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The objective of this article is to examine potential techniques for suppressing solar heat flow on the optical antenna of a laser communication sensor. Firstly, the characteristics of the geosynchronous Earth orbit’s (GEO) space radiation environment are analysed, and a combined passive and active thermal control solution is proposed. Secondly, the temperature distribution of the lasercom sensor under extreme operating conditions is simulated utilising IDEAS-TMG (6.8 NX Series) software, which employs Monte Carlo and radiative heat transfer numerical calculation methods. Finally, a strategy for avoiding direct sunlight around midnight is proposed. The simulation results demonstrated that the thermal control solution and solar avoidance strategy proposed in this paper achieved long-term fine-stable control of the temperature field of the optical antenna, which met the thermal permissible communication hours per daily orbit cycle in excess of 14 h per day.

Ultrasonic vibration-assisted machining is a promising technique for improving the removability of materials, especially for difficult-to-machine materials, but the material removal mechanism under ultrasonic vibration status is still far from clear. Scratch testing is generally employed to study the material removal mechanism, but currently, there is a lack of instruments capable of performing scratch testing under ultrasonic vibration. To address this gap, this study developed an ultrasonic vibration scratch tester that can perform quantitative ultrasonic vibration-assisted scratch (UVAS) testing of materials. A prototype was designed and fabricated, followed by characterizing its performance parameters. Comparative experiments of conventional scratch (CS) testing and UVAS testing were performed on AL1050 to investigate the effects of ultrasonic vibration on scratch characteristics, such as the scratch depth and coefficient of friction. It was found that compared to CS testing, UVAS testing, with an amplitude of 1.45 µm and a frequency of 20 kHz, achieved a maximum reduction in the coefficient of friction of approximately 22.5% and a maximum increase in the depth of the residual scratch of approximately 175%. These findings confirm the superiority of ultrasonic vibration-assisted machining and demonstrate the requirement for the development of ultrasonic vibration scratch testers.

In this paper, an electro-hydraulic servo resonance technology is proposed to meet the loading requirements of a high-frequency sound fatigue test for large tonnage. First of all, we analyze the static and dynamic loading structure of electro-hydraulic servo vibration and establish the vibration equation of the system. Additionally, the modal and vibration characteristics of the system are analyzed by simulation, which verified the feasibility of the proposed electro-hydraulic servo resonant loading technology. Finally, the influence of various factors such as sample stiffness, lead screw stiffness, class II spring stiffness, class II weight mass, lower beam mass, and upper beam mass on the natural frequency and amplification coefficient of the system is analyzed. In this paper, a new technology is proposed to provide theoretical support for the research and development of large-tonnage high-frequency noise fatigue testing equipment.

Aiming at the shortcomings of single-sensor sensing information characterization ability, which is easily interfered with by external environmental factors, a method of intelligent perception is proposed in this paper. This method integrates multi-source and multi-level information, including spindle temperature field, spindle thermal deformation, operating parameters, and motor current. Firstly, the internal and external thermal-error-related signals of the spindle system are collected by sensors, and the feature parameters are extracted; then, the radial basis function (RBF) neural network is utilized to realize the preliminary integration of the feature parameters because of the advantages of the RBF neural network, which offers strong multi-dimensional solid nonlinear mapping ability and generalization ability. Thermal-error decision values are then generated by a weighted fusion of different pieces of evidence by considering uncertain information from multiple sources. The spindle thermal-error sensing experiment was based on the spindle system of the VMC850 (Yunnan Machine Tool Group Co., LTD, Yunnan, China) vertical machining center of the Yunnan Machine Tool Factory. Experiments were designed for thermal-error sensing of the spindle under constant speed (2000 r/min and 4000 r/min), standard variable speed, and stepped variable speed conditions. The experiment’s results show that the prediction accuracy of the intelligent-sensing model with multi-source information fusion can reach 98.1%, 99.3%, 98.6%, and 98.8% under the above working conditions, respectively. The intelligent-perception model proposed in this paper has higher accuracy and lower residual error than the traditional BP neural network perception and wavelet neural network models. The research in this paper provides a theoretical basis for the operation, maintenance management, and performance optimization of machine tool spindle systems.

Citrus Huanglongbing (HLB), caused by the phloem-inhibiting bacterium Candidatus Liberibacter asiaticus (CLas), is the most devastating citrus disease, intimidating citrus production worldwide. Although commercially cultivated citrus cultivars are vulnerable to CLas infection, HLB-tolerant attributes have, however, been observed in certain citrus varieties, suggesting a possible pathway for identifying innate defense regulators that mitigate HLB. By adopting transcriptome and small RNAome analysis, the current study compares the responses of HLB-tolerant lemon (Citrus limon L.) with HLB-susceptible Shatangju mandarin (Citrus reticulata Blanco cv. Shatangju) against CLas infection. Transcriptome analysis revealed significant differences in gene expression between lemon and Shatangju. A total of 1751 and 3076 significantly differentially expressed genes were identified in Shatangju and lemon, respectively. Specifically, CLas infected lemon tissues demonstrated higher expressions of genes involved in antioxidant enzyme activity, protein phosphorylation, carbohydrate, cell wall, and lipid metabolism than Shatangju. Wet-lab experiments further validated these findings, demonstrating increased antioxidant enzyme activity in lemon: APX (35%), SOD (30%), and CAT (64%) than Shatangju. Conversely, Shatangju plants exhibited higher levels of oxidative stress markers like H2O2 (44.5%) and MDA content (65.2%), alongside pronounced ion leakage (11.85%), than lemon. Moreover, microscopic investigations revealed that CLas infected Shatangju phloem exhibits significantly more starch and callose accumulation than lemon. Furthermore, comparative sRNA profiles revealed the potential defensive regulators for HLB tolerance. In Shatangju, increased expression of csi-miR166 suppresses the expression of disease-resistant proteins, leading to inadequate defense against CLas. Conversely, reduced expression of csi-miR166 in lemon plants enables them to combat HLB by activating disease-resistance proteins. The above findings indicate that when infected with CLas, lemon exhibits stronger antioxidative activity and higher expression of disease-resistant genes, contributing to its enhanced tolerance to HLB. In contrast, Shatangju shows lower antioxidative activity, reduced expression of disease-resistant genes, significant ion leakage, and extensive callose deposition, possibly related to damage to plant cell structure and blockage of phloem sieve tubes, thereby promoting the development of HLB symptoms.

With the development of rice varieties and mechanized planting technology, reliable and efficient nitrogen and planting density status diagnosis and recommendation methods have become critical to the success of precise nitrogen and planting density management in crops. In this study, we combined population structure, plant shape characteristics, environmental weather conditions, and management information data using a machine learning model to simulate the responses of the yield and nitrogen nutrition index and developed an ensemble learning model-based nitrogen and planting density recommendation strategy for different varieties of rice types. In the third stage, the NNI and yield prediction effect of the ensemble learning model was more significantly improved than that of the other two stages. The scenario analysis results show that the optimal yields and nitrogen nutrition indices were obtained with a density and nitrogen amount of 100.1 × 104 plant/ha and 161.05 kg·ha−1 for the large-spike type variety of rice, 75.08 × 104 plant/ha and 159.52 kg·ha−1 for the intermediate type variety of rice, and 75.08 × 104 plant/ha and 133.47 kg·ha−1 for the panicle number type variety of rice, respectively. These results provide a scientific basis for the nitrogen application and planting density for a high yield and nitrogen nutrition index of rice in northeast China.

Influenza A virus (IAV) can cause infection and illness in a wide range of animals, including humans, poultry, and swine, and cause annual epidemics, resulting in thousands of deaths and millions of hospitalizations all over the world. Thus, there is an urgent need to develop novel anti-IAV drugs with high efficiency and low toxicity. In this study, the anti-IAV activity of a marine-derived compound mycophenolic acid methyl ester (MAE) was intensively investigated both in vitro and in vivo. The results showed that MAE inhibited the replication of different influenza A virus strains in vitro with low cytotoxicity. MAE can mainly block some steps of IAV infection post adsorption. MAE may also inhibit viral replication through activating the cellular Akt-mTOR-S6K pathway. Importantly, oral treatment of MAE can significantly ameliorate pneumonia symptoms and reduce pulmonary viral titers, as well as improving the survival rate of mice, and this was superior to the effect of oseltamivir. In summary, the marine compound MAE possesses anti-IAV effects both in vitro and in vivo, which merits further studies for its development into a novel anti-IAV drug in the future.

Hyperspectral image (HSI) classification tasks have been adopted in huge applications of remote sensing recently. With the rise of deep learning development, it becomes crucial to investigate how to exploit spatial–spectral features. The traditional approach is to stack models that can encode spatial–spectral features, coupling sufficient information as much as possible, before the classification model. However, this sequential stacking tends to cause information redundancy. In this paper, a novel network utilizing the channel attention combined discrete cosine transform (DCTransformer) to extract spatial–spectral features has been proposed to address this issue. It consists of a detail spatial feature extractor (DFE) with CNN blocks and a base spectral feature extractor (BFE) utilizing the channel attention mechanism (CAM) with a discrete cosine transform (DCT). Firstly, the DFE can extract detailed context information using a series of layers of a CNN. Further, the BFE captures spectral features using channel attention and stores the wider frequency information by utilizing the DCT. Ultimately, the dynamic fusion mechanism has been adopted to fuse the detail and base features. Comprehensive experiments show that the DCTransformer achieves a state-of-the-art (SOTA) performance in the HSI classification task, compared to other methods on four datasets, the University of Houston (UH), Indian Pines (IP), MUUFL, and Trento datasets. On the UH dataset, the DCTransformer achieves an OA of 94.40%, AA of 94.89%, and kappa of 93.92.

Low-temperature chilling is a major abiotic stress leading to reduced rice yield and is a significant environmental threat to food security. Low-temperature chilling studies have focused on physiological changes or coding genes. However, the competitive endogenous RNA mechanism in rice at low temperatures has not been reported. Therefore, in this study, antioxidant physiological indices were combined with whole-transcriptome data through weighted correlation network analysis, which found that the gene modules had the highest correlation with the key antioxidant enzymes superoxide dismutase and peroxidase. The hub genes of the superoxide dismutase-related module included the UDP-glucosyltransferase family protein, sesquiterpene synthase and indole-3-glycerophosphatase gene. The hub genes of the peroxidase-related module included the WRKY transcription factor, abscisic acid signal transduction pathway-related gene plasma membrane hydrogen-ATPase and receptor-like kinase. Therefore, we selected the modular hub genes and significantly enriched the metabolic pathway genes to construct the key competitive endogenous RNA networks, resulting in three competitive endogenous RNA networks of seven long non-coding RNAs regulating three co-expressed messenger RNAs via four microRNAs. Finally, the negative regulatory function of the WRKY transcription factor OsWRKY61 was determined via subcellular localization and validation of the physiological indices in the mutant.

The long-term presence of PPCPs in the aqueous environment poses a potentially significant threat to human life and physical health and the safety of the water environment. In our previous work, we investigated low-cost pitch-based HCP adsorbents with an excellent adsorption capacity and magnetic responsiveness through a simple one-step Friedel–Crafts reaction. In this work, we further investigated the adsorption behavior of the prepared pitch-based adsorbents onto three PPCP molecules (DFS, AMP, and antipyrine) in detail. The maximum adsorption capacity of P-MPHCP for DFS was 444.93 mg g−1. The adsorption equilibrium and kinetic processes were well described through the Langmuir model and the proposed secondary kinetic model. The negative changes in Gibbs free energy and enthalpy reflected that the adsorption of HCPs onto PPCPs was a spontaneous exothermic process. The recoverability results showed that the adsorption of MPHCP and P-MPHCP onto DFS remained above 95% after 10 adsorption–desorption cycles. The present work further demonstrates that these pitch-based adsorbents can be used for multiple applications, which have a very extensive practical application prospect.

A superpixel is a group of pixels with similar low-level and mid-level properties, which can be seen as a basic unit in the pre-processing of remote sensing images. Therefore, superpixel segmentation can reduce the computation cost largely. However, all the deep-learning-based methods still suffer from the under-segmentation and low compactness problem of remote sensing images. To fix the problem, we propose EAGNet, an enhanced atrous extractor and self-dynamic gate network. The enhanced atrous extractor is used to extract the multi-scale superpixel feature with contextual information. The multi-scale superpixel feature with contextual information can solve the low compactness effectively. The self-dynamic gate network introduces the gating and dynamic mechanisms to inject detailed information, which solves the under-segmentation effectively. Massive experiments have shown that our EAGNet can achieve the state-of-the-art performance between k-means and deep-learning-based methods. Our methods achieved 97.61 in ASA and 18.85 in CO on the BSDS500. Furthermore, we also conduct the experiment on the remote sensing dataset to show the generalization of our EAGNet in remote sensing fields.

The rapid proliferation of handheld intelligent devices and the advent of 5G technology have brought about convenient and fast services for people. In perception-oriented application services, participating users will upload sensitive mobile data in order to obtain benefits. While devising privacy protection strategies to ensure data security, it is crucial to accomplish task perception related to data collection to the fullest extent possible. To address this challenge, this paper proposes a personalized data privacy protection algorithm based on an adaptive dynamic adjustment grid and the minimum wage task allocation strategy. According to the different levels of users’ needs for privacy protection, combined with the privacy budget allocation strategy, we design a different-level differential privacy protection mechanism and consider the reward mechanism in task allocation to balance the effectiveness and security of the location data uploaded by users. Experiments show that the strategy proposed in this paper can not only protect the data but also enable users to freely choose the level of privacy protection.

In-season crop-type maps are required for a variety of agricultural monitoring and decision-making applications. The earlier the crop type maps of the current growing season are obtained, the more beneficial it is for agricultural decision-making and management. With the availability of a large amount of high spatiotemporal resolution remote sensing data, different data sources are expected to increase the frequency of data acquisition, which can provide more information in the early season. To explore the potential of integrating different data sources, a Dual-1DCNN algorithm was built based on the CNN model in this study. Moreover, an incremental training method was used to attain the network on each data acquisition date and obtain the best detection date for each crop type in the early season. A case study for Hengshui City in China was conducted using time series of Sentinel-1A (S1A) and Sentinel-2 (S2) attained in 2019. To verify this method, the classical methods support vector machine (SVM), random forest (RF), and Mono-1DCNN were implemented. The input for SVM and RF was S1A and S2 data, and the input for Mono-1DCNN was S2 data. The results demonstrated the following: (1) Dual-1DCNN achieved an overall accuracy above 85% at the earliest time.; (2) all four types of models achieved high accuracy (F1s were greater than 90%) on summer maize after sowing one month later; (3) for cotton and common yam rhizomes, Dual-1DCNN performed best, with its F1 reaching 85% within 2 months after cotton sowing, 15 days, 20 days, and 45 days ahead of Mono-1DCNN, SVM, and RF, respectively, and its extraction of the common yam rhizome was achieved 1–2 months earlier than other methods within the acceptable accuracy. These results confirmed that Dual-1DCNN offered significant potential in the in-season detection of crop types.

An edge computing offloading strategy was proposed with the goal of addressing the issue of low edge computing efficiency and service quality in the multi-service and multi-user intersections of networked vehicles. This strategy took into account all relevant factors, including the matching of users and service nodes, offloading ratio, bandwidth and computing power resource allocation, and system energy consumption. It is mainly divided into 2 tasks: (1) Service node selection: A fuzzy logic-based service node selection algorithm (SNFLC) is proposed. The linear equation for node performance value is determined through fuzzy reasoning by specifying three performance indexes as input. Gradient descent method is used to find the optimal value of the objective function, and the Lyapunov criterion coefficient is introduced to improve the stability of the algorithm. (2) Offload ratio and resource allocation are solved: The coupling between offload ratio and bandwidth resource allocation is confirmed by relaxing integer variables because the optimization goal problem is a NP problem, and the issue is divided into two sub-problems. At the same time, a low-complexity alternate iteration resource allocation algorithm (LC-IRA) is proposed to solve the bandwidth resource and computational power resource allocation. According to simulation results, the performance of genetic ant colony algorithm (G_ACA), non orthogonal multiple access technology (NOMA) and LC-IRA are improved by 26.5%, 31.37%, and 45.52%, respectively, compared with the random unloading allocation (RUA) and average distribution (AD).

Understanding the vibrational information encoded within the terahertz (THz) spectrum of biomolecules is critical for guiding the exploration of its functional responses to specific THz radiation wavelengths. This study investigated several important phospholipid components of biological membranes—distearoyl phosphatidylethanolamine (DSPE), dipalmitoyl phosphatidylcholine (DPPC), sphingosine phosphorylcholine (SPH), and lecithin bilayer—using THz time-domain spectroscopy. We observed similar spectral patterns for DPPC, SPH, and the lecithin bilayer, all of which contain the choline group as the hydrophilic head. Notably, the spectrum of DSPE, which has an ethanolamine head group, was different. Interestingly, density functional theory calculations confirmed that the absorption peak common to DSPE and DPPC at approximately 3.0 THz originated from a collective vibration of their similar hydrophobic tails. Accordingly, the cell membrane fluidity of RAW264.7 macrophages with irradiation at 3.1 THz was significantly enhanced, leading to improved phagocytosis. Our results highlight the importance of the spectral characteristics of the phospholipid bilayers when studying their functional responses in the THz band and suggest that irradiation at 3.1 THz is a potential non-invasive strategy to increase the fluidity of phospholipid bilayers for biomedical applications such as immune activation or drug administration.

To investigate the normal section strength and cracking bending moment of normal concrete–ultra-high-performance concrete (NC-UHPC) composite beams, calculation formulas were established considering the tensile strength of UHPC based on the current railway bridge design code. Using the railway T-beam as a template, prestressed NC-UHPC composite beams with different NC layer heights were built. A static bending test was performed, the pressure of the steel strand and the deflection and strain of the beam were measured, and the evolution of cracks in each beam was observed. The calculation formulas of the normal section strength and cracking bending moment of NC-UHPC composite beam were verified by the test. The results showed that the type of strain was similar to load-deflection curves with increasing load; the bending failure process of the NC-UHPC composite beam showed four obvious stages: elasticity, uniform cracking, crack development, and yield. Cracks in the beam started to appear at stage II, developed rapidly at stage III, and stopped emerging at stage IV. The calculation formulas for the normal section strength and the cracking bending moment of the NC-UHPC composite beam were in good agreement with the test values. Normal concrete with a compressive strength of 80 MPa can replace UHPC for the design of NC-UHPC composite beams.

Information exchange between building information modelling (BIM) and geographic information system (GIS) is problematic, especially in terms of semantic information. Graph-based technologies, such as the resource description framework (RDF) and the labelled property graph (LPG), are promising in solving this problem. These two technologies are different but have not been systematically investigated in the context of BIM/GIS integration. This paper presents our systematic investigation into these two technologies, trying to propose the proper one for BIM/GIS data integration. The main findings are as follows. (1) Both LPG-based databases and RDF-based databases can be generally considered graph databases, but an LPG-based database is considered a native graph database, while an RDF-based database is not. (2) RDF suits applications focusing more on linking data and sharing data, and (3) LPG-based graph database suits applications focusing more on data query and analysis. An LPG-based graph database is thus proposed for BIM/GIS data integration. This review can facilitate the use of graph technology in BIM/GIS integration.

Given the lack of primary data on heat-resistant composites under high-temperature conditions, the focus of this paper is the development of an in-plane biaxial apparatus under high temperatures and complex loads. Besides loading complex loads up to 80 kN, the apparatus can load under high-temperatures up to 2500 °C. A C/C tensile/compression test at 1700 °C illustrates the successful use of high-temperature digital speckle pattern technology to evaluate the in-plane mechanical properties of heat-resistant composites at 1700 °C under biaxial stress. A high-temperature impact test of a graphite specimen at 2500 °C shows that this apparatus can load at a high temperature in a vacuum and inert gas atmosphere. The yield characteristics of the Q235 steel sheet under in-plane stress show that the apparatus can conduct various mechanical loads, including tension–tension, tension–compression, and compression–compression loads. The proposed equipment can measure the in-plane mechanical properties of composite materials, particularly heat-resistant composites. The obtained results can be applied to structural design, life prediction, and reliability evaluation, as well as for the development, research, and design of aerospace instruments and critical materials.

Although ground-based precipitable water vapor (PWV) can be obtained with a high temporal resolution and spatial resolution of tens of kilometers in an urban area using Global Navigation Satellite System (GNSS) observation, it remains fairly sparse in the vast regions over Mainland China. Satellite-derived PWV has a high spatial resolution, thereby enabling the accurate investigation of regional climate change. However, understanding the quality of satellite-derived PWV products is a prerequisite before use, which has become the focus of this study. PWV products, namely, MOD05_L2 and MYD05_L2, over the entirety of Mainland China derived from MODerate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites are validated over the period 2000–2017 using multiple sources, including GNSS, radiosonde, AErosol RObotic NETwork (AERONET), and European Center for Medium-Range Weather Forecasts ERA-Interim (ECMWF). The accuracy of MODIS PWV products is less than 4.3 mm over the entirety of Mainland China; however, it varies in four areas of South China (SC), North China (NC), Northwest China (NWC), and Tibetan Plateau (TP), separately with values ranging from 3 mm to 6 mm. A linear fit model is applied to calibrate the MODIS PWV products, and the accuracies of the corrected PWV from MODIS infrared (IR) and near-infrared (NIR) products have been improved by approximately 7.5% and 50.6%, respectively. The MODIS PWV is compared and calibrated in the four areas, and the improved accuracies vary widely. The root mean square error (RMSE) of IR PWV is approximately less than 4 mm over China except for the SC area with a value of approximately 5.3 mm after calibration, whereas the values of NIR PWV are approximately 2 mm over the entirety of Mainland China, except for the TP area, with a value of approximately 2.6 mm. The MODIS NIR PWV performs better than that of IR PWV data in most areas of Mainland China regardless of whether with calibration. The validation and calibration of MODIS water vapor products over Mainland China also indicate their capability to investigate the seasonal and annual variations as well as long-term trend changes in water vapor in China.

Porous carbon has been one desirable electrode material for supercapacitors, but it is still a challenge to balance the appropriate mesopore volume and a high specific surface area (SSA). Herein, a good balance between a high SSA and mesopore volume in biomass-derived porous carbon is realized by precarbonization of wheat husk under air atmosphere via a chloride salt sealing technique and successive KOH activation. Due to the role of molten salt generating mesopores in the precarbonized product, which can further serve as the active sites for the KOH activation to form micropores in the final carbon material, the mesopore–micropore structure of the porous carbon can be tuned by changing the precarbonization temperature. The appropriate amount of mesopores can provide more expressways for ion transfer to accelerate the transport kinetics of diffusion-controlled processes in the micropores. A high SSA can supply abundant sites for charge storage. Therefore, the porous carbon with a good balance between the SSA and mesopores exhibits a specific gravimetric capacitance of 402 F g−1 at 1.0 A g−1 in a three-electrode system. In a two-electrode symmetrical supercapacitor, the biomass-derived porous carbon also delivers a high specific gravimetric capacitance of 346 F g−1 at 1.0 A g−1 and a good cycling stability, retaining 98.59% of the initial capacitance after 30,000 cycles at 5.0 A−1. This work has fundamental merits for enhancing the electrochemical performance of the biomass-derived porous carbon by optimizing the SSA and pore structures.

Drought stress at jointing–booting directly affects plant growth and productivity in rice. Limited by natural factors, the jointing and booting stages of short-growth-period rice varieties are highly overlapped in high-latitude areas, which are more sensitive to water deficit. However, little is known about the dry matter translocation in rice and the strategies of starch synthesis and filling of superior and inferior grains under different drought stress was unclear. In this study, the rice plants were subjected to three degrees of drought stress (−10 kPa, −25 kPa, −40 kPa) for 15 days during the jointing–booting stage; we investigated dry matter accumulation and translocation, grain filling and enzyme activities to starch synthesis of superior and inferior grains in rice with overlapping growth stages from 2016 to 2017. The results showed that drought stress significantly reduced dry matter accumulation in the stems and leaves. Mild and moderate drought increased dry matter translocation efficiency. However, severe drought stress largely limited the dry matter accumulation and translocation. A large amount of dry matter remains in vegetative organs under severe drought stress. The high content in NSC in stem and sheath plays a key role in resisting drought stress. The drought stress at jointing–booting directly caused a change in the grain filling strategy. Under moderate and severe drought, the grain-filling active period of the superior grains was shortened to complete the necessary reproductive growth. The grain-filling active period of the inferior grains was significantly prolonged to avoid a decrease in grain yield. The significant decrease in the grain-filling rate of the superior and inferior grains caused a reduction in the thousand-grain weight. In particular, the influence of the grain-filling rate of inferior grains on the thousand-grain weight was more significant. Drought stress changed the starch synthesis strategies of the superior and inferior grains. Soluble starch synthase and starch branching enzyme activities of inferior grains increased significantly under drought stress. GBSS activity was not sensitive to drought stress. Therefore, amylose content was decreased and amylopectin synthesis was enhanced under drought stress, especially in inferior grains.

The loading coaxiality of an in-plane biaxial test system and the structure of a cruciform specimen markedly affect the test results. However, due to the lack of methods for correcting the loading coaxiality and designing the cruciform specimen, the data scatter of the test results of the in-plane biaxial test systems varies from the laboratory to different tests. To quantify the loading coaxiality of the in-plane biaxial test system, we first developed a model to calculate alignment deviations with strain distribution of the shape-optimised cruciform specimen with Automated Machine Learning (AutoML). Our results demonstrated that 99.2% (54,536 of 54,976) of the quantified errors are less than 5%. Quantifying alignment deviations for an in-plane biaxial test system has been solved. The quantified method of alignment deviations could enhance the reliability of test data, improve assembly efficiency, and aid in constructing failure criteria of materials under biaxial stress.

Stress uniformity within the gauge zone of a cruciform specimen significantly affects materials’ in-plane biaxial mechanical properties in material testing. The stress uniformity depends on the load transmission of the cruciform specimen from the fixtures to the gauge zone. Previous studies failed to alter the nature of the load transmission of the geometric features using parametric optimisations. To improve stress uniformity in the gauge zone, we optimised the cross-arms to design a centre-reduced cruciform specimen with topology and shape optimisations. The simulations show that the optimised specimen obtains significantly less stress variation and range in the gauge zone than the optimised specimen under different observed areas, directions, and load ratios of von Mises, S11, S22, and S12. In the quantified gauge zone, a more uniform stress distribution could be generated by optimizing specimen geometry, whose value should be estimated indirectly each time through simulations. We found that topology and shape optimisations could markedly improve stress uniformity in the gauge zone, and stress concentration at the cross-arms intersection. We first optimised the cruciform specimen structure by combining topology and shape optimisations, which provided a cost-effective way to improve stress uniformity in the gauge zone and reduce stress concentration at the cross-arms intersection, helping obtain reliable data to perform large strains in the in-plane biaxial tensile test.

The factors affecting the internal stress of specimens during indentation tests were investigated by finite element analysis (FEA) modelling. This was carried out to gain a qualitative understanding of the test errors introduced by the temperature environment during the indentation process. In this study, the influence of thermal expansion of fixed stage on upper specimen (currently neglected in temperature indentation) was explored in detail. Technical issues associated with the parameters of the specimen (such as thickness, width, and elastic modulus) and external conditions (such as stage and glue) were identified and addressed. The test error of the calculated hardness and elastic modulus of the specimen reached up to more than 3% simultaneously at −196 °C (temperature of liquid nitrogen). Based on these considerations, the preferred operation conditions were identified for testing in specific temperature environment. These results can guide experiments aimed at obtaining precise mechanical parameters.

Indentation experiments on differently oriented faces of monocrystalline copper were conducted to investigate the micro-deformation process at temperatures ranging from room temperature to 150 K. The morphologies and textures of the residual imprints were observed using electron microscopy. Distinct slip bands were observed inside the imprints at 150 K compared to smooth surfaces at room temperature. Molecular dynamics simulations were performed to identify the deformation process beneath the indentation region. The results showed that plastic deformation was inhibited with decreasing temperature, but elastic recovery during the unloading process was enhanced, resulting in inner slip bands (ISBs) being observable in the residual imprints. The performances of these ISBs were strongly associated with the angles between the indentation direction and major slip surfaces and could be considered microscopic forms on the surfaces of aggregated geometrically necessary dislocations (GNDs). This work helped reveal the micro-deformation mechanism of indentations inside imprints.

Histidine (His) is widely involved in the structure and function of biomolecules. Transition-metal ions, such as Zn2+ and Cu2+, widely exist in biological environments, and they are crucial to many life-sustaining physiological processes. Herein, by employing density function calculations, we theoretically show that the water affinity of His can be enhanced by the strong cation–π interaction between His and Zn2+ and Cu2+. Further, the solubility of His is experimentally demonstrated to be greatly enhanced in ZnCl2 and CuCl2 solutions. The existence of cation–π interaction is demonstrated by fluorescence, ultraviolet (UV) spectroscopy and nuclear magnetic resonance (NMR) experiments. These findings are of great importance for the bioavailability of aromatic drugs and provide new insight for understanding the physiological functions of transition metal ions.

Mobile crowdsensing utilizes the devices of a group of users to cooperatively perform some sensing tasks, where finding the perfect allocation from tasks to users is commonly crucial to guarantee task completion efficiency. However, existing works usually assume a static task allocation by sorting the cost of users to complete the tasks, where the cost is measured by the expense of time or distance. In this paper, we argue that the task allocation process is actually a dynamic combinational optimization problem because the previous allocated task will influence the initial state of the user to finish the next task, and the user’s preference will also influence the actual cost. To this end, we propose a personalized task allocation strategy for minimizing total cost, where the cost for a user to finish a task is measured by both the moving distance and the user’s preference for the task, then instead of statically allocating the tasks, the allocation problem is formulated as a heterogeneous, asymmetric, multiple traveling salesman problem (TSP). Furthermore, we transform the multiple-TSP to the single-TSP by proving the equivalency, and two solutions are presented to solve the single-TSP. One is a greedy algorithm, which is proved to have a bound to the optimal solution. The other is a genetic algorithm, which spends more calculation time while achieving a lower total cost. Finally, we have conducted a number of simulations based on three widely-used real-world traces: roma/taxi, epfl, and geolife. The simulation results could match the results of theoretical analysis.

In this work, a crosslinked polycarboxylate superplasticizer (crosslinked-PC) was synthesized via the free radical polymerization reaction. Pentaerythritol tetraacrylate (PETA) was used as the crosslinked agent. A comparative comb-like polycarboxylate superplasticizer (comb-like-PC) was prepared under the same reaction conditions. The dispersion retention capacity, dispersion capability, hydration characteristics of the cement paste and setting time were investigated in detail. At the dosage of 0.6% bwoc, the fluidity of the cement/crosslinked-PC paste was about 340 mm, which was 40~50 mm larger than the cement/comb-like-PC paste. The dispersion retention capacity of the cement/crosslinked-PC paste was observed to be much superior due to higher adsorbed amounts on the cement particles. Moreover, the cement/crosslinked-PC paste exhibited the initial and final setting durations of 196 and 356 min, respectively, which indicated an enhancement of 18 and 68 min compared to the cement/comb-like paste. The crosslinked copolymers exhibit a stronger retardation effect than the comb-like copolymers due to their enhanced adsorbed amounts and stronger steric hindrance effect. This is further illustrated by the characterization of the hydration process and hydration products. It can be concluded that it is feasible to improve the dispersive capacity and the dispersion retention capacity of PC by changing the molecule structure from comb-like to slightly crosslinked.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus 2 (SARS-CoV-2) is still raging all over the world. Hence, the rapid and sensitive screening of the suspected population is in high demand. The nucleocapsid protein (NP) of SARS-CoV-2 has been selected as an ideal marker for viral antigen detection. This study describes a lateral flow immunoassay (LFIA) based on colloidal gold nanoparticles for rapid NP antigen detection, in which sensitivity was improved through copper deposition-induced signal amplification. The detection sensitivity of the developed LFIA for NP antigen detection (using certified reference materials) under the optimized parameters was 0.01 μg/mL and was promoted by three orders of magnitude to 10 pg/mL after copper deposition signal amplification. The LFIA coupled with the copper enhancement technique has many merits such as low cost, high efficiency, and high sensitivity. It provides an effective approach to the rapid screening, diagnosis, and monitoring of the suspected population in the COVID-19 outbreak.

Remote sensing scene classification remains challenging due to the complexity and variety of scenes. With the development of attention-based methods, Convolutional Neural Networks (CNNs) have achieved competitive performance in remote sensing scene classification tasks. As an important method of the attention-based model, the Transformer has achieved great success in the field of natural language processing. Recently, the Transformer has been used for computer vision tasks. However, most existing methods divide the original image into multiple patches and encode the patches as the input of the Transformer, which limits the model’s ability to learn the overall features of the image. In this paper, we propose a new remote sensing scene classification method, Remote Sensing Transformer (TRS), a powerful “pure CNNs → Convolution + Transformer → pure Transformers” structure. First, we integrate self-attention into ResNet in a novel way, using our proposed Multi-Head Self-Attention layer instead of 3 × 3 spatial revolutions in the bottleneck. Then we connect multiple pure Transformer encoders to further improve the representation learning performance completely depending on attention. Finally, we use a linear classifier for classification. We train our model on four public remote sensing scene datasets: UC-Merced, AID, NWPU-RESISC45, and OPTIMAL-31. The experimental results show that TRS exceeds the state-of-the-art methods and achieves higher accuracy.

Capability assessment plays a crucial role in the demonstration and construction of equipment. To improve the accuracy and stability of capability assessment, we study the neural network learning algorithms in the field of capability assessment and index sensitivity. Aiming at the problem of overfitting and parameter optimization in neural network learning, the paper proposes an improved machine learning algorithm—the Ensemble Learning Based on Policy Optimization Neural Networks (ELPONN) with the policy optimization and ensemble learning. This algorithm presents an optimized neural network learning algorithm through different strategies evolution, and builds an ensemble learning model of multi-intelligent algorithms to assess the capability and analyze the sensitivity of the indexes. Through the assessment of capabilities, the algorithm effectively avoids parameter optimization from entering the minimum point in performance to improve the accuracy of equipment capability assessment, which is significantly better than previous neural network assessment methods. The experimental results show that the mean relative error is 4.10%, which is better than BP, GABP, and early stopping. The ELPONN algorithm has better accuracy and stability performance, and meets the requirements of capability assessment.

Accurate crop type maps play an important role in food security due to their widespread applicability. Optical time series data (TSD) have proven to be significant for crop type mapping. However, filling in missing information due to clouds in optical imagery is always needed, which will increase the workload and the risk of error transmission, especially for imagery with high spatial resolution. The development of optical imagery with high temporal and spatial resolution and the emergence of deep learning algorithms provide solutions to this problem. Although the one-dimensional convolutional neural network (1D CNN), long short-term memory (LSTM), and gate recurrent unit (GRU) models have been used to classify crop types in previous studies, their ability to identify crop types using optical TSD with missing information needs to be further explored due to their different mechanisms for handling invalid values in TSD. In this research, we designed two groups of experiments to explore the performances and characteristics of the 1D CNN, LSTM, GRU, LSTM-CNN, and GRU-CNN models for crop type mapping using unfilled Sentinel-2 (Sentinel-2) TSD and to discover the differences between unfilled and filled Sentinel-2 TSD based on the same algorithm. A case study was conducted in Hengshui City, China, of which 70.3% is farmland. The results showed that the 1D CNN, LSTM-CNN, and GRU-CNN models achieved acceptable classification accuracies (above 85%) using unfilled TSD, even though the total missing rate of the sample values was 43.5%; these accuracies were higher and more stable than those obtained using filled TSD. Furthermore, the models recalled more samples on crop types with small parcels when using unfilled TSD. Although LSTM and GRU models did not attain accuracies as high as the other three models using unfilled TSD, their results were almost close to those with filled TSD. This research showed that crop types could be identified by deep learning features in Sentinel-2 dense time series images with missing information due to clouds or cloud shadows randomly, which avoided spending a lot of time on missing information reconstruction.

For full description of images’ semantic information, image retrieval tasks are increasingly using deep convolution features trained by neural networks. However, to form a compact feature representation, the obtained convolutional features must be further aggregated in image retrieval. The quality of aggregation affects retrieval performance. In order to obtain better image descriptors for image retrieval, we propose two modules in our method. The first module is named generalized regional maximum activation of convolutions (GR-MAC), which pays more attention to global information at multiple scales. The second module is called saliency joint weighting, which uses nonparametric saliency weighting and channel weighting to focus feature maps more on the salient region without discarding overall information. Finally, we fuse the two modules to obtain more representative image feature descriptors that not only consider the global information of the feature map but also highlight the salient region. We conducted experiments on multiple widely used retrieval data sets such as roxford5k to verify the effectiveness of our method. The experimental results prove that our method is more accurate than the state-of-the-art methods.

Seed germination is an important phase transitional period of angiosperm plants during which seeds are highly sensitive to different environmental conditions. Although seed germination is under the regulation of salicylic acid (SA) and other hormones, the molecular mechanism underlying these regulations remains mysterious. In this study, we determined the expression of SA methyl esterase (MES) family genes during seed germination. We found that MES7 expression decreases significantly in imbibed seeds, and the dysfunction of MES7 decreases SA content. Furthermore, MES7 reduces and promotes seed germination under normal and salt stress conditions, respectively. The application of SA restores the seed germination deficiencies of mes7 mutants under different conditions. Taking together, our observations uncover a MeSA hydrolytic enzyme, MES7, regulates seed germination via altering SA titer under normal and abiotic stress conditions.

Generally, there is an inconsistency between the total regional crop area that was obtained from remote sensing technology and the official statistical data on crop areas. When performing scale conversion and data aggregation of remote sensing-based crop mapping results from different administrative scales, it is difficult to obtain accurate crop planting area that match crop area statistics well at the corresponding administrative level. This problem affects the application of remote sensing-based crop mapping results. In order to solve the above problem, taking Fucheng County of Hebei Province in the Huanghuaihai Plain of China as the study area, based on the Sentinel-2 normalized difference vegetation index (NDVI) time series data covering the whole winter wheat growth period, the statistical data of the regional winter wheat planting area were regarded as reference for the winter wheat planting area extracted by remote sensing, and a new method for winter wheat mapping that is based on similarity measurement indicators and their threshold optimizations (WWM-SMITO) was proposed with the support of the shuffled complex evolution-University of Arizona (SCE-UA) global optimization algorithm. The accuracy of the regional winter wheat mapping results was verified, and accuracy comparisons with different similarity indicators were carried out. The results showed that the total area accuracy of the winter wheat area extraction by the proposed method reached over 99.99%, which achieved a consistency that was between the regional remote sensing-based winter wheat planting area and the statistical data on the winter wheat planting area. The crop recognition accuracy also reached a high level, which showed that the proposed method was effective and feasible. Moreover, in the accuracy comparison of crop mapping results based on six different similarity indicators, the winter wheat distribution that was extracted by root mean square error (RMSE) had the best recognition accuracy, and the overall accuracy and kappa coefficient were 94.5% and 0.8894, respectively. The overall accuracies of winter wheat that were extracted by similarity indicators, such as Euclidean distance (ED), Manhattan distance (MD), spectral angle mapping (SAM), and spectral correlation coefficient (SCC) were 94.1%, 93.9%, 93.3%, and 92.8%, respectively, and the kappa coefficients were 0.8815, 0.8776, 0.8657, and 0.8558, respectively. The accuracy of the winter wheat results extracted by the similarity indicator of dynamic time warping (DTW) was relatively low. The results of this paper could provide guidance and serve as a reference for the selection of similarity indicators in crop distribution extraction and for obtaining large-scale, long-term, and high-precision remote sensing-based information on a regional crop spatial distribution that is highly consistent with statistical crop area data.

Carbofuran (CBF) is an efficient and broad-spectrum insecticide. As testing indicators for water quality and agricultural products, CBF and its metabolite 3-hydroxy-carbofuran (3-OH-CBF) are regulated by many countries. The detection of CBF and 3-OH-CBF is of great importance for the environment and human health. However, an immunosensor detection method for the simultaneous analysis of CBF and 3-OH-CBF remains unavailable. Herein, we report a waveguide-based fluorescent immunosensor for detecting CBF and 3-OH-CBF, synchronously. The immunosensor is based on a broad-spectrum monoclonal antibody with high binding affinity against CBF and 3-OH-CBF. The linear detection ranges for CBF and 3-OH-CBF are 0.29–2.69 and 0.12–4.59 μg/L, with limits of detection of 0.13 μg/L for CBF and 0.06 μg/L for 3-OH-CBF, respectively. The whole detection process for each cycle is approximately 30 min. The results show a good application prospect for the rapid detection of CBF and 3-OH-CBF in water or agricultural products.

Roots provide anchorage and enable the absorption of water and micronutrients from the soil for plants. Besides these essential functions, roots are increasingly being recognized as an important organ for the production of diverse secondary metabolites. The goal of this study was to investigate the chemical composition and function of terpenoid secondary metabolites in roots of different cultivars of the popular ornamental plant Chrysanthemum morifolium Ramat. Although C. morifolium is known for rich production of secondary metabolites in its flower heads and leaves, the diversity of secondary metabolites in roots remains poorly characterized. In this study, 12 cultivars of C. morifolium were selected for comparative analysis. From their roots, a total of 20 terpenoids were detected, including four monoterpenes, 15 sesquiterpenes, and one diterpene. The cultivar ‘She Yang Hong Xin Ju’ exhibited the highest concentration of total terpenoids at approximately 730 µg·g−1 fresh weight. Most cultivars contained sesquiterpenes as the predominant terpenoids. Of them, (E)-β-farnesene was detected in all cultivars. Based on their terpenoid composition, the 12 cultivars were planed into four groups. To gain insights into the function of root secondary metabolites, we performed bioassays to assess their effects on growth of three species of pathogenic fungi: Fusarium oxysporum, Magnaporthe oryzae, and Verticillium dahliae. Significant variability in antifungal activity of the root extracts among different cultivars were observed. The cultivar ‘Xiao Huang Ju’ was the only cultivar that had significant inhibitory effects on all three species of fungi. Our study reveals the diversity of terpenoids in roots of C. morifolium and their function as a chemical defense against fungi.

Nanoindentation technique at low temperatures have developed from initial micro-hardness driving method at a single temperature to modern depth-sensing indentation (DSI) method with variable temperatures over the last three decades. The technique and implementation of representative cooling systems adopted on the indentation apparatuses are discussed in detail here, with particular emphasis on pros and cons of combination with indentation technique. To obtain accurate nanoindentation curves and calculated results of material properties, several influence factors have been carefully considered and eliminated, including thermal drift and temperature induced influence on indenter and specimen. Finally, we further show some applications on typical materials and discuss the perspectives related to low temperature nanoindentation technique.

Remote sensing images are featured by massiveness, diversity and complexity. These features put forward higher requirements for the speed and accuracy of remote sensing image retrieval. The extraction method plays a key role in retrieving remote sensing images. Deep metric learning (DML) captures the semantic similarity information between data points by learning embedding in vector space. However, due to the uneven distribution of sample data in remote sensing image datasets, the pair-based loss currently used in DML is not suitable. To improve this, we propose a novel distribution consistency loss to solve this problem. First, we define a new way to mine samples by selecting five in-class hard samples and five inter-class hard samples to form an informative set. This method can make the network extract more useful information in a short time. Secondly, in order to avoid inaccurate feature extraction due to sample imbalance, we assign dynamic weight to the positive samples according to the ratio of the number of hard samples and easy samples in the class, and name the loss caused by the positive sample as the sample balance loss. We combine the sample balance of the positive samples with the ranking consistency of the negative samples to form our distribution consistency loss. Finally, we built an end-to-end fine-tuning network suitable for remote sensing image retrieval. We display comprehensive experimental results drawing on three remote sensing image datasets that are publicly available and show that our method achieves the state-of-the-art performance.

Recently, with the rapid growth of the number of datasets with remote sensing images, it is urgent to propose an effective image retrieval method to manage and use such image data. In this paper, we propose a deep metric learning strategy based on Similarity Retention Loss (SRL) for content-based remote sensing image retrieval. We have improved the current metric learning methods from the following aspects—sample mining, network model structure and metric loss function. On the basis of redefining the hard samples and easy samples, we mine the positive and negative samples according to the size and spatial distribution of the dataset classes. At the same time, Similarity Retention Loss is proposed and the ratio of easy samples to hard samples in the class is used to assign dynamic weights to the hard samples selected in the experiment to learn the sample structure characteristics within the class. For negative samples, different weights are set based on the spatial distribution of the surrounding samples to maintain the consistency of similar structures among classes. Finally, we conduct a large number of comprehensive experiments on two remote sensing datasets with the fine-tuning network. The experiment results show that the method used in this paper achieves the state-of-the-art performance.

Timely and accurate estimation of the area and distribution of crops is vital for food security. Optical remote sensing has been a key technique for acquiring crop area and conditions on regional to global scales, but great challenges arise due to frequent cloudy days in southern China. This makes optical remote sensing images usually unavailable. Synthetic aperture radar (SAR) could bridge this gap since it is less affected by clouds. The recent availability of Sentinel-1A (S1A) SAR imagery with a 12-day revisit period at a high spatial resolution of about 10 m makes it possible to fully utilize phenological information to improve early crop classification. In deep learning methods, one-dimensional convolutional neural networks (1D CNNs), long short-term memory recurrent neural networks (LSTM RNNs), and gated recurrent unit RNNs (GRU RNNs) have been shown to efficiently extract temporal features for classification tasks. However, due to the complexity of training, these three deep learning methods have been less used in early crop classification. In this work, we attempted to combine them with an incremental classification method to avoid the need for training optimal architectures and hyper-parameters for data from each time series. First, we trained 1D CNNs, LSTM RNNs, and GRU RNNs based on the full images’ time series to attain three classifiers with optimal architectures and hyper-parameters. Then, starting at the first time point, we performed an incremental classification process to train each classifier using all of the previous data, and obtained a classification network with all parameter values (including the hyper-parameters) at each time point. Finally, test accuracies of each time point were assessed for each crop type to determine the optimal time series length. A case study was conducted in Suixi and Leizhou counties of Zhanjiang City, China. To verify the effectiveness of this method, we also implemented the classic random forest (RF) approach. The results were as follows: (i) 1D CNNs achieved the highest Kappa coefficient (0.942) of the four classifiers, and the highest value (0.934) in the GRU RNNs time series was attained earlier than with other classifiers; (ii) all three deep learning methods and the RF achieved F measures above 0.900 before the end of growth seasons of banana, eucalyptus, second-season paddy rice, and sugarcane; while, the 1D CNN classifier was the only one that could obtain an F-measure above 0.900 for pineapple before harvest. All results indicated the effectiveness of the solution combining the deep learning models with the incremental classification approach for early crop classification. This method is expected to provide new perspectives for early mapping of croplands in cloudy areas.

Chrysanthemum morifolium Ramat is an ornamental plant of worldwide cultivation. Like many other species in the family Asteraceae, C. morifolium is a rich producer of secondary metabolites. There are two objectives in this study: (I) to determine and compare the diversity of apolar secondary metabolites among different cultivars of C. morifolium and (II) to compare their properties as antifungal agents. To attain these objectives, we selected 13 cultivars of C. morifolium that are commonly used for making chrysanthemum tea as experimental materials. Leaves at the same developmental stage were collected from respective mature plants and subjected to organic extraction. The extracts were analyzed using gas chromatography−mass spectrometry. A total of 37 apolar secondary metabolites including 26 terpenoids were detected from the 13 cultivars. These 13 cultivars can be largely divided into three chemotypes based on chemical principal components analysis. Next, the extracts from the 13 cultivars were examined in in vitro assays for their antifungal properties against three species of pathogenic fungi: Fusarium oxysporum, Magnaporthe oryzae, and Verticillium dahliae. Significant variability in antifungal activity of the leaf extracts among different cultivars was observed. The 13 cultivars can be divided into four groups based on their antifungal activities, which could be partly correlated to the contents of terpenoids. In short, this study reveals large variations in chemical composition, particularly of terpenoids, of leaf secondary metabolites among different cultivars of C. morifolium and their different abilities in functioning as antifungal agents.

The massive number of images demands highly efficient image retrieval tools. Deep distance metric learning (DDML) is proposed to learn image similarity metrics in an end-to-end manner based on the convolution neural network, which has achieved encouraging results. The loss function is crucial in DDML frameworks. However, we found limitations to this model. When learning the similarity of positive and negative examples, the current methods aim to pull positive pairs as close as possible and separate negative pairs into equal distances in the embedding space. Consequently, the data distribution might be omitted. In this work, we focus on the distribution structure learning loss (DSLL) algorithm that aims to preserve the geometric information of images. To achieve this, we firstly propose a metric distance learning for highly matching figures to preserve the similarity structure inside it. Second, we introduce an entropy weight-based structural distribution to set the weight of the representative negative samples. Third, we incorporate their weights into the process of learning to rank. So, the negative samples can preserve the consistency of their structural distribution. Generally, we display comprehensive experimental results drawing on three popular landmark building datasets and demonstrate that our method achieves state-of-the-art performance.

We propose a new geometric verification method in image retrieval—Hierarchical Geometry Verification via Maximum Entropy Saliency (HGV)—which aims at filtering the redundant matches and remaining the information of retrieval target in images which is partly out of the salient regions with hierarchical saliency and also fully exploring the geometric context of all visual words in images. First of all, we obtain hierarchical salient regions of a query image based on the maximum entropy principle and label visual features with salient tags. The tags added to the feature descriptors are used to compute the saliency matching score, and the scores are regarded as the weight information in the geometry verification step. Second we define a spatial pattern as a triangle composed of three matched features and evaluate the similarity between every two spatial patterns. Finally, we sum all spatial matching scores with weights to generate the final ranking list. Experiment results prove that Hierarchical Geometry Verification based on Maximum Entropy Saliency can not only improve retrieval accuracy, but also reduce the time consumption of the full retrieval.

Small RNAs function to regulate plant defense responses to pathogens. We previously showed that miR825 and miR825* downregulate Bacillus cereus AR156 (AR156)-triggered systemic resistance to Pseudomonassyringae pv. tomato DC3000 in Arabidopsis thaliana (Arabidopsis). Here, Northern blotting revealed that miR825 and miR825* were more strongly downregulated in wild type Arabidopsis Col-0 (Col-0) plants pretreated with AR156 than in nontreated plants upon Botrytis cinerea (B. cinerea) B1301 infection. Furthermore, compared with Col-0, transgenic plants with attenuated miR825 and miR825* expression were more resistant to B. cinerea B1301, yet miR825- and miR825*-overexpressing (OE) plants were more susceptible to the pathogen. With AR156 pretreatment, the transcription of four defense-related genes (PR1, PR2, PR5, and PDF1.2) and cellular defense responses (hydrogen peroxide production and callose deposition) were faster and stronger in miR825 and miR825* knockdown lines but weaker in their OE plants than in Col-0 plants upon pathogen attack. Also, AR156 pretreatment caused stronger phosphorylation of MPK3 and MPK6 and expression of FRK1 and WRKY53 genes upon B. cinerea B1301 inoculation in miR825 and miR825* knockdown plants than in Col-0 plants. Additionally, the assay of agrobacterium-mediated transient co-expression in Nicotiana benthamiana confirmed that AT5G40910, AT5G38850, AT3G04220, and AT5G44940 are target genes of miR825 or miR825*. Compared with Col-0, the target mutant lines showed higher susceptibility to B. cinerea B1301, while still expressing AR156-triggered induced systemic resistance (ISR). The two-way analysis of variance (ANOVA) revealed a significant (P < 0.01) interactive effect of treatment and genotype on the defense responses. Hence, miR825 and miR825*act as negative regulators of AR156-mediated systemic resistance to B. cinerea B1301 in Arabidopsis.

Image retrieval based on a convolutional neural network (CNN) has attracted great attention among researchers because of the high performance. The pooling method has become a research hotpot in the task of image retrieval in recent years. In this paper, we propose the feature distribution entropy (FDE) to measure the difference of regional distribution information in the feature maps from CNNs. We propose a novel pooling method, which fuses our proposed FDE with region maximum activations of convolutions (R-MAC) features to improve the performance of image retrieval, as it takes the advantage of regional distribution information in the feature maps. Compared with the descriptors computed by R-MAC pooling, our proposed method considers not only the most significant feature values of each region in feature map, but also the distribution difference in different regions. We utilize the histogram of feature values to calculate regional distribution entropy and concatenate the regional distribution entropy into FDE, which is further normalized and fused with R-MAC feature vectors by weighted summation to generate the final feature descriptors. We have conducted experiments on public datasets and the results demonstrate that our proposed method could produce better retrieval performances than existing state-of-the-art algorithms. Further, higher performance could be achieved by performing these post-processing on the improved feature descriptors.

Image retrieval applying deep convolutional features has achieved the most advanced performance in most standard benchmark tests. In image retrieval, deep metric learning (DML) plays a key role and aims to capture semantic similarity information carried by data points. However, two factors may impede the accuracy of image retrieval. First, when learning the similarity of negative examples, current methods separate negative pairs into equal distance in the embedding space. Thus, the intraclass data distribution might be missed. Second, given a query, either a fraction of data points, or all of them, are incorporated to build up the similarity structure, which makes it rather complex to calculate similarity or to choose example pairs. In this study, in order to achieve more accurate image retrieval, we proposed a method based on learning to rank and multiple loss (LRML). To address the first problem, through learning the ranking sequence, we separate the negative pairs from the query image into different distance. To tackle the second problem, we used a positive example in the gallery and negative sets from the bottom five ranked by similarity, thereby enhancing training efficiency. Our significant experimental results demonstrate that the proposed method achieves state-of-the-art performance on three widely used benchmarks.

In a Cyber-Physical Power System (CPPS), the interaction between the power cyber system and the power physical system becomes more extensive and more in-depth. The failure of a cyber component could have an impact on the security and reliability of the power physical system. Existing publications have focused on the impacts of the power cyber network on the power physical network, while a general CPPS model considering the mutual impacts of these two networks is less studied. Given this background, a game-theoretic approach for a cyber-physical power system vulnerability analysis is proposed. First, a CPPS interactive model framework is structured, consisting of five types of elements: P-nodes, PP-links, C-nodes, CC-links and CP-links. The interactions among these elements are considered. On this basis, the system cascading failure under potential attacks is analyzed, followed with an optimal load curtailment operation when in an emergency. To further illustrate the system vulnerability, a bi-level optimization model under a game-theoretic framework is presented to describe the interactions between a CPPS attacker and a system defender. Optimal resource allocation by the system defender for maintaining system reliability can be obtained by solving the problem. The feasibility and effectiveness of the proposed method are demonstrated by a revised version of the IEEE 14-bus power system.

SiCp/6061Al composites have been developed and widely applied in many fields, such as automobile, aerospace, shipping, and so on. Considering heat treatment, service environment, and strength of composites, this paper comprehensively studies the mechanical properties of SiCp/6061Al composites with a large range of SiC mass fractions under T6 treatments and different tensile temperatures. SiCp/6061Al composites were successfully prepared by hot press sintering at various SiC mass fractions (0–30%), and the influences of SiC concentration and T6 treatment on the mechanical properties of composites were characterized via tensile tests at room temperature, 100, and 200 °C. Microstructure and fracture surfaces of composites with various SiC concentrations were further analyzed by optical microscope and SEM. The formula for the biggest critical reinforcement concentration for the saturated distribution of SiC is proposed to reveal the strengthening rule of different SiC concentrations. Results show that the effect of T6 treatment on the mechanical properties of composites is a marked increase in tensile strength and an obvious decrease in elongation. The increase in the SiC mass fraction, except at 30%, is able to bring an increase in tensile strength and a decrease in elongation, and the change of the elongation is insignification in T6-treated specimens. The tensile strength of T6-treated specimens decreases as temperature increases, and the composite has a maximum elongation at 100 °C.