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The United Nations Sustainable Development Goal (SDG) 11.1 emphasizes improving well-being, ensuring housing security, and promoting social equity. Informal settlements, one of the most vulnerable groups, require significant attention due to their dynamic changes and habitat quality. These areas limit the ability to comprehensively capture spatial heterogeneity and dynamic shifts in regional sustainable development. This study proposes an integrated approach using multi-source remote sensing data to extract the spatial distribution of informal settlements in Mumbai and assess their habitat environment quality. Specifically, seasonal spectral indices and texture features were constructed using Sentinel and SAR data, combined with the mean decrease impurity (MDI) indicator and hierarchical clustering to optimize feature selection, ultimately using a random forest (RF) model to extract the spatial distribution of informal settlements in Mumbai. Additionally, an innovative habitat environment index was developed through a Gaussian fuzzy evaluation model based on entropy weighting, providing a more robust assessment of habitat quality for informal settlements. The study demonstrates that: (1) texture features from the gray level co-occurrence matrix (GLCM) significantly improved the classification of informal settlements, with the random forest classification model achieving a kappa coefficient above 0.77, an overall accuracy exceeding 0.89, and F1 scores above 0.90; (2) informal settlements exhibited two primary development patterns: gradual expansion near formal residential areas and dependence on natural resources such as farmland, forests, and water bodies; (3) economic vitality emerged as a critical factor in improving the living environment, while social, natural, and residential conditions remained relatively stable; (4) the proportion of highly suitable and moderately suitable areas increased from 65.62% to 65.92%, although the overall improvement in informal settlements remained slow. This study highlights the novel integration of multi-source remote sensing data with machine learning for precise spatial extraction and comprehensive habitat quality assessment, providing valuable insights into urban planning and sustainable development strategies. Full article

L-rhamnose has recently gained attention for its potential to enhance vaccine antigenicity. To optimize its use as a vaccine adjuvant, it is important to understand the dehydration behavior of L-rhamnose monohydrate, which plays a critical role in modifying its physicochemical properties. This study investigated the spectroscopic characteristics of L-rhamnose and its monohydrate using terahertz time-domain spectroscopy (THz-TDS), Raman spectroscopy, and powder X-ray diffraction (PXRD). The results indicate that THz-TDS can more effectively distinguish the spectral features of these two compounds and can be used to reflect the structural changes in L-rhamnose monohydrate before and after dehydration. THz spectral data show that dehydration of L-rhamnose occurs at 100 °C, and continuous heating at 100 °C can complete the dehydration process within 6 min. Density functional theory (DFT) calculations revealed that water molecule vibrations significantly affect the THz absorption peaks. These findings indicate that removing water during dehydration causes substantial changes in molecular structure and dynamics. Overall, this study highlights the value of combining THz-TDS with DFT calculations to investigate the structures of carbohydrates and their hydrates, providing an accurate method for understanding the dehydration process and molecular interactions in hydrated systems. This approach holds significant importance for the development of effective vaccine adjuvants. Full article

The accurate extraction of cultivated land information is crucial for optimizing regional farmland layouts and enhancing food supply. To address the problem of low accuracy in existing cultivated land products and the poor applicability of cultivated land extraction methods in fragmented, small parcel agricultural landscapes and complex terrain mapping, this study develops an advanced cultivated land extraction model for the western part of Henan Province, China, utilizing Gaofen-2 (GF-2) imagery and an improved U-Net architecture to achieve a 1 m resolution regional mapping in complex terrain. We obtained optimal input data for the U-Net model by fusing spectral features and vegetation index features from remote sensing images. We evaluated and validated the effectiveness of the proposed method from multiple perspectives and conducted a cultivated land change detection and agricultural landscape fragmentation assessment in the study area. The experimental results show that the proposed method achieved an F1 score of 89.55% for the entire study area, with an F1 score ranging from 83.84% to 90.44% in the hilly or transitional zones. Compared to models that solely rely on spectral features, the feature selection-based model demonstrates superior performance in hilly and adjacent mountainous regions, with improvements of 4.5% in Intersection over Union (IoU). Cultivated land mapping results show that 83.84% of the cultivated land parcels are smaller than 0.64 hectares. From 2017 to 2022, the overall cultivated land area decreased by 15.26 km², with the most significant reduction occurring in the adjacent hilly areas, where the land parcels are small and fragmented. This trend highlights the urgent need for effective land management strategies to address fragmentation and prevent further loss of cultivated land in these areas. We anticipate that the findings can contribute to precision agriculture management and agricultural modernization in complex terrains of the world. Full article

Geological hazards in densely vegetated mountainous regions are challenging to detect due to terrain concealment and the limitations of traditional visualization methods. This study introduces the LiDAR image highlighting algorithm (LIHA), a novel approach for enhancing micro-topographical features in digital elevation models (DEMs) derived from airborne LiDAR data. By analogizing terrain profiles to non-stationary spectral signals, LIHA applies locally estimated scatterplot smoothing (Loess smoothing), wavelet decomposition, and high-frequency component amplification to emphasize subtle features such as landslide boundaries, cracks, and gullies. The algorithm was validated using the Mengu landslide case study, where edge detection analysis revealed a 20-fold increase in identified micro-topographical features (from 1907 to 37,452) after enhancement. Quantitative evaluation demonstrated LIHA’s effectiveness in improving both human interpretation and automated detection accuracy. The results highlight LIHA’s potential to advance early geological hazard identification and mitigation, particularly when integrated with machine learning for future applications. This work bridges signal processing and geospatial analysis, offering a reproducible framework for high-precision terrain feature extraction in complex environments. Full article

High-resolution visible remote sensing imagery, as a fundamental contributor to Earth observation, has found extensive application in land use classification. However, the heterogeneous array of optical sensors, distinguished by their unique design architectures, exhibit disparate spectral responses and spatial distributions when observing ground objects. These discrepancies between multi-sensor data present a significant obstacle to the widespread application of intelligent methods. In this paper, we propose a method tailored to accommodate these disparities, with the aim of achieving a smooth transfer for the model across diverse sets of images captured by different sensors. Specifically, to address the discrepancies in spatial resolution, a novel positional encoding has been incorporated to capture the correlation between the spatial resolution details and the characteristics of ground objects. To tackle spectral disparities, random amplitude mixup augmentation is introduced to mitigate the impact of feature anisotropy resulting from discrepancies in low-level features between multi-sensor images. Additionally, we integrate convolutional neural networks and Transformers to enhance the model’s feature extraction capabilities, and employ a fine-tuning strategy with dynamic pseudo-labels to reduce the reliance on annotated data from the target domain. In the experimental section, the Gaofen-2 images (4 m) and the Sentinel-2 images (10 m) were selected as training and test datasets to simulate cross-sensor model transfer scenarios. Also, Google Earth images of Suzhou City, Jiangsu Province, were utilized for further validation. The results indicate that our approach effectively mitigates the degradation in model performance attributed to image source inconsistencies. Full article

Seafloor topography data are fundamental for marine resource development, oceanographic research, and maritime rights protection. However, approximately 75% of the ocean remains unsurveyed for bathymetry. Sole reliance on shipborne measurements is insufficient for constructing a global bathymetric model within a short timeframe; consequently, satellite altimetry-based inversion techniques are essential for filling data gaps. Recent advancements have improved the variety and quality of satellite altimetry gravity data. To leverage the complementary advantages of multiparameter gravity data, we propose a 1D convolutional neural network based on a convolutional attention module, termed the Attention Channel 1D Convolutional Network (AC1D). Results of a case study of the Mariana Trench indicated that the AC1D grid predictions exhibited improved agreement with single-beam depth checkpoints, with standard deviation reductions of 6.32%, 20.79%, and 36.77% and root mean square error reductions of 7.11%, 22.82%, and 50.99% compared with those of parallel linked backpropagation, the gravity–geological method, and a convolutional neural network, respectively. The AC1D grid demonstrated enhanced stability in multibeam bathymetric validation metrics and exhibited better consistency with multibeam bathymetry data and the GEBCO2023 grid. Power spectral density analysis revealed that AC1D effectively captured rich topographic signals when predicting terrain features with wavelengths below 6.33 km. Full article

Partial discharge is an important symptom of cable aging, and timely detection of potential defects is of great significance to ensure the stability and safety of the power supply. However, due to the diversity of inspection equipment and information blockage, the staff often show blindness to the partial discharge spectrum and the defects corresponding to the spectrum. In view of this phenomenon, a partial discharge spectrum recognition method based on a convolutional neural network was developed. Firstly, a database of typical partial discharge spectrum was established, including partial amplifiers in the laboratory and at the work site, and then the convolutional neural network was used to train the defect spectral library. This paper proposes a processing technology for the on-site partial discharge spectrum; the unified grayscale image is obtained by grayscale processing, linearized stretching and size unification, and then the shape and color feature parameters are extracted according to the grayscale image, which solves the image distortion and statistical spectrum movement caused by the on-site environment or photographic angle on the user side. The partial discharge type can be obtained by comparing the processed spectrum with the database through the intelligent terminal, which greatly improves the accuracy and efficiency of on-site operations. Full article

Accurate identification of tea plantation distribution is critical for optimizing agricultural practices, informing land-use policies, and preserving ecological balance. However, challenges persist in mountainous regions with persistent cloud cover and heterogeneous vegetation, where conventional methods relying on single-source remote sensing features face limitations due to spectral confusion and information redundancy. This study proposes a novel framework integrating multisource remote sensing data and feature optimization to address these challenges. Leveraging the Google Earth Engine (GEE) cloud platform, this study synthesized 108 spectral, textural, phenological, and topographic features from Sentinel-1 SAR and Sentinel-2 optical data. SVM-RFE (support vector machine recursive feature elimination) was employed to identify the optimal feature subset, prioritizing spectral indices, radar texture metrics, and terrain parameters. Comparative analysis of three classifiers, namely random forest (RF), support vector machine (SVM), and decision tree (DT), revealed that RF achieved the highest accuracy, with an overall accuracy (OA) of 95.03%, a kappa coefficient of 0.95. The resultant 10 m resolution spatial distribution map of tea plantations in Shiyan City (2023) demonstrates robust performance in distinguishing plantations from forests and farmlands, particularly in cloud-prone mountainous terrain. This methodology not only mitigates dimensionality challenges through feature optimization but also provides a scalable solution for large-scale agricultural monitoring, offering critical insights for sustainable land management and policy formulation in subtropical mountainous regions. Full article

The mapping and monitoring of rice fields on a large scale using medium and high spatial resolution data (<10 m) is essential for efficient agricultural management and food security. However, challenges such as managing large volumes of data, addressing data gaps, and optimizing available data are key focuses in remote sensing research using automated machine learning models. In this sense, the objective of this study was to propose a pipeline to characterize and classify three different irrigated rice-producing regions in the state of Santa Catarina, Brazil. To achieve this, we used Sentinel-1 Synthetic Aperture Radar (SAR) polarizations and Sentinel-2 optical multispectral spectral bands along with multiple time series indices. The processing of input data and exploratory analysis were performed using a clustering algorithm based on Dynamic Time Warping (DTW), with K-means applied to the time series. For the classification step in the proposed pipeline, we utilized five traditional machine learning models available on the Google Earth Engine platform to determine which had the best performance. We identified four distinct irrigated rice cropping patterns across Santa Catarina, where the northern region favors double cropping, the south predominantly adopts single cropping, and the central region shows both, a flattened single and double cropping. Among the tested classification models, the SVM with Sentinel-1 and Sentinel-2 data yielded the highest accuracy (IoU: 0.807; Dice: 0.885), while CART and GTBoost had the lowest performance. Omission errors were reduced below 10% in most models when using both sensors, but commission errors remained above 15%, especially for patches in which rice fields represent less than 10% of area. These findings highlight the effectiveness of our proposed feature selection and classification pipeline for improving the generalization of irrigated rice mapping in large and diverse regions. Full article

For reducing the parameters and computational complexity of networks while improving the classification accuracy of hyperspectral remote sensing images (HRSIs), a dynamic split pointwise convolution (DSPC) strategy is presented, and a lightweight convolutional neural network (CNN), i.e., CSM-DSPCss-Ghost, is proposed based on DSPC. A channel switching module (CSM) and a dynamic split pointwise convolution Ghost (DSPC-Ghost) module are presented by combining the presented DSPC with channel shuffling and the Ghost strategy, respectively. CSM replaces the first expansion pointwise convolution in the MobileNetV2 bottleneck module to reduce the parameter number and relieve the increasing channel correlation caused by the original channel expansion pointwise convolution. DSPC-Ghost replaces the second pointwise convolution in the MobileNetV2 bottleneck module, which can further reduce the number of parameters based on DSPC and extract the depth spectral and spatial features of HRSIs successively. Finally, the CSM-DSPCss-Ghost bottleneck module is presented by introducing a squeeze excitation module and a spatial attention module after the CSM and the depthwise convolution, respectively. The presented CSM-DSPCss-Ghost network consists of seven successive CSM-DSPCss-Ghost bottleneck modules. Experiments on four measured HRSIs show that, compared with 2D CNN, 3D CNN, MobileNetV2, ShuffleNet, GhostNet, and Xception, CSM-DSPCss-Ghost can significantly improve classification accuracy and running speed while reducing the number of parameters. Full article

This article reviews recent developments in flow cytometry that have a significant impact on both scientific research and clinical applications in the field of single-cell analysis, from the perspective of instrumentation and technical advances. As a starting point, this article investigates the latest state-of-the-art instruments of flow cytometry including different types in spectral, mass, imaging, nano, and label-free flow cytometry. A comparative analysis of the parameters and features of instruments from different companies elucidates the development trends in flow cytometry instrumentation. Following this, this article delves into cutting-edge technical advancements in flow cytometry. It summarizes the current research status of flow cytometry not only globally but also within China, highlighting emerging trends and innovations in the field. Finally, this article outlines future directions for the development of flow cytometry, indicating that each type of flow cytometry will follow its own trajectory toward achieving enhanced performance and broader applications in diverse fields. Full article

To achieve the rapid and accurate classification and identification of soybean components, this study selected soybeans harvested by the 4LZ-1.5 soybean combine harvester as the research subject. Hyperspectral images of soybean samples were collected using the Pika L spectrometer, and spectral information was extracted from the regions of interest (ROI) in the images. Eight preprocessing methods, including baseline correction (BC), moving average (MA), Savitzky–Golay derivative (SGD), normalization, standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative (DS), and Savitzky–Golay smoothing (SGS), were applied to the raw spectral data to eliminate irrelevant information. Feature wavelengths were selected using the successive projections algorithm (SPA) and the competitive adaptive reweighted sampling (CARS) algorithm to reduce spectral redundancy and enhance model detection performance, retaining eight and ten feature wavelengths, respectively. Subsequently, a random forest (RF) model was developed for soybean component classification. The model parameters were optimized using particle swarm optimization (PSO) and differential evolution (DE) algorithms to improve performance. Experimental results showed that the RF classification model based on SPA-BC preprocessed spectra and DE-tuned parameters achieved an optimal prediction accuracy of 1.0000 during training. This study demonstrates the feasibility of using hyperspectral imaging technology for the rapid and accurate detection of soybean components, providing technical support for the assessment of breakage and impurity levels during soybean harvesting and storage processes. It also offers a reference for the development of future machine-harvested soybean breakage and impurity detection systems. Full article

With growing consumer concerns about food safety, developing methods for the field-based, non-destructive, and rapid detection of pesticide residues is becoming increasingly critical. This study introduces a field-based, non-destructive, and rapid method for detecting pesticide residues on kumquat surfaces. Initially, spectral data from the visible/near-infrared (VNIR) light bands were collected using a handheld spectrometer from kumquats treated with three pesticides at various gradient concentrations and water. The data were then preprocessed and analyzed using machine learning (SPA-SVM) and deep learning models (1D-CNN, 1D-ResNet) to determine the optimal model. Features from the convolutional layer of the 1D-ResNet model were extracted for visualization and analysis, highlighting significant differences in features between the different pesticides and across varying concentrations. The results indicate that the 1D-ResNet model achieved 97% overall accuracy, with a macro average of 0.96 and a weighted average of 0.97, and that precision, recall, and F1-score approached 1.00 for most pesticide treatment gradients. The results of this research verified the feasibility of the handheld spectrometer combined with 1D-Resnet for the detection of pesticide residues on the surface of kumquat, realized the visualization of pesticide residue characteristics, and also provided a reference for the detection of pesticide residues on the surface of other fruits. Full article

Transformers have performed favorably in recent hyperspectral unmixing studies in which the self-attention mechanism possesses the ability to retain spectral information and spatial details. However, the lack of reliable prior information for correction guidance has resulted in an inadequate accuracy and robustness of the network. To benefit from the advantages of the Transformer architecture and to improve the interpretability and robustness of the network, a dual-branch network with prior information correction, incorporating a Transformer network (PICT-Net), is proposed. The upper branch utilizes pre-extracted endmembers to provide pure pixel prior information. The lower branch employs a Transformer structure for feature extraction and unmixing processing. A weight-sharing strategy is employed between the two branches to facilitate information sharing. The deep integration of prior knowledge into the Transformer architecture effectively reduces endmember variability in hyperspectral unmixing and enhances the model’s generalization capability and accuracy across diverse scenarios. Experimental results from experiments conducted on four real datasets demonstrate the effectiveness and superiority of the proposed model. Full article

The inherent complexity of modern software frequently leads to critical issues such as defects, performance degradation, and system failures. Among these, system crashes pose a severe threat to reliability, as they demand rapid fault localization to minimize downtime and restore functionality. A critical step of fault localization is predicting the residence of crashing faults, which involves determining whether a fault is located within the stack trace or outside it. This task plays a crucial role in software quality assurance by enhancing debugging efficiency and reducing testing costs. This study introduces SCM, a two-stage composite feature selection framework designed to address this challenge. The SCM framework integrates spectral clustering for feature grouping, which organizes highly correlated features into clusters while reducing redundancy and capturing non-linear relationships. Maximal information coefficient analysis is then applied to rank features within each cluster and select the most relevant ones, forming an optimized feature subset. A decision tree classifier is then applied to predict the residence of crashing faults. Extensive experiments on seven open-source software projects show that the SCM framework outperforms seven baseline methods, which include four classifiers and three ranking approaches, across four evaluation metrics such as F-measure, g-mean, MCC, and AUC. These results highlight its potential in improving fault localization. Full article