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Aiming at the narrow and long tunnel structure, few internal features, and a large amount of point cloud data, the existing registration algorithms and commercial software registration results are not ideal, an iterative global registration algorithm is proposed for massive underground tunnel point cloud registration, which is composed of local initial pose acquisition and global adjustment. Firstly, the feature point coordinates in the point cloud are extracted, and then the station-by-station registration is performed according to the Rodrigues matrix. Finally, the registration result is considered as the initial value of the parameter, and the global adjustment of all observations is carried out. The observation values are weighted by the selection weight iteration method and the weights are constantly modified in the iteration process until the threshold conditions are met and the iteration stops. In this paper, the experimental data, made up of 85 stations of point cloud data, are from the Xiamen subway tunnel, which is about 1300 m long. When the accumulated error of station-to-station registration is too large, several stations are regarded as partial wholes, and the optimal registration is achieved through multiple global adjustments, and the registration accuracy is within 5 mm. Experimental results confirm the feasibility and effectiveness of the algorithm, which provides a new method for point cloud registration of underground space tunnel.

Scene classification for remote sensing is a popular topic, and many recent convolutional neural networks (CNNs)‐based methods have shown the great model capacity and learning ability of highly discriminative features. Given a large number of training data, CNN can extract extensive features and learn to predict a remote sensing image. However, for supervised learning tasks, deep models often rely on a large number of labelled remote sensing images, which are difficult to pre‐process. Thus, training a lightweight deep learning model is essential. Easy‐classified and hard samples may also cause an imbalance of training set and lead the model to overwhelm the loss function. Accordingly, a novel Efficient Recurrent Attention Network (ERANet) for remote sensing scene classification is proposed. Different from traditional deep learning methods, Efficientnet‐B0 is introduced as a lightweight backbone for the ARCNet framework, replacing the original one. By applying the modified efficient backbone, the low Floating Point Operations (FLOPs) and parameter numbers of the proposed ERANet are maintained. The significance of focal loss is determined and applied to address the sample imbalance problem and yield a desirable performance. Extensive experiments on several challenging remote sensing scene classification data sets prove the efficiency of the proposed ERANet.

A large density difference of a basin-type insulator in gas-insulated metal-enclosed switchgear (GIS) weakens its mechanical strength, which will threaten the safe and stable operation of GIS. In this article, an ultrasonic nondestructive testing method for density uniformity of GIS basin-type insulators was proposed. First, seven standard samples with different Al2O3 content were tested by an ultrasonic testing system, and then a mapping relationship between density and ultrasonic velocity was established. Second, the uncertainty of the testing system was obtained, and the ultrasonic velocities of 192 positions in a 126 kV three-phase basin-type insulator were measured. Finally, the density distribution of the insulator was obtained based on the mapping equation of velocity-density, and a contour plot of density was established by the cubic spline interpolation. The results show that there was a positive linear correlation between the density of standard samples and the ultrasonic velocity, and the determination coefficient was 0.992. Notably, the expanded uncertainty of ultrasonic propagation time for detecting 45 mm-thick EP/Al2O3 composites was only $0.0112~\mu \text{s}$ . The ultrasonic propagation time of detection positions in the insulator was from 31.0634 to $31.4708~\mu \text{s}$ and the difference was greater than $0.0112~\mu \text{s}$ , which illustrated that the testing system meets the requirements for detecting the density of the insulator. According to the contour plot of density, the density distribution of the insulator was shown clearly and efficiently. The density was higher at the bottom edge of the insulator and the upper part of the insert, whereas the density was smaller at the lateral upper part of the insulator and the lower part of the insert. Specifically, the maximum density of the insulator was 2.36 g/cm3 and the minimum density was 2.25 g/cm3. The feasibility of nondestructive testing density by the ultrasonic method is verified, which provides a new method for density evaluation of GIS insulators.

Face manipulation has shown remarkable advances with the flourish of Generative Adversarial Networks. However, due to the difficulties of controlling structures and textures, it is challenging to model poses and expressions simultaneously, especially for the extreme manipulation at high-resolution. In this article, we propose a novel framework that simplifies face manipulation into two correlated stages: a boundary prediction stage and a disentangled face synthesis stage. The first stage models poses and expressions jointly via boundary images. Specifically, a conditional encoder-decoder network is employed to predict the boundary image of the target face in a semi-supervised way. Pose and expression estimators are introduced to improve the prediction performance. In the second stage, the predicted boundary image and the input face image are encoded into the structure and the texture latent space by two encoder networks, respectively. A proxy network and a feature threshold loss are further imposed to disentangle the latent space. Furthermore, due to the lack of high-resolution face manipulation databases to verify the effectiveness of our method, we collect a new high-quality Multi-View Face (MVF-HQ) database. It contains 120,283 images at $6000\times 4000$ resolution from 479 identities with diverse poses, expressions, and illuminations. MVF-HQ is much larger in scale and much higher in resolution than publicly available high-resolution face manipulation databases. We will release MVF-HQ soon to push forward the advance of face manipulation. Qualitative and quantitative experiments on four databases show that our method dramatically improves the synthesis quality.

Here, we have developed an approach termed methylamine stable isotope labeling (MeSIL) to relatively quantify N-glycopeptides through one step labeling. It is the first time that this approach is applied to measure N-glycopeptide changes in huh7 cells after Zika virus infection and it has been revealed that differentially expressed N-glycopeptides played important roles in virus infection at the glycosylation site-specific level.

With andromonoecious Momordica charantia L. (bitter gourd) as material, three light qualities (50 μmol·m-2·s-1) including white LED light (WL), blue monochromatic light (B, 465 nm), and red monochromatic light (R, 650 nm) were carried out to investigate their effects on seed germination, physiological and biochemical parameters, sex differentiation and photosynthetic characteristics of bitter gourd. The results showed that compared to the WL treatment, the R treatment significantly promoted seed germination, seedling height elongation and soluble sugar content, the B treatment significantly increased seedling stem diameter, reducing sugar content and soluble protein content, the R and B treatments both significantly reduced sucrose content, but their POD activity showed no significant difference. Compared with the R treatment, the B treatment significantly increased the total female flower number and female flower nod ratio in 30 nods of main stems. The study of photosynthetic characteristics found that the R and B treatments could effectively increase the stomatal conductance (GS) of leaves, significantly improved the net photosynthetic rate (Pn) compared to the WL treatment, and the effect of the B treatment was better. Compared to the R and WL treatments, the B treatment increased the maximum photosynthetic rate (Pmax), apparent quantum efficiency (AQE) and light saturation point (LSP), and reduced the dark respiration rate (Rd) and light compensation point (LCP) of the leaves. Fit light response curves showed that the adaptability and utilization of weak light in bitter gourd were middle or below, but it showed higher adaptability and utilization of strong light. Thus, it suggests that Momordica charantia is a typical sun plan with lower Rd. In summary, it is concluded that blue light has a positive effect on the seed germination, seedling growth, sex differentiation and improving the photosynthetic performance, and this will lay the foundation for artificially regulating optimum photosynthesis using specific LEDs wavelength, and help to elucidate the relationship how light quality influences the sex differentiation of plant.

The multifactor accelerated aging test is an important means to predict the lifetime of gas-insulated switchgear (GIS) O-ring seals, while the leakage rate (LR) is a potential index for failure evaluation. This article proposed a three-factor accelerated aging test platform and a leakage test system (LTS) for GIS seal rings. The aging factors are thermal, mechanical compression, and pressured SF6 gas. The pressure aging vessel (PAV) can be filled with various gases to simulate the operating conditions of the GIS and age multiple samples simultaneously. During the aging process, leakage tests are conducted on flange pairs without disassembling and separating the seal rings. After the test, the aging can be continued so as to study the evolution of LR with aging time. In order to analyze the properties of leakage test and validate the platform, a three-factor accelerated aging and a traditional two-factor accelerated aging (thermal and compression) were conducted on GIS O-ring seals of EPDM material. Compression set (CS) and LR were tested. The result showed that a steady inherent LR $F_{0}$ exists for the LTS. The LR rose linearly with accumulating time, allowing simplified calculation of LR. The three-factor aged samples had significantly smaller CS than those of the two-factor aged samples, indicating that the working condition of GIS with SF6 atmosphere significantly slows the aging rate of GIS seal rings. The findings proved that the three-factor accelerated aging test platform and LTS effectively simulate the operating conditions of GIS seal rings and evaluate their properties.

Polymer-based dielectric films with desirable heat dissipation property hold great promise as thermal management materials in advanced electronics and high power devices. Herein, we report a new st...

S-Nitrosylation is an important post-translational modification that occurs on cysteine amino acid and regulates signal transduction in diverse cell processes. Dysregulation of protein nitrosylation has shown close association with cardiovascular and neurological diseases, thus demanding further precise and in-depth understanding. Mass spectrometry-based proteomics has been the method of choice for analyzing S-nitrosylated (SNO-) proteins. However, due to their extremely low expression level and rapid turnover rate, quantitative analysis of the S-nitrosylation at the proteomic level remains challenging. Herein, we developed a novel approach termed FluoroTRAQ, which combined the fluorous solid-phase extraction of SNO-peptides and iTRAQ labeling for the quantitative analysis of the SNO-proteome with high sensitivity and specificity. This new analytical strategy was subsequently applied to examine the dynamic SNO-proteome changes of human umbilical vein endothelial cells upon in vitro S-nitrosoglutathione induction. Our data identified a number of novel SNO-proteins and revealed their temporal modulation as validated by biotin switch assay. Our study offered a practical approach for quantitative analysis of protein S-nitrosylation.

Background Pancreaticopleural fistula (PPF) is a rare disease, especially in children. Conservative treatment and surgery are traditional therapies, but surgery is invasive. The emergence of endoscopic retrograde cholangiopancreatography (ERCP) has provided a new noninvasive treatment for PPF and may become the first choice for children with PPF. Aim To explore the treatment response to ERCP for PPF in children. Methods Seven children with PPF were hospitalized in the Gastroenterology Department of Beijing Children's Hospital from December 2007 to May 2019. Data on these seven patients' clinical characteristics, diagnosis, treatments, and outcomes were analyzed, and their treatment responses following surgery and ERCP were compared. The correlation between the length of hospital stay and conservative treatment was analyzed. Peer-reviewed articles written in English and Chinese published from January 2009 to December 2019 were obtained from various open data sources and reviewed. Results The seven patients comprised three boys and four girls with a mean age of 6.57 ± 3.26 years. The main symptoms were chest tightness and pain (n = 4), intermittent fever (n = 3), dyspnea (n = 3), and abdominal pain (n = 1), and all patients had bloody pleural effusion. All seven patients were diagnosed with PPF by magnetic resonance cholangiopancreatography, and all were initially treated conservatively for a mean of 34.67 ± 22.03 d with a poor response. Among five patients who underwent ERCP, one required surgery because of intubation failure; thus, the success rate of ERCP was 80%. Two patients were successfully treated with surgery (100%). The postoperative hospital stay of the two patients treated by surgery was 20 and 30 d, respectively (mean of 25 d), and that of the four patients treated by ERCP ranged from 12 to 30 d (mean of 19.25 ± 8.85 d). The recovery time after ERCP was short [time to oral feeding, 4-6 d (mean, 5.33 ± 1.15 d); duration of closed thoracic drainage, 2-22 d (mean, 13.3 d)]. Analysis of previous cases of PPF published worldwide during the past decade showed that the treatment success rate of ERCP is not lower than that of surgery. There was no significant difference in the postoperative hospital stay between surgery (16 ± 10.95 d) and ERCP (18.7 ± 6.88 d, P > 0.05). A positive linear correlation was found between the overall hospital stay and ERCP intervention time (R 2 = 0.9992). Conclusion ERCP is recommended as the first-choice treatment for PPF in children. ERCP should be performed as early as possible if conditions permit.

It is always an important research direction to improve the performance of solid oxide fuel cells (SOFCs) through structural optimization. For anode-supported SOFC, the thin cathode thickness results in an uneven distribution of oxygen concentration in the porous electrode, which limits the output performance. In this paper, a three-dimensional model of the anode-supported planar SOFC was established, and the thickness of the cathode diffusion layer in the area covered by the connector was increased to improve the uniformity of oxygen distribution in the cathode. The results show that increasing the thickness of the cathode diffusion layer under the connector can significantly improve the overall output performance of SOFC. The uniformity of oxygen concentration distribution in the cathode is improved, the local current density under the connector is increased, and the maximum output power density of the cell is increased by 29.14%. The results show that the output performance of the SOFC and the uniformity of gas distribution in the porous electrode can be improved by increasing the thickness of the cathode diffusion layer under the connector in the structure design and practical application of the SOFC. The research of this paper provides a reference for the optimization design of SOFC.

Discriminative feature learning is critical for person re-identification. To obtain abundant visual information from the input person image, we first propose a novel network that extracts multi-scale patch-level deep features. Then, we propose an improved softmax loss function for learning more compact and more discriminative feature vectors. Specifically, we integrate feature pyramid blocks and region-level global average pooling functions into the feature extraction network, introduce the well-established normalization techniques in face recognition algorithms into person re-ID, and penalize the redundancy in feature vectors by minimizing the l1,2 norm of the weight matrix in the softmax layer. Experiments on three large-scale datasets under the standard settings show the effectiveness of the proposed method. Moreover, we report our cross-domain re-ID results by training re-ID models on source datasets and testing them on other target datasets.

The attosecond (as) pulses are of great significance to observe and control the ultrafast electron dynamics in atoms, molecules and condensed matter. By utilizing some sub-cycle gating methods, i.e., amplitude gating, polarization gating, and double optical gating, isolated attosecond pulses (IAP) have been generated experimentally from high-order harmonics generation (HHG) by the interaction of intense femtosecond laser fields and gas targets. At present, about 50-as IAPs are now available only in few laboratories in the world. Thus, searching for the ways to generate more short IAPs is still desirable and important for the strong field physics and attosecend science. The color gating is another potential technique generating ultrashort IAP, which could generate continuum high-order harmonics without using sub-cycle laser pulses. On the theoretical side, many schemes with two- and three-color combination field have been proposed to generate short IAP, and some works propose that in this way the IAP with width less than 10 as may be generated. However, these investigations are performed only on the single-atom level, and the real experimental conditions and strength of IAPs are not considered in their simulations. In this work, we perform a numerical experiment on the generation of strong IAP from HHG driven by multicolor driving fields. Unlike the previous studies, the macroscopic effects are included in the present simulations. Considering the detection range of spectrograph, the centering energy of IAP is confined as ~200 eV. To obtain single attosecond pulse with short width, a genetic algorithm is used to optimize waveforms of two- and three-color excitation laser pulses, which is determined by intensity, wavelength and carrier envelope phase for each component. We choose the fundamental pulse as Ti:sapphire laser field with the wavelength of 800 nm to ensure the intensity of the generated attosecond pulse, and the second or third component is not confined as the commensurate harmonics of fundamental pulse. In order to possess an acceptable computation time, the model of strong field approximation is adopted in the simulations of HHG. Our simulations show that by coherent synthesizing two or three multi-cycle pulses (the full width at half maximum of each laser field is 6 fs) we obtain sub-optical-cycle laser pulse, which can easily generate supercontinuum high-order harmonic spectra with wide bandwidth. Different from the single-color driving field, which produces the attosecond pulse train, the single attosecond pulses are generated from optimized laser fields. The width of IAP decreases with the increase of intensity of driving laser field. On the single atom level, our simulations show that about 50 as and 40 as IAP can be obtained by superimposing appropriate harmonics generated by optimized two- and three-color laser pulses, respectively. More importantly, after considering the propagation effects, about 50 as IAPs are obtained with spherical spatial filtering of harmonics in the far field, under reasonable conditions, i.e., the length and position of gas jet, gas pressure. With great advent of the ultrafast lasers technology, such as coherent waveform synthesizing, optical parametric amplification and optical parametric chirped-pulse amplification, this work gives helpful guidance for intense short IAP generation in the experiment.

Person re-identification (ReID) aims to predict whether two images from different cameras belong to the same person. Due to low image quality and variance in view point and body pose, it remains a difficult task. To solve the task, a model is supposed to appropriately capture features that describe body regions for identification. With the simple intuition that explicitly incorporating ReID model with part awareness could be beneficial for learning a more discriminative feature space, we propose part segmentation as an assistant body perception task during the training of a ReID model. Specifically, we add a lightweight segmentation head to the backbone of ReID model during training, which is supervised with part labels. Note that our segmentation head is only introduced during training and that it does not change network input or the way of extracting ReID feature. Experiments show that part segmentation considerably improves the performance of ReID. Through quantitative and qualitative analyses, we further reveal that body part perception helps ReID model to capture a set of more diverse features from the body, with decreased similarity between part features and increased focus on different body regions. We experiment with various representative ReID models and achieve consistent improvement on several large-scale datasets including Market1501, CUHK03, DukeMTMC-reID and MSMT17. E.g . on MSMT17, our method increases Rank-1 Accuracy of GlobalPool-ResNet-50, PCB and MGN by 2.3%, 2.9% and 3.9%, respectively. Incorporated with MGN, our model achieves state-of-the-art performance, with Rank-1 Accuracy 95.8%, 78.8%, 90.0% and 84.0% on four datasets, respectively.

Neuronal dendrites and dendritic spines are essential for normal synaptic transmission and may be critically involved in the pathophysiology of various neurological disorders, including depression. Emerging data supports the role of mitochondria in dendritic protrusions in modulating the development and morphological plasticity of spines. Mitophagy, a mitochondria-specific form of autophagy, is the fundamental process of clearing damaged mitochondria to maintain cellular homeostasis. As a brain-specific microRNA, miR-134 is localized to the synaptodendritic compartment of hippocampal neurons and negatively regulates the development of dendritic spines. However, the role of miR-134 in mitophagy related to dendritic deficits in the pathophysiology of depression remains unclear. In this study, we showed that miR-134-5p knockdown abrogated depression-like behavioral symptoms and corrected aberrant spine morphology in hippocampal neurons of chronic unpredictable mild stress (CUMS) mice. Moreover, knockdown of miR-134-5p triggered autophagy in dendrites, improved mitochondrial impairment, and induced the generation of autophagosomes in the hippocampus of CUMS mice. We further found that AMP-activated protein kinase (AMPK), which mediates the impairment of defective mitochondria via mitophagy, can bind directly to miR-134-5p and is negatively regulated by this miRNA. This study demonstrates that miR-134-5p exerts an enormous effect on dendritic deficits by promoting AMPK-mediated mitophagy and provides a potential new target for antidepressant drug research and development.

Distinction between menstrual blood and peripheral blood is vital for forensic casework, as it could provide strong evidence to figure out the nature of some criminal cases. However, to date no single blood-specific gene, including the most variable microRNAs (miRNAs) could work well in identification of blood source. In this study, we developed a new strategy for identification of human blood samples by using the copy number ratios of miR-451a to miR-21-5p based on 133 samples, including 56 menstrual blood and 47 peripheral blood, as well as 30 non-blood samples of saliva (10), semen (10) and vaginal secretion (10). The cut-off value and efficacy of the identification strategy were determined through receiver operating characteristic (ROC) analysis. Our results showed that when the miR-451a/miR-21-5p ratio below 0.929, the sample should be non-blood. In contrast, when the miR-451a/miR-21-5p ratio above 0.929 and below 10.201, the sample should be menstrual blood; and when this ratio above 10.201, the sample should be peripheral blood. External validation using 86 samples (62 menstrual blood and 24 peripheral blood samples) fully supported this strategy with the 100% sensitivity and 100% specificity. We confirmed that this result accuracy was not affected by various potential confounding factors of samples and different experimental platforms. We showed that 0.2 ng of total RNA from menstrual blood and peripheral blood was sufficient for qPCR quantification. In conclusion, our results provide an accurate reference to distinguish menstrual blood from peripheral blood for forensic authentication.

The residual stress could be produced during the curing process of GIS basin insulators. The coupling of residual stress and external stress may cause stress concentration, which threaten the safe operation of the insulators. In this paper, the method for detecting residual stress of a 252kV GIS basin insulator by critical refraction longitudinal wave (L CR ) is presented. The residual stress detection system is built. The main frequency of excitation and receiving probe is 2 MHz. The ultrasonic velocity of epoxy resin standard sample is measured. Two checkpoints on the basin insulator are selected for the circumferential residual stress detection with a distance of 5 mm. The results show that ultrasonic velocity of the standard sample is 2927 m/s. The ultrasonic propagation time of the two checkpoints is different. Near the central conductor, the ultrasonic velocity is 3399 m/s and the compressive residual stress is 0.38 MPa. The ultrasonic velocity is 3073 m/s and the compressive residual stress is 0.12 MPa far away from the central conductor.

Stress concentration will cause mechanical strength deterioration of three-post insulators in GIL, which may threaten the safe and stable operation of GIL. In this paper, the simulation model of a 550 kV GIL three-post insulator is established, and the stress and strain simulation of three post insulator under horizontal and vertical installation conditions are carried out. The stress and strain development laws of the insulator under different radial pressures and axial tensions are studied. The results show that the maximum radial pressure is 9197.1 N in horizontal installation and 2975.2 N in vertical installation. Regardless of the loading mode, the maximum stress of the insulator is at the interface between the epoxy and the grounding insert. With the in-crease of load, the maximum stress and strain of the three-post insulator increase linearly.

In order to realize the storage of the residual coal in the goaf on the flue gas of the power plant, the adsorption characteristics of nitrogen dioxide in the flue gas of the power plant were studied. The Gaussian09 was used to study the adsorption process of NO2 molecules on coal at the density functional (DFT) B3LYP/6-311G level, and the model of NO2 adsorption by coal was established. Different quantities were obtained using orbital energy changes and molecular bond length changes. According to the principle of molecular adsorption, the adsorption of NO2 by coal is considered to be physical adsorption with endothermic heat. On the basis of simulation, using self-organized experimental devices, the single-component NO2 gas and the simulated coal-fired power plant flue gas were introduced into anthracite, bituminous coal and lignite. In single-component adsorption, the adsorption of NO2 by lignite increases with time. The time to reach equilibrium is related to the properties of the coal itself. In the process of simulated flue gas adsorption, the order of the adsorption amount of coal to flue gas is CO2 > NO2 > N2 > O2. In the simulated flue gas, coal is easy to absorb NO2 and CO2, and the competition between gases reduces the frequency of contact between NO2 and the coal surface. Simulation and experimental results show that coal has obvious adsorption characteristics for NO2, and it is feasible for the residual coal in the goaf to adsorb NO2 in the flue gas of power plants.

With the change in environment and lifestyle, the number of diabetic patients is increasing rapidly. Diabetes has become one of the most important chronic diseases affecting the health of the Chinese people, and complications, disability, death and treatment costs caused by diabetes have placed a heavy burden on families and society. If the high-risk population of diabetes can be identified and the adverse lifestyle can be changed as soon as possible, the incidence of diabetes can be reduced or the onset of diabetes can be slowed down. Risk prediction model can accurately predict the risk of disease and has been widely used in the field of health management and medical care. This study was based on the special occupational group of steel workers, the risk prediction model of type 2 diabetes was established by using convolutional neural network, and the feasibility of the model was discussed. The results showed that the prediction accuracy of the established model in the training set, verification set, and test set is relatively high, which is 94.5%, 91.0%, and 89.0%, respectively. The area under the ROC curve was 0.950 (95 CI 0.938–0.962), 0.916 (95 CI 0.888–0.945), and 0.899 (95 CI 0.899–0.939), respectively, indicating that the model can accurately predict the risk of type 2 diabetes among steel workers, provide a basis for self-health management of steel workers, facilitate the rational allocation of medical and health resources and the development of health services, and provide a basis for government departments to make decisions.

Recent years have witnessed the great success of deep learning models in semantic segmentation. Nevertheless, these models may not generalize well to unseen image domains due to the phenomenon of domain shift. Since pixel-level annotations are laborious to collect, developing algorithms which can adapt labeled data from source domain to target domain is of great significance. To this end, we propose self-ensembling attention networks to reduce the domain gap between different datasets. To the best of our knowledge, the proposed method is the first attempt to introduce selfensembling model to domain adaptation for semantic segmentation, which provides a different view on how to learn domain-invariant features. Besides, since different regions in the image usually correspond to different levels of domain gap, we introduce the attention mechanism into the proposed framework to generate attention-aware features, which are further utilized to guide the calculation of consistency loss in the target domain. Experiments on two benchmark datasets demonstrate that the proposed framework can yield competitive performance compared with the state of the art methods.

Buckling of elastomeric materials is recoverable and can be harnessed to provide useful functions. Soft machines enabled by buckling actuators have been demonstrated experimentally, while no efforts have been conducted to model these actuators and machines numerically. This paper fills this blank and presents numerical modelling of buckling actuators and the derived soft machines. Key design parameters are obtained through the simulation, which are missing and typically difficult to be obtained in experiment. The numerical results are helpful to understanding the working mechanism of buckling actuators, and shed light on design and control of soft machines enabled by buckling actuators. The idea and the strategy can be developed further for the design of other soft machines.

To control the polarization of attosecond pulse trains (APTs), the most widely used way is to adjust the intensity ratio between the two components in a bicircular laser field. Here we demonstrate ...

Optimal contract regulates the expected activities of both principals and agents, and influences how the gains from the cooperation are shared between the two participators. Thus, it is necessary and wise for principals to seek for the optimal contracts during the period of negotiations. To determine the optimal contracts, there are several kinds of agency models based on different measures, such as probability measure, capacity measure, credibility measure and uncertainty measure. This paper primarily presents a comparative review of random agency model, fuzzy agency model and uncertain agency model. This comparative review is aimed at not only summarizing the structure and feature of each agency model but also guiding on how to identify the most suitable agency model for each specific principal agent problem. Motivated by this idea, these three classes of agency models are respectively investigated about their structure, feature and application, and then an empirical comparison among these models is basically carried out from several aspects, such as information diversity, decision rule and calculation. As a significant contribution, the comparative result in this paper provides the guidance for the principals on how to identify the most suitable agency model for each special principal agent problem in a certain setting.

Recent works have demonstrated that using a properly structured prior for model-based compressive sensing (CS), can improve the recovery performance. However, there exists the low prior utilisation in previous works. Therefore, in this study, we introduce a multi-structured-wavelet Bayesian CS framework (MSW-BCS) that works for image compression with fully employing the prior knowledge. Our work can be briefly summarised by the following three aspects. Firstly, we explore the multi-structured prior knowledge, including the cluster structure and tree structure in the wavelet transform coefficients of images via statistical analyses. Secondly, a multi-variable joint recovery model is designed to describe this multi-structured prior. Finally, the detailed learning algorithm of model parameters based on variational Bayesian inference is given. The simulation experiments show that the proposed recovery model can effectively merge the different priors and achieve the superior performance compared with that of the other well-known CS algorithms under both noise-free and noisy measurement environments.

Pyroelectric infrared (PIR) sensor is widely used as motion detector, due to its sensitivity to the infrared radiation changes (IRC) induced by human motion. In particular, it is also possible to determine the human activity patterns based on the IRC signal by improving the feature-specific sensitivity of the PIR sensors. In this paper, the feature-specific sensitivity is enhanced by visibility modulation at the geometric sensing layer. At the cooperative sensing layer, a novel stereo configuration is used in developing the infrared sensing system, which aims to handle the difficulties caused by occlusion and limited coverage area. The experimental results demonstrate the effectiveness of the developed system in daily activity recognition.