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Accurate urban land cover information is crucial for effective urban planning and management. While convolutional neural networks (CNNs) demonstrate superior feature learning and prediction capabilities using image-level annotations, the inherent mixed-category nature of input image patches leads to classification errors along object boundaries. Fully convolutional neural networks (FCNs) excel at pixel-wise fine segmentation, making them less susceptible to heterogeneous content, but they require fully annotated dense image patches, which may not be readily available in real-world scenarios. This paper proposes an object-based semi-supervised spatial attention residual UNet (OS-ARU) model. First, multiscale segmentation is performed to obtain segments from a remote sensing image, and segments containing sample points are assigned the categories of the corresponding points, which are used to train the model. Then, the trained model predicts class probabilities for all segments. Each unlabeled segment's probability distribution is compared against those of labeled segments for similarity matching under a threshold constraint. Through label propagation, pseudo-labels are assigned to unlabeled segments exhibiting high similarity to labeled ones. Finally, the model is retrained using the augmented training set incorporating the pseudo-labeled segments. Comprehensive experiments on aerial image benchmarks for Vaihingen and Potsdam demonstrate that the proposed OS-ARU achieves higher classification accuracy than state-of-the-art models, including OCNN, 2OCNN, and standard OS-U, reaching an overall accuracy (OA) of 87.83% and 86.71%, respectively. The performance improvements over the baseline methods are statistically significant according to the Wilcoxon Signed-Rank Test. Despite using significantly fewer sparse annotations, this semi-supervised approach still achieves comparable accuracy to the same model under full supervision. The proposed method thus makes a step forward in substantially alleviating the heavy sampling burden of FCNs (densely sampled deep learning models) to effectively handle the complex issue of land cover information identification and classification. [ABSTRACT FROM AUTHOR]

The band gap is a key parameter in semiconductor materials that is essential for advancing optoelectronic device development. Accurately predicting band gaps of materials at low cost is a significant challenge in materials science. Although many machine learning (ML) models for band gap prediction already exist, they often suffer from low interpretability and lack theoretical support from a physical perspective. In this study, we address these challenges by using a combination of traditional ML algorithms and the 'white-box' sure independence screening and sparsifying operator (SISSO) approach. Specifically, we enhance the interpretability and accuracy of band gap predictions for binary semiconductors by integrating the importance rankings of support vector regression (SVR), random forests (RF), and gradient boosting decision trees (GBDT) with SISSO models. Our model uses only the intrinsic features of the constituent elements and their band gaps calculated using the Perdew–Burke–Ernzerhof method, significantly reducing computational demands. We have applied our model to predict the band gaps of 1208 theoretically stable binary compounds. Importantly, the model highlights the critical role of electronegativity in determining material band gaps. This insight not only enriches our understanding of the physical principles underlying band gap prediction but also underscores the potential of our approach in guiding the synthesis of new and valuable semiconductor materials. [ABSTRACT FROM AUTHOR]

Sucrose synthase (SUS) and sucrose phosphate synthase (SPS) are essential in plant sucrose metabolism. The potato is an important crop worldwide, but systematic analyses of the StSUS and StSPS gene families in potatoes are still lacking. Ten sucrose metabolism-related genes were identified in this study. The SUSs and SPSs could each be split into three subgroups through phylogenetic analysis. StSUSIc was the most highly expressed gene in different developmental tissues. Ka/Ks analysis showed that StSUSIb and StSUSIc were subjected to more-significant homozygous selection pressure. Our cis-acting element analysis of the StSUS and StSPS promoter sequences showed four elements: defense- and stress-responsive, hormone-responsive, light-responsive, and transcription factor elements. The expression of StSUS and StSPS genes was found to be regulated by circadian rhythm. In the treatments of 1% to 5% sucrose, glucose, and fructose, the expression of StSUS and StSPS family genes was enhanced by sucrose, but inhibited at high-glucose and fructose concentrations. This study identified six StSUS and four StSPS genes and analyzed their gene structure, conserved motifs, chromosome position, promoter elements, phylogenetic tree, and tissue-specific expression patterns. Our results will motivate more research into the biological process underlying the genes of sucrose metabolism in potatoes. [ABSTRACT FROM AUTHOR]

Axial piston pumps are critical components of hydraulic systems due to their compact design and high volumetric efficiency, making them widely used. However, they are prone to failure in harsh environments characterized by high pressure and heavy loads over extended periods. Therefore, detecting abnormal behavior in axial piston pumps is of significant importance. Traditional detection methods often rely on vibration signals from the pump casings; however, these signals are susceptible to external environmental interference. In contrast, pressure signals exhibit greater stability. In this study, we propose a novel anomaly detection method for axial piston pumps, referred to as DTW-RCK-IF, which combines dynamic time warping (DTW) for data segmentation, a random convolutional kernel (RCK) for feature extraction, and isolation forest (IF) for anomaly detection using pressure signals. The model is trained using normal operating data to enable the effective detection of abnormal states. First, the DTW algorithm is employed to segment the raw data, ensuring a high degree of similarity between the segmented data. Next, the random convolutional kernel approach is used in a convolutional neural network for feature extraction, resulting in features that are representative of normal operating conditions. Finally, the isolation forest algorithm calculates the anomaly scores for anomaly detection. Experimental simulations on axial piston pumps demonstrate that, compared with vibration signals, the DTW-RCK-IF approach using pressure signals yields superior results in detecting abnormal data, with an average F1 score of 98.79% and a good fault warning effect. Validation using the publicly available CWRU-bearing and XJTU-SY-bearing full-life datasets further confirms the effectiveness of this method, with average F1 scores of 99.35% and 99.73%, respectively, highlighting its broad applicability and potential for widespread use. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) remains the first cause of mortality globally. Diet plays a fundamental role in cardiovascular health and is closely linked to the development of CVD. Numerous human studies have provided evidence on the relationship between diet and CVD. By discussing the available findings on the dietary components that potentially influence CVD progression and prevention, this review attempted to provide the current state of evidence on healthy dietary choices for CVD. We focus on the effects of individual macronutrients, whole food products, and dietary patterns on the risks of CVD, and the data from population-based trials, observational studies, and meta-analyses are summarized. Unhealthy dietary habits, such as high intake of saturated fatty acids, sugar-sweetened beverages, red meat, and processed meat as well as high salt intake are associated with the increased risk of CVD. Conversely, increased consumption of plant-based components such as dietary fiber, nuts, fruits, and vegetables is shown to be effective in reducing CVD risk factors. The Mediterranean diet appears to be one of the most evidence-based dietary patterns beneficial for CVD prevention. However, there is still great debate regarding whether the supplementation of vitamins and minerals confers cardioprotective benefits. This review provides new insights into the role of dietary factors that are harmful or protective in CVD, which can be adopted for improved cardiovascular health. [ABSTRACT FROM AUTHOR]

Spectrum-based fault localization (SBFL), a widely recognized technique in automated fault localization, has limited effectiveness due to its disregard for the internal information of the program under test suites. To overcome this limitation, a novel TrustRank-based fault localization (TRFL) technique is introduced. TRFL enhances traditional SBFL by incorporating internal data dependencies of the program under the test suite, thereby providing a more comprehensive analysis. It constructs a node-weighted program execution network and employs the TrustRank algorithm to analyze network centrality and re-rank program entities based on their suspiciousness. Furthermore, a bidirectional TrustRank algorithm (Bi-TRFL) is extended that takes into account the influence relationship between network nodes for more accurate fault localization. When applied to large-scale datasets with real faults, such as Defects4J, TRFL, and Bi-TRFL, it significantly outperforms traditional SBFL methods in fault localization. They demonstrate up to 40% and 13% improvement in Top-1 and Top-5 rankings, respectively, proving their robustness and efficiency with minimal sensitivity to related parameters. [ABSTRACT FROM AUTHOR]

This study delves into the intricate usage and interpretation issues of the Chinese term "體" (tǐ) in Xuanzang's translation of the Abhidharmakośa (AKBh[X]) by providing a Sanskrit-Chinese comparative investigation. Xuanzang's translations are pivotal in understanding certain Abhidharma scriptures, as some of them are the sole complete versions available. This study focuses on the term "體" in AKBh[X], evaluating its usage in relation to 16 corresponding Sanskrit equivalents and the instances where Xuanzang introduced "體" without a Sanskrit equivalent. The analysis uncovers translation errors, potential misinterpretations, and the lack of clarity in certain contexts, emphasizing the need for readers to be cautious and consult additional sources for a comprehensive understanding of his translations. [ABSTRACT FROM AUTHOR]

The vibration signal of a pump is often used for analysis in the study of hydraulic-pump fault diagnosis methods. In this study, for the analysis, sound signals were used, which can be used to acquire data in a non-contact manner to expand the use scenarios of hydraulic-pump fault-diagnosis methods. First, the original data are denoised using complete ensemble empirical mode decomposition with adaptive noise and the minimum redundancy maximum relevance algorithm. Second, the noise-reduced data are plotted as mean spectrogram bar graphs, and the datasets are divided. Third, the training set graphs are input into the ResNet-50 network to train the base model for fault diagnosis. Fourth, all the layers of the base model are frozen, except for the fully connected and softmax layers, and the support set graphs are used to train the base model through transfer learning. Finally, a fault diagnosis model is obtained. The model is tested using data from two test pumps, resulting in accuracies of 86.1% and 90.8% and providing evidence for the effectiveness of the proposed method for diagnosing faults in hydraulic plunger pumps. [ABSTRACT FROM AUTHOR]

Hibiscus syriacus, a woody ornamental plant with great economic value, is vulnerable to salinity. Hence, its cultivation in saline areas is severely restricted. Although grafting H. syriacus onto H. hamabo rootstock can greatly improve H. syriacus's salt resistance, the photosynthetic response of H. syriacus to grafting and salt stress remains largely unknown. To address this question, self-rooted (Hs), self-grafted (Hs/Hs), and H. hamabo-grafted (Hs/Hh) H. syriacus were exposed to 0 or 300 mM NaCl. Salt significantly reduced the net and maximum photosynthetic rates, chlorophyll content, and maximum (Fv/Fm) and actual (ΦPSII) photochemical quantum yield of photosystem II (PSII), as well as the apparent electron transport rate, in Hs and Hs/Hs. However, these reductions were largely alleviated when H. syriacus was grafted onto H. hamabo. In line with the changes in the chlorophyll fluorescence parameters, the expression of genes encoding subunits of PSII and PSI in Hs/Hh was higher than that in Hs and Hs/Hs under saline conditions. Moreover, H. hamabo rootstock grafting upregulated the genes involved in the Calvin–Benson–Bassham cycle in H. syriacus under salt conditions. These results indicate that grafting can ameliorate the inhibition of salinity on the photosynthetic capacity of H. syriacus, mainly resulting from alleviated limitations on photosynthetic pigments, photochemical efficiency, and the Calvin–Benson–Bassham cycle. [ABSTRACT FROM AUTHOR]

Circular RNA (circRNA) is a newly discovered noncoding RNA that regulates gene transcription, binds to RNA-related proteins, and encodes protein microRNAs (miRNAs). The development of molecular biomarkers such as circRNAs holds great promise in the diagnosis and prognosis of clinical disorders. Importantly, circRNA-mediated maternal-fetus risk factors including environmental (high altitude), maternal (preeclampsia, smoking, and chorioamnionitis), placental, and fetal (preterm birth and low birth weight) factors are the early origins and likely to contribute to the occurrence and progression of developmental and pediatric cardiopulmonary disorders. Although studies of circRNAs in normal cardiopulmonary development and developmental diseases have just begun, some studies have revealed their expression patterns. Here, we provide an overview of circRNAs' biogenesis and biological functions. Furthermore, this review aims to emphasize the importance of circRNAs in maternal-fetus risk factors. Likewise, the potential biomarker and therapeutic target of circRNAs in developmental and pediatric lung diseases are explored. [ABSTRACT FROM AUTHOR]

A Ni60 cladding layer with addition of 6.0% CeO2 was prepared on 35CrMoV steel by laser cladding technology. The prepared sample was placed at 500 °C, 600 °C and 700 °C for 60 min to explore the effects of heat treatment on the tribological properties of the composite coating. The microstructure, phase composition, microhardness and tribological properties of the composite coating were characterized by optical microscopy and scanning electron microscopy (SEM), X-ray diffraction (XRD), micro-Vickers hardness tester and MicroXAM-800 optical surface photometer, respectively. According to the above experimental results analysis, the main components of 6.0% CeO2/Ni60 cladding layer are γ-(Fe,Ni),Cr7C3,Cr23C6,CrB, CrFeB and Cr2Ni3. By calculating the FWHM value and the left shift of the XRD diffraction peak, it is found that the coating grains are remarkably refined and the microstructure uniformity is significantly improved under the condition of heat treatment at 500 °C. The experimental results show that the Ni60 composite coating with 6.0% CeO2 has the best friction and wear performance at 500 °C. The wearing quality of the composite coating at 500 °C was reduced by 43%. [ABSTRACT FROM AUTHOR]

Prediction of remaining useful life is crucial for mechanical equipment operation and maintenance. It ensures safe equipment operation, reduces maintenance costs and economic losses, and promotes development. Most of the remaining useful life prediction studies focus on bearings, gearboxes, and engines; however, research on hydraulic pumps remains limited. This study focuses on gear pumps that are commonly used in the hydraulic field and develops a practical method of predicting remaining useful life. The deep sparse autoencoder is used to extract multi–dimensional features. Subsequently, the feature vectors are inputted to the support vector data description to calculate the machine degradation degree at the corresponding time and obtain the health indicator curve of the machine's life cycle. In building the health state degradation curve, data are processed in an unsupervised manner to avoid the influence of artificial feature selection on the test. The method is validated on the public bearing and self–collected gear pump datasets. The results are better than those of the comparative algorithms: (1) commonly used time–frequency characteristics with principal component analysis and (2) deep sparse autoencoder with self–organizing mapping. Next, the multilayer bidirectional long–short–term memory network is trained as a prediction model using the gear pump health indicator curves obtained previously and applied to the test data. Finally, the proposed method is compared with two others of the same type and the evaluation indexes are calculated based on the prediction results of the three algorithms. From the evaluation indexes, the mean absolute error of the proposed method is reduced by 2.53, and the normalized mean squared error is reduced by 0.36. This result indicates that the prediction results of the method for the remaining useful life of the gear pump are closer to the actual situation. [ABSTRACT FROM AUTHOR]

This paper addresses the difficulty of evaluating operating status in widely used gear pumps. A method for constructing hydraulic pump health indicators and evaluating health status is proposed based on LSTM–VAE. In this study, the vibration signal data source of gear pumps was assessed in the accelerated life test. Firstly, the normalized feature vectors of the whole-life operation data of gear pumps were extracted by wavelet packet decomposition and amplitude feature extraction. Combining an LSTM algorithm with a VAE algorithm, a method for constructing hydraulic pump health indicators based on LSTM–VAE is proposed. By learning the feature vectors of gear pumps in varying health conditions, a one-dimensional HI curve of the gear pumps was obtained. Then, LSTM was used to predict the HI curve of gear pumps. According to the volume efficiency of the gear pumps, the health status of gear pumps is divided into four states: health, sub-health, deterioration, and failure. The health status of the hydraulic pump is accurately evaluated by the health indicator. Finally, the proposed method is compared with the traditional method based on feature selection and PCA dimensionality reduction. The health indicator constructed by the method proposed in this paper is superior to the traditional method in terms of tendency, robustness, and monotonicity, which proves the effectiveness of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

With the advantage of integrating distributed energy, storage and DC load with high efficiency, the DC distribution network recently attracted wide attention in the field of nearly zero energy buildings. Considering the large number of buildings and the enormous energy-saving potential, the distribution form and the revenue distribution are key factors affecting energy efficiency and operational economy. Aiming at the characteristics of AC and DC distribution topologies in nearly zero energy buildings, an energy efficiency evaluation method is proposed based on time-sequential power flow simulation, where the piecewise linear efficiency function of converters and dynamic line power loss model are established. Based on a DC distribution demonstration project, an AC power distribution system with the same boundary conditions is designed. The results show that the efficiency of DC distribution topology is about 6% higher than the AC system, while the efficiency advantage in office, hotel, commercial, educational and residential buildings in Beijing, Shenzhen and Shanghai varies from 0.6–4.96%, which shows a high correlation with the proportion of a high-power load, with Pearson correlation coefficient of 0.794. With the increase of DC load ratio and distributed power supply access capacity, the efficiency of the system will be improved accordingly. On the basis of this, a method to equalize DC distribution income in the nearly zero energy buildings power market is proposed. In addition, 15 typical scenarios of different cities were selected to evaluate efficiency and influencing factors. Finally, assessing the multi-stakeholder impact of DC retrofitting to balance the revenue generated by the DC distribution model suggests that transport costs should be raised by 8.2–13.1% to balance revenue with the equipment prices decreased by about 0.1 yuan/W. [ABSTRACT FROM AUTHOR]

Melia azedarach L. is a native tree species that can be used in a comprehensive way and is widely distributed in all provinces south of the Yellow River in China. Genetic diversity analysis of different M. azedarach germplasm sources is an important basic work for the selection, evaluation, and genetic improvement of M. azedarach germplasm resources. In this study, 100 pairs of SSR primers were designed and synthesized based on M. azedarach transcriptome data, and 16 pairs of reliable SSR primers were finally selected. The developed primers were used to analyze the genetic diversity of M. azedarach from 15 sources in 10 provinces in East, Central, and South China. The results showed that the frequency of the M. azedarach transcriptome SSR loci was high, and the distribution density was high. There were 15 sources of M. azedarach genetic diversity at a moderate level, and genetic variation was mainly present within the sources. The present study further enriches the existing SSR marker database of the M. azedarach family and can provide a reference for genetic diversity analysis and molecularly assisted breeding of M. azedarach plants at the genomic level. [ABSTRACT FROM AUTHOR]

As a hydraulic pump is the power source of a hydraulic system, predicting its remaining useful life (RUL) can effectively improve the operating efficiency of the hydraulic system and reduce the incidence of failure. This paper presents a scheme for predicting the RUL of a hydraulic pump (gear pump) through a combination of a deep convolutional autoencoder (DCAE) and a bidirectional long short-term memory (Bi-LSTM) network. The vibration data were characterized by the DCAE, and a health indicator (HI) was constructed and modeled to determine the degradation state of the gear pump. The DCAE is a typical symmetric neural network, which can effectively extract characteristics from the data by using the symmetry of the encoding network and decoding network. After processing the original vibration data segment, health indicators were entered as a label into the RUL prediction model based on the Bi-LSTM network, and model training was carried out to achieve the RUL prediction of the gear pump. To verify the validity of the methodology, a gear pump accelerated life experiment was carried out, and whole life cycle data were obtained for method validation. The results show that the constructed HI can effectively characterize the degenerative state of the gear pump, and the proposed RUL prediction method can effectively predict the degeneration trend of the gear pump. [ABSTRACT FROM AUTHOR]

Predicting the remaining useful life (RUL) of mechanical equipment can improve production efficiency while effectively reducing the life cycle cost and failure rate. This paper proposes a method for predicting the remaining service life of equipment through a combination of a deep convolutional autoencoder (DCAE) and a convolutional neural network (CNN). For rolling bearings, a health indicator (HI) could be built by combining DCAE and self-organizing map (SOM) networks, performing more advanced characterization against the original vibration data and modeling the degradation state of the rolling bearings. The HI serves as the label of the original vibration data, and the original data with such label is input into the prediction model of the RUL based on a one-dimensional convolutional neural network (1D-CNN). The model was trained for predicting the RUL of a rolling bearing. The bearing degradation dataset was evaluated to verify the method's effectiveness. The results demonstrate that the constructed HI can characterize the bearing degradation state effectively and that the method of predicting the RUL can accurately predict the bearing degradation trend. [ABSTRACT FROM AUTHOR]

Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just in time learning (JITL) method was proposed to solve this problem. First, as the research object, the non-substitute time tac-tail life experiment pressure signals of gear pumps were collected. Following the removal and denoising of the DC component of the pressure signals by the wavelet packet method, multiple characteristic indices were extracted. Subsequently, the KPCA method was used to calculate the weighted fusion of the selected feature indices. Then the state evaluation indices were extracted to characterize the performance degradation of the gear pumps. Finally, an RUL prediction method based on the k-vector nearest neighbor (k-VNN) and JITL methods was proposed. The k-VNN method refers to both the Euclidean distance and angle relationship between two vectors as the basis for modeling. The prediction results verified the feasibility and effectiveness of the proposed method. Compared to the traditional JITL RUL prediction method based on the k-nearest neighbor algorithm, the proposed prediction model of the RUL of a gear pump presents a higher prediction accuracy. The method proposed in this paper is expected to be applied to the RUL prediction and condition monitoring and has broad application prospects and wide applicability. [ABSTRACT FROM AUTHOR]

Featured Application: This work is performed for the compensation of the laser phase noise (LPN) and the equalization enhanced phase noise (EEPN) in high-capacity, long-haul, coherent optical fiber networks. Long-haul optical fiber communication employing digital signal processing (DSP)-based dispersion compensation can be distorted by the phenomenon of equalization-enhanced phase noise (EEPN), due to the reciprocities between the dispersion compensation unit and the local oscillator (LO) laser phase noise (LPN). The impact of EEPN scales increases with the increase of the fiber dispersion, laser linewidths, symbol rates, signal bandwidths, and the order of modulation formats. In this work, the phase noise cancellation (PNC) employing a radio frequency (RF) pilot tone in coherent optical transmission systems has been investigated. A 28-Gsym/s QPSK optical transmission system with a significant EEPN has been implemented, where the carrier phase recovery (CPR) was realized using the one-tap normalized least-mean-square (NLMS) estimation and the differential phase detection (DPD), respectively. It is shown that the RF pilot tone can entirely eliminate the LPN and efficiently suppress the EEPN when it is applied prior to the CPR. [ABSTRACT FROM AUTHOR]

Clinically, intrauterine hypoxia is the foremost cause of perinatal morbidity and developmental plasticity in the fetus and newborn infant. Under hypoxia, deviations occur in the lung cell epigenome. Epigenetic mechanisms (e.g., DNA methylation, histone modification, and miRNA expression) control phenotypic programming and are associated with physiological responses and the risk of developmental disorders, such as bronchopulmonary dysplasia. This developmental disorder is the most frequent chronic pulmonary complication in preterm labor. The pathogenesis of this disease involves many factors, including aberrant oxygen conditions and mechanical ventilation-mediated lung injury, infection/inflammation, and epigenetic/genetic risk factors. This review is focused on various aspects related to intrauterine hypoxia and epigenetic programming in lung development and disease, summarizes our current knowledge of hypoxia-induced epigenetic programming and discusses potential therapeutic interventions for lung disease. [ABSTRACT FROM AUTHOR]

The field of mechanical fault diagnosis has entered the era of "big data". However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved. [ABSTRACT FROM AUTHOR]

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives. [ABSTRACT FROM AUTHOR]

The grouping of parcel data based on proximity is a pre-processing step of GIS and a key link of urban structure recognition for regional function discovery and urban planning. Currently, most literature abstracts parcels into points and clusters parcels based on their attribute similarity, which produces a large number of coarse granularity functional regions or discrete distribution of parcels that is inconsistent with human cognition. In this paper, we propose a novel parcel grouping method to optimise this issue, which considers both the urban morphology and the urban functional connectivity. Infiltration behaviours of urban components provide a basis for exploring the correlation between morphology mechanism and functional connectivity of urban areas. We measured the infiltration behaviours among adjacent parcels and concluded that the occurrence of infiltration behaviours often appears in the form of groups, which indicated the practical significance of parcel grouping. Our method employed two parcel morphology indicators: the similarity of the line segments and the compactness of the distribution. The line segment similarity was used to establish the adjacent relationship among parcels and the compactness was used to optimise the grouping result in obtain a satisfactory visual expression. In our study, constrained Delaunay triangulation, Hausdorff distance, and graph theory were employed to construct the proximity, delineate the parcel adjacency matrix, and implement the grouping of parcels. We applied this method for grouping urban parcel data of Beijing and verified the rationality of grouping results based on the quantified results of infiltration behaviours. Our method proved to take a good account of infiltration behaviours and satisfied human cognition, compared with a k-means++ method. We also presented a case using Xicheng District in Beijing to demonstrate the practicability of the method. The result showed that our method obtained fine-grained groups while ensuring functional regions-integrity. [ABSTRACT FROM AUTHOR]