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.

At present, pre-trained models such as BERT have achieved good results in many NLP tasks, but the pre-trained models are difficult to deploy in small configuration environments because of their large parameter scale, large computation and high requirements on hardware resources. Model compression is the key to solve this problem, and knowledge distillation is currently a better model compression method.A joint model of sentence intent recognition and slot filling based on multi-task distillation is proposed.The model applies ALBERT to task-based dialogue system, and uses the knowledge distillation strategy to migrate the ALBERT model knowledge to the BiLSTM model. Experimental results show that the sentence accuracy rate of the ALBERT based joint model in the SMP 2019 evaluation data set is 77.74%, the sentence accuracy rate of the BiLSTM model trained separately is 58.33%, and the sentence accuracy rate of the distillation model is 67.22%, which is 8.89% higher than the BiLSTM model while offering an inference speed approximately 18.9 times faster than ALBERT.

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.

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.

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.

DPC (clustering by fast search and find of density peaks) algorithm is a density based clustering algorithm. It is one of the milestone clustering algorithms. It can find any arbitrary shapes of clusters embedded within any dimensional spaces. However, its local density definition of a point is not appropriate for simultaneously detecting the cluster centers of dense and sparse clusters, nor detecting the sparse and dense clusters subsequently. In addition, its one-step assignment strategy leads to a fatal problem, that is, once a point is assigned to an incorrect cluster, there are more subsequent points being assigned erroneously, resulting in the domino effect.To address the aforementioned problems, this paper redefines the local density of a point based on the local standard deviation, and proposes a two-step assignment strategy, resulting in the ESDTS-DPC algorithm. The ESDTS-DPC algorithm is compared with the original DPC and its variations including KNN-DPC, FKNN-DPC, DPC-CE and the classic density based clustering algorithm, such as DBSCAN. The extensive experiment results demonstrate superiority of the proposed ESDTS-DPC in detecting the clustering within a dataset.

During consumer financial services, there is a headache with overdue repayment. When negotiating with customers, a small number of them try to use tampered certificate images to achieve illegal benefits. These tampering focuses on content with strong contextual semantic connections such as personal information, seals, and issuing units. Based on the traditional spatial domain RGB and frequency domain DCT as discriminative features, the position of text blocks, seal blocks, and deconvolution network to realize an end-to-end fully convolutional neural network that includes semantic relations are introduced. Compared with the traditional models, it has a 3.97% higher mIoU in “Tianchi’s 2022 Real Scene Tampering Image Detection Challenge” dataset. In our service scenario, the accuracy of tampering detection has been improved by 3.7%.

In practical domains such as finance and healthcare, decision-making problems necessitate through the consideration of risks, where precise prediction and accurate risk classification hold crucial significance. Nevertheless, traditional group decision-making studies prioritize the consistency and consensus of expert evaluations while allocating lesser attention to acquiring objective evaluations and the decision quality. Consequently, a data-driven approach is introduced to assist experts in discovering evaluation through data and clustering results, optimizing group opinions within the three-way decision framework so as to improve and calculate the discriminative point of logistic regression for the results of risk rating classification. The risk rating is determined based on four publicly available datasets of credit risk and disease diagnosis from UCI and Kaggle. Empirical results from data experiments indicate that our proposed three-way classification method focuses more on risk avoidance compared to classical machine learning methods,and achieves stable and superior performance across all datasets. This implies that utilizing objective information from data to assist expert evaluations in risk assessment can help to solve decision problems within different domains.

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.

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.

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 rational and effective allocation of medical resources constitutes a crucial aspect of contemporary urban public space governance. Employing the framework of adaptability theory, an analysis framework to examine the spatial adaptability between urban medical resources and residential populations is constructed. Using Xi’an main urban area as a case study and leveraging point of interest (POI) data on medical resources, the research employs kernel density analysis, Ripley’s K function, location correlation, and Gaussian two-step floating catchment area method to explore the spatial agglomeration modes, scale thresholds, and spatial adaptability relationships with population scale of urban medical resources in Xi’an. Results reveal that urban medical resources in Xi’an exhibit a typical “core-periphery” structure and demonstrate a dependence on the main axis of urban development, with significant agglomeration effects observed in high-level development zones and satellite cities with CBD characteristics. Moreover, the characteristic scales corresponding to peak intensity of various medical resources are consistent, typically representing about 1/4 of the corresponding circle diameter when abstracted into circular areas of equal size. Additionally, the bidirectional location correlation model between urban medical resources and residential communities demonstrates an “asymmetric” structure, with significant leading effects of large-scale public medical resources in population allocation. Overall, the adaptability relationship between urban medical resources and population scale in Xi’an follows a circle decay structure of “strong center, weak periphery”, with different types of medical resources exhibiting differentiated characteristics in various urban circles. This reflects the compensation substitution effect and its spatial manifestation among hierarchical medical resource diagnosis and treatment systems under the dual allocation of urban macro planning and market resources.

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.

Based on the location information of 650 intangible cultural heritage items and 735 traditional villages in Wuling Mountain area, the spatial distribution pattern and structural characteristics of the intangible cultural heritage items and traditional villages were discussed by using kernel density analysis and gravity center model, and the spatial location relationship was revealed. The heterogeneous factors affecting the distribution of intangible cultural heritage and traditional villages were analyzed with the help of ArcGIS 10.5, location entropy and geographic detector. The findings are as follows: 1) The spatial distribution of intangible cultural heritage and traditional villages in Wuling Mountain area has typical non-equilibrium characteristics. The spatial distribution pattern of intangible cultural heritage is significant in “large agglomeration but small dispersion”, and the spatial distribution law of traditional villages is prominent in “ring core group-zonal area”. From the perspective of spatial distance, the dislocation relationship between the two is obvious. 2) Under the influence of natural environment and cultural environment, the spatial distribution preferences of intangible cultural heritage and traditional villages are different, and the driving factors affecting the spatial distribution of intangible cultural heritage and traditional villages are different. The responses of the two to different factors are not only factor comprehensiveness, but also scale difference and spatial heterogeneity.3)The spatial distribution of intangible cultural heritage in the region is more significantly affected by cultural environment factors, and the spatial distribution of traditional villages in different regions is significantly different from each other. Different responses to heterogeneity factors are the cause of the dislocation relationship between intangible cultural heritage and traditional villages in spatial distribution.

Multispectral ghost imaging (MGI) based on code-multiplexing is a novel imaging technology that significantly enhances spectral imaging performance through cleverly designed encoding and decoding strategies, characterized by simplification and intelligence. This paper starts from the fundamental principles and key technologies of the MGI and thoroughly explores various coding multiplexing strategies and their reconstruction algorithms, primarily including coding multiplexing strategies based on random patterns, Hadamard patterns, and Fourier patterns, as well as corresponding reconstruction algorithms such as compressed sensing, Fourier inverse transform, and deep learning. These approaches exhibit unique characteristics in extracting the spatial structure and spectral properties of the target scene, showing different imaging advantages and applicable scenarios. However, the technique still faces challenges such as low projection efficiency and high computational complexity. In the future, it is expected to further improve the imaging performance of the MGI system through more efficient image reconstruction algorithms, intelligent technologies and advanced optics to meet the high-quality imaging needs of complex scenes.

Scenic byways serve as crucial conduits for fostering high-quality rural revitalization along their routes. Taking the viewpoint of scenic byways, this study analyzes the temporal and spatial evolution of transportation dominance and rural revitalization in regions along scenic byways in 2015, 2019 and 2021. Bivariate spatial autocorrelation methods are employed to elucidate their spatial adaptability. Findings reveal that transportation dominance along scenic byways is continually on the rise, with geographical location and tourism resource endowments being primary factors driving spatial disparities in transportation dominance. Additionally, the comprehensive value of rural revitalization in each region exhibits an upward trend, showcasing an inverted “U-shaped” spatial pattern with higher values in the east and lower values in the west, as well as higher values in the north and lower values in the south, and with developmental disparities observed between regions and dimensions. Furthermore, global analysis indicates a strong spatial alignment between transportation dominance and rural revitalization. Local spatial analysis categorizes their spatial alignment into four types: high adaptability, transportation dominance lagging, rural revitalization lagging, and double lagging.

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.

The national tourism leisure street block is a brand of urban street blocks recognized by the Ministry of Culture and Tourism and the National Development and Reform Commission, to meet the people’s pursuit of quality life, stimulate the vitality of cultural and tourism deep integration, and empower urban development. Based on the theory of urban spatial quality and the systematic summary of the national tourism leisure street block construction practice, this study draws the following conclusions. The essence of the tourism leisure street block is the practice of upgrading leisure needs and optimizing functional experience in block construction, which is the leisure upgrading of block space. The national street blocks are characterized by the focus on history and local traditional culture, the expansion of urban center, and the tourism experience and retail layout with local cultural themes. There are three major paths for the future development of tourism leisure blocks, namely, cultural inheritance of placeness, space placement of renewability, and protective operation of systematicness. The cultural inheritance of placeness should pay attention to location, locale, and sense of place. The space placement of renewability should focus on the people oriented, function, and vitality. The protective operation of systematicness should attach importance to integration, optimization and control of essential factors.

Developing flexible land use optimization allocation plans based on mathematical models and scenario simulations is crucial for promoting the coordinated development of ecological, agricultural, and economic lands in ecologically fragile agricultural areas. Taking the Ulan Buh irrigation district in the Hetao irrigation area as an example, this study utilizes morphological spatial pattern analysis (MSPA) and patch importance index to identify and extract ecological source areas. Subsequently, the NSGA-Ⅲ(non-dominated sorting genetic algorithm Ⅲ) and FLUS model are coupled to formulate elastic land use optimization allocation plans for ecologically fragile agricultural areas based on four scenario simulations. Results indicate that NSGA-Ⅲ can efficiently generate a Pareto solution set with multiple objectives, outperforming the 2020 land use benefits. However, scenarios prioritizing ecological security, food production, and economic development show marginal improvements in other objective benefits. Conversely, scenarios emphasizing balanced benefits development under minimal land use change conditions exhibit higher and more balanced comprehensive benefits. When devising land use optimization allocation plans, identifying land use coordination and conflict zones based on scenario-specific optimal allocations aids in formulating flexible plans to accommodate potential changes in economic policies and environments. Overall, this study provides insights for optimizing land use plans in ecologically sensitive and fragile agricultural areas.

In the early stages of drug discovery, deep generative models are emerging as crucial tools for molecular design. The simplified molecular input line entry system(SMILES) serves as a standard chemical representation widely used for model training and generation. However, due to the non-uniqueness and non-directionality of linear representations of molecular ring systems, existing unidirectional encoders face limitations in capturing the global semantic structure of samples and generating valid molecular rings. Therefore, a method called Chemical RWKV BERT (ChemRB) is proposed, which aims to deeply extract bidirectional information from a large amount of unlabeled data. To achieve this, two pre-training tasks are innovatively designed: ring-level feature prediction and global-span closure prediction. These pre-training tasks not only provide the model with rich contextual information but also further enhance its in-depth understanding of the structural properties of complex molecules. Experimental results show that the ChemRB model not only achieves significant performance improvements but also reaches optimal baseline performance on new molecular/sample evaluation metrics. This excellent performance fully validates the effectiveness of ChemRB in accurately capturing the inherent structural information of molecules, providing a solid empirical foundation for its application in related fields. Finally, through testing and application on EGFR inhibitors, the practical utility and broad application prospects of the ChemRB model are further validated.

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.

PsaA and PsaB are central proteins in photosystem Ⅰ, and play important roles in plant photosynthesis and stress regulation. Tobacco (Nicotiana tabacum L.) is a model crop for plant functional genomics research, while there are few studies on the functions of PsaA and PsaB genes. In order to understand the response of tobacco PsaA/PsaB genes to stress, the PsaA/PsaB gene family in tobacco genome were identified, and their physicochemical properties, subcellular localization, phylogenetic evolution, motifs and promoter regulatory elements were analyzed. The results showed that there are 27 PsaA/PsaB genes in tobacco. Compared with Arabidopsis thaliana, the tobacco PsaA/PsaB gene family undergone significant expansion and can be classified into three subfamilies based on their phylogenetic and structural features. The promoters of tobacco PsaA/PsaB genes contain numerous cis-regulatory elements responsive to light, low temperature, drought and plant hormones. RT-qPCR analysis revealed that most tobacco PsaA/PsaB genes were up-regulated under low temperature stress (8 up-regulated genes) and down-regulated under polyethylene glycol and jasmonic acid methyl ester stresses(8 and 7 down-regulated genes respectively). After potato virus Y(PVY) and tobacco mosaic virus (TMV) infection, the expression of most tobacco PsaA/PsaB genes were down-regulated. The down-regulated genes in the lower mesophyll and vein tissues were 19 and 17 after PVY infection, and 9 genes were down-regulated after TMV infection. In conclusion, tobacco PsaA/PsaB genes are involved in the response process of tobacco to various abiotic stresses, and the study results provide a reference for further exploring the role of PsaA/PsaB genes in regulating plant response to stress.

The cultural and entertainment industry plays a pivotal role in the urban economic transformation and the enhancement of residents’ quality of life. Based on point of interest (POI) data of Xi’an from 2010 to 2020, the spatial structural evolution of cultural and entertainment industry is investigated by employing Geo-information Tupu methodology and geographically and temporally weighted regression models. Specifically focusing on symmetry and hierarchy, the research aims to uncover the underlying mechanisms shaping the industry’s spatial configuration. Findings reveal a notable outward expansion of cultural and entertainment industry in Xi’an from the city center during the study period, exhibiting a multi-centralized trend characterized by discernible symmetry and hierarchical laws. The formation of diverse spatial structures is attributed to the interplay of transportation, demographic, regional economic, commercial, and industrial policy factors across multiple dimensions. Basic, developmental, and auxiliary factors influence the spatial distribution and site selection of industry hotspots through mechanisms involving supply and demand mechanism, trigger mechanism, and strengthening mechanism.

Grounded in humanistic principles and the needs of the main groups served by urban outdoor worker service stations, this study constructs evaluation indicators and quantitative assessment models from three dimensions: demand objectives, model evaluation, and feedback evaluation. Specifically, indicators such as transportation convenience degree, supply and demand index of roadside service stations, and supply and demand index of service stations within various land use categories are developed. Using Xiamen Island in Xiamen city as a case study, the rationality of the spatial distribution of urban outdoor worker service stations is investigated, along with an assessment of the extent to which these stations meet the needs of the target groups. Findings reveal significant spatial differentiation in the transportation convenience of service stations on Xiamen Island. While the number of service stations along urban branch roads and main roads/expressways generally matches supply and demand, there is a slight undersupply of stations along secondary trunk roads. Moreover, the matching degree of supply and demand varies across different land use categories, with relatively good matching observed in residential, commercial, business, and transportation facilities, but poorer matching in education, scientific research, cultural, sports, and medical healthcare facilities. Feedback evaluations from target groups align with the observed supply and demand matching status. Recommendations for optimizing the layout of urban outdoor worker service stations include the formulation of planning and construction standards, coordination in station planning and layout, and the revitalization of existing environments through renewal efforts.

Based on knowledge graphs, recommendation methods have become one of the hot research topics in recommender systems. They utilize user historical behaviors and item features with the assistance of structured representation of data in knowledge graphs to address the sparsity and cold-start problems in recommendation systems. However, user interests are easily influenced by specific items, while knowledge graphs store data in structured forms with entities linked only through relational edges. This leads to poor performance in click-through rate prediction when solely relying on knowledge graph-based recommendation methods.A recommendation method called local influence and deep preference propagation(LIDP) is proposed, which fully exploits the advantages of entity influence in preference propagation within structured data of knowledge graphs. The LIDP model first propagates preferences layer by layer in the knowledge graph to obtain data influence weights, and then calculates local influence based on these weights. Next, it enhances user representations based on the enhanced representation of user interests through their historical behaviors. Finally, it calculates the final interaction probability by taking the inner product of user representations and item vector representations. On the MovieLens-1M dataset, compared to the optimal baseline model GNRF, LIDP improves AUC, ACC, MAE, and F₁ score by 0.16%, 0.52%, 0.87%, and 0.21% respectively. On the Book-Crossing dataset, these improvements are 0.45%, 2.14%, 1.29%, and 0.93% respectively. Experimental results demonstrate that the LIDP model effectively captures deep-level user interest preferences, exhibiting good performance and effectiveness in recommendation systems, thereby providing users with better personalized recommendation services.

Diabetic nephropathy (DN) is a major complication of diabetes mellitus and a major causative agent of chronic kidney disease.UBE2V1, as the only E2 enzyme involved in K63 ubiquitination, is involved in the development of diabetic nephropathy, while UHRF2 acts as E3 ubiquitin ligase in the K63 ubiquitination pathway. Real-time fluorescence quantification, gene silencing and overexpression, scratching test, western blot analysis, K63 ubiquitination protein isolation and immunoprecipitation were used to explore the mechanism of UHRF2 and UBE2V1 in the process of epithelial-mesenchymal transition of renal tubular epithelial cells induced by high glucose. It was found that high glucose could increase the expression of UHRF2 and UBE2V1 in renal tubular epithelial cells, leading to the development of epithelial-mesenchymal transition. The expression of epithelial-mesenchymal transition marker proteins α-SMA and ZEB1 was significantly up-regulated in renal tubular epithelial cells cultured with high glucose, while E-cadherin expression was significantly down-regulated. Knocking down of UHRF2 or UBE2V1 inhibited the up-regulation of α-SMA and ZEB1, and weakened the down-regulation of E-cadherin.UBE2V1 may mediate the up-regulation of UHRF2 transcription in renal tubular epithelial cells cultured with high glucose and may increase the ubiquitination of K63 in UHRF2.The results reflect that both UHRF2 and UBE2V1 are involved in the epithelial-mesenchymal transition of renal tubular epithelial cells under the condition of high glucose, which may synergistic promote the fibrosis of diabetic nephropathy. These findings provide a new idea for the treatment of diabetic nephropathy.

Empirical analysis of the spatial misalignment characteristics and driving factors of tourism urbanization quality and scale in the Wuling Mountain area of Hunan from 2011 to 2021 using the center of gravity model, spatial misalignment index, and econometric model. The results show that: 1) The quality and scale development level of tourism urbanization in the Wuling Mountain area of Hunan are generally on the rise, with the quality growth rate faster than the scale, but the overall scale is higher than the quality.In space, the quality and scale of tourism urbanization show a differentiation pattern of “high in the northeast and low in the southwest”. Wulingyuan, Yongding and Fenghuang, as “double high areas” of quality and scale, have become “leaders” in the development of tourism urbanization. 2) There is a spatial mismatch between the quality and scale of tourism urbanization in the Wuling Mountain area of Hunan: the degree of spatial mismatch shows a trend of first decreasing and then increasing with 2015 as the node.The direction of spatial displacement has shifted from a scale first positive displacement area to a quality first negative displacement area.The level of spatial misalignment presents a pyramid structure from high to low, with the proportion of low misalignment areas consistently remaining above 50%.3) Per capita GDP, per capita disposable income ratio of urban and rural residents, passenger turnover, and tourism industry agglomeration have a negative impact on the quality and spatial mismatch of tourism urbanization, while tourism population density has a positive impact on the quality and spatial mismatch of tourism urbanization.

The MADS-box family is one of the largest transcription factor families in plants, and plays an important role in adversity stress and flowering response. The function of AGL12-like subgroup in Vitis vinifera remains unclear. The VvAGL12 promoter (proVvAGL12) was cloned from the Pinot Noir grape genome, and the sequence elements of the promoter were analyzed. It was found that there were several cis-acting elements related to adversity stress in the promoter region. GUS expression vector driven by proVvAGL12 was constructed and transformed in Arabidopsis thaliana and tobacco. It was found that proVvAGL12 promoter fragment had activation activity in Arabidopsis thaliana, and it can drive GUS expression in leaves, stem segments, flower organs, roots and fruit pods in transgenic Arabidopsis thaliana plants.The expression activity can last the whole growth cycle. Transgenic Arabidopsis thaliana and tobacco stress treatment showed that the activity of proVvAGL12-driven GUS was regulated by gibberellin, abscisic acid, polyethylene glycol and low temperature.

In order to achieve accurate and rapid detection for Staphylococcus aureus in drugs, through adding internal amplification control (IAC) and optimizing PCR reaction system, an internal reference based fluorescence quantitative PCR rapid detection method of Staphylococcus aureus that can monitor the process in real time was established, and its specificity, detection limit, reproducibility and feasibility were verified. The verification results showed that the specificity of the established method was good with only Staphylococcus aureus showed typical amplification curve. The detection limit of Staphylococcus aureus genomic DNA was 0.23 pg/μL. There was a good linear relationship between C_t values and the copy numbers of template (R²=0.99). The relative standard deviations of reproducibility experiments were all less than 3.0%.Staphylococcus aureus can be detected 10 h after the bacteria increase in artificially contaminated drugs. The method can not only shorten the detection time of Staphylococcus aureus in drugs, but can also monitor the PCR reaction process, effectively prevent the occurrence of “false negative” result, thus can be used as a supplementary method for staphylococcus aureus detection.

Compared with the traditional nickel-based materials and rare earth-based materials, the magnetostrictive coefficient is small, the eddy current loss is large, and the preparation cost is high. Utilizing cobalt ferrite as the driving core of magnetostrictive ultrasonic transducer can achieve stable service and high-power output at elevated frequencies. In this paper, a longitudinal magnetostrictive ultrasonic transducer with resonant frequency of 30 kHz is designed based on the magnetostrictive characteristics of cobalt ferrite and the finite element simulation is used to analyze the dynamics and magnetic field of the transducer. The output characteristics of the cobalt ferrite magnetostrictive ultrasonic transducer are tested by impedance matching. The resonant frequency, output amplitude and working temperature of the prototype are tested. The results show that the magnetic field intensity is 0.1~0.25 T (301~653 Oe), which can meet the driving requirement of cobalt ferrite in the best driving range(250~750 Oe).When the driving voltage is 12 V and the signal gain is 20 dB, the actual resonant frequency of the transducer is 29.8 kHz, the output amplitude is 3.75 μm, and the stable working temperature is 60 ℃. The experimental results verify the reliability and application potential of cobalt ferrite in the field of magnetostrictive transducers.

To clarify the dynamic changes and clinical significance of heart rate variability in adolescent depression with aerobic exercise therapy,60 students of mild to moderate depression enrolled in 2022 were divided into the study group (5 cases dropped, 25 cases completed ) and the control group (2 cases dropped, 28 cases completed) equally. The patients in the study group received aerobic exercise therapy for 12 weeks, the patients in the control group did not receive any treatment.The clinical efficacy and heart rate variability index between the two groups were compared.The clinical efficacy and cure rate of the study group were significantly higher than those of the control group at the end of the 12th week (P<0.05).The 17-item Hamilton Depression Scale (HAMD-17) score in the two groups was significantly lower than that at baseline (P<0.01).Compared with the control group, the study group showed a more significant decrease in HAMD-17 at the end of the 12th week (P<0.001). The standard deviation of NN intervals(SDNN),standard deviation average of NN intervals(SDANN), and root mean square of successive RR interval differences(RMSSD) in the study group at the end of the 12th week were significantly increased than those before treatment (P<0.001).Compared with the control group, the scores of SDNN, SDANN, and RMSSD in the study group at the end of the 12th week changed more significantly (P<0.001). Negative correlations were found between baseline HAMD-17 score and SDNN, SDANN, and RMSSD (r=-0.343, P=0.012; r=-0.328, P=0.017; r=-0.401, P=0.003; respectively). The results indicate that 12-week aerobic exercise can significantly improve depression symptoms and autonomic nervous system function in adolescent depression patients. The severity of depression is related to impaired autonomic nervous system function, and heart rate variability can become a biological indicator for evaluating the efficacy of aerobic exercise in treating depression.

A high-resolution reconstruction method of split-focal plane mosaic images is proposed due to the low spatial resolution of split-focal plane multispectral cameras. The method realizes the acquisition of a demosaicking reflectance image, which shoot a diffuse reflective white plate to calibrate a division-of-focal-plane (DoF) mosaic sensor. The reflectance image and the DoF mosaic image are substituted into the fusion model of multispectral and hyperspectral images for high-resolution reconstruction. The reconstruction results of two reconstruction methods based on superpixel segmentation operators (least squares-multispectral data fusion method and regularized minimum rank-multispectral data fusion method) are analyzed, and the root mean square error of the two methods is about 0.12. The results show that the demosaicking method achieves better results in the migration of the high-resolution fusion model for spectral images, and the DoF mosaic images are highly restored in both the spatial and the spectral information.

Aegilops tauschii, the D genome donor of wheat (Triticum aestivum), has a variety of genes resistant to biotic and abiotic stresses, and is the main gene pool for wheat genetic improvement. Heavy metal-associated isoprene plant protein (HIPP) plays an important role in regulating metal ion balance and detoxification in plants. In this study, the transgenic Arabidopsis thaliana overexpressing AetHIPP28 was obtained by Agrobacterium-mediated method, and the response of transgenic Arabidopsis thaliana to cadmium (Cd) stress was studied by a series of physiological and biochemical analyses.The results showed that AetHIPP28 was localized in the nucleus and plasma membrane. Overexpression of AetHIPP28 significantly enhanced Arabidopsis thaliana tolerance to Cd, and promoted the root growth of Arabidopsis thaliana under Cd stress. The ROS accumulation of transgenic Arabidopsis thaliana under Cd stress was significantly lower than that of the wild type, while the activity of antioxidant enzymes was significantly higher than that of the wild type. Meanwhile, overexpressing AetHIPP28 inhibited Cd²⁺influx and increased Cd accumulation in roots. These results suggest that overexpression of AetHIPP28 improves Cd tolerance in Arabidopsis thaliana by regulating ROS accumulation, antioxidant enzyme activity, Cd²⁺ influx and distribution in roots, which provide a reference for investigating the response mechanism of AetHIPP28 to Cd stress.

Polygonatum kingianum Coll. et Hemsl. (PK), a well-known Food and Medicine homology in China, riches in various bioactive ingredients and has been consumed for thousands of years with a wide range of applications. PK has shown great tonic effects. Previous studies have consistently shown nutritional functions of PK including liver protection. The Nine Steaming Nine Drying is a traditional processing technique that has been considered as an effective method in enriching tonic properties, but its impacts on improving efficacy of PK on liver protection remain largely unexplored. This study herein applied integrative analysis of metabolomics and bioinformatics, as well as molecular docking, to reveal the core bioactive compounds responsible for the improved effects of Nine Steaming Nine Drying processed PK extracts (PPK) on the prevention and treatment of alcoholic liver injury and its underlying mechanisms.Through constructing an alcohol induced injury model using LO₂ cells, the regulatory effects of PPK on cell activity, oxidative stress, and alcohol metabolism enzymes (ethanol dehydrogenase and acetaldehyde dehydrogenase) were investigated. A total of 204 metabolites in the PPK were compared with crude PK extracts (CPK). Alcohol induced liver injury related targets were obtained by searching for DisGeNET, TTD, GeneCard, and OMIM databases, and a compound-target network was established. Results from protein-protein interaction networks and enrichment analysis identified 23 functional metabolites (including rutin, furanone, caffeic acid, gallic acid, etc.) that may be responsible for the protective effects of PPK on alcoholic liver injury via regulating 15 action targets involved in PI₃K-Akt, AMPK, NF- kB signaling pathways. High affinity between the identified key constituents of PPK and their predicted acting targets was confirmed using molecular docking. Results indicate that PPK can alleviate alcoholic liver injury by regulating oxidative stress, inflammation, and ethanol metabolisms. It provides novel insights in nutritional functions of PPK, and an effective strategy for systematically elucidating the mechanisms of functional foods.

Janus-Helmholtz (JH) transducers and Janus-Hammer Bell (JHB) transducers have similar appearance and structure, but their vibration modes are different. This article firstly analyzes the structural parameters corresponding to different vibration modes in JH transducer and JHB transducer. Under the conditions determined by other parameters, through theoretical calculation, it is concluded that the relationship between the axial length L of the ring shell in the JH transducer and the inner radius a of the ring shell should satisfy 2<L/a<5.7. The value range corresponding JHB transducer is 0.1<L/a<0.3. Then the finite element special boundary condition setting method is used to analyze the vibration modes corresponding to the resonance peaks of the JH transducer and the JHB transducer. It is concluded that the lower-frequency vibration mode in the JH transducer is the Janus longitudinal vibration, and the high-frequency vibration mode is the cavity resonance mode.The lower-frequency vibration mode in the JHB transducer is the Janus longitudinal resonance, and the higher-frequency vibration mode is the ring breathing mode. Finally, the radial and axial transmitting voltage responses of the JH transducer and the JHB transducer are compared to analyze work performance under different working modes, and it is concluded that the JH transducer can achieve a wider operating bandwidth and a higher transmitting voltage response. The working bandwidth of JHB transducer is narrow, but the transmitting voltage response in the radial direction has small fluctuations, and the transmitting voltage response in the axial direction has large fluctuations.

In order to achieve non-destructive and rapid inspection of tissue microarrays, a fully automatic Mueller matrix microscopic imaging system was constructed by using the polarization imaging principle of the Mueller matrix. By integrating polarization optical components and imaging equipment with automatic control, this system can perform automatic imaging. The collected polarized image data is processed by using the Mueller matrix polar decomposition and Mueller matrix transformation algorithms to extract the key polarization characteristic parameters describing the sample. The results showed that in the experiment of distinguishing cervical cancer tissue samples from normal cervical tissue samples, the equivalent waveplate fast-axis azimuth θ and linear phase retardance δ are effective polarization parameters. By using statistical analysis, gray level co-occurrence matrix analysis and Tamura image processing methods to process the polarization parameter images, clear indications for diagnosing cervical cancer can be obtained. The fully automatic Mueller matrix microscopic imaging system can achieve rapid diagnosis of cancer by integrating multiple samples without frequently changing slices or switching to a high power objective.

This study introduces a technique known as large-field optical sectioning structured illumination microscopy(large-field optical sectioning structured illumination microscopy, LF-OS-SIM) designed for three-dimensional tomographic microscopic imaging of thick specimens. The technique utilizes a one-dimensional grating projection to generate a striped structured light field, in conjunction with a spatial light modulator (spatial light modulator, SLM) for fast phase-shifting of the structured light spectrum. Compared to conventional OS-SIM techniques that are based on SLM/DMD projection, LF-OS-SIM enhances the spatial bandwidth product (spatial bandwidth product, SBP) by 4.7 times. Additionally, digital phase-shifting using the SLM is implemented to achieve a slicing speed of 20 frames per second. The LF-OS-SIM was employed for three-dimensional tomographic microscopic imaging of objects such as coins, three-dimensionally distributed fluorescent beads, and biological specimens. The results indicated that the imaging field of view (FOV) for LS-OS-SIM reached 1 030×780 μm³, with an axial tomographic imaging accuracy of 4.0±0.39 μm. Given its wide field of view, high resolution, and rapid slicing capabilities, LS-OS-SIM is anticipated to be extensively applied in the three dimensional imaging of both industrial microdevices and biological samples.

The polarization parameter images in polarization dehazing algorithms are highly susceptible to noise interference, and the brightness of the dehazed images is unstable. This leads to the algorithm’s inability to adapt universally to different lighting conditions. To address this issue, a new polarization dehazing algorithm is proposed, which introduces a novel polarization parameter image based on low-rank approximation to reduce noise interference. Additionally, a multi-exposure fusion method is employed to compress the image’s dynamic range. Comparative experiments demonstrate that this method effectively removes haze from images, exhibiting strong robustness and significantly improving the overall quality of haze-affected images captured in various environments. In terms of standard deviation, image information entropy, NIQE and PIQE, the indicaters of the proposed method are improved by 22.99%,4.06%,17.42%,32.89% and 33.33%,2.80%,12.31%,76.14% respectively compared with the method proposed by Schechner and dark channel method.

Through artificial rainfall simulations, the variations in surface runoff and sediment yield under different straw return rates (0.30, 0.45, 0.60, and 0.75 kg/m²) and methods (crushed mulch reversion (CV),crushed tillage reversion (RT), and mulch-crushed tillage reversion (CR)) are investigated, and the underlying mechanisms governing surface runoff and sediment yield patterns under different straw return methods and rates are analyzed. The results indicate that: 1)Under the same return method, straw return rates significantly affect the timing of runoff production, with increasing straw return rates leading to longer initial and stable runoff production time. Among the same straw return rates, there is minimal difference in runoff production time between CV and RT, while CR exhibits the most pronounced delay, with an initial runoff production time delayed by 19.42 to 55.17 minutes compared to CK. 2)With increasing straw return rates under the same return method, the initial and stable surface runoff intensities decrease, resulting in a significant reduction in total surface runoff. Notably, there is no significant difference in total surface runoff between RT and CR at straw return rates of 0.60 kg/m² and 0.75 kg/m². Moreover, under the same straw return rate, CR demonstrates significantly greater flow reduction benefits compared to CV and RT, with a decrease in initial runoff intensity ranging from 23.64 to 44.04 mm/h, and a reduction in total surface runoff ranging from 55.74% to 98.92% compared to CK. 3)Under the same return method, increasing straw return rates lead to a continual decrease in sediment yield intensity, resulting in a significant reduction in total surface sediment yield. There is no significant difference in total surface sediment yield among the three return methods at straw return rates of 0.60 kg/m² and 0.75 kg/m². Additionally, under the same straw return rate, CR demonstrates greater sediment reduction benefits compared to CV and RT, with sediment reduction benefits ranging from 93.73% to 99.99% compared to CK. Thus, it is evident that mulch-crushed tillage reversion (CR) with a straw return rate of 0.60 kg/m² is the optimal straw return mode.

This study focuses on the real-time dynamic monitoring of meteorological factors and sap flow rates of Haloxylon ammodendron subjected to different pruning treatments in the northeast part of the Ulan Buh Desert. The sap flow characteristics of Haloxylon ammodendron at different time scales during the vigorous growth season (from June to August) under different pruning treatments were investigated and their responses to meteorological factors were examined. Water consumption model in relation to meteorological factors was established to reveal the patterns of water transportation in Haloxylon ammodendron after pruning. Thermal diffusion sap flow meter and automatic weather station were used to monitor the sap flow in Haloxylon ammodendron subjected to different pruning treatments and solar radiation, air temperature, relative humidity and vapor pressure deficit.Sap flow rates in Haloxylon ammodendron under different pruning treatments (120 cm pruning, 90 cm pruning, 60 cm pruning) exhibited a “broad peak” pattern on sunny days, while unpruned Haloxylon ammodendron displayed a “single-peak” pattern. On rainy days, sap flow rates fluctuated dramatically, presenting an “irregular multi-peak” curve, which are lower compared to those of sunny days. The daily average sap flow rates in unpruned, 120 cm pruned, 90 cm pruned and 60 cm pruned Haloxylon ammodendron were 2.314 6, 3.333 4, 1.333 3 and 0.767 2 cm/h, respectively. The daily water consumptions were 8.62, 13.43, 2.40 and 2.89 kg/d, respectively. On the hour scale, solar radiation was the primary factor influencing sap flow rates in both pruned and unpruned Haloxylon ammodendron.On the daily scale, solar radiation was the main factor affecting sap flow rates in 90 cm pruned and unpruned Haloxylon ammodendron, while air temperature influenced sap flow rates in 120 cm pruned Haloxylon ammodendron and wind speed affected sap flow rates in 60 cm pruned Haloxylon ammodendron.On the month scale, water consumption in 120 cm pruned Haloxylon ammodendron was mainly influenced by solar radiation and air temperature, water consumption in 90 cm pruned Haloxylon ammodendron was primarily influenced by relative humidity and solar radiation, and water consumption in 60 cm pruned Haloxylon ammodendron was mainly influenced by wind speed. With the increase in time scale, the number of meteorological factors included in the model decreased, and the explanatory power for sap flow decreased. The fitted equations in this study reached significant levels and can predict the transpiration characteristics of Haloxylon ammodendron at different time scales. The results of this study are of great significance for understanding the patterns of water transportation in Haloxylon ammodendron after pruning.