A convolutional discriminative feature learning method is presented for induction motor fault diagnosis. The approach firstly utilizes back-propagation (BP)-based neural network to learn local ...filters capturing discriminative information. Then, a feed-forward convolutional pooling architecture is built to extract final features through these local filters. Due to the discriminative learning of BP-based neural network, the learned local filters can discover potential discriminative patterns. Also, the convolutional pooling architecture is able to derive invariant and robust features. Therefore, the proposed method can learn robust and discriminative representation from the raw sensory data of induction motors in an efficient and automatic way. Finally, the learned representations are fed into support vector machine classifier to identify six different fault conditions. Experiments performed on a machine fault simulator indicate that compared with the current state-of-the-art methods, the proposed method shows significant performance gains, and it is effective and efficient for induction motor fault diagnosis.
•Urban population densities persistently decline over time across the world.•Urban from and expansion pattern significantly influence the decline in densities.•Fragmented form and extension expansion ...accelerate the decline in densities.•Compact form and infill expansion alleviate the decline in densities.•Compact growth is encouraged to slow down the decline in urban densities.
The faster growth rate of urban land than population results in the persistent decline in urban densities over time, increasing the cost of land in urbanization. We attempt to quantify influences of urban form and expansion pattern on the decline in densities in 200 global cities. We use openness and proximity to represent the fragmentation and compactness of built-up areas and collect proportions of expansion in three patterns (infill, extension, and leapfrog expansion). We first use correlation analysis to identify significant explanatory variables and then build regression models with the annual change rate of densities as the dependent variable. During two periods (1990–2000 and 2000–2014), urban form and expansion pattern can explain 1/3 variance of density changes. A one standard deviation increase in openness accelerates the decline in densities by 44 %–52 %. A one standard deviation increase in the proportion of infill expansion slows down its decline by 38 %–40 %, but extension expansion accelerates its decline by 20 %–27 %. We further build regression models in five world sub-regions and four groups of different-sized cities, and results verify the stable and robust influences of urban form and expansion pattern. We conclude that compact urban form and expansion pattern can slow down the decline in density. Land-use policies supporting compact growth are encouraged to slow down the decline in urban densities, particularly in fast urbanizing populous regions.
Person re-identification (ReID) is to identify pedestrians observed from different camera views based on visual appearance. It is a challenging task due to large pose variations, complex background ...clutters and severe occlusions. Recently, human pose estimation by predicting joint locations was largely improved in accuracy. It is reasonable to use pose estimation results for handling pose variations and background clutters, and such attempts have obtained great improvement in ReID performance. However, we argue that the pose information was not well utilized and hasn't yet been fully exploited for person ReID. In this work, we introduce a novel framework called Attention-Aware Compositional Network (AACN) for person ReID. AACN consists of two main components: Pose-guided Part Attention (PPA) and Attention-aware Feature Composition (AFC). PPA is learned and applied to mask out undesirable background features in pedestrian feature maps. Furthermore, pose-guided visibility scores are estimated for body parts to deal with part occlusion in the proposed AFC module. Extensive experiments with ablation analysis show the effectiveness of our method, and state-of-the-art results are achieved on several public datasets, including Market-1501, CUHK03, CUHK01, SenseReID, CUHK03-NP and DukeMTMC-reID.
We report the controlled synthesis and structures of two isomeric gold nanoclusters, whose compositions are determined to be Au23(CCBut)15 (denoted as Au 23 -1 and Au 23 -2) by single-crystal X-ray ...diffraction and matrix-assisted laser desorption ionization time-of-flight mass spectrometry. This is the first time isomerism is discovered in alkynyl-protected gold nanoclusters. The metal-to-ligand ratios in these two clusters are different from known Au n (SR) m systems and have not been observed in the Au x (CCPh) y family. This pair of isomers exhibits different optical properties, although they have similar structures and identical components. For both Au23 clusters, time-dependent density functional theory calculations revealed the frontier orbitals highest occupied molecular orbital (HOMO)–1, HOMO, and lowest unoccupied molecular orbital (LUMO) are mainly constructed from the Au15 kernel and V-shaped alkynyl–gold motifs. The HOMO → LUMO transition of Au 23 -1 is optically forbidden, whereas it is allowed in Au 23 -2. It is also found that Au 23 -2 cluster can be transformed to Au 23 -1 spontaneously under ambient conditions. This work offers further insight into the synthesis and isomerism of all-alkynyl-protected gold nanoclusters and will stimulate more investigation of isomeric metal nanoclusters.
State‐of‐the‐art proton exchange membranes (PEMs) often suffer from significantly reduced conductivity under low relative humidity, hampering their efficient application in fuel cells. Covalent ...organic frameworks (COFs) with pre‐designable and well‐defined structures hold promise to cope with the above challenge. However, fabricating defect‐free, robust COF membranes proves an extremely difficult task due to the poor processability of COF materials. Herein, a bottom‐up approach is developed to synthesize intrinsic proton‐conducting COF (IPC‐COF) nanosheets (NUS‐9) in aqueous solutions via diffusion and solvent co‐mediated modulation, enabling a controlled nucleation and in‐plane‐dominated IPC‐COF growth. These nanosheets allow the facile fabrication of IPC‐COF membranes. IPC‐COF membranes with crystalline, rigid ion nanochannels exhibit a weakly humidity‐dependent conductivity over a wide range of humidity (30–98%), 1–2 orders of magnitude higher than that of benchmark PEMs, and a prominent fuel cell performance of 0.93 W cm−2 at 35% RH and 80 °C arising from superior water retention and Grotthuss mechanism‐dominated proton conduction.
A bottom‐up approach based on the diffusion and solvent co‐mediated growth of covalent organic frameworks (COFs) is proposed to synthesize nanosheets of a highly crystalline, intrinsically proton‐conducting COF (IPC‐COF) in aqueous solution. The high‐quality IPC‐COF nanosheets allow the fabrication of defect‐free and robust IPC‐COF membranes that exhibit a weakly humidity‐dependent proton conduction and a prominent fuel‐cell performance.
Abstract
How serpentinites in the forearc mantle and subducted lithosphere become involved in enriching the subarc mantle source of arc magmas is controversial. Here we report molybdenum isotopes for ...primitive submarine lavas and serpentinites from active volcanoes and serpentinite mud volcanoes in the Mariana arc. These data, in combination with radiogenic isotopes and elemental ratios, allow development of a model whereby shallow, partially serpentinized and subducted forearc mantle transfers fluid and melt from the subducted slab into the subarc mantle. These entrained forearc mantle fragments are further metasomatized by slab fluids/melts derived from the dehydration of serpentinites in the subducted lithospheric slab. Multistage breakdown of serpentinites in the subduction channel ultimately releases fluids/melts that trigger Mariana volcanic front volcanism. Serpentinites dragged down from the forearc mantle are likely exhausted at >200 km depth, after which slab-derived serpentinites are responsible for generating slab melts.
Chitin deacetylase (CDA) can accelerate the conversion of chitin to chitosan, influencing the mechanical properties and permeability of the cuticle structures and the peritrophic membrane (PM) in ...insects. Putative Group V CDAs SeCDA6/7/8/9 (SeCDAs) were identified and characterized from beet armyworm
larvae. The cDNAs of
contained open reading frames of 1164 bp, 1137 bp, 1158 bp and 1152 bp, respectively. The deduced protein sequences showed that SeCDAs are synthesized as preproteins of 387, 378, 385 and 383 amino acid residues, respectively. It was revealed via spatiotemporal expression analysis that
were more abundant in the anterior region of the midgut. The
were down-regulated after treatment with 20-hydroxyecdysone (20E). After treatment with a juvenile hormone analog (JHA), the expression of
and
was down-regulated; in contrast, the expression of
and
was up-regulated. After silencing
(the conserved sequences of Group V CDAs) via RNA interference (RNAi), the layer of intestinal wall cells in the midgut became more compact and more evenly distributed. The vesicles in the midgut were small and more fragmented or disappeared after
were silenced. Additionally, the PM structure was scarce, and the chitin microfilament structure was loose and chaotic. It was indicated in all of the above results that Group V CDAs are essential for the growth and structuring of the intestinal wall cell layer in the midgut of
. Additionally, the midgut tissue and the PM structure and composition were affected by Group V CDAs.
A route for the construction of all‐carbon tetra‐substituted olefins through visible‐light promoted coupling of diazo compounds with iodonium ylides was developed. A wide range of all‐carbon ...tetra‐substituted olefins was obtained in moderate to good yields. The synthetic values of the current method were further approved by the synthesis of natural isolates modified alkenes and the successful transformation of final olefins into important heterocycles.
Anomaly detection has become an important topic in hyperspectral imagery (HSI) analysis over the last 20 years. HSIs usually possess complexly cluttered spectral signals due to the complicated ...conditions of the land-cover distribution. This in turn makes it difficult to obtain an accurate background estimation to distinguish the anomaly targets. The sparse learning technique provides a way to obtain an implicit background representation with the learned dictionary and corresponding sparse codes. In this paper, we explore the background/anomaly information content for each atom of the learned dictionary, from an analysis based on the frequency of the dictionary atoms for HSI reconstruction. From this perspective, we propose a novel sparsity score estimation framework for hyperspectral anomaly detection. First, an overcomplete dictionary and the corresponding sparse code matrix are obtained from the HSI. The frequency of each dictionary atom for reconstruction, which is also called the atom usage probability, is then estimated from the sparse code matrix. Finally, the estimated frequencies are transformed to the sparsity score for each pixel, which can be seen as the degree of "anomalousness." In the proposed detection framework, two strategies are proposed to enhance the diversity between the background and anomaly information in the learned dictionary: 1) dictionary-based background feature transformation and 2) dictionary iterative reweighting. A series of real-world HSI data sets is utilized to evaluate the performance of the proposed framework. The experimental results show that the proposed framework achieves a superior performance compared to some of the state-of-the-art anomaly detection methods.
Hexi Corridor is the most important base of commodity grain and producing area for cash crops. However, the rapid development of agriculture and industry has inevitably led to heavy metal ...contamination in the soils. Multivariate statistical analysis, GIS-based geostatistical methods and Positive Matrix Factorization (PMF) receptor modeling techniques were used to understand the levels of heavy metals and their source apportionment for agricultural soil in Hexi Corridor. The results showed that the average concentrations of Cr, Cu, Ni, Pb and Zn were lower than the secondary standard of soil environmental quality; however, the concentrations of eight metals (Cr, Cu, Mn, Ni, Pb, Ti, V and Zn) were higher than background values, and their corresponding enrichment factor values were significantly greater than 1. Different degrees of heavy metal pollution occurred in the agricultural soils; specifically, Ni had the most potential for impacting human health. The results from the multivariate statistical analysis and GIS-based geostatistical methods indicated both natural sources (Co and W) and anthropogenic sources (Cr, Cu, Mn, Ni, Pb, Ti, V and Zn). To better identify pollution sources of heavy metals in the agricultural soils, the PMF model was applied. Further source apportionment revealed that enrichments of Pb and Zn were attributed to traffic sources; Cr and Ni were closely related to industrial activities, including mining, smelting, coal combustion, iron and steel production and metal processing; Zn and Cu originated from agricultural activities; and V, Ti and Mn were derived from oil- and coal-related activities.
Display omitted
•PCA and kriging interpolation introduced natural and anthropogenic sources.•Ni was the most potential for human health in Hexi Corridor farmland.•Using PMF to analyze sources of heavy metals in northwestern China.•Three anthropogenic sources were identified and quantified by PMF.
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