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Traditional methods for evaluating the edibility of lipids involve the use of organic reagents and complex operations, which limit their routine use. In this study, nanocellulose was prepared from bamboo, and a colorimetric reading strategy based on nanocellulose composite hydrogels was explored to monitor the freshness of edible oils. The hydrogels acted as carriers for peroxide dyes that changed color according to the freshness of the oil, and color information was digitized using UV-vis and RGB analysis. The sensitivity and accuracy of the hydrogel were verified using H2O2, which showed a linear relationship between absorbance and H2O2 content in the range of 0–0.5 and 0.5–11 mmol/kg with R2 of 0.9769 and 0.9899, respectively, while the chromatic parameter showed an exponential relationship with R2 of 0.9626. Surprisingly, the freshness of all seven edible oil samples was correctly identified by the hydrogel, with linear correlation coefficients greater than 0.95 in the UV-vis method and exponential correlation coefficients greater than 0.92 in the RGB method. Additionally, a peroxide value color card was established, with an accuracy rate of 91.67%. This functional hydrogel is expected to be used as a household-type oil freshness indicator to meet the needs of general consumers.

Abstract Hybridization is very common in flowering plants and it plays a significant role in plant evolution and adaptation. Melastoma L. (Melastomataceae) comprises about 80–90 species in tropical Asia and Oceania, among which 41 species occur in Borneo. Natural hybridization is frequently reported in Melastoma in China, but so far there have been no confirmed cases of hybridization in Southeast Asia (including Borneo), where most species occur. Here, we identified a case of natural hybridization between Melastoma malabathricum L. and Melastoma beccarianum Cogn. in Sarawak, Malaysia, by using sequence data of three nuclear genes and one chloroplast intergenic spacer. Melastoma malabathricum is the most widespread species of this genus, occurring in almost the whole range of this genus, while M. beccarianum is a local species endemic to northern Borneo. Our results showed that natural hybridization and introgression occur between M. malabathricum and M. beccarianum, and the introgression was asymmetrical, mainly from M. malabathricum to M. beccarianum. As adaptive traits can be transferred by introgression, our study suggests that natural hybridization should be a significant mechanism for the evolution and adaptation of Melastoma in Southeast Asia. However, introgression from the common species M. malabathricum to the relatively rare species M. beccarianum may cause the decline of M. beccarianum, incurring conservation concern. With a large number of species of Melastoma and almost year‐around flowering in Southeast Asia, more cases of natural hybridization are expected to be found and identified in near future.

Ixonanthes chinensis (Ixonanthaceae) is an endangered tree species distributed in South China and Vietnam. The phylogenetic position of Ixonanthes in the order Malpighiales remains controversial. Here we reported and characterized the complete chloroplast genome sequence of I. chinensis assembled from Illumina sequencing reads. The chloroplast genome size is 155,296 bp, containing a large single copy (LSC) region of 83,829 bp, a short single copy (SSC) region of 18,917 bp, and a pair of inverted repeats (IRs) of 26,275 bp. We predicted a total of 139 genes, which included 87 protein-coding genes, 44 tRNA genes, and 8 rRNA genes. Phylogenetic analysis showed that I. chinensis was sister to Linum usitatissimum from the family Linaceae rather than Erythroxylum novogranatense from the family Erythroxylaceae.

Eriocaulaceae, a family of herbaceous plants in the order Poales, includes some well-known species used in traditional Chinese herbal medicine. The phylogenetic position of Eriocaulaceae in Poales remains controversial. In this study, we reported the complete chloroplast genome of Eriocaulon sexangulare (Eriocaulaceae) assembled from Illumina sequencing reads. The complete chloroplast genome was 151,435 bp in length with 38.60% overall GC content, containing a large single copy (LSC) region of 81,533 bp, a small single copy (SSC) region of 17,114 bp and a pair of inverted repeat (IR) regions of 26,394 bp. A total of 134 genes were annotated, including 88 protein-coding genes, 8 rRNAs, and 38 tRNAs. Phylogenetic analysis showed that E. sexangulare is most closely related to Syngonanthus chrysanthus (Eriocaulaceae) and the family Eriocaulaceae is sister to Mayacaceae.

ObjectivesThis study analyzes the influencing factors of the aeroacoustic noise of cruise ship chimney exhaust pipelines, and predicts the noise level in the open area of a domestic cruise ship. MethodsA hybrid approach combining Large Eddy Simulation and Lighthill Analogy is used to conduct a numerical simulation of the pipeline’s aeroacoustic noise under the actual working conditions of a cruise ship. The impacts of the inlet flow rate, outlet pressure, cross-section shape and bending angle on the aerodynamic noise are studied. A real-size cruise ship chimney exhaust pipeline model is then established to analyze the main noise sources and predict the noise level in the open areaa. ResultsThe results show that the larger the inlet velocity and bending angle, the greater the aeroacoustic noise. Furthermore, the aeroacoustic noise of a pipeline with a zero pressure-outlet is greater than that with a non-zero pressure-outlet. It is also found that the aeroacoustic noise of a rectangular cross-sectional pipeline is marginally smaller than that of a circular one. In addition, the narrowband peak frequency decreases as the bending angle increases. Except for the chimney outlet, the noise level in the open area is low. ConclusionsThis study has significant reference value for controlling the aeroacoustic noise of cruise ship chimney exhaust pipelines and improving passenger comfort in open areas.

The existing change detection (CD) methods can be summarized as the visual-first change detection (ViFi-CD) paradigm, which first extracts change features from visual differences and then assigns them specific semantic information. However, CD is essentially dependent on change regions of interest (CRoIs), meaning that the CD results are directly determined by the semantics changes in interest, making its primary image factor semantic of interest rather than visual. The ViFi-CD paradigm can only assign specific semantics of interest to specific change features extracted from visual differences, leading to the inevitable omission of potential CRoIs and the inability to adapt to different CRoI CD tasks. In other words, changes in other CRoIs cannot be detected by the ViFi-CD method without retraining the model or significantly modifying the method. This paper introduces a new CD paradigm, the semantic-first CD (SeFi-CD) paradigm. The core idea of SeFi-CD is to first perceive the dynamic semantics of interest and then visually search for change features related to the semantics. Based on the SeFi-CD paradigm, we designed Anything You Want Change Detection (AUWCD). Experiments on public datasets demonstrate that the AUWCD outperforms the current state-of-the-art CD methods, achieving an average F1 score 5.01% higher than that of these advanced supervised baselines on the SECOND dataset, with a maximum increase of 13.17%. The proposed SeFi-CD offers a novel CD perspective and approach.

The efficient and precise retrieval of desired information from extensive geological databases is a prominent and pivotal focus within the realm of geological information services. Conventional information retrieval methods primarily rely on keyword matching approaches, which often overlook the contextual and semantic aspects of the keywords, consequently impeding the retrieval system’s ability to accurately comprehend user query requirements. To tackle this challenge, this study proposes an ontology-driven information-retrieval framework for geological data that integrates spatiotemporal and topic associations. The framework encompasses the development of a geological domain ontology, extraction of key information, establishment of a multi-feature association and retrieval framework, and validation through a comprehensive case study. By employing the proposed framework, users are empowered to actively and automatically retrieve pertinent information, simplifying the information access process, mitigating the burden of comprehending information organization and software application models, and ultimately enhancing retrieval efficiency.

To obtain satisfying results of individual tree detection from LiDAR points, parameters using traditional methods usually need to be adjusted by trials and errors. When encountering complex forest environments, the detection accuracy cannot be satisfied. To resolve this, a multi-level self-adaptive individual tree detection method was presented in this paper. The proposed method can be seen as a hybrid model, which combined the strength of both raster-based and point-based methods. Raster-based strategy was first used for achieving initial trees detection results, while the point-based strategy was adopted for optimizing the clustered trees. In the proposed method, crown width scales were estimated automatically. Meanwhile, multi-scales segmented results were fused together to take advantage of segmented results of both larger and small scales. Six different coniferous forests were adopted for testing. Experimental result shows that this study achieved the lowest omission and commission errors comparing with other three classical approaches. Meanwhile, the average F1 score in this paper is 0.84, which is much highest out of other methods.

Filtering from airborne LiDAR datasets in urban area is one important process during the building of digital and smart cities. However, the existing filters encounter poor filtering performance and heavy computational burden when processing large-scale and complicated urban environments. To tackle this issue, a self-adaptive filtering method based on object primitive global energy minimization is proposed in this paper. In this paper, mode points were first acquired for generating the mode graph. The mode points were the cluster centers of the LiDAR data obtained in a mean shift algorithm. The graph constructed with mode points was named “mode graph” in this paper. By defining the energy function based on the mode graph, the filtering process is transformed to iterative global energy minimization. In each iteration, the graph cuts technique was adopted to achieve global energy minimization. Meanwhile, the probability of each point belonging to the ground was updated, which would lead to a new refined ground surface using the points whose probabilities were greater than 0.5. This process was iterated until two successive fitted ground surfaces were determined to be close enough. Four urban samples with different urban environments were adopted for verifying the effectiveness of the filter developed in this paper. Experimental results indicate that the developed filter obtained the best filtering performance. Both the total error and the Kappa coefficient are superior to those of the other three classical filtering methods.

Illegal mining is one of the biggest problems in many coal mines. With the rapid development of the economy, driven by huge economic benefits, some outlaws illegally exploit mineral resources without a mining license, which is destructive and a potential safety hazard. In order to avoid inspection by law enforcement officials, some outlaws, regardless of the cost or risk, privately and surreptitiously excavate coal mines in self-built houses. The coal resources they excavate are shallow coal resources. Because surface buildings can maintain strong and stable radar scattering characteristics over a long time series, in this study, we combined PS-InSAR technology and high-resolution optical remote sensing to extract the subsidence information of surface buildings corresponding to PS point sets and analyzed their spatiotemporal characteristics. Finally, we developed a fast and accurate method for detecting suspected illegal mining sites from building subsidence information over a larger area. We also carried out a case study using Shandi Village, a suburb of Yangquan City, Shanxi Province, China, as our research object. QuickBird-2, WorldView-2 data, and 20 PALSAR scenes were selected for the experimental research, and two illegal mining sites were detected from 29 December 2006 to 9 January 2011. By comparing our results with previous investigation data, it was found that the accuracy rate reached 40% in local areas, and the detection rate reached 66.67%. In addition, the mining periods were basically consistent. This research shows that our method is feasible and has certain engineering applicability and practical value.

Polarimetric synthetic aperture radar is an important technology for Earth observation. Generally, polarimetric calibration (PolCAL) should be performed before any quantitative applications. The reflection symmetric targets can relieve the dependence of PolCAL on calibrators. In this work, we use a helix-ratio feature to extract these targets. Through analysis, we find that the helix-ratio feature has a high tolerance to polarimetric distortions and is suitable for extracting samples in un-calibrated images for PolCAL. An adaptive segmentation algorithm is also developed to extract reflection symmetric targets automatically. Experiments with C- and X-band airborne images show that the extraction of reflection symmetric targets can improve the calibration accuracy of crosstalk. In addition, the proposed method can maintain the polarimetric signature of the targets in the calibration and can improve calibration performance when compared with other reflection symmetric target extraction methods.

Airborne Light Detection and Ranging (LiDAR) is a popular active remote sensing technology that has been developing very rapidly in recent years. To solve the problems of low filtering accuracy of airborne LiDAR point clouds in complex terrain environments and avoiding too much human intervention, this paper proposes a point cloud filtering method based on active learning. In the proposed method, the initial training samples are acquired and marked automatically by multi-scale morphological operations. In so doing, no training samples are selected and labeled manually, i.e., the training samples are added gradually according to the oracle used in active learning. In this paper, the oracle is set to a sigmoid function of residuals from the points to the fitted surface. Subsequently, the training model is revised progressively using the updated training samples. Finally, the classification results are further optimized by a slope-based method. Three datasets with different filtering challenges provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) were used to test the proposed method. Comparing with the other ten famous filtering methods, the proposed method can achieve the smallest average total error (5.51%). Thus, it can be concluded that the proposed method performs very well toward different terrain environments.

Background Microglia, neuron, and vascular cells constitute a dynamic functional neurovascular unit, which exerts the crucial role in functional recovery after ischemic stroke. Paeoniflorin, the principal active component of Paeoniae Radix, has been verified to exhibit neuroprotective roles in cerebralischemic injury. However, the mechanisms underlying the regulatory function of Paeoniflorin on neurovascular unit after cerebral ischemia are still unclear. Methods In this study, adult male rats were treated with Paeoniflorin following transient middle cerebral artery occlusion (tMCAO), and then the functional behavioral tests (Foot-fault test and modified improved neurological function score, mNSS), microglial activation, neurogenesis and vasculogenesis were assessed. Results The current study showed that Paeoniflorin treatment exhibited a sensorimotor functional recovery as suggested via the Foot-fault test and the enhancement of spatial learning as suggested by the mNSS in rat stroke model. Paeoniflorin treatment repressed microglial cell proliferation and thus resulted in a significant decrease in proinflammatory cytokines IL-1β, IL-6 and TNF-α. Compared with control, Paeoniflorin administration facilitated von Willebrand factor (an endothelia cell marker) and doublecortin (a neuroblasts marker) expression, indicating that Paeoniflorin contributed to neurogenesis and vasculogenesis in rat stroke model. Mechanistically, we verified that Paeoniflorin repressed JNK and NF-κB signaling activation. Conclusions These results demonstrate that Paeoniflorin represses neuroinflammation and facilitates neurogenesis in rat stroke model and might be a potential drug for the therapy of ischemic stroke.

Individual tree modeling for terrestrial LiDAR point clouds always involves heavy computation burden and low accuracy toward a complex tree structure. To solve these problems, this paper proposed a self-adaptive optimization individual tree modeling method. In this paper, we first proposed a joint neighboring growing method to segment wood points into object primitives. Subsequently, local object primitives were optimized to alleviate the computation burden. To build the topology relation among branches, branches were separated based on spatial connectivity analysis. And then the nodes corresponding to each object primitive were adopted to construct the graph structure of the tree. Furthermore, each object primitive was fitted as a cylinder. To revise the local abnormal cylinder, a self-adaptive optimization method based on the constructed graph structure was proposed. Finally, the constructed tree model was further optimized globally based on prior knowledge. Twenty-nine field datasets obtained from three forest sites were adopted to evaluate the performance of the proposed method. The experimental results show that the proposed method can achieve satisfying individual tree modeling accuracy. The mean volume deviation of the proposed method is 1.427 m3. In the comparison with two other famous tree modeling methods, the proposed method can achieve the best individual tree modeling result no matter which accuracy indicator is selected.

Glioblastoma (GBM) is the most malignant form of astrocytoma with short survival and a high recurrence rate and remains a global problem. Currently, surgery, chemotherapy, radiotherapy, and other comprehensive treatments are the main treatment modalities, but patients still have a poor prognosis mainly due to the infiltrative growth of GBM and the protective effect of the blood–brain barrier on tumor cells. Therefore, immunotherapy is expected to be a good option for GBM. In the immune system, different cells play varying roles in the treatment of GBM, so understanding the roles played by various immune cells in treating GBM and considering how to combine these effects to maximize the efficacy of these cells is important for the selection of comprehensive and optimal treatment plans and improving GBM prognosis. Therefore, this study reviews the latest research progress on the role of various types of immune cells in the treatment of GBM.

Land surveyors, photogrammetrists, remote sensing engineers and professionals in the Earth sciences are often faced with the task of transferring coordinates from one geodetic datum into another to serve their desired purpose. The essence is to create compatibility between data related to different geodetic reference frames for geospatial applications. Strictly speaking, conventional techniques of conformal, affine and projective transformation models are mostly used to accomplish such task. With developing countries like Ghana where there is no immediate plans to establish geocentric datum and still rely on the astro-geodetic datums as it national mapping reference surface, there is the urgent need to explore the suitability of other transformation methods. In this study, an effort has been made to explore the proficiency of the Extreme Learning Machine (ELM) as a novel alternative coordinate transformation method. The proposed ELM approach was applied to data found in the Ghana geodetic reference network. The ELM transformation result has been analysed and compared with benchmark methods of backpropagation neural network (BPNN), radial basis function neural network (RBFNN), two-dimensional (2D) affine and 2D conformal. The overall study results indicate that the ELM can produce comparable transformation results to the widely used BPNN and RBFNN, but better than the 2D affine and 2D conformal. The results produced by ELM has demonstrated it as a promising tool for coordinate transformation in Ghana.

Building extraction from airborne Light Detection and Ranging (LiDAR) point clouds is a significant step in the process of digital urban construction. Although the existing building extraction methods perform well in simple urban environments, when encountering complicated city environments with irregular building shapes or varying building sizes, these methods cannot achieve satisfactory building extraction results. To address these challenges, a building extraction method from airborne LiDAR data based on multi-constraints graph segmentation was proposed in this paper. The proposed method mainly converted point-based building extraction into object-based building extraction through multi-constraints graph segmentation. The initial extracted building points were derived according to the spatial geometric features of different object primitives. Finally, a multi-scale progressive growth optimization method was proposed to recover some omitted building points and improve the completeness of building extraction. The proposed method was tested and validated using three datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). Experimental results show that the proposed method can achieve the best building extraction results. It was also found that no matter the average quality or the average F1 score, the proposed method outperformed ten other investigated building extraction methods.

Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.

The all-embracing inspection of geometry structures of revetments along urban rivers using the conventional field visual inspection is technically complex and time-consuming. In this study, an approach using dense point clouds derived from low-cost unmanned aerial vehicle (UAV) photogrammetry is proposed to automatically and efficiently recognize the signatures of revetment damage. To quickly and accurately recover the finely detailed surface of a revetment, an object space-based dense matching approach, that is, region growing coupled with semi-global matching, is exploited to generate pixel-by-pixel dense point clouds for characterizing the signatures of revetment damage. Then, damage recognition is conducted using a proposed operator, that is, a self-adaptive and multiscale gradient operator, which is designed to extract the damaged regions with different sizes in the slope intensity image of the revetment. A revetment with slope protection along urban rivers is selected to evaluate the performance of damage recognition. Results indicate that the proposed approach can be considered an effective alternative to field visual inspection for revetment damage recognition along urban rivers because our method not only recovers the finely detailed surface of the revetment but also remarkably improves the accuracy of revetment damage recognition.

Most filtering algorithms suffer from complex parameter settings or threshold adjusting. To solve this problem, this paper proposes an improved skewness balancing filtering algorithm based on thin plate spline (TPS) interpolation. The proposed algorithm filters the nonground points in an iterative manner. A reference surface that reflects the fluctuation of the terrain is generated using the TPS interpolation method. Accordingly, the elevation difference from each point to the surface can be calculated. By applying the skewness balancing principle to these elevation differences, nonground points can be removed automatically. To verify the validity and robustness of the proposed method, the datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) were adopted. The experimental results show that this presented method can adapt to complex environments and achieve a higher filtering accuracy than the traditional skewness balancing algorithm. Moreover, in comparison with the other eight filtering methods tested by the ISPRS and four improved filtering methods proposed recently, the proposed method achieved an average total error of 5.39%, which is smaller than that of most of these other methods.

Filtering is one of the core post-processing steps for airborne LiDAR point cloud. In recent years, the morphology-based filtering algorithms have proven to be a powerful and efficient tool for filtering airborne LiDAR point cloud. However, most traditional morphology-based algorithms have difficulties in preserving abrupt terrain features, especially when using larger filtering windows. In order to suppress the omission error caused by protruding terrain features, this paper proposes an improved morphological algorithm based on multi-level kriging interpolation. This algorithm is essentially a combination of progressive morphological filtering algorithm and multi-level interpolation filtering algorithm. The morphological opening operation is performed with filtering window gradually downsizing, while kriging interpolation is conducted at different levels according to the different filtering windows. This process is iterative in a top to down fashion until the filtering window is no longer greater than the preset minimum filtering window. Fifteen samples provided by the ISPRS commission were chosen to test the performance of the proposed algorithm. Experimental results show that the proposed method can achieve promising results not only in flat urban areas but also in rural areas. Comparing with other eight classical filtering methods, the proposed method obtained the lowest omission error, and preserved protruding terrain features better.