To further explore the law of hydraulic properties of overland flow, the hydrodynamic characteristics of overland flow under different flow rates and slopes with different roughness (0.2~0.6 mm, 0.6~1 mm, 1~1.4 mm, 1.4~2 mm, 2~3 mm particle size and smooth control group) were studied by the indoor fixed-bed water scouring test. The results show that the increase of the underlying surface roughness slows down the overland flow velocity and increases the water depth. The flow velocity and water depth are well related to the flow, slope, and roughness factors as a power function. The Reynolds number reaches the maximum value in the control group, and the flow pattern tends to be slow flow with increasing roughness. The resistance coefficient positively changes with roughness and is in a good regression relationship with the power function of flow, slope, and roughness factors. The calculated runoff shear stress is well related to the measured runoff shear stress on the smooth underlying surface, and the increase of roughness has a certain effect on the increase of measured runoff shear stress. The results are significant for a deeper understanding of soil erosion mechanisms and slope erosion control.

Graph convolutional neural network (GCN) has emerged as the intersection of graph theory and deep learning, becoming the hotspot research field of machine learning.Therefore, a comprehensive overview of the GCN is provided, and the available studies of GCN into two typical categories are summarized: spectral-based methods and spatial-based methods.These two typical types of GCN models are extensively discussed, the fundamental theoretical underpinnings of the graph convolution operations are delved into, diverse applications of GCN across various domains are showcased, the major challenges encountered by GCN are summarized, and valuable insights into the future trends of GCN advancement are offerred. Additionally, the potential utilization of GCN in butterfly recognition tasks is investigated, particularly in identifying butterfly species by leveraging images captured in natural habitats.

Three-way decision (3WD) is an important research direction in the field of granular computing, which conforms to human thinking and can effectively deal with uncertainty problems in practical decision-making processes. Three-way decision can effectively reduce the cost of decision-making, enhance the control of uncertain decision-making processes and improve the interpretability of model by introducing delay strategy. Therefore, a three-way decision based on machine learning is worth researching deeply. Firstly, the basic model of three-way decision is introduced.Secondly, the research status of three-way decision based on machine learning at domestic and foreign search results is analyzed by using CiteSpace and VOSviewer. Thirdly, from the perspectives of research questions, model methods, and application backgrounds, the integration of three-way decisions with clustering models, classification models, recommendation systems, and deep learning models are focused on.Finally, the future research directions of three-way decisions based on machine learning are given.

Individual behavior plays an important role in the spread of emerging infectious diseases, and the ability to mitigate and control a disease through behavior changes is a vital aspect of response strategies. It is essential and important to understand how individuals’ behavior changes affect the spread of emerging infectious diseases. To address this, a psychological model of individual decision-making on social networks with infectious disease model (LHBM) is developed, and its applicability to the course of the 2009 A/H1N1 influenza outbreak is demonstrated.Individual behavior models (IBMs) are formalized to incorporate the rate of change of individuals’ behavior and investigated how individuals’ behavior changes affect the dynamical development of A/H1N1. In addition, approximate Bayesian computation sequential Monte Carlo (ABC SMC) methods are used to evaluate unknown parameters. The main results indicated that the novel model can depict the real transmission process and reveal the effects of individual behavior changes on A/H1N1 outbreak more accurately. Moreover, as far as mitigating and controlling emerging infectious diseases are concerned, it is essential for media publicity to focus on how to guide individuals’ behavior changes and get individuals to response positively.

In recent years, ultrasonic imaging testing technology has made rapid development and become a research hotspot in the field of nondestructive testing. This paper focuses on the development history, research status and progress of six ultrasonic imaging detection methods: acoustic time reversal imaging, ultrasonic phased array imaging, synthetic aperture imaging, total focusing imaging, dispersive guided wave imaging and ultrasonic multi wave imaging. It shows that acoustic time reversal imaging is an adaptive focusing imaging method, which is not applicable to nonlinear and dissipative media in principle, and can only be applied after appropriate improvement. Ultrasonic phased array imaging, synthetic aperture imaging and total focusing imaging are all phase control methods based on ultrasonic array, which can realize adjustable sound beam. With the development of electronic hardware and software technology, ultrasonic array imaging will develop towards three-dimensional real-time imaging. By analyzing the focusing and imaging characteristics of dispersive guided waves and multi waves, several methods to focusing and imaging of dispersive guided waves and multi waves by controlling the excitation signal waveform of each array element of the transducer are discussed. By analyzing the latest research progress of these imaging detection methods, the development trend and existing problems of ultrasonic imaging detection are studied and summarized. It is shown that the integration of multiple imaging methods and multi-information imaging will become an important development direction of ultrasonic imaging detection, which can provide reference and research ideas for the research of ultrasonic imaging detection.

Aiming at the problems of low cutting efficiency of ultrasonic bone scalpel and the generation of thermal damage of bone tissue during surgery, a high-capacity ultrasonic bone scalpel with a secondary amplification structure was designed. Based on the equivalent length method of standing wave theory and finite element simulation, the design and optimization of the secondary amplified structure were carried out, and the vibration performance was tested and the bone cutting test was conducted. The results show that the resonance frequency of the ultrasonic bone scalpel with secondary amplification structure is 23 306 Hz, the amplitude is 140 μm, and the bone cutting surface is flat and damage-free, which meets the needs of clinical surgery.

Photocatalytic technique is an effective method to solve the problems of energy conversion and environmental remediation, and graphitic carbon nitride (g-C₃N₄) has been considered as a promising photocatalytic material. However, the high carrier recombination rate severely limits the photocatalytic activity of g-C₃N₄. Constructing core-shell structure to form heterojunction with tight connection interfaces and large contact area has proved to be an excellent strategy to improve the carrier separation efficiency. Therefore, the research on core-shell structured g-C₃N₄-based heterojunction photocatalysis becomes a hot spot. In this review, the main functions of core-shell structure and the applications of core-shell structured materials are introduced. The performance and advantages of g-C₃N₄-based core-shell heterojunction photocatalytic materials are elaborated. The preparation methods of core-shell structured g-C₃N₄-based heterojunctions and the growth mechanism of g-C₃N₄ in the synthetic process are emphatically discussed. Finally, challenges and perspectives for the future research and development of g-C₃N₄-based core-shell heterojunctions are analyzed. This review will provide guidance for the development of new and efficient g-C₃N₄-based core-shell structured photocatalytic materials.

The resilience and efficiency of the inbound tourism economy are two significant factors affecting the economic growth recovery and high-quality development of inbound tourism.The entropy method and the DEA-Tobit model were used to measure the resilience level of Chinas inbound tourism economy from 2010 to 2019, the temporal and spatial pattern and obstacle factors were analyzed to explore the impact of resilience on the economic efficiency of inbound tourism. In the end, the growth recovery mechanism of inbound tourism was constructed.The results show that: the overall resilience of Chinas inbound tourism economy is improving, and areas of high resilience gradually spread from the eastern coast to the central and western regions. However, the national average is only 0.246 7, which has a large room for improvement. The positive spatial autocorrelation of inbound tourism economic resilience weakens gradually. The radiation effect of high-high areas and the diffusion effect of low-low areas decrease, while the polarization effect of high-low areas and the hollow effect of low-high areas increase. The main obstacle factors affecting the economic resilience of inbound tourism include the number of inbound tourists, inbound tourism income, economic development level, industrial structure level, market development and openness. When the economic resilience of inbound tourism increases by 1%, its efficiency increases by 0.243 2%. The economic resilience and efficiency of inbound tourism play a role in promoting the growth recovery of inbound tourism.

Understanding the relationship between infrastructure and the tourism economy is crucial for optimizing infrastructure planning and enhancing the tourism sector’s quality and efficiency. Focusing on the Yangtze River Delta(YRD) urban agglomeration, this study establishes a comprehensive evaluation index system for infrastructure levels and tourism economic development. Using a coupling coordination model, this research analyzes the spatio-temporal evolution characteristics of their development and employs geographic detectors to investigate the driving mechanisms. Results indicate: 1)A steady upward trend in both the comprehensive evaluation index and coupling coordination degree of infrastructure and tourism economic development, with their coordination level transitioning rapidly between moderate and extreme coordination. Significant inter-provincial differences in coupling coordination are observed, with a spatial heterogeneity pattern of “Shanghai>Jiangsu>Zhejiang>Anhui”. 2)The coupling coordination degree exhibits a spatial distribution of “high in the east and low in the west, high in the center and low in the north and south”, with high and low coordination zones showing a spatial “expansion-convergence” trend. During the study period, the number of cities with lagging infrastructure increased gradually, mainly concentrated in Zhejiang and Shanghai regions. Meanwhile, cities with lagging tourism economic development converged and gathered in the northern part of the Yangtze River Delta. 3)The coupling coordination of infrastructure and tourism economic development is driven by various factors, including economic pulling force, government regulation force, urbanization driving force.The coordinated development of the two is driven by a comprehensive mechanism formed by eight driving forces,including economic operation and structural transformation mechanism, policy regulation and open cooperation mechanism, population agglomeration and consumption driving mechanism, and talent guarantee and technological innovation mechanism.

When dealing with complex issues, developing comprehensive strategies, or making critical decisions, it is necessary to think and analyze from multiple perspectives,levels, or dimensions. Three-way decision is triadic thinking, triadic method, and triadic computing. Following the principles of three-way decision, the concept of double triadic thinking and the associated 3×3 methods and structures are introduced.Double triadic thinking is based on a combination of two triadic structures, which allows us to think, analyze and solve problems from nine different perspectives or dimensions.Two particular 3×3 methods are proposed by combining trilevel hierarchical thinking and triangular thinking. One is the application of triangular methods at each of the three levels of a hierarchy, which is called a (3-level)×(3-angle) method. The other is the application of trilevel methods at each of the three vertices of a triangle, which is called a (3-angle)×(3-level) method. As a case study, 3×3 methods are applied to explainable artificial intelligence. By means of the concept of Symbols-Meaning-Value (SMV) space, we consider specific semantics of the nine elements of a 3×3 method. The SMV space based 3×3 method can analyze and interpret the data, assumptions, principles, and outcomes of an intelligent system at multiple levels. It provides a construction process and structure of explanation for intelligent systems, making an explanation easier to communicate,understand, and accept.

Sodium-ion batteries have attracted much attentions in recent years and are anticipated to become the next-generation power supply for mobile electronic device because of the rich natural sources and low cost of sodium element. However, the large volume changes of anodes during continuous charging-discharging as well as thir low conductivity hinder the applications of sodium-ion batteries. In this work, sheet-like γ-FeOOH are electrochemically deposited on the commercially available carbon cloth to fabricate CC-FeOOH composites as anode in sodium-ion battery. The FeOOH on carbon cloth present vertically intersecting nanosheet structure, which allows fast Na⁺ transport among the nanosheets. The carbon cloth not only provides the good conductivity, but also prevents the detaching of FeOOH nanosheets from the CC substrate during charging and discharging process. Due to the nanosheet morphology of γ-FeOOH and the good conductivity of carbon cloth, the CC-FeOOH-3h electrode exhibits high specific capacity, long cycle life, and excellent rate performance. It delivers a specific capacity of 684 mA·h/g and 181 mA·h/g after cycling 100 and 500 times at a current density of 0.1 A/g and 1 A/g, respectively. Moreover, a specific capacity of 135 mA·h/g can still be retained even at a high current density of 5 A/g. The fabricated CC-FeOOH electrode is expected to become a high-performance anode for future sodium ion batteries.

Based on the China health and retirement longitudinal study(CHARLS) data, the frailty index (FI) is used to assess the frailty of the elderly in China. The Logistic regression is used to analyze the influence of physical activity on weakness, supplemented by propensity score matching method for robustness test.The results show that the frailty rate of the elderly in China has increased.Among the elderly without frailty, 26.0% of the elderly changed into frailty, while 19.89% of the elderly with frailty improved into non-frailty.The proportion of women from non frailty state to frailty state is higher than that of men. The proportion of low level physical activity among the frail elderly increased.The risk of changed from non-frailty state to frailty state is 2.59 times that of the moderate to vigor level elderly, and the possibility of improving the frailty state to non-frailty state is reduced by 62%. The results demonstrate that the low level physical activity is a risk factor in the transformation of the elderly from a non-frailty state to a frailty state, while moderate to vigor level physical activity is conducive to the transformation of the frailty state to a non-frailty state.

In order to improve the ability of ultrasonic guided wave technology in accurately detecting damage location, size and shape in plate-like structures, the low-resolution imaging results of the total focusing method(TFM) and the multi-scale deep learning algorithm model are combined to study a high-precision imaging method for targeting two type of damage:round holes and cracks.The high-precision imaging algorithm based on deep learning is composed of two parts: a convolutional neural network and a deconvolutional neural network. The multi-scale analysis, nonlinear enhancement and multi-level fusion functions of the neural network are used to improve the resolution. On the basis of the imaging results of the all-focus imaging algorithm, a network training database is constructed, and the trained network is used to detect and verify the two cases of circular hole-crack double damage and circular hole-circular hole double damage in the plate. The results show that the imaging method has high imaging accuracy, and can further obtain the fine features of the damage based on the precise location of the damage.

The purpose of this study is to optimize PCR system to achieve efficient amplification for AT-rich DNA fragments.Four additives and four KOD-based high-fidelity DNA polymerases were studied for their amplification effects of four AT-rich fungal mitochondrial DNA fragments (AT contents: 72%~80%) with variable lengths of 1.0~5.4 kb. When applied alone, only magnesium ion facilitated the amplification, and the other three additives (bovine serum albumin(BSA), dimethyl sulfoxide(DMSO) and formamide) failed to produce the expected amplicon, which were contrary to their well-known facilitative effects on GC-rich DNA amplification. The presence of BSA or DMSO showed no impact on the amplification effect of magnesium ion, whereas the presence of formamide expressed inhibition effect on the facilitation function of magnesium ion. The four KOD-based polymerases (KOD-Plus-, KOD-Plus-Neo, KOD FX and KOD FX Neo) generated expected amplicons when magnesium ion was present in the PCR system, but KOD-Plus- and KOD-Plus-Neo failed to generate expected amplicon when magnesium ion was absent. This study verified that magnesium ion was critical for the successful amplification of AT-rich DNA fragments, and the optimal concentration was 1.75 mmol/L.The results provide a basis for the amplification of AT-rich DNA fragments in other eukaryotes.

Based on the reversible Diels-Alder reaction between furan and maleimide, a water-borne polyacrylate coating with self-healing properties driven by heat was successfully prepared, and the self-healing conditions were explored and optimized. A novel acrylate monomer with furan functional group (FEEMA) was synthesized. Stable acrylate emulsion was obtained by soap-free emulsion copolymerization. The results were verified by infrared spectroscopy, gel permeation chromatography (GPC) and particle size analysis (DLS). Then, polyether chain (D-400-BMI) was designed and synthesized, which could be dispersed in water. The crosslinked films were prepared by Diels-Alder reaction with water-based acrylate emulsion. The process of Diels-Alder and retro-DA reactions under different conditions were followed by infrared spectroscopy and differential scanning calorimetry. The results showed that the crosslinked membranes prepared by D-400-BMI had better repair performance when the content of FEEMA was 30% and the mass ratio of MMA and BA was 1/5. After the crosslinked membrane was scratched, the surface of the scratch and friction traces showed a good repair effect by heating for 140 ℃ and then slowly cooling to room temperature. The crosslinked membrane also has good heat resistance and mechanical properties, so it has potential application prospect in the self repair of scratch and microcrack of surface coatings.

Small RNAs are a class of non-coding RNA with a length of 20~24 nt that regulate target genes through RNA-RNA interaction. Small RNAs have become a hot spot in the research and development of new drugs due to their wide range of targets and the ability to regulate the expression of pathogenic genes in multiple ways. The research and development of small RNA drugs depend on the acquisition of RNA raw materials. Based on this, three small RNA synthesis methods including chemical synthesis, in vitro transcription and in vivo biosynthesis were summarized. Four small RNA biosynthesis technologies including siRNA expressed by si-RNA-binding protein, rRNA scaffold, tRNA scaffold and modified tRNA scaffold were mainly introduced. It proposed that the use of hybrid tRNA scaffold can produce recombinant small RNA on a large scale by biological fermentation, which has the advantages of high activity, low cost, natural modification and high safety,and provides an alternative source for clinical RNA drug raw materials. In addition, 14 kinds of small RNA drugs approved by the American Food and Drug Administration (FDA) as well as the latest research progress of small RNA drugs in research were summarized. The challenges and future development directions of small RNA drugs were analyzed. This review is helpful to deepen the understanding of small RNA synthesis technology and small RNA drug research progress.

The carbon based hole transport material free perovskite solar cells (C-PSCs) that employ inorganic CsPbI₂Br perovskite, have gained extensive attention. Two preparation methods for efficient and stable planar-heterojunction CsPbI₂Br C-PSCs under fully ambient environment are presented. Firstly, by optimizing the type and dosage of antisolvent materials, CsPbI₂Br C-PSCs with power conversion efficiency of 9.87% were successfully prepared by one-step solution method under the conditions of 800 μL antisolvent and 1.2 mol/L perovskite precursor solution concentration.In order to get rid of the dependence of one-step solution method on toxic antisolvent, the low-temperature pre-annealing process is introduced. By optimizing the pre-annealing time and temperature, as well as the concentration of perovskite precursor solution, the optimal efficiency of 10.52% is achieved with 80 ℃ pre-annealing temperature and 1.6 mol/L perovskite precursor solution concentration in fully ambient without anti-solvents. Meanwhile, the annealing temperature of CsPbI₂Br perovskite is reduced to 240 ℃, and the unencapsulated device shows excellent stability in the ambient.

Based on the panel data of 26 cities in the Yangtze River Delta urban agglomeration from 2010 to 2020, the coupling coordination level, spatial agglomeration characteristics and driving factors of urban resilience and land use benefit are analyzed by using the models of coupling coordination, spatial autocorrelation and geographical detector. The results show that the urban resilience and land use benefit of the Yangtze River Delta urban agglomeration continue to rise, but there are obvious differences in spatial distribution. The coupling coordination of the two systems is also increasing year by year. The coordination types are mainly mild imbalance, near imbalance, primary coordination and intermediate coordination. In the east-west direction, it has always presented the spatial distribution characteristics of “high in the east and low in the west”. In the north-south direction, it has evolved from the spatial differentiation pattern of “prominent in the middle and collapsed in the north and south” to “high in the middle, followed by the south and lowest in the north”. Moreover, the spatial agglomeration effect of coupling coordination degree is not strong, and the spatial heterogeneity of local areas does not change much with time. The level of economic development, government regulation ability and labor factor are the main driving factors affecting the coordinated development of the two systems. Among them, the interaction between government regulation ability and other factors is the strongest. The research on the correlation between urban resilience and land use benefit is helpful to provide a reference for the Yangtze River Delta urban agglomeration to do a good job in risk prevention and control, intensive land use, and to provide relevant experience for the sustainable development of other regions in China.

By sequencing and analyzing the transcriptome of Picea wilsonii, the potential drought-related gene PwRhomboid was screened out, and its functional characteristics of drought resistance in Picea wilsonii were analyzed. Through bioinformatics analysis, the amino acid sequence and homologous gene of PwRhomboid were compared. The responses of PwRhomboid to different abiotic stresses and hormones were analyzed by RT-qPCR. Instantaneous transformation of tobacco leaves was conducted to detect the subcellular localization of PwRhomboid protein. PwRhomboid transgenic strains of Arabidopsis and potato were obtained and their drought phenotypes were verified. The results showed that the amino acid sequence of PwRhomboid has significant difference with those of its homologous species at N-terminus, but shows high similarity and good conservation at C-terminus. PwRhomboid protein is mainly localized on cell membrane. The expression level of PwRhomboid was significantly induced by drought, low temperature and abscisic acid, with the highest expression level observed in mature leaves.PwRhomboid can improve the drought resistance in transgenic Arabidopsis and potato. After drought treatment, compared with the control group Arabidopsis WT and pCM1205 strains, PwRhomboid overexpression strains PwRhomboid-L1 and PwRhomboid-L2 had higher survival rates, chlorophyll fluorescence F_v/F_m and Φ_PSⅡ. After polyethyleneglycol simulated drought treatment, the overexpression strains PwRhomboid-L3 and PwRhomboid-L4 resulted in higher potato seedling heights compared to the wild-type. The results showed that the expression level of PwRhomboid is affected by drought and hormones, and overexpression of PwRhomboid could improve the drought resistance of Arabidopsis and potato.

One new species of Hyboella Hancock from Guangxi was described. Type specimens were deposited in the Institute of Zoology, Shaanxi Normal University..

Big data helps to reveal the migration patterns and spatial characteristics of tourists in the destination area in detail. Northwest China is the main destination region of China's “Silk Road”. Based on extensive travel route data obtained from online booking platforms, this paper employs social network analysis to study the network and spatial characteristics of tourist flows and connections between destinations in Northwest China. It aims to uncover regional community structures and analyze the factors influencing touring route. The results indicate that the tourist flow network in Northwest China is loose and unbalanced, with distinct core and peripheral areas. The peripheral areas are mainly located in western Xinjiang, most of Shaanxi, and southern Gansu, while the core areas are mainly distributed in Hexi Corridor. In Northwest China, a small number of nodes possess absolute network power, demonstrating clear hierarchical levels. Nodes at all levels are highly dependent on core nodes such as Lanzhou, Xi'an, and Urumqi. The Northwest tourism network is composed of 3 secondary communities and 8 tertiary communities around the core nodes. These communities demonstrate strong regional proximity and administrative regionalism within their internal structures. Tourism flow in Northwest China is influenced by various factors, including the tourism resources endowment, tourism reception capacity, regional economic levels, transportation infrastructure, and more. Finally, taking into consideration the characteristics of the Northwest China tourist flow network and the factors influencing it, this paper offers relevant recommendations for tourism enhancement and regional development.

Sediment connectivity is an important parameter to characterize the physical cascade relationship of sediment between geomorphic units.It is of great significance to explore the response of sediment connectivity to land use change, which is helpful to reveal the dynamic of runoff and sediment load and evaluate the soil and water conservation capacity of different land use types. In this study, based on the data about land use, runoff and sediment load from 1990 to 2019, the index of connectivity (IC) was used to quantify the spatial and temporal variation characteristics of sediment connectivity and its relationship with runoff and sediment load under the background of land use change in Yanhe River Basin. The results showed that: 1) In recent 30 years, the land use change has been dominated by the Grain for Green, coupled with a small amount of the expansion of construction land in this basin. 2) From 1990 to 2019, the IC showed a downward trend as a whole, with a decrease of 17.85% in 2019. Grain for Green and the expansion of construction land could effectively reduce sediment connectivity, and the former also had long-term ecological benefits. 3) IC had obvious spatial distribution characteristics, which showed an increasing trend from south to north and east to west. Among them, the middle of the basin was the main region of Grain for Green and construction land expansion, and thus significantly reduced sediment connectivity. The northwest of the basin had higher sediment connectivity, and its decreasing extent became smaller with land use change.The sediment connectivity in the south of the basin was lower. 4) There was a significant positive correlation between IC and sediment load (P<0.05), and thus it was more suitable for the prediction of sediment load. The results can provide a scientific basis for the research of sediment transport process in the basin, and further provide a reference for the layout and optimization of land use.

The self-assembly effect of non-conjugated polymers, such as polyvinylpyrrolidone (PVP), can migrate to the surface of the active layer to form the cathode interface layer of non-fullerene positive organic solar cells, aiming to improve device efficiency and simplify the manufacturing process. The quasi-planar heterojunction structure of the active layer prepared by sequential deposition can make better use of the self-assembly effect of PVP additive compared to the traditional bulk heterojunction structures. The direction and process of the self-assembly PVP migration are characterized by the contact angle and atomic force microscopy. In addition, the intrinsic mechanism of PVP affecting the device efficiency is analyzed from the light intensity dependence of the devices. In the OSCs based on quasi-planar heterostructure with PM6:Y6 as the active layer, the self-assembled cathode interface layer formed by introducing PVP, can achieve the highest power conversion efficiency of 11.32%, which is 25.5% higher than that of the reference device. Finally, for high-performance organic solar cells using low boiling point solvents, the combination of quasi-planar heterojunction active layer structure and non-conjugated polymer of self-assembled electron transport layer will be a more effective and convenient fabrication technology.

Pancreatic cancer is a highly aggressive malignancy with a low survival rate. The high degree of interstitial fibrosis in the tumor microenvironment of pancreatic cancer plays an important role in the occurrence and development of pancreatic cancer, and seriously affects the therapeutic effects of pancreatic tumors. In recent years, reversing the tumor microenvironment of pancreatic cancer fibrosis has become a new strategy in the treatment of pancreatic cancer. On these basis, this article summarizes the latest research progress in inhibiting fibrosis treatment of pancreatic cancer from three perspectives, including the targeted mesenchymal cells treatment (pancreatic stellate cells and related fibroblasts), the targeted stromal elements treatment (collagen and hyaluronic acid) and the targeted fibrosis mechanism treatment (epithelial-mesenchymal transformation, hedgehog signaling pathways and wound healing). The construction of cell and animal fibrosis models in current anti-fibrosis research is also introduced，so as to provide references for the tumor microenvironment treatment of pancreatic cancer.

Understanding the microbe-disease relationship can not only reveal the pathogenesis of diseases, but also promote the diagnosis and prognosis of diseases.Based on the structured deep network embedding algorithm, a new method (NEMDA) is proposed to identify potential microbial-disease associations. First, by integrating the human microbe-disease association database (HMDAD) and Disbiome databases, the number of microbes and diseases as well as the known microbial-disease associations have been expanded. Next, the structural deep network is embedded to extract features on the microbe-disease bipartite network, and microbe functional similarity, microbe interaction profile similarity, disease semantic similarity and disease symptom similarity are introduced as the biological characteristics of microbes and diseases, respectively. Then, these three characteristics are combined to form the characteristics of the microorganism-disease pair, and the deep neural network model is used to make predictions. Finally, the performance of NEMDA is evaluated through five-fold cross validation and case analysis. Under five-fold cross validation, NEMDA performs well, and its prediction performance is higher than the six comparison methods of KATZMDA, NCPHMDA, LRLHMDA, PBHMDA, NTSHMDA and BRWMDA. The case studies of asthma, inflammatory bowel disease and colorectal cancer further show that NEMDA has good predictive performance. Therefore, NEMDA is an effective tool for predicting microbial-disease associations.

The relationship between the number of features and classification accuracy in the feature selection can usually be considered as a multimodal multi-objective optimization problem (MMOP). However, most existing multimodal multi-objective evolutionary algorithms (MMOEAs) have weak search capability in solving high-dimensional MMOPs. To solve this problem, a two-stage feature selection method based on causal model and MMOEA is proposed. In the proposed algorithm, the causal model is firstly used to select features to reduce the problem dimensionality, and then a competitive MMOEA is used to find a subset of features with multimodal characteristics. To demonstrate the performance of the proposed algorithm, it is used to solve the feature selection problem of intraoperative hypothermia prediction model. Experimental results show that the proposed two-stage feature selection method not only combines the advantages of two different methods, but also provides more decision support for the intraoperative hypothermia prediction.

Surface-enhanced Raman spectroscopy (SERS) can be used as an assay for single-cell oxidative damage. A Raman spectroscopic characterization method was developed to study cellular damage using copper ion-induced oxidative damage in cells as a model and surface-enhanced Raman spectroscopy as a detection method. Gold nanoflowers with peptide modifications were prepared as Raman-enhanced substrate to enable them to enter cells and achieve signal enhancement. The extent of cell damage caused by the substrate was tested using CCK-8 cytotoxicity assay, and concentration of the substrate was determined to be 160 μg/mL. The cell oxidative damage caused by Cu²⁺ was also verified through CCK-8 cytotoxicity assay, and significant damage and death of cells were observed under the inducement of Cu²⁺. Finally, a model of Cu²⁺-induced cell oxidative damage was established and Raman spectra presented by cells under different conditions were obtained. It was found that cells treated with oxidation were damaged to different degrees, and intracellular glycans, nucleic acids, lipids and proteins were altered to some extent, causing damage to the organism.

The effect of transition-metal-doping on the electrocatalytic performance of MoS₂ towards hydrogen evolution reaction (HER) is studied by using of density functional theory calculation. The plane, Mo-edge S-edge structures of MoS₂ are taken into consideration during calculation. The study doping elements are 3d transition metals. For intrinsic MoS₂, the S-edge has the highest HER performance, the Mo-edge has the second highest performance, and the plane has the lowest performance. Both the doping and surface adsorption of transition metals enhance the HER performance of the MoS₂ planar site. The effect of the transition-metal doping on the HER performance of Mo-edge and S-edges is dependent on the metal types. Among all of doped MoS₂, Co-doped MoS₂ has the best HER performance at the planar site, S-edge, and Mo-edge, with free energy change of 0.01 eV, -0.03 eV, and 0.07 eV for H adsorption, respectively.

The pylon is one of the most important components in the entire power transmission system.It is necessary to timely inspect the tower to ensure the stability of the base for the later use. There are problems of the transmission tower images collected by UAV have complex backgrounds, the background is similar to the base of target tower, as well as small objects and incomplete tower base, this paper proposes an improved YOLOv7 algorithm for detecting the base of tower. Firstly, using the pylon images of different landforms to construct high-quality data sets. Then CBAM attention mechanism is added to the Backbone layer of the original YOLOv7 to improve the feature extraction ability of the pylon. Finally, introducing WIoU v3 instead of the original coordinate loss function CIoU to improve the veracity and stability of target detection tasks. On this dataset, a comparative experiment was conducted using the improved YOLOv7 algorithm and the current mainstream object detection algorithm. The mAP value of our algorithm is as high as 99.93% in the experimental results, it is 2.19% higher than the original YOLOv7, the FPS value is 37.125, which meets the real-time detection requirements, and the overall performance of the algorithm is good. It’s feasible and effective in detection tasks of towers’ base for our algorithm, which has been proven by the experiments in this paper, and laying the foundation for future research on the soil and water around the base of tower.

The concept of triadic thinking is given. From the perspective of triadic thinking, the basic ideas ofthree-way decision is introduced, that is, to explain and deal with an whole system through three independent and relevant parts, reasonably divide the whole system into three parts, and take effective strategies to deal with each part, so as to obtain the required results. Then, several related models and specific applications of three-way decision are summarized. Finally, the research status of three-way decision is analyzed and the future research direction is prospected from two aspects of model and application.

Under the influence of complex global climate change and industrial globalization, plants with fixed growth characteristics face the threats of diverse abiotic stresses. The successful germination of seeds under various abiotic stresses is of great important for plant reproduction, geographical distribution, crop yield and quality. With the development of plant molecular biology, significant progress has been achieved in the regulatory mechanisms of seed germination under abiotic stresses. In this review, the molecular mechanisms of regulating seed germination under salt stress, extreme temperature stress (high and low temperatures), drought stress, shade stress, osmotic stress and waterlogging stress were summarized in terms of phytohormone, reactive oxygen species (ROS) and energy metabolism pathways. The crosstalk network among these cascades and the potential research hotspots in this field were discussed，so as to elucidate the main modulators/gene functions in response to abiotic stresses during seed germination, and provide references for breeding design in agricultural production and environmental modification.

The visible optical polarization simulation data set in the scattering medium is relatively scarce, which limits the polarization imaging algorithm in the development and verification for the scattering medium. By considering the effects of scattering degeneration and light in the scattered environment on polarizing imaging, a polarizing image simulation method in the scattered medium is proposed. First, the physical render is used to simulate the propagation path of light and obtain the non-degraded polarization images through the polarized filter. After that, the real scattering environment data set (underwater and foggy environment) was used to determine the scattering degradation parameters in different environments. These parameters are then integrated with the scattering degradation model to generate simulated scattering polarization images. The simulation results closely match the image in real scattering environments. The DoLP(degree of linear polarization) images and AoP (angle of polarization) images reflect the phenomenon of depolarization in real scattering environments. Simulating polarization images can be used to analyze the polarization properties of the object and background in the scattered medium, and can provide a solid foundation for improving various polarized scattering algorithms.

Moving coil transducers have been widely applied to such fields as ship noise simulation and sonar system calibration because of their small size, ultra-low frequency, and ultra-wideband emission. This paper summarized the structure and performance of moving coil transducer. The principle of ultra-low frequency and ultra-wideband emission was described in this paper. The research work of domestic and foreign scholars on the optimization of the driving magnetic circuit, the expansion of the working bandwidth, and the improvement of the working depth were also reviewed. Several hot issues in the research of moving coil transducer, such as nonlinear distortion and the influence of acoustic cavity on the performance, were discussed. It was proposed that optimizing the driving magnetic circuit to reduce the nonlinearity of the output force and improving the impedance matching between the moving coil transducer and the power amplifier can reduce the waveform distortion and harmonic distortion. The fluctuation of the source level can be suppressed by filling the gas with small characteristic impedance into the acoustic cavity. The future trend of moving coil transducer was also prospected. The research of moving coil transducer should focus on improving the ultra-low frequency acoustic radiation performance in large depth environment, increasing the continuous working time and reliability, and reducing the nonlinear distortion, so as to meet the application requirements.

Pinus kwangtungensis is a relatively primitive and ancient single vascular plant, which belongs to national second-class key protected wild plant. Predicting the distribution changes of Pinus kwangtungensis under different climatic backgrounds is of great significance for the protection of its wild resources. Based on the existing distribution points of Pinus kwangtungensis and the climate data, this study used the maximum entropy model (MaxEnt) and ArcGIS software to predict the suitable habitats and future changes of Pinus kwangtungensis under different climate scenarios, and revealed the main climatic factors affecting the distribution of Pinus kwangtungensis through the contribution value of climatic variables, the importance value of replacement and the knife cut method. The results show that the AUC values of the model simulation training set and the test set are all higher than 0.995, and the simulation results are reliable. The precipitation in the warmest quarter is the key climatic factor affecting the distribution of Pinus kwangtungensis. At present, the suitable habitats of Pinus kwangtungensis are mainly located in northern Guangdong, Guangxi and southern Guizhou, accounting for 17% land area of China. Under different climatic backgrounds in the future, the area and distribution of the suitable habitats of Pinus kwangtungensis change significantly.From 2041 to 2060, the total area of suitable habitats decreases first and then increases with the increase of greenhouse gas concentrations.From 2061 to 2080, the total area of suitable habitats gradually decreases with the increase of greenhouse gas concentrations. The above results suggest that the stable suitable habitats of Pinus kwangtungensis in Guangxi (except the southern region), northwestern Guangdong and the southern edge of Guizhou should be taken as the key protection areas to cope with future climate change, and priority protection should be given to the southern edge of Guangxi and southern Guangdong. This study provides a scientific basis for coping with different climate changes and ex-situ protection of Pinus kwangtungensis in the future.

In this paper, two three-way decision models based on interval-valued hesitant fuzzy multi-granularity rough set are proposed for the interval-valued hesitant fuzzy information system. First, the optimistic and pessimistic interval-valued hesitant fuzzy multi-granularity rough set models are determined by means of multi-granularity rough set theory. Then, the concept of interval valued hesitant fuzzy continuous cross-entropy is introduced to calculate the conditional probabilities under different cases through the technique for order preference by similarity to an ideal solution (TOPSIS). Based on this, interval-valued hesitant fuzzy decision-theoretic rough sets and relevant three-way decision rules are proposed. Finally, it is illustrated that these models adopt different attitudes and decision-making schemes for target evaluation through an example, and the effectiveness of the algorithm is verified.

Based on the simulated rainfall test, the mechanism of slope effect of soil erosion was discussed. The influence of slope in typical areas of the Loess Plateau on runoff depth and erosion modulus was analyzed by multiple linear regression with control covariates. The results showed that: (1) The slope effect of soil erosion was closely related to soil texture and slope erosion mode. On the sandy loam slope, soil infiltration and runoff were affected by the slope. The minimum infiltration and maximum runoff rate appeared on the 15° slope. For sandy clay loam, the 25° and 15° slopes had higher soil infiltration and lower runoff, while the 20° and 10° slopes had lower soil infiltration and higher runoff. On the clay loam slope, the infiltration on the 20° and 25° slopes was larger and the runoff was smaller, while the infiltration on the 10° and 15° slopes was lower and the runoff was higher. For loam clay slope, soil infiltration and runoff rate were approximately the same under different slopes, and soil infiltration was almost not affected by the slope. (2)There was a good linear relationship between the slope and erosion dynamic parameters. On sandy loam slope, the critical runoff shear force and power increased with the increase of slope. On sandy clay loam slope, the maximum critical runoff shear force and power appeard at 20° slope. The critical runoff shear force and power increased with the increase of slope on the clay slope.Moreover, the critical runoff shear force and power decreased with the increase of slope from 15° to 25° on the loamy clay slope. This paper provides theoretical support for soil erosion prevention and control and regional land use management on the Loess Plateau.

The floral organs of Ranunculales show complex and diverse characteristics, which are ideal plant group to study the related problems of morphology and genetic evolution of floral organs. This paper reviewed the floral organ morphological diversity of Ranunculales. Combining the genome and transcriptome of Ranunculales as well as the flower development researches of representative species including Thalictrum thalictroides, Aquilegia coulela, Nigella damascena and Eschscholzia californica, the current research results on the function of MADS-box gene family A, B, C, D, E genes and their downstream target genes were elaborated.Besides, the breakthrough points and possible challenges of flower development research in Ranunculales in the future were forecasted. The aim is to enrich the understanding on the molecular pathways of floral organ development in basal eudicots.

The structure from motion (SfM) photogrammetry was used to measure seven typical gullies in the Liudaogou catchment located in the Loess Plateau, and the accuracy in quantifying gully and gully erosion was evaluated via comparing the results to those acquired with the laser scanning (LS) and real-time kinematic (RTK) GPS. The DEM measured with SfM photogrammetry for each gully were similar with those obtained using LS and RTK GPS, and the average elevation differences ranged from -0.162 to 0.436 m when subtracting the former from the latter. SfM photogrammetry exhibited high precisions in quantifying the length, surface width, perimeter and area of each gully, and the corresponding relative errors barely exceeded ±7%. In contrast, the measurement errors of SfM photogrammetry were relatively greater for gully depth and volume, which ranged from -37.4% to 18.0%. Regarding gully erosion monitoring, both the spatial distribution of soil erosion and gully morphological parameter changes obtained by SfM photogrammetry were similar with those by RTK GPS. These results demonstrate that SfM photogrammetry can serve as a high-precision method for gully and gully erosion monitoring in the Loess Plateau.

Existing studies on the three-class classification of molecular subtypes of low-grade glioma (LGG) rely on LGG medical imaging data. The scarcity and difficulty of obtaining data samples make it challenging for models to learn the differences between LGG molecular subtypes, reducing the model’s classification performance.A three-class classification method for LGG molecular subtypes called MODDA is proposed, which utilizes a gene attention network to extract important features from LGG multi-omics data and employs an embedding network to process clinical data to obtain clinical data features. Then fuses clinical data features with important omics data features and uses a dense deep neural network for the classification of LGG molecular subtypes. Experimental results show that MODDA’s classification performance surpasses existing LGG molecular subtype classification methods and also exhibits good generalization performance on external validation datasets. Moreover, an enrichment analysis of important genes identified during the chi-square testing process for gene ontology(GO) terms and biological pathways is conducted, aiding in the personalized treatment of LGG.

Protein-ligand binding affinity prediction is a challenging task in drug repositioning regression. Deep learning methods can effectively predict the binding affinity of protein-ligand interactions,reducing the time and cost of drug discovery. This study proposes a deep convolutional neural network model (DLLSA) based on long short-term memory module (LSTM) and attention mechanism module.The model is constructed using a convolutional network parallel pattern embedded with LSTM and spatial attention module. The LSTM module focuses on the long sequence information of protein ligand contact features, while the spatial attention module aggregates local information of contact features. PDBbind (v. 2020) dataset was used for training, and CASF-2013 and CASF-2016 datasets were used for validating. Pearson correlation coefficients of the model were improved by 0.6% and 3% compared to the PLEC model, and the experimental results were significantly better than the current correlation methods.