Search

Abstract After thermal treatment, quail eggs pruduce an attractive flavor that is favored by consumers. In this study, the key aroma volatiles and their lipid precursors during thermal treatment were investigated by the electronic nose, GC-MS, GC-O-MS and UPLC-Q-Exactive HF-X. The results exhibited that the raw and fresh flavor of raw egg yolks came from 1-Octen-3-ol, 2-Ethyl-1-hexanol, 1-Decene, and 1-Undecene. 2-Methyl-3-octanone, Toluene, and some aromatic compounds worked synergistically to contribute to the aroma profile of boiled eggs. (+)-2-Bornanone, Octanal, 2-Methyl-butanal, Nonanal, (.+Dihydro-3-hydroxy-4,4-dimethyl-2(3 H)-furanone, 6,7-Dihydro-2,5-dimethyl-5 H-cyclopentapyrazine, (E)-2-methyl-6-(1-propenyl)-pyrazine, 2-Ethyl-3,5-dimethyl-pyrazine, 3,5-Diethyl-2-methyl-pyrazine, 2,5-Dimethyl-pyrazine were the key flavor compounds in the fried egg and gave it the popcorn and roasted flavors. The statistical analysis of the lipid profile revealed that brief, high-temperature heating (100 or 200 °C for 10 min) in typical boiled and fried quail eggs had minor effects on the lipid nutritional value. PE-related lipids, particularly those containing 18-carbon fatty acids, contributed to the aroma formation of fried quail eggs. Graphical Abstract

Satellite Interferometric Synthetic Aperture Radar (InSAR) is widely used for topographic, geological and natural resource investigations. However, most of the existing InSAR studies of ground deformation are based on relatively short periods and single sensors. This paper introduces a new multi-sensor InSAR time series data fusion method for time-overlapping and time-interval datasets, to address cases when partial overlaps and/or temporal gaps exist. A new Power Exponential Knothe Model (PEKM) fits and fuses overlaps in the deformation curves, while a Long Short-Term Memory (LSTM) neural network predicts and fuses any temporal gaps in the series. Taking the city of Wuhan (China) as experiment area, COSMO-SkyMed (2011–2015), TerraSAR-X (2015–2019) and Sentinel-1 (2019–2021) SAR datasets were fused to map long-term surface deformation over the last decade. An independent 2011–2020 InSAR time series analysis based on 230 COSMO-SkyMed scenes was also used as reference for comparison. The correlation coefficient between the results of the fusion algorithm and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval dataset. The correlation coefficient of the overall results is 0.78, which fully demonstrates that the algorithm proposed in our paper achieves a similar trend as the reference deformation curve. The experimental results are consistent with existing studies of surface deformation at Wuhan, demonstrating the accuracy of the proposed new fusion method to provide robust time series for the analysis of long-term land subsidence mechanisms.

Nowadays, the rapid development of hypersonic vehicles brings great challenges to the missile defense system. As achieving successful interception depends highly on terminal guidance laws, research on guidance laws for intercepting highly maneuvering targets has aroused increasing attention. Artificial intelligence technologies, such as deep reinforcement learning (DRL), have been widely applied to improve the performance of guidance laws. However, the existing DRL guidance laws rarely consider the partial observability problem of onboard sensors, resulting in the limitations of their engineering applications. In this paper, a deep recurrent reinforcement learning (DRRL)-based guidance method is investigated to address the intercept guidance problem against maneuvering targets under partial observability. The sequence consisting of previous state observations is utilized as the input of the policy network. A recurrent layer is introduced into the networks to extract hidden information behind the temporal sequence to support policy training. The guidance problem is formulated as a partially observable Markov decision process model, and then a range-weighted reward function that considers the line-of-sight rate and energy consumption is designed to guarantee convergence of policy training. The effectiveness of the proposed DRRL guidance law is validated by extensive numerical simulations.

Optical and Synthetic Aperture Radar (SAR) remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications, yet further advances are viable through the exploitation of novel sensor data and imaging modes, big data and high-performance computing, advanced and automated analysis methods. This paper showcases the main research avenues in this field, with a focus on archaeological prospection and heritage site protection. Six demonstration use-cases with a wealth of heritage asset types (e.g. excavated and still buried archaeological features, standing monuments, natural reserves, burial mounds, paleo-channels) and respective scientific research objectives are presented: the Ostia-Portus area and the wider Province of Rome (Italy), the city of Wuhan and the Jiuzhaigou National Park (China), and the Siberian “Valley of the Kings” (Russia). Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite (e.g. Copernicus Sentinels and ESA Third Party Missions) and aerial (e.g. Unmanned Aerial Vehicles, UAV) platforms, as well as field-based evidence and ground truth, auxiliary topographic data, Digital Elevation Models (DEM), and monitoring data from geodetic campaigns and networks. The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes, identify threats to cultural heritage assets due to ground instability and urban development in large metropolises, and monitor post-disaster impacts in natural reserves.

Background Rat models are valuable tools to study the lung microbiota in diseases. Yet the impacts of different lung parts, young and mature adult stages, and the different batches of the same conditions on the healthy rat lung microbiome have not been investigated.Methods The rat lung microbiome was analyzed to clarify the lung part-dependent and age-dependent differences and to evaluate the effects of several ‘batch environmental factors’ on normal rats, after eliminating potential contamination.Results The results showed that the contamination could be identified and excluded. The lung microbiome from left and right lung parts was very similar so one representative part could be used in the microbiome study. There were significantly different lung microbial communities between the young and mature adult groups, and also between the different feeding batches groups of the same repetitive feeding conditions, but a common lung microbiota characterized by Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria as the most dominant phyla were present in all adult rats. It indicated that the experiment under the same condition of the same rats batch was needed to compare the difference in the lung microbiota and repeated experiments were necessary to confirm the results.Conclusion These data represented that the lung bacterial communities were dynamic and rapidly susceptible to environmental influence, clustered strongly by age or different feeding batches but similar in the different lung tissue parts. This study improved the basic understanding of the potential effects on the lung microbiome of healthy rats.

This paper considers the problem of tracking a three-dimensional target under the condition that only a single two-dimensional radar is available. Since a two-dimensional radar can only measure the slant range and azimuth information relative to the target, an unobservability issue arises in this tracking application. Therefore, we first investigate the observability issue of tracking a three-dimensional target with a single two-dimensional radar from two perspectives, including intuitive illustration and quantitative analysis. From the perspective of intuitive illustration, we demonstrate “What is the unobservability issue” and “How does the relative target-radar geometry influence the observability of the tracking system”. From the perspective of quantitative analysis, we construct a novel observability metric for this special tracking problem. Second, aiming at improving tracking performance under the unobservability of target height, we propose an observability-based Gaussian sum cubature Kalman filter. Built within the Gaussian sum framework and based on the height-parameterized strategy, this novel algorithm uses a set of independent fifth-degree cubature Kalman filters, each of which can detect the system observability variation and enhance the tracking accuracy by using a Gaussian splitting scheme under low-degree observability. Finally, the effectiveness of the presented filtering algorithm is validated through lots of simulation experiments.

The escalating proliferation of space debris poses an increasing risk to spinning satellites, elevating the probability of hazardous collisions that can result in severe damage or total loss of functionality. To address this concern, a pioneering inflatable protective structure is employed to ensure the optimal functionality of spinning satellites. Additionally, a multi-body dynamic modeling method based on spring hinge unfolding/spring expansion is proposed to tackle the complex dynamics of spinning satellites with inflatable protective structures during flight. This method enables analysis of the motion parameters of spinning satellites. First, the structural composition of a spinning satellite with inflatable protective structures is introduced and its flight process is analyzed. Then, an articulated spring hinge unfolding model or a spring expansion model using the Newton–Euler method is established to describe the unfolding or expansion of the spinning satellite with inflatable protective structures during flight. Finally, the effects on the motion parameters of a spinning satellite are analyzed through simulation under various working conditions.

A short-term rapid subsidence event occurred in the Bi Guiyuan community in Balitai Town, Tianjin City, leading to the tilting of high-rise buildings and the emergency evacuation of over 3000 residents. In response to this incident, InSAR (Interferometric Synthetic Aperture Radar) technology was swiftly employed to monitor the subsidence in the area before and after the event. Our observations indicate that the region had maintained stability for 8 months prior to the incident. However, over the course of the 15-day event, the ground experienced more than 10mm of subsidence. By integrating the findings from an InSAR analysis with geological studies, we speculate that the rapid subsidence in the region is related to the extraction of geothermal resources. It is suspected that during drilling operations, the wellbore mistakenly penetrated a massive underground karst cavity. Consequently, this resulted in a sudden rapid leakage of drilling fluid, creating a pressure differential that caused the overlying soil layers to collapse and rapidly sink into the cavity. As a result, short-term rapid subsidence on the ground surface and tilting of high-rise buildings occurred.

IntroductionPesticides are widely and excessively used in the world. Reducing pesticide overuse is an important measure to protect the environment and human health.MethodsBased on the survey data of 518 farmers in Shandong Province, China, using the Logit model to empirically test the effect of risk cognition on farmers' pesticide overuse behavior and the moderating effect of cooperatives training on the effect of risk cognition on farmers' pesticide overuse behavior.Results and discussionWe found that 21.24% of farmers overused pesticides. The three dimensions of risk cognition have significant negative effects on farmers' behavior of excessive pesticide use, among which the human health risk cognition has the largest impact (0.74), followed by food safety risk cognition (0.68) and ecological environment risk cognition (0.63). Cooperatives training has a positive moderating effect on the relationship between risk cognition and pesticide overuse behavior, that is, when risk cognition matches farmers participating in cooperatives training, the effect on reducing pesticide overuse is more significant. Years of education, planting scale and detection frequency of pesticide residues have significant effects on farmers' pesticide overuse.ConclusionsThe government should help farmers reduce pesticide overuse by improving risk cognition, developing agricultural cooperatives and perfecting guarantee conditions.

The reliability of the high-voltage drive motor system for pure electric commercial vehicles is in premium demand. Conventional reliability based on fault tree analysis methods is not suitable for the quantitative assessment of polymorphic systems. As an example of a pure electric commercial vehicle, this paper combines polymorphic theory and Bayesian theory to establish a polymorphic Bayesian network model of a high-voltage drive motor system in terms of a polymorphic fault tree and to quantitatively judge the system. The polymorphic Bayesian network (BN) model can accurately depict the high-voltage drive motor system’s miscellaneous fault states and solve the top event’s probability in every state, also solving the system and drawing the consistent conclusion that the presence of abrasive particles, high-temperature gluing, moisture, and localized high temperatures are the system’s weak links by solving the critical importance, probabilistic importance, and posterior probability of the underlying event, which provides a theoretical reference for structure contrive optimization and fault diagnosis. This is extremely important in terms of improving pure electric commercial vehicles’ high-voltage drive motor systems.

Due to the simplified assumptions or unascertained equipment parameters, traditional mechanism models of boiler system in coal-fired power plant usually have predictive errors that cannot be ignored. In order to further improve the predictive accuracy of the model, this paper proposes a novel recurrent neural network-based hybrid modeling method for digital twin of boiler system. First, the mechanism model of boiler system is described through recurrent neural network (RNN) to facilitate training and updating parameters, while the interpretability of the model does not degenerate. Second, for the time-varying parameters in the mechanism model, the functional relationship between them and the state variables is constructed by neurons to improve the predictive accuracy. Third, the long short-term memory (LSTM) neural network model is established to describe the unascertained dynamic characteristics to compensate the predictive residual of the mechanism model. Fourth, the update architecture and training algorithm applicable to the hybrid model are established to realize the iterative optimization of model parameters. Finally, experimental results show that the hybrid modeling method proposed in this paper can improve the predictive performance of traditional models effectively.

The measurement of viscosity coefficient of liquids is vital in plentiful fields. There are numerous ways to measure the viscosity coefficient such as capillary and rotary rheometry in which falling ball method is mostly widely used. The uncertainty of the terminal velocity and the limit of the viscosity coefficient scale are the principled disadvantages of the method. In our work, the relationship between the displacement and time is obtained by integral calculation. What’s more, we considered the radius of the ball, the radius of the graduated cylinder, and the height of the graduated cylinder by theoretical correction of the stokes expression. The new correction expression broadens the measured viscosity coefficient scale and also improves the precision dramatically. Experimental verification is also well processed which fits well with our theory. The deviation between the viscosity coefficient of castor oil measured by the new correction method and the standard value is only 0.045. The relationship between displacement and time is consistent with the theoretical prediction, and the room mean square error of the two is 0.065. Based on them, this method can be widely used in scientific researches and teaching practices.

This paper presents a zero-optical-distance mini-LED backlight with cone-shaped light coupling microstructures to achieve an ultra-thin backlight architecture (~0.1 mm thickness) by combining the characteristics of direct-lit and edge-lit backlights. There is no gap between the light guide plate (LGP) and the reflector, as well as between the LGP and the mini-LED embedded in the reflector. The illuminance uniformity and light extraction efficiency (LEE) of the whole structure reach 91.47% and 77.09%, respectively. Nine sub-modules are spliced together to realize 2D local dimming with 0.29% crosstalk. The structure shows high optical performance while reducing the thickness of the backlight module, which is of great significance for the development of mini-LED backlights.

In the growing mass of social media data, how to efficiently extract the collection of interested concerns has become a research hotspot. Due to the large size and regularity of social media data, traditional indexing techniques are not applicable. Our “Learned Index”, which is a part of social media intelligence solutions, uses mathematical principles to summarize the laws from the data. It predicts the location of the data by learning the mathematical properties of the data distribution to build the model. Although existing methods over single dimension and multi-dimension such as setting gaps are proposed to further optimize the performance of index, they do not consider the update-distribution of data. In this paper, we propose an update-distribution-aware learned index for social media data (TALI) to support update operations and handle the data sliding. In TALI, underlying data are learned through machine learning models, and a recursive hierarchical model is built. It also learns the update-distribution of data to adjust the size of each leaf node. Thus, it can more effectively support all kinds of operations in databases due to the decrease of the leaf nodes’ sliding. In addition, TALI uses the model-based insertion method for bulkload and query, resulting in a small prediction error. Thus, exponential search is used to perform secondary lookup to improve query efficiency. Experiments were tested and compared on four realistic and synthetic social media datasets. Through extensive experiments, TALI performed better than the existing state-of-the-art learned index with less space occupancy on four realistic and synthetic social media datasets.

Background: Maximum oxygen uptake (VO2max) is an important respiratory physiological index of the aerobic endurance of the body, especially for special groups such as drug addicts, and it is an important indicator for assessing the cardiopulmonary function and formulating exercise prescriptions. Although the cardiopulmonary exercise test (CPX) is a classic method to directly measure VO2max, this method is limited by factors such as cumbersome operating procedures and expensive equipment, resulting in its relatively low applicability. Recently, many studies have begun to focus on the estimation of VO2max in different groups of people, but few studies have focused on drug addicts.Methods: Fifteen chemically synthesized drug addicts (such as amphetamines) and Fifteen plant-derived drug addicts (such as heroin) were recruited at the Chongqing Compulsory Isolation and Drug Rehabilitation Center in China. First, the VO2max of subjects was directly measured through the CPX. Second, after subjects were fully rested, they were required to complete the 30-s high-leg raise, 1,000-m walk, and 3-min step experiment. Finally, SPSS 21.0 software was used to perform the correlation and linear regression analysis to verify the estimated effectiveness.Results: (1) Regardless of chemically synthesized or natural plant-derived drug addicts, the years of drug use and walking time of 1,000 m were significantly negatively correlated with VO2max (chemically synthesized: P < 0.01 and natural plant-derived: P < 0.05), the number of 30-s high-leg raises was a significantly positive correlation with VO2max (P < 0.05 and P < 0.01), and the 3-min step index was significantly positively correlated with VO2max (P < 0.01 and P < 0.01). (2) Regression analysis shows that the 30-s high-leg lift, 1,000-m walking, and 3-min step experiment could effectively estimate the VO2max of chemically synthesized and natural plant-derived drug addicts. (3) Multiple linear regression constructed by the years of drug use combined with the step index has the highest estimated accuracy for the VO2max of chemically synthesized drug addicts (96.48%), while the unary regression equation established by a single step index has the highest prediction accuracy for the VO2max of natural plant-derived addicts (94.30%).Conclusion: The indirect measurement method could effectively estimate the VO2max of drug addicts, but different measurement methods have certain differences in the estimation accuracy of VO2max of different drug addicts. In the future, the physical characteristics of drug users can be fully considered, combined with more cutting-edge science and technology, to make the estimation accuracy of VO2max closer to the real level.

BackgroundAs a global infectious disease, Acquired Immune Deficiency Syndrome (AIDS) poses a serious threat to the safety and health of the society. In recent years, the proportion of drug addicts infected with HIV has been increased, and drug addicts became one of the main carriers of the spread of AIDS, which has attracted worldwide attention. It has been reported that physical activity has positive effects on improving the inhibitory function of drug addicts and reducing their drug craving, but the mechanism of the internal inhibition remains to be further explored.MethodThe drug addicts in an AIDS treatment center in Chongqing were investigated by means of a Physical Activity Rating Scale (PARS – 3), Internal Inhibition Scale and Drug Craving Scale, and a structural equation model was established.Results(1) There is no gender difference in the internal inhibition and drug craving of drug addicts with AIDS, and there are significant differences across the types and years of drug abuse. (2) The amount of physical activity in drug addicts with AIDS is positively correlated with the intrinsic inhibition, while the internal inhibition is negatively correlated with the drug craving, and the physical exercise amount is negatively correlated with the drug craving. The years of drug abuse were negatively correlated with internal inhibition and positively correlated with drug craving. (3) Internal inhibition plays a partly mediatory role between the physical activity amount and the craving for drugs, and internal inhibition plays a partly mediatory role between the number of years of drug abuse and the craving for drugs, which indicates that internal inhibition has a dual mediating effect.ConclusionActively participate in physical activity, especially high-intensity physical activity, can effectively enhance the internal inhibition of drug addicts with AIDS and reduce their drug craving. Meanwhile, the difference in the drug-taking years among addicts should be paid attention to, and the physical activity prescription should be formulated according to the actual situation.

BackgroundMaximal oxygen uptake (VO2max), a vital physiological indicator, has been widely used in many fields. In recent years, the measurement method of VO2max has been widely explored in various populations, but few studies have been conducted for women drug abusers. For the importance of VO2max in the formulation of aerobic exercise intensity for drug users, the present study estimated VO2max using the step test index combined with heart rate variability in women with drug use disorder.MethodsForty women methamphetamine (MA) users without cardiovascular disease and dyskinesia participated in a cardiopulmonary exercise test (CPX) and a 3-minute step test. Each of them performed a heart rate variability (HRV) monitoring test after the step test, and VO2max was estimated by step test index and HRV.Results(1) The step test index had a significant positive correlation with VO2max. The standard deviation of normal-to-normal interval (SDNN) had a significant positive correlation with VO2max and a significant positive correlation with the step test index; (2) the R-square values of the estimated VO2max by step test index and post-SDNN for overall MA users were 0.29 and 0.22, with an accuracy of 93.19 and 92.85%, respectively; (3) the R-square values of the estimated VO2max by step test index and post-SDNN in group I were 0.27 and 0.36, respectively, with an accuracy of 94.04 and 93.99%. The R-square value of the estimated VO2max by step test index in group II was 0.44, with an accuracy of 92.65%, however, post-SDNN cannot adequately estimate the VO2max in group II; and (4) there was no significant difference in VO2max obtained by CPX, step test index, or post-SDNN, regardless of overall or grouping variable analysis.ConclusionThe 3-minute step test combined with HRV can estimate the VO2max of women MA users to a certain extent, but the size and the coverage of the sample size should be further considered. In the future, more methods, such as machine learning or artificial neural networks, should be used.

Cotton is an important economic crop, but large-scale field extraction and estimation can be difficult, particularly in areas where cotton fields are small and discretely distributed. Moreover, cotton and soybean are cultivated together in some areas, further increasing the difficulty of cotton extraction. In this paper, an innovative method for cotton area estimation using Sentinel-2 images, land use status data (LUSD), and field survey data is proposed. Three areas in Hubei province (i.e., Jingzhou, Xiaogan, and Huanggang) were used as research sites to test the performance of the proposed extraction method. First, the Sentinel-2 images were spatially constrained using LUSD categories of irrigated land and dry land. Seven classification schemes were created based on spectral features, vegetation index (VI) features, and texture features, which were then used to generate the SVM classifier. To minimize misclassification between cotton and soybean fields, the cotton and soybean separation index (CSSDI) was introduced based on the red band and red-edge band of Sentinel-2. The configuration combining VI and spectral features yielded the best cotton extraction results, with F1 scores of 86.93%, 80.11%, and 71.58% for Jingzhou, Xiaogan, and Huanggang. When CSSDI was incorporated, the F1 score for Huanggang increased to 79.33%. An alternative approach using LUSD for non-target sample augmentation was also introduced. The method was used for Huangmei county, resulting in an F1 score of 78.69% and an area error of 7.01%. These results demonstrate the potential of the proposed method to extract cotton cultivated areas, particularly in regions with smaller and scattered plots.

The small scale of transmission towers and the environmental diversity around their situations make their detection in Synthetic Aperture Radar (SAR) images a challenging task. This paper presents a new hierarchical detection algorithm for transmission towers. First, Signal-to-Clutter Ratios (SCRs) of pixels are calculated. Afterwards, a SCR threshold is set. Since transmission towers possess strong scattering characteristics, pixels with SCRs above the threshold are considered as potential transmission tower pixels. Second, spatial densities of potential transmission tower pixels are calculated. According to the aggregation characteristics of transmission tower pixels, some potential transmission tower pixels with small spatial densities are removed. The remained potential transmission tower pixels are considered as candidate transmission tower pixels. The candidate transmission tower pixels are grouped by the nearest neighbour scheme such that in each group the distance between pixels is under a given threshold. Thus, each of the groups is viewed as a quasi-transmission tower. Convex-hulls of quasi-transmission towers are built, and then Minimum Bounding Rectangle (MBR) for each convex-hull is generated. According to the rectangle aspect ratios of MBRs, the real transmission towers are extracted. C-band HH-polarization GaoFen-3 (GF-3) amplitude images are used for experiments and four of the most popular transmission tower detection algorithms are selected as comparing algorithms to validate the proposed algorithms. The detection performance of transmission towers is evaluated with detection rate and quality factor. Experimental results verify that the proposed algorithm can efficiently and accurately detect transmission towers while maintaining the transmission tower geometry to a certain extent, which indicates that the proposed algorithm is efficient and promising.

Aims and Hypothesis: NEDD9 is highly expressed in gastric cancer and has a significant involvement in its pathogenesis. However, the mechanism behind hypoxia-promoted cancer cell migration and its regulation because of NEDD9 is still unknown. The aim of this study is to investigate the involvement of NEDD9 in gastric cancer cell migration under hypoxia and explore the underlying potential molecular mechanisms.MethodsCell motility was measured by wound healing and transwell assay. NEDD9 and MICAL1 expressions were examined by western blot analysis. Interaction between NEDD9 and MICAL1 was assessed by immunohistochemistry and co-immunoprecipitation assay, respectively. Cells were transfected with plasmids or siRNA to upregulate or downregulate the expression of NEDD9 and MICAL1. Rac1, Cdc42, and RhoA activation was assessed by pulldown assay.ResultsThe mRNA and protein level of NEDD9 increased as a result of hypoxia in gastric cancer cell lines BGC-823 and SGC-7901 while decreased levels of NEDD9 caused reduced cell migratory potential in response to hypoxia. Hypoxia also caused the enhancement of MICAL1 expression. Furthermore, it was revealed that there is a positive correlation between NEDD9 and MICAL1 protein while hypoxia played role in increasing their interaction. Under hypoxic conditions, silencing of NEDD9 caused reduction in the stability of MICAL1 protein, while depletion of MICAL1 also inhibited the migration of NEDD9-overexpressing gastric cancer cells. In addition, silencing of NEDD9 or MICAL1 expression reversed the increased GTP forms of Rac1 and Cdc42 in hypoxic cells. However, only the upregulation of Rac1-GTP level was observed in gastric cancer cells that were already overexpressed by MICAL1.ConclusionIn all, it is concluded that MICAL1 is regulated by NEDD9 that facilitates hypoxia-induced gastric cancer cell migration via Rac1-dependent manner.

Wuhan is an important city in central China, with a rapid development that has led to increasingly serious land subsidence over the last decades. Most of the existing Interferometric Synthetic Aperture Radar (InSAR) subsidence monitoring studies in Wuhan are either short-term investigations—and thus can only detect this process within limited time periods—or combinations of different Synthetic Aperture Radar (SAR) datasets with temporal gaps in between. To overcome these constraints, we exploited nearly 300 high-resolution COSMO-SkyMed StripMap HIMAGE scenes acquired between 2012 and 2019 to monitor the long-term subsidence process affecting Wuhan and to reveal its spatiotemporal variations. The results from the Persistent Scatterer Interferometric SAR (PSInSAR) processing highlight several clearly observable subsidence zones. Three of them (i.e., Houhu, Xinrong, and Guanggu) are affected by serious subsidence rates and non-linear temporal behavior, and are investigated in this paper in more detail. The subsidence in Houhu is caused by soft soil consolidation and compression. Soil mechanics are therefore used to estimate when the subsidence is expected to finish and to calculate the degree of consolidation for each year. The COSMO-SkyMed PSInSAR results indicate that the area has entered the late stage of consolidation and compression and is gradually stabilizing. The subsidence curve found for the area around Xinrong shows that the construction of an underground tract of the subway Line 21 caused large-scale settlement in this area. The temporal granularity of the PSInSAR time series also allows precise detection of a rebound phase following a major flooding event in 2016. In the southern industrial park of Guanggu, newly detected subsidence was found. The combination of the subsidence curve with an optical time-series image analysis indicates that urban construction is the main trigger of deformation in this area. While this study unveils previously unknown characters of land subsidence in Wuhan and clarifies the relationship with the urban causative factors, it also proves the benefits of non-linear PSInSAR in the analysis of the temporal evolution of such processes in dynamic and expanding cities.

The excellent generalization ability of deep learning methods, e.g., convolutional neural networks (CNNs), depends on a large amount of training data, which is difficult to obtain in industrial practices. Data augmentation is regarded commonly as an effective strategy to address this problem. In this paper, we attempt to construct a crack detector based on CNN with twenty images via a two-stage data augmentation method. In detail, nine data augmentation methods are compared for crack detection in the model training, respectively. As a result, the rotation method outperforms these methods for augmentation, and by an in-depth exploration of the rotation method, the performance of the detector is further improved. Furthermore, data augmentation is also applied in the inference process to improve the recall of trained models. The identical object has more chances to be detected in the series of augmented images. This trick is essentially a performance–resource trade-off. For more improvement with limited resources, the greedy algorithm is adopted for searching a better combination of data augmentation. The results show that the crack detectors trained on the small dataset are significantly improved via the proposed two-stage data augmentation. Specifically, using 20 images for training, recall in detecting the cracks achieves 96% and Fext(0.8), which is a variant of F-score for crack detection, achieves 91.18%.

Standard Bayesian filtering algorithms only work well when the statistical properties of system noises are exactly known. However, this assumption is not always plausible in real target tracking applications. In this paper, we present a new estimation approach named adaptive fifth-degree cubature information filter (AFCIF) for multi-sensor bearings-only tracking (BOT) under the condition that the process noise follows zero-mean Gaussian distribution with unknown covariance. The novel algorithm is based on the fifth-degree cubature Kalman filter and it is constructed within the information filtering framework. With a sensor selection strategy developed using observability theory and a recursive process noise covariance estimation procedure derived using the covariance matching principle, the proposed filtering algorithm demonstrates better estimation accuracy and filtering stability. Simulation results validate the superiority of the AFCIF.

We established and validated a radiant thermal test system using a miniaturized quartz lamp heating device designed with a novel search algorithm to meet aero-engine blade heating requirements. The device can perform rapid, high-temperature gradient tests on new-material aero-engine blades, which cannot be achieved through electromagnetic induction. The algorithm, derived from the Monte Carlo method (MCM) and pattern search, can solve the problems of the classical iterative search algorithm by searching lamp parameters and reducing the algorithm's time complexity. These searched power parameters enable the system's closed-loop control to achieve temperature gradients easily. The corresponding heating process was also simulated using commercial numerical analysis software, serving as a numerical validation for the algorithm-based system. The device could meet the thermal fatigue test requirements for the blade at six different temperature control points, including a maximum temperature exceeding 1150 K, a maximum temperature difference exceeding 160 K within 20 mm, and a heating rate exceeding 30 K/s. Thus, the device provides a promising technique for rapid, high-temperature heat treatment of complex small components, and the algorithm makes designing miniaturized quartz lamp heating devices more accessible and versatile for small components. [ABSTRACT FROM AUTHOR]