearance of the targets in this sensing region, determined the targets’ positions and their types. Here, we buried the targets in the media of seedless cotton and wheat, and detected static targets with a non-contact method in the NLOS. Researches illustrated that by deploying appropriate tag matrices and adjusting the angle of a single RFID antenna, the matrices signals can be sensitive to the static targets’ positions and their properties,i.e. matrices signals vary with different targets and their positions. Specifically, we achieved a 100% success rate in locating metallic targets, while the success rate for clods was the lowest at 86%. We achieved a 100% recognition rate for the types of all the four objects.

The advancement of sequencing technology and molecular breeding methods have provided technical support and assurance for accurate breeding. Genomic Selection (GS) utilizes genomic information to improve livestock breeding and, it is more accurate and more efficient than traditional selection methods. GS has been widely applied in domestic animals breeding, especially in cattle. However, there is still limited study on the application and research of GS in sheep and goat. In this paper, we outlined the principle, analysis methods, the influential factors of GS, and elaborated on the research progress, challenges, and prospects of applying GS in sheep and goat breeding. Through the review of these aspects this paper is expected to provide valuable references for the implementation of GS in the field of sheep and goat breeding.

Photovoltaic is one of the promising renewable sources of power to meet the future challenge of energy need. Organic and perovskite thin film solar cells are an emerging cost-effective photovoltaic technology because of low-cost manufacturing processing and a light-weight. The main barrier of commercial use of organic and perovskite solar cells is the poor stability of devices. Encapsulation of these photovoltaic devices is one of the best ways to address this stability issue and enhance the device lifetime by employing materials and structures that possess high barrier performance for oxygen and moisture. The aim of this review paper is to find different encapsulation materials and techniques for perovskite and organic solar cells according to the present understanding of reliability issues. It discusses the available encapsulate materials and their utility in limiting chemicals such as water vapour, oxygen penetration. It also covers the mechanisms of mechanical degradation within the individual layers and solar cell as a whole, and possible obstacles to their application in both organic and perovskite solar cells. The contemporary understanding of these degradation mechanisms, their interplay and their initiating factors (both internal and external) are also discussed.

This study extends the CGAN-based MODIS cloud vertical-profile (64×64-scene, about 70km width×15km height) retrieval technique developed by Leinonen et al. (2019) [1] to construct seamless 3D cloud fields for the MODIS granules [2]. Firstly, the accuracy and spatial continuity of the Leinonen et al. [1] retrievals are statistically evaluated. Then, according to the characteristics of the retrieval error, a spatially overlapping-scene ensemble generation method and a bi-directional Ensemble Binning Probability Fusion (CGAN-BEBPF) technique are developed, which improves the CGAN retrieval accuracy and support to construct seamless 3D clouds. The CGAN-BEBPF technique involves three steps: cloud masking, intensity scaling, and optimal value selection. It ensures adequate coverage of the low reflectivity areas while preserving the high reflectivity cloud cores. The technique is applied to retrieve the 3D cloud fields of Typhoon Chaba and a multi-cell convective system. The cloud structures of the CGAN-BEBPF results are highly consistent with the ground-based radar observations. It can retrieve weak clouds at the top levels of convective storms and hurricanes that were missed by ground-based radars and fill the gaps of the ground-based radar lower-level PPI scan. Overall, CGAN-BEBPF can be automated to retrieve the rich informative 3D structures of typhoons and severe convective systems over the oceans along the MODIS swath, which certainly presents great value for improving maritime and near-shore typhoons and convection prediction and other cloud-sensitive applications.

This paper gives both a general canonical quantum gravity theory and the general canonical quantum gravity theories of the Universe and general black hole, and discovers the relations reflecting symmetric properties of the standard nonlinear gravitational Lagrangian, which are not relevant to any concrete metric models. This paper concretely shows the general commutation relations of the general gravitational field operators and their zeroth, first, second and third style, respectively, of high order canonical momentum operators for the general nonlinear system of the standard gravitational Lagrangian, and then has finished all the four styles of the canonical quantization of the standard gravity.

An accurate assessment of the stage-discharge relationship in open channel flows is necessary and important to the design and management of hydraulic structures and engineering in practical hydrosystems such as rivers and streams. While the flow structures and patterns at open channel junctions are interesting and have been widely studied in the literature, this paper focuses further on the effect of flow junctions on stage-discharge relationship at mountain river confluences. In this study, both the flume and physical model experiments are designed and performed carefully to test and analyze the complex flow structures and characteristics at river confluences with different configurations and hydraulic conditions. The impacts of the flow junctions on the traditional stage-discharge relationship are analyzed in this study. The results of this study are discussed in the paper for the understanding of flow structures at flow junctions and the design and management of hydraulic structures in river engineering.

By repeated unloading and loading of the excavation dynamic and static loads, repeated train dynamic and static loads of multi-factors, the underpass tunnel, cross tunnel engineering influence area rock layer, the existing tunnel structure of the three constitutes a multifaceted structure, multi-factor interaction system. Existing tunnels into the prediction theory model of the new tunnel excavation process of dynamic and static loads, dynamic and static loads of existing tunnels are mostly analyzed individually, the results of synergistic analysis needs to be improved. Based on the theory of the current layer method, the paper comprehensively considers the stiffness of the existing tunnel, the stiffness of the new tunnel, the excavation method load and the existing tunnel load, coupled with Peck's empirical formula, the theoretical model of deformation prediction for excavation of a two-lane highway tunnel under the tunnel through the existing railroad is proposed, and verified and optimized by numerical calculations and monitoring data, which proves the applicability and reliability of the theoretical model in the similar strata and the project promotion.

The research on the excavation mechanical properties of underpass tunnels has already had certain results, but fewer of them consider the effects of dynamic and static loads on the excavation mechanical properties of underground tunnels at the same time, especially the lack of research cases of double-line highway tunnels with angled underpasses of existing railway tunnels. In this paper, based on the tunnel project of the new double-line Shiqian Highway Tunnel passing under the Hurong Railway at an oblique angle, the mechanical parameters of the rock body in the cross-tunneling area under the influence of static and dynamic load coupling are obtained by using the disturbance quantification coefficients and the Hoek-Brown criterion based on the method of over-advanced geological prediction and the investigation of the surrounding rock on the palisade face, and the engineering-scale finite element model is constructed and the mechanical characteristics of the excavation in the tunnel of the double-lane highway tunnel passing under the existing railroad under the influence of the static and dynamic loads are analyzed. The results show that: the new tunnel rock movement law for the top of the arch sinking, bottom plate bulging, side wall to move outward, the height and width of the arch and bottom plate arch with the tunnel excavation and increase, the side wall rock displacement effect is smaller; left and right lines of the tunnel disturbed area of the rule of change is similar; the existing tunnel bottom plate displacement is more than the top plate and the left and right side walls by the excavation of the time step influence is larger. Typical profile displacement is mainly determined by the distance from the excavation surface; the new tunnel in the four types of calculation conditions of the rock disturbance angle is the same; the existing tunnel roof and side wall Von Mises stress extreme occurs in the tunnel structure, the bottom plate there are pressure relief and pressure-bearing areas; based on the displacement control criteria, the recommended upper and lower step blasting method.

This paper gives solutions to puzzles of double Wheeler’s delayed choice experiments, a photon’s passing two paths simultaneously and wave collapse velocity measure, and shows new quantum control, new quantum oscillation and new controlling photon oscillator (that can be arranged in quantum computer and quantum network as their classical correspondences) in experiments. Furthermore, this paper obtains the measured attractive state and the new useful key technology of altering state’s evolving paths for quantum systems. We discover the reason and the technological way for the present changes the past, further show their feasible experiments of new quantum control, new quantum oscillation and new quantum oscillator. Finally, this paper achieves the collapse response time t of wave, further concretely deduces the measured collapse velocity of quantum system. In the past, the collapse velocity cannot be objectively deduced and experimentally measured.

The key to building a strong bridge country lies in talent, and the foundation lies in education. The cultivation of bridge engineering talents is related to the future and destiny of building a strong bridge country. This course adopts the educational philosophy of "holographic teaching method" centered on the comprehensive development of students, cultivating "engineering thinking" guided by engineering education certification, enhancing "abilities and qualities" through professional knowledge, reconstructing the course knowledge content system through engineering philosophy thinking, and implementing the ideological and political construction of the course in a multi-level unified manner of "explicit + implicit". Based on restructuring the course content system, we will strengthen the construction of diversified course resources, integrate theory with practice throughout the entire process of the course, practice the concept of "bridge architects + bridge engineers" parallel development, and cultivate cross-border and compound talents with comprehensive qualities of "engineering + art + humanities".

Due to the impacts from climate change, the allocation of water resources must urgently be optimized worldwide to ensure that the needs of both water managers and farmers are balanced. In this study, manager-oriented and farmer-oriented assessment models were developed for irrigation water optimization and allocation. The distance from water sources and hydraulic head were the main factors in the manager-oriented assessment model; crop value, water demand of crops, and soil type were additional factors in the farmer-oriented assessment model. The developed assessment models were used to assess irrigation water allocation in five villages in Neimen District. Cadasters at high elevation were discovered to not be suitable for cultivation of crops because of the difficulties in constructing irrigation facilities and the loss of irrigation water during transportation. The result obtained from the manager-oriented assessment system was related to the costs involved in the construction and maintenance of irrigation facilities, which indicated that cadasters located at long distances from water sources and at high elevation are unsuitable for cultivation. By contrast, the result obtained from the farmer-oriented assessment system was related to the profits of farmers and revealed that more cadasters would be suitable for cultivation if suitable crops were chosen.

Insulated Gate Bipolar Transistor (IGBT) may convey high current and sustain high breakdown voltages; it may provide high power control. In a sense, how IGBT demonstrates the capability of power control was not specifically described, and the equivalent bipolar junction transistors (BJT) activated by an applied bias to the insulated gate is intriguing. As seen in Figure 1, IGBT shows an equivalent circuit structure containing two types of BJT, (NPN-type Bipolar transistor and PNP-type Bipolar transistor), which can be modeled as diode-associated transistor. Apparently, as a bias applies to the controlling gate, the channel beneath the gate oxide gets strongly inversed and turns out to be conductive just like what happens on metal-oxide-silicon field effect transistor (MOSFET), which provides necessary igniting currents to both BJTs and thus causes high current gain flowing through them-selves from the bottom. A proposed and renewed formula provides addressing the above scenario and successfully fitting the measured data that forms the characteristic curves. Some parameters in the proposed formula, which includes size and mobility associated constant, threshold voltage, and Early-Voltage-related lambda for MOSFET, explain and explore the underlying physics. IGBT thus enjoy high input impedance and high current gain due to MOSFET (Insulated Gate) and BJT, respectively. This formula combines both merits of MOSFET and BJT in order to fit the measured data. Indeed, the formula intimately fits IGBT electrical characteristic curves, and thus may be qualified and served as a model. The new model is then advisable for designing power integrated circuit to exactly achieve power saving, power control, and power efficiency.

This paper uncovers that quantum uncertain principle makes the single particle with global property have no certain path, and then wave of quantum particle can simultaneously do pass the double slits. The two subwaves after passing Young’s double slits are entanglement, they may form interference of subwaves. Consequently, we find a kind of quantum probabilistic entanglements with Wheeler's delayed choice. Quantum particles such as photons, electrons, neutrons, protons etc mean that wave of the quantum particle can simultaneously do pass through Young's double slits, rather than individual quantum particle may pass through Young's double slits at the same time. When considering wave property, we cannot consider particle property (Just as in the photoelectric effect, considering the particle nature of the system, people cannot consider wave property, otherwise the photoelectric effect cannot appear). Therefore, this paper discovers that the ability of single photon to hit electrons out in photoelectric effect is complementarily equivalent to the ability of wave of the single photon to simultaneously pass through Young's double slits in wave-particle duality. Objective criteria for distinguishing classical and quantum particles are discovered and objectively give the applicable realm of quantum mechanics for the first time. The crisis of the single particle’s simultaneously passing through Young's double slits, which has been plaguing physicists in the whole world up to now for decades, is solved, in which the studies are classified as classical and quantum particles, the classical particle and quantum particle wave cannot and can pass the Young’s slits, respectively. This paper discovers both the new physics mechanism of passing the double slits of the wave with the amplitude of 4-dimensional momentum representation wave function reflecting particle nature and the principle of self-adaptive emergence of wave-particle duality, and then using the principle, this paper gives both direct explanations to the current experiments and new predictions of new some experiments for wave-particle duality. All the deduced results here are consistent with all relevant physics experiments.

Studies over the past three years have substantially expanded the involvements of eIF3 in mRNA translation. It now appears that this multi-subunit complex is involved in every possible form of mRNA translation, controlling every step of protein synthesis from initiation to elongation, termination and quality control in positive as well as negative fashion. Through the study of eIF3, we are beginning to appreciate protein synthesis as a highly integrated process coordinating protein production with protein folding, subcellular targeting, and degradation. At the same time, eIF3 subunits appear to have specific functions that probably vary between different tissues and individual cells. Considering the broad functions of eIF3 in protein homeostasis, it comes as little surprise that eIF3 is increasingly implicated in major human diseases and first attempts at therapeutically targeting eIF3 have been undertaken. Much remains to be learned, however, about subunit- and tissue-specific functions of eIF3 in protein synthesis and disease and their regulation by environmental conditions and posttranslational modifications.

This paper presented a two-dimensional, well-balanced hydrodynamic and sediment transport model based on the solutions of variable density shallow water equations (VDSWEs) for sediment-laden flows, and the Exner equation for bed changes. Those equations are solved in a coupled way by the first-order Godunov-type finite volume method. The Harten-Lax-van Leer-Contact (HLLC) Riemann solver is extended to find the local Riemann fluxes in order to maintain the exact balance between the momentum term and the bed slope term. The advantage of a well-balanced scheme over an unbalanced scheme is demonstrated by the synthetic standing contact-discontinuity test case. Then, the model is employed to simulate two laboratory experiments. At last, a field case, the 1996 Lake Ha! Ha! flood event (Canada), is simulated. Results of cross sectional geometries and profiles of longitudinal thalweg agree well with measurements. The accuracy and simplicity of the numerical model, together with the robust implementation, make the model a good candidate for practical engineering applications.

Density distribution function of classical statistical mechanics is generally generalized as a product of a general complex function and its complex Hermitian conjugate function, and the average of classical statistical mechanics is generalized as the average of the quantum mechanics. Furthermore, this paper derives three ones of the five axiom presumptions of quantum mechanics, e.g., deduces Schrȍdinger equation by two general ways, makes the three axiom presumptions into three theorems of quantum mechanics, not only solves the crisis to hard understand, but also gets new theories and new discoveries, e.g., this paper solves the crisis of the origin of the wave-particle duality, derives operators, eigenvalues and eigenstates, deduces commutation relations for coordinate and momentum as well as the time and energy, and discovers quantum mechanics is just a generalization ( mechanics ) theory of the complex square root of ( real density function of ) classical statistical mechanics. Quantum mechanics being just a generalization theory of the complex square root of classical statistical mechanics is both new physics and revolutionary discovery, which are affecting people’s deep philosophical thinking for modern physics development, solve all the crisises of quantum mechanics, quantum information and so on, and make quantum mechanics have scientific solid bases being checked and both no basic axiom presumption and no all the quantum strange incomprehensible properties, because classical statistical mechanics and the complex square root of classical statistical mechanics have the scientific solid bases being checked. In addition, this paper discovers the reason no taking the time derivative of space coordinates in Schrȍdinger equation. Therefore, this paper gives solution to the crisis of the first quantization origin, and mainly deduces quantum physics no all the quantum current strange incomprehensible properties.

This paper derives measurement and identical principles, then makes the two principles into measurement and identical theorems of quantum mechanics, plus the three theorems derived earlier, we deduce the axiom system of current quantum mechanics, the general quantum theory no axiom presumptions not only solves the crisis to understand in current quantum mechanics, but also obtains new discoveries, e.g., discovers the velocities of quantum collapse and entanglement are instantaneously infinitely large. We deduce the general Schrȍdinger equation of any n particles from two aspects, and the wave function not only has particle properties of the complex square root state vector of the classical probability density of any n particles, but also has the plane wave properties of any n particles. Thus, the current crisis of the dispute about the origin of wave- particle duality of any n microscopic particles is solved. We display the classical locality and quantum non-locality for any n particle system, show entanglement origins, and discover not only any n-particle wave function system has the original, superposition and across entanglements, but also the entanglements are of interactions preserving conservation or correlation, three kinds of entanglements directly give lots of entanglement sources. This paper discovers, one of two pillars of modern physics, quantum mechanics of any n particle system is a generalization ( mechanics ) theory of the complex square root ( of real density function ) of classical statistical mechanics, any n particle system’s quantum mechanics of being just a generalization theory of the complex square root of classical statistical mechanics is both a revolutionary discovery and key new physics, which are influencing people’s philosophical thinking for modern physics, solve all the crisises in current quantum theories, quantum information and so on, and make quantum theory have scientific solid foundations checked, no basic axiom presumption and no all quantum strange incomprehensible properties, because classical statistical mechanics and its complex square root have scientific solid foundations checked. Thus, all current studies on various entanglements and their uses to quantum computer, quantum information and so on must be further updated and classified by the new entanglements. This and our early papers derive quantum physics, solve all crisises of basses of quantum mechanics, e.g., wave-particle duality & the first quantization origins, quantum nonlocality, entanglement origins & classifications, wave collapse and so on.Key words: quantum mechanics, operator, basic presumptions, wave-particle duality, principle of measurement, identical principle, superposition principle of states, entanglement origin, quantum communication, wave collapse, classical statistical mechanics, classical mechanics

Hydro generator sets serve as the peak and frequency regulation of the power system,and play a very important role in maintaining system stability and improving power quality.Based on the internal characteristics model of a specific type of hydro generator,this paper constructs a hydroelectric coupling model for hydropower units,studies the influence of the regulation system of hydropower units on low-frequency oscillations under different load conditions,and adopts variable speed reaching law and fuzzy sliding mode control strategy to improve the regulation quality of the system and ensure the stable operation of the units.Results of simulation and water turbine set under different load conditions show that varible speed reaching law can effectively improve the power-angle oscillation behavior of the hydropower set and suppress the occurrence of low-frequency oscillations of the system when the system is subject to power disturbance,which provide a new theoretical basis and solution for the vibration analysis and control optimization of hydraulic turbine units.

Curriculum teaching is the basic unit to realize the cultivation and achievement of students’ ability in undergraduate colleges, and it is very important to investigate the influence mechanism of the curriculum system on the achievement states of students' ability. This paper first puts forward the theoretical system and framework of curriculum chains, and constructs a “double-energy and double-space” (DEDS) which consist of the curriculum energy efficiency space and student ability space respectively. Investigating the space-time transformation relationship of curriculum chains in the DEDS, the forward transformation model, inverse transformation model and the rheological model of the achievement states in the curriculum energy efficiency space and student ability space are established. Studying the dynamic evolution mechanism of curriculum chains within the DEDS, the dynamic evolution model, which presents the relationship between achievement states of curriculum energy efficiency/students' ability and curriculum/students' dynamic potential, are established; the control methods of courses/students' dynamic potential based on the proposed dynamic evolution model are developed to ensure the achievements of the graduates’ social ability; the evaluation and detection methods of achievement states of curriculum chains in DEDS are proposed to obtain the actual achievement states of curriculum energy efficiency and students’ ability, and this will provide effective input for the control of curriculums/students’ dynamic potential; the planning and design methods of curriculum chains facing social needs are presented, which can provide theoretical basis for the formulation of the talent training scheme and curriculum system in colleges and universities.

Lake sediment records are of great importance for understanding the evolution of watershed environments. Various studies have been carried out to constrain the depositional ages of lake sediments and examine their physical, chemical, and biological characteristics, aiming to construct historical vegetation, environment and climate patterns in Chinese lake watersheds. In this study, we obtained relevant literature on lake sediment records by searching the key word 'age-depth' from the databases – Web of Science and China National Knowledge Infrastructure (CNKI, the largest Chinese academic database). We analyzed the literature based on its type (published in a Chinese/English journal or as a Master’s/PhD thesis), period of publication, journal (if published in a journal), key authors, study area, dating scale, and main aims. The results suggest that the lakes in the plateau regions are the most popular research topic typically covering 100–200 years (short-term) and 500–30,000 years (long-term). The literature focuses on a wide range of topics, from past environmental evolution in the watershed to lake ecology, and provides a solid foundation for better understanding regional climate change and the preservation of lake environments and ecosystems. In the future, resulting data from environmental reconstruction with lake sediments will be needed to integrate with emerging information processing technologies (e.g., artificial intelligence and meta-analysis), to disentangle the complex interplay between Earth surface processes and global climate change; furthermore, strengthening interdisciplinary collaboration will deepen our comprehension of man-land relationship and promote the sustainable management of lake ecosystems in the context of global climate change.

Fosmidomycin (FOS) is a naturally occurring compound active against the enzyme DXR in the MEP pathway, and using it as a template for lead structure design is an effective strategy in developing new active compounds. In this work, by replacing the hydroxamate unit of FOS with pyrazole, isoxazole and the related heterocycles that also have the metal ion binding affinity, while retaining the monophosphonic acid in FOS or replacing it with a bisphosphonic acid group, heterocycle-containing mono- and bisphosphonic acid compounds as FOS analogs were designed. The key steps involved in the facile synthesis of these FOS analogs include the Michael addition of diethyl vinylphosphonate or tetraethyl vinylidenebisphosphonate to β-dicarbonyl compounds and the subsequent cyclic condensation with hydrazine or hydroxylamine. Two additional isoxazolinone-bearing FOS analogs were synthesized by the Michaelis-Becker reaction with diethyl phosphite as a key step. The bioactivity evaluation on model plants demonstrated that several compounds have better herbicidal activities compared to FOS, with the most active compound showing a 3.7-fold inhibitory activity on Arabidopsis thaliana, while on the root and stalk of Brassica napus L. and Echinochloa crus-galli in pre-emergence inhibitory activity test, the activities of this compound were found to be 3.2- and 14.3-, 5.4- and 9.4-fold, respectively, and in post-emergency activity test on Amaranthus retroflexus and Echinochloa crus-galli, 2.2- and 2.0-fold inhibition activities were displayed. Despite the significant herbicidal activity, this compound exhibited a DXR inhibitory activity lower than that of FOS but comparable to that of other non-hydroxamate DXR inhibitors, and the dimethylallyl pyrophosphate rescue assay gave no statistical significance, suggesting a different target might be involved in the inhibiting process. This work demonstrates that using bioisosteric replacement can be considered a valuable strategy for discovering new FOS analogs that may have high herbicidal activity.

Objective: The aim of this study was to optimize the fermentation process of Radix Ranunculi ternate via microbial fermentation and analyze the changes in the contents of the main components, the antioxidant and hypoglycemic capacities of the extract before and after fermentation. Methods: The solid-state fermentation process was optimized using single-factor tests and the response surface method, with the yield of the alcohol extract of R. ternate as an evaluation index. Results: The best fermentation process was optimized using solid-state endophytic fungus fermentation technology as follows: strain addition ratio of Chaetomium globosum/Fusarium equiseti =1:1, fermentation for 5 d, sieve size of 40 mesh, liquid/material ratio of 0.8:1 mL·g−1, fermentation temperature of 31 °C, and inoculation amount of 7.5%. Under the optimized conditions, the contents of the water-soluble extract and total polysaccharides decreased by 12.71% and 12.95%, respectively, in the fermentation, The contents of ethanol-soluble-extract, flavonoids, saponins, polyphenols, organic acids, and total amino acids of fermented R. ternate increased by 19.77%, 57.14%, 79.67%, 14.29%, 17.63% and 3.82%, respectively, and the scavenging rate for DPPH, ABTS+, ·OH free radicals and inhibitory rate for α-amylase also increased by 19.02%, 14.17%, 7.53% and 34.54%, respectively, compared with unfermented R. ternate. Conclusions: Solid-state fermentation opens new avenues for the development and application of R. ternate as natural antioxidant and hypoglycemic food.

Translation is spatiotemporally regulated and ER-associated mRNAs are generally in efficient translation. It is unclear whether the ER-associated mRNAs are deadenylated or degraded on the ER surface in situ or in the cytosol. Here, we showed that ER possessed active deadenylases, particularly the poly(A)-specific ribonuclease (PARN), in common cell lines and mouse tissues. Consistently, purified recombinant PARN exhibited a strong ability to insert into the Langmuir monolayer and liposome. ER-anchored PARN was found to be able to reshape the poly(A) length profile of the ER-associated RNAs by suppressing long poly(A) tails without significantly influencing the cytosolic RNAs. The shortening of long poly(A) tails did not affect global translation efficiency, suggesting that the non-specific action of PARN towards long poly(A) tails was beyond the scope of translation regulation on the ER surface. Transcriptome sequencing analysis indicated that the ER-anchored PARN trigged the degradation of a small subset of ER-enriched transcripts. The ER-anchored PARN modulated the translation of its targets by redistributing ribosomes to heavy polysomes, suggesting that PARN may play a role in dynamic ribosome reallocation. During DNA damage response, MK2 phosphorylated PARN-Ser557 to modulate PARN translocation from the ER to cytosol. By promoting the decay of ER-associated MDM2 transcripts with low ribosome occupancy, the ER-anchored PARN modulated DNA damage response and thereby cell viability. These findings revealed that a highly regulated communication between mRNA degradation rate and translation efficiency is present on the ER surface in situ and that PARN may contribute to this communication by modulating the dynamic ribosome reallocation between transcripts with low and high ribosome occupancies.

As a fundamental, overall, and strategic issue facing human society, human migration is a key factor affecting the development of countries and cities given constantly changing population numbers. The fuzziness of the spatiotemporal attributes of human migration limits the pool of open-source data for human migration prediction, leading to a relative lag in human migration prediction algorithm research. This study expands the definition of human migration research, reviews the progress of research into human migration prediction, and classifies and compares human migration algorithms based on open-source data. It also explores the critical uncertainty factors restricting the development of human migration prediction. Given the effect of human migration prediction, in combination with artificial intelligence and big data technology, the paper concludes with specific suggestions and countermeasures aimed at enhancing human migration prediction research results to serve economic and social development and national strategy.

The human mandible's cancellous bone, which is characterized by its unique porosity and directional sensitivity to external forces, is crucial for sustaining biting stress. Traditional CAD models fail to fully represent bone's anisotropic structure and thus depend on simple isotropic assumptions. For our research, we use the latest versions of nTopology and Creo Parametric software to make biomimetic Voronoi lattice models that accurately reflect the complex geometry and mechanical properties of trabecular bone. The porosity of human cancellous bone is accurately modeled in this work using biomimetic Voronoi lattice models. The porosities range from 70% to 95%, which can be achieved by changing the pore sizes to 1.0 mm, 1.5 mm, 2.0 mm, and 2.5 mm. Finite element analysis (FEA) was used to examine the displacements, stresses, and strains acting on dental implants with a buttress thread, abutment, retaining screw, and biting load surface. The results show that the Voronoi model accurately depicts the complex anatomy of the trabecular bone in the human jaw, compared to standard solid block models. The ideal pore size for biomimetic Voronoi lattice trabecular bone models is 2 mm, taking into account both the von Mises stress distribution over the dental implant, screw retention, cortical bone, cancellous bone, and micromotions. This pore size displayed balanced performance by successfully matching natural bone's mechanical characteristics. Advanced finite element analysis (FEA) improves biomechanical understanding of how bones and implants interact by creating more accurate models of biological problems and dynamic loading situations. This makes biomechanical engineering better.

Macula fovea detection is a crucial prerequisite towards screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither accuracy nor effectiveness of the diagnose process could be guaranteed. In this project, we proposed a deep learning approach on ultra-widefield fundus (UWF) images for macula fovea detection. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. Methods based on U-shape network (Unet) and Fully Convolutional Networks (FCN) are implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. Results are measured by calculating the Euclidean distance between proposed approaches and the accurate grounded standard, which is detected by Ultra-widefield swept-source optical coherence tomograph (UWF-OCT) approach. Through a comparation of proposed methods, we conclude that, deep learning approach of Unet outperformed other methods on macula fovea detection tasks, by which outcomes obtained are comparable to grounded standard method.

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL through a wide range of capabilities from reinforcement learning (RL) and deep learning (DL) for handling sophisticated dynamic business environments offers vast opportunities. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this work, we first consider a brief review of DL, RL, and deep RL methods in diverse applications in economics providing an in-depth insight into the state of the art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher accuracy as compared to the traditional algorithms while facing real economic problems at the presence of risk parameters and the ever-increasing uncertainties.

The correlation and comorbidity between depression and chronic pain have been observed for a long time. Generally, it is considered that the two conditions reinforce each other, whereas the causal relationship between them is not clear. However, some evidence suggested that chronic pain may reverse the progression of depression in some cases. This article presents a selective review of clinical and pharmacological relationship between depression and pain, and their interactions at neurochemical and neurobiological levels. In addition, we open a discussion on a recent case report of repeated success of using short but continuous pain (SCP) during meditation as the only treatment for depression, compared to initial success but no remission with other conventional antidepressants on the same patient. Together this review proposes an updated model for depression and its various treatments that is based on synaptic and system homeostasis. More importantly, it suggests that SCP may benefit depression recovery through its properties that are different from either acute or chronic pain and represents a novel research area that has been largely neglected to date.

The technological development of piezoelectric materials is crucial for developing wearable and flexible electromechanical devices. There are many inorganic materials with piezoelectric effects, such as piezoelectric ceramics, aluminum nitride, and zinc oxide. They all have very high piezoelectric coefficients and large piezoelectric response ranges. The characteristics of high hardness and low tenacity make inorganic piezoelectric materials unsuitable for flexible devices that require frequent bending. Polyvinylidene fluoride (PVDF) and its derivatives are the most popular materials used in flexible electromechanical devices in recent years and have high flexibility, high sensitivity, high ductility, and a certain piezoelectric coefficient. Owing to increasing the piezoelectric coefficient of PVDF, researchers are committed to optimizing PVDF materials and enhancing their polarity by a series of means to further improve their mechanical–electrical conversion efficiency. This paper reviews the latest PVDF-related optimization materials, related processing and polarization methods, and the applications of these materials such as those in wearable functional devices, chemical sensors, biosensors, and flexible actuator devices for flexible micro-electromechanical devices. We also discuss the challenges of wearable devices based on flexible piezoelectric polymer, consider where further practical applications could be.

This study investigated whether reciprocal collaborative pedagogy significantly impacts Indonesian electrical engineering polytechnic students in reducing reading anxiety and improving self-efficacy in ESP reading comprehension skills. Fifty Polytechnic students were equally assigned to two groups. Control groups learned in the traditional reciprocal teaching method individually. Experimental group engaged in reciprocal collaborative pedagogy. Each group received a 6-week reading strategy training. E-learning platforms were used in this study as a complementary tool to distribute ESP reading materials, announcements, and the experiment details being taught. The quantitative and qualitative results demonstrated that the tasks in reciprocal collaborative pedagogy positively affects Indonesian electrical engineering polytechnic students' learning experience and outcomes. This pedagogy promotes a productive learning environment through peer interaction, active engagement, and peer group discussions of the whole learning activities, reducing feelings of anxiety and building confidence in ESP-specialized text.

Complex models have received significant interest in recent years and are being increasingly used to explain the stochastic phenomenon with upward and downward fluctuation such as the stock market. Different from existing semi-variance methods in traditional integer dimension construction for two variables, this paper proposes a simplified multi-factorized fractional dimension derivation with the exact Excel tool algorithm involving the fractional center moment extension to covariance, which is a complex parameter average that is a multi-factorized extension to Pearson covariance. By examining the peaks and troughs of gold price averages, the proposed algorithm provides more insight into revealing underlying stock market trends to see who is the financial market leader during good economic times. The calculation results demonstrate that the complex covariance is able to distinguish subtle differences among stock market performances and gold prices for the same field that the two variable covariance may overlook. We take the London, Tokyo, Shanghai, Toronto and Nasdaq as the representative examples.

Using data from the China Family Panel Studies, this study examines the socioeconomic characteristics of Internet users, as well as the relationships between the dynamics of different forms of online activities and the subjective well-being of urbanites and rural migrants in urban China. The study finds that online behaviour may clearly reflect differences in individuals’ personal traits and socioeconomic positions. Patterns of the association between online activities and subjective well-being tend to differ among rural migrants and urbanites, especially in terms of depression. A difference-in-differences model is employed to estimate the impact of intensified engagement in online activities on depression and life satisfaction from 2010 to 2016. The results show that increased frequency of online entertainment exhibits a comparatively positive effect on depression and life satisfaction. Spending more time on online social networking has a similar impact on rural migrants, but not on urbanites. These findings suggest that the rapid development of urban China’s online community has important implications for residents’ subjective well-being.

In the process of climate change mitigation and adaptation, the focus on climate risk driven by transition innovations has received widespread attention from governments, regulators, and investors. Based on the identification of low-carbon patents through the Green and Low-carbon Technology Inventory in China, this study constructs low-carbon innovation measurements of listed firms in China from 2015 to 2021. Then, an empirical model is used to investigate the impact and mechanism of climate transition innovations on the default risk. The baseline regression model shows that low-carbon innovations can mitigate the default risk of listed firms. Moreover, our findings are supported by instrumental variable regressions that use the time costs of innovation. Finally, the mediation effect demonstrates that investor attention, total factor productivity, and technology spillovers are paths through which transition innovations can affect the default risk. This study reveals that low-carbon technological advances have a positive effect on climate transition risks.

Existing research indicates that mineralization of Carlin-type gold deposit is related to geological structures, but current studies mostly focus on the qualitative association, and lack quantitative analysis of the relationship between mineralization and structural features, which limits the potential for predictive mineral exploration. In this study, the Nibao Carlin-type gold deposit was selected as the research area to conduct three-dimensional geological modeling, spatial analysis, and three-dimensional prospectivity modeling. Spatial analysis revealed that the majority of gold ore bodies are situated near the main fault, with gold mineralization primarily occurring within a spatial range of approximately -80 meters to 20 meters from the main fault plane. This spatial distribution predominantly aligns in a SW-NE direction. Spatial characteristic extraction of fault slope indicators suggests that the slope angles are conducive to the migration and accumulation of ore-forming hydrothermal fluids, with ore body metal content rapidly increasing within an angle range of approximately 20° to 60°. The shape analysis of the mainfault indicates that the principal spaces of mineralization enrichment are within -10m to 20m of the fault plane undulations, where gold resources are concentrated in a relatively flat area. However, the degree of mineralization is more pronounced in the protruding sections than in the depressed areas. The areas with significant dip changes in faults display higher variations in orebody attitudes, with mineralization noticeably weaker in areas where the fault steepness changes markedly. Ore-bearing units exhibit significantly higher grades within the -17° to 3° interval of fault steepness transition. This interval also sees a rapid accumulation of gold resources. The Nibao gold mine's mineralization is derived from deep, ore-bearing hydrothermal fluids ascending along major fractures, precipitating, and accumulating in favorable ore-hosting spaces (faults, anticline cores) and lithological combinations (sedimentary tuffs, tuffs). Three deep potential gold targets were identified, highlighting the deep western edge of the F1 fault and the deep southeast-trending lower plate as prospective areas for future exploration. This study underscores the effectiveness of 3D prospective modeling as an indispensable tool for elucidating the spatial distribution of fault-controlled Carlin-type gold deposits and their tectonic determinants.

The collapsible loess will rapidly soften and lose its bearing capacity when soaked in water. Under a mild condition (20°C), the biomimetic inorganic agent, Diammonium phosphate (DAP), reacts with calcite in the collapsible loess, producing a stronger bonding material, hydroxyapatite (HAP), to modify and stabilize the soil. Uniaxial compression, permeability tests and morphological analysis using XRD and SEM/EDX microscopy were carried out to assess the effectiveness of DAP stabilization on the collapsible loess. The results indicated that HAP improved the inter-particle bonding within loess, filled the pores within particles, reduced the permeability, and consequently mitigated collapsibility of loess. The compressive strength of DAP-treated loess increased as DAP concentration increased. Following 28-days curing, the compressive strength of the loess treated with a 3.0 mol/L DAP solution was six times greater than that of the untreated group. DAP's reinforcement effect on loess was superior to that of cement. The compressive strength of DAP-treated loess was about double that of cement-treated loess and the permeability coefficient was reduced by more than 50% at equivalent solid content. Furthermore, DAP generated 68% fewer carbon emissions compared to Portland cement. Considering eco-friendly and sustainable development, DAP offers a more competitive alternative for modification and stabilization of loess.

A simple design of an ultrathin six-band polarization-insensitive terahertz perfect metamaterial absorber (PMMA) composed of a metal cross-cave-patch resonator (CCPR) placed over a ground plane was proposed and investigated numerically. The numerical simulation results demonstrate that the average absorption peaks are up to 95% at six resonance frequencies with high quality-factors (>65). In addition, the absorption properties can be kept stability for both normal incident transverse magnetic (TM) and transverse electric (TE) waves. The physical mechanism behind the observed high level absorption is illustrated by the electric and power loss density distributions. The different absorption mainly originates from the higher order multipolar and multipolar plasmon resonance of the structure, which are sharp different to the most previous studies of the PMMAs. Furthermore, the resonance absorption of the PMMA can be tunable by varying the geometric parameters of the unit cell.

A simple design of an ultrathin six-band polarization-insensitive terahertz perfect metamaterial absorber (PMMA) composed of a metal cross-cave-patch resonator (CCPR) placed over a ground plane was proposed and investigated numerically. The numerical simulation results demonstrate that the average absorption peaks are up to 95% at six resonance frequencies with high quality-factors (>65). In addition, the absorption properties can be kept stability for both normal incident transverse magnetic (TM) and transverse electric (TE) waves. The physical mechanism behind the observed high level absorption is illustrated by the electric and power loss density distributions. The different absorption mainly originates from the higher order multipolar and multipolar plasmon resonance of the structure, which are sharp different to the most previous studies of the PMMAs. Furthermore, the resonance absorption of the PMMA can be tunable by varying the geometric parameters of the unit cell.

Cesarean Section (CS) is one of the most frequently executed surgical procedures in gynecology and obstetrics. After a cesarean section, surgical site infection (SSI) increases hospital stay, lengthens maternal morbidity, and upsurges treatment costs. The current study determines the prevalence and risk factors for surgical site infection following cesarean section in China. A retrospective study was conducted on 23 cases of pregnant women who underwent cesarean section and incision severe infection and detection from March 2017 to January 2020 at Wuhan Maternal and Child Healthcare Hospital in China as the study group, and 20 cases of uninfected cesarean section during the same period were selected as the control group. Data were compared with the controls based on study variables and the presence of SSI. The mean age was 31±2.6. High fever and blood loss were observed in serous SSI-infected patients. The incidence rate of severe surgical site infection was 0.15 %. SSI was observed to be expected in pregnant women who had premature rupture of membrane before surgery (p < 0.001), who underwent postoperative antibiotic therapy (p < 0.001), and the patients who had gestational diabetes mellitus (p <0.001) and hematoma (p < 0.001) during surgery. Hence, following a cesarean section, surgical site infection is common. This research discovered several modifiable risk factors. SSI is associated with multifactorial rather than a single one. The development and strict implementation of a procedure by all health care practitioners can successfully reduce and prevent infection rates following cesarean section.

The multidrug resistance transporter NorM is an important drug resistance pump and plays a critical role in multidrug resistance in bacteria and mammals. In this study we carried out molecular dynamics simulation to study the mechanism of Na+ binding and dynamical structures of two long loops in the substrate-releasing process in substrate binding NorM. Our simulation study identified several key residues (D41, E261 D377) along the Na+ binding pathway and a multi-state ion-binding mechanism is proposed based on the simulation study. In this proposed model, the transport of Na+ is a multi-stage process with D41 being the first station for binding to Na+, followed by Na+ binding to the second station E262 and finally to the cation-binding site of E262 and D377. During the transport of Na+, the transmembrane components TM1, TM7 and TM2 are rearranged to facilitate the ion transport as well conformational changes of NorM to a closed state. Further, substrate-bound simulation revealed that Loop3-4 and Loop9-10 control the substrate-releasing process.

In this paper we report a new development on the numerical model for aluminum-steam combustion. This model is based on diffusion flame of continuum regime and the thermal equilibrium between the particle and the flow field, which can be used to calculate the aluminum particle combustion model for two phase calculation conditions. The model prediction is in good agreement with the experimental data. A new type of vortex combustor was proposed for the combustion of aluminum and steam, and the mathematical model of the two phase reacting flow with in this combustor was established. The turbulence effects are modeled using the Reynolds Stress Model (RSM) with Linear Pressure-Strain approach, and the Eddy-Dissipation model is used to simulate the gas phase combustion. Aluminum particles are injected into the vortex combustor and form a swirling flow around the chamber and their trajectories are traced using the Discrete Phase Model (DPM). The simulation results show that the vortex combustor can achieve high efficient combustion of aluminum and steam. The influencing factors, such as the eccentric distance of the inlet of aluminum particles, particle size and steam inlet diameter, etc., are studied. The work described in this paper represents an attempt to the design of a vortex combustor in order to increase aluminum combustion efficiency.

With the increasingly online exposure of urban public spaces, the new concept of "Internet-celebrity cities" has emerged in China. The online internet-famous sites are the main characteristics of "Internet-celebrity cities". To fully understand online users' preferences in such kind of public spaces, this article took 27 typical riverfront internet-famous sites (RIFS) in Changsha city (China), as an example. Through the online images and review comments on social media platform "Xiaohongshu", the study analyzed users’ perception of RIFS specially on visual and emotional preferences. It revealed the following findings:(1) The popularity of RIFS had a significant head effect and there were far more positive emotions than neutral and negative emotions in review comments. (2) According to 5 different categories of RIFS: modern buildings with river views, restaurants with strong visual impact became the main attractions on commercial RIFS. The art exhibition RIFS attracted visitors to experience traditional culture and highlight their aesthetic pursuits. In the historical and cultural RIFS, people preferred to see the historical buildings with surrounding natural environment, as well as river and mountains views. Ecological Recreational RIFS showed the similar results with the uncultivated RIFS, for the main focus was on the ecological environment, a broader vision and the specific atmosphere. Moreover, the uncultivated RIFS showed more diversified attraction points. This study can provide a new perspective on the waterfront vitality and can offer a targeted and attractive manner for waterfront regeneration that are different from traditional methods.

Ornamental kale (Brassica oleracea var. acephala) is an attractive ornamental plant with a range of leaf colors and shapes. Breeding new varieties of ornamental kale has proven challenging due to its lengthy breeding cycle and the limited availability of genetic markers. Microspore culture is an effective approach to generate new materials and genetic maps are a prerequisite for quantitative trait loci analysis, MAS, fine gene mapping, and genome sequence assembly. In this study, a F1DH ornamental kale population comprising 300 DH lines was constructed using microspore culture. A high-density genetic map was developed by conducting whole-genome sequencing on 150 individuals from the F1DH population. The genetic map contained 1,696 bin-markers with 982,642 single-nucleotide polymorphisms (SNPs) spanning a total distance of 775.81 cM on all nine chromosomes with an average distance between markers of 0.46 cM. The ornamental kale genetic map contained substantially more SNP markers compared with published genetic maps for other B. oleracea crops. The F1DH progenies provide an excellent resource for germplasm innovation and breeding new varieties of ornamental kale. The high-density genetic map provides crucial insights for gene mapping and unraveling the molecular mechanisms behind important agronomic traits in ornamental kale.

This paper uses probability method to evaluate the fire safety performance of prestressed steel-concrete beam bridges based on simulation experimental research. Firstly, fire simulation experimental sample analysis was conducted on actual small box girder bridges to obtain the structural response of prestressed steel-concrete structures under fire, which is in line with engineering practice. Next, construct a reliability analysis model for the fire resistance performance of prestressed steel-concrete beam bridges; Combining reliability theory with finite element method, establish a reliability analysis method for the fire resistance performance of prestressed steel-concrete beam bridges. Subsequently, a safety factor evaluation model for the fire resistance performance of prestressed steel-concrete beam bridges was proposed, and a safety factor evaluation method for the fire resistance performance of prestressed steel-concrete beam bridges based on reliability back analysis was established. Finally, based on the analysis of the structural response after a fire in a specific case of a simply supported to continuous prestressed steel-concrete continuous beam bridge project, a structural resistance sample of the prestressed steel-concrete beam bridge is generated through uniform design method, and statistical analysis is conducted. Subsequently, probability methods are used to evaluate the safety of the prestressed steel-concrete beam bridge after a fire. Through analysis, it can be concluded that the time of fire has a significant impact on the structural performance of prestressed steel-concrete beam bridges, and the randomness of parameters has a significant impact on the safety reserve of prestressed steel-concrete beam bridges after fire. It is necessary to pay attention to it in specific engineering practice and strengthen the monitoring and statistics of structural random characteristics.

The load of power system changes with the development of economy, short-term load forecasting play a very important role in dispatching and management of power system. In this paper, the Ant Lion Optimizer (ALO) is introduced to improve the input weights and hidden-layer Matrix of extreme learning machine (ELM), after the parameters of ELM are optimized by ALO, then input nodes, hidden layer nodes and output nodes are determined, so a load forecasting model based on ALO-ELM combined algorithm is established. The proposed method is illustrated based on the historical load data of a city in China. The results show that the average absolute error of short-term load demand predicted by ALO-ELM model is 1.41, while that predicted by ELM is 4.34, the proposed ALO-ELM algorithm is superior to the ELM and meet the requirements of engineering accuracy, which proves the effectiveness of proposed method.

Ischemic stroke (IS) is a group of vascular disease concomitant with high morbidity and mortality. Berberine is a bioactive substance and it has known to improve stroke, but the mechanism is yet to be proven. Mice were fed with BBR for 14 days. Then, mice were made into MCAO/R models. Neurological score, infarct volume, neuronal damage and markers associated with inflammation were detected. We tested the changes of intestinal flora in model mice after BBR administration by 16SrRNA sequencing. Chromatography-mass spectrometry was used to detect butyrate chemically. Tissue immunofluorescence was used to detect the changes of microglia and astroglia in mice brain. Our findings suggest that berberine improves stroke outcomes by modulating gut microbiota. Specifically, after MCAO/R mice were given berberine, beneficial bacteria producing butyric acid increased significantly, and the mice also had significantly higher levels of butyric acid. Administration of butyric acid and an inhibitor of butyric acid synthesis, heptyl-coA, showed that butyric acid improved stroke outcomes in model mice. In addition, butyric acid could inhibit the activation of microglia and astrocytes in the brain of model mice, thereby inhibiting the release of inflammatory cytokines IL-6, IL-1β, TNF-α and improving stroke outcomes. Our results suggest that berberine may improve stroke outcomes by modulating the gut flora to increase the abundance of butyric acid. These findings elucidate the mechanisms by which berberine improves stroke outcomes and provide some basis for clinical treatment.

Mechanical ventilation (MV), used in patients with acute lung injury (ALI), induces diaphragmatic myofiber atrophy and contractile inactivity, termed ventilator-induced diaphragm dysfunction. Phosphoinositide 3-kinase- (PI3K-) is crucial in modulating fibrogenesis during the reparative phase of ALI; however, the mechanisms regulating the interactions among MV, myofiber fibrosis, and PI3K- remain unclear. We hypothesized MV with or without bleomycin treatment would increase diaphragm muscle fibrosis through the PI3K- pathway. Five days after receiving a single bolus of 0.075 units bleomycin intratracheally, C57BL/6 mice were exposed to 6 or 10 mL/kg MV for 8 h after receiving 5 mg/kg of AS605240 intraperitoneally. In wild-type mice, bleomycin exposure followed by MV 10 mL/kg prompted significant increases in disruptions of diaphragmatic myofibrillar organization, transforming growth factor-1, oxidative loads, Masson’s trichrome staining, extracellular collagen levels, positive staining of -smooth muscle actin, PI3K- expression, and myonuclear apoptosis (P &lt; 0.05). Decreased diaphragm contractility and peroxisome proliferator activated receptor- coactivator-1 levels were also observed (P &lt; 0.05). MV-augmented bleomycin-induced diaphragm fibrosis and myonuclear apoptosis were attenuated in PI3K-γ-deficient mice and through AS605240-induced inhibition of PI3K- activity (P &lt; 0.05). MV-augmented diaphragm fibrosis after bleomycin-induced ALI is partially mediated through PI3K-γ. Therapy targeting PI3K-γ may ameliorate MV-associated diaphragm fibrosis.

This study presents a novel approach for quantifying the dynamics of monthly hydraulic geometry relationships in the Jingjiang reach of the Yangtze River by integrating leaf area index (LAI) data as an indicator of riparian vegetation. A comprehensive system of ordinary differential equations (ODEs) couples traditional power-law hydraulic geometry equations with LAI, capturing the influence of erosion control on channel morphology. The periodic delayed response theory and automatic differentiation techniques are employed to enhance the modeling framework's capabilities. Calibration and verification demonstrate the models' effectiveness in capturing overall trends in discharge, width, and depth dynamics, outperforming traditional constant-parameter approaches. However, model predictive abilities are limited under extreme climate conditions due to training on normal climate data. Key assumptions, including the periodic delayed response theory's neglect of peak discharges, simplified representation of artificial cut-offs, and reliance on remotely sensed data with limited local validation, introduce uncertainties. The proposed modeling framework shows potential for broader application to other river systems with appropriate adjustments. Future research could incorporate additional environmental factors, integrate models across scales, couple with advanced simulation techniques, and expand stability assessments beyond the riverbed index. By addressing related challenges, this integrated approach lays a foundation for enhancing understanding of river morphodynamics and channel stability.

The basic oxygen furnace slag (BOF) was wide used in road construction, but there was a lack of characteristics in different asphalt mixtures. This study investigates the properties of hot-mixed asphalt (HMA) containing stone mastic asphalt (SMA), porous asphalt (PA) and dense-graded BOF as a partial substitution for natural aggregates. The purpose of this study is to evaluate various BOF slag contents in the asphalt mixtures would affect the cooling behavior after compaction. Asphalt mixture specimens contained 0%, 20%, 40% and 60% BOF slag, respectively, as coarse aggregate. Test results showed that BOF slag has a lipophilic property, so it can be adsorbed by asphalt cement, thereby reducing the cost of asphalt. The stability value of all asphalt mixtures increases with the proportion of BOF slag replacement. In addition, the voids in the mineral aggregate (VMA) value variable exhibited significant differences among asphalt mixtures, and could determine the deviation of the cooling trend of asphalt mixtures. Furthermore; it was found that the cooling procedure of the BOF slag used in dense-graded asphalt mixture takes about 100 min, and that the temperature tends to be moderate; however, it took about 120 min of cooling the SMA and PA mixture with BOF slag. In addition, the voids distribution of dense asphalt mixture was not uniform. It would result in various locations of thermal energy temperature on asphalt mixtures that were inconsistent.

Various factors, such as uncertainty of component parameters and uncertainty of sensor meas-urement values, contribute to the difficulty of fault detection in the landing gear retrac-tion/extension hydraulic system. In this paper, we introduce linear fractional transformation technology and uncertainty analysis theory for the construction of the diagnostic bond graph of the landing gear retraction/extension hydraulic system. In this way, interval analytical redundancy relations and fault signature matrix can be derived. Using the fault signature matrix, existing faults of the system can be preliminarily detected and isolated. Additionally, interval analytical re-dundancy relations can be used to detect system faults in detail, and cases analysis can be carried out to determine if the actuator is externally or internally leaky, and if the landing gear selector valve is reversing stuck. Compared to the traditional analytical redundancy relations, this method takes into account the negative factors of uncertainty; and compared to the traditional absolute diagnostic threshold, the interval diagnostic threshold is more accurate and sensitive.

COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, support quantative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation and multi-year daily use for public health and clinical support decision-making of a particle filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian Sequential Monte Carlo algorithm of Particle Filtering allows the model to be re-grounded daily and adapt to new trends within daily incoming data – including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of dynamic model state, support probabilistic model projection of epidemiology and health system capacity utilization and service demand and probabilistically evaluate trade-offs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision making across Canada, such methods offer strong support for evidence-based public health decision making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams.

Epitranscriptomic RNA modifications play a crucial role in the posttranscriptional regulation of gene expression. N6-methyladenosine (m6A) is the most prevalent internal modification of eukaryotic RNA and plays a pivotal role in RNA fate. RNA m6A modification is regulated by a group of cellular proteins, methyltransferases (writers) and demethylases (erasers), which add and remove the methyl group from adenosine, respectively. m6A modification is recognized by a group of cellular RNA-binding proteins (readers) that specifically bind to m6A-modified RNA, mediating effects on RNA stability, splicing, transport, and translation. The functional significance of m6A modification of viral and cellular RNA is an active area of virology research. In this review, we summarize and analyze current literature on m6A modification of HIV-1 RNA, the multifaceted functions of m6A in regulating HIV-1 replication, and the role of viral RNA m6A modification in evading the innate immune responses to infection. Furthermore, we briefly discuss future directions and therapeutic implications of mechanistic studies of HIV-1 epitranscriptomic modifications.

The health of the landing gear retraction and extension hydraulic system may be assessed using fuzzy comprehensive evaluation (FCE), however the traditional FCE method depends solely on human assessment by specialists, which is excessively subjective. To address the issue of excessive human subjective variables in the assessment, an improved FCE model based on enhanced risk coefficient is provided, which includes four consideration indexes: failure probability, failure severity, failure detection difficulty, and failure repair difficulty. To reduce subjective human judgment errors entirely due to expert experience, the improved FCE takes into account the likelihood of failure using a statistical method, the severity of failure using a fault simulation analysis based on the LMS Imagine.Lab AMESim simulation platform, and the difficulty of fault detection and repair using the aircraft manufacturer's professional maintenance information. As part of the evaluation model, the range of health assessment values and accompanying treatment methods are included, making it easier to implement on a daily basis in aircraft maintenance. As a final step, the simulation is evaluated and the simulated faults are calculated.

With the widespread use of new equipment such as distributed photovoltaics, distributed energy storage, electric vehicles, and distributed wind power, the control of low-voltage distribution networks (LVDNs) has become increasingly complex. Acquiring the most recent topological structure is essential for conducting accurate analysis and real-time control of LVDNs. The signal-injection-based topology identification algorithm is favored for its speed and efficiency. This study proposes a new signal-injection-based topology identification algorithm aimed at solving the issues of reduced completeness and correctness of identification caused by missing feature signal records (FSRs). By analyzing the correlations between FSRs of different positions, the algorithm employs vertical and horizontal completion methods to effectively address missing data, combined with inclusion detection algorithms, to identify the topology of LVDNs. Based on the study of actual LVDN data, the results indicate that the algorithm not only significantly improves the completeness and correctness of topology identification for LVDNs but can also increase the number of devices in operation by evaluating the status of topological equipment, further enhancing the topology identification performance.

Littoraria melanostoma (Gray, 1839) is a most common species of gastropods in mangroves. They quickly respond during the early stage of mangrove restoration and usually form a dominant community within a certain period. We characterized the complete mitochondrial genome of the species. The whole mitogenome of L. melanostoma was 16,149 bp in length and its nucleotide composition showed high AT-content of 64.16%. It had 37 genes, including 13 protein-coding genes, two ribosomal RNA genes, and 22 transfer RNA genes, one control region between tRNA-Phe and COX3. The A/T compositions in the control region is 74.7%, respectively, and are much higher than the overall A/T composition of the mitochondrial genomes. The amino acid composition and codon usage of the mitochondrial genomes of from eight superfamilies of Littorinimorpha were analyzed, and the results showed that CUU (Leu), GCU (Ala), AUU (Ile), UCU (Ser), UUA (Leu), GUU (Gly) and UUU (Phe) are the commonly used codons in this order. The results of phylogenetic analysis were roughly consistent with morphological classification, which showed that L. melanostoma is closely related to L. sinensis, a rock-dwelling species that is widespread in the coastal intertidal zone of China. These results may provide a basis to understand the phylogeny and evolution of the Littorinimorpha.

Turbidity is an important water quality parameter, especially for drinking water. The ability to actively monitor the turbidity level in Drinking Water Distribution Systems is of critical importance to the safety and wellbeing of the public. Traditional turbidity monitoring methods involve manual collection of water samples at set locations and times followed by laboratory analysis, which are labor intensive and time consuming. Fiber-optic measurement permits real-time, in-situ turbidity monitoring. But the current technology is based on plastic fibers, which suffer from high optical attenuation and hence are unsuitable for large-scale remote monitoring. In this paper, we report the demonstration of a fiber-optic turbidity sensor based on multi-mode glass fibers. The system uses a single fiber to both deliver laser light into the water sample and collect the back-scattered light for detection. A balanced-detection scheme is utilized to remove the common-mode noise to enhance the turbidity sensitivity. Highly linear turbidity responses are obtained and a turbidity resolution as low as 0.1 NTU is achieved. The test unit is also shown to have excellent reproducibility against repeated measurements and good stability against temperature changes. Turbidity measurement in real environmental matrices such as tap water and pond water is also reported with an assessment of the impact of flow rate. This work demonstrates the feasibility of future large scale distributed fiber-optic turbidity monitoring networks.

Considering that the electric refrigeration temperature range of 0.94BNT-0.06BT ceramic materials is 100~140˚C, the electric refrigeration performance of the 0.94BNT-0.06BT ceramic material system was modified by LiNbO3 doping to reduce the cooling temperature. As a result, the refrigeration temperature range of the 0.94BNT-0.06BT ceramic material system was lowered to 25~80 ˚C, achieving its cooling effect near room temperature, and in this temperature range, the adiabatic temperature changes ∆T&gt;0.6K.

With the rise of the Internet of Things (IoT), maintaining data confidentiality and protecting user privacy have become increasingly challenging. End devices in IoT are often deployed in unattended environments and connected to open networks, which can make them vulnerable to physical tampering and other security attacks. Different authentication key agreement (AKA) schemes have been validated to date, but most schemes do not cover the necessary security features or are incompatible with resource-constrained end devices. Besides, their security proofs have been performed under the real-or-random model, which is not guaranteed to be secure in real applications. To reduce the weaknesses, we present an AKA protocol for end devices and servers. The proposal leverages the ECC-based key exchange mechanism and one-way hash function-based message authentication method to achieve mutual authentication, user anonymity, and forward security. Formal security proof of the proposed scheme is performed under the standard model with the elliptic curve encryption computational assumptions, and an automatic formal verification was performed with ProVerif. Further, the performance comparison verifies that our scheme reduces computation and communication costs while providing improved security features.

In order to foster a more sustainable and eco-friendly trajectory for the construction industry, while concurrently mitigating environmental pollution and energy inefficiency, it is imperative to cultivate an environmentally conscious building and urban environment. Under the background of carbon peak and carbon neutrality, green building evaluation system (GBRS) has become a research hotspot in the field of green building. This paper systematically summarizes the current research progress based on GBRS, focuses on the current weight and indicators setting, analyzes it with Cite Space software, and puts forward its future development direction. The results show that the weight setting method of green building evaluation system is determined by multidimensional and multi-method. From the perspective of environment, society and economy, the scope of indicators setting is expanding. The green building evaluation system will play a guiding role in green building design, and the evaluation process will continue to be optimized and developed in the direction of intelligence and automation.

An accurate, economical, and reliable device for detecting falls in persons ambulating with the assistance of an orthopedic walker is crucially important for the elderly and patients with limited mobility. Existing wearable devices such as wristbands are not designed for walker users, and patients may not wear them at all times. This research proposes a novel idea of attaching an internet-of-things (IoT) device with an inertial measurement unit (IMU) sensor directly to an orthopedic walker to perform real-time fall detection as well as activity logging. A dataset is collected and labeled for walker users in four activities, including idle, motion, step, and fall. Classic machine learning algorithms are evaluated using the dataset by comparing their classification performance. Deep learning with convolutional neural network (CNN) is also explored. Furthermore, the hardware prototype is designed by integrating a low-power microcontroller for onboard machine learning, an IMU sensor, a rechargeable battery, and Bluetooth wireless connectivity. The research results show the promise of improved safety and well-being of walker users.

Studying the interaction between the upper ocean and the typhoons is crucial to improve our understanding of heat and momentum exchange between the ocean and the atmosphere. In recent years, the upper ocean responses to typhoons have received considerable attention. The sea surface cooling (SSC) process has been repeatedly discussed. In the present work, case studies were examined on five strong and super typhoons that occurred in 2016—LionRock, 1610; Meranti, 1614; Malakas, 1616; Megi, 1617; and Chaba 1618—to search for more evidence and new features of typhoon’s impact on the sea surface environment. The typhoon monitoring data from the Central Meteorological Observatory, the sea surface temperature (SST) data from satellite microwave and infrared remote sensing, and the sea surface height anomaly (SSHA) data from satellite altimeters were used to analyze in detail: the SSC features caused by typhoons, the relationship between the SSC and the typhoon travelling speed, and the variations in cold and warm eddies during typhoon passage. Results showed that: (1) SSC generally occurred during typhoon passage and the degree of SSC was always determined by the strength and the travelling speed of the typhoon, as well as the initial SST. (2) One day before or on the day of typhoon passage, the SSHA slightly increased due to low surface pressure. After the typhoon passed, the SSHA obviously decreased along with the SSC. The pre-existing positive SSHAs, which always represent warm eddies, decreased or disappeared during typhoon passage, whereas negative SSHAs or cold eddies were enhanced. (3) New cold eddies were generated, especially at the turning points of the typhoon path. The presence of warm eddies is suggested to have a strengthening effect on the typhoons.

In the field of delta-sigma modulators, reducing system power consumption without sacrificing accuracy has become a challenge. The summing circuit, as the main part of the cascade-of-integrator-in-feedforward delta-sigma modulator, consumes a significant amount of power in active summing. On the other hand, passive summing circuits have low power consumption but can cause attenuation in the summing results. This article proposes a low-power multi-bit delta-sigma modulator based on passive and attenuationless summation scheme. The summation circuit achieves multiplication of the voltage signal carried on the summation capacitor through bidirectional sampling technique, compensating for the inherent attenuation caused by passive summation, thus eliminating the need for active OTA to achieve perfect summation. A 2-order delta-sigma modulator based on 4-bit 1-order passive noise-shaping (NS) SAR quantizer is designed to verify the scheme. By combining a passive NS SAR as the 4-bit quantizer and the third-stage integrator of the system, and incorporating an energy-efficient cascode floating inverter amplifier (FIA) structure as the core active OTA, these elements have significantly enhanced the energy-efficiency of the modulator. Simulation results show that at a supply voltage of 1.2 V and a bandwidth of 20 kHz, the SNDR reaches 102.62 dB, power consumption is only 148.32 μW, and Schreier figure-of-merit (FoM) of SNDR is 183.92 dB.

Contrapose the highly integrated, multiple types of faults and complex working conditions of aircraft Electro Hydrostatic Actuator (EHA), to effectively identify its typical faults, we propose a fault diagnosis method based on the fusion convolutional neural networks (FCNN). First, the aircraft EHA fault data is encoded by GADF to obtain the fault feature images. Then we build an FCNN model that integrates the 1DCNN and 2DCNN, where the original 1D fault data is the input of the 1DCNN model, and the feature images obtained by GADF transformation are used as the input of 2DCNN. Multiple convolution and pooling operations are performed on each of these inputs to extract the features, next these feature vectors are spliced in the convergence layer, and the fully connected layers and the Softmax layers are finally used to attain the classification of aircraft EHA faults. Furthermore, the multi-strategy hybrid particle swarm optimization (MSPSO) algorithm is applied to optimize the FCNN to obtain a better combination of FCNN hyperparameters; MSPSO incorporates various strategies, including an initialization strategy based on homogenization and randomization, and an adaptive inertia weighting strategy, etc. The experimental result indicates that the FCNN model optimized by MSPSO achieves an accuracy of 96.86% for identifying typical faults of the aircraft EHA, respectively higher than the 1DCNN and the 2DCNN about 16.5% and 5.7%. Additionally, the FCNN model improved by MSPSO has a higher accuracy rate when compared to PSO.

An in-depth understanding of the Se uptake and metabolism in plants is necessary for developing Se biofortification strategies. Thus, hydroponic experiments were conducted to investigate the associated processes and mechanisms of organic Se [selenomethionine (SeMet) and selenomethionine-oxide (SeOMet)] uptake, translocation, transformation and their interaction in wheat, in comparison to inorganic Se. Results showed that Se uptake by roots and the root-to-shoot translocation factor under the SeMet treatment were higher than those under the selenite, selenate, and SeOMet treatments. The uptake and translocation of SeMet were higher than those of SeOMet within 72 h, although the differences gradually narrowed with time. The uptake of SeMet and SeOMet was also sensitive to the aquaporin inhibitor: AgNO3 addition resulted in 99.5% and 99.9% inhibition of Se in root in the SeMet and SeOMet treatments, respectively. Once absorbed by root, were rapidly assimilated to other Se forms, and SeMet and Se-methyl-selenocysteine (MeSeCys) were the dominant species in SeMet and SeOMet-treated plants, while notably, an unidentified Se form was also found in the root and xylem sap under the SeMet treatment. In addition, within 16 h, SeOMet inhibited the uptake and translocation of SeMet, while the interaction was weakened with longer treatment time. Taken together, the present study provides new insights for the uptake and transformation processes of organic Se within plants.

In order to investigate the size distributions and seasonal variations of carbonaceous aerosols (OC and EC), the carbonaceous species were collected and then analyzed by using a 9-stage Anderson-type aerosol sampler and DRI Model 2001A Thermal/Optical Carbon Analyzer on the typical industrial city Nanjing in Yangtze River Delta, China in the summer, autumn and winter of 2013 and spring of 2014. OC, EC, SOC and POC exhibited obvious seasonal variations, with the highest level in winter (39.1±14.0, 5.7±2.1, 23.6±11.7 and 14.1±5.7 μg•m^-3) and the lowest level in summer (20.6±6.7, 3.3±2.0, 12.2±3.8 and 8.4±4.1 μg•m^-3), and were mainly centralized in PM_1.1 in four seasons. The concentrations of OC in PM_1.1 varied in the order of winter > autumn > spring > summer, while EC ranked in the order of autumn > winter > summer > spring. In the PM_1.1-2.1 and PM_2.1-10, the concentrations of OC and EC decreased in the sequence of winter > spring > autumn > summer. The size spectra of OC, EC and SOC had bimodal distributions in four seasons, except for EC with four peaks in summer. The size spectra of POC varied greatly with seasons, exhibiting bimodal distribution in winter, trimodal distribution in spring and summer, and four peaks in autumn. The OC/EC ratios were 7.0, 6.3, 7.6 and 6.9 in spring, summer, autumn and winter, respectively, which demonstrated the abundance of secondary organic aerosols in Nanjing. The sources of carbonaceous aerosol varied significantly with seasons, and were dominated by vehicle exhaust, coal and biomass burning in PM_2.1, and dominant by dust, coal and biomass burning in PM_2.1-10.

Soil extracellular enzyme activity (EEAs) and enzymatic stoichiometry (ES) can provide a crucial indication of changes in soil ecosystem's nutrient availability and the microbial resource limitations. However, the changing characteristics of soil EEAs and ES at different stages of the native succession process and their key drivers are unclear. In order to investigate the soil EEAs, ES and driving factors of soil under vegetation at different succession stages, we adopted the "spatio-temporal substitution" method to collect the surface soil of bryophyte community, herbaceous community, shrub community and tree community in the new volcanic lava platform of Wudalianchi Volcanic Nature Reserve. We measured seven soil EEA, including carbon(C)-acquiring enzyme (β-1,4-glucosidase (BG)), N-acquiring enzymes (β-N-acetyl-glucosaminidase (NAG) and leucine aminopeptidase (LAP)) and phosphorus (P)-acquiring enzyme (acid phosphatase (AP)) activities. The length and angle of vectors defined by ratios of enzyme activities (BG/(NAG + LAP) vs. BG/AP) were used to indicate relative microbial investments in C- (length), and N- and P- (angle) acquiring enzymes. Our results showed that the contents of TC, TN, TP, MBC, DOC and NO3-N in shrub community soil were significantly higher than those in bryophyte, herb and tree communities, and increased by 441%, 246%, 137%, 5570%, 12% and 484%, respectively. The highest soil EEA of C-, N- and P-acquiring were found in shrub community, and the soil EEAs/MBC of C-, N- and P-acquiring were the highest in bryophyte community. Enzyme C:N, C:P and N:P ratios increased progressively in the order of bryophyte, herb and shrub community, but the enzyme C:N, C:P and N:P ratios of tree community were both far less than shrub community. Vector lengths increased progressively in the order of bryophyte (1.16), herb (1.27), tree (1.29) and shrub (1.40), and Vector angles decreased progressively in the order of bryophyte (49.15°), herb (45.65°), Tree (45.31°) and shurb (44.54°), suggested that as succession progresses, soil microbial nutrients transforms from P limitation (angle＞45°) to N limitation (angle＜45°). Redundancy analysis showed that TC, TN, EC and C:N were important drivers of variation in soil EEAs and ES in vegetation at different succession stages. Our findings highlight that the primary succession process cause nutrient limitation transformation. Soil ES might be a sensitive indicator mediated by soil microorganisms to the relative resource limitation at different stages of the primary succession process.

The pharmacological effects of the fruiting body of Ophiocordyceps sinensis (Os) exerted antioxidant, anti-virus, and immunomodulatory, whereas the anti-inflammatory effects and active components of Os mycelial submerged culture (OMSC) are not known. This study aimed to investigate the active compounds in the fermented liquid (FLOS), hot water (WEOS), and 50-95% (EEOS-50, EEOS-95) ethanol extracts of OMSC and their anti-inflammatory effects and potential mechanisms in LPS-stimulated microglial BV2 cells. The results demonstrated that all of the OMSC extracts could inhibit NO production in BV2 cells. EEOS-95 exhibited the strongest inhibitory effects (41 % inhibitory ability at 500 µg/mL), and its ergosterol, GABA, total phenolic, and total flavonoid contents were significantly higher than those of the other extracts (18.60, 18.60, 2.28, and 2.14 mg/g, p &lt; 0.05, respectively). EEOS-95 also has a strong inhibitory ability against IL-6, IL-1β and TNF-α with IC50 of 617, 277 and 507 µg/mL, respectively, which is higher than that of 1 mM melatonin. The anti-inflammatory mechanism of EEOS-95 seems to be associated with up-regulation of PPAR-γ/Nrf-2/HO-1 antioxidant-related expression and down-regulation of NF-κB/COX-2/iNOS pro-inflammatory expression signaling. Therefore, we hypothesized that EEOS-95 can prevent neurodegenerative diseases, but the effect on the application in vivo should be further investigated.

Based on the analysis of hydrochemical data of 18 samples of shallow groundwater around Hancheng mining area in Guanzhong Basin, Gibbs semi-logarithmic diagram and Piper diagram were used to study the hydrogeochemical behavior of shallow groundwater around Hangcheng mining area in Guanzhong Basin. Chemical composition analysis showed that most of the groundwater was SO₄ · Cl-Ca · Mg type, accounting for 61.1 %. Hydrogen and oxygen isotope data show that groundwater in the study area is recharged by modern precipitation. The main chemical components of most groundwater are Ca²⁺, Mg²⁺, HCO₃^-, Cl^-and SO₄^2-. Ca²⁺, Mg²⁺, and HCO₃^- are mainly dolomite, calcite, and gypsum dissolution precipitation results. Na⁺ and Cl^- originate from the dissolution of halite. The main ion chemistry of shallow groundwater in this area is controlled by rock weathering, and pyrite oxidation is an important factor affecting SO₄^2-concentration. The research results will help analyze the formation mechanism of chemical components and provide some basic data for the evolution of mine water in this area in the future.

Compared to wide-field telescopes, small-field detection systems have higher spatial resolution, resulting in stronger detection capabilities and higher positioning accuracy. When detecting by small-field in synchronous orbit, both space debris and fixed stars are imaged as point targets, making it difficult to distinguish them. In addition, with the improvement of detection capabilities, the number of stars in the background rapidly increases, which puts higher requirements on recognition algorithms. Therefore, star detection is indispensable for identifying and locating space debris in complex backgrounds. To address these difficulties, this paper proposes a real-time star extraction method based on adaptive filtering and multi frame projection. We use bad point repair and background suppression algorithms to preprocess star image. Afterwards, we analyze and enhance the target signal-to-noise ratio(SNR). Then, we use multi frame projection to fuse information. Subsequently, adaptive filtering, adaptive morphology, and adaptive median filtering algorithms are proposed to detect trajectories. Finally, the projection is released to locate the target. Our recognition algorithm has been verified by real star images, and the images were captured using small-field telescope. The experimental results demonstrate the effectiveness of the algorithm proposed in this paper. We successfully extracted hip-27066 star, which has magnitudes about 12 and SNR about 1.5. Compared with existing methods, our algorithm has advantages in both recognition rate and false-alarm rate, and can be used as real time target recognition algorithm for space-based synchronous orbit detection payload.

In this work, we develop a self-consistent physical model of bubbles in a gas solid two-phase flow. Using the Peng-Robonson state equation and a detailed specific heat ratio equation of bubbles, we obtain the kinetic equations of the bubbles on the basis of the Ergun equation, thermodynamic equations, and kinetic equations. It is found that the specific heat ratio of bubbles in such systems strongly depends on bubble pressures and temperatures, which play an important role in the characteristics of the bubbles. The theoretical studies show that with increasing height in the systems, the gas flow rate shows a downward trend. Moreover, the larger particles in the gas solid flows are, the greater the gas velocity is. The bubble sizes increase with the increasing heights of the gas solid systems, and then decrease. The bubble velocity is affected by the gas velocity and the bubble size, which gradually increase and eventually quasi-stabilize. This shows that gas and solid phases in a gas solid two-phase flow interact with each other and a self-consistent system comes into being. The theoretical results have exhibited important value as a guide for understanding the properties and effects of bubbles in gas solid two-phase flows.

Precipitation and human activities are two essential forcing dynamics that influence hydrological processes. To investigate those impacts, the Zuli River Basin (ZRB, a typical tributary basin of the Yellow River in China) was chosen to identify the impact of precipitation and human activities on runoff and sediment discharge. A double mass curve (DMC) analysis and the test methods, including accumulated variance analysis, sequential cluster, Lee-Heghnian, and moving t-test methods was utilized to determine the abrupt change point based on data from 1956 to 2015. Correlation formulas and multiple regression methods were used to calculate the runoff and sediment discharge reduction effects of soil conservation measures and to estimate the contribution rate of precipitation and soil conservation measures to runoff and sediment discharge. Our results show that the runoff reduction effect of soil conservation measures (45%) is greater than the sediment discharge reduction effect (32%). Soil conservation measures were the main factor controlling the 74.5% and 75.0% decrease in runoff and sediment discharge, respectively. Additionally, the contribution rate of vegetation measures was higher than that of engineering measures. This study provides scientific strategies for water resource management and soil conservation planning at catchment scale to face future hydrological variability.

A novel drive current model covering the effects of source/drain voltage (VDS) and gate voltage (VGS) and incorporating drift and diffusion current on the surface channel at the nano-node level, especially beyond 28nm node is presented. The effect of the diffusion current added is more satisfactory to describe the behavior of the drive current in nano-node MOSFETs, fabricated with the atomic-layer-deposition (ALD) technology. This breakthrough in model establishment can expose the long and short channel devices together. Introducing the variables of VDS and VGS, the mixed current model more effectively and meaningfully demonstrates the drive current of MOSFETs under the operation of horizontal, vertical, or mixed electrical field. In comparison between the simulation and experimental consequences, the electrical performance is impressive. The error between both is less than 1%, better than the empirical adjustment to issue a set of drive current models.

The quality of healthcare services is a very important indicator of social welfare in developed and emerging countries. Good quality levels are critical to ensuring patient safety, improving health outcomes, increasing patient satisfaction, optimizing resource utilization, and meeting regulatory requirements. The stable and sustainable operation of medical institutions urgently requires the establishment of a reliable and effective medical quality performance evaluation platform. However, the complexity of the evaluation process, involving numerous evaluators and the consideration of different subjective and objective criteria, poses significant challenges and essentially constitutes a group multi-criteria decision-making problem. This article uses the overall medical environment of the institution, medical service attitude, medical security, administrative service measures, etc. as measurement standards to deal with this challenge. It proposes a medical service quality measurement model suitable for both qualitative and quantitative analysis. In view of the heterogeneity of information, the two-tuple fuzzy language calculation method is introduced to enable evaluators to skillfully manage diverse information and effectively avoid potential information omission problems in the subjective evaluation integration process. The effectiveness and feasibility of the model were demonstrated by applying it to the operational framework of a medical institution in central Taiwan.

The substitution of chemical fertilizers by organic manure plays a critical role in sustainable crop production. Nevertheless, organic amendments promote the global warming potential (GWP) in rice paddies due to increased methane (CH4) emissions. Increasing evidence shows that lime application can reduce CH4 emissions from acidic paddy soils, while it is still not clear whether liming can reduce the GWP in rice fields under organic manure substitution. A two-year field experiment was conducted to investigate the impacts of pig manure substitution and lime application on grain yield, CH4 and nitrous oxide (N2O) emissions in a subtropical double-cropped rice field in China. The experiment consisted of three treatments: chemical fertilization (CF, 100% N from urea), 50% of chemical N substituted by pig manure (1/2N + M), and the 1/2N + M treatment plus lime amendment (1/2N + M + L). On average, 1/2N + M reduced rice yield by 5.65% compared to CF, while lime application had no effect on rice yield. Mean cumulative CH4 emissions were 218.8% higher in 1/2N + M than in CF, whereas 1/2N + M + L reduced CH4 emissions by 36.6% compared to 1/2N + M. Neither pig manure substitution nor lime application affected N2O emissions. Consequently, 1/2N + M increased the GWP and greenhouse gas intensity (GHGI) by 214.6% and 228.3%, respectively, compared to CF. In contrast, 1/2N + M + L reduced the GWP and GHGI by 36.4% and 36.5% compared to 1/2N + M. Lime application can mitigate CH4 emissions and GWP induced by pig manure amendment in double-cropped rice fields.

Sign language recognition technology can help people with hearing impairments to communicate with those who are hearing impaired. At present, with the rapid development of society, deep learning technology also provided certain technical support for sign language recognition work. In sign language recognition tasks, the use of traditional convolutional neural networks to extract spatio-temporal features from sign language videos suffers from insufficient feature extraction, resulting in low recognition rates. Nevertheless, video-based sign language datasets are very large and require a lot of computational resources for training and generalisation must be ensured, which poses a challenge for recognition. In this paper, we have presented a video-based sign language recognition method based on resnet and lstm. As the number of network layers increases, the ResNet network can effectively solve the granularity explosion problem and obtain better time series features. We use the Resnet convolutional network as the backbone model. At the initialisation stage, we obtain sign language features using ResNet; then, the learned feature space is used as the input of LSTM network to obtain long sequence features. The experimental results show that the accuracy of the above model is better than the mainstream model, and it can effectively extract the spatio-temporal features in sign language videos and improve the recognition rate of sign language actions.

The flowering period of rice significantly impacts variety adaptability and yield formation. Properly shortening the reproductive period of rice varieties can expand their ecological range without significant yield reduction. Targeted genome editing, like CRISPR/Cas9, is an ideal tool to fine-tune rice growth stages and boost yield synergistically. In this study, we developed a CRISPR/Cas9-mediated multiplex genome editing vector containing five genes related to three traits: Hd2, Ghd7, and DTH8 (flowering stage genes), along with the recessive rice blast resistance gene Pi21 and the aromatic gene BADH2. This vector was introduced into the high-quality Geng rice variety in Zhejiang province, Jiahe212 (JH212), resulting in 34 T0 plants with various effective mutations. Among the 17 mutant T1 lines, several displayed diverse flowering dates, but most exhibited undesirable agronomic traits. Notably, three homozygous mutant lines (JH-C15, JH-C18, and JH-C31) showed slightly earlier flowering dates without significant differences in yield-related traits compared to JH212. Through special Hyg and Cas marker selection of T2 plants, we identified 7, 6, and 2 fragrant glutinous plants devoid of transgenic components, respectively. These single plants will serve as sib lines of JH212 and potential resources for breeding applications, including maintenance lines for Xian-Geng interspecific three-line hybrid rice. In summary, our research offers practical insights and potential resources for targeted improvement of Geng rice varieties, and would also promote the Geng rice varieties expending from the southward to the northward.

High-quality rice (Oryza sativa L.) is increasingly widely planted in China with the improvement of people's living standards and the achievement of rice breeding efforts in recent years. However, the agronomic and physiological performances of high-quality indica rice (HQIR) under different nitrogen(N) application conditions in southern China are little known. Two-year consecutive field experiments were conducted with two HQIR and two ordinary-quality indica rice (OQIR) varieties under moderate and high N application rates, with yield and yield components, biomass, N up-take, and their related traits investigated. We found that grain yields of HQIR were slightly de-creased, but grain yields of OQIR were significantly increased by 6.60%-8.88% under high N rate compared with moderate N rate in both years. Thereby, OQIR produced 8.34%-11.87% and 22.00%-22.50% higher grain yield than HQIR under moderate and high N rates, respectively. The different responses of grain yield to N application rates were mainly due to decreased grain set-ting rate in HQIR and increased spikelets m-2 in OQIR under high N rate. Furthermore, high N rate significantly reduced pre-anthesis AE and improved grain-leaf area ratio, while did not in-crease post-anthesis DM compared with moderate N rate in HQIR, which might result in carbon metabolic deterioration and imbalance of source-sink relationship and subsequently lower supply of carbohydrate to panicle. Our results suggest that a moderate N rate (165 kg N ha-1) is beneficial for HQIR varieties to balance the maximum grain yield and high quality in southern China