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The current deep learning methods for copy–move forgery detection (CMFD) are mostly based on deep convolutional neural networks, which frequently discard a large amount of detail information throughout convolutional feature extraction and have poor long-range information extraction capabilities. The Transformer structure is adept at modeling global context information, but the patch-wise self-attention calculation still neglects the extraction of details in local regions that have been tampered with. A local-information-refined dual-branch network, LBRT (Local Branch Refinement Transformer), is designed in this study. It performs Transformer encoding on the global patches segmented from the image and local patches re-segmented from the global patches using a global modeling branch and a local refinement branch, respectively. The self-attention features from both branches are precisely fused, and the fused feature map is then up-sampled and decoded. Therefore, LBRT considers both global semantic information modeling and local detail information refinement. The experimental results show that LBRT outperforms several state-of-the-art CMFD methods on the USCISI dataset, CASIA CMFD dataset, and DEFACTO CMFD dataset. [ABSTRACT FROM AUTHOR]

Object detection based on Knowledge Distillation can enhance the capabilities and performance of 5G and 6G networks in various domains, such as autonomous vehicles, smart surveillance, and augmented reality. The integration of object detection with Knowledge Distillation techniques is expected to play a pivotal role in realizing the full potential of these networks. This study presents Shared Knowledge Distillation (Shared-KD) as a solution to overcome optimization challenges caused by disparities in cross-layer features between teacher–student networks. The significant gaps in intermediate-level features between teachers and students present a considerable obstacle to the efficacy of distillation. To tackle this issue, we draw inspiration from collaborative learning in real-world education, where teachers work together to prepare lessons and students engage in peer learning. Building upon this concept, our innovative contributions in model construction are highlighted as follows: (1) A teacher knowledge augmentation module: this module is proposed to combine lower-level teacher features, facilitating the knowledge transfer from the teacher to the student. (2) A student mutual learning module is introduced to enable students to learn from each other, mimicking the peer learning concept in collaborative learning. (3) The Teacher Share Module combines lower-level teacher features: the specific functionality of the teacher knowledge augmentation module is described, which involves combining lower-level teacher features. (4) The multi-step transfer process can be easily optimized due to the minimal gap between the features: the proposed approach breaks down the knowledge transfer process into multiple steps, which can be easily optimized due to the minimal gap between the features involved in each step. Shared-KD uses simple feature losses without additional weights in transformation, resulting in an efficient distillation process that can be easily combined with other methods for further improvement. The effectiveness of our approach is validated through experiments on popular tasks such as object detection and instance segmentation. [ABSTRACT FROM AUTHOR]

Recently, numerous spatial prediction methods with diverse characteristics have been developed. Selecting an appropriate spatial prediction method, along with its data preprocessing and parameter settings, presents a challenging task for many users, especially for non-experts. This paper addresses this challenge by exploring the potential of automated machine learning method proposed in artificial intelligent domain to automatically determine the most suitable method among various machine learning methods. As a case study, the automated machine learning method was applied to predict the spatial distribution of soil subgroups in Heshan farm. A total of 110 soil samples and 10 terrain variables were utilized in the designed experiments. To evaluate the performance, the proposed method was compared to each machine learning method with default parameters values or parameters determined by expert knowledge. The results showed that the proposed method typically achieved higher accuracy scores than the two alternative methods. This suggests that automated machine learning performs effectively in scenarios where numerous machine learning methods are available and offers practical utility in reducing the dependence on users' expertise in spatial prediction. However, a more robust automated framework should be developed to encompass a broader range of spatial prediction methods, such as spatial statistic methods, rather than only focusing on machine learning methods. [ABSTRACT FROM AUTHOR]

In addressing the issues of low efficiency in bridge maintenance decision making, the inaccurate estimation of maintenance costs, and the lack of specificity in decision making regarding maintenance measures for specific defects, this study utilizes data from regular bridge inspections. It employs a two-parameter Weibull distribution to model the duration variables of the states of bridge elements, thereby enabling the prediction of the duration time of bridge elements in various states. Referring to existing bridge maintenance and repair regulations, the estimation process of maintenance costs is streamlined. Taking into account the specific types and development state of bridge defects, as well as considering the adequacy of maintenance and the restorative effects of maintenance measures, an intelligent agent sequential decision-making model for bridge maintenance decisions is established. The model utilizes dynamic programming algorithms to determine the optimal maintenance and repair measures for elements in various states. The decision results are precise, all the way down to the specific bridge elements and maintenance measures for individual defects. In using the case of the regular inspection data of 222 bridges along a highway loop, this study further validates the effectiveness of the proposed research methods. By constructing an intelligent agent sequential decision-making model for bridge element maintenance, the optimal maintenance measures for 21 bridge elements in different states are obtained, thereby significantly enhancing the efficiency of actual bridge maintenance and the practicality of decision results. [ABSTRACT FROM AUTHOR]

Due to the low viscosity of water, water-lubricated bearings are susceptible to significant wear and noise in demanding operating conditions. It has been demonstrated that a small quantity of secondary lubricating medium can improve the lubrication performance of water-lubricated contact surfaces and achieve the purpose of temporary risk aversion. As a further step, the feasibility of the proposed idea is experimentally validated on a water-lubricated bearing test bench. A numerical model that couples the N–S equation and the VOF model is then developed to investigate the behavior of the flow field lubricated by pure water and water with a small quantity of the secondary lubricating medium. This model provides the predictions of important quantities such as the load-carrying capacity, the secondary lubricating medium volume fraction and the contact pressure under different lubricated conditions. The results show that the secondary lubricating medium can enter into the contact region and improve the lubrication performance of water-lubricated bearings, especially at lower shaft rotational speeds. Therefore, the feasibility of our proposed idea is verified, which provides a promising approach to reduce the wear and friction of water-lubricated bearings when they encounter short-time severe working conditions. [ABSTRACT FROM AUTHOR]

In March 1966, Ms 6.8 and 7.2 earthquakes occurred in Xingtai, North China, resulting in widespread soil liquefaction that caused severe infrastructure damage and economic losses. Using Keyhole satellite imagery combined with aerial images and fieldwork records, we interpreted and identified 66,442 liquefaction points and analyzed the coseismic liquefaction distribution characteristics and possible factors that influenced the Xingtai earthquakes. The interpreted coseismic liquefaction was mainly concentrated above the IX-degree zone, accounting for 80% of all liquefaction points. High-density liquefaction zones (point density > 75 pieces/km2) accounted for 22% of the total liquefaction points. Most of the interpreted liquefaction points were located at the region with a peak ground acceleration (PGA) of >0.46 g. The liquefaction area on 22 March was significantly larger than that on 8 March. The region of liquefaction was mainly limited by sandy soil conditions, water system conditions, and seismic geological conditions and distributed in areas with loose fine sand and silt deposits, a high water table (groundwater level increases before both mainshocks corresponding to the liquefaction intensive regions), rivers, and ancient river channels. Liquefaction exhibited a repeating characteristic in the same region. Further understanding of the liquefaction characteristics of Xingtai can provide a reference for the prevention of liquefaction in northern China. [ABSTRACT FROM AUTHOR]

The goal of low-light image enhancement (LLIE) is to enhance perception to restore normal-light images. The primary emphasis of earlier LLIE methods was on enhancing the illumination while paying less attention to the color distortions and noise in the dark. In comparison to the ground truth, the restored images frequently exhibit inconsistent color and residual noise. To this end, this paper introduces a Wasserstein contrastive regularization method (WCR) for LLIE. The WCR regularizes the color histogram (CH) representation of the restored image to keep its color consistency while removing noise. Specifically, the WCR contains two novel designs including a differentiable CH module (DCHM) and a WCR loss. The DCHM serves as a modular component that can be easily integrated into the network to enable end-to-end learning of the image CH. Afterwards, to ensure color consistency, we utilize the Wasserstein distance (WD) to quantify the resemblance of the learnable CHs between the restored image and the normal-light image. Then, the regularized WD is used to construct the WCR loss, which is a triplet loss and takes the normal-light images as positive samples, the low-light images as negative samples, and the restored images as anchor samples. The WCR loss pulls the anchor samples closer to the positive samples and simultaneously pushes them away from the negative samples so as to help the anchors remove the noise in the dark. Notably, the proposed WCR method was only used for training, and was shown to achieve high performance and high speed inference using lightweight networks. Therefore, it is valuable for real-time applications such as night automatic driving and night reversing image enhancement. Extensive evaluations on benchmark datasets such as LOL, FiveK, and UIEB showed that the proposed WCR method achieves superior performance, outperforming existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Water-lubricated bearings are widely used in marine equipment, and the lubricating water often contains hard particles. Once these particles enter the gap between the bearing and the shaft, they can scratch the smooth surfaces of the shaft and bearing, influencing the working performance of the bearing system. To investigate the effect of scratch parameters on tribological performance, this paper conducts multiple block-on-ring experiments and constructs a mixed-lubrication model under water-lubrication conditions. The results show that among the three commonly used bearing materials, the tribological performance of graphite block is the most sensitive to scratches on the test ring surface. Under the condition of one scratch (N = 1), the loading area of water film pressure is divided into two separate zones (a trapezoidal pressure zone and an extremely low-pressure zone). In addition, the variation of maximum water film pressure is determined by the positive effect (hydrodynamic pressure effect of fluid) and negative effect ("piercing effect" of the asperities). Compared with the scratch depth and scratch location, the scratch width has the most significant effect on the tribological performance of the block-on-ring system. The maximum contact pressure is located at both edges of the scratch due to the formation of a water sac structure. The scratch has a great influence on the transition of the lubrication state of the block-on-ring system. The existence of scratches increases the critical speed at which the lubrication state transits from mixed-lubrication to elastohydrodynamic lubrication, and the critical speed is directly proportional to the scratch width. [ABSTRACT FROM AUTHOR]

This paper develops a coupling model of cam-roller contact and roller-pin contact considering thermal effects based on the exhaust cam-roller unit of an internal combustion engine. For the steady/startup running processes, the skidding and the lubrication performance are obtained in a complete cam rotation cycle, and the effects of oil viscosity or modified load are also discussed. The results show that the most significant effect of skidding on the cam-roller unit is the increase in friction between the cam and the roller. In a cam rotation cycle, the slide-roll ratio is not constant, and its value may even be negative. Compared with the steady running process, skidding is more pronounced during the startup running process, especially at the beginning of the acceleration stage (0–18°). Reducing oil viscosity or increasing modified load can effectively reduce the skidding situation, and its effect is more obvious in the steady running process than in the startup running process. [ABSTRACT FROM AUTHOR]

(1) Background: Sleep deprivation (SD) triggers a range of neuroinflammatory responses. Dexmedetomidine can improve sleep deprivation-induced anxiety by reducing neuroinflammatory response but the mechanism is unclear; (2) Methods: The sleep deprivation model was established by using an interference rod device. An open field test and an elevated plus maze test were used to detect the emotional behavior of mice. Mouse cortical tissues were subjected to RNA sequence (RNA-seq) analysis. Western blotting and immunofluorescence were used to detect the expression of p38/p-p38, MSK1/p-MSK1, and NFκBp65/p- NFκBp65. Inflammatory cytokines were detected using enzyme-linked immunosorbent assay (ELISA); (3) Results: SD triggered anxiety-like behaviors in mice and was closely associated with inflammatory responses and the MAPK pathway (as demonstrated by transcriptome analysis). SD led to increased expression levels of p-p38, p-MSK1, and p-NFκB. P38 inhibitor SB203580 was used to confirm the important role of the p38/MSK1/NFκB pathway in SD-induced neuroinflammation. Dexmedetomidine (Dex) effectively improves emotional behavior in sleep-deprived mice by attenuating SD-induced inflammatory responses and oxidative stress in the cerebral cortex, mainly by inhibiting the activation of the p38/MSK1/NFκB pathway; (4) Conclusions: Dex inhibits the activation of the p38/MSK1/NFκB pathway, thus attenuating SD-induced inflammatory responses and oxidative stress in the cerebral cortex of mice. [ABSTRACT FROM AUTHOR]

Lipid metabolism disorder has become an important hidden danger threatening human health, and various supplements to treat lipid metabolism disorder have been studied. Our previous studies have shown that DHA-enriched phospholipids from large yellow croaker (Larimichthys Crocea) roe (LYCRPLs) have lipid-regulating effects. To better explain the effect of LYCRPLs on lipid regulation in rats, the fecal metabolites of rats were analyzed from the level of metabolomics in this study, and GC/MS metabolomics measurements were performed to figure out the effect of LYCRPLs on fecal metabolites in rats. Compared with the control (K) group, 101 metabolites were identified in the model (M) group. There were 54, 47, and 57 metabolites in the low-dose (GA), medium-dose (GB), and high-dose (GC) groups that were significantly different from that of group M, respectively. Eighteen potential biomarkers closely related to lipid metabolism were screened after intervention with different doses of LYCRPLs on rats, which were classified into several metabolic pathways in rats, including pyrimidine metabolism, the citric acid cycle (TCA cycle), the metabolism of L-cysteine, carnitine synthesis, pantothenate and CoA biosynthesis, glycolysis, and bile secretion. L-cysteine was speculated to be a useful biomarker of LYCRPLs acting on rat fecal metabolites. Our findings indicated that LYCRPLs may regulate lipid metabolism disorders in SD rats by activating these metabolic pathways. [ABSTRACT FROM AUTHOR]

Large-volume waste products, such as refrigerators and automobiles, not only consume resources but also pollute the environment easily. A two-sided disassembly line is the most effective method to deal with large-volume waste products. How to reduce disassembly costs while increasing profit has emerged as an important and challenging research topic. Existing studies ignore the diversity of waste products as well as uncertain factors such as corrosion and deformation of parts, which is inconsistent with the actual disassembly scenario. In this paper, a partial destructive mode is introduced into the mixed-model two-sided disassembly line balancing problem, and the mathematical model of the problem is established. The model seeks to comprehensively optimize the number of workstations, the smoothness index, and the profit. In order to obtain a high-quality disassembly scheme, an improved non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. The proposed model and algorithm are then applied to an automobile disassembly line as an engineering illustration. The disassembly scheme analysis demonstrates that the partial destructive mode can raise the profit of a mixed-model two-sided disassembly line. This research has significant application potential in the recycling of large-volume products. [ABSTRACT FROM AUTHOR]

China's first optical stereo mapping satellite with a sub-meter resolution, GaoFen-7 (GF-7), launched in November 2019, shows significant potential for providing high-resolution topographic and geomorphic data for quantitative research on active tectonics. However, no studies have evaluated the capability of the GF-7-generated digital elevation model (DEM) for quantitatively studying active tectonics. This study aimed to validate the accuracy of the DEMs extracted from GF-7 stereo imagery, with or without ground control points (GCPs), and evaluated the potential of applying GF-7 DEMs to active tectonics. First, GF-7 stereo images were processed to obtain DEMs with a spatial resolution of 2 m, utilizing three different methods, including block adjustment without GCPs, block adjustment with the aid of Google Earth images and SRTM DEM, and block adjustment with GCPs derived from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data. These three generated DEMs were called GF-7 DEMMethod1, GF-7 DEMMethod2, and GF-7 DEMMethod3, respectively, and were verified by the airborne LiDAR data in the Hasishan section of the Haiyuan fault. Second, the capability of the GF-7 DEMs for identifying active faults, fault scarps, and horizontal offsets was evaluated. Finally, 8 vertical and 13 horizontal offsets were measured based on three different GF-7 DEMs, and airborne LiDAR data were used to verify the measurements' accuracies. The results indicated that the accuracy of GF-7 DEMMethod1 was the worst and that of GF-7 DEMMethod3 was superior to that of GF-7 DEMMethod2. The GF-7 DEMs could effectively identify the apparent fault scarps and horizontal offsets. The RMSE values of the vertical offsets measured based on GF-7 DEMMethod1, GF-7 DEMMethod2, and GF-7 DEMMethod3 were 0.55 m, 0.55 m, and 0.41 m, respectively. The horizontal offsets yielded RMSE values of 3.98 m, 2.52 m, and 1.37 m, respectively. These findings demonstrated that vertical and horizontal offsets could be accurately measured using the DEMs generated from GF-7 stereo images. Meanwhile, our study indicated that the GCPs derived from ICESat-2 data could be utilized to improve the accuracies of the GF-7 DEM, and the measurements of vertical and horizontal offsets. [ABSTRACT FROM AUTHOR]

Karst bauxite is a major source of aluminum and contains high concentrations of trace elements such as Li, Ga, Sc, and rare earth elements (REEs). It is regarded as a potential non-conventional REE source due to the increasing demand for REEs. This study provides new mineralogical and geochemical evidence of a Lindai bauxite deposit in central Guizhou Province, aiming to analyze the genesis of REE minerals and reveal the forms of REEs occurring in these deposits. The results indicate that a large number of detrital and authigenic rare-earth phosphate minerals, including monazite and xenotime, were identified. In terms of their genesis, the authigenic monazite was mainly precipitated under alkaline and reducing conditions, whereas the authigenic xenotime was formed in acidic and reducing conditions. The mineralogical evidence detected by scanning electron microscopy with energy dispersive spectrometry (SEM-EDS) and a TESCAN Integrated Mineral Analyzer (TIMA) suggests that the REEs in the Lindai bauxite exhibit multiple forms, including rare-earth phosphate minerals (monazite and xenotime) and REE scavenging by hematite and anatase phases. Among them, the anatase controls some LREE (such as La, Ce, and Pr) distributions in addition to monazite, whereas the hematite also controls relatively large amounts of Sm, Eu, and HREE (such as Gd, Tb, Dy, Ho, Er, Tm, and Lu) distributions. According to the calculation of the percentage of REE-bearing mineral phases obtained by a TIMA analysis, REE scavenging by hematite accounted for 93.28% of all forms of REEs, REE scavenging by anatase accounted for 5.88%, monazite accounted for 0.67%, and xenotime accounted for 0.17%. This study provides new evidence of the forms of REEs occurring in Carboniferous karst bauxite in central Guizhou Province. [ABSTRACT FROM AUTHOR]

Layered δ-MnO2 catalysts were prepared using the one-step redox method in precursor solutions with five different pH values (pH = 7, 9, 11, 13, and 14). The effects of pH on the physical properties and catalytic activity of the catalyst were investigated through XRD, SEM, TEM, BET, XPS, H2-TPR, and HCHO degradation tests at room temperature. The results showed that the layer spacing, manganese vacancy content, Mn4+/Mn3+ ratio, and surface-reactive oxygen species content of MnO2 increased with the increase in pH value in the alkaline range. When the catalyst was prepared at pH = 13, the above characteristics of the catalyst reached the optimal value which contributed to the high catalytic activity. Combined with the related characterization results, it was proved that changing the pH can affect the degree of oxidation in the catalyst synthesis process, increase the number of active oxygen and the oxygen mobility of the catalyst, and effectively improve the catalytic activity of the manganese dioxide catalyst for HCHO. This work represents a giant step toward the preparation of an effective catalyst for practical applications of HCHO removal at room temperature at a low concentration and high velocity. [ABSTRACT FROM AUTHOR]

Green technology innovation is increasingly becoming an important driver of modern development. Facing the pressure of the carbon neutrality target, China has been strengthening environmental regulations in recent years, and the green technology innovation ability of market players has been affected. Moreover, the impact of environmental regulations on green technology innovation and the strategies to develop green technology innovation still need to be further explored. Here, we used 285 cities from 2010–2020 as research subjects, constructed indicators such as environmental regulation and green technology innovation capability, and used panel regression, threshold effect and mediating effect methods. The results reveal that: (1) environmental regulation has a disincentive effect on green technology innovation, as increased environmental regulation increases production costs, external costs and opportunity costs for firms, causing a mismatch of resources and creating a "crowding-out effect" that inhibits innovation development; (2) further study finds that when the human capital level reaches a certain threshold value, the impact of environmental regulation on green technology innovation shifts significantly, i.e., there is a threshold effect; (3) industrial structure can further be upgraded and optimized by environmental regulation, which will have an impact on green technology innovation, thus industrial structure optimization plays a mediating role. We conclude with recommendations for improving green innovation techniques for market players, and provide some lessons for accelerating the goal of carbon neutrality and promoting quality economic development. [ABSTRACT FROM AUTHOR]

Unequal flaws are widespread in rock engineering, in which the extensions induced by excavation and unloading are the main factors leading to engineering instability. Rock deformation and failure are essentially the results of energy transmission. In order to study the influence of flaw type and angle on the mechanical properties and energy characteristics of the flawed rock mass, uniaxial compression tests were conducted using particle flow numerical analysis software. The results show that the crack initiation strength and peak strength of the sample increase with the increase in the flaw angle. For smaller flaw angles, the sample shows obvious plastic deformation during uniaxial compression. For larger flaw angles, the sample shows elastic–brittle properties, whose storage energy is larger. The peak strength of samples with flaws of unequal length presents an obvious decrease compared with that of flawed samples with flaws of equal length, and the extent of the reduction is 6.15% on average. Unequal flaws decrease the ability of the flawed sample to absorb strain energy. Compared with flawed samples with equal flaws, flawed samples with unequal flaws decrease the boundary energy and elastic strain energy by 9.29% and 9.95% on average, respectively. Flaws of unequal length in this sample can weaken the performance of the flawed sample. [ABSTRACT FROM AUTHOR]

Under the influence of various forces, the conflict between Russia and Ukraine is violent and changeable. The obtaining of battlefield data by conventional means is difficult but necessary in order to ensure security, reliability, and comprehensiveness. The use of remote sensing technology can make up for the deficiencies of conventional methods. By using night-time light data, the total number of night-time lights in the built-up areas of Ukrainian cities within 36 days of the outbreak of the Russian–Ukrainian conflict is compiled in this paper. Furthermore, the dynamic changes in night-time light at the national, regional, and urban scales are analyzed by using the night-time light ratio index and the dynamic degree model combined with the time-series night-time light data. The results show that (1) after the outbreak of the war, more than 60% of the night-time lights in Ukrainian cities were lost. In terms of the night-time light recovery speed, the night-time lights in the pro-Russian areas recovered significantly faster, followed by Russian-controlled areas, and the recovery speed in areas of conflict was the lowest. (2) Decision-making by belligerents affects non-combatant activities and thus corresponds to light at night. The loss of night-time light will be reduced if military operations are reduced and mitigated if humanitarian operations are increased. (3) The changes in night-time light reflect the changes in the conflict situation well. When the conflict between Russia and Ukraine intensifies, the overall change of night-time light shows a downward trend. In this context, night-time light data can be used as an effective source to deduce and predict battlefield situations. [ABSTRACT FROM AUTHOR]

The variable noise spectrum for many actual application scenarios requires a sound absorber to adapt to this variation. An adjustable sound absorber of multiple parallel-connection Helmholtz resonators with tunable apertures (TA–MPCHRs) is prepared by the low-force stereolithography of photopolymer resin, which aims to improve the applicability of the proposed sound absorber for noise with various frequency ranges. The proposed TA–MPCHR metamaterial contains five metamaterial cells. Each metamaterial cell contains nine single Helmholtz resonators. It is treated as a basic structural unit for an array arrangement. The tunable aperture is realized by utilizing four segments of extendable cylindrical chambers with length l0, which indicates that the length of the aperture l is in the range of [l0, 4l0], and that it is tunable. With a certain group of specific parameters for the proposed TA–MPCHR, the influence of the tunable aperture with a variable length is investigated by acoustic finite element simulation with a two-dimensional rotational symmetric model. For the given noise spectrum of certain actual equipment with four operating modes, the TA–MPCHR sample with a limited total thickness of 40 mm is optimized, which is made of photopolymer resin by the low-force stereolithography, and its actual average sound absorption coefficients for the frequency ranges of 500–800 Hz, 550–900 Hz, 600–1000 Hz and 700–1150 Hz reach 0.9203, 0.9202, 0.9436 and 0.9561, respectively. Relative to common non-adjustable metamaterials, the TA–MPCHR made of photopolymer resin can reduce occupied space and improve absorption efficiency, which is favorable in promoting its practical applications in the noise pollution prevention. [ABSTRACT FROM AUTHOR]

A new approach for the synthesis of 2-aminobenzofurans has been described via Sc(OTf)3 mediated formal cycloaddition of isocyanides with the in situ generated ortho-quinone methides (o-QMs) from o-hydroxybenzhydryl alcohol. Notably, as a class of readily available and highly active intermediates, o-QMs were first used in the construction of benzofurans. This [4 + 1] cycloaddition reaction provides a straightforward and efficient methodology for the construction of 2-aminobenzofurans scaffold in good yield (up to 93% yield) under mild conditions. [ABSTRACT FROM AUTHOR]

Rapid extraction of liquefaction induced by strong earthquakes is helpful for earthquake intensity assessment and earthquake emergency response. Supervised classification methods are potentially more accurate and do not need pre-earthquake images. However, the current supervised classification methods depend on the precisely delineated polygons of liquefaction by manual and landcover maps. To overcome these shortcomings, this study proposed two binary classification methods (i.e., random forest and gradient boosting decision tree) based on typical samples. The proposed methods trained the two machine learning methods with different numbers of typical samples, then used the trained binary classification methods to extract the spatial distribution of liquefaction. Finally, a morphological transformation method was used for the postprocessing of the extracted liquefaction. The recognition accuracies of liquefaction were estimated by four evaluation indices, which all showed a score of about 90%. The spatial distribution of liquefaction pits is also consistent with the formation principle of liquefaction. This study demonstrates that the proposed binary classification methods based on machine learning could efficiently and quickly provide the spatial distribution of liquefaction based on post-earthquake emergency satellite images. [ABSTRACT FROM AUTHOR]

Bridge management systems (BMSs) are widely used to assist an inspector in performing element-level bridge inspection. Retrieving and determining target elements to be inspected becomes an important factor in the efficiency of bridge inspection. This paper presents an enhanced information retrieval (IR) method based on ontology to predict the target elements. The novelty of this method is that an improved seven-step method based on automatic mapping technology is proposed to construct a new bridge inspection ontology (BIontology), which provides a knowledge base for the present IR method. A further novelty is that a new software architecture is designed for integrating ontology, and a promising prototype system based on the software architecture is developed to realize the present IR method using SPARQL query. In addition, a novel prediction algorithm based on the present IR method is proposed to automatically recommend the target elements. A case study of ontology construction is performed to demonstrate that the improved seven-step method can accelerate the construction of the BIontology compared with the manual method. A case study of bridge inspection is implemented to verify that the proposed algorithm outperforms an existing method, thereby validating the effectiveness of the present IR method. [ABSTRACT FROM AUTHOR]

Passive heat dissipation cooling technologies based on natural convection in open channels can effectively control the maximum temperature and improve the temperature homogeneity of 5G base stations, data centers and other equipment. In this paper, the flow and heat transfer of natural convection in an open-ended square channel with two suspending heat sources are studied through numerical simulation. The distributions of the temperature field and flow field in the channel with different horizontal distances and vertical altitude differenced of the heat sources are acquired via the finite element method (FEM)-based COMSOL Multiphysics. The changes in local temperature and the local Nusselt number are obtained. The relationships between the temperature field, flow field, and Nusselt number with respect to the geometric parameters of the heat sources are discussed. With different geometric parameters of the two suspending heat sources, the average surface temperature at the bottom is always lower than the top, while the average Nusselt number reaches maximum and minimum values at the bottom and top surfaces, respectively. As the horizontal distance increases, the maximum vertical airflow velocity decreases. The average surface temperature and local Nusselt number go through a V-shape and reverse V-shape tendency, respectively. The maximum temperature at the surface of the heat source is 397 K at a horizontal distance of 0.36 m. The local Nusselt number on the side of the heat source reaches its maximum at a horizontal distance of 0.28 m with an average value of 33.5. As the vertical altitude difference increases, the temperature difference between the heat sources increases from 0 K to 54 K, and the maximum vertical airflow velocity goes through a reverse V-shape tendency. The Nusselt number of the right heat source decreases to a certain value of about 20, while that of the left heat source goes through a fluctuating tendency. The results show that the best arrangement of the heat sources is a vertical altitude difference of 0 m and a horizontal distance of 0.28 m. [ABSTRACT FROM AUTHOR]

This paper proposes a double propeller wall-climbing robot (DP-Climb) with a hybrid adhesion system based on the biomimetic design principle to address the problems of single adhesion-powered wall climbing robots (WCRs). Such problems include poor maneuverability and adaptability to orthogonal working surfaces with different roughness and flatness, weak flexibility of ground-wall transition motion, and easy stand stilling of transition,. Based on the clinging characteristics of different creatures, the hybrid system combines the rotor units' reverse thrust, the drive wheels' driving torque, and the adhesion force offered by the coating material to power the robot through a coupled control strategy. Based on the Newton–Euler equations, the robot's kinematic characteristics during the ground-wall internal transition motion were analyzed, the safe adhesion conditions were obtained, and a dynamics model of the robot's ground-wall transition was established. This provided the basis for the coupling control between different power units. Finally, an internal transition PID control strategy based on DP-Climb was proposed. Through mechanical and aerodynamic characteristic experiments, it is verified that the robot's actual output pulling force can meet the transition motion demand. The experimental results show that the proposed strategy can enable the DP-Climb to complete the ground-wall mutual transition motion smoothly with a speed of 0.12 m/s. The robot's maximum wall motion speed can reach 0.45 m/s, which verifies that the hybrid adhesion system can flexibly and quickly reach the specified position in a target area flexibly and quickly. The robustness and adaptability of WCR to complex application environments are improved. [ABSTRACT FROM AUTHOR]

"Nature education" has become a striking trend in the field of environmental and sustainable development education in mainland China in recent years, which is considered to be a powerful force to realize the 11th goal of the 2030 Agenda for Sustainable Development, namely, "Sustainable Cities and Communities". Chinese urban residents' perceptions of "nature education" are fundamental to the success of "nature education" but we still know very little about it. This study aims to gather insights on the awareness, attitudes, and behaviors towards nature and nature education. In this paper, online questionnaires and random sampling methods were used. An online questionnaire survey was conducted among urban residents in eight representative cities in China, namely, Beijing (n = 313), Shanghai (n = 314), Guangzhou (n = 307), Shenzhen (n = 308), Chengdu (n = 206), Xiamen (n = 207), Hangzhou (n = 203), and Wuhan (n = 206). This study implied that Chinese urban residents are willing to accept nature education. There is a strong sense among the Chinese public in cities that living in harmony with nature, improving wellbeing through healthy and sustainable actions, and enjoying and protecting nature are desirable actions. However, respondents were slightly less likely to consider the emotional benefits and the development of social and functional (technical) skills as important elements of participating in nature education. The results also confirm that Chinese urban residents' perceptions of nature education can be positively predicted by the individual's relationship with nature and the individual's experience related to nature education. These are important key messages for nature education organizations to communicate. The results of the study put forward some questions worthy of in-depth consideration for the development of China's nature education, which can provide a foundation and direction for the further development of nature education in China. [ABSTRACT FROM AUTHOR]

Kelp and laver are large economic macroalgae in China, which are rich in nutrients, especially Mn and Zn. Excessive intake of Mn and Zn can be harmful to the human body. Therefore, it is necessary to develop a convenient and efficient method to detect the contents of Mn and Zn in macroalgae. In this experiment, red carbon dots (R-CDs) doped with N and S elements were prepared by the thermal solvent method. The obtained R-CDs displayed excitation wavelength-independent fluorescent emission in the red spectral region. The R-CDs were used to construct a fluorescent probe for specific recognition of Mn2+ and Zn2+, achieving high-sensitivity detection of Mn2+ and Zn2+. The detection results showed a good linear relationship between fluorescence intensity and Mn2+ concentration, and the calculated detection limit was 0.23 nmol/L. For the detection of Zn2+, the detection limit was estimated as 19.1 nmol/L. At the same time, the content distribution of Mn and Zn elements in macroalgae produced in Fujian was investigated by the constructed fluorescence probe. It was found that kelp, laver, and their products are rich in Mn and Zn elements, and the content of Mn and Zn elements in laver is higher than that in kelp, which can be used as the optimal food supplement for Mn and Zn elements. [ABSTRACT FROM AUTHOR]

The flexible job-shop scheduling problem with indirect energy and time-of-use (ToU) electricity pricing (FJSP-IT) is investigated. Considering the production cost, which includes the indirect energy cost, direct energy cost and time cost, the cost evaluation model under ToU pricing is built. To minimize the total production cost of the FJSP-IT, an approach based on a genetic algorithm and Petri nets (GAPN) is presented. Under this approach, indirect energy and direct energy are modeled with Petri net (PN) nodes, the operation time is evaluated through PN simulation, and resource allocation is fine-tuned through genetic operations. A group of heuristic operation time policies, especially the exhausting subsection policy and two mixed policies, are presented to adapt to the FJSP-IT with vague cost components. Experiments were performed on a data set generated from the banburying shop of a rubber tire plant, and the results show that the proposed GAPN approach has good convergence. Using the proposed operation time policies makes it possible to save 10.81% on the production cost compared to using the single off-peak first or passive delay policy, and considering indirect energy makes it possible to save at least 2.09% on the production cost compared to ignoring indirect energy. [ABSTRACT FROM AUTHOR]

A constrained least-squares (CLS) 3D source localization method is presented for acoustic sensor networks with sensor position errors. The proposed approach uses angles of arrivals (AOAs) and gain ratios of arrival (GROAs) measured simultaneously at each node to estimate the source position jointly. Compared to AOA-only localization methods, the GROAs can be used in conjunction with AOA measurements so as to get more accurate results by exploiting the geometrical relationship between these two measurements. Compared to time difference of arrival localization methods, the proposed algorithm does not require accurate time synchronization over different nodes. The theoretical mean-square error matrices of the proposed approach are derived and they are exactly equal to the Cramér-Rao bound for Gaussian noise under the small error condition. Simulations validate the performance of the proposed estimator. [ABSTRACT FROM AUTHOR]

Developing a three-dimensional laminar flow in the entrance region of rectangular microchannels has been investigated in this paper. When the hydrodynamic development length is the same magnitude as the microchannel length, entrance effects have to be taken into account, especially in relatively short ducts. Simultaneously, there are a variety of non-continuum or rarefaction effects, such as velocity slip and temperature jump. The available data in the literature appearing on this issue is quite limited, the available study is the semi-theoretical approximate model to predict pressure drop of developing slip flow in rectangular microchannels with different aspect ratios. In this paper, we apply the lattice Boltzmann equation method (LBE) to investigate the developing slip flow through a rectangular microchannel. The effects of the Reynolds number (1 < Re < 1000), channel aspect ratio (0 < ε < 1), and Knudsen number (0.001 < Kn < 0.1) on the dimensionless hydrodynamic entrance length, and the apparent friction factor, and Reynolds number product, are examined in detail. The numerical solution of LBM can recover excellent agreement with the available data in the literature, which proves its accuracy in capturing fundamental fluid characteristics in the slip-flow regime. [ABSTRACT FROM AUTHOR]

Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy, a new remote sensing image classification method inspired by deep learning is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder. Then, with noised input, the unsupervised Greedy layer-wise training algorithm is used to train each layer in turn for more robust expressing, characteristics are obtained in supervised learning by Back Propagation (BP) neural network, and the whole network is optimized by error back propagation. Finally, Gaofen-1 satellite (GF-1) remote sensing data are used for evaluation, and the total accuracy and kappa accuracy reach 95.7% and 0.955, respectively, which are higher than that of the Support Vector Machine and Back Propagation neural network. The experiment results show that the proposed method can effectively improve the accuracy of remote sensing image classification. [ABSTRACT FROM AUTHOR]

Porous polymeric materials, such as textile fabrics, are elastic and widely used in our daily life for garment and household products. The mechanical and dynamic heat transfer properties of porous polymeric materials, which describe the sensations during the contact process between porous polymeric materials and parts of the human body, such as the hand, primarily influence comfort sensations and aesthetic qualities of clothing. A multi-sensory measurement system and a new method were proposed to simultaneously sense the contact and characterize the mechanical and dynamic heat transfer properties of porous polymeric materials, such as textile fabrics in one instrument, with consideration of the interactions between different aspects of contact feels. The multi-sensory measurement system was developed for simulating the dynamic contact and psychological judgment processes during human hand contact with porous polymeric materials, and measuring the surface smoothness, compression resilience, bending and twisting, and dynamic heat transfer signals simultaneously. The contact sensing principle and the evaluation methods were presented. Twelve typical sample materials with different structural parameters were measured. The results of the experiments and the interpretation of the test results were described. An analysis of the variance and a capacity study were investigated to determine the significance of differences among the test materials and to assess the gage repeatability and reproducibility. A correlation analysis was conducted by comparing the test results of this measurement system with the results of Kawabata Evaluation System (KES) in separate instruments. This multi-sensory measurement system provides a new method for simultaneous contact sensing and characterizing of mechanical and dynamic heat transfer properties of porous polymeric materials. [ABSTRACT FROM AUTHOR]

Post-translational modifications have been identified to be of great importance in cancers and lysine acetylation, which can attract the multifunctional transcription factor BRD4, has been identified as a potential therapeutic target. In this paper, we identify that BRD4 has an important role in colorectal cancer; and that its inhibition substantially wipes out tumor cells. Treatment with inhibitor MS417 potently affects cancer cells, although such effects were not always outright necrosis or apoptosis. We report that BRD4 inhibition also limits distal metastasis by regulating several key proteins in the progression of epithelial-to-mesenchymal transition (EMT). This effect of BRD4 inhibitor is demonstrated via liver metastasis in animal model as well as migration and invasion experiments in vitro. Together, our results demonstrate a new application of BRD4 inhibitor that may be of clinical use by virtue of its ability to limit metastasis while also being tumorcidal. [ABSTRACT FROM AUTHOR]

The digital elevation model (DEM) acquired through photogrammetry or LiDAR usually exposes voids due to phenomena such as instrumentation artifact, ground occlusion, etc. For this reason, this paper proposes a multiattention generative adversarial network model to fill the voids. In this model, a multiscale feature fusion generation network is proposed to initially fill the voids, and then a multiattention filling network is proposed to recover the detailed features of the terrain surrounding the void area, and the channel-spatial cropping attention mechanism module is proposed as an enhancement of the network. Spectral normalization is added to each convolution layer in the discriminator network. Finally, the training of the model by a combined loss function, including reconstruction loss and adversarial loss, is optimized. Three groups of experiments with four different types of terrains, hillsides, valleys, ridges and hills, are conducted for validation of the proposed model. The experimental results show that (1) the structural similarity surrounding terrestrial voids in the three types of terrains (i.e., hillside, valley, and ridge) can reach 80–90%, which implies that the DEM accuracy can be improved by at least 10% relative to the traditional interpolation methods (i.e., Kriging, IDW, and Spline), and can reach 57.4%, while other deep learning models (i.e., CE, GL and CR) only reach 43.2%, 17.1% and 11.4% in the hilly areas, respectively. Therefore, it can be concluded that the structural similarity surrounding the terrestrial voids filled using the model proposed in this paper can reach 60–90% upon the types of terrain, such as hillside, valley, ridge, and hill. [ABSTRACT FROM AUTHOR]

Meso-structure is an important factor affecting the characteristics of rock fracture. To determine the factors influencing the internal meso-structural characteristics upon the crack generation and extension, rock samples were tested under uniaxial cyclic loading and unloading and examined using computed tomography (CT) scanning. CT scanning was used to visualize and investigate the entire process of fracture source generation and its development in three dimensions, and finally the location information of the fracture source was determined. The mineral composition and structure along the fracture path inside the specimen were studied by using a polarizing microscope, and the evolution of fracture propagation around mineral particles was revealed based on its mineralogical characteristics. Results indicate that based on the fracture source around different rock meso-structure types, the initial fracture source can also be divided into different types, namely, the primary porosity type, the micro-crack type, and the mineral grain type. The strength characteristics of mineral grains can determine whether the crack extends around the gravel or through it. The hard grains at the crack-tip promote the transformation of tensile stress to shear stress, which lead to the change in the direction of crack extension and bifurcation. The spatial shape of the cracks after rock fracture is related to the initial distribution of minerals and is more complicated in areas where minerals are concentrated. The crack extension around gravel particles also generates a mode of failure, affecting large grains with gravel spalling from the matrix. The findings provide a study basis for identifying the potentially dangerous areas and provide early warning for the safety of underground engineering construction operations. [ABSTRACT FROM AUTHOR]

Low-temperature storage is one of the most important preservation methods for aquatic product storage. However, the effects of low-temperature storage on the lipid profiles of shrimp are unclear. Herein, UPLC-Q-Exactive Orbitrap/MS combined with LipidSearch software was applied to analyze the effect of three low storage temperatures (4 °C, −2 °C, and −18 °C) on the lipidomics of Litopenaeus vannamei. A total of 15 lipid classes were analyzed, and PC, PE, DG, and TG accounted for vast majority of peak areas. Furthermore, 531 individual lipid variables enriched in 12 metabolic pathways were identified via bioinformatics analysis methods. A total of 56 significantly different lipid molecular species (55 belonging to PC, PE, DG, and TG) were selected as potential biomarkers of lipid oxidation via correlational analysis between physical properties (texture and color) and individual lipid variables. The results indicated that the three low storage temperatures caused different effects on the lipidomics profile of L. vannamei, and PC, PE, DG, and TG could become potential focuses in further studies of lipid oxidation in L. vannamei. [ABSTRACT FROM AUTHOR]

Intelligent geoprocessing relies heavily on formalized parameter constraints of geoprocessing tools to validate the input data and to further ensure the robustness and reliability of geoprocessing. However, existing methods developed to formalize parameter constraints are either designed based on ill-suited assumptions, which may not correctly identify the invalid parameter inputs situation, or are inefficient to use. This paper proposes a novel method to formalize the parameter constraints of geoprocessing tools, based on a high-level and standard constraint language (i.e., SHACL) and geoprocessing ontologies, under the guidance of a systematic classification of parameter constraints. An application case and a heuristic evaluation were conducted to demonstrate and evaluate the effectiveness and usability of the proposed method. The results show that the proposed method is not only comparatively easier and more efficient than existing methods but also covers more types of parameter constraints, for example, the application-context-matching constraints that have been ignored by existing methods. [ABSTRACT FROM AUTHOR]

A non-contact method was proposed to monitor the freshness (based on TVB-N and TBA values) of large yellow croaker fillets (Larimichthys crocea) by using a visible and near-infrared hyperspectral imaging system (400–1000 nm). In this work, the quantitative calibration models were built by using feed-forward neural networks (FNN) and partial least squares regression (PLSR). In addition, it was established that using a regression coefficient on the data can be further compressed by selecting optimal wavelengths (35 for TVB-N and 18 for TBA). The results validated that FNN has higher prediction accuracies than PLSR for both cases using full and selected reflectance spectra. Moreover, our FNN based model has showcased excellent performance even with selected reflectance spectra with rp = 0.978, R2p = 0.981, and RMSEP = 2.292 for TVB-N, and rp = 0.957, R2p = 0.916, and RMSEP = 0.341 for TBA, respectively. This optimal FNN model was then utilized for pixel-wise visualization maps of TVB-N and TBA contents in fillets. [ABSTRACT FROM AUTHOR]

Accurate and up-to-date road network information is very important for the Geographic Information System (GIS) database, traffic management and planning, automatic vehicle navigation, emergency response and urban pollution sources investigation. In this paper, we use vector field learning to extract roads from high resolution remote sensing imaging. This method is usually used for skeleton extraction in nature image, but seldom used in road extraction. In order to improve the accuracy of road extraction, three vector fields are constructed and combined respectively with the normal road mask learning by a two-task network. The results show that all the vector fields are able to significantly improve the accuracy of road extraction, no matter the field is constructed in the road area or completely outside the road. The highest F1 score is 0.7618, increased by 0.053 compared with using only mask learning. [ABSTRACT FROM AUTHOR]

Cellular vehicle-to-everything (C-V2X) is essential in enabling safe, reliable, and efficient transportation services. It serves as serve as the foundation for vehicles to communicate with each other and everything around them. One fundamental element in C-V2X is positioning, namely extracting the vehicle's absolute and relative positions concerning other objects such as buildings, pedestrians, traffic signs, and other vehicles. However, its feasibility in enabling vehicular positioning has not been fully explored yet. In this paper, key performance indicators (KPIs) for C-V2X positioning have been described firstly. Then positioning challenges and conventional positioning methods for C-V2X are reviewed. Afterward, two user equipment (UE)-based and UE-assisted C-V2X positioning architectures are proposed, and key technologies are also described. Lastly, testing and typical application cases are provided. [ABSTRACT FROM AUTHOR]

This article introduces a wall-climbing robot that uses the reverse thrust of the propeller as the adsorption force. The robot is symmetrically distributed in structure and the adsorption force is symmetrically distributed before and after so that it can adapt to the surface of a variety of different media materials and achieve stable adsorption and movement of a variety of wall surfaces. The robot mainly uses the reverse thrust of the aircraft propeller as the adsorption force to achieve wall adsorption. The robot relies on four wheels to move forward. The forward power mainly comes from the combined action of the propeller reverse thrust component and the front wheel driving force. During the movement of the robot, the steering is realized by the front wheel differential control. In this paper, we design the structure of a dual-propeller dynamic adsorption wall mobile robot, plan the movement process of the robot from the ground to the wall, analyze the stable adsorption conditions of the robot wall, and carry out the robot's motion performance and adaptability test under different ground/wall environments to verify that the robot is stable and feasible. [ABSTRACT FROM AUTHOR]

Environmental covariates are fundamental inputs of digital soil mapping (DSM) based on the soil–environment relationship. It is normal to have invalid values (or recorded as NoData value) in individual environmental covariates in some regions over an area, especially over a large area. Among the two main existing ways to deal with locations with invalid environmental covariate data in DSM, the location-skipping scheme does not predict these locations and, thus, completely ignores the potentially useful information provided by valid covariate values. The void-filling scheme may introduce errors when applying an interpolation algorithm to removing NoData environmental covariate values. In this study, we propose a new scheme called FilterNA that conducts DSM for each individual location with NoData value of a covariate by using the valid values of other covariates at the location. We design a new method (SoLIM-FilterNA) combining the FilterNA scheme with a DSM method, Soil Land Inference Model (SoLIM). Experiments to predict soil organic matter content in the topsoil layer in Anhui Province, China, under different test scenarios of NoData for environmental covariates were conducted to compare SoLIM-FilterNA with the SoLIM combined with the void-filling scheme, the original SoLIM with the location-skipping scheme, and random forest. The experimental results based on the independent evaluation samples show that, in general, SoLIM-FilterNA can produce the lowest errors with a more complete spatial coverage of the DSM result. Meanwhile, SoLIM-FilterNA can reasonably predict uncertainty by considering the uncertainty introduced by applying the FilterNA scheme. [ABSTRACT FROM AUTHOR]

Frequent waterlogging near anthesis is a primary constraint for wheat production in the Yangtze River Basin, China. This study aimed to identify the critical stage for waterlogging on wheat yield and its components and identify the possible factors determining this stage. In the first experiment (E1), we evaluated three varieties and initiated a 10-day waterlogging starting at the stem elongation (S1), booting (S2), and anthesis (S3) stages. In the second experiment (E2), waterlogging was applied at the stem elongation (S1) and anthesis (S3) stages and for three durations (five, 10, and 15 days). In E1, waterlogging at S1 and S2 resulted in a lower grain yield than at S3. In the second year of E1, grain yield under waterlogging at S1 and S3 was similar but significantly lower than under waterlogging conditions at S2. Waterlogging at S1 and S2 greatly decreased the kernel number per spike and 1000-kernel weight, but waterlogging at S3 only decreased the 1000-kernel weight. Moreover, different varieties did not change the stages mostly reducing grain yield and yield components. In E2, grain yield decreased with prolonged exposure to waterlogging and was significantly lower under 15-day conditions than the five-day conditions. There was no significant interaction between the stage and duration of waterlogging. Finally, our results indicate that in yield formation, a greater reliance on kernel weight instead of the kernel number per spike can contribute to a reduction in yield loss for waterlogging at S1 and S2. This study, therefore, indicates that the stem elongation stage is the most affected by waterlogging. [ABSTRACT FROM AUTHOR]

Waterlogging remains a critical constraint to wheat production in areas with high rainfall. Exogenous application of nitrogen (N) can effectively diminish the adverse effects of waterlogging, but varies with specific events. To provide highly efficient remedial strategies, this pot study investigated the effects of urea application following 10 days of waterlogging initialing at the stem elongation stage (Zadoks growth stage, GS33). The remedial measures included foliar spray of urea solution at a single dose (0.108 g urea per pot) at the 0th day after finishing waterlogging (FU1) and twice at the 0th and 7th day (FU2), and soil surface spray of urea solution at single and double doses when soil water content was approximately 80% of field capacity (SU1 and SU2, respectively). Waterlogging significantly reduced grain yield, total N uptake, and N partial factor productivity (by 11%, 18%, and 11%, respectively), but subsequent remedial measures improved these to variable degrees. Reduction in grain yield owing to waterlogging could be effectively alleviated and even eliminated using these remediations. Grain yield and N partial factor productivity were higher under FU2 and SU2 than FU1 and SU1. Among all treatments, plants under SU2 exhibited the highest total N uptake and top-dressing N recovery. Diminished yield losses were attributed to (1) increased kernel number per spike resulting from increased spikelet fertility and kernel number per spikelet and (2) increased photosynthetic production by delaying senescence (improved chlorophyll content and maintained green leaf area) of the top leaves. This study suggests that urea application for a brief period of time following waterlogging during the stem elongation stage has remarkable remedial effects. [ABSTRACT FROM AUTHOR]

One of the key concerns in geographic modeling is the preparation of input data that are sufficient and appropriate for models. This requires considerable time, effort, and expertise since geographic models and their application contexts are complex and diverse. Moreover, both data and data pre-processing tools are multi-source, heterogeneous, and sometimes unavailable for a specific application context. The traditional method of manually preparing input data cannot effectively support geographic modeling, especially for complex integrated models and non-expert users. Therefore, effective methods are urgently needed that are not only able to prepare appropriate input data for models but are also easy to use. In this review paper, we first analyze the factors that influence data preparation and discuss the three corresponding key tasks that should be accomplished when developing input data preparation methods for geographic models. Then, existing input data preparation methods for geographic models are discussed through classifying into three categories: manual, (semi-)automatic, and intelligent (i.e., not only (semi-)automatic but also adaptive to application context) methods. Supported by the adoption of knowledge representation and reasoning techniques, the state-of-the-art methods in this field point to intelligent input data preparation for geographic models, which includes knowledge-supported discovery and chaining of data pre-processing functionalities, knowledge-driven (semi-)automatic workflow building (or service composition in the context of geographic web services) of data preprocessing, and artificial intelligent planning-based service composition as well as their parameter-settings. Lastly, we discuss the challenges and future research directions from the following aspects: Sharing and reusing of model data and workflows, integration of data discovery and processing functionalities, task-oriented input data preparation methods, and construction of knowledge bases for geographic modeling, all assisting with the development of an easy-to-use geographic modeling environment with intelligent input data preparation. [ABSTRACT FROM AUTHOR]

The phospholipids (PLs) of large yellow croaker (Pseudosciaena crocea, P. crocea) roe contain a high level of polyunsaturated fatty acids, especially docosahexaenoic acid (DHA), which can lower blood lipid levels. In previous research, PLs of P. crocea roe were found able to regulate the accumulation of triglycerides. However, none of these involve the function of DHA-containing phosphatidylcholine (DHA-PC), which is the main component of PLs derived from P. crocea roe. The function by which DHA-PC from P. crocea roe exerts its effects has not yet been clarified. Herein, we used purified DHA-PC and oleic acid (OA) induced HepG2 cells to establish a high-fat model, and the cell activity and intracellular lipid levels were then measured. The mRNA and protein expression of Fatty Acid Synthase (FAS), Carnitine Palmitoyl Transferase 1A (CPT1A) and Peroxisome Proliferator-Activated Receptor α (PPARα) in HepG2 cells were detected via RT-qPCR and western blot as well. It was found that DHA-PC can significantly regulate triglyceride accumulation in HepG2 cells, the effect of which was related to the activation of PPARα receptor activity, upregulation of CPT1A, and downregulation of FAS expression. These results can improve the understanding of the biofunction of hyperlipidemia mediated by DHA-PC from P. crocea roe, as well as provide a theoretical basis for the utilization of DHA-PC from P. crocea roe as a functional food additive. [ABSTRACT FROM AUTHOR]

Oxidation ditches are popularly used in rural areas and decentralized treatment facilities where energy deficiency is of concern. Aeration control technologies are well established for diffusion systems in order to improve energy efficiency, but there are still challenges in their application in oxidation ditches because surface aerators have unique characteristics with respect to oxygen transfer and energy consumption. In this paper, an integral energy model was proposed to include the energy, aeration, and fluidic effects of surface aerators, by which the energy for aeration of each aerator can be estimated using online data. Two types of rotating disks with different diameters (1800 mm and 1400 mm) were monitored in situ to estimate the model parameters. Furthermore, a feedforward–feedback loop control strategy was proposed using the concept of energy analysis and optimization. The simplified control system was implemented in a full-scale Orbal oxidation ditch, achieving an approximately 10% saving in full-process energy consumption. The cost–benefit analysis and carbon emission assessment confirmed the economic feasibility and environmental contribution of the control system. The energy model can help process designers and operators to better understand and optimally control the aeration process in oxidation ditches. [ABSTRACT FROM AUTHOR]

In the monitoring of pollutants in an aquatic environment, it is important to preserve water quality safety. Among the available analysis methods, the microbial fuel cell (MFC) sensor has recently been used as a sustainable and on-line electrochemical microbial biosensor for biochemical oxygen demand (BOD) and toxicity, respectively. However, the effect of the background organic matter concentration on toxicity monitoring when using an MFC sensor is not clear and there is no effective strategy available to avoid the signal interference by the combined shock of BOD and toxicity. Thus, the signal interference by the combined shock of BOD and toxicity was systematically studied in this experiment. The background organic matter concentration was optimized in this study and it should be fixed at a high level of oversaturation for maximizing the signal output when the current change (ΔI) is selected to correlate with the concentration of a toxic agent. When the inhibition ratio (IR) is selected, on the other hand, it should be fixed as low as possible near the detection limit for maximizing the signal output. At least two MFC sensors operated with high and low organic matter concentrations and a response chart generated from pre-experiment data were both required to make qualitative distinctions of the four types of combined shock caused by a sudden change in BOD and toxicity., Competing Interests: The authors declare no conflict of interest.

Powdery mildew is an important disease of rubber trees caused by Oidium heveae B. A. Steinmann. As far as we know, none of the resistance genes related to powdery mildew have been isolated from the rubber tree. There is little information available at the molecular level regarding how a rubber tree develops defense mechanisms against this pathogen. We have studied rubber tree mRNA transcripts from the resistant RRIC52 cultivar by differential display analysis. Leaves inoculated with the spores of O. heveae were collected from 0 to 120 hpi in order to identify pathogen-regulated genes at different infection stages. We identified 78 rubber tree genes that were differentially expressed during the plant-pathogen interaction. BLAST analysis for these 78 ESTs classified them into seven functional groups: cell wall and membrane pathways, transcription factor and regulatory proteins, transporters, signal transduction, phytoalexin biosynthesis, other metabolism functions, and unknown functions. The gene expression for eight of these genes was validated by qRT-PCR in both RRIC52 and the partially susceptible Reyan 7-33-97 cultivars, revealing the similar or differential changes of gene expressions between these two cultivars. This study has improved our overall understanding of the molecular mechanisms of rubber tree resistance to powdery mildew.