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As the information age progresses, the electronics industry is evolving towards smaller and more sophisticated products. However, electrostatic potentials easily penetrate these components, causing damage. This underscores the urgent need for materials with superior antistatic properties to safeguard electronic devices from such damage. Antistatic coatings typically rely on polymers as the primary material, enhanced with conductive fillers and additives to improve performance. Despite significant progress, these coatings still face challenges related to advanced processing technologies and the integration of electrical and mechanical properties. Among various conductive fillers, the conducting polymer PEDOT:PSS stands out for its exceptional conductivity, environmental stability, and long cycle life. Additionally, epoxy resin (EP) is widely utilized in polymer coatings due to its strong adhesion to diverse substrates during curing. Here, we develop highly conductive and strongly adhesive PEDOT:PSS inks by combining PEDOT:PSS with EP using a composite engineering approach. These inks are used to fabricate PEDOT:PSS coatings by direct ink writing (DIW). We systematically evaluate the DIW of PEDOT:PSS-EP coatings, which show high electrical conductivity (ranging from 0.59 ± 0.07 to 41.50 ± 3.26 S cm−1), strong adhesion (ranging from 15.84 ± 2.18 to 99.3 ± 9.06 kPa), and robust mechanical strength (8 MPa). Additionally, we examine the surface morphology, wettability, and hardness of the coatings with varying PEDOT:PSS content. The resultant coatings demonstrate significant potential for applications in antistatic protection, electromagnetic shielding, and other flexible electronic technologies. [ABSTRACT FROM AUTHOR]

The accurate assessment of the rise in nitrous oxide (N2O) under global changes in grasslands has been hindered because of inadequate annual observations. To measure the seasonal response of N2O emissions to increased water and nitrogen (N) deposition, one year round N2O emissions were investigated by chamber weekly in the growing season and every two weeks in the non-growing season in semi-arid temperate grasslands northern China. The results showed the temperate semi-arid grassland to be a source of N2O with greater variability and contribution during the non-growing season. The individual effects of water or N addition increased N2O emissions during the growing season, while the effects of water or N addition depended on the N application rates during the non-growing season. Soil properties, particularly soil temperature and water-filled pore space (WFPS), played key roles in regulating N2O emissions. Structural equation modeling revealed that these factors explained 71% and 35% of the variation in N2O fluxes during the growing and non-growing season, respectively. This study suggested that without observations during the non-growing season it is possible to misestimate the annual N2O emissions and the risk of N2O emissions increasing under global change. This would provide insights for future management strategies for mitigating greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

Chromatin remodelers are essential for regulating plant growth, development, and responses to environmental stresses. HIT4 (HEAT-INTOLERANT 4) is a novel stress-induced chromatin remodeling factor that has been less studied in abiotic stress and stress resistance, particularly in cotton. In this study, we conducted a comprehensive analysis of the members of the HIT4 gene family in Gossypium hirsutum using bioinformatics methods, including phylogenetic relationships, gene organization, transcription profiles, phylogenetic connections, selection pressure, and stress response. A total of 18 HIT4 genes were identified in four cotton species, with six HIT4 gene members in upland cotton. Based on the evolutionary relationships shown in the phylogenetic tree, the 18 HIT4 protein sequences were classified into four distinct subgroups. Furthermore, we conducted chromosome mapping to determine the genomic locations of these genes and visually represented the structural characteristics of HIT4 in G. hirsutum. In addition, we predicted the regulatory elements in HIT4 in G. hirsutum and conducted an analysis of repetitive sequences and gene collinearity among HIT4 in four cotton species. Moreover, we calculated the Ka/Ks ratio for homologous genes to assess the selection pressure acting on HIT4. Using RNA-seq, we explored the expression patterns of HIT4 genes in G. hirsutum and Gossypium barbadense. Through weighted gene co-expression network analysis (WGCNA), we found that GHHIT4_4 belonged to the MEblue module, which was mainly enriched in pathways such as DNA replication, phagosome, pentose and glucuronate interconversions, steroid biosynthesis, and starch and sucrose metabolism. This module may regulate the mechanism of upland cotton resistance to Verticillium wilt through DNA replication, phagosome, and various metabolic pathways. In addition, we performed heterologous overexpression of GH_D11G0591 (GHHIT4_4) in tobacco, and the results showed a significant reduction in disease index compared to the wild type, with higher expression levels of disease resistance genes in the transgenic tobacco. After conducting a VIGS (virus-induced gene silencing) experiment in cotton, the results indicated that silencing GHHIT4_4 had a significant impact, the resistance to Verticillium wilt weakened, and the internode length of the plants significantly decreased by 30.7% while the number of true leaves increased by 41.5%. qRT-PCR analysis indicated that GHHIT4_4 mainly enhanced cotton resistance to Verticillium wilt by indirectly regulating the PAL, 4CL, and CHI genes. The subcellular localization results revealed that GHHIT4_4 was predominantly distributed in the mitochondria and nucleus. This study offers preliminary evidence for the involvement of the GHHIT4_4 in cotton resistance to Verticillium wilt and lays the foundation for further research on the disease resistance mechanism of this gene in cotton. [ABSTRACT FROM AUTHOR]

Glutathione S-transferases (GSTs) are a major class of phase II metabolic enzymes. Besides their essential role in detoxification, GSTs also exert diverse biological activities in the occurrence and development of various diseases. In the past few decades, much research interest has been paid to exploring the mechanisms of GST overexpression in tumor drug resistance. Correspondingly, many GST inhibitors have been developed and applied, solely or in combination with chemotherapeutic drugs, for the treatment of multi-drug resistant tumors. Moreover, novel roles of GSTs in other diseases, such as pulmonary fibrosis and neurodegenerative diseases, have been recognized in recent years, although the exact regulatory mechanisms remain to be elucidated. This review, firstly summarizes the roles of GSTs and their overexpression in the above-mentioned diseases with emphasis on the modulation of cell signaling pathways and protein functions. Secondly, specific GST inhibitors currently in pre-clinical development and in clinical stages are inventoried. Lastly, applications of GST inhibitors in targeting cell signaling pathways and intracellular biological processes are discussed, and the potential for disease treatment is prospected. Taken together, this review is expected to provide new insights into the interconnection between GST overexpression and human diseases, which may assist future drug discovery targeting GSTs. [ABSTRACT FROM AUTHOR]

Multi-source image registration has often suffered from great radiation and geometric differences. Specifically, grayscale and texture from similar landforms in different source images often show significantly different visual features, and these differences disturb the corresponding point extraction in the following image registration process. Considering that edges between heterogeneous images can provide homogeneous information and more consistent features can be extracted based on image edges, an edge consistency radiation-change insensitive feature transform (EC-RIFT) method is proposed in this paper. Firstly, the noise and texture interference are reduced by preprocessing according to the image characteristics. Secondly, image edges are extracted based on phase congruency, and an orthogonal Log-Gabor filter is performed to replace the global algorithm. Finally, the descriptors are built with logarithmic partition of the feature point neighborhood, which improves the robustness of the descriptors. Comparative experiments on datasets containing multi-source remote sensing image pairs show that the proposed EC-RIFT method outperforms other registration methods in terms of precision and effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we investigate deep-learning-based image inpainting techniques for emergency remote sensing mapping. Image inpainting can generate fabricated targets to conceal real-world private structures and ensure informational privacy. However, casual inpainting outputs may seem incongruous within original contexts. In addition, the residuals of original targets may persist in the hiding results. A Residual Attention Target-Hiding (RATH) model has been proposed to address these limitations for remote sensing target hiding. The RATH model introduces the residual attention mechanism to replace gated convolutions, thereby reducing parameters, mitigating gradient issues, and learning the distribution of targets present in the original images. Furthermore, this paper modifies the fusion module in the contextual attention layer to enlarge the fusion patch size. We extend the edge-guided function to preserve the original target information and confound viewers. Ablation studies on an open dataset proved the efficiency of RATH for image inpainting and target hiding. RATH had the highest similarity, with a 90.44 % structural similarity index metric (SSIM), for edge-guided target hiding. The training parameters had 1 M fewer values than gated convolution (Gated Conv). Finally, we present two automated target-hiding techniques that integrate semantic segmentation with direct target hiding or edge-guided synthesis for remote sensing mapping applications. [ABSTRACT FROM AUTHOR]

To determine the optimal process parameters for the preparation of a Co-based alloy cladding layer, the experimental research of laser cladding Co-based alloy was carried out based on the optimal process window and grey relational analysis methods with 42CrMo as the substrate. The analysis of variance (ANOVA) was used to explore the influence laws of laser process parameters on the forming characteristics of the cladding layer within the optimal process window range. Furthermore, the optimal process parameter combination was obtained by grey relational analysis, and the experimental verification of the optimization results was conducted. It was found that the process parameter interval determined by the optimal process window was laser power 1300–2100 W, scanning speed 6–14 mm/s, and powder feeding rate 17.90–29.84 g/min. The influence order of each process parameter was: laser power > scanning speed > powder feeding rate. The optimal process parameters of laser power 2100 W, scanning speed 6 mm/s, and powder feeding rate 17.90 g/min were obtained. The experimental verification results of optimal process parameters proved that the grey correlation grade of the optimized parameters was improved by 0.260 compared with the initial parameters and agreed well with the prediction value with an accuracy of 96%. After optimization, the cross-sectional area, the ratio of the width to height, cladding efficiency, and powder utilization rate of the cladding track increased by 4.065 mm2, 1.031, 19.032, and 70.3%, respectively, and the fluctuation ratio decreased by 60.9%. The optimal cladding track was well bonded to the substrate without cracks, holes, and evident element segregation, and included the phases of Cr3C7, CoCx, fcc-Co, and WC. [ABSTRACT FROM AUTHOR]

Constructing a Regional Innovation Ecosystem (RIE) can be an effective way to utilize innovation resources by breaking the existing regional barriers. Existing research focuses more on exploring the characteristics, evolution, and impact of innovation ecosystems from a theoretical perspective, while few studies combine reality with exploring how to build an effective innovation ecosystem. Given that the concept of an innovation ecosystem is inspired by natural ecosystems, using the method of comparative analysis, this article begins by analyzing the characteristics of tropical rainforests as the most stable and efficient natural ecosystem and deeply explores the essential features of an efficient RIE, including an innovative soft and hard environment, multi-level and differentiated producers, consumers and decomposers of innovative activities, and a virtuous cycle of innovation resources. By comparing Beijing-Tianjin-Hebei's current reality with these characteristics, this article systematically analyzes the advantages of the innovation environment, the further improvement of innovation resource aggregation and innovation chains, as well as the disadvantages of an unbalanced distribution of innovation resources, the leapfrogging transformation of scientific and technological achievements, low innovation levels of market-oriented innovation entities, and loss of innovative factors, such as talents. Based on this analysis, targeted suggestions to construct a RIE in Beijing-Tianjin-Hebei, including using both market and administrative approaches to allocating innovation factors, stimulating the innovation vitality of market-oriented innovation entities, taking multiple measures to enhance Hebei Province's capacity, building an innovative environment that is livable and business-friendly are proposed. [ABSTRACT FROM AUTHOR]

Simple Summary: Bacterial endosymbionts play important roles in the life history of herbivorous insects, including supplying nutrients allowing exploitation of unbalanced diets, increasing the survivorship and fecundity of hosts, protecting hosts against entomopathogenic fungi and parasitoid wasps, ameliorating the detrimental effects of heat, and broadening the range of suitable food plants. The important function of bacterial endosymbionts is that they can manipulate the reproduction of their hosts. However, all functions of endosymbionts must rely on vertical transmission to spread within the population, such as Wolbachia, Rickettsia, and Cardinium. So, we studied the spatial and temporal distribution of Wolbachia and Rickettsia in Asia II 1 Bemisia tabaci. The results showed that the titers of Wolbachia and Rickettsia in the 3–120 h old eggs changed in a "w" pattern, and the location of Wolbachia and Rickettsia in the egg changed from egg stalk to egg base, and then from the egg base to egg posterior, and finally back to the middle of egg in a Rickettsia and Wolbachia coinfected whitefly host. Our study helps to explain the vertical transmission mechanism of bacterial endosymbionts and the distribution of bacterial endosymbionts in different tissues in the host. Wolbachia and Rickettsia are bacterial endosymbionts that can induce a number of reproductive abnormalities in their arthropod hosts. We screened and established the co-infection of Wolbachia and Rickettsia in Bemisia tabaci and compared the spatial and temporal distribution of Wolbachia and Rickettsia in eggs (3–120 h after spawning), nymphs, and adults of B. tabaci by qPCR quantification and fluorescent in situ hybridization (FISH). The results show that the titer of Wolbachia and Rickettsia in the 3–120 h old eggs showed a "w" patterned fluctuation, while the titers of Wolbachia and Rickettsia had a "descending–ascending descending–ascending" change process. The titers of Rickettsia and Wolbachia nymphal and the adult life stages of Asia II1 B. tabaci generally increased with the development of whiteflies. However, the location of Wolbachia and Rickettsia in the egg changed from egg stalk to egg base, and then from egg base to egg posterior, and finally back to the middle of the egg. These results will provide basic information on the quantity and localization of Wolbachia and Rickettsia within different life stages of B. tabaci. These findings help to understand the dynamics of the vertical transmission of symbiotic bacteria. [ABSTRACT FROM AUTHOR]

As an important strategic material in China, cotton policy has positive significance in stabilizing the safety of domestic cotton, ensuring cotton income, and stabilizing cotton output. At the same time, affected by the future market price, the cotton industry supply chain cost management is particularly important. Based on the data of China's cotton price, consumption, yield, and wheeling-in and wheeling-out from 1978 to 2016, the simultaneous equation model is used to empirically analyze the transmission effect of the rotation mechanism of reserve cotton (RMRC) on cotton supply and demand. Studies have shown that China's cotton reserve policy and the RMRC play a positive role in balancing the supply and demand of the domestic cotton market and preventing excessive fluctuations in cotton prices. The transmission effect of the RMRC on domestic cotton yield and consumption is significant and there are certain differences. The transmission effect of domestic cotton yield is stronger than the transmission effect on domestic cotton consumption. The difference is owing to the transfer path of the RMRC being different. We should improve the regulation and control effect of the RMRC on cotton demand, realize the normalization of the RMRC, and clarify the policy objectives of the RMRC. [ABSTRACT FROM AUTHOR]

A disposable and portable electrochemical sensor was fabricated by integrating vertically-ordered silica mesoporous films (VMSF) and electrochemically reduced graphene (ErGO) on a screen-printed carbon electrode (SPCE). Such VMSF/ErGO/SPCEs could be prepared by a simple and controllable electrochemical method. Stable growth of VMSF on SPCE could be accomplished by the introduction of an adhesive ErGO nanolayer owing to its oxygen-containing groups and two-dimensional (2D) planar structure. An outer VMSF layer acting as a protective coating is able to prevent the leakage of the inner ErGO layer from the SPCE surface. Thanks to the electrostatic permselectivity and anti-fouling capacity of VMSF and to the good electroactive activity of ErGO, binary nanocomposites of VMSF and ErGO endow the SPCE with excellent analytical performance, which could be used to quantitatively detect doxorubicin (DOX) in biological samples (human serum and urine) with high sensitivity, good long-term stability, and low sample amounts. [ABSTRACT FROM AUTHOR]

Rectangular double-laminated piezoelectric elements are widely used in low frequency transducers. At present, most of these piezoelectric elements use a piezoelectric ceramic laminated structure. In this work, the bending vibration properties of double-laminated piezoelectric ceramic elements with different transverse dimensions were analyzed by the finite element method. It was found that there was a strong transverse mechanical coupling, and the transverse dimensions had a great effect on the bending vibration. To suppress the transverse mechanical coupling effect, a double-laminated piezoelectric element based on a 2-2 piezoelectric composite was designed. The simulation results show that the bending vibration performance of the double-laminated composite element is not affected by the transverse size. The 2-2 piezoelectric composite double-laminated and ceramic elements were prepared. In addition, simulation and experimental results indicate that the transverse mechanical coupling of the bending vibration of the piezoelectric composite double-laminated element is effectively suppressed, the vibration frequency is reduced by about 100 Hz, and the vibration displacement is increased by 2.2 times, in comparison with the piezoelectric ceramic double-laminated element. [ABSTRACT FROM AUTHOR]

The quality of the filling algorithm in additive manufacturing directly affects the strength of the part. The commonly used 3D printing filling algorithm at this stage is the classic filling algorithm. The density of each part in the filling region is the same, and there is a cavity structure in the transverse direction, which makes the strength of the part in the transverse direction lower. Therefore, this paper proposed a new filling algorithm—variable density filling algorithm based on Delaunay triangulation. First, we performed concave-polygon-convex decomposition on the filling area to form printing sub-regions; then, the filling density value was set according to the required intensity of each region, and we used the Poisson disk sampling algorithm to generate the filling point set. Finally, Delaunay triangulation was performed on the generated point set to generate filled traces. The comparison with the two commonly used classical filling algorithms proves that the algorithm can improve the strength of the part to a certain extent, and the printing time and the consumption of consumables will not increase significantly. [ABSTRACT FROM AUTHOR]

The characteristics of fused deposition 3D printing lead to the inevitable step effect of surface contour in the process of forming and manufacturing, which affects molding accuracy. Traditional layering algorithms cannot take into account both printing time and molding accuracy. In this paper, an adaptive layering algorithm based on the optimal volume error is proposed. The angle between the normal vector and the layering direction is used for data optimization. The layer thickness is determined by calculating the volume error, and based on the principle of the optimal volume error, the unequal thickness adaptive layering of each printing layer of the model is realized. The experimental results show that the self-adaptive layering algorithm based on the optimal volume error has a better layering effect, greatly improves the forming efficiency and surface forming accuracy, and has a good adaptability to models with complex surfaces. [ABSTRACT FROM AUTHOR]

Rapid and highly sensitive determination of clozapine (CLZ), a psychotropic drug for the treatment of refractory schizophrenia, in patients is of great significance to reduce the risk of disease recurrence. However, direct electroanalysis of CLZ in human whole blood remains a great challenge owing to the remarkable fouling that occurs in a complex matrix. In this work, a miniaturized, integrated, disposable electrochemical sensing platform based on the integration of nanochannel arrays on the surface of screen-printed carbon electrodes (SPCE) is demonstrated. The device achieves high determination sensitivity while also offering the electrode anti-fouling and anti-interference capabilities. To enhance the electrochemical performance of SPCE, simple electrochemical polarization including anodic oxidation and cathodic reduction is applied to pretreat SPCE. The electrochemically polarized SPCE (p-SPCE) exhibits an enhanced electrochemical peak signal toward CLZ compared with bare SPCE. An electrochemically assisted self-assembly method (EASA) is utilized to conveniently electrodeposit a vertically ordered mesoporous silica nanomembrane film (VMSF) on the p-SPCE, which could further enrich CLZ through electrostatic interactions. Owing to the dual signal amplification based on the p-SPCE and VMSF nanochannels, the developed VMSF/SPCE sensor enables determination of CLZ in the range from 50 nM to 20 μM with a low limit of detection (LOD) of 28 nM (S/N = 3). Combined with the excellent anti-fouling and anti-interference abilities of VMSF, direct and sensitive determination of CLZ in human blood is also achieved. [ABSTRACT FROM AUTHOR]

The surface defects of industrial structural parts have the characteristics of a large-scale span and many small objects, so a novel YOLOv3 model, the YOLOv3-ALL algorithm, is proposed in this paper to solve the problem of precise defect detection. The K-means++ algorithm is combined with the intersection-over-union (IoU) and comparison of the prior box for clustering, which improves the clustering effect. The convolutional block attention module (CBAM) is embedded in the network, thus improving the ability of the network to obtain key information in the image. By adding fourth-scale prediction, the detection capability of a YOLOv3 network for small-object defects is greatly improved. A loss function is designed, which adds the generalized intersection-over-union (GIoU) loss combined with focal loss to solve the problems of L2 loss and class imbalance in samples. Experiments regarding contour-defect detection for stamping parts show that the mean average precision (mAP) of the YOLOV3-ALL algorithm reaches 75.05% in defect detection, which is 25.16% higher than that of the YOLOv3 algorithm. The average detection time is 39 ms/sheet. This proves that the YOLOv3-ALL algorithm has good real-time detection efficiency and high detection accuracy. [ABSTRACT FROM AUTHOR]

The 3D printing process lacks real-time inspection, which is still an open-loop manufacturing process, and the molding accuracy is low. Based on the 3D reconstruction theory of machine vision, in order to meet the applicability requirements of 3D printing process detection, a matching fusion method is proposed. The fast nearest neighbor (FNN) method is used to search matching point pairs. The matching point information of FFT-SIFT algorithm based on fast Fourier transform is superimposed with the matching point information of AKAZE algorithm, and then fused to obtain more dense feature point matching information and rich edge feature information. Combining incremental SFM algorithm with global SFM algorithm, an integrated SFM sparse point cloud reconstruction method is developed. The dense point cloud is reconstructed by PMVs algorithm, the point cloud model is meshed by Delaunay triangulation, and then the accurate 3D reconstruction model is obtained by texture mapping. The experimental results show that compared with the classical SIFT algorithm, the speed of feature extraction is increased by 25.0%, the number of feature matching is increased by 72%, and the relative error of 3D reconstruction results is about 0.014%, which is close to the theoretical error. [ABSTRACT FROM AUTHOR]

To improve the transmission performance and preparation of a transducer array, two planar array transducers based on connected 1–3 piezoelectric composites as a matrix were designed. Each transducer contained 25 array elements with a gap of 1 mm between them. The length, width and height of each array element were 1 mm, 26 mm and 5 mm, respectively. Two kinds of array transducers were tested through finite element simulation and experiments. The array transducer prototype was fabricated based on two kinds of composite materials, and the fabrication cycle was short. Our results show that the maximum transmission voltage response of the two-phase 1–3 full array driver is up to 179 dB at 200–400 kHz and the acoustic radiation intensity can be increased by up to 22% compared with the traditional splicing transducer array. It is suitable for short-range target positioning and measurement. Moreover, in the single element mode, the beam of the three-phase 1–3 transducer has no sidelobe and a single element −3 dB beam width of up to 91°. Furthermore, the beam width of the two-phase 1–3 type is 54°, and the acoustic radiation breadth is improved by 40.6%. The three-phase 1–3 type array transducer has the characteristics of concentrated acoustic transmission energy of the whole array, and its −3 dB beam width is 3.5°. The beam width decreased by 12.5%, indicating that the three-phase 1–3 type transducer is suitable for short-range target detection and perception. The two array transducers have their own advantages in transmitting the voltage response and beam width, which must be selected on the basis of the requirements of practical applications. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification is essential for satellite Internet of Things (IoT) to build a large scale land-cover surveillance system. After acquiring real-time land-cover information, the edge of the network transmits all the hyperspectral images by satellites with low-latency and high-efficiency to the cloud computing center, which are provided by satellite IoT. A gigantic amount of remote sensing data bring challenges to the storage and processing capacity of traditional satellite systems. When hyperspectral images are used in annotation of land-cover application, data dimension reduction for classifier efficiency often leads to the decrease of classifier accuracy, especially the region to be annotated consists of natural landform and artificial structure. This paper proposes encoding spectral-spatial features for hyperspectral image classification in the satellite Internet of Things system to extract features effectively, namely attribute profile stacked autoencoder (AP-SAE). Firstly, extended morphology attribute profiles EMAP is used to obtain spatial features of different attribute scales. Secondly, AP-SAE is used to extract spectral features with similar spatial attributes. In this stage the program can learn feature mappings, on which the pixels from the same land-cover class are mapped as closely as possible and the pixels from different land-cover categories are separated by a large margin. Finally, the program trains an effective classifier by using the network of the AP-SAE. Experimental results on three widely-used hyperspectral image (HSI) datasets and comprehensive comparisons with existing methods demonstrate that our proposed method can be used effectively in hyperspectral image classification. [ABSTRACT FROM AUTHOR]

Dephosphorization slag contains considerable quantities of valuable components, such as P2O5 and FeOx. To recover P from dephosphorization slag, selective leaching has been adopted to separate the P-concentrating mineral phase. In this study, the effect of Fe2O3 content in slag and acid on the leaching behavior of P from dephosphorization slag was investigated. It was found that a higher Fe2O3 content in slag resulted in a higher P2O5 content in the C2S–C3P solid solution. Increasing the Fe2O3 content in slag promoted the dissolution of P and simultaneously suppressed the dissolution of other elements, facilitating the selective leaching of P. In the hydrochloric acid solution, more than 81% of P could be dissolved from dephosphorization slag at pH 4, and the dissolution ratio of Fe was nearly zero, achieving excellent selective leaching. Although better selective leaching was also realized in the citric acid solution at pH 5, hydrochloric acid was considered the appropriate leaching agent from the perspective of leaching cost. Through selective leaching, almost all the C2S–C3P solid solution was dissolved from dephosphorization slag, and the Fe-bearing matrix phase and magnesioferrite remained in the residue. The residue with low P2O5 content can be reutilized in ironmaking or steelmaking processes. [ABSTRACT FROM AUTHOR]

In this work, the bending vibration characteristics of the 2-2 piezoelectric composite trilaminar vibrator are studied by the finite element simulation and experiment. The simulation results show that the trilaminar vibrator has lower resonant frequency and larger vibration displacement under the fixed boundary condition compared with that of the free boundary condition, and its performance is relatively good. Then, the 2-2 piezoelectric composite and piezoelectric ceramic trilaminar vibrators are fabricated and their performances are tested under the fixed boundary condition. The experimental and simulation results show that the vibrator has pure bending vibration characteristics in the frequency band of 1.2–1.4 kHz, especially the 2-2 piezoelectric composite vibrator, which has lower frequency, higher electromechanical coupling coefficient and larger bending vibration displacement; thus, the 2-2 piezoelectric composite trilaminar vibrator is a better choice for the fabrication of a low-frequency transducer. [ABSTRACT FROM AUTHOR]

Separating P2O5 from steelmaking slag is the key to achieving optimum resource utilization of slag. If the P-concentrating 2CaO∙SiO2–3CaO∙P2O5 solid solution was effectively separated, it can be a potential phosphate resource and the remaining slag rich in Fe2O3 and CaO can be reutilized as a flux in steelmaking process. In this study, a low-cost method of selective leaching was adopted, and hydrochloric acid was selected as leaching agent. The dissolution behavior of quenched steelmaking slags with different composition in the acidic solution was investigated and the dissolution mechanism was clarified. It was found that the P dissolution ratio from each slag was higher than those of other elements, achieving an effective separation of P and Fe. The dissolution ratios of P, Ca, and Si decreased as the P2O5 content in slag increased. A higher Fe2O3 content in slag led to a lower P dissolution ratio. Increasing slag basicity facilitated the dissolution of P from slag. The residue mainly composed of matrix phase and the P2O5 content decreased significantly through selective leaching. The P dissolution ratio from slag was primarily determined by the P distribution ratio in the 2CaO∙SiO2–3CaO∙P2O5 solid solution and the precipitation of ferric phosphate in the leachate. The P-concentrating solid solution was effectively separated from quenched steelmaking slag, even though hydrochloric acid was used as leaching agent. [ABSTRACT FROM AUTHOR]

Since remote sensing images are difficult to obtain and need to go through a complicated administrative procedure for use in China, it cannot meet the requirement of huge training samples for Waterside Change Detection based on deep learning. Recently, data augmentation has become an effective method to address the issue of an absence of training samples. Therefore, an improved Generative Adversarial Network (GAN), i.e., BTD-sGAN (Text-based Deeply-supervised GAN), is proposed to generate training samples for remote sensing images of Anhui Province, China. The principal structure of our model is based on Deeply-supervised GAN(D-sGAN), and D-sGAN is improved from the point of the diversity of the generated samples. First, the network takes Perlin Noise, image segmentation graph, and encoded text vector as input, in which the size of image segmentation graph is adjusted to 128 × 128 to facilitate fusion with the text vector. Then, to improve the diversity of the generated images, the text vector is used to modify the semantic loss of the downsampled text. Finally, to balance the time and quality of image generation, only a two-layer Unet++ structure is used to generate the image. Herein, "Inception Score", "Human Rank", and "Inference Time" are used to evaluate the performance of BTD-sGAN, StackGAN++, and GAN-INT-CLS. At the same time, to verify the diversity of the remote sensing images generated by BTD-sGAN, this paper compares the results when the generated images are sent to the remote sensing interpretation network and when the generated images are not added; the results show that the generated image can improve the precision of soil-moving detection by 5%, which proves the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Pea aphid (Acyrthosiphon pisum) is a worldwide pest that feeds exclusively on the phloem sap of numerous host plants. It harbours a well-known primary endosymbiont Buchneraaphidicola that helps to overcome the nutritional deficiency of a plant-based diet. However, how the Buchnera contributes to the nutritional and energy metabolism of its aphid host is unclear to date. In the current study, the function of Buchnera in relation to nutritional synthesis of pea aphid was investigated by disrupting the primary endosymbiont with an antibiotic rifampicin. Our findings revealed that the disruption of Buchnera led to infertility and higher loss in body mass of aphid hosts. Body length and width were also decreased significantly compared to healthy aphids. The detection of nutrition indicated that the quantity of proteins, soluble sugars, and glycogen in aposymbiotic pea aphids increased slowly with the growth of the aphid host. In comparison, the quantities of all the nutritional factors were significantly lower than those of symbiotic pea aphids, while the quantity of total lipid and neutral fat in aposymbiotic pea aphids were distinctly higher than those of symbiotic ones. Thus, we concluded that the significant reduction of the total amount of proteins, soluble sugars, and glycogen and the significant increase of neutral fats in aposymbiotic pea aphids were due to the disruption of Buchnera, which confirmed that the function of Buchnera is irreplaceable in the pea aphid. [ABSTRACT FROM AUTHOR]

Vehicle counting from an unmanned aerial vehicle (UAV) is becoming a popular research topic in traffic monitoring. Camera mounted on UAV can be regarded as a visual sensor for collecting aerial videos. Compared with traditional sensors, the UAV can be flexibly deployed to the areas that need to be monitored and can provide a larger perspective. In this paper, a novel framework for vehicle counting based on aerial videos is proposed. In our framework, the moving-object detector can handle the following two situations: static background and moving background. For static background, a pixel-level video foreground detector is given to detect vehicles, which can update background model continuously. For moving background, image-registration is employed to estimate the camera motion, which allows the vehicles to be detected in a reference coordinate system. In addition, to overcome the change of scale and shape of vehicle in images, we employ an online-learning tracker which can update the samples used for training. Finally, we design a multi-object management module which can efficiently analyze and validate the status of the tracked vehicles with multi-threading technique. Our method was tested on aerial videos of real highway scenes that contain fixed-background and moving-background. The experimental results show that the proposed method can achieve more than 90% and 85% accuracy of vehicle counting in fixed-background videos and moving-background videos respectively. [ABSTRACT FROM AUTHOR]

Engineering vehicles intrusion detection is a key problem for the security of power grid operation, which can warn of the regional invasion and prevent external damage from architectural construction. In this paper, we propose an intelligent surveillance method based on the framework of Faster R-CNN for locating and identifying the invading engineering vehicles. In our detection task, the type of the objects is varied and the monitoring scene is large and complex. In order to solve these challenging problems, we modify the network structure of the object detection model by adjusting the position of the ROI pooling layer. The convolutional layer is added to the feature classification part to improve the accuracy of the detection model. We verify that increasing the depth of the feature classification part is effective for detecting engineering vehicles in realistic transmission lines corridors. We also collect plenty of scene images taken from the monitor site and label the objects to create a fine-tuned dataset. We train the modified deep detection model based on the technology of transfer learning and conduct training and test on the newly labeled dataset. Experimental results show that the proposed intelligent surveillance method can detect engineering vehicles with high accuracy and a low false alarm rate, which can be used for the early warning of power grid surveillance. [ABSTRACT FROM AUTHOR]