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In particle image velocimetry (PIV), overexposure is particularly common in regions with high illumination. In particular, strong scattering or background reflection at the liquid–gas interface will make the overexposure phenomenon more obvious, resulting in local pixel saturation, which will significantly reduce the particle image quality, and thus reduce the particle recognition rate and the accuracy of velocity field estimation. This study addresses the overexposure challenges in particle image velocimetry applications, mainly to address the challenge that the velocity field cannot be measured due to the difficulty in effectively detecting particles in the exposed area. In order to address the challenge of overexposure, this paper does not use traditional frame-based high-speed cameras, but instead proposes a particle image velocimetry algorithm based on adaptive integral spike camera data using a neuromorphic vision sensor (NVS). Specifically, by performing target-background segmentation on high-frequency digital spike signals, the method suppresses high illumination background regions and thus effectively mitigates overexposure. Then the spike data are further adaptively integrated based on both regional background illumination characteristics and the spike frequency features of particles with varying velocities, resulting in high signal-to-noise ratio (SNR) reconstructed particle images. Flow field computation is subsequently conducted using the reconstructed particle images, with validation through both simulation and experiment. In simulation, in the overexposed area, the average flow velocity estimation error of frame-based cameras is 8.594 times that of spike-based cameras. In the experiments, the spike camera successfully captured continuous high-density particle trajectories, yielding measurable and continuous velocity fields. Experimental results demonstrate that the proposed particle image velocimetry algorithm based on the adaptive integration of the spike camera effectively addresses overexposure challenges caused by high illumination of the liquid–gas interface in flow field measurements.

Urban interstices are transitional spaces that emerge between expanding metropolitan regions. Despite increasing scholarly interest, the empirical analysis of these cities’ spatial development and functional integration remains scarce, particularly within the contexts of state-led urbanization, where administrative boundaries significantly shape development outcomes. This study quantitatively investigates urban interstice dynamics through a detailed analysis of Jurong City, which is located between the cities of Nanjing and Zhenjiang in the Chinese Yangtze River Delta. Utilizing mobile phone signaling data and origin–destination (OD) flow analysis, this research study systematically measures the intensity, directionality, and spatial patterns of human mobility flows between Jurong and its neighboring cities. The findings demonstrate that Jurong has a strong functional connection to Nanjing, with nearly 60% of its outbound mobility directed toward the city, despite being governed by Zhenjiang. This misalignment reveals a structural tension between functional integration and administrative hierarchy, fostering distinct subcenters such as Baohua (residential) and Guozhuang (innovation). Overall, the findings highlight the need to move beyond territorially bounded governance toward functionally coordinated regional strategies. Urban interstices can serve as effective connectors across fragmented systems, supporting more balanced and adaptive metropolitan integration. Leveraging real-time mobility data enables planners to identify spatial–functional linkages that transcend administrative boundaries, informing more responsive governance without requiring formal realignment.

The spatial genes of rural settlements show a lot of different traditional settlement traits, which makes them a great starting point for studying rural spatial morphology. However, qualitative and macro-regional statistical indicators are usually used to find and extract rural settlement spatial genes. Taking Yubei Village in the Nanxi River Basin as an example, this study combined remote sensing images, real-time drone mapping, GIS (geographic information system), and space syntax, extracted 12 key indicators from five dimensions (landform and water features (environment), boundary morphology, spatial structure, street scale, and building scale), and quantitatively “decoded” the spatial genes of the settlement. The results showed that (1) the settlement is a “three mountains and one water” pattern, with cultivated land accounting for 37.4% and forest land accounting for 34.3% of the area within the 500 m buffer zone, while the landscape spatial diversity index (LSDI) is 0.708. (2) The boundary morphology is compact and agglomerated, and locally complex but overall orderly, with an aspect ratio of 1.04, a comprehensive morphological index of 1.53, and a comprehensive fractal dimension of 1.31. (3) The settlement is a “clan core–radial lane” network: the global integration degree of the axis to the holy hall is the highest (0.707), and the local integration degree R3 peak of the six-room ancestral hall reaches 2.255. Most lane widths are concentrated between 1.2 and 2.8 m, and the eaves are mostly higher than 4 m, forming a typical “narrow lanes and high houses” water town streetscape. (4) The architectural style is a combination of black bricks and gray tiles, gable roofs and horsehead walls, and “I”-shaped planes (63.95%). This study ultimately constructed a settlement space gene map and digital library, providing a replicable quantitative process for the diagnosis of Jiangnan water town settlements and heritage protection planning.

The existing motor fault classification methods mostly use sensors to detect a single fault feature, which makes it difficult to ensure high diagnostic accuracy. In this paper, a motor fault classification method based on multi-source information fusion Naive Bayes classification algorithm is proposed. Firstly, this paper introduces the concept and advantages of multi-source information fusion, as well as its problems of miscellaneous information and inconsistent data magnitude. For example, as this paper classifies the fault of generators, there are many physical quantities, such as voltage, current and temperature, which are not in the same dimension, therefore it is difficult to fuse. Then, aiming at the corresponding problems, this paper uses a PCA dimension reduction method to remove redundant information and reduce the dimension of multi-dimensional complex information. Aiming at the problem of unequal data magnitude, the interval mapping method is adopted to effectively solve the misjudgment caused by unequal data magnitude. After the initial multi-source information processing, the classical Naive Bayes classification algorithm is used for fault classification, and the algorithm diagnosis and verification are carried out according to the statistical fault data. Use of the algorithm increases accuracy to more than 97%.

To make sure that regional reconstruction goes smoothly, it is important to know how rural construction areas in a river basin change over time and space and what factors affect those changes. This study focuses on the rural construction areas in the Nanxi River Basin. Through geographic information systems’ spatial analysis methods, the construction area morphology, center of gravity migration, and agglomeration degree are analyzed to reveal its spatiotemporal evolution from 1990 to 2020. The geographical detector is used to explore the interaction of multidimensional driving factors such as natural geography, socio-economic development, and cultural heritage protection. The research results show that (1) the rural construction area in the Nanxi River Basin shows an evolution trend of “agglomeration expansion and northward shift of the center of gravity.” (2) Cultural, economic, and natural factors all play a part in how rural construction areas change over time. Cultural factors, like the distance between farms and the layout of educational resources, have the most significant impact, followed by economic and natural factors. (3) The study also suggests a “cultural gene-natural base-economic potential” model that can help us understand how to protect cultural heritage and boost the economy at the same time. This result has direct guiding significance for the implementation of China’s rural revitalization strategy. It can give natural resource planning departments a scientific way to figure out the best way to use land and give cultural heritage management agencies a way to come up with safe development plans. It also provides a reference for the sustainable development path of resource-rich villages around the world.

Change detection for synthetic aperture radar (SAR) images effectively identifies and analyzes changes in the ground surface, demonstrating significant value in applications such as urban planning, natural disaster assessment, and environmental protection. Since speckle noise is an inherent characteristic of SAR images, noise suppression has always been a challenging problem. At the same time, the existing unsupervised deep learning-based methods relying on the pseudo labels may lead to a low-performance network. These methods are high data-dependent. To this end, we propose a novel unsupervised change detection method based on curvelet fusion and local patch similarity information clustering (CF-LPSICM). Firstly, a curvelet fusion module is designed to utilize the complementary information of different difference images. Different fusion rules are designed for the low-frequency subband, mid-frequency directional subband, and high-frequency subband of curvelet coefficients. Then the proposed local patch similarity information clustering algorithm is used to classify the image pixels to output the final change map. The pixels with similar structures and the weight of spatial information are incorporated into the traditional clustering algorithm in a fuzzy way, which greatly suppresses the speckle noise and enhances the structural information of the changing area. Experimental results and analysis on five datasets verify the effectiveness and robustness of the proposed method.

In the new paradigm where the digital economy is profoundly reshaping urban spatial organization, how the platform economy transcends traditional geographical constraints to restructure the urban system has become a strategic issue in urban geography and regional economics. This study develops an innovative measurement framework based on Business-to-Business (B2B) e-commerce enterprises to analyze platform-driven urban systems across 337 Chinese cities. Using spatial autocorrelation, rank-size distributions, and urban scaling laws, we reveal spatial differentiation patterns of cities’ B2B platforms. Combining Ordinary Least Squares (OLS) and random forest models with Partial Dependence Plots (PDP), Individual Conditional Expectations (ICE), and Locally Weighted Scatterplot Smoothing (LOWESS), we uncover non-linear mechanisms between platform development and urban attributes. Results indicate that (1) B2B platforms exhibit “superliner agglomeration” and “gradient locking”, reinforcing advantages in top-tier cities; (2) platform effects are non-linear, with Gross Domestic Product (GDP), Information Technology (IT) employment, and service sector shares showing threshold-enhanced marginal effects, while manufacturing bases display saturation effects; and (3) regional divergence exists, with eastern consumer-oriented platforms forming digital synergies, while western manufacturing platforms face path dependence. The findings highlight that platform economy evolution is shaped by a “threshold–adaptation–differentiation” mechanism rather than neutral diffusion. This study provides new insights into urban system restructuring under digital transformation.

Background Coccinellini lady beetles have been applied as biological control agent of aphids, however, not all of these species are obligately aphidophagous. Thus, a comprehensive understanding of the molecular mechanisms behind predaceous specificity of Coccinellini lady beetles can provide important clues for evaluating their performance and ecological risk assessment in biological control. Post-transcriptional regulations act a key role in shaping organisms’ rapid adaptation to changing environment, yet, little is known about their role in the acclimation of Coccinellini lady beetles to non-target preys. Results In this study, we conducted a genome-wide investigation to alternative splicing (AS) dynamics in three Coccinellini species Propylea japonica , Coccinella septempunctata and Harmonia axyridis in response to feeding shift from natural prey bean aphids ( Megoura japonica ) to non-target insect citrus mealybugs ( Planococcus citri ). Compared to aphid-feeding, all three lady beetles were subject to substantial splicing changes when preying on mealybugs. Most of these differentially spliced genes (DSGs) were not differentially expressed, and regulated different pathways from differentially expressed genes, indicating the functionally nonredundant role of AS. The DSGs were primarily associated with energy derivation, organ formation and development, chemosensation and immune responses, which may promote tolerance of lady beetles to nutrient deprivation and pathogen challenges induced by prey shift. The lady beetles feeding on mealybugs moreover downregulated the generation of splicing products containing premature termination codons (PTCs) for the genes involved in energy derivation and stimulus responses, to fine-tune their protein expression and rationalize energy allocation. Conclusion These findings unraveled the functional significance of AS reprogramming in modulating acclimation of Coccinellini lady beetles to prey shift from aphids to non-target insects and provided new genetic clues for evaluating their ecological safety as biological control agents.

The insulo-opercular network functions critically not only in encoding taste, but also in guiding behavior based on anticipated food availability. However, there remains no direct measurement of insulo-opercular activity when humans anticipate taste. Here, we collect direct, intracranial recordings during a food task that elicits anticipatory and consummatory taste responses, and during ad libitum consumption of meals. While cue-specific high-frequency broadband (70–170 Hz) activity predominant in the left posterior insula is selective for taste-neutral cues, sparse cue-specific regions in the anterior insula are selective for palatable cues. Latency analysis reveals this insular activity is preceded by non-discriminatory activity in the frontal operculum. During ad libitum meal consumption, time-locked high-frequency broadband activity at the time of food intake discriminates food types and is associated with cue-specific activity during the task. These findings reveal spatiotemporally-specific activity in the human insulo-opercular cortex that underlies anticipatory evaluation of food across both controlled and naturalistic settings. Animal studies have shown that insulo-opercular network function is critical in gustation and in behaviour based on anticipated food availability. The authors describe activities within the human insulo-opercular cortex which underlie anticipatory food evaluation in both controlled and naturalistic settings.

Pain remains poorly understood in task-free contexts, limiting our understanding of its neurobehavioral basis in naturalistic settings. Here, we use a multimodal, data-driven approach with intracranial electroencephalography, pain self-reports, and facial expression analysis to study acute pain in twelve epilepsy patients under continuous neural and audiovisual monitoring. Using machine learning, we successfully decode individual participants’ high versus low pain states from distributed neural activity, involving mesolimbic regions, striatum, and temporoparietal cortex. Neural representation of pain remains stable for hours and is modulated by pain onset and relief. Objective facial expressions also classify pain states, concordant with neural findings. Importantly, we identify transient periods of momentary pain as a distinct naturalistic acute pain measure, which can be reliably discriminated from affect-neutral periods using neural and facial features. These findings reveal reliable neurobehavioral markers of acute pain across naturalistic contexts, underscoring the potential for monitoring and personalizing pain interventions in real-world settings. This study uses brain recordings, self-reports, and facial analysis to decode acute pain in epilepsy patients. Machine learning reveals stable neural markers in mesolimbic, striatal, and cortical regions, plus facial cues, enabling reliable pain detection in naturalistic settings.