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.

Railways play an irreplaceable role in the long-distance transportation of bulk energy materials such as coal. In the context of uncertainty becoming the new normal in transportation operations, it has far-reaching strategic significance of exploring the reliability of railway transportation networks in energy-rich regions to enhance the resilience of railway transportation networks and ensure national energy security.On the basis, the railway network in Shanxi-Shaanxi-Inner Mongolia energy-rich region, one of China’s most important energy and chemical bases, is selected as the research object.A railway network model of Shanxi-Shaanxi-Inner Mongolia energy-rich region is constructed by using the Space-L method, the reliability of the railway transportation network is analyzed systematically by using two attack strategies: random attack and deliberate attack, and key transportation corridors are identified.The research results indicate that the railway network in Shanxi-Shaanxi-Inner Mongolia energy-rich region is relatively sparse and exhibits scale-free network characteristics. Small-scale random attacks render the network more reliable, while the network becomes more robust under high-betweenness attacks but exhibits vulnerability under high-degree attacks. Under different attack strategies, the cumulative effect of multiple node failures leads to a phased and sharp decline in network performance. The key corridors of the railway network in Shanxi-Shaanxi-Inner Mongolia energy-rich region are highly concentrated in small areas, mainly located at the intersections of coal transportation dedicated lines and external transportation railways. In future energy transportation planning, efforts of protecting these key corridors must be strengthened to ensure their continuous and stable operation, maintaining the safe operation of the energy transportation network.

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.

With the rapid development of information and communication technology and transportation infrastructure, inter-city traffic is a vital foundation for regional integration. Based on multi-source data such as mobile phone signaling and social and economic development, this paper employs the rank-scale method and MRQAP regression to investigate the characteristics and influencing factors of inter-city flow network structure in Hunan province counties during the week and weekend. The results show that: the scale polarization effect of daily inter-city mobility in each county is significant. There are more counties with low-order and low-floating populations, and the main source of population mobility is high-order counties. The daily inter-city flow network of Hunan province takes Changsha city as the absolute center, and the prefecture-level cities take the main urban area of the city as the center, forming an inter-city flow network of “core-hinterland”. Changsha city is the hub of the province on weekdays and weekends. Regarding the inter-city flow group structure, the network aggregation on weekdays and weekends shows the characteristics of grade and group interaction. The inter-city group has a high degree of coupling with the Changsha-Zhuzhou-Xiangtan intercity railway, Shaolin expressway, and Beijing-Hong Kong-Macao expressway. The inter-city flow within the city group is greatly affected by distance. The spatial agglomeration effect of population will generate more demand for daily intercity mobility, and the population size will strengthen intercity mobility between counties. The smaller the gap between wage income and housing price levels between adjacent counties, within the same city area, and the shorter the travel time between counties, the more daily intercity mobility there will be. Among them, the population agglomeration effect and administrative resource control during weekdays are stronger.Economically developed counties may intercept potential migrant populations through the “income highland effect”.The study of daily intercity flow at the county level is helpful in formulating targeted facility allocation strategies based on regional differentiated needs, and provides reference significance for the high-quality integrated development layout of Hunan province.

In ecologically fragile areas, road expansion exacerbates issues such as landscape fragmentation and biodiversity loss.The study takes the Qinba Mountain region of Southern Shaanxi, a typical ecologically fragile region, as the research subject, constructs a five-dimensional landscape fragmentation evaluation system encompassing quantity, shape, area, aggregation, and connectivity.The spatial heterogeneity effects of different road structure characteristics on multi-dimensional landscape fragmentation are further investigated based on multi-scale geographically weighted regression (MGWR). The results show that: regional landscape fragmentation exhibits significant spatial differentiation, with Baoji, Xi’an, and Weinan forming a low-fragmentation core area, Shangluo serving as a moderate fragmentation transition zone, and central Hanzhong and Ankang constituting high fragmentation clustering areas. Sensitivity to road disturbance varies across multi-dimensional fragmentation features, with quantity and area characteristics being the most sensitive, followed by shape and connectivity features, while aggregation features exhibit the weakest sensitivity.Road density and distance from roads have the strongest explanatory power for the number, area, and connectivity of ecological patches, with lower-grade roads exerting a greater influence than higher-grade roads. Road curvature shows a significant negative correlation with the shape of ecological patches, whereas network degree centrality correlates with both patch shape and area.The study can provide a reference basis for future road construction and ecological conservation in the Qinba Mountain region of Southern Shaanxi.

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.

The ecological environment quality of Fujian province remains excellent and stable. It is helpful to reveal the effects of the provincial expressway network evolution on the landscape ecology by studying the expressway network landscape and its impact on ecological quality. This study selects 3 km on both sides of the expressways in Fujian province as the research area, utilizes GIS technology, Fragstats software with landscape pattern indices, the remote sensing ecological index (RSEI), and the InVEST model, to investigate the evolution of landscape patterns and ecological quality of expressway corridor in Fujian province from 1994 to 2022. The results show that: the 30-90 m range constitutes the primary scale domain for the expressway corridor scale effect analysis, with 40 m being identified as the optimal grain size for landscape pattern analysis of the expressway corridor. The fragmentation index of cultivated land, forestland, grassland, water area and unused land have increased in varying degrees, while the fragmentation index of construction land has continued to decline. The overall fragmentation of the expressway corridor has intensified, the dispersion of various types has increased, and the stability of the ecosystem structure has declined. Carbon storage of the expressway corridor, forest land, and grassland has declined, while cultivated land, water area, unused land, and construction land increased in varying degrees. Areas with greater changes in carbon storage are mainly located in coastal cities with faster development. The average RSEI value of the expressway corridor is higher than 0.6, and the ecological environment quality is good. The areas with poor and relatively poor RSEI levels in the expressway corridor are mainly distributed in the southeastern coastal cities of Fujian province.The ecological environment quality of the expressway corridor improved and deteriorated are roughly the same over the 28 years. The fragmentation index and RSEI value within 1 000 m of the expressway corridor are most significantly affected, and the impact beyond 1 000 m gradually weakens.

While the development of high-speed rail (HSR) has notably reduced travel time, its implications for equitable accessibility within urban clusters and spatial equity remain critical concerns. The study divides the medium-term and long-term intercity railway initiatives in the Beijing-Tianjin-Hebei urban into three phases from 2016 to 2030. Using road network datasets and socio-economic indicators, accessibility is analyzed through the average travel time, while spatial equity is evaluated using the accessibility dispersion index and the territorial cohesion index. The results indicate that the intercity HSR network substantially improves accessibility and strengthens regional connectivity at the municipal level. From 2016 to 2023, average intercity travel time decreases by 33.1% at the municipal level and 20.7% at the county level. From 2023 to 2030, accessibility increases by 9.5% at the municipal level and 10.2% at the county level, with a primary emphasis on improving the city connectivity in the capital region. Spatial equity and cohesion within the region are enhanced at the municipal level, whereas equity at the county level tends to polarization. The study recommends that both national and local governments should consider policies and coordination measures at different planning levels to promote spatial development and regional equity.

Enhancing the transportation and tourism compatibility and promoting the integration of transportation and tourism are important levers for achieving rural revitalization and urban-rural integration in the context of high-quality development. Based on the framework of cognition-experience perspective for rural transportation-tourism coupling coordination measurement, this paper integrates the elements of visitor experience and perception satisfaction in transportation and tourism, and uses the statistical analysis and barrier model method to scientifically measure the level of rural transportation-tourism coupling coordination in the research area of Nanbaozhai village, Fangyan village and Liujiagou village in the suburbs of Xi’an city,identify the influencing factors of coordinated development of transportation and tourism coupling. The results show that: the coordination degree of transportation-tourism coupling in the research villages shows significant heterogeneity in both cognition and experience perspectives, and the order of coordination degree between different perspectives forms a strong contrast. Liujiagou village, Nanbaozhai village and Fangyan village show different misalignment directions and increasing misalignment degrees in coordination of transportation-tourism coupling. The main reason for the cognition-experience compatibility of rural transportation-tourism coupling coordination is the interaction between the objective conditions of rural transportation and tourism and the visitor’s transportation and tourism experience. The cognition dimension is mainly determined by the level of development of transportation and tourism and their synchronized development level, while the experience dimension is mainly affected by the tourism characteristics and the traffic and tourism environment pressure caused by the number of tourists. The study provides scientific basis for optimizing the measurement method of transportation-tourism coupling coordination level, promoting the continuous improvement of transportation and tourism functions in suburban areas of large cities, and driving the construction of beautiful rural areas that are suitable for tourism and industry.

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.

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.

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.

Based on social sensing theory, multi-source data from the online toll collection system (OTC) are used and methods such as graph theory modeling, complex networks, and function analysis are employed to explore the spatial interaction patterns of expressway traffic flow from the “section-station-city” scale. The results indicate that: 1)Section flow as a “flow process” which provides a micro perspective on the decay process of perceived distance and the spatial organization of traffic. At the section scale, densely trafficked sections of expressways in Guangdong province belong to the network area of “two loops, four horizontals and seven radials”, particularly concentrated on national expressways. Power functions have a high degree of explanatory power for the distance decay law of passenger and freight flows on expressways, and the aggregation level of motor vehicles in the Pearl River Delta urban agglomeration decreases with increasing vehicle size, with small vehicles (cars, light trucks) having the highest degree of aggregation. 2)Compared to section flow, as a “flow result”,station flow intuitively exhibits the characteristics of expressways serving short-distance transportation. Core station flows exhibit spatial proximity and primarily appear in short-cluster forms. Boundary station flows can be categorized into three interaction relationships: Guangdong-Guangxi, Guangdong-Hunan and Guangdong-Jiangxi,and Guangdong-Fujian. 3)In contrast to station flow, city flow offers a macro perspective on perceived urban spatial interactions. Based on city flow, the study examines the current development status of the “five major urban agglomerations” in Guangdong province. It reveals that internal traffic connections within the urban agglomeration on the west bank of the Pearl River are relatively loose. Conversely, the core urban agglomerations of Guangzhou and Shenzhen exhibit a trend of integrated traffic development. This study elucidates the methods and model correlations for mining multi-scale traffic flow big data, summarizes the perceptual differences in socio-economic phenomena under different scales of interaction patterns, and extends the methods and theoretical research of social perception from the perspective of scale differences.

Taking the urban agglomeration in the middle reaches of the Yangtze River as the research object, the RMP theory is introduced into the theoretical analysis framework and an appraisal index system for the integration efficiency of the culture and tourism industry is constructed.DEA-Malmquist index, center of gravity and standard deviation ellipse, and geodetector models are used to explore the time-space development landscape of the integration efficiency of the culture and tourism industry for the urban agglomeration in the middle reaches of the Yangtze River and its influencing factors from 2009 to 2021. The findings suggested that: in terms of time-series evolution, the general degree of integration efficiency of cultural and tourism industry is comparatively high during the study period, with a tendency of increasing firstly and then reducing secondly. The Malmquist index is rising at an average annual rate of 11.9%, and technological progress and scale effect are the primary reasons for sustained growth in total factor productivity.From the perspective of spatial distribution, the integration efficiency of cultural and tourism industry shows the “core-edge”structure with “Wuhan-Changsha-Nanchang”as the core and other cities as the edges. The integration efficiency of culture and tourism industry in the three regions has been improving, and the advantages of the “core” region have been highlighting, with significant regional differences.In an evolutionary space-time dimension, the integration efficiency of the cultural and tourism industry as a whole shows a spatial distribution pattern dominated by the “southeast-northwest” direction. The trends of the gravity center show in the direction of “northeast-southwest-southeast”, and the spatial pattern shows the characteristics of agglomeration.The results of geographic exploration show that the integration efficiency of cultural and tourism industry is the result of a combination of factors, such as consumer demand, industrial structure, digitalization level, government regulation, transportation level and human capital.

Scientific identification of the intrinsic connection between digital infrastructure construction and tourism economic resilience is of great significance for driving the transformation of the digitalization of the Yangtze River Economic Belt and improving the modern tourism system. The entropy method is used to assess the degree of development of the two systems during 2011-2022, and the characteristics of the spatio-temporal coupling between digital infrastructure construction and tourism economic resilience in 108 prefectural-level and above cities in the Yangtze River Economic Belt.As well as the influencing factors are profoundly revealed through the coupling coordination degree model, kernel density estimation, exploratory spatial analysis (ESDA) and geographic detector. Research shows that: during the study period, the composite index of digital infrastructure construction and tourism economic resilience of the Yangtze River Economic Belt showed a fluctuating upward trend, and presented multi-level “core-edge” characteristics, and the phenomenon of polarization is becoming more and more obvious. The coupling and coordination degree between the construction of digital infrastructure and the resilience of the tourism economy in the Yangtze River Economic Belt has been increasing year by year, with significant spatial differences between cities. There is a spatial clustering phenomenon in the coupling coordination degree of the two systems, but the spatial clustering characteristics are weakened. The level of economic growth, human capita and travel resources endowment are the main factors affecting the degree of coupling coordination. The impact of human capital on factor interaction is the strongest. The interaction between economic development level and human capital, human capital and transportation accessibility, and human capital and tourism resource endowment has the strongest explanatory power for the coupling and coordination level.

Cities are the spatial carriers of corporate activities, and the cross-regional investment behavior of tourism enterprises represents and maps the interregional tourism economic competition relationship. Based on the construction of the theoretical framework, using the off-site investment data of 265 tourism enterprises, we conduct an in-depth analysis of the complexity characteristics and driving mechanism of the off-site investment network in the three time periods of 2008-2012, 2013-2017, and 2018-2023, with the help of spatial analysis, social network analysis and weighted index random graph model. The study finds that: the spatio-temporal complexity of Chinese tourism enterprises’ cross-regional investment network is significant. The network has shifted from Beijing and Shanghai as a single center to a triangular structure with “Beijing-Shanghai-Guangzhou”,“Shanghai-Xi’an-Haikou”,“Shanghai-Shenzhen-Kunming” as the core nodes. Beijing, Shanghai and Haikou were the major outward investment cities during the study period, Xi’an, Shenzhen and Kunming were important inward investment cities. The cross-regional investment network of Chinese tourism enterprises has developed a typical “core-edge” circle structure, showing a trend of evolution from “dominated by a single core city” to “interconnected by multiple core cities”.At the same time, it is characterized by “small world” and scalelessness.The formation of tourism enterprises’ cross-regional investment network is the result of endogenous structural effect, actor-relationship effect and multi-dimensional neighboring mechanism. Among them, tourism resource endowment, tourism market size, government support level, factor agglomeration and diffusion level, degree of openness to the outside world, and the convergence and transferability of investment have a significant contribution to the formation of the investment network. Tourism enterprises tend to establish investment links between cities with strong economic ties, geographical proximity, fewer cultural differences and similar institutions.

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.

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.

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.

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.

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.

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.

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 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.

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 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 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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.