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Abstract The intelligent fault diagnosis model has made a significant development, whose high-precision results rely on a large amount of labeled data. However, in the actual industrial environment, it is very difficult to obtain a large amount of labeled data. It will make it difficult for the fault diagnosis model to converge with limited labeled industrial data. To address this paradox, we propose a novel unsupervised domain adaptation framework (M-Net) for fault diagnosis of rotating machinery, which only requires unlabeled industrial data. The M-Net will be pretrained using the labeled data, which can be accessed through the labs. In this stage, we propose a multi-scale feature extractor that can extract and fuse multi-scale features. This operation will generalize the features further. Then, we will align the distribution of the labeled data and unlabeled industrial data using the generator model based on multi-kernel maximum mean discrepancy. This will reduce the distribution distance between the labeled data and the unlabeled industrial data. For now, the unsupervised domain adaptation problem has shifted to a semi-supervised domain adaptation problem. The results, obtained through experimental comparison, demonstrate that the M-Net can achieve an accuracy of over 99.99% with labeled data and a maximum transfer accuracy of over 99% with unlabeled industrial data.

Incorporating green and low-carbon building targets into the rural planning management system requires scientific and quantitative methods for assessing carbon emissions from rural land use. At present, the research in this field mainly focuses on urban areas, but there are fewer theoretical and practical studies on the assessment of carbon emissions from rural land use. This paper proposes a new carbon emission assessment method based on land use modes, and the model can not only assess carbon emissions but also directly reflect the carbon emission intensity of different land use spaces in rural areas and guide the carbon emission control of construction land in village planning. In this paper, we take suburban rural areas in Hunan Province as an example and establish a land use carbon emission assessment model with 13 indicators in five dimensions: total carbon emission, carbon emission efficiency, carbon emission intensity per unit of land use, carbon emission structure of land use, and carbon emission intensity of buildings, based on the bottom-up field research data. We practised our method in Jinlong Town, Hunan Province, and gave examples of model applications. It was found that the carbon emission calculation method based on the carbon emission intensity of land use can be used to calculate the current status of carbon emissions in different villages in Jinlong Town. At the same time, the carbon emission assessment results can be used as a scientific basis for carbon emission control in detailed village planning in Jinlong Town. In general, the carbon emission assessment model can complete the assessment of land carbon emissions in rural areas and provide a low-carbon land use management tool for the government.

One of human brain’s remarkable traits lies in its capacity to dynamically coordinate the activities of multiple brain regions or networks, adapting to an externally changing environment. Studying the dynamic functional brain networks (DFNs) and their role in perception, assessment, and action can significantly advance our comprehension of how the brain responds to patterns of sensory input. Movies provide a valuable tool for studying DFNs, as they offer a naturalistic paradigm that can evoke complex cognitive and emotional experiences through rich multimodal and dynamic stimuli. However, most previous research on DFNs have predominantly concentrated on the resting-state paradigm, investigating the topological structure of temporal dynamic brain networks generated via chosen templates. The dynamic spatial configurations of the functional networks elicited by naturalistic stimuli demand further exploration. In this study, we employed an unsupervised dictionary learning and sparse coding method combing with a sliding window strategy to map and quantify the dynamic spatial patterns of functional brain networks (FBNs) present in naturalistic functional magnetic resonance imaging (NfMRI) data, and further evaluated whether the temporal dynamics of distinct FBNs are aligned to the sensory, cognitive, and affective processes involved in the subjective perception of the movie. The results revealed that movie viewing can evoke complex FBNs, and these FBNs were time-varying with the movie storylines and were correlated with the movie annotations and the subjective ratings of viewing experience. The reliability of DFNs was also validated by assessing the Intra-class coefficient (ICC) among two scanning sessions under the same naturalistic paradigm with a three-month interval. Our findings offer novel insight into comprehending the dynamic properties of FBNs in response to naturalistic stimuli, which could potentially deepen our understanding of the neural mechanisms underlying the brain’s dynamic changes during the processing of visual and auditory stimuli.

With the implementation of China’s rural revitalization strategy, the societies and economies of villages have been comprehensively developed, but the carbon emissions in rural areas have also been increasing year by year. Therefore, low-carbon control of the rural spatial form has become an important element of rural revitalization. This paper takes 18 suburban integrated villages in the plain terrain within Hunan Province, China, as the research object, quantifies the spatial morphology indicators of the overall rural community and the neighborhood building groups, and investigates the relationship between rural spatial form and carbon emissions in plain terrain, aiming to clarify the content of low-carbon control in rural spatial planning. The main conclusions are as follows. (1) The correlation between spatial form and carbon emissions at different levels of suburban integrated villages is “total volume form > neighborhood building groups combination form > overall layout form > neighborhood connection form”. When the scale of the villages is fixed, the spatial layout of the neighborhood building groups has a more direct influence on the carbon emissions of the residents. (2) The building density in the overall spatial form of the village has the greatest influence on the carbon emissions of the suburban integrated villages, and it is positively correlated. (3) There is a negative correlation between the form of neighborhood building groups and carbon emissions within a certain range. When the distance between the front and back of a building is 8–12 m, the carbon emissions of the building decrease with the increase in the degree of aggregation on the building, but when the distance between the front and back of a building reaches 12 m or more, the influence of the group layout form on the carbon emissions of the building is weakened. (4) Finally, based on the principle of “macro-control quantity and meso-control shape”, this paper proposes new control content and indicators for Hunan’s rural territorial space planning, which can provide a reference for low-carbon control in rural space form planning with suburban integration.

Machine vision can prevent additional stress on yarn caused by contact measurement, as well as the risk of hairiness and breakage. However, the speed of the machine vision system is limited by image processing, and the tension detection method based on the axially moving model does not take into account the disturbance on yarn caused by motor vibrations. Thus, an embedded system combining machine vision with a tension observer is proposed. The differential equation for the transverse dynamics of the string is established using Hamilton’s principle and then solved. A field-programmable gate array (FPGA) is used for image data acquisition, and the image processing algorithm is implemented using a multi-core digital signal processor (DSP). To obtain the yarn vibration frequency in the axially moving model, the brightest centreline grey value of the yarn image is put forward as a reference to determine the feature line. The calculated yarn tension value is then combined with the value obtained using the tension observer based on an adaptive weighted data fusion method in a programmable logic controller (PLC). The results show that the accuracy of the combined tension is improved compared with the original two non-contact methods of tension detection at a faster update rate. The system alleviates the problem of inadequate sampling rate using only machine vision methods and can be applied to future real-time control systems.

Naturalistic functional magnetic resonance imaging (NfMRI) has become an effective tool to study brain functional activities in real-life context, which reduces the anxiety or boredom due to difficult or repetitive tasks and avoids the problem of unreliable collection of brain activity caused by the subjects’ microsleeps during resting state. Recent studies have made efforts on characterizing the brain’s hierarchical organizations from fMRI data by various deep learning models. However, most of those models have ignored the properties of group-wise consistency and inter-subject difference in brain function under naturalistic paradigm. Another critical issue is how to determine the optimal neural architecture of deep learning models, as manual design of neural architecture is time-consuming and less reliable. To tackle these problems, we proposed a two-stage deep belief network (DBN) with neural architecture search (NAS) combined framework (two-stage NAS-DBN) to model both the group-consistent and individual-specific naturalistic functional brain networks (FBNs), which reflected the hierarchical organization of brain function and the nature of brain functional activities under naturalistic paradigm. Moreover, the test-retest reliability and spatial overlap rate of the FBNs identified by our model reveal better performance than that of widely used traditional methods. In general, our model provides a promising method for characterizing hierarchical spatiotemporal features under the natural paradigm.

Metal component surfaces are random textured and non-smooth. There are many stains on the surface of metal component that are similar to the gray scale of the scratches. The scratches have non-uniform gray distribution, various shapes, and low contrast in their background, posing challenges in accurate scratch detection. This paper presents a method for detecting weak scratches on metal component surfaces based on deep convolutional neural networks (DCNNs). First, a DCNN is trained using labeled scratch images. Then, the scratches and some faults are detected by the trained DCNN, and most of the faults can be removed through properly thresholding based on the size of connected regions. Finally, the scratch length united in the number of pixels is obtained by the skeleton extraction. The experimental results show that the proposed method can effectively deal with background noise, thereby achieving accurate scratch detection.

This paper presents an approach to implement multi-parameter (i.e., pressure, temperature, vibration, current, and liquid level) signals for fault diagnosis of the reciprocating compressor (RC). Due to the complexity of structure and motion of such compressor, the acquired signals involve transient impacts and noises. This causes the useful information to be corrupted and makes it difficult to diagnose the fault patterns accurately. A component estimating empirical mode decomposition (CEEMD) method is proposed to remove the random noise and improve data quality. Furthermore, a new template matching algorithm called de-dimension template with double-loop correction (DDT-DLC) is applied to diagnose the fault pattern contained in the time series signals. The DDT employs a judging criterion for key characterization parameters extraction and a multicellular parameter fusion method to reduce the dimension of the matching template, and then, the DLC supplies a double-loop correction algorithm to build a parameter state array computing model of the time series data by adjusting the dynamic factors. The proposed approach is validated with three fault patterns and the healthy pattern in a two-stage reciprocating air compressor. To confirm the superiority of the proposed method, its performance is compared with that of the traditional methods. The results have indicated that the proposed approach is of highly diagnostic accuracy and shortly computing time in the fault diagnosis.

Nedd4 family interacting protein 1 (Ndfip1) is an adaptor of Nedd4-family ubiquitin ligases. Experimental results showed that Ndfip1 had a potential neuroprotective effect in neurology diseases. However, the neuroprotective effect and the underlying mechanisms of Ndfip1 in Parkinson's disease (PD) have not yet been fully elucidated. Therefore, in this study, we explored the neuroprotective effect of Ndfip1 against mitochondrial complex I inhibitor rotenone in a human dopaminergic neuroblastoma SH-SY5Y cell line and further elucidated its possible underlying mechanisms. Our results showed that rotenone could induce the up-regulation of α-synuclein (α-syn) in both mRNA and protein levels. The expression of Ndfip1 decreased at 24 h after rotenone treatment. Further study showed that high expression of Ndfip1 could protect SH-SY5Y cells against rotenone-induced neurotoxicity and antagonize the rotenone-induced increase in α-syn protein levels. In addition, high expression of Ndfip1 inhibited rotenone-induced increase in the protein levels of caspase-3 and decrease in tyrosine hydroxylase (TH). Further study showed that Ndfip1 did not affect the protein expression of iron regulatory protein 1 (IRP1), transferrin receptor 1 (TfR1), while antagonized the increase in protein levels of P62 and ferritin L caused by rotenone. Our findings provide specific identification of Ndfip1 proteins to inhibit the increase of α-syn in rotenone-induced SH-SY5Y cells. Ndfip1 might be a new theoretical drug target for the prevention and treatment of PD.

An outstanding chess player needs to accumulate massive visual and spatial information for chess configurations. Visual motion area (MT) is considered as a brain region specialized for visual motion perception and visuospatial attention processing. However, how long-term chess training shapes the functional connectivity patterns of MT, especially its functional subregions, has rarely been investigated. In our study, using resting-state functional connectivity (RSFC) and Granger causality analysis (GCA), we studied the changed functional couplings of MT subregions between 28 chess master players and 27 gender- and age-matched healthy novices to reveal the neural basis of long-term professional chess training. RSFC analysis identified decreased functional connections between right dorsal-anterior subregion (CI1.R) and left angular gyrus, and increased functional connections between right ventral-anterior MT subregion (CI2.R) and right superior temporal gyrus in chess experts. Moreover, GCA analyses further found increased mutual interactions of left angular gyrus and CI1.R in chess experts compared to novice players. These findings demonstrate that long-term professional chess training could enhance spatial perception and reconfiguration and semantic processing efficiency for superior performance.

The spotted alfalfa aphid [Therioaphis trifolii (Monell), Homoptera, Drepanosiphidae] is a well-known destructive pest that can significantly reduce alfalfa yields. Herein, the morphology of antennal sensilla of T. trifolii has been examined by using scanning electron microscopy and the ultrastructure of sensilla stellate and placoidea was described by transmission electron microscopy. Stellate sensilla, placoid sensilla, and coeloconic sensilla were found on the 6th segment, and a single sensillum placoidea was located on the 5th segment. Placoid sensilla were also present on the 3rd antennal segment of alate and apterous aphids, and the number was similar between two morphs. Two types of trichoid sensilla and coeloconic sensilla were found on the antennae, respectively. The results of ultrastructure showed that stellate sensilla are innervated by three neurons, while placoid sensilla present three groups of neurons, equipped with 2–3 dendrites in each neuron group. Immunocytochemical localization of odorant-binding proteins (OBPs) was performed on ultrathin sections of sensilla stellate and placoidea, and we observed that the antiserum against OBP6 intensively labeled all placoid sensilla from both primary and secondary rhinaria. OBP7 and OBP8 could also be detected in placoid sensilla, but less strongly than OBP6. In addition, OBP6, OBP7, and OBP8 were densely labeled in stellate sensilla, suggesting OBP6, OBP7, and OBP8 may sense alarm pheromone germacrene A in T. trifolii.

Knowledge of the bubble-forming regime is important for studying the mechanism of gas-liquid two-phase flow. The process of bubble formation through an underwater nozzle at the bottom of a rectangular tank was observed using a high-speed camera. Five bubble-forming regimes were classified by considering both the bubble interaction and the periodic behavior. To automatically identify the bubble-forming regimes, 22 image textural features were extracted based on the grey-level co-occurrence matrix and grey-level gradient co-occurrence matrix. A feature selection method, the mathematical correlation weight algorithm (MCWA), was proposed to select the optimal textural feature subset for bubble-forming regime recognition. Experimental results demonstrated that the identification rates based on the feature subset selected by MCWA were 99.81% and 99.24% for support vector machine and artificial neural network, respectively, which were higher than those by the between-class distance method and the Fisher algorithm. The MCWA proposed in this paper is an effective feature selection method and can be widely used in pattern recognition.

Fringe projection profilometry (FPP) is a popular optical 3-D imaging approach, in which the images of deformed fringe patterns are analyzed to extract object surfaces (i.e., height maps of object surfaces). As an object surface normally does not change independently, height correlations of an object surface can be used to denoise and improve the measurement performance of the FPP. This paper investigates the issue of exploiting height correlations in FPP fringe pattern analysis. The challenge lies in that height correlations are unknown and they are different from object to object. In addition, the problem of interest is normally in a large scale. In this paper, we use autoregressive (AR) models with unknown parameters to model the unknown height correlations and formulate the FPP analysis problem (with height correlations exploited) under the framework of expectation maximization (EM). With EM, the unknown AR model parameters are determined based on observations, and the estimates of the heights with their correlations exploited can also be extracted. To deal with the large-scale problem, a message passing-based implementation of the formulated EM problem is studied and the relevant message updating rules are developed. The proposed approach has a linear complexity and it allows parallel processing due to the nature of message passing. Simulation and experimental results demonstrate a significant performance improvement by the proposed approach.

Wing dimorphism is a widespread phenomenon in insects with an associated trade-off between flight capability and fecundity. Despite the molecular underpinnings of phenotypic plasticity that has already been elucidated, it is still not fully understood. In this study, we focused on the differential proteomics profiles between alate and apterous morphs of the pea aphid, Acyrthosiphon pisum at the fourth instar nymph and adult stages, using isobaric tags for relative and absolute quantitation (iTRAQ) in a proteomic-based approach. A total of 5,116 protein groups were identified and quantified in the three biological replicates, of which 836 were differentially expressed between alate and apterous morphs. A bioinformatics analysis of differentially expressed protein groups (DEPGs) was performed based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). KEGG enrichment analysis showed that DEPGs mainly participated in energy metabolism, amino acid biosynthesis and metabolism, and signal sensing and transduction. To verify the reliability of proteomics data, the transcriptional expression of 29 candidates of differentially expressed proteins were analyzed by quantitative real-time PCR (qRT-PCR), showing that 26 genes were consistent with those at proteomic levels. In addition, differentially expressed proteins between winged and wingless morphs that were linked to olfactory sense were investigated. Quantitative real-time PCR revealed the tissue- and morph-biased expression profiles. These results suggested that olfactory sense plays a key role in wing dimorphism of aphids. The comparative proteomic analysis between alate and apterous morphs of the pea aphid provides a novel insight into wing development and dimorphism in aphids and will help facilitate our understanding of these concepts at molecular levels.

Complicated molecular and cellular processes take place in a spatiotemporally heterogeneous and precisely regulated pattern in the human fetal brain, yielding not only dramatic morphological and microstructural changes, but also macroscale connectomic transitions. As the underlying substrate of the fetal brain structural network, both dynamic neuronal migration pathways and rapid developing fetal white matter (WM) fibers could fundamentally reshape early fetal brain connectome. Quantifying structural connectome development can not only shed light on the brain reconfiguration in this critical yet rarely studied developmental period, but also reveal alterations of the connectome under neuropathological conditions. However, transition of the structural connectome from the mid-fetal stage to birth is not yet known. The contribution of different types of neural fibers to the structural network in the mid-fetal brain is not known, either. In this study, diffusion tensor magnetic resonance imaging (DT-MRI or DTI) of 10 fetal brain specimens at the age of 20 postmenstrual weeks (PMW), 12 in vivo brains at 35 PMW, and 12 in vivo brains at term (40 PMW) were acquired. The structural connectome of each brain was established with evenly parcellated cortical regions as network nodes and traced fiber pathways based on DTI tractography as network edges. Two groups of fibers were categorized based on the fiber terminal locations in the cerebral wall in the 20 PMW fetal brains. We found that fetal brain networks become stronger and more efficient during 20–40 PMW. Furthermore, network strength and global efficiency increase more rapidly during 20–35 PMW than during 35–40 PMW. Visualization of the whole brain fiber distribution by the lengths suggested that the network reconfiguration in this developmental period could be associated with a significant increase of major long association WM fibers. In addition, non-WM neural fibers could be a major contributor to the structural network configuration at 20 PMW and small-world network organization could exist as early as 20 PMW. These findings offer a preliminary record of the fetal brain structural connectome maturation from the middle fetal stage to birth and reveal the critical role of non-WM neural fibers in structural network configuration in the middle fetal stage.

A new solution to the high-quality 3D reverse modeling problem of complex surfaces for fine workpieces is presented using a laser line-scanning sensor. Due to registration errors, measurement errors, deformations, etc., a fast and accurate method is important in machine vision measurement. This paper builds a convenient and economic multi-view stereo (MVS) measurement system based on a linear stage and a rotary stage to reconstruct the measured object surface completely and accurately. In the proposed technique, the linear stage is used to generate the trigger signal and synchronize the laser sensor scanning; the rotary stage is used to rotate the object and obtain multi-view point cloud data, and then the multi-view point cloud data are registered and integrated into a 3D model. The measurement results show a measurement accuracy of 0.075 mm for a 360° reconstruction in 34 s, and some evaluation experiments were carried out to demonstrate the validity and practicability of the proposed technique.

This paper proposes a deep convolutional neural network (CNN) -based technique for the detection of micro defects on metal screw surfaces. The defects we consider include surface damage, surface dirt, and stripped screws. Images of metal screws with different types of defects are collected using industrial cameras, which are then employed to train the designed deep CNN. To enable efficient detection, we first locate screw surfaces in the pictures captured by the cameras, so that the images of screw surfaces can be extracted, which are then input to the CNN-based defect detector. Experiment results show that the proposed technique can achieve a detection accuracy of 98%; the average detection time per picture is 1.2 s. Comparisons with traditional machine vision techniques, e.g., template matching-based techniques, demonstrate the superiority of the proposed deep CNN-based one.

Phase retrieval from single frame projection fringe patterns, a fundamental and challenging problem in fringe projection measurement, attracts wide attention and various new methods have emerged to address this challenge. Many phase retrieval methods are based on the decomposition of fringe patterns into a background part and a fringe part, and then the phase is obtained from the decomposed fringe part. However, the decomposition results are subject to the selection of model parameters, which is usually performed manually by trial and error due to the lack of decomposition assessment rules under a no ground truth data situation. In this paper, we propose a cross-correlation index to assess the decomposition and phase retrieval results without the need of ground truth data. The feasibility of the proposed metric is verified by simulated and real fringe patterns with the well-known Fourier transform method and recently proposed Shearlet transform method. This work contributes to the automatic phase retrieval and three-dimensional (3D) measurement with less human intervention, and can be potentially employed in other fields such as phase retrieval in digital holography.

The non-contact three-dimensional measurement and reconstruction techniques have played a significant role in the packaging and transportation of precious cultural relics. This paper develops a structured light based three-dimensional measurement system, with a low-cost for cultural relics packaging. The structured light based system performs rapid measurements and generates 3D point cloud data, which is then denoised, registered and merged to achieve accurate 3D reconstruction for cultural relics. The multi-frequency heterodyne method and the method in this paper are compared. It is shown that the relative accuracy of the proposed low-cost system can reach a level of 1/1000. The high efficiency of the system is demonstrated through experimental results.

In an attempt to explore a new class of epidermal growth factor receptor (EGFR) inhibitors, novel 4-stilbenylamino quinazoline derivatives were synthesized through a Dimorth rearrangement reaction and characterized via IR, 1H-NMR, 13C-NMR, and HRMS. Methoxyl, methyl, halogen, and trifluoromethyl groups on stilbeneamino were detected. These synthesized compounds were evaluated for antitumor activity in vitro against eight human tumor cell lines with an MTS assay. Most synthesized compounds exhibited more potent activity (IC50 = ~2.0 μM) than gefitinib (IC50 > 10.0 μM) against the A431, A549, and BGC-823 cell lines. Docking methodology of compound 6c and 6i binding into the ATP site of EGFR was carried out. The results showed that fluorine and trifluoromethyl played an important role in efficient cell activity.