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To address the challenges posed by the complex shapes of hollow parts, this study examined the ultrasonic-assisted granular medium hydroforming (UGMF) process for tubular components. The dynamics of the deformation behavior and deformation control during 6063-T5 aluminum alloy tube free forming by UGMF were studied via simulations and experiments. Based on the ABAQUS software platform, a coupled method based on finite element (FE) simulation analysis and discrete element (DE) analysis for the UGMF free forming process was used. The results showed that ultrasonic vibration (UV) could reduce the forming force required for expansion and promote the flow of material at the end to the forming area as well as inhibit the decrease in the wall thickness. The accuracy of the FE-DE coupled simulation model and a parabolic geometric model was verified by testing. The results found that UV enhances material flow, decreases the forming force needed, and minimizes damage to the granular surface.

Due to the irregularity and complexity of ground and non-ground objects, filtering non-ground data from airborne LiDAR point clouds to create Digital Elevation Models (DEMs) remains a longstanding and unresolved challenge. Recent advancements in deep learning have offered effective solutions for understanding three-dimensional semantic scenes. However, existing studies lack the capability to model global semantic relationships and fail to integrate global and local semantic information effectively, which are crucial for the ground filtering of point cloud data, especially for larger objects. This study focuses on ground filtering challenges in large scenes and introduces an elevation offset-attention (E-OA) module, which considers global semantic features and integrates them into existing network frameworks. The performance of this module has been validated on three classic benchmark models (RandLA-Net, point transformer, and PointMeta-L). It was compared with two traditional filtering methods and the advanced CDFormer model. Additionally, the E-OA module was compared with three state-of-the-art attention frameworks. Experiments were conducted on two distinct data sources. The results show that our proposed E-OA module improves the filtering performance of all three benchmark models across both data sources, with a maximum improvement of 6.15%. The performance of models was enhanced with the E-OA module, consistently exceeding that of traditional methods and all competing attention frameworks. The proposed E-OA module can serve as a plug-and-play component, compatible with existing networks featuring local feature extraction capabilities.

Recent studies have introduced transformer modules into convolutional neural networks (CNNs) to solve the inherent limitations of CNNs in global modeling and have achieved impressive performance. However, some challenges have yet to be addressed: first, networks with simple connections between the CNN and transformer perform poorly in small change areas; second, networks that only use transformer structures are prone to attaining coarse detection results and excessively generalizing feature boundaries. In addition, the methods of fusing the CNN and transformer have the issue of a unilateral flow of feature information and inter-scale communication, leading to a loss of change information across different scales. To mitigate these problems, this study proposes a full-scale connected CNN–Transformer network, which incorporates the Siamese structure, Unet3+, and transformer structure, used for change detection in remote sensing images, namely SUT. A progressive attention module (PAM) is adopted in SUT to deeply integrate the features extracted from both the CNN and the transformer, resulting in improved global modeling, small target detection capacities, and clearer feature boundaries. Furthermore, SUT adopts a full-scale skip connection to realize multi-directional information flow from the encoder to decoder, enhancing the ability to extract multi-scale features. Experimental results demonstrate that the method we designed performs best on the CDD, LEVIR-CD, and WHU-CD datasets with its concise structure. In particular, based on the WHU-CD dataset, SUT upgrades the F1-score by more than 4% and the intersection over union (IOU) by more than 7% compared with the second-best method.

Many cities worldwide have large amounts of industrial vacant land (IVL) due to development and transformation, posing a growing problem. However, the large-scale identification of IVL is hindered by obstacles such as high cost, high variability, and closed-source data. Moreover, it is difficult to distinguish industrial vacant land from operational industrial land based solely upon image features. To address these issues, we propose a method for the large-scale automatic identification of IVL. The framework uses deep learning to train remote-sensing images of potential industrial vacant land to generate a semantic segmentation model and further use population density and surface temperature data to filter model predictions. The feasibility of the proposed methodology was validated through a case study in Tangshan City, Hebei Province, China. The study indicates two major conclusions: (1) The proposed IVL identification framework can efficiently generate industrial vacant land mapping. (2) HRNet exhibits the highest accuracy and strongest robustness after training compared with other semantic segmentation backbone networks, ensuring high-quality performance and stability, as evidenced by a model accuracy of 97.84%. Based on the above advantages, the identification framework provides a reference method for various countries and regions to identify industrial vacant land on a large scale, which is of great significance for advancing the research and transformation of industrial vacant land worldwide.

Image dense matching plays a crucial role in the reconstruction of three-dimensional models of buildings. However, large variations in target heights and serious occlusion lead to obvious mismatches in areas with discontinuous depths, such as building edges. To solve this problem, the present study mines the geometric and semantic information of line segments to produce a constraint for the dense matching process. First, a disparity consistency-based line segment matching method is proposed. This method correctly matches line segments on building structures in discontinuous areas based on the assumption that, within the corresponding local areas formed by two corresponding line pairs, the disparity obtained by the coarse-level matching of the hierarchical dense matching is similar to that derived from the local homography estimated from the corresponding line pairs. Second, an adaptive guide parameter is designed to constrain the cost propagation between pixels in the neighborhood of line segments. This improves the rationality of cost aggregation paths in discontinuous areas, thereby enhancing the matching accuracy near building edges. Experimental results using satellite and aerial images show that the proposed method efficiently obtains reliable line segment matches at building edges with a matching precision exceeding 97%. Under the constraint of the matched line segments, the proposed dense matching method generates building edges that are visually clearer, and achieves higher accuracy around edges, than without the line segment constraint.

This work assesses the flexural performance of prestressed concrete beams with external carbon fiber-reinforced polymer (CFRP) tendons, focusing on tendon-related variables. A finite element analysis (FEA) method is verified. A numerical parametric analysis of prestressed concrete beams with external CFRP tendons is carried out. Four tendon-related variables are considered, namely, the area, initial prestress, depth and elastic modulus of tendons. The analysis shows that flexural ductility decreases as the tendon area, initial prestress or elastic modulus increases but is insensitive to the tendon depth. The ultimate tendon stress increment (Δσp) is influenced by all of the four variables investigated. JGJ 92-2016 (Chinese technical specification for concrete structures prestressed with unbonded tendons) significantly underestimates Δσp and, hence, is over-conservative for the strength design of these beams. An equation is proposed for calculating Δσp, taking into account all four variables investigated. An analytical model is then developed to estimate the flexural strength (Mu) of prestressed concrete beams with external CFRP tendons. The proposed analytical model shows good agreement with FEA, i.e., the mean discrepancy for Δσp is 0.9% with a standard deviation of 11.1%; and the mean discrepancy for Mu is −1.6% with a standard deviation of 2.1%.

Over-activation of Toll-like receptor 4 (TLR4) is the key mechanism in Gram-negative bacterial infection-induced sepsis. SAM and HD domain-containing deoxynucleoside triphosphate triphosphohydrolase 1 (SAMHD1) inhibits multiple viruses, but whether it plays a role during bacterial invasion remains unelucidated. Monocyte-macrophage specific Samhd1 knockout (Samhd1−/−) mice and Samhd1−/− macrophage cell line RAW264.7 were constructed and used as research models to evaluate the role of SAMHD1 in TLR4-activated inflammation. In vivo, LPS-challenged Samhd1−/− mice showed higher serum inflammatory factors, accompanied with more severe inflammation infiltration and lower survival rate. In vitro, Samhd1−/− peritoneal macrophages had more activated TLR4 pathway upon LPS-stimulation, accompanied with mitochondrial depolarization and dysfunction and a higher tendency to be M1-polarized. These results could be rescued by overexpressing full-length wild-type SAMHD1 or its phospho-mimetic T634D mutant into Samhd1−/− RAW264.7 cells, whereas the mutants, dNTP hydrolase-function-deprived H238A and phospho-ablative T634A, did not exert the same effect. Lastly, co-IP and immunofluorescence assays confirmed that SAMHD1 interacted with an outer mitochondrial membrane-localized protein, voltage-dependent anion channel-1 (VDAC1). SAMHD1 inhibits TLR4-induced acute inflammation and M1 polarization of macrophages by interacting with VDAC1 and maintaining mitochondria function, which outlines a novel regulatory mechanism of TLR signaling upon LPS stimulation.

Protein kinase Cα (PKCα/PRKCA) is a crucial regulator of circadian rhythm and is associated with human mental illnesses such as autism spectrum disorders and schizophrenia. However, the roles of PRKCA in modulating animal social behavior and the underlying mechanisms remain to be explored. Here we report the generation and characterization of prkcaa-deficient zebrafish (Danio rerio). The results of behavioral tests indicate that a deficiency in Prkcaa led to anxiety-like behavior and impaired social preference in zebrafish. RNA-sequencing analyses revealed the significant effects of the prkcaa mutation on the expression of the morning-preferring circadian genes. The representatives are the immediate early genes, including egr2a, egr4, fosaa, fosab and npas4a. The downregulation of these genes at night was attenuated by Prkcaa dysfunction. Consistently, the mutants demonstrated reversed day–night locomotor rhythm, which are more active at night than in the morning. Our data show the roles of PRKCA in regulating animal social interactions and link the social behavior defects with a disturbed circadian rhythm.

As position sensorless control technology can avoid many disadvantages caused by mechanical position sensors, improve the reliability of the motor, reduce costs and other advantages, a large number of researchers have conducted research on compound control technology in order to achieve position sensorless control technology in a wide speed range. In this article, the position sensorless compound control technology of a permanent magnet synchronous motor is reviewed, and the compound control technology of a permanent magnet synchronous motor without a position sensor is elaborated. Finally, the existing problems and development trend of sensorless compound control technology are summarized and prospected.

Due to the façade visibility, intuitive expression, and multi-view redundancy, oblique photogrammetry can provide optional data for large-scale urban LoD-2 reconstruction. However, the inherent noise in oblique photogrammetric point cloud resulting from the image-dense matching limits further model reconstruction applications. Thus, this paper proposes a novel method for the efficient reconstruction of LoD-2 building models guided by façade structures from an oblique photogrammetric point cloud. First, a building planar layout is constructed combined with footprint data and the vertical planes of the building based on spatial consistency constraints. The cells in the planar layout represent roof structures with a distinct altitude difference. Then, we introduce regularity constraints and a binary integer programming model to abstract the façade with the best-fitting monotonic regularized profiles. Combined with the planar layout and regularized profiles, a 2D building topology is constructed. Finally, the vertices of building roof facets can be derived from the 2D building topology, thus generating a LoD-2 building model. Experimental results using real datasets indicate that the proposed method can generate reliable reconstruction results compared with two state-of-the-art methods.

This paper investigated the effects of different particle sizes and addition levels of purple sweet potato flour (PSPF) on the rheological properties and moisture states of wheat dough. There was deterioration in the pasting and mixing properties of the dough, due to the addition of PSPF (0~20% substitution), which was reduced by decreasing the particle size of the PSPF (260~59 μm). Dynamic rheology results showed that PSPF enhanced the elasticity of the dough, providing it solid-like processability. PSPF promoted the binding of gluten proteins and starch in the dough, resulting in a denser microstructure. Differential scanning calorimetry and low-field nuclear magnetic resonance showed that PSPF converted immobilized water and freezable water to bound water and non-freezable water in the dough, making the dough more stable, and that the reduction in PSPF particle size facilitated these processes. Our results provide evidence for the great application potential of purple sweet potatoes for use in flour-based products.

Compared with the existing modes of LiDAR, single-photon LiDAR (SPL) can acquire terrain data more efficiently. However, influenced by the photon-sensitive detectors, the collected point cloud data contain a large number of noisy points. Most of the existing denoising techniques are based on the sparsity assumption of point cloud noise, which does not hold for SPL point clouds, so the existing denoising methods cannot effectively remove the noisy points from SPL point clouds. To solve the above problems, we proposed a novel multistage denoising strategy with fused multiscale features. The multiscale features were fused to enrich contextual information of the point cloud at different scales. In addition, we utilized multistage denoising to solve the problem that a single-round denoising could not effectively remove enough noise points in some areas. Interestingly, the multiscale features also prevent an increase in false-alarm ratio during multistage denoising. The experimental results indicate that the proposed denoising approach achieved 97.58%, 99.59%, 95.70%, and 77.92% F1-scores in the urban, suburban, mountain, and water areas, respectively, and it outperformed the existing denoising methods such as Statistical Outlier Removal. The proposed approach significantly improved the denoising precision of airborne point clouds from single-photon LiDAR, especially in water areas and dense urban areas.

The cultivated land-use transition (CLUT) is the morphological result of changes in the cultivated land-use mode over time, and the result of the interaction and mutual restriction of the human land system. This paper applies a “spatial–functional” integrated framework to understand the structure and functioning of CLUTs, and quantitatively evaluates and visualizes CLUTs in the major grain-producing area in southern China. The results show that (1) the comprehensive CLUT index in the middle and lower reaches of the Yangtze River changed from 0.0480 to 0.0711 from 2001 to 2019 and indicated significant differences in the transition index between different regions. (2) The CLUT identified a positive aggregation effect under a 5% significance during the period, and the agglomeration degree of the spatial and functional transitions strengthened, which increased from 0.3776 to 0.4673 and from 0.2127 to 0.2952, respectively. (3) The gravity center of the CLUT demonstrated a pattern of migration from the southwest to the northeast, and the migration speed of the gravity center decreased from 2.9401 km/year to 1.2370 km/year. The migration direction of the gravity center for the spatial transition is opposite to the functional transition, and the migration speed of the gravity center for the spatial and functional transitions decreased from 8.3573 km/year to 1.0814 km/year, and from 3.2398 km/year to 1.0254 km/year, respectively. To address this transition, policymakers should formulate differentiated policies to promote the sustainable use of cultivated land through the spatial and functional transition of major grain-producing areas.

Reinforced concrete (RC) frame beams are subject to axial restriction at the ends, which plays an important role in the nonlinear behavior of these beams. This paper presents a numerical and theoretical investigation into the flexural behavior of RC beams axially restricted with external steel or fiber reinforced polymer (FRP) reinforcement. A numerical procedure for RC beams axially restricted with external reinforcement has been developed and it is verified against available experimental results. A numerical parametric study is then performed on axially restricted RC beams, focusing on the effect of type, area, and depth of external reinforcement. The results show that axial restriction increases the post-cracking stiffness and ultimate load-carrying capacity but reduces the flexural ductility. The ultimate stress in external reinforcement is substantially impacted by reinforcement type, area, and depth. A simplified model is developed to predict the ultimate load of RC beams axially restricted with external steel/FRP reinforcement. The predictions of the proposed simplified model agree favorably with the numerical results. The correlation coefficient for the ultimate load is 0.984, and the mean difference is −2.11% with a standard deviation of 3.62%.

To investigate the effect of oat bran on bread quality and the mechanism of reducing the glycemic index (GI) of bread, wheat bran (10%, w/w, flour basis), oat bran (10%), and β-glucan (0.858%) were individually added to determine the expansion of dough, the specific volume, texture, color, GI, starch digestion characteristics, and α-amylase inhibition rate of bread. The results showed that the incorporation of wheat bran and oat bran both reduced the final expanded volume of the dough, decreased the specific volume of the bread, and increased the bread hardness and crumb redness and greenness values as compared to the control wheat group. The above physical properties of bran-containing bread obviously deteriorated while the bread with β-glucan did not change significantly (p < 0.05). The GI in vitro of bread was in the following order: control (94.40) > wheat bran (69.24) > β-glucan (65.76) > oat bran (64.93). Correspondingly, the oat bran group had the highest content of slowly digestible starch (SDS), the β-glucan group had the highest content of resistant starch (RS), and the control group had the highest content of rapidly digestible starch (RDS). For the wheat bran, oat bran, and β-glucan group, their inhibition rates of α-amylase were 9.25%, 28.93%, and 23.7%, respectively. The β-glucan reduced the bread GI and α-amylase activity by intertwining with starch to form a more stable gel network structure, which reduced the contact area between amylase and starch. Therefore, β-glucan in oat bran might be a key component for reducing the GI of whole oat bread.

Accurate combined bundle adjustment (BA) is a fundamental step for the integration of aerial and terrestrial images captured from complementary platforms. In traditional photogrammetry pipelines, self-calibrated bundle adjustment (SCBA) improves the BA accuracy by simultaneously refining the interior orientation parameters (IOPs), including lens distortion parameters, and the exterior orientation parameters (EOPs). Aerial and terrestrial images separately processed through SCBA need to be fused using BA. Thus, the IOPs in the aerial–terrestrial BA must be properly treated. On one hand, the IOPs in one flight should be identical for the same images in physics. On the other hand, the IOP adjustment in the cross-platform-combined BA may mathematically improve the aerial–terrestrial image co-registration degree in 3D space. In this paper, the impacts of self-calibration strategies in combined BA of aerial and terrestrial image blocks on the co-registration accuracy were investigated. To answer this question, aerial and terrestrial images captured from seven study areas were tested under four aerial–terrestrial BA scenarios: the IOPs for both aerial and terrestrial images were fixed; the IOPs for only aerial images were fixed; the IOPs for only terrestrial images were fixed; the IOPs for both images were adjusted. The cross-platform co-registration accuracy for the BA was evaluated according to independent checkpoints that were visible on the two platforms. The experimental results revealed that the recovered IOPs of aerial images should be fixed during the BA. However, when the tie points of the terrestrial images are comprehensively distributed in the image space and the aerial image networks are sufficiently stable, refining the IOPs of the terrestrial cameras during the BA may improve the co-registration accuracy. Otherwise, fixing the IOPs is the best solution.

Efficient and accurate road extraction from remote sensing imagery is important for applications related to navigation and Geographic Information System updating. Existing data-driven methods based on semantic segmentation recognize roads from images pixel by pixel, which generally uses only local spatial information and causes issues of discontinuous extraction and jagged boundary recognition. To address these problems, we propose a cascaded attention-enhanced architecture to extract boundary-refined roads from remote sensing images. Our proposed architecture uses spatial attention residual blocks on multi-scale features to capture long-distance relations and introduce channel attention layers to optimize the multi-scale features fusion. Furthermore, a lightweight encoder-decoder network is connected to adaptively optimize the boundaries of the extracted roads. Our experiments showed that the proposed method outperformed existing methods and achieved state-of-the-art results on the Massachusetts dataset. In addition, our method achieved competitive results on more recent benchmark datasets, e.g., the DeepGlobe and the Huawei Cloud road extraction challenge.

This paper investigates the feasibility of replacing steel bars with carbon-fiber-reinforced polymer (CFRP) bars in continuous reinforced concrete (RC) beams. A numerical model is introduced. Model predictions are compared with the experimental results that are available in the literature. A comprehensive numerical investigation is then performed on two-span CFRP/steel RC beams with ρb2 = 0.61–3.03% and ρb1/ρb2 = 1.5, where ρb1 and ρb2 are tensile bar ratios (ratios of tensile bar area to effective cross-sectional area of beams) over positive and negative moment regions, respectively. The study shows that replacing steel bars with CFRP bars greatly improves the crack mode at a low bar ratio. The ultimate load of CFRP RC beams is 89% higher at ρb2 = 0.61% but 7.2% lower at ρb2 = 3.03% than that of steel RC beams. In addition, CFRP RC beams exhibit around 13% greater ultimate deflection compared to steel RC beams. The difference of moment redistribution between CFRP and steel RC beams diminishes as ρb2 increases. ACI 318-19 appears to be conservative, and it leads to more accurate predictions of moment redistribution in CFRP RC beams than that in steel RC beams.

Landslides frequently occur because of natural or human factors. Landslides cause huge losses to the economy as well as human beings every year around the globe. Landslide susceptibility prediction (LSP) plays a key role in the prevention of landslides and has been under investigation for years. Although new machine learning algorithms have achieved excellent performance in terms of prediction accuracy, a sufficient quantity of training samples is essential. In contrast, it is hard to obtain enough landslide samples in most the areas, especially for the county-level area. The present study aims to explore an optimization model in conjunction with conventional unsupervised and supervised learning methods, which performs well with respect to prediction accuracy and comprehensibility. Logistic regression (LR), fuzzy c-means clustering (FCM) and factor analysis (FA) were combined to establish four models: LR model, FCM coupled with LR model, FA coupled with LR model, and FCM, FA coupled with LR model and applied in a specific area. Firstly, an inventory with 114 landslides and 10 conditioning factors was prepared for modeling. Subsequently, four models were applied to LSP. Finally, the performance was evaluated and compared by k-fold cross-validation based on statistical measures. The results showed that the coupled model by FCM, FA and LR achieved the greatest performance among these models with the AUC (Area under the curve) value of 0.827, accuracy of 85.25%, sensitivity of 74.96% and specificity of 86.21%. While the LR model performed the worst with an AUC value of 0.736, accuracy of 77%, sensitivity of 62.52% and specificity of 72.55%. It was concluded that both the dimension reduction and sample size should be considered in modeling, and the performance can be enhanced by combining complementary methods. The combination of models should be more flexible and purposeful. This work provides reference for related research and better guidance to engineering activities, decision-making by local administrations and land use planning.

Most of the existing approaches to the extraction of buildings from high-resolution orthoimages consider the problem as semantic segmentation, which extracts a pixel-wise mask for buildings and trains end-to-end with manually labeled building maps. However, as buildings are highly structured, such a strategy suffers several problems, such as blurred boundaries and the adhesion to close objects. To alleviate the above problems, we proposed a new strategy that also considers the contours of the buildings. Both the contours and structures of the buildings are jointly learned in the same network. The contours are learnable because the boundary of the mask labels of buildings implicitly represents the contours of buildings. We utilized the building contour information embedded in the labels to optimize the representation of building boundaries, then combined the contour information with multi-scale semantic features to enhance the robustness to image spatial resolution. The experimental results showed that the proposed method achieved 91.64%, 81.34%, and 74.51% intersection over union (IoU) on the WHU, Aerial, and Massachusetts building datasets, and outperformed the state-of-the-art (SOTA) methods. It significantly improved the accuracy of building boundaries, especially for the edges of adjacent buildings. The code is made publicly available.

Three-dimensional (3D) building models play an important role in digital cities and have numerous potential applications in environmental studies. In recent years, the photogrammetric point clouds obtained by aerial oblique images have become a major source of data for 3D building reconstruction. Aiming at reconstructing a 3D building model at Level of Detail (LoD) 2 and even LoD3 with preferred geometry accuracy and affordable computation expense, in this paper, we propose a novel method for the efficient reconstruction of building models from the photogrammetric point clouds which combines the rule-based and the hypothesis-based method using a two-stage topological recovery process. Given the point clouds of a single building, planar primitives and their corresponding boundaries are extracted and regularized to obtain abstracted building counters. In the first stage, we take advantage of the regularity and adjacency of the building counters to recover parts of the topological relationships between different primitives. Three constraints, namely pairwise constraint, triplet constraint, and nearby constraint, are utilized to form an initial reconstruction with candidate faces in ambiguous areas. In the second stage, the topologies in ambiguous areas are removed and reconstructed by solving an integer linear optimization problem based on the initial constraints while considering data fitting degree. Experiments using real datasets reveal that compared with state-of-the-art methods, the proposed method can efficiently reconstruct 3D building models in seconds with the geometry accuracy in decimeter level.

Royal jelly (RJ), a natural honeybee product, has a wide range of antibacterial activities. N-glycosylated major royal jelly protein 2 (N-MRJP2), purified from RJ, can inhibit the growth of Paenibacillus larvae (P. larvae, Gram-positive), a contagious etiological agent of the American foulbrood disease of honeybees. However, the inhibitory mechanism is largely unknown. Antibacterial assay and membrane proteome were conducted to investigate the inhibition capacity of RJ from different instar larvae and P. larvae treated by N-MRJP2, respectively. The similar antibacterial efficiency of RJ from different larval instar indicates that RJ is vital for the adaptive immune defense of small larvae. The killing of P. larvae by N-MRJP2 is achieved by disturbing the cell wall biosynthesis, increasing the permeability of cell membrane, hindering aerobic respiration, restraining cell division and inducing cell death. This demonstrates that RJ is critical for the passive immunity of immature larvae and N-MRJP2 can be used as natural antibiotic substance to resist P. larvae, even for other gram-positive bacteria. This constitutes solid evidence that RJ and N-MRJP2 have potentials as novel antibacterial agents.

Heterogeneous cellular networks (HCNs) have emerged as the primary solution for explosive data traffic. However, an increase in the number of base stations (BSs) inevitably leads to an increase in energy consumption. Energy efficiency (EE) has become a focal point in HCNs. In this paper, we apply tools from stochastic geometry to investigate and optimize the energy efficiency (EE) for a two-tier HCN. The average achievable transmission rate and the total power consumption of all the BSs in a two-tier HCN is derived, and then the EE is formulated. In order to maximize EE, a one-dimensional optimization algorithm is used to optimize picocell BS density and transmit power. Based on this, an alternating optimization method aimed at maximizing EE is proposed to jointly optimize transmit power and density of picocell BSs. Simulation results validate the accuracy of the theoretical analysis and demonstrate that the proposed joint optimization method can obviously improve EE.

There are normally three main steps to carrying out the labeling of airborne laser scanning (ALS) point clouds. The first step is to use appropriate primitives to represent the scanning scenes, the second is to calculate the discriminative features of each primitive, and the third is to introduce a classifier to label the point clouds. This paper investigates multiple primitives to effectively represent scenes and exploit their geometric relationships. Relationships are graded according to the properties of related primitives. Then, based on initial labeling results, a novel, hierarchical, and optimal strategy is developed to optimize semantic labeling results. The proposed approach was tested using two sets of representative ALS point clouds, namely the Vaihingen datasets and Hong Kong’s Central District dataset. The results were compared with those generated by other typical methods in previous work. Quantitative assessments for the two experimental datasets showed that the performance of the proposed approach was superior to reference methods in both datasets. The scores for correctness attained over 98% in all cases of the Vaihingen datasets and up to 96% in the Hong Kong dataset. The results reveal that our approach of labeling different classes in terms of ALS point clouds is robust and bears significance for future applications, such as 3D modeling and change detection from point clouds.

Aerial laser scanning or photogrammetric point clouds are often noisy at building boundaries. In order to produce regularized polygons from such noisy point clouds, this study proposes a hierarchical regularization method for the boundary points. Beginning with detected planar structures from raw point clouds, two stages of regularization are employed. In the first stage, the boundary points of an individual plane are consolidated locally by shifting them along their refined normal vector to resist noise, and then grouped into piecewise smooth segments. In the second stage, global regularities among different segments from different planes are softly enforced through a labeling process, in which the same label represents parallel or orthogonal segments. This is formulated as a Markov random field and solved efficiently via graph cut. The performance of the proposed method is evaluated for extracting 2D footprints and 3D polygons of buildings in metropolitan area. The results reveal that the proposed method is superior to the state-of-art methods both qualitatively and quantitatively in compactness. The simplified polygons could fit the original boundary points with an average residuals of 0.2 m, and in the meantime reduce up to 90% complexities of the edges. The satisfactory performances of the proposed method show a promising potential for 3D reconstruction of polygonal models from noisy point clouds.

It is difficult to recover the residual heat from flue gas when supercritical carbon dioxide (S-CO2) cycle is used for a coal fired power plant, due to the higher CO2 temperature in tail flue and the limited air temperature in air preheater. The combined cycle is helpful for residual heat recovery. Thus, it is important to build an efficient bottom cycle. In this paper, we proposed a novel exergy destruction control strategy during residual heat recovery to equal and minimize the exergy destruction for different bottom cycles. Five bottom cycles are analyzed to identify their differences in thermal efficiencies (ηth,b), and the CO2 temperature entering the bottom cycle heater (T4b) etc. We show that the exergy destruction can be minimized by a suitable pinch temperature between flue gas and CO2 in the heater via adjusting T4b. Among the five bottom cycles, either the recompression cycle (RC) or the partial cooling cycle (PACC) exhibits good performance. The power generation efficiency is 47.04% when the vapor parameters of CO2 are 620/30 MPa, with the double-reheating-recompression cycle as the top cycle, and RC as the bottom cycle. Such efficiency is higher than that of the supercritical water cycle power plant.

Oblique photogrammetric point clouds are currently one of the major data sources for the three-dimensional level-of-detail reconstruction of buildings. However, they are severely noise-laden and pose serious problems for the effective and automatic surface extraction of buildings. In addition, conventional methods generally use normal vectors estimated in a local neighborhood, which are liable to be affected by noise, leading to inferior results in successive building reconstruction. In this paper, we propose an intact planar abstraction method for buildings, which explicitly handles noise by integrating information in a larger context through global optimization. The information propagates hierarchically from a local to global scale through the following steps: first, based on voxel cloud connectivity segmentation, single points are clustered into supervoxels that are enforced to not cross the surface boundary; second, each supervoxel is expanded to nearby supervoxels through the maximal support region, which strictly enforces planarity; third, the relationships established by the maximal support regions are injected into a global optimization, which reorients the local normal vectors to be more consistent in a larger context; finally, the intact planar surfaces are obtained by region growing using robust normal and point connectivity in the established spatial relations. Experiments on the photogrammetric point clouds obtained from oblique images showed that the proposed method is effective in reducing the influence of noise and retrieving almost all of the major planar structures of the examined buildings.

Automatic road extraction from remote-sensing imagery plays an important role in many applications. However, accurate and efficient extraction from very high-resolution (VHR) images remains difficult because of, for example, increased data size and superfluous details, the spatial and spectral diversity of road targets, disturbances (e.g., vehicles, shadows of trees, and buildings), the necessity of finding weak road edges while avoiding noise, and the fast-acquisition requirement of road information for crisis response. To solve these difficulties, a two-stage method combining edge information and region characteristics is presented. In the first stage, convolutions are executed by applying Gabor wavelets in the best scale to detect Gabor features with location and orientation information. The features are then merged into one response map for connection analysis. In the second stage, highly complete, connected Gabor features are used as edge constraints to facilitate stable object segmentation and limit region growing. Finally, segmented objects are evaluated by some fundamental shape features to eliminate nonroad objects. The results indicate the validity and superiority of the proposed method to efficiently extract accurate road targets from VHR remote-sensing images.

Porcine epidemic diarrhea (PED) has re-emerged in recent years and has already caused huge economic losses to the porcine industry all over the world. Therefore, it is urgent for us to find out efficient ways to prevent and control this disease. In this study, the antiviral activity of a cationic amphibian antimicrobial peptide Caerin1.1 against porcine epidemic diarrhea virus (PEDV) was evaluated by an in vitro system using Vero cells. We found that even at a very low concentration, Caerin1.1 has the ability to destroy the integrity of the virus particles to block the release of the viruses, resulting in a considerable decrease in PEDV infections. In addition, Caerin1.1 showed powerful antiviral activity without interfering with the binding progress between PEDV and the receptor of the cells, therefore, it could be used as a potential antiviral drug or as a microbicide compound for prevention and control of PEDV.

This study investigated the influence of Canarium album extract (CAext) on intestinal microbiota composition of mice fed a high-fat diet (HFD). Kun Ming (KM) mice were fed either a normal chow diet or a HFD for six weeks. At the seventh week, HFD-fed mice were gavaged daily with saline, or a different dose of CAext for four weeks, respectively. Then, the composition of the gut microbiota was analyzed by high-throughput sequencing technology. Analysis of fecal microbial populations, grouped by phyla, showed significant increases of Firmicutes and Verrucomicrobia, but a decrease of Bacteroidetes in all CAext-fed mice. Particularly, CAext gavage in a low dose or a medium dose caused a significant increase in the proportion of Akkermansia. These findings suggested that CAext can alter the gut microbiota composition of HFD-fed mice, and had a potential prebiotic effects on Akkermansia.

Traditionally, visual-based RGB-D SLAM systems only use correspondences with valid depth values for camera tracking, thus ignoring the regions without 3D information. Due to the strict limitation on measurement distance and view angle, such systems adopt only short-range constraints which may introduce larger drift errors during long-distance unidirectional tracking. In this paper, we propose a novel geometric integration method that makes use of both 2D and 3D correspondences for RGB-D tracking. Our method handles the problem by exploring visual features both when depth information is available and when it is unknown. The system comprises two parts: coarse pose tracking with 3D correspondences, and geometric integration with hybrid correspondences. First, the coarse pose tracking generates the initial camera pose using 3D correspondences with frame-by-frame registration. The initial camera poses are then used as inputs for the geometric integration model, along with 3D correspondences, 2D-3D correspondences and 2D correspondences identified from frame pairs. The initial 3D location of the correspondence is determined in two ways, from depth image and by using the initial poses to triangulate. The model improves the camera poses and decreases drift error during long-distance RGB-D tracking iteratively. Experiments were conducted using data sequences collected by commercial Structure Sensors. The results verify that the geometric integration of hybrid correspondences effectively decreases the drift error and improves mapping accuracy. Furthermore, the model enables a comparative and synergistic use of datasets, including both 2D and 3D features.

RGB-D sensors (sensors with RGB camera and Depth camera) are novel sensing systems that capture RGB images along with pixel-wise depth information. Although they are widely used in various applications, RGB-D sensors have significant drawbacks including limited measurement ranges (e.g., within 3 m) and errors in depth measurement increase with distance from the sensor with respect to 3D dense mapping. In this paper, we present a novel approach to geometrically integrate the depth scene and RGB scene to enlarge the measurement distance of RGB-D sensors and enrich the details of model generated from depth images. First, precise calibration for RGB-D Sensors is introduced. In addition to the calibration of internal and external parameters for both, IR camera and RGB camera, the relative pose between RGB camera and IR camera is also calibrated. Second, to ensure poses accuracy of RGB images, a refined false features matches rejection method is introduced by combining the depth information and initial camera poses between frames of the RGB-D sensor. Then, a global optimization model is used to improve the accuracy of the camera pose, decreasing the inconsistencies between the depth frames in advance. In order to eliminate the geometric inconsistencies between RGB scene and depth scene, the scale ambiguity problem encountered during the pose estimation with RGB image sequences can be resolved by integrating the depth and visual information and a robust rigid-transformation recovery method is developed to register RGB scene to depth scene. The benefit of the proposed joint optimization method is firstly evaluated with the publicly available benchmark datasets collected with Kinect. Then, the proposed method is examined by tests with two sets of datasets collected in both outside and inside environments. The experimental results demonstrate the feasibility and robustness of the proposed method.

Waste mushroom residues are often returned to fields as organic amendments. Here, we estimated the effects of the continuous applications of different spent mushroom substrates for 2 years on crop yields, soil nutrients, and heavy metals in paddy fields. The study comprised seven treatments: no fertilization (CK) and mineral NPK fertilizer (CF), as well as NPK fertilizer combined with Enoki mushroom residue (EMR50), Oyster mushroom residue (OMR50), Auricularia polytricha mushroom residue (APR50), Shiitake mushroom residue (SMR50), and Agaricus bisporus residue (ABR50). The grain yield was highest under the APR50 treatment. The short-term application of waste mushroom residue significantly increased SOC, TN, TP, and TK content relative to the CK treatment. The SOC, TP, and TK were highest under ABR50. Both total Cr and Cd contents were highest under CF treatment. The highest cumulative ecological risk was observed under OMR50 treatment. In addition, crop yield was positively correlated with SOC, TN, TP, and TP. Our results highlight that further research and innovation are needed to optimize the benefits and overcome the challenges of mushroom residue application.

The brain storm optimization (BSO) algorithm has received increased attention in the field of evolutionary computation. While BSO has been applied in numerous industrial scenarios due to its effectiveness and accessibility, there are few theoretical analysis results about its running time. Running-time analysis can be conducted through the estimation of the upper bounds of the expected first hitting time to evaluate the efficiency of BSO. This study estimates the upper bounds of the expected first hitting time on six single individual BSO variants (BSOs with one individual) based on the average gain model. The theoretical analysis indicates the following results. (1) The time complexity of the six BSO variants is O(n) in equal coefficient linear functions regardless of the presence or absence of the disrupting operator, where n is the number of the dimensions. Moreover, the coefficient of the upper bounds on the expected first hitting time shows that the single individual BSOs with the disrupting operator require fewer iterations to obtain the target solution than the single individual BSOs without the disrupting operator. (2) The upper bounds on the expected first hitting time of single individual BSOs with the standard normally distributed mutation operator are lower than those of BSOs with the uniformly distributed mutation operator. (3) The upper bounds on the expected first hitting time of single individual BSOs with the U−12,12 mutation operator are approximately twice those of BSOs with the U(−1,1) mutation operator. The corresponding numerical results are also consistent with the theoretical analysis results.