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.

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.

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.

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.

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 2016, the National Development and Reform Commission and the National Energy Administration proposed the construction of clean energy demonstration provinces and regions, and planned key pilot provinces, aiming to reduce the level of carbon emissions across the country. In order to clarify the implementation of clean energy demonstration provincial policies can effectively curb carbon emissions in the pilot areas, the impact of the implementation of clean energy demonstration provincial policies on carbon emissions was studied with the help of a multi-period DID model based on provincial panel data from 2005 to 2022. The results showed that the regression coefficient of the carbon emission reduction effect of the clean energy demonstration province policy was significantly negative(P<0.05), indicating that the clean energy demonstration provincial policy had a significant inhibitory effect on the carbon emission level of the pilot areas. In addition, through the parallel trend test, it also concluded that there was a time lag in the implementation effect of the clean energy demonstration provincial policy, and the impact effect gradually increased with time. Robustness test, intermediary effect test and heterogeneity analysis were carried out to verify the impact of technical level, industrial pollution control level, business environment and other factors on the carbon emission reduction effect of the policy.

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.

Under the dual context of global climate governance and China's “dual carbon” goals, the study focuses on transportation carbon emissions in cities along the New International Land-Sea Trade Corridor. The spatiotemporal evolution patterns, spatial correlations, driving mechanisms are explored, and optimization strategies are proposed by selecting data from 2010, 2015 and 2020, employing the Gini coefficient, spatial autocorrelation analysis, and the spatial Durbin model (SDM) alongside multi-source datasets. The results show that transportation carbon emissions show significant regional differences and dynamic disequilibrium, the trend of disequilibrium is gradually weakening and structural problems need to be solved urgently. Emissions are synergistically influenced by multiple drivers.Transportation infrastructure exert positive effects, while urbanization exhibits a significant inhibitory role. The spatial spillover effects of each factor differ in intensity and underlying mechanisms. The results offer theoretical and policy insights for low-carbon transitions in the corridor, supporting China's “dual carbon” goals.Furthermore, the study expands the empirical diversity of transportation carbon emission research, providing incremental contributions to the field.

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.

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.

Walking score is a widely used diagnostic tool for urban walkability in the world, and its scientific measurement has become a hot topic in the academic and practical fields. However, most of the existing measurement methods are still based on the background of western countries, and have not yet been adapted to China's national conditions, and in terms of the impact factors, the correlation analysis is mainly focused on the single factors at the plot scale, but lacks the cross analysis of muti-scale and muti-dimensional elements. Therefore, this paper constructs a walking score measurement system suitable for China's national conditions, and conducts an empirical study using 138 cities in the three major urban agglomerations (the Yangtze River Delta, Beijing-Tianjin-Hebei and the Pearl River Delta urban agglomerations) as case studies. It is found that the cities in the Yangtze River Delta urban agglomeration have the highest walking score, followed by Beijing-Tianjin-Hebei urban agglomeration, and the cities in the Pearl River Delta urban agglomeration have the lowest score. City scale, management level, social development level, and planning concept have significant effects on the walking score. The study suggests that improving a region's walking score requires not only consideration of the region itself, but also targeted policy interventions from the city's perspective.

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.

As a core fundamental component in various large-scale mechanical systems, bearings play a critical role in ensuring the safe and reliable operation of equipment, making high-precision detection and imaging of internal defects of significant theoretical and engineering importance. To address the challenges associated with bearings featuring complex curved surfaces, this study proposes a high signal-to-noise ratio ultrasonic imaging method based on the delay multiply and sum (DMAS) algorithm. In the experiments, a wedge in combination with an ultrasonic phased-array transducer was employed to inspect internal flat-bottom hole defects in bearings using oblique ultrasonic incidence. Signal-to-noise ratio (SNR), array performance index (API), and other evaluation metrics were introduced to quantitatively assess the imaging performance for different types of damage. The experimental results demonstrate that the surface-corrected p-DMAS imaging algorithm exhibits superior performance across all image quality metrics, achieving a maximum SNR improvement of 25.8 dB and an API increase of up to 8.4, thereby significantly enhancing defect detection capability. This method provides a novel approach and practical solution for in situ nondestructive testing of bearing-like curved structures.

Detection of brain networks based on functional magnetic resonance imaging data is crucial for understanding cognitive and functional aspects of the brain, as well as exploring brain disorders. With the development of deep learning techniques, an increasing number of researchers have applied them in the field of brain network detection. The main research achievements and advancements in this field are summarized. Firstly, it introduces the basic principles of brain network detection based on fMRI. Then, it discusses the deep learning models and their applications in brain network detection, analyzing their strengths and limitations. Finally, it summarizes the challenges and future research directions in applying deep learning methods to brain network detection. An important reference for further promoting research and applications in brain network detection using deep learning and fMRI imaging is provided.

The identification of crack tips in total focusing method (TFM) is easily affected by diffraction waves. In this study, the ultrasonic TFM based on Otsu thresholding is proposed for crack characterization. The full-matrix amplitude map is first constructed from the full matrix capture (FMC) data. Then, the adaptive algorithm of Otsu threshold is applied to automatically segment the reflection and diffraction regions. Only the reflection signals are used for delay-and-sum process and imaging to avoid diffraction interference and improve efficiency. Detection results of internal cracks in aluminum alloy show that the proposed method outperforms the conventional TFM using complete FMC data in crack measurement and imaging efficiency. The error of crack length is less than 0.3 mm, the error of angular is within 2.0°, and the required imaging time is reduced by more than 60%.

To address the challenge of extracting subtle ultrasonic defect features in low signal-to-noise ratio environments, this study proposes a gated residual and dual-attention collaborative enhancement network for low SNR ultrasonic signals. Based on convolutional neural network, the model adopts a ‘residual block squeeze-excitation(SE) module-pooling’ cascaded structure: a standard SE module is embedded in the residual block for initial channel screening, a locally enhanced SE module is used at the end of network stages to focus on peak signals, and gated residual connections are employed to dynamically preserve original subtle features, thus realizing collaborative optimization of noise suppression and feature enhancement. Experimental results show that the improved model achieves a mean root mean square error (RMSE) of 0.068 3 and a mean absolute error (MAE) of 0.047 1, which are 49.7% and 41.7% lower than those of the baseline CNN, respectively. It also outperforms models with only a single attention mechanism or residual blocks, verifying the superiority of dual-mechanism collaboration, while exhibiting excellent training stability and maintaining high accuracy in low SNR environments. In conclusion, the proposed model effectively overcomes the bottlenecks of noise interference and subtle feature learning. Its prediction accuracy, anti-interference capability, and stability are significantly superior to traditional methods and existing models, providing an efficient technical solution for ultrasonic non-destructive testing of steel pipes with important industrial application value.

Segmenting the butterflies from ecological images will provide accurate butterfly masks, guaranteeing the accuracy of the automatic butterfly species identification using the ecological images of butterflies. Therefore, the segmentation study of butterfly ecological images is of great significance. However, existing butterfly ecological image dataset cannot train an excellent butterfly segmentation model with strong generalization due to the small number of samples in the dataset and the mimicries and wing folds of butterflies in the butterfly ecological images. To address these issues, a new enhanced SAM (segment anything model) with good and robust segmentation capability is proposed. This enhanced SAM is named as SABM (segment any butterfly model) for segmenting the butterfly ecological images. This SABM introduces two-way convolution module, butterfly token, and a 3-layer MLP (multi-layer perceptron) to enhance SAM to adapt to the ecological butterfly image segmentation task. The 2-fold cross validation experimental results on the available butterfly ecological image dataset containing 707 ecological butterfly images demonstrate that this proposed SABM obtains an excellent segmentation performance for the ecological butterfly images. It is superior to SAM and its variants, particularly the SOTA model of SAM variants. Additionally, the segmentation experiments on the entirely new 7 645 butterfly ecological images show that this SABM has strong generalization capability, and it can segment all these 7 645 ecological butterfly images efficiently. This segmentation results provide a 10 times larger dataset than the available one for future butterfly segmentation task utilizing the ecological images while providing a much better dataset for the automatic butterfly species identification task through ecological images of butterflies, and a very challenging dataset for testing the performance of a clustering algorithm. Furthermore, the robust of the proposed SABM is tested on medical image datasets.

To address the challenge of non-destructively characterizing deep three-dimensional residual stress fields in critical load-bearing hot-section components of domestically developed large wide-body aircraft engines, this study investigates ultrasonic-based methods for non-destructive residual stress characterization and imaging. An ultrasonic acoustoelastic theoretical model capable of describing the influence of arbitrary three-dimensional stresses is established, revealing a quantitative relationship between the relative velocity variations of ultrasonic bulk longitudinal waves and triaxial stresses. By developing an ultrasonic wave-propagation simulation framework for components containing residual stresses, multi-angle ultrasonic transmission data are designed and acquired for representative high-temperature alloy forgings. Two imaging approaches are proposed: a tomographic imaging method based on iterative reconstruction algorithms and an inversion imaging method based on neural networks. The results demonstrate that the tomographic approach can sensitively capture the characteristic “tensile core-compressive surface” stress distribution within the forging and exhibits high sensitivity to process-induced asymmetric distributions. The neural network method, in contrast, shows strong nonlinear fitting capability for complex patterns and yields smaller average errors in high-stress regions. The two methods are complementary, their inversion results remain at the same stress level as the ground truth and effectively reflect the magnitudes and distributions of radial, circumferential, and axial residual stresses within the actual forging. The proposed methodology provides important data support for ensuring the dimensional stability, reliability, and in-service safety of critical load-bearing hot-section components in aero-engines.

To address the challenge of extracting weak nonlinear effects, a method for extracting mixed nonlinear components is proposed based on sliding correlation analysis. Finite element simulations are conducted to investigate the influence of time window width on the extraction results of mixed components. A sine signal modulated with a Hann window is selected as the reference signal, and when the time window width matches the excitation signal length, the extraction of mixed nonlinear components achieves the best results. Mixed nonlinear detection experiments are carried out on specimens with different bonding strengths, and the mixed components are extracted using three different methods. The results indicate that, compared with the three-excitation difference and polarity reversal methods, the sliding correlation analysis method simplifies the nonlinear detection process, allowing accurate extraction of the nonlinear mixed-frequency components using only a single detection signal. The normalized nonlinear coefficient obtained can effectively characterize the bond strength of the adhesive structure.

Scientifically identifying key areas of ecological restoration and rationally planning the ecological restoration space across the region are among the major tasks currently faced by territorial space planning. This study takes the Qingling-Daba Mountain area as an example, by employing the INVEST model, the landscape value, water conservation, soil retention, carbon fixation, and habitat quality of the Qinling-Daba Mountain area are evaluated to identify ecological source areas. Using circuit theory, ecological corridors and intersections are extracted to construct an ecological security pattern for territorial space. Furthermore, key areas of ecological restoration are identified using small watersheds as basic units. The research findings indicate that: high-value ecological resistance areas are primarily distributed in the eastern and northeastern regions of Qinling-Daba Mountain area.The areas of low-resistance, medium-low resistance, medium-high resistance, and high resistance zones are 31 682.38, 179 998.17, 11 346.18 and 1 404.08 km², respectively. The Qinling-Daba Mountain area encompasses a total of 26 ecological source areas covering a combined area of 34 646.87 km². There are 51 potential ecological corridors with a total length of 3 274.111 km and 141 intersections and obstacles. The ecological source areas exhibit the largest coverage in the central and western regions. The spatial distribution of ecological corridors demonstrates longer corridors in the east-west direction and shorter ones in the middle. Intersection points are predominantly concentrated in the eastern part of the Qinling-Daba Mountain area, while barrier points are more densely distributed in the northern part. Five key restoration areas are identified with small watersheds serving as the basic units. Specifically, it includes 10 small watersheds for habitat quality and water conservation restoration, 74 small watersheds for carbon fixation and landscape value restoration, 18 small watersheds for habitat quality and soil conservation restoration, 7 small watersheds for comprehensive restoration, 58 small watersheds for comprehensive restoration of corridors, pinch points, and barrier points.Finally,the strategies for ecological restoration of territorial space are proposed,which provide scientific references for ecological restoration of Qingling-Daba Mountain area.

Based on the GPU-parallelized algorithm of the time-domain spectral element method (SEM), this study investigates the S₀ mode guided waves in anisotropic plates. Parallelization is achieved by integrating high-order spectral element discretization with the CUDA computing platform, thereby establishing a numerical model for guided wave propagation in composite plates. The proposed model accurately simulates the excitation and propagation processes of guided waves in composite plates, extracts the characteristics of the S₀ mode, calculates its wave velocity, and subsequently plots the distribution curves of S₀ mode wave velocity. To validate the model, an experimental system was set up using piezoelectric sensors as excitation units to conduct S₀ mode guided wave propagation tests on T300 composite plates. By incorporating element-level parallel computation and a matrix-free assembly strategy, the proposed method significantly improves computational efficiency and remarkably reduces memory consumption. Numerical verification demonstrates that the method achieves high accuracy while offering superior computational performance and resource efficiency compared with traditional SEM. The simulation results show excellent agreement with the experimental measurements and accurately capture the wave-velocity curve of the S₀ mode, thereby verifying the accuracy and feasibility of the proposed parallel time-domain spectral element method. This study offers an effective technical route for the simulation of guided wave propagation in composite plates and demonstrates broad prospects for application in the field of health monitoring of composite plates.

Exercise ameliorates obesity-induced fat accumulation, insulin resistance and inflammatory response through AMPK/SIRT1/PPARs signaling pathways, yet its efficacy may be modulated by dietary factors. Notably, the excessive consumption of sugar adversely affects host health. The substitution of high-calorie sucrose with low-calorie functional sweeteners has gained widespread popularity among sports enthusiasts and people seeking for weight reduction. Nevertheless, the intricate relationship between sweeteners and host energy metabolism, fat metabolism, and immune health remains largely unexplored. Given the pivotal roles played by exercise and functional sweetener intake in the physiology of obesity and its related complications, we systematically reviewed the impacts of exercise on mitigating obesity-induced dysfunctions across multiple organs(e.g., cardiovascular, cerebral vascular, hepatic, renal, intestinal, skeletal muscle, and adipose tissues). Moreover, impacts of distinct sweeteners on core regulating targets(e.g., AMPK/SIRT1/PPARs, STAT3/NF-κB/Smad, PI3K/Akt signaling pathways) of exercise for combating obesity and its related metabolic diseases were then summarized to illustrate potential synergistic or antagonistic effects of sweeteners on the health effects of exercise. This review furnishes theoretical basis and practical guidance aiming for optimization of exercise strategies that could successfully prevent obesity and improve weight maintenance.

The propagation behavior of Lamb waves in variable thickness plates is complex and exhibits significant dispersion effects, which leads to the difficulty of defect localization and imaging. A defect imaging method for slowly varying-thickness plates based on a Lamb wave propagation model is proposed in this paper. A Lamb wave propagation model for variable thickness plates is established, enabling the prediction of response signals through segmented constant thickness approximation and dispersion curve interpolation. A backward propagation phase compensation algorithm is introduced to eliminate signal distortion induced by dispersion effects. Combined with a delay-and-sum imaging algorithm, this approach achieves high-resolution defect imaging in the slowly varying-thickness plates. Numerical simulations and experimental studies are conducted to validate the proposed method. Numerical results demonstrate a positioning error of approximately 5.8 mm for an 8 mm diameter circular hole defect in a 500 mm×500 mm aluminum plate with thickness varying linearly from 2 mm to 4 mm. Experimental results confirm clear defect visualization with a positioning error of about 10 mm, verifying the method’s applicability in defect detection for slowly varying-thickness plates.

Territorial spatial planning is an important tool for promoting high-quality urban and rural development, while the study of emotional well-being during public transit commutes represents a key aspect of this development in the context of urban public transportation systems.Commuters, as the main users of public transit, face numerous challenges during their commutes. However, current academic research in China on the impact of commuting behavior on the emotional well-being of commuters within the framework of territorial spatial planning is still insufficient. This study focuses on commuters in Shenzhen, using smartphone software developed by the University of Minnesota to collect 4 097 valid travel records. A linear mixed-effects model was employed to explore the relationship between commuting behavior and emotional well-being during public transit commutes, aiming to comprehensively reveal the differences in emotional well-being among commuters in various urban areas. The findings provide theoretical support for integrating master-level spatial planning into more detailed planning stages. The main findings are as follows: 1)For bus users, travel companions and activities undertaken during the trip significantly influenced positive emotions such as happiness and a sense of meaning, and trip purpose largely determined the level of happiness. Moreover, factors including trip distance, duration, the presence of companions, and activities conducted during the trip were significantly related to feelings of fatigue and frustration. 2) For subway users, trips accompanied by partners, roommates, or colleagues were more likely to bring about happiness, especially in the Futian central area, but were also associated with higher stress levels. 3) Socio-economic factors including residential area, marital status, and household income significantly influenced the emotional well-being of public transit users. Finally, this study proposes planning recommendations aligned with territorial spatial planning to enhance the emotional well-being of commuters during public transit commutes.

The fragmentation of cultivated land caused by urban expansion poses a serious threat to the sustainable use of natural resources and food security. Taking the Guanzhong Plain urban agglomeration as a case study, this paper constructs a cultivated land fragmentation index (CLFI) from the perspective of landscape pattern.By integrating the CRITIC weighting method, concentric zone gradient analysis, and land use transition matrix, it systematically explores the spatiotemporal characteristics and response mechanisms of cultivated land fragmentation driven by urban expansion from 1990 to 2023. The results reveal that the total area of cultivated land in the study area decreased by 6.01×10³ km² from 1990 to 2023, of which 4.30×10³ km² was converted to construction land, accounting for 46.62% of new construction land. Fragmentation was primarily concentrated at urban fringes and higher elevations. The degree of cultivated land fragmentation exhibits a significant gradient across urban-rural zones, reflecting variations in urban development scale and stage across cities. The difference in the CLFI (ΔI) indicates an overall increase in fragmentation between 1990 and 2023, with Xianyang city showing the greatest increase in ΔI, which also intensifies with distance from the city center, whereas in most other cities, fragmentation decreases outward. These findings reveal the spatiotemporal evolution patterns of cultivated land fragmentation along the urban-rural gradient under the backdrop of urban agglomeration expansion, offering scientific insights for cultivated land protection, urban expansion planning, and land use optimization.

Although the results of the SASRec(self-attention sequential recommendation)model on both sparse and dense datasets are superior to various sequence recommendation models, in sequence recommendation, it also suffers from representation degradation, that is, frequently occurring Items are often concentrated in a small region of the representation space, degrading recommendation performance. In order to solve this problem, a comparative learning loss function is introduced. Gaussian noise was added in the embedding space for data enhancement, and the original item sequence and the item sequence after data enhancement were used to construct positive sample pairs to promote similar instances in the mapping the closer the distance in the space, and the distribution of different instances in the mapping space showed uniformity. As far as possible, the instance can retain its own personalized information after being mapped to embedding. A comprehensive experimental study on two benchmark datasets shows that, although it appears to be very simple, the proposed method can smoothly adjust the popularity bias of the learned representations.The contrastive learning is based on the graph recommendation model SGL(self-supervised graph learning for recommendation), which suffers from negative sampling bias in representation learning.The model proposed in this paper can effectively improve the recommendation performance.

The aim of this study is to optimize the preparation process of enteric plant dropping pills and to enhance their comprehensive properties in order to promote their application in food and biomedical fields.Single factor experiments and response surface methodology were employed to optimize the optimal formula: pullulan 2.875%, sodium alginate 0.702%,m_{κ-carrageenan}∶m_{gellan gum}=3∶1,glycerol 2.5%,KCl 0.20%. The results of the verification experiments showed that the tensile strength of the resulting film was 10.98±0.42 MPa and the elongation at break was 55.31%±0.65%, which were similar to the theoretical predictions and showed good mechanical properties. Moreover, the enteric plant dropping pills made from this formula were tested in simulated gastric fluid and simulated intestinal fluid for 2 h and 3 h, respectively, and showed no signs of disintegration or softening. Whereas the dropping pills completely disintegrated in simulated colonic fluid within 11±3 min, meeting the requirements of the Pharmacopoeia of the People’s Republic of China(2020 edition). Meanwhile, the dissolution rates of the dropping pills in gastric and intestinal fluids were 38.32%±4.63% and 294.92%±7.33%, respectively, and there was no erosion or deformation, showing good dissolution properties. This study provides a theoretical basis and technical support for the preparation of enteric plant dropping pills with excellent mechanical properties, and strongly promotes their application expansion in the fields of food and medicine.

To improve the “pitch-catch” detection mode in the method for micro-damage detection based on the static component of nonlinear ultrasound, a detection method based on the pulse-echo approach is proposed, which requires only a straight probe to achieve both transmission and reception of ultrasonic waves.Taking an aluminum plate specimen as an example, finite element simulations and experimental measurements were conducted.Nonlinear ultrasonic longitudinal waves were excited using a single straight probe, and their reflected signals were collected.The fundamental frequency and static component were extracted from these signals, and the relative nonlinear coefficient was calculated to evaluate the degree of plastic damage in the aluminum plate.The results show that when using an ultrasonic transducer with a center frequency of 5 MHz for excitation and reception, the static component of the reflected wave can be effectively obtained, verifying the feasibility of the single transducer pulse-echo method for acquiring the static component.The relative nonlinear coefficient of the static component of the reflected wave increases with the thickness of the aluminum plate and the degree of plastic damage, further demonstrating the effectiveness and applicability of this method for micro-damage detection.

Ancient murals carry profound historical, cultural, and artistic value. A network based on 3D hyperspectral Transformer for the classification of style features of Chinese Central Plains style and Western Regions style murals is proposed. By collecting hyperspectral images of murals and constructing corresponding datasets. On this basis, the proposed network is trained using transfer learning methods. The experimental results show that compared with other traditional methods, the accuracy of this study has increased by 0.92%, the precision has increased by 0.12%, the recall has increased by 1.4%, and the F₁ score has increased by 0.75% compared to the optimal algorithm. In addition, compared with the deep learning classification method based on color images, the accuracy improved by 4.5%, the precision improved by 1.9%, the recall improved by 5.4%, and the F₁ score improved by 3.7% compared to the method with the best results.The study verified the unique advantages of hyperspectral information in improving classification accuracy.

As a distinctive historical and cultural heritage, rock art faces significant challenges in image classification due to its vast quantity, wide distribution and complex overlapping elements, which hinder efficient and accurate identification. Recent advances in deep learning offer new opportunities for rock art conservation research. A dual-branch attention fusion network (DBAFN) that integrates the local feature extraction capability of ResNet50 with the global semantic modeling strength of ViT(vision Transformer) is proposed. Using Helan Mountain rock art as a case study, our method employs a gated attention mechanism to dynamically weight features, significantly enhancing classification accuracy. Evaluated on a dataset containing 1 200 Helan Mountain rock art images (human faces,animals,hunting scenes), DBAFN achieves a weighted average classification accuracy of 85.62%, outperforming standalone ResNet50 (81.46%) and ViT (80.02%) models. Notably, the F₁-score for hunting scenes reaches 82.35%. Experiments demonstrate that DBAFN effectively resolves misclassification caused by interleaved elements, providing an innovative technical pathway for semantic analysis of cultural heritage and interdisciplinary research while advancing the application of artificial intelligence in digital conservation of cultural relics.

A novel vortex ultrasonic hole-detection methodology is proposed to address the pressing need for rapid and cost-effective internal defect detection within materials, leveraging the unique properties of vortex ultrasound.A systematic analysis was conducted on the propagation characteristics of vortex ultrasound within titanium alloy materials. The research findings reveal that the second harmonic signal detected at the center of the vortex ultrasound field can effectively ascertain the presence of internal pores in the material. Furthermore, the signal amplitude diminishes as the radial distance and depth between the pore and the center position increase, thereby defining the effective radial and depth detection ranges of vortex ultrasonic inspection and enabling qualitative localization of the pore’s position. Concurrently, the amplitude at the vortex ultrasound center position escalates with the enlargement of the pore size, indicating its potential for quantitative assessment of pore dimensions. Numerical simulation results further corroborate that variations in the presence, position, and size of pores induce alterations in the amplitude at the vortex ultrasound center, thereby validating the efficacy and feasibility of vortex ultrasound for detecting pore defects in titanium alloys. This study is anticipated to furnish novel theoretical insights and methodologies for the swift detection of internal pores in materials.

Gold nanoclusters (AuNCs) have been widely used in the sensing of various medicines. However, they have not been reported in the sensing of glucocorticoids. Based on this, this study prepared an AuNCs nanocomposite CB[7]/AuNCs by combining cucurbit urea CB[7] with the surface ligand ATT of ATT-AuNCs. This CB[7]/AuNCs nanocomposite demonstrated excellent aggregation induced fluorescence enhancement effect. Compared with ATT-AuNCs, the fluorescence intensity of CB[7]/AuNCs was enhanced by 15 fold. Moreover, the CB[7]/AuNCs could induce disaggregation by binding CB[7] to dexamethasone, leading to fluorescence quenching of CB[7]/AuNCs. Based on this mechanism, a sensitive and rapid dexamethasone fluorescence sensing method was developed. The detection limit of the established method was 22 nmol/L and the linear range was 0.05~40 μmol/L. The method can be applied to the rapid detection of dexamethasone in skin care products, and has the advantages of high sensitivity and simple operation.

A fluorescent aptamer probe for highly sensitive sensing detection of microcystin-LR (MC-LR) was developed based on double-stranded DNA-silver nanoclusters (dsDNA-AgNCs) coupled with gold nanorods (AuNRs). Three DNA nucleotides were designed in this work, including one MC-LR aptamer chain and two C-riched single stranded DNA S1 and S2 complementary to aptamer. Single stranded DNA-AgNCs (ssDNA-AgNCs) with red fluorescence were synthesized by using single stranded DNA S1 and S2 as templates and reduction of silver ions (Ag⁺) with sodium borohydride (NaBH₄). Two ssDNA-AgNCs hybridized with aptamer strands at its two ends to form negatively charged dsDNA-AgNCs with a maximum fluorescence emission at 624 nm. In the presence of positively charged AuNRs, fluorescent resonance energy transfer (FRET) occurred between dsDNA-AgNCs and AuNRs due to the electrostatic interaction, resulting in fluorescent quenching of dsDNA-AgNCs. After addition of the target MC-LR, MC-LR specifically bonded with aptamer of dsDNA-AgNCs, resulting in dissociation of dsDNA-AgNCs to ssDNA-AgNCs and restoring of fluorescence intensity. An “off-on” fluorescent aptamer probe was constructed for MC-LR quantitative detection. The proposed method exhibited a linear response for MC-LR detection in the concentration range of 5 ng/L to 500 μg/L with a detection limit of 1.7 ng/L. The proposed fluorescent aptamer probe possesses the advantages of simple preparation and high selectivity and sensitivity, and can be applied for the MC-LR detection in actual water samples.

The limited visible light absorption and relatively large bandgap of zeolitic imidazolate framework-8 (ZIF-8) substantially restrict its antibacterial efficacy under visible light irradiation. Herein, a novel visible-light-responsive antibacterial nanocomposite, EGCG@ZIF-8, was synthesized via post-synthetic modification through the etching of ZIF-8 with epigallocatechin gallate (EGCG). Under visible light irradiation, EGCG@ZIF-8 can efficiently generate reactive oxygen species (ROS), demonstrating a antibacterial rate of 99.999 9% against Staphylococcus aureus within 60 min, which significantly surpassed the performance of pristine ZIF-8. Furthermore, EGCG@ZIF-8 retained robust antibacterial activity even after five consecutive usage cycles. The incorporation of EGCG narrowed the bandgap of ZIF-8, promoting the separation and migration of photogenerated electron-hole pairs, which enhanced the visible-light photocatalytic performance of the composite and subsequently boosted ROS generation. In addition, in vitro cytotoxicity assays confirmed the outstanding biocompatibility of EGCG@ZIF-8. This study not only offers a straightforward and efficient approach for synthesizing high-performance antibacterial composites, but also paves the way for the development of innovative visible-light-driven antibacterial materials, underscoring its substantial scientific and practical significance.

In recent years, advances in semiconductor silicon wafer manufacturing have increased the demand for accurate detection of subsurface defects. When inspecting microcracks far smaller than the wavelength, nonlinear Lamb waves offer distinct advantages such as high efficiency, high sensitivity, and nondestructive evaluation. However, their application in silicon wafer inspection remains limited, partly due to the unclear relationship between nonlinear Lamb wave signal characteristics and subsurface microcrack features. In this work, finite element models of Lamb wave propagation in silicon wafers with subsurface microcracks are established. The Lamb wave mode pair S₀-s₀, satisfying phase-velocity matching, is employed to investigate how the acoustic nonlinearity parameter (ANP) of the second-harmonic correlates with propagation distance and microcrack characteristics. Simulation results indicate that the ANP increases with propagation distance, and the presence of subsurface microcracks significantly amplifies its amplitude. Moreover, the relative ANP increases with the number,length, density of subsurface microcracks, and subsurface damage layer thickness. Additionally, for a given density, the length of subsurface microcracks has a more significant influence on the relative ANP than their number. This study illustrates the potential of nonlinear Lamb waves for detecting subsurface microcracks in silicon wafers and provides simulation-based validation for its feasibility.

To study the variations of phylogenetic diversity and species diversity of woody plant communities in Karst areas under different terrains and their influencing factors, woody plants in different terrains within the Maolan Dynamic Plot of Karst Forest Eco-system in South China were selected as the research objects. Utilizing the hierarchical partitioning (HP) method and Pearson correlation analysis, it examined the changing trends of diversity indices under different terrains (depression, valley, hillside, saddle, ridge and summit), the key terrain factors driving the diversity indices, and the interrelationship between species diversity and phylogenetic diversity. The results demonstrate that: 1)There is a significant difference in the community species richness index between depressions and saddles (P<0.05), while there is no significant difference in the community Pielou evenness index and Simpson dominance index among the six terrains. The Faith’s phylogenetic diversity index and the mean nearest taxon phylogenetic distance show significant differences between depressions and saddles. The mean pair phylogenetic distance shows a significant difference between hillsides and depressions,valleys. 2)The relative influence of aspect on the diversity indices is the largest and plays a leading role, followed by slope, and the influence of four topographic factors on the diversity indices is not significant.3)Species richness index is significantly positively correlated with Faith’s phylogenetic diversity index (P<0.000 1), significantly negatively correlated with mean nearest taxon phylogenetic distance and Simpson dominance index. Faith’s phylogenetic diversity index is significantly negatively correlated with mean nearest taxon phylogenetic distance. This study reveals that different terrains in the Maolan Karst area have different effects on species diversity and phylogenetic diversity, and the correlation between species diversity and phylogenetic diversity is not high. The aspect and slope jointly drive the variations of species diversity and phylogenetic diversity.

This study investigated the effects of composite spices (five-spice powder and thirteen-spice powder) marinating treatment on the quality and microbial properties of ground beef.The five-spice powder and thirteen-spice powder were added to the ground beef and marinated at 4 ℃. The changes of TVB-N value and the total number of colonies in ground beef during marinating were measured. Electronic nose was used to analyze the volatile flavor substances in ground beef. The bacterial flora structure in marinated ground beef was analyzed by 16S rRNA high-throughput sequencing technique. Results showed that the two spices could significantly inhibit the increase of TVB-N value and the total number of colonies in ground beef (P<0.05), improve the flavor of ground beef and change the microbial diversity and richness of ground beef. The two spices had obvious inhibitory effect on Pseudomonas.The above results confirmed that the two spices have significant improvement effect on the quality of marinated ground beef, and the improvement effect of thirteen-spice powder is superior to that of five-spice powder.

Triadic concept analysis is a theoretical approach to knowledge discovery of a triadic context by constructing triadic concepts. A triadic concept reduct is a minimal triadic concept set that preserves triadic relation of a triadic context unchanged.The problems of triadic concept reduction based on the representative triadic concept matrix are studied. Firstly, the representative triadic concept matrix is defined. On the basis of the matrix, judging theorems of a triadic concept consistent set and a triadic concept reduct are given. Secondly, the minimum representative triadic concept matrix is defined and the algorithm to obtain triadic concept reducts through the matrix is given. Finally, the characteristics of core, relatively necessary and absolutely unnecessary triadic concepts are given respectively from the perspective of the minimum representative triadic concept matrix.