Multimodal sentiment analysis, an inherently challenging research area, integrates textual, audio, and visual modalities to analyze human emotional tendencies. Existing studies suggest that the textual modality plays a dominant role in sentiment prediction. However, this predominance raises a potential issue: during training, machine learning models tend to learn spurious correlations between the input and the output, leading to an overreliance on textual information. This overreliance may cause models to incorrectly model spurious correlations between textual and sentiment labels, thus undermining the model's generalization ability. To address this challenge, an innovative counterfactual text debiasing(CFTB) algorithm is proposed for multimodal sentiment analysis. Our framework first employs causal graph to thoroughly analyze the causal relationships among the three modalities and the sentiment labels.Then, an auxiliary textual model is designed to precisely quantify the direct impacts of the textual modality to sentiment prediction and leverage an attention mechanism to accurately capture textual features that might introduce spurious correlations. During the inference phase, the CFTB algorithm demonstrates its unique advantage: it intelligently isolates the negative impacts caused by spurious textual associations from the overall multimodal information, while retaining and enhancing the beneficial information within the textual modality that genuinely contributes to sentiment prediction. Experiments on the MOSEI and MOSI datasets show that this framework can be effectively integrated into existing methods and has good generalization performance.

Aiming at the difficulties present in current automatic sleep staging methods, a method for automatic sleep staging of EEG and ECG dual modal signals by combining U²-Net and CBAM fusion attention is proposed.Firstly, the EEG-ECG signals in the MIT-BIH public dataset used in this paper are preprocessed. Then, the U²-Net network with multi-scale feature extraction module is used to extract waveform features in EEG and ECG in parallel. Secondly, CBAM fusion attention is used to assign weights to all features. Finally, the Softmax activation function is used to classify sleep periods into six. The results show that when sleep staging is performed based on U²-Net and CBAM fusion attention models, the overall accuracy of hexaclassification using ECG single-modal signals is 80.2%, and the F1 score is 75.3%. The overall accuracy of six classifications using EEG single-modal signals was 85.8%, and the F1 score was 81.7%;The overall accuracy of the six classifications using EEG-ECG dual-modal signals was 90.4%, and the F1 score was 85.6%. This shows that the bimodal sleep staging model proposed in this paper is feasible and effective, and provides a new idea for automatic sleep staging.

Though online reviews on social media platforms have been widely used in tourism research as data analytical sources in recent years, how they can be applied to destination recommendation needs further investigation.The review data of 239 5A scenic spots in China was retrieved from ctrip.com by Python programming and web-crawling technology. Natural language processing and deep learning technologies including BERT (Bidirectional Encoder Representations from Transformers) and word embedding were then imported to build a destination recommendation system for tourist destinations. The model was trained and validated on a dataset containing 57 360 reviews, with a classification accuracy of around 78% reached on 14 340 pieces of test data. Experimental results show that, with the aid of other tourists’ travel experiences and image perception, the proposed model can facilitate potential tourists in finding their ideal destinations to explore the first step of itinerary planning. The findings of this study extend the research scope of online reviews within tourism and hospitality and provide new insights into pre-trip travel counseling.

Imaging genetics suggests that there is a certain degree of correlation between neuroimaging and genes, leading researchers to pay attention to the analysis of diseases using genetic variations and imaging data. In practice, clinical doctors usually have limited data availability but still aspire to employ deep learning method for real-world problems. Considering the expanding data scale and expensive annotation costs, it becomes essential to develop an unsupervised learning method capable of utilizing multimodal data. To meet these needs, a representation learning method based on multimodal tabular data with contrastive learning (MTCL) is proposed. The model leverages resting-state functional magnetic resonance imaging (rs-fMRI) and single nucleotide polymorphisms (SNP) data without requiring any labeled information. To enhance interpretability, the model first transforms rs-fMRI and SNP data into a tabular structure through a feature extraction module. Then, a multimodal tabular data contrastive learning method is employed to fuse the dataset and obtain the fused data representation. On the dataset of major depressive disorder (MDD), our proposed method effectively improves the diagnostic performance of MDD. Additionally, the MTCL method combines model attribution techniques to explore imaging and genetic biomarkers associated with MDD, enhancing the interpretability of the model and aiding researchers in understanding the mechanisms underlying the disease.

Taking all cities in Hunan province as the research object, the theoretical analysis framework of the integration level of cultural tourism industry was defined and the evaluation index system of the integration level of cultural tourism industry was constructed. The coupling coordination degree model, temporal evolution characteristics and spatial pattern characteristics visualization methods were used to measure the spatio-temporal evolution trend of the level of the cultural tourism industry, and the PVAR model was used to analyze the dynamic coupling relationship among the subsystems of the integration level of cultural tourism industry. The geo-detector model was used to explore the dominant factors affecting the integration level of cultural tourism industry and to detect the interaction effects among the factors. The main conclusions are as follows: 1) In 2009-2021, the integration level of culture tourism industry of cities in Hunan province can be divided into three types, the overall level shows an upward trend, but the differences within the region are also widened, and the expansion trend is significant. 2) During 2009-2021, the overall coupling coordination level of cities in Hunan province is still mainly in dysfunction class, with Changsha demonstrates a high degree of coordination class, Yueyang, Zhuzhou, Hengyang and Changde exhibit basic coordination class, and the remaining 9 cities are still in basic dysfunction class. 3) There exists an obvious dynamic coupling relationship among the subsystems of the industrial integration foundation, industrial integration support, and industrial integration volume in each city during the study period. 4) The 7 driving factors led by fiscal policy are the dominant factors influencing the integration level of cultural tourism industry of cities in Hunan province, and the factor interaction effects of fiscal policy, science and technology innovation and consumption capacity are strong.

Multi-view imaging technology obtains rich scene information by capturing images from different angles, which can provide key visual data for fields such as autonomous driving, intelligent manufacturing and robot navigation. This paper first summarizes the components of common multi-view imaging systems, their working principles and implementation methods, and provides an in-depth analysis of the significant advantages of different multi-view imaging systems and their limitations from the key dimensions of dynamic imaging adaptability, imaging accuracy, cost-effectiveness, and comprehensive system performance. Then for the visual image enhancement technology, the application and effect in improving the quality of multi-view imaging are discussed by combining traditional image processing methods and deep learning techniques. Finally, the current status and future trends of the development of multi-view imaging technology are considered, and forward-looking predictions are made in the development directions of hardware innovation, algorithm optimisation and multi-modal data fusion.

Plant secondary metabolites are a class of biologically active organic compounds synthesized through secondary metabolic pathways during plant growth. These compounds are not essential for plant development but play crucial roles in many aspects including plant survival, environmental adaptation and stress resistance. The WRKY transcription factor family, named for its characteristic conserved WRKYGQK domain, is one of the largest and most functionally diverse transcription factor families in higher plants. These factors are widely distributed across the plant kingdom and regulate the synthesis of secondary metabolites, thus participating in various physiological processes including plant growth, environmental responses, and chemical defense. This review discusses the structural features and classification of WRKY transcription factors, with a focus on their role in regulating the synthesis of plant secondary metabolites such as terpenes, phenolics and alkaloids. Future research should delve deeper into the mechanisms by which WRKY transcription factors function within the plant secondary metabolism regulatory network and their potential applications in enhancing plant stress resistance and improving the quality of secondary metabolites. Such studies will provide theoretical and practical insights for the advancement of agricultural biotechnology and the functional improvement of plants.

As a driving force for new industries, the digital economy has emerged as a pivotal force in enhancing the efficiency of the tourism sector, critically contributing to the attainment of high-quality development in China’s tourism industry.Utilizing provincial panel data spanning from 2011 to 2020, this study employs the entropy weight TOPSIS and super efficiency SBM models to gauge the level of digital economy development and tourism industry efficiency. Furthermore, a comprehensive analysis is conducted using the two-way fixed effects model, adjustment effect model, and spatial Dubin model to explore the influence mechanism and effects of digital economy development on tourism industry efficiency. Findings reveal that digital economy development significantly boosts tourism industry efficiency, a conclusion upheld even after rigorous robustness tests. Analysis of regulatory mechanisms indicates that tourism intensity and the structure of the tourism industry positively moderate the relationship between digital economy development and enhanced tourism industry efficiency. Results from the spatial Dubin model demonstrate that the digital economy development within a province notably enhances its own tourism industry efficiency while exhibiting negligible spatial spillover effects on neighboring provinces’ tourism industry efficiency. Heterogeneity analysis further reveals that, compared with the eastern region, digital economy development in the central and western regions exerts a more pronounced influence on tourism industry efficiency.

The flood event has always been endangered high-quality development as for human society. Reconstructing and analyzing the impact-response process of the past flood events would be helpful to realize the mechanism and process of extreme climate events to human response, which could improve the adaptive capacity for natural disasters among human beings. In this study, archives and other historical documents were collected to reconstruct the spatial and temporal process of the flood event in 1811 Aksu. Furthermore, 26 points and 73 lines were extracted from above documents to reconstruct the impact-response process of this flood event, and social network analysis was applied to analyse the patterns of human interactions with climate change in the arid region. The main conclusions are as follows: 1) The flood event in 1811 Aksu was happened in the background of climate warming and humidification, and the interaction within the natural system among constant rainfall, ice and snow melt water, steep terrain exacerbated the impact of this flood event. 2) The main high-risk areas centralized in the Aksu city and adjacent villages, farmland. It was partly because the lower terrain and intense populations, various commodities near the city were prone to suffered by flood and waterlogging. 3) The flood affected the supporting system and population subsystem directly and indirectly affected the population subsystem, economic subsystem and social subsystem. In this process, there were 4 points (urban waterlogging, food production damaged, infrastructure damaged, famine) represented the main impacts of this flood event. 4) The human responses changed over time influenced by different disaster phases, including adjustment and adaption, which jointly effected to avoid the occurrence of post-disaster malignant events. And people constructed flood control engineering to prevent the occurrence of flood events that maintained the security and stability in northwestern frontier society during the middle and late Qing dynasty.

Infrared polarization imaging detection technology introduces polarization information on the basis of traditional intensity information, which can effectively improve target detection and recognition capabilities under specific conditions. It has advantages such as high signal-to-noise ratio, anti camouflage, and anti-interference, and has broad application potential and good development prospects in target reconnaissance, detection, and strike fields. This article first introduces the theory of surface polarization of objects and related phenomena, especially the discovery of high-temperature polarization phenomenon of targets, which expands new fields for polarization detection applications.Based on the theory of target polarization, the research results of target polarization characteristics under different environments and application backgrounds were analyzed. Secondly, the development of infrared polarization detectors and the latest progress in target infrared polarization imaging detection in recent years were reviewed. The new requirements for infrared polarization detection technology in the current complex battlefield environment were summarized, including breaking through the bottleneck of real-time high-precision imaging technology, strengthening cloud and fog penetration, and anti occlusion interference performance.Finally, based on the summary of the target polarization mechanism, target polarization characteristics, and the development trend of polarization detector technology, it is proposed to deepen the theoretical research on target surface polarization, promote the development of high-precision infrared polarization detector preparation technology, explore multi-dimensional information fusion processing, and further look forward to the future development and application prospects of infrared polarization detection technology in military fields such as complex target recognition, anti stealth operations, and battlefield monitoring.

The data from the fifth to seventh national censuses was used to explore a grid based method for agricultural population based on land use types, 1.5 km of agricultural population grid density data in Shaanxi province was obtained to reveal the current characteristics and spatiotemporal evolution process of agricultural population distribution. Research found that in 2020, the agricultural population in Shaanxi province was densely distributed in the Guanzhong Plain and Hanzhong Basin, while the Northern Shaanxi Plateau and Qinba Mountains were relatively sparse. At the same time, the agricultural population is relative densely distributed in river valleys, while the density is extremely low in urban core areas, difficult to use land, and ecological protection and water conservation areas. From 2000 to 2020, the overall agricultural population density in Shaanxi province has generally decreased, but there are differences in the decline rates among different regions, leading to increased spatial heterogeneity. The reason may lie in the different changes in land use types and the slowdown in agricultural population size. The accuracy verification found that the agricultural population grid data has good consistency with the census data, and the fitting accuracy of the agricultural population grid dataset is relatively high. The research has alleviated the problem of plasticity area units, fully captured the detailed information of agricultural population distribution, and has policy reference value for accelerating the shaping of reasonably distributed modern agricultural human resources and achieving comprehensive rural revitalization.

Aiming at the disadvantage that it is hard to obtain multiple Pareto sets in solving multimodal multi-objective optimization problems, a decomposition-based differential evolution algorithm is presented. In the proposed algorithm, multiple individuals that are assigned to the same weight vector form a subpopulation for finding multiple different Pareto sets. Then, an environmental selection method is designed to locate multiple different Pareto optimal solutions in the subpopulation. Finally, two differential evolution strategies are utilized to generate the offspring. The simulation results of the IEEE CEC 2019 benchmark test suite show that the proposed algorithm has good distribution ability in the decision space and can find more Pareto optimal solutions.

When mobile robots perform path planning, the traditional or classical ant colony algorithm often encounters problems such as fewer movement directions, smaller fields of view, non-optimal paths, and unsmooth paths. Aiming at the inherent shortcomings of the ant colony algorithm mentioned above, a parallel bidirectional 24 neighborhoods 16 directions ant colony algorithm is proposed. First, the 24 neighborhoods 16 directions path search method can expand the field of view of path search. Second, combining the 24 neighborhoods 16 directions path search method with the bidirectional alternating search strategy can better reach the endpoint and enhance the global search ability. Subsequently, the heuristic function includes starting point, current point, candidate node, and endpoint, as well as adaptive factors. At the same time, an improved transition probability formula is introduced to enhance the guidance of path search. Then, the crossing strategy is introduced to avoid getting stuck in local optima. Finally, the path node transfer strategy is adopted to smooth the path, resulting in fewer inflection points and the shortest path. On grid maps with different complexity, the improved ant colony algorithm proposed in this paper was compared with the traditional ant colony algorithm and other improved ant colony algorithms through simulation experiments. The simulation results proved that the algorithm proposed in this paper is feasible and effective.

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.

To establish a tourism-friendly bay area, it is essential to assess the cross-sea accessibility of the Pearl River Estuary, thereby effectively addressing the connectivity issues between the eastern and western shores of the bay area. Utilizing the cost-weighted grid method, this study examines the impact of newly built cross-sea channels on the accessibility of tourist attractions in the Greater Bay Area (GBA) from four aspects: changes in isochrone area, daily accessibility, weighted average travel time, and tourism economic linkage intensity. Taking the Hong Kong-Zhuhai-Macao Bridge and the Shenzhen-Zhongshan Channel (hereinafter referred to as “B&C”) as examples, the study analyzes their influence. The conclusions are as follows: 1) Within a certain spatial scope, there is a marginal diminishing effect on the improvement of tourist attraction accessibility, and the enhancement effect declines with distance from the starting and ending points of the cross-sea tunnels. The main contradiction in improving GBA accessibility has shifted from absolute value enhancement to controlling the expansion of regional disparities and structural imbalances. 2) The impact of B&C on tourism accessibility is concentrated mainly on the eastern and western shores of the Pearl River Estuary, with insignificant effects on the central region. Specifically, the HKZMB primarily enhances the area of the 2-hour traffic circle in the GBA, while the Shenzhen-Zhongshan Channel mainly expands the 1-hour traffic circle. 3) In terms of attraction accessibility, B&C significantly increase the number of attractions reachable within 1 hour on both shores of the Pearl River, enabling the areas around the Pearl River Estuary in the GBA to form a relatively close tourism circle. The overall attraction accessibility is compressed from 1.20 hours to 1.12 hours, with Zhaoqing, Jiangmen, and Huizhou representing weaknesses. 4) Measuring the changes in the tourism economic landscape of the GBA based on tourism economic linkage intensity reveals that B&C have not altered the three core clusters of Guangzhou-Foshan, Shenzhen-Hong Kong, and Zhuhai-Macao, but rather further consolidated their agglomeration trends.

Multi-functional active peptide is a protein-derived compound that can act on multiple targets and deliver a variety of physiological effects, and has significant therapeutic effects on a variety of diseases. The existing multi-functional active peptide prediction model fails to fully consider the correlation between amino acids in the feature representation stage, which reduces the feature representation ability of the model, and the existing method adopts the strategy of converting the multi-label classification problem into multiple binary classification prediction problems, which leads to the inability of the model to consider the dependence between multiple functions of the active peptide in the prediction stage, which reduces the prediction accuracy of the model for multi-functional active peptides. In order to solve the above problems, a multi-functional active peptide prediction model based on label dependence is proposed, TCLD, which extracts the correlation between amino acids in the active peptide sequence through the Transformer encoder, and uses the ZLPR loss function to capture the dependence between multiple functions, which is used to improve the performance of the multi-functional active peptide prediction model. The experimental results show that the prediction performance of TCLD is better than that of the existing multi-functional active peptide prediction methods, which is helpful for researchers to quickly screen out multi-functional active peptide candidates with potential therapeutic value, thereby accelerating the research and development process of new drugs.

Polarization spectral imaging technology provides an effective solution to the imaging problems in complex scenes by combining intensity, polarization, and spectral information. Division-of-focal-plane polarization spectral imaging technology has become a significant development direction in this field due to its high compactness and strong real-time performance. This paper first reviews the development history of polarization spectral imaging technology and systematically compares the advantages and disadvantages of related technologies. Then, it focuses on the research progress of the division-of-focal-plane polarization spectral imaging system, providing a detailed overview of polarization spectral splitting elements, polarization spectral demosaicking algorithms, and existing polarization spectral image databases. It also systematically summarizes the core advantages of this technology, including high compactness, strong real-time performance, and low power consumption. Finally, it summarizes the applications of polarization spectral imaging technology in military reconnaissance, space exploration, medical diagnosis, and remote sensing detection. The analysis indicates that this technology has broad prospects in fields such as target detection, environmental monitoring, and medical diagnosis, but it still faces challenges such as low spatial resolution and insufficient accuracy in reconstructing polarization information currently. Based on this, future research should focus on optimizing the design and fabrication of polarization spectral splitting elements, improving demosaicking algorithms for high-quality image restoration, and further expanding its application capabilities in dynamic scenes and complex environments.

Compound erosion by multiple forces is the most significant feature of soil erosion on slope cropland in rolling hill regions with black soil in northeastern China. Compound erosion by wind and water is one of the important erosion types to aggravate the effects of soil erosion on slope cropland. In this study, on the experimental slope cropland in rolling hill regions, field observations of meteorological conditions, wind erosion and water erosion, and statistical methods were used to reveal the intra-annual alteration of erosion forces on compound erosion by wind and water and to analyze the intra-annual temporal succession characteristics of wind erosion and water erosion.Based on the threshold of wind speed for sand saltation (5 m/s) and erosive rainfall amount (12 mm/d) in rolling hill regions with black soil in northeastern China, the criteria for dividing the wind-dominated force and rainfall-dominated force of complex erosion on the experimental slope cropland was determined. The alteration of erosion forces of compound erosion by wind and water on the experimental slope cropland was divided into three stages: wind-dominated period Ⅰ(WDⅠ: Mid-March to Mid-July), rainfall-dominated period (RD: Mid July to Early October), wind-dominated period Ⅱ(WDⅡ: Early October to Mid-November). Surface creep erosion by wind accounted for about 92.95% of the total erosion amount by wind on the experimental slope cropland. Wind erosion rate in wind-dominated period was calculated at 432.9 g/m² in WDⅠ and 40.95 g/m² in WDⅡ. The rainfall erosion rate in rainfall-dominated period was estimated at 485.15 g/m².The wind erosion and water erosion on the experimental slope cropland showed temporal succession characteristics with the intra-annual alternations of erosion forces, following the order of water erosion rate in RD > wind erosion rate in WDⅠ>wind erosion rate in WDⅡ.

Dietary biomarkers offer an objective means of assessing contemporary food exposures, serving as an alternative or supplementary tool to self-reported food intake. They are also utilized to explore the functional characteristics of dietary factors. The precise measurement of dietary biomarkers promotes our understandings of the relationship between diet and risk of developing metabolic diseases, helping to achieve personalized and precise nutrition. Metabolomics is a key method for screening dietary biomarkers. This article reviews the research results on dietary biomarkers related to the intake of grains, fruits and vegetables, meat, fish, dairy products, and nuts based on large-scale population cohort studies and randomized controlled dietary intervention studies from 2013 to 2023. It also summarizes the relationship between specific dietary biomarkers and the risk of 35 dietary nutrition related metabolic diseases, including obesity, cardiovascular and cerebrovascular diseases, cancer, and Alzheimer’s disease. The current status and challenges of discovering dietary biomarkers based on metabolomics are discussed, in particular on the issues related with research design, complex food composition, inter-individual differences and metabolomics data processing techniques. This review emphasizes the enormous potentials and research prospects of using dietary biomarkers to uncover the relationship between diet and health.

Most Transformer-based object tracking models have limited extraction of target's local spatial feature information and insufficient utilization of temporal features, significantly affecting the performance of object tracking models in handling complex scenarios such as target occlusion, deformation, or scale changes. Therefore, a visual object tracking method with spatial-temporal feature enhancement and perception (STFEP) are proposed in this paper. On one hand, this method uses Transformer for the extraction and fusion of search region and temporal context features to obtain global feature information. By designing a local convolutional neural network, it extracts the target's local feature information and associates it with the target's global feature information, further enhancing the target's feature representation. On the other hand, a spatial-temporal feature perception mechanism is proposed to analyze the reliability and necessity of feature information at different moments, constructing dynamic templates to perceive richer spatial-temporal information, enabling the model to adapt to complex changes in targets and scenes. Experimental results on multiple datasets such as TrackingNet, GOT-10k, LaSOT and UAV123 show that the proposed method can track the target accurately and robustly, and the optimal results are obtained on GOT-10k dataset. AO, SR_0.5 and SR_0.75 were 73.7%, 83.8% and 70.6%, respectively.

Photoacoustic imaging is an efficient, non-invasive biomedical imaging technique that combines the high contrast of optical imaging with the deep tissue penetration of ultrasound imaging. By reducing the effects of optical scattering, it provides clear internal imaging views. This paper discusses traditional photoacoustic imaging techniques, including photoacoustic tomography, photoacoustic microscopy, photoacoustic endoscopy, and photoacoustic molecular functional imaging. It also highlights four novel photoacoustic imaging technologies: photoacoustic elastography, photoacoustic-guided wavefront shaping, polarization photoacoustic imaging, and optical detection methods for photoacoustic signals. Compared to traditional methods, these new approaches incorporate advanced optical control and signal processing techniques to improve imaging accuracy and resolution. The main challenges faced by new photoacoustic imaging technologies include improving imaging speed, enhancing signal detectability, and optimizing system user-friendliness. This paper summarizes key scientific achievements in photoacoustic imaging for achieving high resolution and deep tissue imaging and provides an outlook for future development. In the future, photoacoustic imaging technology is expected to overcome current limitations through further hardware innovations and algorithm optimizations, particularly in real-time imaging, system simplicity. With the development of multimodal imaging systems, photoacoustic imaging may be combined with other imaging techniques, such as magnetic resonance imaging (MRI), computed tomography (CT), or positron emission tomography (PET), to provide more comprehensive biomedical imaging solutions.

Lead-free metal halide biperovskite is composed of non-toxic elements, stable in air and has a long carrier lifetime. The physical properties of bismuth based Cs₂MBiX₆(M=Cu, Ag, Au, X=Cl, Br, I) double perovskite materials with excellent photovoltaic properties were calculated theoretically.In order to analyze the effects of different lead-free metal halides on battery performance, first-principles calculations were performed to systematically investigate the crystal structures,electronic structures and optical properties of four materials Cs₂AgBiI₆, Cs₂AuBiCl₆, Cs₂CuBiBr₆, and Cs₂AgBiBr₆.Finally, the absorption rate, carrier collection efficiency, external quantum efficiency, short-circuit current density, open circuit voltage and volt-ampere characteristics for the layered architecture consists of FTO/c-TiO₂/Cs₂MBiX₆/spiro-OMeTAD/Au structure perovskite solar cell are analyzed by performing equivalent optical admittance method. The results show that:when the thickness of the absorption layer is 0.6 μm.The short-circuit current densities of perovskite solar cells prepared with Cs₃AgBiI₆, Cs₂AuBiCl₆, Cs₂CuBiBr₆ and Cs₂AgBiBr₆ are 27.6, 26.0, 22.3 and 10.9 mA/cm², respectively, corresponding to open circuit voltages of 0.83, 0.87, 1.08 and 1.1 V. The photoelectric conversion efficiency of the device is 19.3%, 16.6%, 21.3% and 10.9%, respectively.It is found that 4 kinds of materials have high thermodynamic stability, suitable band gap and high absorption coefficient of ~10⁵cm^-1 in the visible light range, and the cell with optimized device structure also has considerable photoelectric conversion efficiency.

Rural tourism is an important engine for achieving the strategy of rural revitalization. Exploring the spatio-temporal evolution laws of rural tourism destinations is of great significance for promoting high-quality development of rural tourism. Based on the theory of “production-living-ecological” function, 303 provincial-level key rural tourism villages in Shaanxi province are taken as research objects to study the temporal-spatial distribution characteristics and influence mechanism of rural tourism destinations in Shaanxi province by using the methods of GIS spatial analysis and geographical detector. The conclusions are as follows: 1)The spatial distribution of all rural tourism destinations in Shaanxi province is characterized by gathering in mid to low altitude terrain, near the main transportation line, along rivers and lakes, showing a developing trend of polarization and sheet diffusion. Rural tourism destinations with dominant function production have the greatest spatial dependence on transportation and hydrological conditions.2)There are certain differences in the spatial distribution and evolution of rural tourism destinations dominated by different functions. Rural tourism destinations primarily driven by production functions exhibit the highest degree of agglomeration, displaying a contiguous expansion trend, and are predominantly situated in areas with flat terrain and dense river networks.The distribution of rural tourism destinations driven by life functions is largely consistent with the overall distribution, and there is a noticeable increase in the agglomeration of the high-density core areas.Rural tourism destinations driven by ecological functions exhibit a clear trend of outward expansion. These destinations are predominantly located in the border regions between cities, forming a point-to-axis distribution pattern. Additionally, a high-density distribution area has emerged at the southern foothills of the Qinling Mountains.3)There are differences in the influencing factors of spatial differentiation of rural tourism destinations dominated by different functions. Relevant policies exert the strongest influence on the spatial distribution of rural tourism destinations dominated by production functions. Rural tourism destinations dominated by life functions have a high spatial dependence on population distribution and surrounding scenic areas, while rural tourism destinations dominated by ecological functions are significantly affected by climate conditions and air quality.4)Natural ecological environment determines the basic spatial distribution pattern of rural tourism destinations, economic development level is an important driving force for the formation and development of rural tourism destinations, social and political conditions are guiding factors. Resource endowment foundation plays a prominent driving role in the spatial distribution differentiation of rural tourism destinations across various functional types. Furthermore, the interactive enhancement effect with economic development level surpasses that with social and political conditions.

In order to explore the water extraction process and quality standard of compound Juncao Ganoderma lucidum granules, liquid extract yield and liquid extract polysaccharide content were taken as indexes, the effects of extraction temperature, extraction time, ratio of material to liquid, extraction times and soaking time on extraction yield and polysaccharide content were investigated by single factor and response surface experiments. The particle size, moisture content, weight loss on drying, loading difference, solubility, microbial limit and heavy metal content of granules were detected according to the 2015 edition of Pharmacopoeia of the People’s Republic of China and the national standard. The raw materials of Juncao Ganoderma lucidum, Perilla frutescens and selenium-enriched tea in compound Juncao Ganoderma lucidum granules were identified by thin layer chromatography, and the contents of monosaccharides and tea polyphenols in compound Juncao Ganoderma lucidum granules were determined through high performance liquid chromatography. The results showed that the optimal extraction conditions were as follows: extraction temperature 80 ℃, extraction time 2.5 h, ratio of material to liquid(g/mL) 1∶25, two times of extraction, and immersion time 6 h. The particle size, water content, solubility, weight loss on drying and loading difference of the granules reached the requirements of the 2015 edition of Pharmacopoeia of the People’s Republic of China.The microbial limit and heavy metal content of the granules met the requirements of the national standard, and the average selenium content was 0.058 3 mg/kg. The TLC bands of Juncao Ganoderma lucidum, Perilla frutescens and selenium-enriched tea were clear and well separated, with no interference from the negative control. The contents of mannose, rhamnose, glucose, galactose, arabinose, epigallocatechin gallate, epicatechin gallate, catechin, gallic acid in compound Juncao Ganoderma lucidum granules were 1.310, 1.166, 8.708, 1.265, 0.824, 5.79, 2.58, 1.72 and 1.67 mg/g, respectively. The optimized water extraction process of the compound Juncao Ganoderma lucidum granules is stable and reliable, all the indexes of the granules meet the requirements, and the established quality standard for the water extraction process of compound Juncao Ganoderma lucidum granules is feasible.

This study utilized eight kiwifruit cultivars as materials to investigate kiwifruit quality traits, including soluble solid content (SSC), soluble sugar content, organic acid content, vitamin C content and aroma components. These traits were analyzed in conjunction with resequencing data. The results indicated that the SSCs of the eight kiwifruit cultivars ranged from 10.90% to 19.28%, the soluble sugar contents ranged from 80.24 g/kg to 132.82 g/kg, and the organic acid contents ranged from 7.28 g/kg to 30.99 g/kg. Among these kiwifruits, the average single fruit weight of Actinidia chinensis was significantly higher than those of the other species. A. eriantha exhibited the highest vitamin C content, reaching up to 6 541.32 mg/kg.A. arguta had the highest sugar-acid ratio. The main aroma components of A. chinensis, A. deliciosa and A. rufa were esters, whereas the aroma components of A. arguta and A. eriantha were mainly alkenes. The hybrid cultivars ‘Jinyan’ and ‘Mantianhong’ also showed esters as their main aroma components. By comparing the quality parameters of the hybrid kiwifruit cultivars with those of their parental species, it was demonstrated that the quality parameters of ‘Jinyan’ and ‘Mantianhong’ were closer to those of A. chinensis.Genetic clustering analysis also indicated that the two hybrid cultivars were more closely related to their paternal species A. chinensis.

To improve the segmentation accuracy of cataract surgical instruments, an EE-DANet was constructed in this paper. EE-DANet adopts a double-branched structure: The edge branch is responsible for extracting edge features, using augmented spatial attention (ASA) to address the issue of edge information loss in traditional image segmentation; The main branch adopts a U-shaped structure, and the multi-scale feature fusion module is used in the feature extraction stage to solve the problem of multiple types of surgical instrument images and large scale transformation; in the decoder section, a strip coordinate attention (SCA) is used to globally model the image, capturing the direction and position information of the surgical instrument, and address the problem of mirror reflection of the surgical instrument. The IoU and Dice reached 75.5% and 83.9% on the Dataset_instrument dataset, respectively. The proposed model effectively improves the segmentation performance of cataract surgery instruments, and has a positive significance for the clinical diagnosis of cataracts.

Multi-frame blind deconvolution (MFBD) is one of the current mainstream image restoration algorithms. It uses less frames (less than 20 frames) of degraded images to restore the high-resolution images. MFBD uses an iterative optimization method to obtain the optimal estimates of the target by minimizing the cost function. There are two main optimization strategies for the MFBD algorithm, namely the joint iteration strategy and the alternating iteration strategy. Currently, there are few publicly available research reports comparing the advantages and disadvantages of these two strategies. At the same time, the point spread function (PSF) parameterization method can also affect the restoration results. In order to obtain the optimal iteration strategies and parameterization methods, two iterative strategies are adopted with three different PSF parameterization methods based on the MFBD, to conduct comparative analysis on the restoration results under different signal-to-noise ratios and different initial value conditions through the normalized mean squared error(NMSE) and frequency spectrum curve of the result. Simulation experiments have shown that the joint iteration strategy with phase parameterization is able to handle more various complex degraded types. The mean square errors of the restoration results for three types of degraded images are 0.046, 0.194 and 0.342, respectively, which is the least compared with the joint iteration strategy with other PSF parameterization. For the alternating iteration strategy, only the gray matrix parameterization can obtain acceptable results (mean square errors of 0.109, 0.159, 0.332, respectively), but the spectral curve indicates that there is an amplification problem of iterative noise. In summary, joint iteration with phase parameterization can obtain better restoration results and handle more complex degraded situation.

Mixed attribute data is one of the most common types of datasets, and clustering algorithms tailored for this type of data are a research hotspot in clustering analysis. Due to the advantages of spectral clustering algorithms in handling clustering problems of arbitrary-shaped data and converging to global optimal solutions, an improved spectral clustering algorithm(improved Jaccard and Mahalanobis-spectral clustering, IJM-SC) from the perspective of similarity measurement formulas is proposed.Based on the ideas of Jaccard distance and Mahalanobis distance, a similarity measurement suitable for mixed attribute data is designed, and its application in spectral clustering of mixed attribute data is explored. The developed algorithm is applied to cluster three mixed attribute datasets including the UCI heart disease dataset, demonstrating its superiority in clustering mixed attribute data. By comparing the performance metrics with existing algorithms, the results demonstrate that the proposed algorithm achieves better clustering of mixed attribute data.

To enhance the measurement accuracy of a compact full-Stokes vector aperture-division polarimetric camera, a series of polarimetric image processing techniques have been proposed and established. These techniques specifically encompass dark current correction, bilateral filtering for noise reduction, image distortion correction, polarization parameter calibration, and channel image registration, followed by experimental research on polarimetric imaging. The results demonstrate that these techniques effectively mitigate the impact of various non-ideal factors on the camera’s imaging process. After image processing, the reprojection errors of the four polarization channels are all less than 0.2 pixels, and the average structural similarity index (SSIM) between the four polarimetric sub-images is improved by 15.2%. This signifies a significant enhancement in the accuracy of polarimetric information measurement.

Calcineurin B-like proteins (CBLs) are involved in plant-specific Ca²⁺ signaling as calcium (Ca) sensors and are essential for plant response to abiotic stress. Peanut is an important oilseed crop, and salt stress seriously affects the growth and development of peanut and its seed quality.At present, the response of peanut CBL genes to salt stress is unknown. In this study, members of the CBL gene family were identified from the peanut genome-wide database using bioinformatics methods and were analyzed for physicochemical properties, gene structure, phylogenetic tree and colinearity. The study found that there are 89 CBL genes in the peanut genome distributed on 19 chromosomes. Analysis of the physicochemical properties of the proteins showed that peanut CBLs are mainly composed of acidic amino acids. Subcellular localization predictions revealed that most of the CBLs in peanut are localized in the nucleus. To investigate the response of peanut CBL genes to salt stress, the expression patterns of five CBL genes were analyzed under salt stress. After treating with 200 mmol/L NaCl for 3 h, the expression of AhCBL46.1 was significantly increased, and the expressions of AhCBL4, AhCBL33 and AhCBL66 were significantly increased after 12 h of NaCl treatment. However, AhCBL61.3 showed a negative response to salt stress, suggesting that the peanut CBL genes play an important role in salt stress regulation. This research lays the groundwork for further exploration into the functionality of AhCBL genes.

Based on the land use data of the Yihe River Basin from 1975 to 2020, the GIS technology was used to construct and analyze the land use change trajectories. It also used the FLUS (further land use simulation) model to simulate land use scenarios of the basin in 2030 and 2050 under the “production-living-ecological” space paradigm, and visualize development patterns of the land use under different management modes. The results showed that: 1) From 1975 to 2020, the land use was primarily characterized by the reduction of arable land (accounting for about 10% of the basin area) and the expansion of construction land (accounting for about 8% of the basin area). 2) The land use change the Yihe River Basin could be divided into two stages: the early stage (1975-1995) and the later stage (1995-2020). During the early stage, about 22% of the area suffered land use transfer, mainly manifesting as mutual change of arable land, forest, and grassland. In the later stage, about 46% of the total area changed to another land use type, with a major tendency of arable land transferring to construction land. Both the trajectory changes and the spatial pattern of land use were more complex and significant. 3) The land use pattern of the basin was transferring towards a focus on ecological construction. From 2020 to 2030, the natural development scenarios was more consistent with the ecological space priority scenarios, but this convergence declined by 2050. This study can serve as a reference for further optimization of the “production-living-ecological” space pattern and ecological construction in the basin.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has had a profound and severe impact on human health and socio-economic structures. Dissecting the core aspects of the replication mechanism of this virus is particularly important for the development of effective prevention and control strategies. As a key single-stranded nucleic acid binding protein, nsp9 plays a pivotal role in the virus life cycle. Overexpression of green fluorescent protein (GFP)-fused nsp9 in cells and observation with confocal laser microscopy revealed that nsp9 is mainly localized in nucleus. The interactions between nsp9 and several intracellular proteins, including YWHAB and TRAF2, were initially identified by immunoprecipitation combined with mass spectrometry, and were verified by immunocoprecipitation and pull-down assay. In addition, the potential effects of nsp9 on the biological characteristics of host cells were investigated by CCK8 cell viability detection, EdU cell proliferation experiment and TUNEL apoptosis test, and it was found that overexpression of nsp9 could not significantly affect the normal proliferation of cells or induce apoptosis. These findings offer a foundation for subsequent comprehensive investigation into the interaction between nsp9 and host protein as well as its molecular mechanism in SARS-CoV-2 infection.

In underwater optical imaging applications, the strong scattering effect of particles in the water on reflected light often leads to poor imaging results. To address this issue, a pseudo-polarization de-scattering imaging method based on a single image is proposed, building on the foundation of underwater polarization difference imaging. By separating the spectral information of turbid underwater images, a pair of virtual orthogonal polarization images is constructed, which are then processed for polarization de-scattering to obtain a clear underwater image. Theoretical analysis and experimental results demonstrate that the proposed method outperforms the original images in complex underwater environments and under various distance conditions. Compared to the original images, the processed results show significant improvements in the following metrics: natural image quality evaluation (NIQE) increased by more than 50%, root mean square contrast (RMSC) increased by more than 1.5 times, and information entropy increased by more than 10%. Moreover, the enhancement effect of the method becomes more pronounced as the turbidity of the underwater environment increases. Additionally, compared to traditional underwater polarization de-scattering methods, the proposed method offers advantages such as fast processing speed and wide applicability.

The cavitation phenomenon of centrifugal pumps will seriously affect the hydraulic performance of the pump, especially the use of higher speed semi-open impeller high-speed miniature pumps, the impact of its tip clearance on the cavitation performance of the impeller is more significant, and is the main problem faced by its current research and application. The high-speed miniature semi-open impeller centrifugal pump applied to the thermal management system of airborne equipments is taken as the research object, and its full-flow field cavitation simulation is carried out. Using a combination of numerical simulation and experimental study, the cavitation performance of the centrifugal pump is investigated under different inlet cavitation margins and different tip clearance ratios (0.05, 0.08, 0.11 and 0.14). The results show that: the effective cavitation margin of the experimental pump increases with the increase of flow rate; the necessary cavitation margin of the pump decreases by 0.10 m for every 0.1 mm increase in the tip clearance, when the tip clearance ratio is increased by 0.03, resulting in a decrease in the anti-cavitation performance of the pump. Under different tip clearance, the smaller the tip clearance is the smaller the tip leakage flow can be obtained,the possibility of leakage vortex is reduced, improving the anti-cavitation performance of the pump.

The traditional flower pollination algorithms tend to exhibit poor exploitation when facing some complex optimization problems. Aiming at this weakness of the traditional flower pollination algorithms, an adaptive guidance flower pollination algorithm (AGFPA) is proposed. In the proposed AGFPA, an adaptive guidance mechanism (AGM) is introduced, which combines the global best individual surrounding strategy and the global best approaching strategy.The introduced AGM adaptively utilizes the global best individual to guide the population evolution, enhancing the exploitation capabilities while preserving the population diversity as much as possible. Specifically, the global best individual surrounding strategy focuses on exploiting the neighborhoods around the global best individual. Meanwhile, the global best approaching strategy utilizes the global best individual to guide the search directions, enabling the algorithm to explore a wide unknown area. In addition, an adaptive parameter control strategy is presented in the proposed AGFPA. The two key parameters, global pollination transform probability and step size factor, are adjusted according to the needs of different evolution stages, maintaining a good balance between exploitation and exploration. To test the performance of AGFPA, 18 benchmark functions are utilized in the experiments, which are commonly used in the field of swarm intelligence. The effectiveness of the strategies is discussed. Moreover, AGFPA is compared with several existing flower pollination algorithms and particle swarm optimization algorithms. Additionally, AGFPA is also used to estimate the fermentation kinetic parameters in the biochemical engineering. The experimental results show that AGFPA can exhibit promising performance on the most unimodal, multimodal and complex functions. Moreover, AGFPA can yield excellent results in the biochemical engineering applications.

Based on the panel data of 30 provinces in China from 2006 to 2020, the article constructs an index system for evaluating the development efficiency of rural public cultural services, uses the super-efficient SBM model and Malmquist index model to conduct static and dynamic analyses of the development efficiency in the east, west and national regions. The study also analyzed the spatial distribution of the development efficiency through ArcGIS, and analyzed the drivers and threshold effects of the development efficiency of rural public cultural services with the help of fixed-effects and threshold models. The study found that:during the study period, the efficiency of rural public cultural services in China was generally on the rise, and the growth rate of total factor productivity continues to slow down.The efficiency of rural public cultural services among regions shows an imbalanced state, forming a regional pattern of “east>west>central”.Analysis based on a panel data fixed-effects model shows that residents’ demand for cultural consumption, the degree of government attention and the scale effect all significantly affect the efficiency of rural public cultural services. In addition, there are single and double threshold effects on the efficiency of rural public cultural services based on the input of rural public cultural services in the national and east-central regions respectively.

The spatial correlation of carbon emissions is important for resource allocation optimization, environmental governance and policy formulation, but the study of the spatial correlation structure of urban agglomeration under the perspective of carbon sink potential has not yet been explored in depth. Based on the data of 41 cities in the Yangtze River Delta (YRD), a spatial correlation network of carbon emissions in YRD is constructed by combining the modified gravity model with the carbon ecological carrying capacity coefficient (CECC). The temporal and spatial evolution of the spatial correlation network structure of carbon emissions from the perspective of carbon sink potential and its driving factors are analyzed by using social network analysis and geographic detector. The results show that: in spatial, the carbon ecological carrying capacity of the YRD urban agglomeration has the Shanghai metropolitan area as the center of low value, and the ecological barrier of southern Anhui and western Zhejiang as the center of high value, which shows a core-periphery structure of “low in the middle and high in the periphery”. In time, the carbon carrying capacity of the Yangtze River Delta urban agglomeration as a whole declined significantly, showing a trend of equalization. The YRD urban agglomeration has formed a carbon emission correlation structure of carbon emission core, carbon flow axis and carbon sink core from east to west. The intensity of carbon emission correlation gradually narrows with the time gap. The spatial correlation network of carbon emissions in the Yangtze River Delta is at a low level, with obvious differences between the north and the south, relying on a few core cities and critical paths to maintain the effectiveness of carbon correlation. The spatial linkage network of carbon emissions in the YRD urban agglomeration is developing from the binary structure of “economic radiation and ecological absorption” to “multi-polarization and multi-threading”. The differences in population concentration and resource inclination are conducive to the establishment of carbon emission spatial correlation among cities. The heterogeneity of carbon emission correlation among cities mainly comes from the resource tilting power, infrastructure power, and other factors, and the potential factor is the guidance power of the government. This study can provide scientific basis and practical reference for the Yangtze River Delta urban agglomeration to realize low-carbon transition and synergistically promote the “dual-carbon” goal.

As the aging problem of Chinese society becomes more and more serious, the contradiction between the demand and supply of urban community in-home elderly service facilities continues to highlight, and there is an urgent need to rationally configure and optimize the elderly service facilities. Taking Xi'an as an example, the spatial configuration characteristics of urban community elderly service facilities and the daily travel pattern of the elderly are revealed,the rationalization of spatial configuration of urban community elderly service facilities layout is evaluated, and suggestions for the optimal allocation of elderly service facilities are put forward, using GIS and mathematical and statistical methods based on multi-source data, such as the urban point of interest(POI) data,statistical data and questionnaire survey data. The results show that elderly service facilities in Xi'an are concentrated in the traditional inner city and mature built-up areas, with a distribution extending to the emerging expansion areas and urban-rural transition areas along the direction of urban expansion. Fitness and recreation facilities in Xi'an have the widest distribution, with accessibility covering the six districts of the city. In contrast, other types of facilities in the urban-rural transition areas are less accessible. Community residents express satisfaction with the current state of elderly service facilities, but there are still some elderly people who lack understanding of the elderly service facilities, especially the social service facilities. The configuration of elderly service facilities in Xi'an is generally in a relatively irrational state, showing an unbalanced distribution pattern, with the phenomenon of lack of accessibility of facilities in some areas. The findings of the study can not only deepen the theory of spatial allocation of public service facilities in urban communities, but also provide a reference for the optimal layout of elderly service facilities in Xi'an.

In order to explore effective embedding technology of goat milk fat (GMF) and improve the storage quality of GMF, GMF was used as the core material, and soy bean protein isolate (SPI) and maltodextrin (MD) were used as the wall materials to prepare GMF microcapsules with different wall material ratios.The embedding rate of the microcapsules and the optimal conditions were measured. The basic physical properties (water content, water activity, solubility, particle size, color value), structure (Fourier transform infrared spectroscopy, X-ray diffraction), moisture absorption and peroxide value (POV) during storage of the optimized microcapsules were determined. The results showed that the optimum wall material ratio was m_SPI∶m_MD=1∶4. The water content, embedding rate, particle size and solubility of the microcapsule were 4.5%±0.4%, 92.5%±1.3%, 954.20±11.35 nm and 73.02%±2.90%,respectively. Fourier transform infrared spectroscopy found that C-O in the goat milk fat disappeared in the microcapsule, and the strength of C=O and -CH₂ decreased slightly in the microcapsule.A new characteristic peak was found in the microcapsule by X-ray diffraction,indicating that GMF is successfully embedded in the wall material. The moisture absorption of the microcapsule was stable in the range of 11%~12%. The POV of the microcapsule increased rapidly from day 0 to day 21.In summary, a GMF microcapsule with high embedding rate, small particle size and stable properties was prepared.

In order to explore the influence of spontaneous fermentation (SF) on wine quality,‘Cabernet Sauvignon’ grapes from Yinchuan (Ningxia), Xiangning (Shanxi) and Taigu (Shanxi) were used as raw materials for spontaneous fermentation, and wine samples of inoculated fermentation (IF) were used as control.Basic physical and chemical indicators, and the contents of phenolic and volatile substances of these wine samples were detected. The results showed that the alcohol content of SF samples was significantly lower than that of the IF samples, while the total phenolic content of SF samples was significantly higher than that of the IF samples. The alcohol fermentation of SF samples was more thorough with reducing sugar content of less than 4 g/L. The pH value,contents of dry extract and volatile acid showed no significant difference between SF and IF samples. A total of 45 volatile substances were detected in SF and IF samples, including 26 esters, 13 alcohols, 2 acids, 3 terpenes and 1 ether. The contents of esters and alcohols were high, accounting for 40.21%~67.14% and 24.98%~58.13% of the total volatile substances, respectively. The total amount of esters in SF samples was significantly lower than that in IF samples. There was no significant difference in the total amount of alcohols between SF and IF samples. However, the proportion of alcohols in SF samples was higher than that in IF samples. In wine samples from Yinchuan and Taigu,there was no significant difference in the total amount of volatile substances between SF and IF samples, while the total amount of volatile substances in IF samples from Xiangning was significantly higher than that in SF samples. By partial least squares analysis, it was found that ethyl trans-4-decenoate and 1-octanol had significant difference between SF and IF samples, making the main contribution to sample classification. Sensory analysis of the wine samples showed that the clarity of SF samples was lower than that of IF samples, and the color of wine samples from Yinchuan was the darkest. The SF samples had a fresh fruit aroma with no off flavor. Compared with IF samples, the aroma of SF samples was more complex, with less green flavor, while IF samples had a higher intensity of aroma. There was no significant difference in taste between SF and IF samples, while Yinchuan samples had stronger tannins and a fuller body. The study is of great significance in enhancing the style diversity and regional characteristics of wine, providing reference for the development and utilization of local yeast resources.