In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.

In this paper, in order to reduce the energy consumption and delay of data transmission, the non-orthogonal multiple access (NOMA) and edge caching technologies are jointly considered. As for the cache-assisted vehicular NOMA-MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under the latency and resource constraints, which jointly optimizes the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task caching algorithm based on the twin delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm may achieve lower local energy consumption than several benchmark algorithms, and HA may achieve a lower one than the completely offloading algorithm and local execution algorithm.

The 3D reconstruction method using RGB-D camera has a good balance in hardware cost, point cloud quality and automation. However, due to the limitation of inherent structure and imaging principle, the acquired point cloud has problems such as a lot of noise and difficult registration. This paper proposes a three-dimensional reconstruction method using Azure Kinect to solve these inherent problems. Shoot color map, depth map and near-infrared image of the target from six perspectives by Azure Kinect sensor. Multiply the 8-bit infrared image binarization with the general RGB-D image alignment result provided by Microsoft to remove ghost images and most of the background noise. In order to filter the floating point and outlier noise of the point cloud, a neighborhood maximum filtering method is proposed to filter out the abrupt points in the depth map. The floating points in the point cloud are removed before generating the point cloud, and then using the through filter filters out outlier noise. Aiming at the shortcomings of the classic ICP algorithm, an improved method is proposed. By continuously reducing the size of the down-sampling grid and the distance threshold between the corresponding points, the point clouds of each view are continuously registered three times, until get the complete color point cloud. A large number of experimental results on rape plants show that the point cloud accuracy obtained by this method is 0.739mm, a complete scan time is 338.4 seconds, and the color reduction is high. Compared with a laser scanner, the proposed method has considerable reconstruction accuracy and a significantly ahead of the reconstruction speed, but the hardware cost is much lower and it is easy to automate the scanning system. This research shows a low-cost, high-precision 3D reconstruction technology, which has the potential to be widely used for non-destructive measurement of crop phenotype.

Brain metastasis (BrM), involving the spread of cells from a primary tumor through the blood circulation system to the brain microvasculature, eventually progresses despite multiple treatments and remains a substantial contributor to major mortality in patients with advanced- stage cancer. Molecular signatures and immune cellular components of the tumor microenvironment (TME) are emerging as essential regulators involved in establishing an organ-specific metastasis (colonization) and therapeutic response. A comprehensive understanding of detailed characterization and the immune landscape in context of process of BrM formation will greatly expand the horizon of treatments available to target these deadly diseases. In this review, we provide a comprehensive picture of the complex interactions between tumor cells and immune cellular components participated in the BrM process. Based on this knowledge, we will discuss opportunities and challenges for therapeutic strategies against BrM.

γ-Aminobutyric acid (GABA) plays an important role in plant development and postharvest properties of fruits. However, studies on regulation of kiwifruit ripening by GABA are scarce. Here, we evaluted physicochemical characters and the expression profile of genes related to ripening in hardy kiwifruit treated with exogenous GABA compared to control during postharvest storage period. The results showed that (1) exogenous GABA treatment not only improved the fruit quality but also inhibited respiration rate and ethylene production, as well as reduced the enzyme activities of ACC oxidase (ACO) and ACC synthase (ACS), via down-regulating the expression of AaACO1 and AaACO3, AaACS1 and AaACS2 during kiwifruit storage period; (2) on the one hand, overexpression of AaGAD1 or AaGAD4 gene which is involved in GABA biosynthesis inhibited ethylene production by reducing the enzyme activities of ACO and ACS, whereas silencing of AaGAD1 or AaGAD4 gene led to the reverse effect. On the other hand, overexpression of AaGAD1 or AaGAD4 decreased the expression levels of AaACO1 and AaACO3, AaACS1 and AaACS2, whereas silencing of AaGAD1 or AaGAD4 gene increased the expression levels of these four genes. Above all, our findings demonstrate that exogenous GABA treatment could improve the storage quality and extend the shelf life of kiwifruit, Additionally, the content of GABA was participated in the regulation of ethylene biosynthesis at the molecular level.

The images captured by UAVs during inspection often contain numerous small targets related to transmission lines, which are critical and vulnerable elements for ensuring the safe operation of these lines. However, due to various factors such as the small size of the targets, low resolution, complex background, and potential line aggregation, achieving accurate and real-time detection becomes challenging. To address these issues, this paper proposes a detection algorithm called P2-ECA-EIOU-YOLOv5 (P2E-YOLOv5). Firstly, in order to address the challenges posed by the complex background and environmental interference that impact small targets, an ECA attention module is integrated into the network. This module effectively enhances the network's focus on small targets while concurrently mitigating the influence of environmental interference. Secondly, considering the characteristics of small target size and low resolution, a new high-resolution detection head is introduced, which is more sensitive to small targets. Lastly, the network utilizes the EIOU-Loss as the regression Loss function to improve the positioning accuracy of small targets, as they tend to aggregate. Experimental results demonstrate that the proposed P2E-YOLOv5 detection algorithm achieves an accuracy P of 96.0% and an average accuracy (mAP) of 97.0% for small target detection in transmission lines.

The purpose of this study is to examine the relationship between the institutional investors which can affect financial performance for corporate sustainability on the income smoothing. Therefore, this study focus on the connection between the nature of stock rights and income smoothing in China. For this study, hypotheses were established on the relationship each state-controlled companies, income smoothing, and information equilibrium of individual investors, and empirical analysis was conducted through related variables. The analysis results are summarized in three categories as follows. First, this research finds that state-controlled firms (CONTs) prefer income smoothing activities compared to non-state-controlled firms for the long-term sustainable development of firms using data from 2011 to 2019. Second, this study found out that Institutional investors support the behavior of CONTs to smooth their earnings because this behavior is seen as an attempt by CONTs to convey valuable private information to other investors. Third, we was able to discover that institutional investors' monitoring effect is predominantly driven by pressure-resistant institutional investors. This research complements the lack of empirical research on income smoothing and enable to give a guideline that the type of stock rights is a critical key determinant of participation in income smoothing activities for stable growth and sustainability in the future.

: This study focuses on optimizing chlorophyll extraction techniques and strategically placing leaf disks to enhance chlorophyll concentration in sesame (Sesamum indicum L.) leaves. Thirty sesame genotypes, categorized into light green (LG), middle green (MG), and deep green (DG) pigment groups based on leaf coloration, were selected from a larger pool of field-grown accessions. The investigation involved determining optimal SPAD value index measurements, quantifying pigment concentrations, exploring extraction solvents, and selecting suitable leaf disk positions. Significant variations in chlorophyll content were observed across genotypes, greenness categories, and leaf disk positions. The categorization of genotypes into DG, MG, and LG groups revealed a correlation between leaf appearance and chlorophyll content. The study highlighted a consistent relationship between carotenoids and chlorophyll, indicating their role in adaptation to warm environments. Examination of leaf disk positions revealed a significant chlorophyll gradient along the leaf blade, emphasizing the need for standardized protocols. Chlorophyll extraction experiments identified DMSO and 96% ethanol, particularly the later incubated for 10 minutes at 85°C, as effective choices. This recommendation considers factors like cost-effectiveness, time efficiency, safety, and environmental regulations, ensuring consistent and simplified extraction processes. For higher chlorophyll extraction, focusing on leaf tips and the 75% position along the sesame leaf blade is suggested, consistently yielding higher chlorophyll content. Our findings therefore, underscores insightful knowledge on chlorophyll gradients and encouraging for standardized protocols, that enabling researchers to refine experimental designs for precise and comparable chlorophyll measurements. Recommended solvent choices ensure reliable outcomes in plant physiology, ecology, and environmental studies.

As metabolomics is widely used in the study of disease mechanisms, more and more studies have found that metabolites play an important role in the occurrence of diseases. The aim of this study is to investigate the effects and mechanisms of quercetin in high-fat-sucrose diet (HFD)-induced nonalcoholic fatty liver disease (NAFLD) development using nontargeted metabolomics. A rat model of NAFLD was established by feeding with a HFD for 30 and 50 days. Results indicated quercetin exhibited hepatoprotective activity in HFD-induced NAFLD rats in 30 days by regulating fatty acids related metabolites (adrenic acid, etc.), inflammation related metabolites (arachidonic acid, etc.), oxidative stress related metabolites (2-hydroxybutyric acid) and other differential metabolites (citric acid, etc.). However, quercetin couldn’t improve NAFLD in 50 days maybe it couldn’t reverse the inflammation condition induced by long-term high-fat diet. These data indicate that dietary quercetin may be beneficial to NAFLD at early stages. Furthermore, combining metabolomics and experimental approaches opens up avenues of effects and mechanisms of drugs for complex diseases.

(1) Background: Most of the existing studies focus on the evaluation of technology finance; the relationship between technology finance and technology innovation. But there are few studies on the development of technology finance and the quality of economic development in our country; (2) Methods: Based on the panel data of 30 provinces in China, this paper constructs an index system to measure the development of technology finance through the improved entropy method, and tests the spatial correlation of the development of technology finance in China by Moran'I index. According to the test results, this paper constructs a spatial econometric model to empirically analyze the promoting effect of scientific, technological and financial development on high-quality economic development, and analyzes its promoting effect in different regions and different time periods; (3) Results: The results show that the quality of China's economic growth is spatially dependent, and the development of science, technology and finance can significantly promote the quality economic development in China. And the promotion coefficient of the central region is the largest, as well as the coefficient of the eastern region is the smallest. The promotion coefficient was small and not significant before 2015, and was significantly positive after 2015; (4) Conclusions: this paper puts forward the corresponding policy recommendations according to the research results.

This paper analyzed the main diseases of the friction high-strength bolt (FHSB) joints in order to study the characteristics of the corrosion products of the joints in steel bridge and the influence factors. Seven representative FHSB connection pairs and eight steel plate samples were selected from the Dongying Shengli Yellow River Bridge, which has been in operation for 33 years. Then Scanning Electron Microscope (SEM), Energy Dispersive Spectroscopy (EDS) and X-Ray Diffraction (XRD) were used to analyze the microscopic morphology and chemical composition of the corroded surface of the samples. Finally, this paper discussed the relationship between the corroded sample surface and the environmental corrosion. The research results showed that construction quality problems, such as non-standard hole expansion, segmental slab joint dislocation and contact surface pollution, accelerated corrosion of the contact surface. With the increase of corrosion degree of joints, the contact surface of aluminum spraying layer gradually changed from a rugged and dense state to a smooth and porous powder state. With the corrosion of the steel plate substrate, the contact surface gradually changed from a fluffy stratified state to a surface bonding state. It was inferred that the friction coefficient of the FHSB connection node on the corroded aluminum sprayed contact surface first decreased and then increased. The corroded node samples detected not only a large number of S and Cl elements, but also the oxides of Mn, Si and other elements as well as FeS. It was speculated that node corrosion was also related to atmospheric (acid rain) and industrial dust and other corrosive environments. Almost all samples detected the component SiO2 of the Yellow River soil, as well as Mg, Ca, K, Na and other elements at the same time, which is basically consistent with the component elements of the Yellow River soil. Therefore, it was inferred that it was related to the inclusion of the Yellow River soil.

We propose a novel plasmon nano-optical antenna that consists of three vertically superimposed discs with different materials. Two symmetrical nanometer-scale Yagi antenna elements with gold-material are embedded into the surface of the middle disc with silicon dioxide. Based on plasmon theory and Yagi antenna principle, we explore the properties of the far-field radiation and near-field transmission of the antenna in near infrared band. It is found that the pattern of the xoy plane in the antenna can be adjusted in arbitrary direction. The far-field pattern and near-field transmission spectrum can be reconfigured by changing the angle a of the Yagi antenna elements in the middle disc. Moreover, we also discuss the influences of the widths and lengths of the elements on the near-field transmission spectrum when a is fixed at 0 degree.

Revealing the binding properties of calcium ion (Ca2+) and magnesium ion (Mg2+) to chromophoric dissolved organic matter (CDOM) facilities understanding the effect of natural water composition on the photophysics of dissolved organic matter. Steady-state and time-resolved fluorescence spectrometry, and dynamic light scattering were applied to investigate the fluorescence quenching process of CODM by Ca2+ and Mg2+. The binding of Ca2+ and Mg2+ preferred terrestrial CDOM to aquatic CDOM. The fluorescence quenching of CDOM by cations mainly occurred in a static process, which was based on the fact that the decrease of steady-state fluorescence intensity was greater than fluorescence lifetime. The fluorescence quenching was profound under longer excitation and emission wavelength. The binding constant (K, L/mol) for Ca2+ to CDOM from terrestrial source ranged from 4.29 to 5.09 (lgK), which was approximately one order of magnitude higher than that of Mg2+ to CDOM (3.86 to 4.56). Fluorescence decay became faster in the presence of Ca2+ and Mg2+. Lifetime distribution of CDOM excited states shifted to small value side in the presence of metal ions, particularly for Ca2+, indicating fluorescence quenching of CDOM mainly through the interaction of Ca2+/Mg2+ with relatively long-lived fluorophores.

High dimensional statistical analysis for the yield of large-scale circuits is quite difficult due to expensive simulations, especially for the memory circuits with high sigma requirement (e.g., SRAM). In this paper, we developed an efficient sparse additive model to substitute simulations. To fit high sigma region accurately, the modeling center is moved to near failure boundary searched by scaling the shape of sampling function. To solve the model efficiently, the process variables are grouped by standard cells so that the model can be solved by our developed blockwise greedy algorithm. The experiments on the 28nm memory circuits validate that our method achieves high accuracy and efficiency compared with other state-of-art works.

The host-guest inclusion strategy has potential to surpass the limitations of energy density and suboptimal performances of single explosives. The guest molecules can not only enhance the detonation performance of host explosives but can also enhance their stability. Therefore, the deeply analysis the role of guest influence on the pyrolysis decomposition of the host-guest explosive is necessary. The whole decomposition reaction stage of CL-20/H2O, CL-20/CO2, CL-20/N2O, CL-20/NH2OH was calculated by ReaxFF-MD. The incorporation of CO2, N2O and NH2OH significantly increase the energy levels of CL-20. However, different guest has little influence on the initial decomposition paths of CL-20. The Ea1 and Ea2 values of CL-20/CO2, CL-20/N2O, CL-20/NH2OH systems are higher than the CL-20/H2O system. Clearly, incorporation of CO2, N2O, NH2OH can inhibit the initial decomposition and intermediate decomposition stage of CL-20/H2O. Guest molecules get heavily involved in the reaction and influence on the reaction rate. k1 of CL-20/N2O and CL-20/NH2OH systems are significantly larger than that of CL-20/H2O at high temperatures. k1 of CL-20/CO2 system is much complex, which can be affected deeply by temperatures. k2 of CL-20/CO2, CL-20/N2O system is significantly smaller than that of CL-20/H2O at high temperatures. k2 of CL-20/NH2OH system is little difference at high temperatures. For the CL-20/CO2 system, the k3 value of CO2 is slight higher than that for CL-20/H2O, CL-20/N2O, CL-20/NH2OH systems, while the k3 values of N2 and H2O are slight smaller than that for CL-20/H2O, CL-20/N2O, CL-20/NH2OH systems. For the CL-20/N2O system, the k3 value of CO2 is slight smaller than that for CL-20/H2O, CL-20/CO2, CL-20/NH2OH systems. For the CL-20/NH2OH system, the k3 value of H2O is slight larger than that for CL-20/H2O, CL-20/CO2, CL-20/N2O systems. These mechanisms revealed that CO2, N2O and NH2OH molecules inhibit the early stages of the initial decomposition of CL-20, and play an important role for the decomposition subsequently.

Runtime verification (RV) is a lightweight approach to detecting temporal errors of system at runtime. It confines the verification on observed trajectory which avoids state explosion problem.To predict the future violation, some work proposed the predictive RV which uses the information from models or static analysis. But for software whose models and codes cannot be obtained, or systems running under uncertain environment, these predictive methods cannot take effect. Meanwhile, RV in general takes multi-valued logic as the specification languages, for example the " true, false and inconclusive" in three-valued semantics. They cannot give accurate quantitative description of correctness when "inconclusive" is encountered. We in this paper present a RV method which learns probabilistic model of system and environment from history traces and then generates probabilistic runtime monitor to quantitatively predict the satisfaction of temporal property at each runtime state. In this approach, Hidden Markov Model (HMM) is firstly learned and then transformed to Discrete Time Markov Chain (DTMC). To construct incremental monitor, the monitored LTL property is translated into Deterministic Rabin Automaton (DRA). The final probabilistic monitor is obtained by generating the product of DTMC and DRA, and computing the probabilities for each state. With such method, one can give early warning once the probability of correctness is lower than pre-defined threshold, and have the chance to do adjustment in advance. The method has been implemented and experimented on real UAS (Unmanned Aerial Vehicle) simulation platform.

This paper focuses on robust multi-criteria traffic network equilibrium problems with path capacity constraints. The concepts of robust vector equilibrium and robust vector equilibrium with respect to worst case are introduced, respectively. To generate a subset of robust vector equilibrium, an equivalent min-max optimization problem is constructed. In addition, a direct search algorithm, in which the step size without derivatives and redundant parameters, for solving this min-max problem is proposed. To find a subset of robust vector equilibrium with respect to worst case, an equivalent smoothing optimization problem based on a variant version of ReLU activation function is established. Numerical experiments are also presented to expound the advantages of the algorithms.

Understanding the distribution in drought and floods plays an important role in disaster risk management. The present study aims to explore the trends in the standardized precipitation index and extreme precipitation days in China, as well as to estimate the economic losses they cause. We found that in the Northeast China, northern of North China and northeast of Northwest China were severely affected by drought disasters (average damaged areas were 6.44 million hectares) and the most severe drought trend was located in West China. However, in the north of East China and Central China, the northeastern of the Southwest China was severely affected by flood disasters (average damaged areas were 3.97 million hectares) and the extreme precipitation trend is increasing in the northeastern of the Southwest China. In the Yangtze River basin, there were increasing trends in terms of drought and extreme precipitation, especially in the northeastern of the Southwest China, where accompanied by severe disaster losses. By combining the trends in drought and extreme precipitation days with the distribution of damaged areas, we found that the increasing trend in droughts shifted gradually from north to south, especially in the Southwest China, and the increasing trend in extreme precipitation gradually shifted from south to north.

Asphalt binder is a temperature sensitive material, whose performance is greatly affected by the climate change of the environment. Improper selection of asphalt will cause a lot of damage and affect the durability of the road. The establishment of asphalt pavement performance zoning in Xinjiang, a vast area with great temperature difference, will provide reference for the selection of asphalt suitability, the refinement of pavement design and the sustainable development of road engineering. In this study, 11 years of temperature data in Xinjiang region were collected and analyzed, and 98% reliability of pavement design temperature was used to draw a performance grading map of asphalt pavement in Xinjiang region based on ArcGIS platform. Finally, Xinjiang region was divided into 9 performance zones. At the same time, the performance grade (PG) of five kinds of asphalt binders in Karamay were explored. The result shows that there is little difference in continuous PG span between different matrix asphalt binders, the lower penetration grade, the better high temperature performance, the worse low temperature performance. After adding SBS modifier, the continuous PG span can be higher than the matrix asphalt in 20 ℃. All these provide the basis for reasonable selection of asphalt binders in different areas of Xinjiang.

The treatment of large-area bone defects remains a challenge; however, various strategies have been developed to improve the performances of scaffolds in bone tissue engineering. In this study, poly(lactide-co-glycolide)/hydroxyapatite (PLGA/HA) scaffold was coated with Asp-Gly-Glu-Ala (DGEA)-incorporated collagen for the repair of rat skull defect. Our results indicated that the mechanical strength and hydrophilicity of PLGA/HA scaffold were clearly improved and conducive to cell adhesion and proliferation. The collagen-coated scaffold with DGEA significantly promoted the repair of skull defect. These findings indicated that a combination of collagen coating and DGEA improved scaffold properties for bone regeneration, thereby providing a new potential strategy for scaffold design.

Though Cyber Physical Systems (CPS) become very popular in last the decade, dependability of CPS is still a critical issue and related survey is rare. We try to spell out the jigsaw of technologies and figure out the technical trends of dependable self-managing CPS. This survey first recalls the motivation and the similar concepts. By analyzing four generic architectures, we summarize the common characteristics and related assurance technologies, and propose a more generic environment-in-loop processing flow of CPS and a formal interaction flow between physical space and cyber space. Further, the similarity between correctness and dependability is formally analyzed and the new five research questions of dependable self-managing CPS are presented. Then we review the critical technologies and related correctness verification & validation (V&V) methods, the architectures for dependable self-managing CPS. Further, the detail dependability management and V&V technologies are surveyed, which covers the areas of running-time fault management methods and whole life cycle V&V technologies, maintenance and available tool sets. For holistic CPS development, Modeling techniques and MDE (model driven engineering) based V&V methods are analyzed in detail. Then we complete the jigsaw of technologies and figure out the missing part. Further, we propose the technical challenges and the further direction. To our best knowledge, this is the first comprehensive survey on dependable self-managing CPS development and evaluation.

In recent years, how to combine intelligent prospecting algorithms such as random forest with a large number of geological and mineral data for quantitative prediction of exploration geochemistry has become an important topic of concern to quantitatively improve the accuracy of target delineation. The ore-forming geological conditions in the central Kunlun area of Xinjiang are great and have good prospecting prospects. However, due to the exhaustion of shallow deposits and the lag of geological prospecting work in the past ten years, there has been no expected breakthrough in the search for large and super-large metal deposits for many years. There has been a serious shortage of reserve resources. The use of new theories, new methods and new technologies for mineral resources investigation and evaluation has become an urgent need in the current prospecting work. In view of this, based on the existing spatial database of geological and mineral resources in the central Kunlun of Xinjiang, combined with the geological characteristics, genesis and metallogenic regularity of the area, this paper carried out a series of studies on gold polymetallic minerals with the help of geographic information system and data science programming software platform. The researchers integrated geological and regional geochemical data, and constructed a random forest metallogenic discriminant model based on two different sampling methods (integrated random undersampling and selection of training samples) to predict the mineralization of gold polymetallic minerals in the central Kunlun area of Xinjiang and delineate the metallogenic target area. The quantitative prediction of gold polymetallic mineral resources in the central Kunlun area of Xinjiang by two random forest models is compared and discussed: the known ore spots, fault structures and geochemical information are extracted, and the known gold polymetallic ore spots and geochemical data are used to form a training set and a prediction set to construct a machine learning random forest model. The results of prediction evaluation and metallogenic prospect division show that for different sampling methods, the performance evaluation parameters of the training process show that the prediction accuracy of the selected training samples is higher, and the selected training samples are more reliable because they can fully learn the complex information of the original data. In the metallogenic prospect prediction and metallogenic potential division, the random forest model of selecting training samples has more reference value and further exploration research significance in the production problem considering the actual exploration cost because of its small area of high potential prediction area and high proportion of ore bearing per unit area. At the same time, this study innovatively improves the prediction accuracy, reduces the exploration risk, and expands the prospecting idea of machine learning algorithm in mathematical geology in the central Kunlun area of Xinjiang. The delineated metallogenic potential area has positive guiding significance for the actual gold polymetallic prospecting work in this area.

Predicting nurse turnover is a growing challenge within the healthcare sector, profoundly impacting healthcare quality and the nursing profession. This study employs the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues in the 2018 National Sample Survey of Registered Nurses (NSSRN) dataset and predict nurse turnover using machine learning (ML) algorithms. Four ML algorithms, namely logistic regression (LR), random forests (RF), decision tree (DT), and extreme gradient boosting (XGBoost), are applied to the SMOTE-enhanced dataset. The data is randomly split into an 80% training set and a 20% validation set. Eighteen carefully selected variables from the NSSRN database serve as predictive features, and the machine learning model identifies feature importance concerning nurse turnover. The study includes a performance comparison based on metrics such as Accuracy, Precision, Recall (Sensitivity), F1-score, and AUC. In summary, the results demonstrate that SMOTE-enhanced random forests (SMOTE_RT) exhibit the most robust predictive power, both in the classical approach (with all 18 predictive variables) and an optimized approach (utilizing eight key predictive variables). XGBoost, decision tree, and logistic regression follow in performance. Notably, age emerges as the most influential factor in nurse turnover, with working hours, EHR/EMR usability, individual income, and region also playing significant roles. This research offers valuable insights for healthcare researchers and stakeholders, aiding in selecting suitable ML algorithms for nurse turnover prediction.

Stess in laser cladding coating is an important factor affecting the safe operation of remanufacturing components. Ultrasonic testing has become a hot direction in nondestructive evaluation of stress, because it has the advantages of safety, non-destructive and on-line detection. This paper provides a review of ultrasonic testing for stress in remanufacturing laser cladding coating. It summarizes the recent research outcomes on ultrasonic testing for stress, analyzes mechanism of ultrasonic testing for stress. Remanufacturing laser cladding coating shows typical anisotropic behaviors, the ultrasonic testing signal in laser cladding coating is influenced by many complex factors, such as microstructure, defect, temperature and surface roughness, etc. At present, ultrasonic testing for stress in laser cladding coating can only be done roughly. The paper discusses active mechanism of micro / macro factors to the reliability of stress measurement and the impact of stress measurement to the quality and safety of remanufacturing components. Base on the discussion, the paper proposes strategies for acquisition of nondestructive, rapid and accurate measurement of stress in remanufacturing laser cladding coating.

In this paper, a distributed algorithm is proposed to solve a consensus convex optimization problem. It is a Jacobi-proximal alternating direction method of multipliers with a damping parameter $\gamma$ in the iteration of multiplier. Compared with existing algorithms, it has the following nice properties: (1) The restriction on proximal matrix is relaxed substantively, thus alleviating the weight of the proximal term. Therefore, the algorithm has a faster convergence speed. (2) The convergence analysis of the algorithm is established for any damping parameter $\gamma\in(0,2]$, which is larger ones in the literature. In addition, some numerical experiments and an application to a logistic regression problem are provided to validate the effectiveness and the characteristics of the proposed algorithm.

Covid-19 has now become a public health concern worldwide. The infection primarily involves the respiratory tract. Hitherto, some Covid-19 pneumonia patients carry the viral nucleic acids, and the active virus was detected in stool specimens. The virus discharged with feces is a potential contagious source. In the present study, three Covid-19 respiratory tract infection patients showed no gastrointestinal symptoms, and two were positive for viral nucleic acids in anal swab specimens remained positive 6 and at least 14 days after virus turned negative in the respiratory tract, respectively (details of the patients were listed in Fig 1). Thus, for Covid-19-infected patients with or without gastrointestinal symptoms, viral nucleic acids in stool specimens or anal swab specimens should be focused on for testing in order to decide the isolation duration of the patient.

Fault line detection timely and accurately when single-phase-to-earth fault occurs in resonant grounding system is still a focus of research. This paper presents a new approach for fault detection based on data fusion and it has non-artificial setting. Firstly, the fault criterion for interphase difference energy ratio and time-frequency correlation coefficient of each line is proposed. Subsequently, the paper establish a coordinate system with the interphase difference energy ratio as X axis and the time-frequency correlation coefficient as Y axis, and it uses the Euclidean distance algorithm to get the characteristic distance of each line by fusing two-dimensional information. Finally, comparing the sound distance and the fault distance of each line to discriminate the fault line. Electromagnetic Transients Program (EMTP) simulation results and adaptability analysis have confirmed the effectiveness and reliability of the proposed scheme.

Biliary tract cancer, encompassing intrahepatic and extrahepatic cholangiocarcinoma, as well as gallbladder carcinoma, stands as a prevalent malignancy characterized by escalating incidence rates and unfavorable prognoses. The onset of cholangiocarcinoma involves a multitude of risk factors and could potentially be influenced by microbial exposure. The human microbiome, encompassing the entirety of human microbial genetic information, assumes a pivotal role in regulating key aspects such as host digestion, absorption, immune responses, and metabolism. The widespread application of next-generation sequencing technology has notably propelled investigations into the intricate relationship between the microbiome and diseases. An accumulating body of evidence strongly suggests a profound interconnection between biliary tract cancer and the human microbiome. This article critically appraises the existing evidence pertaining to the microbiome milieu within patients afflicted by biliary tract cancer. Furthermore, it delves into potential mechanisms through which dysregulation of the human microbiome could contribute to the advancement of biliary tract cancer. Additionally, the article expounds on its role in the context of chemotherapy and immunotherapy for biliary tract cancer.

The SiBOC precursor ceramics were fabricated by polymer-derived method SiBOC precursors with different B/Si molar ratios were synthesized by controlling monomer content. The effects of different process conditions on SiBOC ceramics and the oxidation resistance of SiBOC ceramics at a high temperature (1400 ℃) were studied. The SiBOC precursor’s bonds of Si-O-C, Si-O-Si, and Si-O-B were detected by FTIR. The precursor is converted to a rigid ceramic material that has an 85.3 wt % ceramic yield of SiBOC ceramics with B/Si=0.4 by TG-DSC. The results of SEM analysis show that the SiBOC ceramics surface bulges and folds increase with the increase of boric acid content. With the increase of pyrolysis temperature and pyrolysis time, the size and number of pores on the surface of SiBOC ceramics also increase. Static oxidation tests show that SiBOC ceramics with B/Si=0.4 have better oxidation resistance under other conditions being the same.

Temper embrittlement is a major challenge encountered during the heat treatment of high-performance steels for large forgings. This study investigates the microstructural evolution and mechanical properties of Cr-Ni-Mo-V thick-walled steel, designed for new large forgings with a tensile strength of 1500 MPa, under different tempering cooling rates. Optical microscopy (OM), scanning electron microscopy (SEM), and electron backscatter diffraction (EBSD) were employed to analyze the microstructural features. The results demonstrate that the embrittlement occurring during air cooling after tempering is attributed to the concentration of impurities near Fe3C at the grain boundaries. The low-temperature impact toughness at -40°C after water quenching reaches 29 J due to the accelerated cooling rate during tempering, which slows down the diffusion of impurity elements towards the grain boundaries, resulting in reduced concentration and dislocation density, and increased stability of the grain boundaries, thereby enhancing toughness. the bainite content decreases and the interface between martensite and bainite undergoes changes after water quenching during tempering. These alterations influence the crack propagation direction within the two-phase microstructure, further modifying the toughness. These findings contribute to the understanding of temper embrittlement and provide valuable guidance for optimizing heat treatment processes to enhance the performance of high-performance steels in large forgings.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

Background: The COVID-19 outbreak has accelerated the huge difference between medical care and disease prevention in Chinese medical institutions. This study aimed to investigated the relationship between the symbiotic units, environments, models, and effects of the integration of medical and disease prevention. Methods: This cross-sectional study involved 762 employees of public hospitals in 11 cities in Zhejiang Province by random stratified sampling. We analyzed the influence paths of elements in the mechanism of integration of medical and disease prevention and the mediating effect of symbiotic models among symbiotic units, symbiotic environments, and effects for on this integration. Results: The path coefficient of symbiotic unit on the symbiosis model was 0.46 (p<.001), the path coefficient of symbiotic environment on symbiosis model was 0.52 (p<.001). The path coefficient of the symbiotic unit and environment was 0.91 (p<.001). The symbiotic models exhibited a partial mediation effect between symbiotic units and the effect of this integration. Sobel test = 3.27, β = 0.152, and the mediating effect accounted for 34.6%. Conclusions: It is suggested that Health policy-makers and public hospital managers should provide sufficient symbiotic units, establish collaborative symbiotic models, and improve the effects of integration of medical and disease prevention in public hospitals.

As a critical transmitter, the compact 532 nm lasers operating on high repetition and narrow pulse widths have been used widely for airborne or space-borne laser active remote sensing. We developed a free space pumped TEM₀₀ mode sub-nanosecond 532 nm laser that occupied a volume of less than 125 mm × 50 mm × 40 mm (0.25 liters). The fundamental 1064 nm laser consists of a passively Q-switched composite crystal microchip laser and an off-axis, two-pass power amplifier. The pump sources were two single-emitter semiconductor laser diodes (LD) of 808 nm with a maximum continuous wave (CW) power of 10 W each. The average power of fundamental 1064 nm laser was 1.26 W with the laser operating at 16 kHz repetition rates, and 857ps pulse widths. Since the beam distortion would be severe in microchip lasers in terms of the increase in heat load, for obtaining a high beam quality of 532 nm, the beam distortion was compensated by adjusting the distribution of pumping beam in our experiment of fundamental amplification. Furthermore, better than 0.6 W average power, 770 ps, beam quality of M² ＜1.2, and 16 kHz pulse output at 532 nm was obtained by a Type I LiB₃O₅ (LBO) crystal in the critical phase matching (CPM) regime for second harmonic generation (SHG).

Flow and transport processes in soil and rock play a critical role in agricultural non-point source pollution (ANSP) loads. In this study, we investigated the ANPS load discharged into rivers from an irrigation district in the Tibetan Plateau, and simulated ANPS load using a distributed model involving detailed descriptions of flow and ANPS transport and transformation processes in the soil and rock. Experiments were conducted for two years to measure soil water content and nitrogen concentrations and the quality and quantity of lateral flow in the rock and at the drainage canal outlet during the highland barley growing period. A distributed model, in which the subsurface lateral flow was described using a step-wise method, was developed to simulate flow and ammonium nitrogen and nitrate nitrogen transport. Sobol’s method was used to evaluate the sensitivity of simulated flow and transport processes to model inputs. The results showed that, with a 21.2% increase of rainfall and irrigation in the highland barley growing period, the average NH4+-N and NO3--N concentrations in the soil layer decreased by 10.8% and 14.3%, respectively, due to increased deep seepage. Deep seepage of rainfall water accounted for 0–52.4% of total rainfall, whereas deep seepage of irrigation water accounted for 36.6–45.3% of total irrigation. NH4+-N and NO3--N discharged into the drainage channel represented 19.9–30.4% and 19.4–26.7% of the deep seepage, respectively. The mean Nash-Sutcliffe coefficients, root mean square errors, and cumulative deviations between the measured and simulated flow rates and NH4+-N and NO3--N concentrations at the surface drainage canal outlet were 0.694, 0.081, and 0.242, respectively, indicating that the proposed method can effectively describe the hydrological and ANPS pollution migration in the plateau irrigation zone. The Sobol’ sensitivity analysis results demonstrated that subsurface lateral flow had the most important first order and total effect on the simulated flow and NH4+-N and NO3--N concentrations at the surface drainage outlet.

In-situ observation on the austenite grain growth and martensite transformation in a developed NM500 wear-resistant steel were conducted on a Confocal Laser Scanning High-temperature Microscope (CSLM). Results indicated that the martensite nucleation did not synchronously proceed during quenching process. The selective prenucleation dominated in martensite nucleation, which dividing untransformed austenite into several regions, and resulting in the larger size of fresh martensite. Martensite can not only nucleate at parent austenite grain boundaries, but also nucleate in the preformed martensite laths and twins. Smaller constraint in the martensite growth was achieved in a larger parent austenite grain size, leading to the longer fresh martensite and secondary martensite. Besides, the transformation kinetics of martensite was accelerated in a higher quenching temperature. Moreover, the martensitic laths presented in parallel laths (0~2°) based on the pre-formed lath or distributed in triangle, parallelogram or hexagon with an angle of 60° or 120°. Interestingly, martensitic lath also traversed the unstable new parent austenitic grain boundaries. In addition, the size of austenite grains increased with the quenching temperature, and austenite grains coarsened in a short time at a higher quenching temperature of 1160 ℃. Furthermore, a large amount fine dispersed particles redissolved and ripped at 1160 ℃, resulting in the many large and visible carbonitrides.

Cyber Physical Systems (CPS) has been a popular research area in the last decade. The dependability of CPS is still a critical issue, and rare survey has been published in this domain. CPS is a dynamic complex system, which involves various multidisciplinary technologies. To avoid human error and to simplify management, self-management CPS (SCPS) is a wise choice. And to achieve dependable self-management, systematic solution is necessary to verify the design and to guarantee the safety of self-adaptation decision, as well as to maintain the health of SCPS. This survey first recalls the concepts of dependability, and proposes a generic environment-in-loop processing flow of self-management CPS, and then analyzes the error sources and challenges of self-management through the formal feedback flow. Focus on reducing the complexity, we first survey the self-adaptive architecture approaches and applied dependability means; then we introduce a hybrid multi-role self-adaptive architecture, and discuss the supporting technologies for dependable self-management at the architecture level. Focus on dependable environment-centered adaption, we investigate the verification and validation (V&V) methods for making safe self-adaptation decision and the solutions for processing decision dependably. For system-centered adaption, the comprehensive self-healing methods are summarized. Finally, we analyze the missing pieces of the technology puzzle and the future directions. In this survey, the technical trends for dependable CPS design and maintenance are discussed, an all-in-one solution is proposed to integrate these technologies and build a dependable organic SCPS. To the best of our knowledge, this is the first comprehensive survey on dependable SCPS building and evaluation.

To meet the International Maritime Organization’s requirements for carbon dioxide emissions from ships, a cost-effective and practical ship decarbonization tower was autonomously designed based on the marine diesel engine model 6135G128ZCa. The tower is internally filled with structured packing, and a slot-type liquid distributor at the top of the packing evenly distributes the NaOH solution to absorb carbon dioxide from the ship’s exhaust gas. Experimental research was conducted to investigate the influence of factors such as liquid-to-gas ratio and NaOH concentration on the performance of the decarbonization tower. The results confirmed that the use of NaOH solution can effectively absorb CO2 from ship exhaust gas, reducing CO2 emissions by more than 81.59% and meeting the EEDI (Energy Efficiency Design Index) requirements specified by the IMO.

G protein-coupled receptor 15 (GPR15, also known as BOB) is an extensively studied orphan GPCR involving HIV infection, colonic inflammation and smoking-related diseases. Recently, GPR15 was deorphanized and its corresponding natural ligand demonstrated an ability of inhibiting cancer cell growth. However, no study reported the potential role of GPR15 in a pan-cancer manner. Using large-scale publicly available data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, we found that GPR15 expression is significantly lower in colon adenocarcinoma (COAD) than in normal tissues. And among 33 cancer types, GPR15 expression is significantly correlated with the prognoses of COAD, neck squamous carcinoma (HNSC), lung adenocarcinoma (LUAD) positively and stomach adenocarcinoma (STAD) negatively. This study also revealed that commonly upregulated gene set in the high GPR15 expression group (stratified via median) of COAD, HNSC, LUAD and STAD are enriched in immune systems, indicating that GPR15 might be considered as a potential target for cancer immunotherapy. Furthermore, we modelled the 3D structure of GPR15 and conducted the structure-based virtual screening. The top 8 hits compounds were screened and then subjected to molecular dynamics (MD) simulation for stability analysis. Our study provided novel insights into the role of GPR15 in a pan-cancer manner and discovered a potential hit compound for GPR15 antagonists.

Intrauterine adhesions (IUA) has become one of the main causes of female infertility, how to effectively prevent postoperative re-adhesion has been a clinical challenge. In this study, a mussel-inspired dual-network hydrogel was proposed for postoperative anti-adhesion of IUA. First, calcium alginate/polyacrylamide (CA-PAM) hydrogel was prepared by covalent and Ca2+ cross-linking. Benefiting from abundant phenolic hydroxyl groups, polydopamine (PDA) was introduced to further enhance the adhesion ability and biocompatibility. This CA-PAM hydrogel immersed in 10 mg/mL dopamine solution possessed remarkable mechanical strength (elastic modulus > 5 kPa) and super stretchability (with a breaking elongation of 720%). At the same time, it shown excellent adhesion more than 6 kPa. Surprisingly, the coagulation index of the hydrogel was 27.27 ± 4.91, demonstrating attractive coagulation performance in vitro and the potential for rapid hemostasis after surgery.

Interactions between proteins and ions are essential for various biological functions like structural stability, metabolism, and signal transport. Given that more than half of all proteins bind to ions, it becomes crucial to identify ion-binding sites. Accurate identification of protein-ion binding sites helps us to understand proteins’ biological functions and plays a significant role in drug discovery. While several computational approaches have been proposed, this remains a challenging problem due to the small size and high versatility of metals and acid radicals. In this study, we propose IonPred, a sequence-based approach that employs ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) to predict ion binding sites using only raw protein sequences. We successfully fine-tuned our pretrained model to predict the binding sites for nine metal ions (Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, and K+) and four acid radical ion ligands (CO32−, SO42−, PO43−, NO2−). IonPred surpassed six current state-of-the-art tools by over 44.65% and 28.46% respectively in F1 score and MCC when compared on an independent test dataset. Our method is more computationally efficient than existing tools producing prediction results for a hundred sequences for a specific ion in under ten minutes.

Urea, a prevalent component found in wastewater, shows great promise as an alternative substrate to water for energy-efficient hydrogen production by electrolysis. However, the sluggish kinetics of the anodic urea oxidation reaction (UOR) significantly hampers the overall reaction rate. To address this challenge, this article presents a comprehensive study focusing on the controlled fabrication of hierarchically structured nanomaterials as potential catalysts for UOR. The prepared MnO2@NiCo-LDH hybrid catalyst demonstrates remarkable improvements in reaction kinetics, benefiting from synergistic enhancements in charge transfer and efficient mass transport facilitated by its unique three-dimensional architecture. Notably, the catalyst exhibits an exceptionally low onset potential of 1.228 V and requires only 1.326 V to achieve an impressive current density of 100 mA cm-2, representing a state-of-the-art performance in UOR. These findings highlight the tremendous potential of this innovative material-designing strategy to drive advancements in electrocatalytic processes.

In order to coordinate the damping performance and energy regenerative performance of energy regenerative suspension, this paper proposes a structure of vehicle semi-active energy regenerative suspension with electro-hydraulic actuator (EHA). In light of the proposed concept, a specific energy regenerative scheme is designed and the mechanical properties test is carried out. Based on the test results, the parameter identification for the system model is conducted using recursive least squares algorithm. On the basis of system principle, the nonlinear model of the semi-active energy regenerative suspension with EHA is built. Meanwhile, LQG control strategy of the system is designed. And then the influence of the main parameters of EHA on the damping performance and energy regenerative performance of suspension is analyzed. Finally, the main parameters of EHA actuator are optimized via genetic algorithm. The test results show that when sinusoidal is input at the frequency of 2Hz and the amplitude of 30mm, the spring mass acceleration RMS value of optimized EHA semi-active energy regenerative suspension is reduced by 22.23% and energy regenerative power RMS value is increased by 40.51%, which means while meeting the requirements of certain vehicle ride comfort and driving safety, energy regenerative performance is improved significantly.

The current deformation and stable state of slopes with historical shatter signs is a concern for engineering construction. Suspected landslide scarps were discovered at the rear edge of the slope of the Genie in the Sichuan-Tibet transportation corridor during a field investigation. In order to qualitatively determine the current status of the surface deformation of this slope, this paper uses high-resolution optical remote sensing, airborne LiDAR and InSAR technologies for comprehensive analysis. The interpretation of high-resolution optical and airborne LiDAR data revealed that the rear edge of the slope exhibits three levels of scarps. However, no deformation was detected with the D-InSAR analysis of ALOS-1 radar images from 2007 to 2008 or with the Stacking-InSAR and SBAS-InSAR processing of Sentinel-1A radar images from 2017 to 2020. A geological model of the slope was established in combination with field investigation stipulating that the slope is composed of steep anti-dip layered dolomite limestone and that the scarps at the rear edge of the slope were caused by historical shallow toppling. Further research is recommended to determine the extent of toppling deformation and evaluate the slope stability under the disturbance of tunnel excavation.

Flaviviruses cause a significant amount of mortality and morbidity, especially in the area where they are endemic. A recent example is the outbreak of Zika virus though out the world. Development of antiviral drugs against different viral targets is as important as development of vaccine. During viral replication, the flavivirus genome is translated as a single polyprotein precursor, which must be cleaved into individual proteins by a complex of the viral protease, NS3, and its cofactor, NS2B. Flavivirus protease is the most attractive target for development of therapeutic antivirals because it is essential for processing of viral polyprotein precursor and generation of functional viral proteins. In this review, we have summarized recent development in drug discovery targeting NS3-NS2B protease of flaviviruses, especially Zika, dengue and West Nile virus.

For Synthetic Aperture Radar (SAR) image registration, successive processes following feature extraction are required by both the traditional feature-based method and the deep learning method. Among these processes, the feature matching process—whose time and space complexity are related to the number of feature points extracted from sensed and reference images, as well as the dimension of feature descriptors—proves to be particularly time-consuming. Additionally, the successive processes introduce data sharing and memory occupancy issues, requiring an elaborate design to prevent memory leaks. To address these challenges, this paper introduces the OptionEM-based reinforcement learning framework to achieve end-to-end SAR image registration. This framework outputs registered images directly without requiring feature matching and calculation of the transformation matrix, leading to significant processing time savings. The Transformer architecture is employed to learn image features, while a correlation network is introduced to learn the correlation and transformation matrix between image pairs. Reinforcement learning, as a decision process, can dynamically correct errors, making it more efficient and robust compared to supervised learning mechanisms like deep learning. We present a hierarchical reinforcement learning framework combined with episodic memory to mitigate the inherent problem of invalid exploration in generalized reinforcement learning algorithms. This approach effectively combines coarse and fine registration, further enhancing training efficiency. Experiments conducted on three sets of SAR images, acquired by TerraSAR-X and Sentinel-1A, demonstrate that the proposed method’s average runtime is sub-second, achieving subpixel registration accuracy.

Urban scene point cloud pose significant challenges for registration due to its large data volume, similar scenarios and dynamic objects. In this paper, we propose PCRMLP, a model for urban scene point cloud registration that achieves comparable registration performance to prior learning-based methods. Compared to previous works which focus on extracting features and estimating correspondence, the model estimates the transformation implicitly from concrete instances. An instance-level urban scene representation method is introduced to extract instance descriptors via semantic segmentation and DBSCAN, which enable the model to obtain robust instance features, filter dynamic objects and estimate transformation in a more logical manner. Then a lightweight network consisting of MLPs is employed to obtain transformation in an encoder-decoder manner. We validate the approach on KITTI dataset. Experimental results demonstrate that PCRMLP can obtain a satisfactory coarse transformation from instance descriptors just in 0.0028s. With a subsequent ICP refinement module, the proposed method achieves higher registration accuracy and computational efficiency than prior learning-based works.

Aiming at the problems that the parameters of YOLOv5s model are too large and the computing resources of development board memory are limited, a new target detection method based on deep learning YOLOv5 algorithm model is proposed. First, the light-weight module Gz-ShffleNetv2 is used to construct cotton top bud feature extraction unit, which reduces the number of parameters and improves the running speed. It can be better applied in image classification, speed up detection and meet the requirements of mobile end of development board. Secondly, in order to solve the problem of decreasing detection accuracy caused by lightweight, BotNet and C3SE attention mechanism are added to focus on specific areas of cotton terminal bud. Combined with YYG loss function XIOU boundary frame regression loss, more feature information and rich feature MAP were obtained to further improve the accuracy of target detection. Through analysis of research and experimental results, the average accuracy map reached 91.3% under the Windos system NVIDIA Geforce RTX 2060 SUPER detection. While maintaining high precision identification, the number of parameters of YOLOv6 and YOLOv7-tiny network model is reduced by 83% and 53%, respectively, and the detection accuracy is increased by 1.2% and 7.7%, respectively. Compared with YOLOv5 reasoning image, the speed of image is increased by 0.035s, and the detection accuracy of MAP_0.5:0.95 is increased by 1%. At the same time, PyQt5 and YOLOv5 target detection algorithms are used to design a cotton top bud identification system, which makes cotton top bud detection more intuitive and convenient for subsequent hardware development and use.

The eutrophication caused by excessive total nitrogen(TN) and total phosphorus(TP) emissions has been widely concerned by the whole society. Studies have revealed the relationship between land use and TN and TP, but the relationship between land use compound topographic position and TP and TN was seldom studied. Therefore, Spearman correlation and redundancy analyses were used to reveal the relationship between land use compound topographic position and TN and TP based on the monthly data of 28 water quality sampling sites and the land use data of 2013 and 2016 in the lakes of Guizhou Plateau. The results show that the nutritional state of the HBA watershed is medium. The trophic level index (TLI) value and TN concentration were high during flood, while TP concentration was high in dry period. The TN concentration in the tributaries was higher than that in the reservoir area. Construction and valley were the sources of the pollution, whereas forest land and gentle slope were the sink. According to the”source-sink”effect, the optimal zoning of land use was completed, and the urban land pollution govern area should be strengthened firstly. This paper can provide decision support for water environment management and sustainable development decision-making.

Cracks on concrete surfaces are vital factors affecting construction safety. Accurate and efficient crack detection can prevent safety accidents. Using drones to photograph cracks on a concrete surface and detect them through computer vision technology has the advantages of accurate target recognition, simple practical operation, and low cost. To solve this problem, an improved CenterNet concrete crack-detection model is proposed. First, a channel-space attention mechanism is added to the original model to enhance the ability of the convolution neural network to pay attention to the image. Second, a feature selection module is introduced to scale the feature map in the downsampling stage to a uniform size and combine it in the channel dimension. In the upsampling stage, the feature selection module adaptively selects the combined features and fuses them with the output features of the upsampling. Finally, the target size loss is optimised from a Smooth L1 Loss to IoU Loss to improve its inability to adapt to targets of different sizes. The experimental results show that the improved CenterNet model reduces the FPS by 123.7, increases the GPU memory by 62MB, increases the FLOPs by 3.81, and increases the AP by 0.154 compared with the original model. The GPU memory occupancy remained stable during the training process and exhibited good real-time performance and robustness.

The optimization of guide vane closing schemes (OGVCS) of pumped storage hydro unit (PSHU) is the research field of cooperative control and optimal operation of pumped storage, wind power and solar power generation. This paper presents a OGVCS model of PSHU considering the rise rate of the unit rotational speed and the specific node pressure of each hydraulic unit, as well as various complicated hydraulic and mechanical constraints. OGVCS model is formulated as a multi-objective optimization problem to optimize conflictive objectives, i.e., unit rotational speed and water hammer pressure criteria. In order to realize the efficient solution of the OGVCS model, an enhanced multi-objective bacterial-foraging chemotaxis gravitational search algorithm (EMOBCGSA) is proposed to solve this problem, which adopts population reconstruction, adaptive selection chemotaxis operator of local searching strategy and Elite archive set to efficiently solve the multi-objective problem. Especially, novel constraints-handling strategy with eliminating and local search based on violation ranking is used to balance various hydraulic and mechanical constraints. Finally, simulation cases of complex extreme operating conditions (i.e., load rejection and pump outage) of ‘single tube-double units’ type PSHU system are conducted to verify the feasibility and effectiveness of the proposed EMOBCGSA in solving OGVCS problem. The simulation results indicate that the proposed EMOBCGSA can provide lower rise rate of the unit rotational speed and smaller water hammer pressure than other method established recently while considering various complex constraints in OGVCS problem.

The nitrogen-doped amorphous oxide semiconductor (AOS) thin film transistors (TFTs) with double-stacked channel layers (DSCL) were prepared and characterized. The DSCL structure composed of nitrogen-doped amorphous InGaZnO and InZnO films (a-IGZO:N/a-IZO:N or a-IZO:N/a-IGZO:N) made the corresponding TFT devices exhibit quite large field-effect mobility due to the existence of double conduction channels. Especially, the a-IZO:N/a-IGZO:N TFTs showed even better electrical performance (μ_FE = 15.0 cm²·V^-1·s^-1, SS = 0.5 V/dec, V_TH = 1.5 V, I_ON/I_OFF = 1.1×10⁸) and stability (V_TH shift of 1.5, -0.5, and -2.5 V for positive bias-stress, negative bias-stress and thermal stress tests, respectively) than the a-IGZO:N/a-IZO:N TFTs. Based on the X-ray photoemission spectroscopy measurements and energy band analysis, it was assumed that the optimized interface trap states, the less ambient gas adsorption, and the better suppression of oxygen vacancies in the a-IZO:N/a-IGZO:N hetero-structures might be responsible for the better behaviors of the corresponding TFTs.

This purpose was to compare the ability of body shape index (ABSI) and body roundness index (BRI) with waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR) and body adiposity index (BAI) to predict metabolic risk. The cross-sectional study was conducted in 17,360 Chinese subjects (were aged 18-95 years old) who escaped cardiovascular disease (CVD) or diabetes. Biochemical and anthropometric variables were measured by trained staff. Receiver operating characteristic curve (ROC) and optimal cutoff values of obesity indices were recruited to compare the predictive ability for metabolic risk factors. The mean age of subjects was 53.7(13.1) years, 41.6 % were males. Within young group (<60 years), the areas under the curve (AUC) demonstrated that WC, BMI, WHR, WHtR and BRI were able to similarly predict high metabolic risk in males (0.74 vs. 0.74 vs. 0.73 vs. 0.73 vs. 0.73) and in females (0.73 vs. 0.73 vs. 0.71 vs. 0.73 vs. 0.73), while the approximate predictive ability were only acquired in males (0.73 vs. 0.73 vs. 0.70 vs. 0.73 vs. 0.73) within elder group (≥60 years). The optimal cut-off values of BRI for high metabolic risk were calculated in males (<60 y: 3.49 vs. ≥60 y: 3.46) and females (<60 y: 3.47 vs. ≥60 y: 3.60). Meanwhile, BRI displayed a strong prediction to elevated BP and elevated TG in males (AUC = 0.64; AUC = 0.70) and to elevated BP, elevated TG and elevated SUA in females (AUC = 0.67; AUC = 0.69; AUC =0.70). BRI was able to similarly predict high metabolic risk compared to WC, BMI, WHR, WHtR and BAI, while ABSI was not. Moreover, BRI revealed specific predictive ability for elevated BP, elevated TG and elevated SUA.

Three new compounds (1, 11 and 12), together with 32 known ones, were isolated from the root bark of Morus alba L. using various chromatographic methods. The structures of the undescribed compounds were elucidated based on 1D, 2D NMR and HRESIMS data analysis, while the known ones were identified by comparison of their spectroscopic data with those reported in the literature. All the isolates were evaluated for their cytotoxic activities against human gastric cancer HGC27 cells by CCK-8 assay. Among them, compounds 5, 8, 10 and 30 exhibited cytotoxic activities on HGC27 cells with IC50 values of 10.24 ± 0.89 μM, 28.94 ± 0.72 μM, 6.08 ± 0.34 μM and 33.76 ± 2.64 μM, respectively. Furthermore, compound 10 was confirmed to reduce proliferation ability, increase apoptosis rate and inhibit cell migration pathway by annexin V/PI double staining experiment, transwell experiment and western blot analysis.

In recent years, cemented tailings/paste backfill (CPB) become more and more important to maintain mine structure and keep mining stope to be stable in deep underground mining. Deep mining process often accompanied by blasting, rock burst and other high-speed dynamic loading. So it’s important to make research on dynamic behavior and characteristics of CPB. In this paper, a series of dynamic loading tests on CPB specimens by SHPB were taken to study the characteristic of stress-strain, dynamic strength, deformation and failure of CPB. The experimental results revealed that some CPB specimens have 1~2 lower stress peaks after reaching the peak stress before getting completely failed. The greater the cement-sand ratio was, the more obvious of strain reaction was. This property mainly showed like follows. The greater the cement-sand ratio was, the greater the dynamic compressive strength was under similar velocity impact; the dynamic compressive strength and the average strain rate were quadratic equation relationship, and the greater the cement-sand ratio was, the higher increase rate of dynamic compressive strength caused by strain rate. The dynamic compressive strength enhancement factor increased with the increase of the strain rate, and is larger than that of rock samples. The failure forms of CPB specimens under low-speed impact were almost the same as them under static uniaxial and triaxle compression. The CPB specimens were crushed broken under the critical strain rate, similar to the failure mode of low strength concrete. The experimental research results will improve our understanding the dynamic mechanical property of CPB and help to guide the strength design of deep mining backfill.

Post-earthquake fire is considered as a catastrophic secondary disaster to personal and property safety. In this article, an evacuation assessment of a complex rail transit station under the post-earthquake fire for sustainable buildings was conducted from the internal environment and external rescue based on Building Information Modeling (BIM) and Fire Dynamic Simulation (FDS). The internal environment evacuation assessment simulation experiments were conducted in six hypothetical high-risk scenarios. Besides, the external rescue assessment was based on investigation of the route and the rescue time required during different periods of holidays and workdays. The results show that: (1) The influence caused by different size of fire area in the power distribution room is smaller than those in the train at the platform floor. (2) In the case of fire scenarios with the same fire area but different fire locations, the impact on temperature is greater than that on CO concentration in power distribution room. (3) It shows slight differences between single-floor fire and double-floor fire on evacuation of small area fire in power distribu-tion room. Meanwhile, optimized design recommendations are proposed to reduce the risk of emergency evacuation in both internal and external environments of rail transit stations for sustainable future buildings.

Wireless Sensor Networks (WSNs) has the characteristics of large-scale deployment, flexible networking, and wide application. It is an important part of the wireless communication networks. However, due to limited energy supply, the development of WSN is greatly restricted. Wireless Rechargeable Sensor Networks (WRSNs) transform the distributed energy around the environment into usable electricity through energy collection technology. In this work, a joint optimization strategy is proposed to improve the energy management efficiency for WRSNs. The joint optimization strategy is divided into two phases. In the first phase, we design an Annulus Virtual Force based Particle Swarm Optimization (AVFPSO) algorithm for area coverage planing. It adopts the multi-parameter joint optimization method to improve the efficiency of the algorithm. In the second phase, a Queuing Game-based Energy Supply (QGES) algorithm is designed for energy scheduling. It converts energy supply and consumption into network service. By solving the game equilibrium of the model, the optimal energy distribution strategy can be obtained. The simulation results show that our scheme improves the efficiency of coverage and energy, and extends the lifetime of WSN.

Effects of plastic deformation on the indentation feature of hot-rolled commercial pure titanium alloy were detected using microindentation. It was found that the most deformed sample experienced the largest indentation resistance and had the maximum dislocation density and the indentation size influenced the CP-Ti indentation feature. The Vicker hardness and energy dissipated demonstrated the significant influence of the dislocation motion and size effects on the indentation in the indentation deformation. The linear relation of the energy ratio which is related to the microstructures and the indentation depth ratio was obtained for hcp materials Titanium alloys.

The exponential growth of digital media content has introduced new challenges in managing and classifying internet traffic. Digital media traffic is composed of various applications such as video, audio, social media, and search, and its data structure is complex, incorporating a vast array of features. The classification of traffic data is a crucial aspect of internet traffic management and network security, and it forms the basis for several scenarios, including content distribution, advertising recommendations, and data analysis. Traditional classification methods rely mainly on deep packet inspection and port-based techniques, which have become increasingly ineffective due to the rapid evolution of network traffic. To address this issue, this study proposes a machine learning-based traffic classification method aimed at enhancing the accuracy and efficiency of digital media traffic classification to meet the current needs of traffic management and network security. The paper also analyzes and evaluates the classification effect and prediction capability of various algorithms under different training set sizes to validate the feasibility and effectiveness of the proposed method. The result demonstrates that the neural network algorithm has superior classification and prediction capabilities compared to the decision tree and support vector machine algorithms. Furthermore, our proposed method achieves the highest accuracy of 96.88% with a large training sample of 40,000 data streams, proving its superiority in handling high-dimensional data and complex datasets. The research results are significant for the development of digital media traffic classification and prediction methods and are expected to be applied in practical scenarios.

Diabetic kidney disease (DKD) is one of the leading causes of end-stage renal disease worldwide and significantly increases the risk of premature death due to cardiovascular diseases. Elevated urinary albumin levels are an important clinical feature of DKD. Effective control of albuminuria not only delays glomerular filtration rate (GFR) decline but also markedly reduces cardiovascular disease risk and all-cause mortality. New drugs for treating DKD proteinuria, including sodium-glucose cotransporter 2 inhibitors (SGLT2i), mineralocorticoid receptor antagonists (MRAs), and endothelin receptor antagonists (REAs), have shown significant efficacy. Auxiliary treatment with proprietary Chinese medicine has also yielded promising results; however, it also faces a broader scope for development. The mechanisms by which these drugs treat albuminuria in patients with DKD should be described more thoroughly. The positive effects of combination therapy with two or more drugs in reducing albuminuria and protecting the kidneys warrant further investigation. Therefore, this review explores the pathophysiological mechanism of albuminuria in patients with DKD, the value of clinical diagnosis and prognosis, new progress and mechanisms of treatment, and multidrug therapy in patients who have type 2 diabetic kidney disease (T2DKD), providing a new perspective on the clinical diagnosis and treatment of DKD.

The ATP-binding cassette (ABC) transporter ABCG2 is a significant urate transporter with a high capacity, and it plays a crucial role in the development of hyperuricemia and gout. Therefore, it has the potential to be targeted for therapeutic interventions. Cortex Fraxini, a traditional Chinese medicine (TCM), has been found to possess anti-hyperuricemia properties. However, the specific constituents of Cortex Fraxini responsible for this effect are still unknown, particularly the compound that is responsible for reducing uric acid levels in vivo. In this study, we propose a target screening protocol utilizing bio-affinity ultrafiltration mass spectrometry (BA-UF-MS) to expediently ascertain ABCG2 ligands from the plasma of rats administered with Cortex Fraxini. Our screening protocol successfully identified fraxin as a potential ligand that interacts with ABCG2 when it functions as the targeted protein. Subsequent investigations substantiated fraxin as an activator of ABCG2. These findings imply that fraxin exhibits promise as a drug candidate for the treatment of hyperuricemia. Furthermore, the utilization of BA-UF-MS demonstrates its efficacy as a valuable methodology for identifying hit compounds that exhibit binding affinity towards ABCG2 within TCMs.

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.

This study presents the Distilled MixUp Squeeze ResNet (DMSResNet), an optimized variant of the Residual Network architecture designed for high accuracy and computational efficiency. By integrating Squeeze-and-Excitation blocks, MixUp data augmentation, and knowledge distillation, it achieves competitive performance within strict parameter constraints and limited training resources. Evaluated on the CIFAR-10 dataset, the model attains a 96.56% accuracy with only 4.3 million total parameters. The results demonstrate that optimization of established architectures can yield performance comparable to newer models, especially in resource-limited scenarios. This work contributes to the development of efficient deep learning models for applications in constrained computational environments.

Abstract: Microflora play an important role in the fermentation of blueberry wine, influencing the flavor and nutrient formation. Commercial yeasts give blueberry wines an average flavor profile that does not highlight the specific aroma and origin of the blueberry. In the present study, ITS1-ITS2 region sequencing analysis was performed using Illumina MiSeq high-throughput technology to sequence fermented blueberry wine samples of three Vaccinium ashei varieties, Gardenblue, Powderblue, and Britewell, from the Majiang appellation in Guizhou province to analyze the trends of fungal communities and the diversity of compositional structures in different periods of blueberry wine fermentation. The study's results revealed that 114 genera from 7 phyla were detected in 9 samples from different fermentation periods of blueberry wine. The main fungal phyla were Ascomycota, Basidiomycota, Kickxellomycota, Chytridiomycota, and Olpidiomycota. The main fungal genera were Hanseniaspora, Saccharomyces, unidentified, Aureobasidium, Penicillium, Mortierella, Colletotrichum, etc. Hanseniaspora was dominant in the pre-fermentation stage of blueberry wine, accounting for more than 82%; Saccharomyces was the dominant genera in the middle and late fermentation stages of blueberry wine, with Saccharomyces accounting for more than 72% in the middle of fermentation and 93% in the late fermentation stage. This study screened indigenous flora for the natural fermentation of blueberry wine in the Majiang production area of Guizhou, improved the flavor substances of the blueberry wine, highlighted the characteristics of the production area, and made the blueberry wine have the characteristic flavor of the production area.

The thoracic surgery has increased drastically in recent years, especially in the light of the severe outbreak of 2019 novel coronavirus disease (COVID-19). Routine “passive” chest computed tomography (CT) screening of inpatients detects some pulmonary diseases requiring thoracic surgeries timely. As an essential device for thoracic anesthesia, the double-lumen tube (DLT) is particularly important for anesthesia and surgery. With the continuous upgrading of the DLTs and the widespread use of the fiberoptic bronchoscopy (FOB), the position of DLT in thoracic surgery is gradually becoming more stable and easier to observe or adjust. However, the DLT malposition still occurs during transferring patients from supine to lateral position in thoracic surgery, which leads to lung isolation failure and hypoxemia during one-lung ventilation (OLV). Recently some innovative DLTs or improved intervention methods have shown good results in reducing the incidence of DLT malposition. This review aims to summarize the recent studies of the incidence of left-sided DLT malposition, the reasons and effects of malposition, and summarize current methods for reducing DLT malposition and prospects for possible approaches. Meanwhile, we use bibliometric analysis to summarize the research trends and hot spots of the DLT research.

Traditional 3D reconstruction models have consistently encountered a challenge: attaining high recall of object edges while preserving precision. In this paper, we introduce a post-processing method Multi-Head Attention Refiner (MA-R) aimed at tackling this challenge by integrating a multi-head attention mechanism within the U-Net style refiner module. The 3D reconstruction model applying this method demonstrates excellence in parsing intricate image details, resulting in significant enhancement of boundary predictions and increased recall rates. In our experiments, our method significantly enhances the reconstruction performance of Pix2Vox++ when multiple images are utilized as input. Specifically, it achieves an enhanced Intersection Over Union score of approximately 1.1% compared to the original Pix2Vox++ model when 16-view images are employed.

Predatory mites are a type of biological control agent used in many countries against various vegetable pests, particularly spider mites. Despite the significant presence of predatory mites in the Tibetan plateau, there is limited research on their fitness and prey capacity in the area. Herein, four predatory mite species, including one species Neoseiulus californicus and three species from genus Amblyseius (Amblyseius swirskii, Amblyseius barkeri, and Amblyseius cucumeris) were investigated. Specifically, their fitness and prey capacity on two-spotted mites (Tetranychus urticae) at highland conditions were evaluated. Compared to the other three predatory species, A. swirskii had the highest fecundity (Mean ± SEM=11.60 ± 0.34) and the highest pre-adult survival rate (Mean ± SEM = 83.33 ± 3.33%). Since their juvenile Survival Rate (SR) was extremely low (Mean ± SEM =13.33% ± 5.77%), most A. barkeri nymphs died before emergence. Compared to the other three predatory mites, A. swirskii had the highest predation capacity on T. urticae adult mites at 15 d post-release (Mean ± SEM =14.28 ± 2.24). Collectively, these findings imply notable advantages in employing A. swirskii for controlling two-spotted mites in the Tibetan Plateau. Furthermore, based on the results, A. swirskii was the most effective, and A. barkeri was the least effective in controlling two-spotted mites in the Tibetan Plateau. These findings could inform the development of a feasible biological control method based on suitable predatory mite species to manage T. urticae in the Tibetan Plateau, and would be helpful in delaying the rise or emergence of chemical pesticide resistance in two-spotted mites.

The present study was to investigate effects of total flavonoids from Taraxacum mongolicum Hand.-Mazz. (FT) on fermentation quality, antioxidant status and microbial community of Caragana korshinskii Kom. (CK) silage. CK was ensiled with no additive (CON), 1% FT and 2% FT on a fresh weight (FW) basis for 60 days. The results showed that 1% FT and 2% FT groups displayed higher DM content than CON group, and 2% FT group had the best effect on nutrient preservation. Compared with CON and 1% FT groups, 2% FT group exhibited the best silage fermentation quality and the highest antioxidant activity, including increased lactic acid, acetic acid concentrations and the activities of antioxidant enzymes, as well as decreased pH and the ammonia nitrogen (NH₃-N) concentration. Moreover, 2% FT addition significantly affected the microbial community, such as increased abundance of Lactobacillus and decreased abundances of Pseudomonas and unidentified Cyanobacteria. The abundances of Lactobacillus parafarraginis and Lactobacillus brevis were negatively correlated to pH, while they were positively correlated with T-AOC, GSH-Px and CAT activities. In conclusion, 2% FT may be used as additives to inhibit the growth of undesirable microorganisms and promote fermentation quality and antioxidant activity of CK silage.

The power conversion efficiency (PCE) of polymer solar cells (PSCs) are strongly depended on the bulk-heterojunction active layer. Here, the 1,8-diiodooctane (DIO) additives have been added into the PCDTBT: PC70BM blend PSCs. Based on the higher boiling point than host solvent ODB and better solubility of PC71BM, the device photovoltaic properties with DIO additive were changed obviously. The investigations of EQE indicate that the DIO can influence the charge recombination and transportation process, and absorption studies demonstrate that the charge carriers generation process can also be affected by DIO. The overall impact reflected on the decreased equivalent resistant. With 3% v/v DIO, the Jsc, Voc and FF are both increased. Correspondingly, a highest PCE is achieved of 6.15%, while the reference device without DIO only has a PCE of 5.23%. Hence, adding DIO solvent additive is a effective method for the photovoltaic properties improving of PCDTBT: PC70BM blend PSCs.

This paper aims to quantify the influence of hydrogen embrittlement on Wierzbicki and Xue (W-X) model parameters. This model has 4 parameters which have been obtained by tensile tests on smooth specimens, tensile notch specimens, single notch tensile specimens (SENT) and pure shear specimens. Hydrogen embrittlement has been obtained by electrolytic method. Application of the (W-X) model has been done to get the local resistance used to predict failure by the Volumetric Method (VM). An example is given for the case of an embedded pipe submitted to service pressure and lateral seismic displacement. The effect of this displacement is then assimilated to the shearing of the straight section of the pipe. The pipe shows a corrosion defect considered a semi-elliptical crack. The influence of hydrogen embrittlement on local strain resistance and safety factors is discussed.

In this work, we studies the principle of ground electrode current field through ground communication technology, signal design, and optimal working mode of excitation source, so as to adapt the working mode, working frequency, and transmission medium condition of signal transmission. Through waveform design, energy is concentrated in the main conduction direction, which is beneficial for signal filtering at the receiving end and achieving high reliability data transmission; On this basis, on-site environmental testing was conducted to verify the detection and recognition technology of weak signals. Signal design and detection recognition are a very important part of the grounding electrode current field through ground communication technology. The grounding electrode current field through ground communication technology is mainly applied to solve the problem of communication between rescue personnel and trapped personnel before and during tunnel engineering collapse and rescue.

This paper aims to build up a preference function to evaluate the public benefits of the type of agricultural farming, biodiversity, water provisions, land use type, ecotourism modes, and a monetary attribute (environmental trust fund and willingness to contribute) associated with an ecosystem service and land use program in a forest park. This study used the choice experiments to build a random utility model, analyze the average preference for the above land use attributes based on the conditional logit and used a latent class model to test the resident’s heterogeneous preferences for land use planning in the forest park. We also estimated the welfare derived from various land use programs. The empirical result had shown that: (1) increasing organic farming area, maintaining the status quo of species biodiversity, increasing the surface water provision, increasing the area of custom flora, increasing the wetland area, and setting up an integrated framework for ecotourism increase the public’s preference for the land use program; (2) we found that farmer and non-farmer haven’t the same land use preferences; (3) the ecotourism development program incorporating biodiversity, organic farming, ethnobotany, and wetland area with integrated ecotourism are more preferred than other land use program scenarios.

In the surgical treatment of lung cancer, systemic mediastinal lymph node dissection, as one important routine procedure, has been accepted by most peers in the world. However, due to the special location of some mediastinal lymph nodes, the difficulty of dissection, and the negative preoperative CT results, the specific scope of lymph node dissection is still controversial. Especially the second group, which is located at the top of thorax, is likely to be overlooked for the above reasons. Here, we report a case of lung adenocarcinoma in which the preoperative CT showed no abnormal lymph nodes in the second group and also no enlarged lymph nodes of the second group were found during the surgery, yet lymphadenectomy was still performed according the routine with the lymph node of station 2 being sampled. In the postoperative pathological report, cancer cells were found in the second group, instead of lung adenocarcinoma, these cells come from thyroid and were proved to be papillary thyroid carcinoma, which is unusual because no obvious indication of thyroid carcinoma was found in preoperative color doppler ultrasound of superficial lymph nodes.

With the rapid development of new energy vehicles and the digital electronics industry, the demand for lithium has surged, necessitating advanced lithium extraction technologies. Electrochemical methods, noted for their high selectivity and efficiency in extracting target ions from liquid sources in an environmentally friendly manner, have become increasingly vital. These methods are versatile, applicable in scenarios such as lithium extraction from saline lakes, mother liquor separation, and lithium enrichment. They include electrochemical deintercalation, electrochemical ion pumps, and electrodialysis, each offering unique benefits and challenges depending on the application context. This review provides a detailed exploration of the research progress in lithium extraction using electrochemical methods and discusses future prospects for these technologies, emphasizing their potential to meet the growing demand for lithium.

A detailed analysis of the dehydrogenation mechanism and reversibility of LiBH₄ doped by active Al* derived from AlH₃ was performed by thermogravimetry (TG), differential scanning calorimetry (DSC), mass spectral analysis (MS), powder X-ray diffraction (XRD), scanning electronic microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). The results show that the dehydrogenation of LiBH₄/Al* is a five-step reaction: (1) LiBH₄ + Al → LiH + AlB₂ + “Li-Al-B-H” + B₂H₆ + H₂; (2) the decomposition of "Li-Al-B-H" compounds liberating H₂; (3) 2LiBH₄ + Al → 2LiH + AlB₂ + 3H₂; (4) LiBH₄ → LiH + B + 3/2H₂; (5) LiH + Al → LiAl + 1/2H₂. And the reversibility of LiBH₄/Al* composite is based on equation as follows: LiH + LiAl + AlB₂ + 7/2H₂ ↔ 2LiBH₄ + 2Al. The extent of dehydrogenation reaction between LiBH₄ and Al* greatly depends on the precipitation and growth of reaction products (LiH, AlB₂ and LiAl, etc.) on the surface of Al*. A passivation shell of Al* formed by these products is the kinetic barrier to the dehydrogenation of LiBH₄/Al* composite.

Conducting related research is crucial since there are still many problems that need to be resolved in the research on soft robots in the areas of material selection, structure design and manufacture, and drive control. Soft manipulators, a subset of soft robots, are now a popular area of study for many researchers. In comparison to typical manipulators, soft manipulators feature a high degree of gripping flexibility and a basic morphological structure. They are composed of flexible materials. It has a wide range of potential applications in healthcare, rehabilitation, bionics, and detection, and it can compensate for the drawbacks of rigid manipulators in some use scenarios. A modular soft-body torsional gripping system is developed after a torsional and gripping actuator is conceived, constructed, and its performance is examined. The torsion actuator and the grasping actuator can be combined in the system in a modular fashion. With the help of RGB-D vision algorithms, this multi-modular setup makes it possible to combine soft actuators with various twisting degrees and achieve exact gripping. Through pneumatic control, the target object is precisely grasped and rotated at various angles, enabling rotation of the target object in three dimensions.

One new cassane diterpene possessing an unusual N bridge between C-19 and C-20 named caesalsappanin R (1), as well as another new diterpene caesalsappanin S (2), were isolated from the seeds of Caesalpinia sappan with methanol extract. Their structures were determined by spectroscopic analysis and examined alongside existing data from prior studies. Their biological activities were profiled by their antiplasmodial activity.

Underwater robotics equipped with visual detection systems have the function of detecting un-derwater artifacts, which is of great significance to deep-sea archaeology. Underwater artifacts are located in complex environments with poor imaging conditions, and the targets have chal-lenging problems such as breakage, stacking, and sediment burial, which causes the marine cul-tural target detection failures. To solve these problems, this paper proposes an underwater cul-tural target detection algorithm based on the deformable deep aggregation network model for underwater robotics exploration. To fully extract the target feature information of underwater targets in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. Besides, the BAM attention module is designed for fea-ture optimization, which enhances the potential feature information of the target while weak-ening the background interference information. Finally, the target prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validat-ed and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, the present al-gorithm has been practically deployed on the underwater robotics. In the deep-sea tests, the ar-tifact detection frame rate reaches up to 19 fps, which satisfies the real-time target detection。

Gastrointestinal bleeding (GIB) after heart transplantation (HT) remains a significant clinical issue. The study aimed to explore its incidence, trends, outcomes and clinical predictors of GIB in HT patients. Adult patients who underwent HT between 2015 and 2021 at Union Hospital were recruited and divided into two groups based on the presence or absence of postoperative GIB. Primary outcomes were evaluated by follow-up. Independent predictors of GIB after HT were identified by logistic regression analysis. A nomogram prediction model was constructed according to these independent variables, and the accuracy of the model was assessed using the receiver operating characteristic (ROC) curve and the calibration curve. Among 461 patients, 40 (8.7%) developed GIB post-HT. HT patients with postoperative GIB exhibited higher in-hospital, 30-day, 90-day and 1-year mortality (all P < 0.05). Multivariate analysis identified age, preoperative warfarin, postoperative continuous renal replacement therapy and postoperative nasogastric tube as independent risk factors for GIB following HT. The nomogram prediction model was performed using the 4 variables. The area under the curve (AUC) of this model was 0.852(95% CI: 0.787-0.917, P < 0.001) and the calibration curve was close to the ideal diagonal line. GIB following HT is associated with a poor clinical prognosis. The constructed nomogram, incorporating its independent risk factors, demonstrated a favorable predictive value for GIB.

Recent prolonged dry periods and lack of irrigation water have severely affected the productivity of coffee farms’ in the Central Highlands of Vietnam. This paper analyzes the efficiency of irrigation water use for Robusta coffee (Coffea canephora) in Lam Dong province, Highlands, Vietnam. A Cobb-Douglas production function was used to determine coffee productivity’s response to the application of irrigation water and other production factors using data collected from 194 farmers while the Technical Efficiency (TE) and Irrigation Water Use Efficiency (IWUE) were analyzed using a Data Envelopment Analysis (DEA) model. The correlation of different factors to IWUE was determined using the Tobit model. The production function analysis using Cobb-Douglas shows that the volume of irrigation water, amount of working capital, labor and farm size significantly influence coffee productivity. It also shows that indigenous farmers are more efficient in utilizing irrigation water than the (mostly Kinh) migrant farmers. The Tobit result, on the other hand. indicates that farmers’ experience, education level, distance of farm to water source, security of access to water source, extension contact and credit access significantly affect IWUE. The study findings further suggest that mitigating water shortages in coffee farms require sub-regional and national policy support such as better access to credit and extension services, training, land management and household-level effort to improve farming practices, through the application of appropriate technologies and traditional knowledge.

Ag+ pollution is great of harm to the human body and the biology. Therefore, it is an urgent need to develop inexpensive and accurate detection methods. Herein, lignin-derived structural memory carbon nanodots (CSM-dots) with outstanding fluorescence property were fabricated via a green method, which reserve functional and structural units of the precursor molecules. The CSM-dots could specifically bind Ag+, accompanied with a remarkable fluorescence quenching response. This “turn-off” fluorescence behavior was used for Ag+ determination in a linear range of 5-290 μM with the detection limit as low as 500 nM. Furthermore, the finding showed that this sensing nano-platform was successfully used for Ag+ determination in real samples and intracellular imaging, showing great potential in biological and environmental monitoring applications.

Cervical cancer is a worldwide public health problem with a high rate of illness and mortality among women. In this study, we proposed a novel framework based on Faster RCNN-FPN ar-chitecture for the detection of abnormal cervical cells in cytology images from cancer screening test. We extended the Faster RCNN-FPN model by infusing deformable convolution layers into the feature pyramid network (FPN) to improve scalability. Furthermore, we introduced a global contextual aware module alongside the Region Proposal Network (RPN) to enhance the spatial correlation between the background and the foreground. Extensive experimentations with the proposed deformable and global context aware (DGCA) RCNN were carried out using the cer-vical image dataset of “Digital Human Body" Vision Challenge from the Alibaba Cloud TianChi Company. Performance evaluation based on the mean average precision (mAP) and receiver operating characteristic (ROC) curve has demonstrated considerable advantages of the proposed framework. Particularly, when combined with tagging of the negative image samples using tra-ditional computer-vision techniques, 6-9% increase in mAP has been achieved. The proposed DGCA-RCNN model has potential to become a clinically useful AI tool for automated detection of cervical cancer cells in whole slide images of Pap smear.

A full connected world is expected to gain in the 6th generation mobile network (6G). As a typi-cal fully connected scenario, the internet of vehicle (IoV) enables intelligent vehicle operations via artificial intelligence (AI) and edge computing technologies. In the future of vehicular net-works, wide variety of services need powerful computing resources and higher quality of ser-vice (QoS). Existing resources are insufficient to match these requirements. Aim to this problem, An intelligent service offloading framework is provided. Based on the framework, an Algorithm of Improved Gradient Descent (AIGD) is created to accelerate the speed of iteration. So, the con-vergence of convolutional neural network (CNN) based on AIGD is able to be accelerated too. Then, an Algorithm of convolutional long short-term memory (CN_LSTM) Based Traffic Predic-tion (ACLBTP) is designed to gain the predicted number of vehicles belonged to the edge node. At last, an Algorithm of Service Offloading Based on CN_LSTM (ASOBCL) is conducted to of-fload these services to the vehicles belonged to the edge node. In ASOBCL, sorting technique is adopted to speed up the offloading work. Simulation results demonstrate the fact that the pre-diction strategy designed in this paper has high accuracy. The low offloading time and main-taining stable load balance is gained via running ASOBCL. Low offloading time means short response time. And, the QoS is guaranteed. So, these strategies designed in this paper are effec-tive and valuable.

Catalyst with high catalytic activity and good stability are desirable in the electrocatalytic oxidation of glucose. Herein, Pt concave nanocubes with high-index facets (HIFs) supported by carbon black (Pt CNC/CB) are prepared through a hydrothermal method. The experimental results demonstrate that the peak current densities in different potential regions on the Pt CNC/CB anode are 0.22, 0.20, and 0.60 mA cm−2, respectively. The glucose oxidation reaction shows superior performances in basic and neutral conditions than in acid conditions. Better stability is achieved by Pt CNC/CB than Pt concave nanocubes (Pt CNCs). Abundant surface defects with low-coordinated atom numbers, such as the steps, kinks, and edges, are served as active sites in the electrocatalytic oxidation of glucose. With the addition of carbon black, the catalytic activity can be improved by facilitating the full exposure of the active surface defects on the HIFs of Pt CNCs. Moreover, to address the aggregation of Pt CNCs, caused by the high surface energy of HIFs, the introduction of carbon material is an effective way to preserve the HIFs, and thus enhance the stability of the catalyst. Hence, the prepared Pt CNC/CB electrocatalyst has great potential to be applied in the electrooxidation of glucose.

The efficiency and performance of Proton Exchange Membrane Fuel Cells (PEMFCs) are primarily influenced by ORR electrocatalysts. In recent years, atomically dispersed met-al-nitrogen-carbon (M-N-C) catalysts have gained significant attention due to their high active center density, high atomic utilization, and high activity. These catalysts are now considered the preferred alternative to traditional noble metal electrocatalysts. The unique properties of M-N-C catalysts are anticipated to enhance the energy conversion efficiency and lower the manufac-turing cost of the entire system, thereby facilitating the commercialization and widespread ap-plication of fuel cell technology. This article initially delves into the origin of performance and degradation mechanisms of Fe-N-C catalysts from both experimental and theoretical perspectives. Building on this foundation, the focus shifts to strategies aimed at enhancing the activity and durability of atomically dispersed Fe-N-C catalysts. These strategies encompass the use of bi-metallic atoms, atomic clusters, heteroatoms (B, S, and P), and morphology regulation to optimize catalytic active sites. The article concludes by detailing the current challenges and future pro-spects of atomically dispersed Fe-N-C catalysts.

Ankle joint flexion and extension exercises play an important role in the rehabilitation training of patients who have been injured or bedridden for a long time before and after surgery. Accurately guiding patients to perform ankle flexion and extension exercises can significantly reduce deep vein thromboembolism. Currently, most ankle rehabilitation devices focus on assisting patients with ankle flexion and extension movements, and there is a lack of devices for effectively monitoring these movements. In this study, we designed an ankle joint flexion and extension movement monitoring device based on a pressure sensor. It is composed of an STM32 microcontroller, a pressure sensor, an HX711A/D conversion chip, and an ESP8266 WiFi communication module. The value of force and effective number of ankle joint flexion and extension movements were obtained. An experimental device was designed to verify the accuracy of the system. The results showed that the device can effectively monitor and control ankle flexion and extension movements remotely, ultimately ensuring that the patient can effectively monitor and grasp the active ankle pump movement.

Background: HMGA2 encodes a small non histone chromatin-associated protein that has no intrinsic transcriptional activity, but can modulate transcription by altering the chromatin architecture. HMGA2 was found overexpressed in a variety of epithelial and mesenchymal tumors and promoted invasion and metastasis in most malignant epithelial tumors. A recent study showed that P53 inhibited CRC progression by targeting HMGA2. However, the mechanism by which HMGA2 affect angiogenesis in CRC has not been clarified. Methods: The expression of HMGA2 was analyzed by IHC, WB and bio infomatic analysis. Cbioportal and mexpress online tools were applied to explore the CNV and methylation of HMGA2 in CRC patients. Single cell data from GEO was used to examine the specific cell type that contribute to the high HMGA2 expression in CRC. Lentivirus was used to knock down HMGA2 in CRC cells and HUVECs was used to study angiogenesis. Results: In the current study, we first detected the expression pattern of HMGA2 in CRC patients and evaluated its clinical values and CNV amplification could possibly contribute to the up regulation of HMGA2 in CRC patients. By analyzing CRC single cell data we found that HMGA2 was specifically up regulated in the colorectal epithelial cells. Furthermore, knocking down of HMGA2 suppresses angiogenesis via dual regulation of VEGF-A and SEMA3A in CRC through inactivating VEGRR2 pathway in HUVECs. Conclusions: HMGA2 might be a promising prognostic marker and target for treating advanced CRC patients.

Digital twin technology is revolutionizing traditional manufacturing paradigms. In modern manufacturing systems, digital twin technology is fraught with challenges due to the scarcity of labeled data. Specifically, existing supervised machine learning algorithms, with their reliance on voluminous training data, find their applicability constrained in real-world production settings. This paper introduces an unsupervised 3D reconstruction approach tailored for industrial applications, aimed at bridging the data void in creating digital twin models. Our proposed model, by ingesting high-resolution 2D images, autonomously reconstructs precise 3D digital twin models without the need for manual annotations or prior knowledge. Through comparisons with multiple baseline models, we demonstrate the superiority of our method in terms of accuracy, speed, and generalization capabilities. This research not only offers an efficient approach to industrial 3D reconstruction but also paves the way for the widespread adoption of digital twin technology in manufacturing.

Both bacteria product flagellin and macrophages are implicated in HIV infection/disease progression. However, the impact of their interaction on HIV infection and the associated mechanisms remain to be determined. We thus examined the effect of the flagellins on HIV infection of primary human macrophages. We observed that the pretreatment of macrophages with the flagellins from the different bacteria significantly inhibited HIV infection. The mechanistic investigation showed that the flagellin treatment of macrophages downregulated the major HIV entry receptors (CD4 and CCR5) and upregulated the CC chemokines (MIP-1α, MIP-1β, and RANTES), the ligands of CCR5. These effects of the flagellin could be compromised by a toll like receptor 5 (TLR5) antagonist. Given the important role of flagellin as a vaccine adjuvant in TLR5 activation-mediated immune regulation and in HIV infection of macrophages, future investigations are necessary to determine the in vivo impact of flagellin-TLR5 interaction on macrophage-mediated innate immunity against HIV infection and the effectiveness of flagellin adjuvant-based vaccines studies.

Rhododendron chrysanthum Pall., live in Changbai Mountain being exposed to chilling temperature, high light intensities and water scarcity condition. To adapt to the harsh environment, the cold resistance mechanisms of R. chrysanthum have been successfully evolved in the long-term adaptive process. In our present work, the methods of proteomics combined with physiological and biochemical analyses were used to investigate the effects of cold stress on the photosynthesis and antioxidant system of Rhododendron chrysanthum Pall. and the molecular mechanisms involved in cold resistance of plants. A total of 153 photosynthesis related proteins were identified in present work, of which 7 proteins including Rubisco large subunit (rbcL) were up-regulated in experiment group (EG) compared with control group (CG). Simultaneously, four chlorophyll fluorescence parameters were measured in present study. The results showed that the maximum photochemical efficiency of photosystem II (Fv/Fm), actual quantum yield of PSII (Y(II)) and photochemical quenching (qP) were significantly higher in EG, whereas the non-photochemical quenching (NPQ) was notably decreased. Cold stress could lead to a significant reduction in electron transport rate (ETR) accompanied with an increase in excitation pressure (1-qP). The abundance of PetE which involved in the plants photosynthetic electron transfer was also significantly influenced by cold stress. Moreover, the up-regulated expressions and higher levels of enzymatic activities of Glutathione peroxidase (GPX) and Ascorbate peroxidases (APXs) were detected in EG. All these changes which can help plants to survive in low temperature are considered as the crucial parts of cold tolerance mechanisms. These results revealed that photosynthesis and redox adjustment play significant roles in the defense of cold-induced damage.

In this study, high-performance FAU (NaY type) zeolite membranes were successfully synthesized using small-sized seeds of 50 nm, and their gas separation performance was systematically evaluated. Employing nano-sized NaY seeds and an ultra-dilute reaction solution with a molar composition of 80 Na2O: 1Al2O3: 19 SiO2: 5000H2O, the effects of synthesis temperature, crystallization time, and porous support (α-Al2O3 or mullite) on the formation of FAU membranes were investigated. The results illustrated that further extending the crystallization time or increasing the synthesis temperature led to the formation of a NaP impurity phase on the FAU membrane layer. The most promising FAU membrane with a thickness of 2.7 µm was synthesized on an α-Al2O3 support at 368 K for 8 h, and had good reproducibility. The H2 permeance of the membrane was as high as 5.34×10-7 mol/(m2 s Pa), and the H2/C3H8 and H2/i-C4H10 selectivities were 183 and 315, respectively. The C3H6/C3H8 selectivity of the membrane was as high as 46 with a remarkably high C3H6 permeance of 1.35× 10-7 mol/(m2 s Pa). The excellent separation performance of the membrane is mainly attributed to the thin, defect-free membrane layer and relatively wide pore size (0.74 nm).

The straightness error of guideway is one of the key indicators of ultra-precision machine, which plays an important role in the machining accuracy of work-piece. In order to measure straightness error of long distance ultra-precision guideway accurately, a splicing measurement for straightness error of guideway using high-precision flat mirror and displacement sensor was proposed in this paper, and the data splicing processing algorithm based on coordinate transformation was studied. Then comparative experiments on splicing measurement and direct measurement of straightness error were carried out on a hydrostatic guideway grinder. The maximum difference between the two measurements was about 0.3μm, which was far less than the straightness error of 5.8μm. The experiment demonstrated the correctness of the proposed splicing measurement method and data processing algorithm. To suppress the influence of straightness error on machining accuracy, a straightness error compensation algorithm based on error rotation transformation and vertical axis position correction was proposed, and the grinding experiment of a plane optics with size of 1400mm×500mm was carried out, whose flatness error after grinding was about 2.98μm. The straightness error of the grinding machine had been well suppressed.

Addressing the contemporary challenges of mitigating and adapting to climate change, conserving biodiversity, and ensuring human well-being constitutes a central focus for humanity. These challenges, though often treated in isolation, are intricately interconnected, sharing common drivers. Current and future research emphasizes the pursuit of an integrated approach that minimizes trade-offs and fosters synergies among the Sustainable Development Goals (SDGs). The nexus between ecosystem service value (ESV) and the social cost of carbon (SCC) is pivotal in informing national and governmental decisions regarding human well-being and climate change mitigation and adaptation, respectively. This study employs the Zhoushan Archipelago as a case study, exploring ESV and SCC dynamics from 2010 to 2020. An in-depth analysis of spatial development characteristics and the temporal and spatial correlation between ESV and SCC is conducted. The findings reveal an increase in Zhoushan Archipelago's land area from 2010 to 2015, stabilizing thereafter, with construction land exhibiting the most notable proportional growth. Carbon emissions were observed during the study period, with forests identified as the primary carbon sink, followed by mudflat and waterbody, while construction land contributed significantly to carbon emissions. Notably, the SCC in Zhoushan Archipelago increased by 2452%, contrasting with a modest 1.5% decrease in ESV during the study period. Global spatial correlation analysis demonstrated a significant positive correlation between ESV and SCC in Zhoushan Archipelago throughout the study period, validated by passing the P-value test. However, local spatial correlation analysis revealed a specific nature of the global spatial correlation between ESV and SCC in the island area. This nuanced understanding enhances our comprehension of the intricacies surrounding ESV and SCC dynamics, contributing valuable insights to the broader discourse on sustainable regional development, economic decarbonization, and human well-being improvement.

A marine fibrinolytic compound FGFC1 enhancing fibrinolysis was obtained involving in enzymatic kinetic parameters of reciprocal activation system with single chain urokinase type plasminogen activator and plasminogen. FGFC1, a kind of bisindole alkaloid from a metabolite of rare marine fungi Starchbotrys longispora FG216, modulated enzymatic kinetic parameters including fibrinolytic reaction rate and fibrin degradation characteristics. The enzymatic kinetics of fibrinolysis was described based on enzymatic reaction of chromogenic-substrate associated with p-nitroaniline (p-NA). While single chain urokinase-type plasminogen activator (pro-uPA) actived plasminogen, K_cat and k_cat/k_m increased significantly with increase of FGFC1 concentration. Moreover, K_cat and k_cat/k_m exhibited 26.5-fold and 22.8-fold enhanced activity at the concentration of 40 μg•mL⁻¹ of FGFC1, respectively. The results suggested that FGFC1 improved significantly the maximum catalytic efficiency and the total catalytic activity of fibrinolysis base on the reciprocal activation of pro-uPA and plasminogen. K_m increased with increasing FGFC1 concentration, which indicated that FGFC1 decreased slightly the affinity activity of pro-uPA and plasminogen versus enzyme substrate. The marine bisindole alkaloid FGFC1 enhanced fibrinolysis which was taken on enzymatic kinetic characteristics.

For the different harvest targets, the requirement for the prolificacy trait of maize was also different, so prolificacy is of great significance for modern production. Although some QTLs and genes associated with prolificacy in teosinte have been reported, the genetic mechanism of prolificacy in maize has not been fully elucidated. In this study, two RIL populations and GWAS population were used to genetic research of prolificacy trait in maize, with multi-environment. Combine linkage analysis and Genome-wide association study has identified a total of 13 QTLs and 8 significant SNPs. There were two genes related to tissue differentiation in the stable QTL qP9-2, and two significant SNPs corresponding to three genes were in QTL qP5-1 and QTL qP7-1, respectively. Four candidate genes GRMZM2G317262, GRMZM2G317584, GRMZM5G882364 and GRMZM2G141679 were finally screened out by qRT-PCR analysis. Based on the function of candidate genes, ethylene signaling pathway plays an important role in the formation of prolificacy in maize. It has deepened our understanding of the formation mechanism of prolificacy and laid a foundation for breeding new varieties with various prolificacy in maize.

At present, farmland abandonment（FA）is a severe phenomenon in China, imposing a serious restriction on agricultural production. In this context, it is of great significance to explore the logical relationship between high-standard farmland construction (HSFC) and FA to optimize land resource allocation and guarantee national food security. Based on 838 samples of farmers in the main rice production area of the Yangtze River Basin in China, this paper employs the Tobit model, the mediating effect model, and the moderated mediating effect model to analyze the impact of HSFC on FA at the micro level. The results show that (1) HSFC inhibits FA, and FA proportion decreases by 1.14% for every 1% increase in HSFC proportion, and the robustness test and endogeneity treatment also yield consistent conclusions. (2) Agricultural socialization services (ASS) play a positive mediating role in the influence path. HSFC promotes farmers' purchase of ASS, which in turn inhibits FA. (3) The agricultural labor transfer distance (ALTD) plays a positive moderating role in the relationship between HSFC and FA. The farther the distance, the more likely HSFC can promote farmers' purchase of ASS and inhibit FA. The results provide enlightenment on how to precisely implement HSFC policy, that is, to inhibit FA by improving the construction of high-standard farmland and the post-construction management as well as protection system. Moreover, this study helps guiding the development of ASS and optimizing the institutional environment for agricultural labor transfer, creating conditions for agricultural scale management.

Background: At present, the treatments for patients with advanced lung cancer focus on chemotherapy, targeted therapy, immunotherapy, or a combination of multiple treatments. Purpose: The main purpose of this study is to compare the various chemotherapy-based combination therapies and find the best one for patients with advanced lung cancer. Methods: Based on database (PubMed, EMBASE and Medline) for randomized controlled trials of advanced lung cancer with combination therapy from 2008 to 2020, we searched literatures with overall survival (OS), progression-free survival (PFS), objective response rate (ORR) and adverse as outcome indicators and established a Bayesian mesh meta-analysis for multiple treatment strategies. Then, we combined the results of four outcome indicators to find out the best chemotherapy-based combination therapy strategy for patients with advanced lung cancer, further, we tried to screen out the best drugs of which were commonly used now. Results: It contained a total of 51 studies, including five combination therapies: Chemotherapy/Chemotherapy plus placebo (CT), chemotherapy plus one targeted therapy drug (CT+T), chemotherapy plus two targeted therapy drugs (CT+T+T), chemotherapy combined with immunotherapy (CT+I) or chemotherapy combined with biotherapy (CT+B). In terms of four outcome indicators, CT+I showed the best therapeutic benefits. In the comparison of immunotherapy drugs, pembrolizumab showed the best effect. Conclusion: Our results showed that, among the multiple chemotherapy-based combination therapy strategies, chemotherapy combined with immunotherapy is the best choice for patients with advanced lung cancer, and pembrolizumab combined with chemotherapy has the best effect.

Previous control methods developed by our research team cannot satisfy the high-accuracy requirements of unmanned surface vessel (USV) path-tracking during bathymetric mapping because of the excessive overshoot and slow convergence speed. For this reason, this study developed an adaptive fuzzy integral-derivative line-of-sight (AFIDLOS) guidance law for USV path-tracking control. Integral and derivative terms were added to counteract the effect of the sideslip angle, with which the USV could be quickly guided to converge to the planned path for bathymetric mapping. To obtain a high-accuracy of the look-ahead distance, a fuzzy control method was proposed. The proposed method was verified using simulations and outdoor experiments. The results demonstrate that the AFIDLOS guidance law can reduce the overshoot by 79.85%, shorten the settling time by 55.32% in simulation experiments, and reduce the average cross-track error by 10.91%, and can ensure a 30% overlap of neighboring strips of bathymetric LiDAR outdoor mapping when compared with the traditional method.

On 17 October 1947, Niels Bohr was made a knight of the Order of the Elephant by the King of Denmark in view of his outstanding achievements and contributions to science. Bohr designed his own coat of arms that featured a pattern of Yin and Yang (Tai Chi symbol) to symbolize the wave-particle complementarity. However, Bohr’s Yin-Yang diagram (YYD) was neither drawn based on the principles of quantum mechanics, nor did it originate from the traditional Taoist YYD. Scientists still have doubts about the legitimacy of using YYD as the icon of the wave-particle complementarity, because the YYD belonging to quantum mechanics itself is unknown so far. This article reports the YYDs existing in quantum mechanics and justifies the role of YYD in the wave-particle duality by showing that any system, whether classical or quantum, has an ideal YYD as long as it satisfies Bohr’s principle of complementarity (BPC). The deviation of a deformed YYD from the ideal YYD indicates the extent to which a real system satisfies BPC. This article constructs the quantum YYD by the complex quantum trajectory of a particle tunneling via a step barrier, which displays the continuous transition between the wave behavior and the particle behavior. It appears that the YYD designed by Bohr in his coat of arms resembles the YYD generated by tunneling motion, not only in appearance but also in the governing equation.

The 2-oxoglutarate dehydrogenase complex (OGDHc) is a key enzyme in the TCA cycle and represents one of the major regulators of mitochondrial metabolism through NADH and reactive oxygen species levels. The OGDHc impacts cell metabolic and cell signaling pathways through the coupling of 2-oxoglutarate metabolism to gene transcription related to tumor cell proliferation and aging. DHTKD1 is a gene encoding 2-oxoadipate dehydrogenase (E1a), which functions in the L-lysine degradation pathway. The potentially damaging variants in DHTKD1 have been associated to the (neuro) pathogenesis of several diseases. Evidence was obtained for the formation of a hybrid complex between the OGDHc and E1a, suggesting a potential cross talk between the two metabolic pathways and raising fundamental questions about their assembly. Here we reviewed the recent findings and advances in understanding of protein-protein interactions in OGDHc and 2-oxoadipate dehydrogenase complex (OADHc), an understanding that will create a scaffold to help design approaches to mitigate the effects of diseases associated with dysfunction of the TCA cycle or lysine degradation. A combination of biochemical, biophysical and structural approaches such as chemical cross-linking MS and cryo-EM appears particularly promising to provide vital information for the assembly of 2-oxo acid dehydrogenase complexes, their function and regulation.

Existing evidence suggest that lactoferrin might be beneficial for Alzheimer’s disease. We aimed to determine the effects of lactoferrin intervention on cognitive function from APP/PS1 mice, and possible mechanisms involved in. Both young and middle-aged male APP/PS1 mice were divided into control and lactoferrin group with 16 weeks’ intervention. Lactoferrin intervention had no effects on cognitive function from both young and middle-aged mice, and no key markers involved in Aβ, tau pathology, neuro-inflammation and synaptic plasticity were altered post lactoferrin intervention. In regards to gut microbiota profiles, in the young mice, lactoferrin elevated α diversity index including ACE and Chao 1, and reduced the relative abundance of the genera Bacteroides and Alistipes and elevated Oscillibacter, in addition, Oscillibacter, Anaerotruncus, EF096579_g, EU454405_g, Mollicutes_RF39, EU474361_g, EU774448_g, and EF096976_g were specifically abundant post Lf intervention via LEfSe analysis. In the middle-aged mice, the relative abundance of the phylum Proteobacteria, as well as the genera Oscillospira, Coprococcus and Ruminococcus was significantly reduced post Lf intervention, additionally, S24_7, Bacteroidia, Bacteroidetes and Methylobacterium were specific via LEfSe analysis post lactoferrin intervention. In conclusion, dietary lactoferrin might be beneficial for gut microbiota homeostasis although might have no effects on cognition.