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When analyzing screening chest X-ray (CXR) images, radiologists can naturally consider information about the relationships between pathologies, namely pathological co-occurrence, and interdependence. Such topologically meaningful pathological relationships provide complementary a priori knowledge and can improve the radiologist’s classification accuracy. Unfortunately, most existing deep learning systems focus only on regression from input to binary labels, lack the ability to jointly analyze and integrate global and local information from these multiple labels, and the ability to exploit this valuable prior knowledge of topology. We suppose that the algorithm will distinguish co-occurring pathologies easily if it is guided to learn potentially valuable information between multiple pathologies. We propose a novel multi-label global–local analysis method named CXR × MLAGCPL for disease recognition in CXR images. We first designed two methods for constructing graphs: building adaptive local graph on a single image and creating global co-occurrence graph in a data-driven manner. Then two modules, local awareness module (LAM) and global co-occurrence priori learning module (GCPL), were designed to jointly learn local class correlation and global inter-class dependency patterns. We have evaluated the effectiveness of the proposed CXR × MLAGCPL in two large-scale CXR datasets (ChestX-Ray14 and CheXpert). Our model achieved state-of-the-art performance: the mean AUC scores for the 14 pathologies were 0.828 and 0.813, respectively. The proposed LAM and GCPL can jointly effectively improve the recognition performance. Our model is accurate in terms of classification accuracy and generalization due to competing approaches. [ABSTRACT FROM AUTHOR]

Restoring high quality images from raw data in low light is challenging due to various noises caused by limited photon count and complicated Image Signal Process (ISP). Although several restoration and enhancement approaches are proposed, they may fail in extreme conditions, such as imaging short exposure raw data. The first path-breaking attempt is to utilize the connection between a pair of short and long exposure raw data and outputs RGB images as the final results. However, the whole pipeline still suffers from some blurs and color distortion. To overcome those difficulties, we propose an end-to-end network that contains two effective subnets to joint demosaic and denoise low exposure raw images. While traditional ISP are difficult to image them in acceptable conditions, the short exposure raw images can be better restored and enhanced by our model. For denoising, the proposed Short2Long raw restoration subnet outputs pseudo long exposure raw data with little noisy points. Then for demosaicing, the proposed Color consistent RGB enhancement subnet generates corresponding RGB images with the desired attributes: sharpness, color vividness, good contrast and little noise. By training the network in an end-to-end manner, our method avoids additional tuning by experts. We conduct experiments to reveal good results on three raw data datasets. We also illustrate the effectiveness of each module and the well generalization ability of this model. • We propose a Short2Long raw restoration strategy to process low quality raw images and restore pseudo raw ones for the subsequent subnet. It extracts sufficient information and removes enough noisy points, thus mainly focuses on the denoising task. • We device a Color consistent RGB enhancement mechanism to rearrange raw images and generate the final RGB outputs. With the residual connection and channel-wise attention, it reconstructs the color spatial information and emphasizes more on the demosaicing task. • Both quantitative and qualitative experiments are conducted to demonstrate the superiority of our proposed method. Ablation studies prove the effectiveness of modules. We also illustrate its promising generalization ability on unseen raw data such as images from smartphones. [ABSTRACT FROM AUTHOR]

LiNi 0.8 Co 0.15 Al 0.05 O 2-δ (NCAL) has been demonstrated to be an excellent electrode with dual catalytic activities of hydrogen oxidation reaction (HOR) and oxygen reduction reaction (ORR) for solid oxide fuel cells (SOFCs). Reports have indicated that the anode NCAL tends to be reduced during cell operation and produce LiOH and Li 2 CO 3. In this work, the effect of the online reaction byproducts of NCAL electrodes, LiOH and Li 2 CO 3 , on the performance of SOFCs is investigated. Different amounts of LiOH and Li 2 CO 3 are separately added to the Ce 0.8 Sm 0.2 O 2-δ (SDC) electrolyte for fabricating SOFCs with NCAL electrodes. The power output under normal and reverse operation is studied for the cells with lithium salt addition from 5 wt% to 30 wt%. Electrochemical impedance spectroscopy (EIS) and distribution of relaxation times (DRT) analysis suggested that the addition of LiOH or Li 2 CO 3 to the SDC reduces HOR and ORR activities of the cells, leading to lower cell performance. The cross-sectional SEM of the cells shows that the added LiOH and Li 2 CO 3 makes the electrolyte porous and densifies the electrode, which is the main reason for the reduction of cell performance. In addition, the porosity of electrolyte is modified during cell fabrication in order to construct flowing pathways for the online reaction byproducts of NCAL electrode to move from electrode to electrolyte, resulting in optimized cell performance. This study provides an insight into facilitating the performance of NCAL electrode-based SOFC. [Display omitted] • Adding LiOH and Li 2 CO 3 in the electrolyte is unfavorable for NCAL electrode fuel cell. • LiOH and Li 2 CO 3 reduce electrode performance from DRT analysis but can improve electrolyte performance. • Constructing porosity helps improve both electrolyte and electrode performance. [ABSTRACT FROM AUTHOR]

Bandgaps are intrinsic physical properties of periodic structures, for example, mechanical metamaterials or phononic crystals, which indicate attenuation of wave propagation within a specific frequency range. Bandgap calculation of mechanical metamaterials is realized via discretizing a representative voluame element (RVE) and imposing complex-valued Bloch wave boundary conditions. It is highly desirable to implement Bloch wave analysis in real-valued commercial finite element software. We present and detail an ABAQUS and Python implementation of Bloch wave analysis. Two-dimensional (2D) soft as well three-dimensional (3D) hard mechanical metamaterials are chosen as numerical examples. Material and geometric nonlinearities are tackled properly and the effects of pre-strains are explored. Our numerical results are validated either by similar results reported in the literature or by our experimental measurement. The presented numerical strategy would aid the design, analysis and utilization of mechanical metamaterials for dynamic applications. The codes and data of the paper could be downloaded from https://github.com/XJTU-Zhou-group/Calculating-band-gaps-of-nonlinear-mechanical-metamaterials. [ABSTRACT FROM AUTHOR]

Activity recognition is fundamental to many applications envisaged in pervasive computing, especially in smart environments where the resident's data collected from sensors will be mapped to human activities. Previous research usually focuses on scripted or pre-segmented sequences related to activities, whereas many real-world deployments require information about the ongoing activities in real time. In this paper, we propose an online activity recognition model on streaming sensor data that incorporates the spatio-temporal correlation-based dynamic segmentation method and the stigmergy-based emergent modeling method to recognize activities when new sensor events are recorded. The dynamic segmentation approach integrating sensor correlation and time correlation judges whether two consecutive sensor events belong to the same window or not, avoiding events from very different functional areas or with a long time interval in the same window, thus obtaining the segmented window for every single event. Then, the emergent paradigm with marker-based stigmergy is adopted to build activity features that are explicitly represented as a directed weighted network to define the context for the last sensor event in this window, which does not need sophisticated domain knowledge. We validate the proposed method utilizing the real-world dataset Aruba from the CASAS project and the results show the effectiveness. [ABSTRACT FROM AUTHOR]

Open-world instance segmentation has recently gained significant popularity due to its importance in many real-world applications, such as autonomous driving, robot perception, and remote sensing. However, previous methods have either produced unsatisfactory results or relied on complex systems and paradigms. We wonder if there is a simple way to obtain state-of-the-art results. Fortunately, we have identified two observations that help us achieve the best of both worlds: (1) query-based methods demonstrate superiority over dense proposal-based methods in open-world instance segmentation, and (2) learning localization cues is sufficient for open-world instance segmentation. Based on these observations, we propose a simple query-based method named OpenInst for open-world instance segmentation. OpenInst leverages advanced query-based methods like QueryInst and focuses on learning localization cues. Notably, OpenInst is an extremely simple and straightforward framework without any auxiliary modules or post-processing, yet achieves state-of-the-art results on multiple benchmarks. Specifically, in the COCO → UVO scenario, OpenInst achieves a mask Average Recall (AR) of 53.3, outperforming the previous best methods by 2.0 AR with a simpler structure. We hope that OpenInst can serve as a solid baseline for future research in this area. The source codes are available at https://github.com/hustvl/OpenInst. • OpenInst achieves state-of-the-art results on multiple datasets. Our study confirms that incorporating box IoU into query-based detectors improves their generalizability. • OpenInst is the first work that achieves both simplicity and high-performance, serving as a solid baseline for future research in the open-world community. • We conduct extensive ablation studies to analyze each component used in our model. We hope those observations benefit future researches in this area. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Soft mechanical metamaterials with hinge-like elements can undergo multi-step reconfiguration through folding and contacts, and thus exhibit highly nonlinear responses. Numerical simulation of the nonlinear behaviors is essential for the design and control of the mechanical metamaterials, but it remains a challenge due to complicated nonlinear effects. Here, we report the finite element modeling of multi-step reconfiguration of a shape-changing metamaterial, and elucidate the underlying mechanism of soft elasticity. The predicted stress–strain curve together with the folding angles of hinge elements shows excellent agreement with experimental data reported in the literature. Moreover, we explore the influence of reconfiguration and folding-induced internal stress on the bandgap distribution of the mechanical metamaterials. Our efforts provide useful guidelines for the design and application of mechanical metamaterials for both static and dynamic situations. [ABSTRACT FROM AUTHOR]

The COVID-19, viral influenza, tuberculosis, and other widespread infectious diseases evidence that pathogenic biological aerosols (PBAs) are a serious threat to public health. Different from traditional inactivation methods, such as ultraviolet (UV) light which are only safe to use when people are not present, and high-efficiency particulate filters (HEPA) which merely filter microbes without killing them, atmospheric pressure nonequilibrium plasma (APNP) has shown its tremendous potential in drastically diminishing the aerosol transmission route of the infectious agents through the abatement of PBAs. The key issues to develop high performance APNP based air purification system are critically reviewed. Systematic studies on the hazards of different PBAs and the spread of PBAs in indoor environments guide the development of APNP sources to control communicable diseases. The key six sampling and seven detection methods on PBAs are introduced to analyze the PBA abatement efficiency by APNP. Seven common APNP sources which can remove viruses and bacteria aerosols efficiently developed during the past 8 years are introduced. For the APNP sources with small plasma volume, the electric field and diffusion driven charging are the dominant mechanisms to charge PBAs, while the common methods of dusty plasma research can be adapted to atmospheric-pressure conditions to describe the charging effects of APNP sources with large plasma volume. Plentiful long- and short-lifetime reactive oxygen and nitrogen species (RONS) generated by APNP effectively contribute to inactivation of bacterial aerosols. Current studies suggest that viral aerosols are mainly inactivated by short-lifetime RONS including 1O2, ONOO− and ONOOH. The study on the dissolution and reaction of gaseous RONS in microdroplets and accurate measurements on the evolution of charged PBAs are envisaged to be the focus of future research. Opportunities for multidisciplinary collaborative research to advance the development of next-generation high-performance plasma-based air purifiers are highlighted. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Nowadays, along with increased public demands on the high-quality green fresh product, the downstream retailer has to spend in the packaging and cold chain transportation and the upstream farmer needs to invest more in green fresh products producing. In this paper, we investigate a green fresh product supply chain problem, in which the retailer transports and sells green fresh products to the ultimate consumer while the farmer produces green fresh product through spending a greenness improvement investment and sells green fresh products to the retailer. Since the fresh product is perishable, the retailer needs to make a costly freshness-keeping effort. Obviously, the freshness-keeping effort, the price, and the greenness improvement level will affect the demand. Thus, to demonstrate the game structure between the retailer and the farmer, a decentralized model without cost sharing, a decentralized Stackelberg cost sharing model, and a Nash bargaining model with cost sharing are formulated. Results show that: (1) The equilibrium decisions under Stackelberg model with cost sharing are larger than that of the Nash bargaining model with cost sharing, while equilibrium decisions in the decentralized model without cost sharing are the least. (2) Both greenness improvement levels in Stackelberg cost sharing contract and Nash bargaining are greater than that in decentralized model without cost sharing. (3) The retailer's profits in Stackelberg cost sharing contract and Nash bargaining are larger than that in decentralized case without cost sharing, while the farmer's profits in Stackelberg cost sharing contract and Nash bargaining are larger than in decentralized model without cost sharing in certain conditions. Meanwhile, a numerical example is given to illustrate the results we obtained in the theoretical analysis process. [ABSTRACT FROM AUTHOR]

Change detection methods for measuring deformation of the LNG storage tank's exterior wall cannot meet the requirements for extensive and comprehensive deformation analysis. The method proposed in this paper uses multi‐source spatial data to detect the deformation of the LNG storage tank's exterior wall. Exterior wall data collected simultaneously by terrestrial and airborne laser scanners, as well as close‐range and oblique photogrammetry, are pre‐processed separately and then registered to the global point clouds. The deformation state of exterior wall is represented by a thorough assessment approach that takes into account four change indices. The results suggest that the registration precision of global point clouds is 6 mm. The global ovality of the LNG storage tank is 0.053%, with a tilt change of 2.58° and a deviation of 27 mm in the direction of the azimuth of 321.98°, an average value of 0.07 mm for depression and protrusion deformation, and a surface flatness of 21.3 mm. In this case, the flatness is greater than the variation threshold of the standard specification, indicating that the tank may have structural uneven settlement or other deformation problems. The attraction of the proposed method resides in its capacity to efficiently and accurately obtain spatial positioning and color information of the LNG storage tank's exterior wall, thereby providing a robust foundation for precise judgments regarding the deformation state. [ABSTRACT FROM AUTHOR]

• First attempts to model soft machines driven by buckling actuators. • Simulation provides missing information vital for real designs. • Results aid the design and control of soft machines. 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. [ABSTRACT FROM AUTHOR]

Most patients with renal cancer will develop resistance to sorafenib therapy and will therefore exhibit disease progression. Effective therapies for these patients are extremely limited. Cyclooxygenase-2 (COX-2) promotes the malignant transformation of cancer cells and drug resistance. The potential of COX-2 inhibitor (celecoxib) administration in combination with sorafenib for the treatment of renal cancer is unclear. The present study demonstrated that sorafenib rapidly increased the expression of COX-2 in renal cancer cells, as determined using reverse transcription-quantitative PCR and western blotting. The results of the MTT assay and cell apoptosis experiment demonstrated that the cytotoxicity of sorafenib was also affected by COX-2 expression and celecoxib enhanced the cytotoxicity of sorafenib against renal cell carcinoma. Immunofluorescence analysis indicated that sorafenib induced the formation of stress granules (SGs) in renal cancer cells. In addition, COX-2 expression was associated with the formation of SGs, and SGs could capture and stabilize COX-2 mRNAs in renal cancer cells; this was confirmed using RNA fluorescence in situ hybridization and an actinomycin D chase experiment. The protective effect of SGs was further demonstrated in cell experiments and xenograft tumor models. Thus, the present study indicated that the use of celecoxib may significantly enhance the sensitivity of renal cancer cells to sorafenib and improve efficacy. Sorafenib-induced SGs may contribute to critical events that promote COX-2 expression and survival in renal cancer cells. Therefore, the present study may provide novel ideas for the treatment of renal cancer. [ABSTRACT FROM AUTHOR]

Differential scanning calorimetry (DSC) and polarized optical microscopy were used to comparatively investigate the multiple melting behavior and isothermal crystallization kinetic of pure PET and PET/Na+-MMT nanocomposites prepared through solid-state mechanochemical method and conventional method. The results showed that both pure PET and Na+-MMT-filled PET system isothermally treated at different crystallization temperature exhibited three melting endotherms, including peak I, II and III in the DSC heating process. The increases in both crystallization temperature and Na+-MMT loading and the mechanochemical pan-milling prove to be beneficial to the formation of crystals related to endotherm II in the primary crystallization process. The mechanochemical pan-milling effect and the increase in the Na+-MMT loading also decrease the size of the formed spherulite. The investigation of isothermal crystallization kinetics indicated that the calculated Avrami exponent n of the milled sample (PETSNx) shows the higher value than that of both pure PET and the conventional unmilled sample (PETCNx) and also increases with increasing the Na+-MMT content. In addition, the calculated crystallization activation energy of PETSNx is found to be lower than that of pure PET as well as PETCNx. [ABSTRACT FROM AUTHOR]

• The activity modeling method based on the emergent paradigm and the directed-weighted network can fully extract feature structure. • The additional context-aware information of each whole activity settles the issue of activity ambiguity. • The heterogeneous mechanism of pheromone keeps the balance between the new and old context-aware information. With the population aging, elderly health monitoring is triggering more studies on daily activity recognition as the fundamental of ambient assisted living. It is remarkable that activity recognition remains difficulties including how to adequately extract feature structure and settle the issue of activity confusion. To address these challenges, we propose a novel activity modeling method under the emergent paradigm with marker-based stigmergy and the directed-weighted network with additional context-aware information. In the modeling process, stigmergy is first introduced to aggregate the context information at the low level for generating activity pheromone trails, and then the constructed stigmergic trails are represented in form of directed-weighted network with distinguishability of individual pheromone source corresponding to location. The potential advantage is that the robust trails with distinguishable individual initial positions are feasible to supplement user's daily habits and thus both inter-class and intra-class distances can be kept at acceptable levels. Experiments on Aruba demonstrates that the proposed emergent modeling method can effectively deal with the problems of feature extraction and activity ambiguity and achieve good classification performance. [ABSTRACT FROM AUTHOR]

This paper proposes a novel rotation invariant feature for object recognition. Firstly, the local Fourier transform features of pixels in the described region are encoded by Fisher Vectors. Then, the encoded vectors are aggregated into a final representation by ordinal pyramid pooling, which hierarchically partitions the described region into sub-regions based on the orders of its pixels’ rotation invariants. Since both the encoded Fisher Vectors and the ordinal pyramid pooling strategy are rotation invariant, the extracted feature is rotation invariant by nature. Two kinds of rotation invariants are investigated in this framework, one is the Radial Gradient Orientation and the other is the Radial Gradient Angle. Experiments on handwritten digit recognition and airplane/car detection in aerial images demonstrate the effectiveness of the proposed method, which outperforms the state of the art. [ABSTRACT FROM AUTHOR]

The photoionization cross sections of the excited levels (3s3p P 0 , 1 , 2 o 3 ) of atomic Mg have been studied theoretically using both the nonrelativistic and fully relativistic R -matrix method. For the threshold cross sections, as previous nonrelativistic studies, present calculations show significant differences (a factor of 3) from former experimental values. Large discrepancies with experiment calls for additional measurements of the photoionization cross sections from the excited states of Mg. [ABSTRACT FROM AUTHOR]

7-Aminocoumarins are commonly used as fluorophores for application in molecular monitoring, cell imaging and many other fields. Herein, a series of fluorescent 7-aminocoumarins were prepared via Er(OTf) 3 -catalyzed Pechmann condensation of 3-aminophenols with β-ketoesters in 19–96% yields. Among them, 7-(dimethylamino)-4-(pyridin-4-yl)-2H-chromen-2-one (3j) displayed a fluorescence maximum with a wavelength of 557 nm and a large Stokes shift of 166 nm in DMSO, and showed strong fluorescence in sub acid to alkaline environment. Besides, 3j exhibited excellent photo stability and stable fluorescence intensity in the presence of some metal cations and biomolecules in neutral solution. A series of fluorescent 7-aminocoumarins were prepared via Er(OTf) 3 -catalyzed Pechmann condensation of 3-aminophenols with β-ketoesters. Among them, 7-(dimethylamino)-4-(pyridin-4-yl)-2H-chromen-2-one (3j) exhibited excellent photophysical properties, and displayed strong fluorescence in sub acid to alkaline environment. [Display omitted] • An efficient approach to access fluorescent 7-aminocoumarins •Easy work-up and broad substrate scopes •Excellent fluorescence properties [ABSTRACT FROM AUTHOR]

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. [Display omitted] • CUMS induced depression-like behavior is accompanied by hippocampal neuron damage and dendritic spine reduction. • Knockdown of miR-134-5p improved depression-like behavior and hippocampal dendritic spine morphology in CUMS mice. • Knockdown of miR-134-5p activated mitophagy in the hippocampal dendrites of CUMS mice. • Knockdown of miR-134-5p rescued hippocampal dendritic spine anomalies by targeting AMPK-mediated mitophagy. [ABSTRACT FROM AUTHOR]

Mechanical metamaterials have attracted tremendous interests, owing to their fruitfulness of underlying physics and mechanics as well as diverse potential applications. Conventional soft mechanical metamaterials contain periodic distribution of holes having identical sizes, which can only display one mechanical function. Periodically dispersing a regular pattern of through-holes with two sizes into an elastomer results in metamaterials with more mechanical functions and deformation scenarios. We numerically investigate the deformation behaviors of this type of metamaterials. Switching of modes from the buckling-dominated deformation to a loss-of-instability deformation is emphasized. Replacing the pattern of holes with the same sizes by a pattern of holes with two sizes imposes higher compliance and even loss of negative Poisson’s ratio of the material. [ABSTRACT FROM AUTHOR]

The employing visible light to drive organic transformations is the most promising choice to meet the needs for green synthesis, in which the reactions promoted by visible light may provide a more efficient and greener method. Currently, the most abundant methods for activation and functionalization of reaction substrates have relied on the direct single‐electron transfer (SET) between the excited photocatalyst and substrates, and these wonderful works were summarized and docoumented in many reviews. As a complement to the above reactions, the photoredox‐mediated atom transfer transformations (photocatalyst‐to‐HAT catalyst‐to‐substrates) to generate radical species are showing an explosively growing trend. In this review, we highlight recent significant developments in this rapidly growing area, mainly focusing on the photoinduced base‐to‐substrates charge transfer reactions. [ABSTRACT FROM AUTHOR]

When a soft mechanical metamaterial, consisting of a regular array of representative volume elements (RVEs), is stressed up to a large strain, the delicately tailored behavior of the RVE does not prevail in the metamaterial due to the boundary effect and manufacturing imperfections. A metamaterial sheet comprising RVEs designed for snapping-back behavior exhibits random snapping-through instability when uniaxially stretched. We conceptualize that loss of representativeness of RVE can be avoided by introducing fiber reinforcement to regulate boundary conditions. Through a combination of experiments and numerical simulation, we demonstrate that fiber reinforcements tune behavior of a metamaterial sheet from random snapping-through to sequential and even selective snapping-back instability by introducing small structural variations. Ideal snapping-back instability, characterized by sharp variations of forces in both loading and unloading processes, is captured, while the latter is typically hard to observe in real experiments. Enhanced energy dissipation rate from 25.3% for the case without fiber to 46.4% for the case with fiber-reinforcement is recorded in experiments, when the metamaterial sheet is stretched up to 200% and then released to restore its original length. [ABSTRACT FROM AUTHOR]

Our previous studies showed that the expressions of miR-148a, miR-152 and miR-148b are altered in gastric cancer (GC). The present study aimed to find relationship between individual single nucleotide polymorphisms (SNPs) or haplotypes of these miRNAs and susceptibility, clinicopathological parameters and prognosis of GC in a large sample of the Han population of Northern China. Twelve SNPs were genotyped using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry in a case–control study of 571 Chinese GC patients and 571 cancer-free controls. The rs11170877 G allele (P = 0.027) and the rs12231393 C allele (P = 0.034) were associated with a decreased risk of GC. However, these associations were lost after Bonferroni correction. The rs4719839 G allele was associated with Borrmann type III–IV GC (P = 0.034), increased tumour size (P = 0.020), an increased rate of lymph node metastasis (P = 0.047) and advanced TNM stage (P = 0.009). These associations were also lost after Bonferroni correction. The haplotype of miR-148b was significantly correlated with GC risk. The haplotypes in miRNA-148a were different in Borrmann types. The haplotype of miR-152 distributed various in the positive lymphovascular invasion group compared to negative group. Polymorphisms of miR-148b rs11170877 and 12231393 and their haplotypes were predictive factors of susceptibility to GC. A functional genetic variant of miRNA rs4719839 and the corresponding haplotype were associated with clinicopathological features and prognosis of advanced GCs. [ABSTRACT FROM PUBLISHER]

• We are the first to explore the uncertainty of hashing bits during approximate optimization. Depending on the magnitude of uncertainty, the corresponding hashing bits and input images receive different attention. • We propose to explicitly model bit-level and image-level uncertainty, resorting to the output discrepancy between the hashing network and the momentum-updated network. • Extensive experiments on the CIFAR-10, the NUS-WIDE, the MS-COCO, and the largescale Clothing1M datasets demonstrate that our method significantly improves retrieval performance when compared with state-of-the-art methods. Combinatorial optimization (CO) has been a hot research topic because of its theoretic and practical importance. As a classic CO problem, deep hashing aims to find an optimal code for each data from finite discrete possibilities, while the discrete nature brings a big challenge to the optimization process. Previous methods usually mitigate this challenge by binary approximation, substituting binary codes for real-values via activation functions or regularizations. However, such approximation leads to uncertainty between real-values and binary ones, degrading retrieval performance. In this paper, we propose a novel Deep Momentum Uncertainty Hashing (DMUH). It explicitly estimates the uncertainty during training and leverages the uncertainty information to guide the approximation process. Specifically, we model bit-level uncertainty via measuring the discrepancy between the output of a hashing network and that of a momentum-updated network. The discrepancy of each bit indicates the uncertainty of the hashing network to the approximate output of that bit. Meanwhile, the mean discrepancy of all bits in a hashing code can be regarded as image-level uncertainty. It embodies the uncertainty of the hashing network to the corresponding input image. The hashing bit and image with higher uncertainty are paid more attention during optimization. To the best of our knowledge, this is the first work to study the uncertainty in hashing bits. Extensive experiments are conducted on four datasets to verify the superiority of our method, including CIFAR-10, NUS-WIDE, MS-COCO, and a million-scale dataset Clothing1M. Our method achieves the best performance on all of the datasets and surpasses existing state-of-the-art methods by a large margin. [ABSTRACT FROM AUTHOR]

This paper investigates an uncertain price discrimination problem in labor market, in which the employee's capability is his/her private information and characterized as an uncertain variable. To maximize the employer's welfare, an uncertain price discrimination model in labor market is established. Moreover, a crisp equivalent form of the model is given, and the analytic solution is therefore obtained by employing variational method. This optimal solution shows that both the productivity and the wage are increasing with respect to the employee's capability. [ABSTRACT FROM AUTHOR]

The glycoproteome has emerged as a prominent target for screening biomarkers, as altered glycosylation is a hallmark of cancer cells. In this work, we incorporated tandem mass tag labeling into quantitative glycoproteomics by developing a chemical labeling-assisted complementary dissociation method for the multiplexed analysis of intact N -glycopeptides. Benefiting from the complementary nature of two different mass spectrometry dissociation methods for identification and multiplex labeling for quantification of intact N -glycopeptides, we conducted the most comprehensive site-specific and subclass-specific N -glycosylation profiling of human serum immunoglobulin G (IgG) to date. By analysing the serum of 90 human patients with varying severities of liver diseases, as well as healthy controls, we identified that the combination of IgG1-H3N5F1 and IgG4-H4N3 can be used for distinguishing between different stages of liver diseases. Finally, we used targeted parallel reaction monitoring to successfully validate the expression changes of glycosylation in liver diseases in a different sample cohort that included 45 serum samples. [ABSTRACT FROM AUTHOR]

Motivation: Pairwise protein sequence alignments are generally evaluated using scores defined as the sum of substitution scores for aligning amino acids to one another, and gap scores for aligning runs of amino acids in one sequence to null characters inserted into the other. Protein profiles may be abstracted from multiple alignments of protein sequences, and substitution and gap scores have been generalized to the alignment of such profiles either to single sequences or to other profiles. Although there is widespread agreement on the general form substitution scores should take for profile-sequence alignment, little consensus has been reached on how best to construct profile–profile substitution scores, and a large number of these scoring systems have been proposed. Here, we assess a variety of such substitution scores. For this evaluation, given a gold standard set of multiple alignments, we calculate the probability that a profile column yields a higher substitution score when aligned to a related than to an unrelated column. We also generalize this measure to sets of two or three adjacent columns. This simple approach has the advantages that it does not depend primarily upon the gold-standard alignment columns with the weakest empirical support, and that it does not need to fit gap and offset costs for use with each substitution score studied.Results: A simple symmetrization of mean profile-sequence scores usually performed the best. These were followed closely by several specific scoring systems constructed using a variety of rationales.Contact: altschul@ncbi.nlm.nih.govSupplementary Information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Drought is one of the major natural disasters causing huge agricultural losses annually. Regional agricultural drought risk assessment has great significance for reducing regional disaster and agricultural drought losses. Based on the fuzzy characteristics of agricultural drought risk, variable fuzzy sets model was used for comprehensively assessing agricultural drought risk of Liaoning Province in China. A multi-layers and multi-indices assessment model was established according to variable fuzzy sets theory, and agricultural drought risk of all 14 prefecture-level cities was respectively estimated in terms of dangerousness, vulnerability, exposure and drought-resistibility. By calculating the combination weights of four drought risk factors, agricultural drought risk grade of each city was obtained. Based on the assessment results, the spatial distribution maps of agricultural drought risk were drawn. The results shows that eastern cities have lower drought dangerousness than western cities in Liaoning Province totally. Most cities are located in low drought vulnerability region and high drought exposure region. Because of frequent and severe drought since 2000, most cities are located in lower drought-resistibility region. Comprehensive agricultural drought risk presents apparent spatial characteristics, escalating from the east to the west. Drought dangerousness is the most important factor influencing comprehensive agricultural drought risk. Through the spatial distribution maps of drought risk, decision makers could find out drought situation and make decisions on drought resistance conveniently. [ABSTRACT FROM AUTHOR]

• Estrogen receptor α deficiency produced a depression-like effect in the tail suspension test and forced swimming test of female mice. • Estrogen receptor α deficiency affected cardiac function by aggravating cardiomyocyte disorders of female mice. • Estrogen receptor α deficiency simultaneously influenced BDNF-AKT/ERK1/2 signaling in the hippocampus and heart of female mice. • Quercetin alleviated ERα-/--induced depression-like responses and cardiac dysfunction through BDNF-TrkB-AKT and ERK1/2 signaling. Brain-derived neurotrophic factor (BDNF) is the potential link between depression and cardiovascular disease and estrogen receptor α (ERα), an estrogen-mediated major regulator, plays an important role in protecting against depression and cardiovascular disease. However, the relationship between BDNF and ERα remains obscure. Herein, quercetin (QUE), a kind of plant flavonoids and existed in many vegetables and fruits, was found to simultaneously reverse ERα−/−-induced depression-like and cardiac dysfunction by reducing immobility time in the tail suspension test (TST) and forced swimming test (FST), and decreasing systolic blood pressure and activating the apoptosis-related proteins, BDNF, tropomyosin-related kinase B (TrkB), protein kinase B (AKT), and extracellular regulatory protein kinase (ERK1/2) in the hippocampal and cardiac tissues of female mice. These findings suggested that ERα might be involved in the regulation of BDNF activity, thereby regulating depression-like and cardiovascular responses in female mice, and QUE exerted significant antidepressant and cardioprotective effects, at least in part, through BDNF-TrkB-AKT/ERK1/2 to effectively inhibit ERα−/−-induced hippocampal and cardiac dysfunction. [ABSTRACT FROM AUTHOR]

With the acceleration of aging process of population structure, the single resident lifestyle is increasing on account of the high cost of care services and the privacy invasion concern. It is essential to monitor the activities of solitary elderly to find the emergency and lifestyle deviation, as independent life cannot be maintained due to physical or mental problems. The unobtrusive systems are the most preferred choice for the real-life long-term monitoring, while the camera and wearable devices based systems are not suitable due to the privacy and uncomfortableness, respectively. We propose a novel sensor-based activity recognition model based on the two-layer multi-granularity framework and the emergent paradigm with marker-based stigmergy. The stigmergy based marking subsystem builds features by aggregating the context-aware information and generating the two-dimensional activity pheromone trail. The two-layer framework consists of coarse-grained and fine-grained classification subsystems. The coarse-grained subsystem identifies whether the input completed activity segmented by the traditional method is easily-confused, and utilizes our generalized segmentation method to increase the inter-cluster distance. The fine-grained subsystem employs machine learning or deep learning classifiers to realize the activity recognition task. The proposed model is a data-driven model based on the information self-organization. It does not need sophisticated domain knowledge, and can fully mine the hidden feature structure containing semantically related information and spatio-temporal characteristics. The experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Currently, there is no universally accepted standard treatment for ocular myasthenia gravis (OMG) in children. We aimed to investigate the possible proper regimens and timing of treatment for pediatric OMG cases based on the clinical manifestations: OMG with ptosis only and OMG with other features. One hundred and forty two OMG cases attended at the Department of Pediatrics, Xiangya Hospital, Central South University, from 2010 to 2019 were included, and information from medical records was reviewed and recorded. Comparisons of clinical characteristics between patients with OMG with ptosis only and patients with OMG with other features as well as between patients treated with glucocorticoid (GC) within or after six months from disease onset were performed. OMG with other features constituted about 54.9% of the cases, and 66.2% of the patients achieved optimal outcome. Patients with OMG with ptosis only responded to pyridostigmine alone more than patients with OMG with other features who required several therapies (P < 0.001). Patients with OMG with ptosis only had a larger proportion of optimal outcome than the patients with OMG with other features (P = 0.002), and the difference remained significant even when the individual outcome groups were compared (P < 0.001). Patients who received GC within six months had a greater proportion of optimal outcome than those who received it after six months (P < 0.001). Although OMG with other features is a more common subtype of OMG, it is also more severe than OMG with ptosis only. An earlier addition of GC leads to optimal outcome. [ABSTRACT FROM AUTHOR]

Terpenoids are a class of structurally complex, naturally occurring compounds found predominantly in plant, animal, and microorganism secondary metabolites. Classical terpenoids typically have carbon atoms in multiples of five and follow well-defined carbon skeletons, whereas noncanonical terpenoids deviate from these patterns. These noncanonical terpenoids often result from the methyltransferase-catalyzed methylation modification of substrate units, leading to irregular carbon skeletons. In this comprehensive review, various activities and applications of these noncanonical terpenes have been summarized. Importantly, the review delves into the biosynthetic pathways of noncanonical terpenes, including those with C6, C7, C11, C12, and C16 carbon skeletons, in bacteria and fungi host. It also covers noncanonical triterpenes synthesized from non-squalene substrates and nortriterpenes in Ganoderma lucidum, providing detailed examples to elucidate the intricate biosynthetic processes involved. Finally, the review outlines the potential future applications of noncanonical terpenoids. In conclusion, the insights gathered from this review provide a reference for understanding the biosynthesis of these noncanonical terpenes and pave the way for the discovery of additional unique and novel noncanonical terpenes. Key points: •The activities and applications of noncanonical terpenoids are introduced. •The noncanonical terpenoids with irregular carbon skeletons are presented. •The microbial biosynthesis of noncanonical terpenoids is summarized. [ABSTRACT FROM AUTHOR]

This article concerns the issue of how to combine the multidomainradar information, including range–Doppler, time–Doppler, and time–range, for human activity recognition. Specifically, to fully make use of radar information, instead of using a single-domain spectrum as inputs, a novel hybrid neural network model is developed for exploring multidomain fusion of radar information. In doing this, three kinds of 2-D domain spectra are used in a fashion of supplementing each other with a hybrid framework that combines three models: 1-D convolution neural network, recurrent neural network, and 2-D convolution network. It is advantageous to use such a hybrid model to capture much rich features through multidomain feature fusion, so as to improve the accuracy of human activity recognition effectively. Experimental results validate the proposed method. [ABSTRACT FROM AUTHOR]

Cooperation between upstream suppliers and downstream manufacturers in technology investment is a popular way to improve production technology for reducing suppliers' production costs of key components. The suppliers' cooperation strategies are mainly influenced by manufacturers' technology investments and wholesale price discount contracts provided by suppliers. This paper explores whether a supplier should cooperate with two downstream competing manufacturers to accept their technology investments to reduce the supplier's production cost of a key component. Specifically, we consider the following three cooperation strategies: The supplier does not accept manufacturers' technology investments, only accepts one manufacturer's technology investment and accepts both manufacturers' technology investments. Our results demonstrate that the wholesale price discount contract and the technology investment can enhance the profits of the supplier and two manufacturers when the discount degree is low. Further, we conclude that when the discount degree is relatively low or when both the discount degree and the technology investment efficiency are relatively high, the supplier's optimal cooperation strategy with two manufacturers is to accept both manufacturers' technology investments and both manufacturers are also willing to invest simultaneously. At last, we extend the model to the asymmetric potential market size and show that our theoretical results are robust. [ABSTRACT FROM AUTHOR]

Hydrogeological parameters uncertainty is a major source of error in groundwater modeling.It is essential to analyze the effects of stochastic parameters uncertainty on predictinghydraulic heads and contaminant concentration. In this paper, a series of numericalexperiments were performed based on a groundwater numerical simulation to discuss theinfluence of parameters uncertainty on the prediction results. Hydrogeological parameterswith high sensitivity are identified by using local sensitivity analysis and taken as randomparameters. In order to analyze parameters uncertainty, the Markov chain Monte Carlomethod is used to select the groups of parameters which converge to the Bayesianposterior distribution. Then, the simulation results come out under different groups ofparameters which can show the distribution of hydraulic heads and contaminantconcentration, and the groundwater pollution risk at every point. It can providereference to groundwater resources evaluation and groundwater pollution control. [ABSTRACT FROM AUTHOR]

It is a conspicuous contradiction between sediment silting, flood control and water utilizationduring flood season for Naodehai Reservoir. Reservoir sediment silting and water level jointcontrol during flood season is in urgent need to study. As a basis for joint control, it isnecessary to study out flow water and sediment relations of the reservoir. In this paper, theauthors obtain practical achievement through the analysis to out flow water and sedimentrelation in different situation. The achievement sets the stage for the future water andsediment joint operation, at the same time; it provides the new thought for the similarproblem in domestic. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We consider an order problem for two channels consisting of one common retailer and two competing suppliers that are subject to supply uncertainty. A new concept of supply risk level (SRL) of a channel is proposed to quantitatively characterize the supply risk of the channel due to supply uncertainty. Under different SRLs, we study the order strategies for the two channels in integrated and decentralized supply chains. Regardless of whether the game is integrated or decentralized, we find that the different SRLs give rise to a difference between the belief-degree costs of the two channels that directly influences the optimal order strategy, market supply and profit of each channel. This implies that the decision maker of the supply chain can take different risk attitudes toward the supply uncertainty of the two channels to adjust the potential market supply and profit of each channel because the decision maker generally replaces the SRL with his or her risk preference. Under given SRLs, we find that integration is a better strategy than decentralization. However, the channel profit with a higher belief-degree cost under decentralization is greater than that under integration in some cases. Finally, proper risk preferences for both channels are suggested to strike a balance between supply reliability and supply risk. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The traditional Chinese medicine (TCM) Rehmanniae Radix (RR) refers to the fresh or dried root tuber of the plant Rehmannia glutinosa Libosch of the family Scrophulariaceae. As a traditional Chinese herbal medicine (CHM), it possesses multiple effects, including analgesia, sedation, anti-inflammation, antioxidation, anti-tumor, immunomodulation, cardiovascular and cerebrovascular regulation, and nerve damage repair, and it has been widely used in clinical practice. In recent years, scientists have extensively studied the active components and pharmacological effects of RR. Active ingredients mainly include iridoid glycosides (such as catalpol and aucuboside), phenylpropanoid glycosides (such as acteoside), other saccharides, and unsaturated fatty acids. In addition, the Chinese patent medicine (CPM) and Chinese decoction related to RR have also become major research subjects for TCM practitioners; one example is the Bolus of Six Drugs, which includes Rehmannia, Lily Bulb and Rehmannia Decoction, and Siwu Decoction. This article reviews recent literature on RR; summarizes the studies on its chemical constituents, pharmacological effects, and clinical applications; and analyzes the progress and limitations of current investigations to provide reference for further exploration and development of RR. [Display omitted] • Rehmanniae Radix possesses multiple effects, including antioxidation and anti-tumor. • Its active ingredients mainly include iridoid and phenylpropanoid glycosides. • Chinese decoctions related to Rehmanniae Radix are widely used in clinical practice. [ABSTRACT FROM AUTHOR]

Terpenoids are a class of structurally complex, naturally occurring compounds found predominantly in plant, animal, and microorganism secondary metabolites. Classical terpenoids typically have carbon atoms in multiples of five and follow well-defined carbon skeletons, whereas noncanonical terpenoids deviate from these patterns. These noncanonical terpenoids often result from the methyltransferase-catalyzed methylation modification of substrate units, leading to irregular carbon skeletons. In this comprehensive review, various activities and applications of these noncanonical terpenes have been summarized. Importantly, the review delves into the biosynthetic pathways of noncanonical terpenes, including those with C6, C7, C11, C12, and C16 carbon skeletons, in bacteria and fungi host. It also covers noncanonical triterpenes synthesized from non-squalene substrates and nortriterpenes in Ganoderma lucidum, providing detailed examples to elucidate the intricate biosynthetic processes involved. Finally, the review outlines the potential future applications of noncanonical terpenoids. In conclusion, the insights gathered from this review provide a reference for understanding the biosynthesis of these noncanonical terpenes and pave the way for the discovery of additional unique and novel noncanonical terpenes. Key points: •The activities and applications of noncanonical terpenoids are introduced. •The noncanonical terpenoids with irregular carbon skeletons are presented. •The microbial biosynthesis of noncanonical terpenoids is summarized. [ABSTRACT FROM AUTHOR]

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 l 1,2 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. [ABSTRACT FROM AUTHOR]

Along with the significant improvement of environmental consciousness, consumers not only consider the price and quality level of products, but also pay more attention to their green level. In order to strengthen the competitive advantage, the manufacturers should consider the green level of products in addition to their price and the quality level. In this paper, we investigate the green investment of two competing manufacturers in a supply chain based on price and quality competition and analyze the effect of green investment on the quality level of the product. The research shows that the manufacturer is willing to make a green investment with a relatively low value of green sensitivity regardless of whether the manufacturer's rival makes a green investment. Further, we find that the profit of the manufacturer who makes a green investment is greater than the profit of the manufacturer who does not invest regardless of the market size. When both competing manufacturers make green investments, the profit of the manufacturer who is in a large potential market is higher than that of the manufacturer who is in a small potential market. While in a same potential market, the profits of the two competing manufacturers are the same. Finally, we conclude that the green investment counterintuitively will not always improve the quality level of the products. [ABSTRACT FROM AUTHOR]

Non-invasive fetal electrocardiography (NI-FECG) plays an important role in fetal heart rate (FHR) measurement during the pregnancy. However, despite the large number of methods that have been proposed for adult ECG signal processing, the analysis of NI-FECG remains challenging and largely unexplored. In this study, we propose a prefix tree-based framework, called QRStree, for FHR measurement directly from the abdominal ECG (AECG). The procedure is composed of three stages: Firstly, a preprocessing stage is employed for noise elimination. Secondly, the proposed prefix tree-based method is used for fetal QRS complexes (FQRS) detection. Finally, a correction stage is applied for false positive and false negative correction. The novelty of the framework relies on using the range of FHR to establish the connections between the FQRS. The consecutive FQRS can be considered as strings composed of alphabet items, thus we can use the prefix tree to store them. A vertex of the tree contains an alphabet, thus a path of the tree gives a string. Such that, by storing the connections of the FQRS into the prefix tree structure, the problem of FQRS detection converts to a problem of optimal path selection. Specifically, after selecting the optimal path of the tree, the nodes in the optimal path are collected as detected FQRS. Since the prefix tree can cover every possible combination of the FQRS candidates, it has the potential to reduce the occurrence of miss detections. Results on two different databases show that the proposed method is effective in FHR measurement from single-channel AECG. The focus on single-channel FHR measurement facilitates the long-term monitoring for healthcare at home. [ABSTRACT FROM AUTHOR]

We combine experiment and finite element simulation and come up with a design for a mechanical metamaterial which demonstrates snap-back induced hysteresis and energy dissipation. The resultant is an elastic system that can be used reversibly for many times. The underlying mechanism of the existence of hysteresis and the physics of snap-back induced elastic instability is unveiled. Our results open an avenue for the design and implementation of recoverable energy dissipation devices by harnessing mechanical instability. [ABSTRACT FROM AUTHOR]

The simplified circuit and modulation scheme is presented for a five‐phase three‐level voltage source inverter (FPTL‐VSI). The simplified FPTL‐VSI is a concatenation of a dual‐buck stage and a two‐level VSI (TL‐VSI), and the space vector pulse width modulation (SVPWM) is used as the modulation scheme. Owing to the merit of the dual‐buck stage which uses only four controllable switching devices, the TL‐VSI can produce a three‐level voltage waveform. By simplification of the space voltage vectors, a simplified SVPWM scheme can be obtained. Thus, the simplified FPTL‐VSI reduces the hardware costs and improves the quality of the output voltage waveforms. Furthermore, the hardware implementation of the simplified SVPWM scheme can be easily realised. The experimental results prove the feasibility and validity of the proposed inverter and the modulation scheme. [ABSTRACT FROM AUTHOR]

The diagenetic mineral assemblages in mudstones during diagenesis can indicate the changing diagenetic environmental characteristics, providing possibility to study the influence of the diagenetic environment on the evolution of organic matter (OM). In the present study, mudstones from the Dongying Depression were selected and evaluated by X-ray diffraction (XRD), thin section, and scanning electron microscopy (SEM). The SEM and XRD results indicated that detrital minerals are depositional minerals, whereas clay minerals are diagenetic minerals; carbonate are a combination of depositional and diagenetic minerals. According to the diagenetic mineral content and content change rates, three diagenetic mineral assemblages were distinguished: I. smectite/I-S (1,000-2,000 m); II. Smectite/I-S + illite + kaolinite (2,000-3,000 m); III. Smectite/I-S + illite + chlorite + ankerite (>3,000 m). Kaolinite is formed in acidic environments, while the other diagenetic minerals are formed and stable under alkaline conditions. Therefore, the evolution of the diagenetic environment was investigated using the K/(I + Ch + A) ratio (K:Kaolinite; I:Illite; Ch:Chlorite; A:Ankerite), and the formations were found to have undergone alkaline-acidic-alkaline changes. For OM with different chemical properties, huge differences exist in hydrocarbon generation between acidic and alkaline diagenetic environments. Therefore, for hydrocarbon generation, attention should be paid to the changes in diagenetic environment, which are important for understanding the hydrocarbon generation, hydrocarbon expulsion and hydrocarbon accumulation in mudstones. [ABSTRACT FROM AUTHOR]