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Objective: The study aimed to investigate the effects of Periplaneta americana glycoprotein (PAG) on the growth and metabolites of Lactobacillus plantarum and Bifidobacterium adolescentis. Methods: With inulin as a positive control, the probiotic bacteria were cultured in medium containing different concentrations of PAG (MRS medium with PAG concentrations of 0.5, 1.0, 2.0, and 4.0 mg/mL, respectively. TPY medium with PAG concentrations of 1.0, 2.0, 4.0, and 8.0 mg/mL, respectively) to determine the OD600 and pH, and the contents of lactic acid, short-chain fatty acid (SCFA, snamely acetic acid, propionic acid and butyric acid) in the bacterial solution. Results: The growth of Lactobacillus plantarum was greatest when the concentration of PAG reached 1.0 mg/mL, while Bifidobacterium adolescentis grew best with a PAG concentration of 2.0 mg/mL. The OD600 value, production of lactic acid and SCFAs of Lactobacillus plantarum solution at 1.0 mg/mL PAG were higher than those of inulin, while the pH value was significantly lower. In the bacterial solution of Bifidobacterium adolescentis with 2.0 mg/mL PAG, the values of OD600 and the production of propionic acid and butyric acid increased compared to the same concentration of inulin, the lactic acid production was better than that of inulin at 12 h, the acetic acid production was better than that of inulin at 24 h, and the pH value decreased, indicating that PAG and inulin had different effects on promoting Bifidobacterium adolescentis to produce lactic acid and SCFAs. Conclusion: The PAG could promote the proliferation and organic acid production of Lactobacillus plantarum and Bifidobacterium adolescentis, and the effect was better than that of inulin.

Abstract Atmospheric soot and organic particles from fossil fuel combustion and biomass burning modify Earth’s climate through their interactions with solar radiation and through modifications of cloud properties by acting as cloud condensation nuclei and ice nucleating particles. Recent advancements in understanding their individual properties and microscopic composition have led to heightened interest in their microphysical properties. This review article provides an overview of current advanced microscopic measurements and offers insights into future avenues for studying microphysical properties of these particles. To quantify soot morphology and ageing, fractal dimension (D f ) is a commonly employed quantitative metric which allows to characterize morphologies of soot aggregates and their modifications in relation to ageing factors like internal mixing state, core-shell structures, phase, and composition heterogeneity. Models have been developed to incorporate D f and mixing diversity metrics of aged soot particles, enabling quantitative assessment of their optical absorption and radiative forcing effects. The microphysical properties of soot and organic particles are complex and they are influenced by particle sources, ageing process, and meteorological conditions. Furthermore, soluble organic particles exhibit diverse forms and can engage in liquid–liquid phase separation with sulfate and nitrate components. Primary carbonaceous particles such as tar balls and soot warrant further attention due to their strong light absorbing properties, presence of toxic organic constituents, and small size, which can impact human health. Future research needs include both atmospheric measurements and modeling approaches, focusing on changes in the mixing structures of soot and organic particle ensembles, their effects on climate dynamics and human health.

As a cross-perception and cognitive research field in video understanding, motion training feature recognition is a very challenging task to establish a good spatio-temporal modeling of human motion due to the uncertainty of human motion speed, start and end time, appearance and posture, as well as the interference of physical factors such as lighting, perspective and occlusion. The purpose of this study is to use artificial intelligence data mining technology to study the feature recognition application of iot voice devices in sports training. Install the sensor in the appropriate position according to the position and posture to be measured. Ensure that the sensor can accurately measure the relevant features and maintain a stable connection. Using iot voice devices for data acquisition, sensors collect data on relevant features in real time to transmit the data to a cloud platform or local processing device via a wireless connection. By analyzing and mining the data collected by iot voice devices, we hope to effectively identify the characteristics of sports training and provide accurate feedback and guidance for athletes and coaches. The experimental results show that the iot voice device based on artificial intelligence data mining has achieved good results in the feature recognition application of sports training. Through the analysis of sports training data, we can successfully identify the characteristic patterns of different movements, and accurately predict the athletic state and posture of athletes.

Abstract Using the event-related potentials (ERPs) technique, this study successively presented names (in either a supra- or subthreshold manner) and emotional words to examine how self-relevant cue (self-name) affects emotional word processing in word class judgment task (to determine whether an emotional word is a noun or adjective) and valence judgment task (to determine whether an emotional word is positive or negative). At the suprathreshold condition, self-relevant positive words elicited a more significant Early posterior negativity (EPN) than negative words only in the valence judgment task. In contrast, at the subthreshold condition, self-relevant negative words elicited an enhanced Late positive potential (LPP) than positive words only in the word class judgment task. These results indicate that self-relevant cue affects emotional word processing at both suprathreshold and subthreshold conditions; nevertheless, the effect manifests as self-positive bias at the suprathreshold condition and self-negative bias at the subthreshold condition. The experimental task modulates these dynamics.

Abstract How to represent a human face pattern? While it is presented in a continuous way in human visual system, computers often store and process it in a discrete manner with 2D arrays of pixels. The authors attempt to learn a continuous surface representation for face image with explicit function. First, an explicit model (EmFace) for human face representation is proposed in the form of a finite sum of mathematical terms, where each term is an analytic function element. Further, to estimate the unknown parameters of EmFace, a novel neural network, EmNet, is designed with an encoder‐decoder structure and trained from massive face images, where the encoder is defined by a deep convolutional neural network and the decoder is an explicit mathematical expression of EmFace. The authors demonstrate that our EmFace represents face image more accurate than the comparison method, with an average mean square error of 0.000888, 0.000936, 0.000953 on LFW, IARPA Janus Benchmark‐B, and IJB‐C datasets. Visualisation results show that, EmFace has a higher representation performance on faces with various expressions, postures, and other factors. Furthermore, EmFace achieves reasonable performance on several face image processing tasks, including face image restoration, denoising, and transformation.

Abstract Sensitive and stable perovskite X-ray detectors are attractive in low-dosage medical examinations. The high sensitivity, tunable chemical compositions, electronic dimensions, and low-cost raw materials make perovskites promising next-generation semiconductors. However, their ionic nature brings serious concerns about their chemical and water stability, limiting their applications in well-established technologies like crystal polishing, micro-processing, photolithography, etc. Herein we report a one-dimensional tryptamine lead iodide perovskite, which is stable in water for several months as the strong cation-π interactions between organic cations. The one-dimensional and two-dimensional tryptamine lead iodide perovskite tablets are switchable through thermal-annealing or water-soaking treatments to relax microstrains. The water-stable and microstrain-free one-dimensional perovskite tablets yield a large sensitivity of 2.5 × 106 μC Gyair −1 cm−2 with the lowest detectable dose rate of 5 nGyair s−1. Microelectrode arrays are realized by surface photolithography to construct high-performance X-ray flat mini-panels with good X-ray imaging capability, and a record spatial resolution of 17.2 lp mm−1 is demonstrated.

Abstract Advanced photodetectors with intelligent functions are expected to take an important role in future technology. However, completing complex detection tasks within a limited number of pixels is still challenging. Here, we report a differential perovskite hemispherical photodetector serving as a smart locator for intelligent imaging and location tracking. The high external quantum efficiency (~1000%) and low noise (10−13 A Hz−0.5) of perovskite hemispherical photodetector enable stable and large variations in signal response. Analysing the differential light response of only 8 pixels with the computer algorithm can realize the capability of colorful imaging and a computational spectral resolution of 4.7 nm in a low-cost and lensless device geometry. Through machine learning to mimic the differential current signal under different applied biases, one more dimensional detection information can be recorded, for dynamically tracking the running trajectory of an object in a three-dimensional space or two-dimensional plane with a color classification function.

Non-routine activities such as startup, shutdown, maintenance, and operation commissioning require increased human interaction with the corresponding process. Owing to operator or procedural violations, the risk of accidents can be high during non-routine activities, even though they are performed less frequently. To identify and evaluate the hazards of non-routine processes, an integrated method combining job hazard analysis (JHA), hazard and operability analysis (HAZOP), and deviation degrees is proposed. JHA is applied to break down an operational process into steps, which are further defined as nodes in HAZOP for hazard scenario analysis. The concept of deviation degree is defined by integrating the operational and control function deviations to quantify the deviation analysis. Finally, the heating-furnace startup process in an oil and gas gathering and transmission station was selected to illustrate the proposed integrated method. The results show that this method constitutes a systematical and intuitive approach to identify hazard scenarios and evaluate risks, as well as to establish preventive measures for non-routine processes.

Summary: Incomplete combustion of fossil fuels and biomass burning emit large amounts of soot particles into the troposphere. The condensation process is considered to influence the size (Dp) and mixing state of soot particles, which affects their solar absorption efficiency and lifetimes. However, quantifying aging evolution of soot remains hampered in the real world because of complicated sources and observation technologies. In the Himalayas, we isolated soot sourced from transboundary transport of biomass burning and revealed soot aging mechanisms through microscopic observations. Most of coated soot particles stabilized one soot core under Dp

Textureless building surfaces composed of homogenized pixels could lead to failure of photometric consistency. However, textureless regions widely present in artificial scenes usually exhibit strong planarity enabling depth estimation of textureless regions with planar priors. However, existing methods for generating planar priors suffer from oversegmentation of large planes with textureless regions, which indicates that planarity is not fully exploited. In this study, we propose a novel generation method of planar prior by combining mean-shift clustering and superpixel segmentation. The planarity is fully utilized given preferential generation of planar priors for large planes with textureless regions in artificial scenes. Finally, a probabilistic graphical model is used to adopt the planar priors and smoothing constraints into depth estimation process. The image gradient is used as a criterion of the degree of texture to adaptively adjust the weights of different constraints. Experimental results on the benchmark dataset ETH3D, UDD5, and SenseFly demonstrate that the proposed method can effectively recover the depth information of textureless regions in high-resolution images to obtain highly complete three-dimensional (3-D) models of artificial scenes.

The task of named entity recognition (NER) is to identify entities in the text and predict their categories. In real-life scenarios, the context of the text is often complex, and there may exist nested entities within an entity. This kind of entity is called a nested entity, and the task of recognizing entities with nested structures is referred to as nested named entity recognition. Most existing NER models can only handle flat entities, and there has been limited research progress in Chinese nested named entity recognition, resulting in relatively few models in this direction. General NER models have limited semantic extraction capabilities and cannot capture deep semantic information between nested entities in the text. To address these issues, this paper proposes a model that uses the GlobalPointer module to identify nested entities in the text and constructs the IDCNNLR semantic extraction module to extract deep semantic information. Furthermore, multiple-head self-attention mechanisms are incorporated into the model at multiple positions to achieve data denoising, enhancing the quality of semantic features. The proposed model considers each possible entity boundary through the GlobalPointer module, and the IDCNNLR semantic extraction module and multi-position attention mechanism are introduced to enhance the model’s semantic extraction capability. Experimental results demonstrate that the proposed model achieves F1 scores of 69.617% and 79.285% on the CMeEE Chinese nested entity recognition dataset and CLUENER2020 Chinese fine-grained entity recognition dataset, respectively. The model exhibits improvement compared to baseline models, and each innovation point shows effective performance enhancement in ablative experiments.

Abstract Strict emission control policies implemented in two megacities of New York City (NYC) and Beijing show impacts on the non-linear relationship of their ozone (O3) and fine particulate matter (PM2.5) during summertime. Here we show these non-linear O3-PM2.5 relationships including a positive linear part reflecting the O3/PM2.5 co-occurrence and a negative power function part reflecting the O3 formation suppression by PM2.5 based on the multiyear surface observations. The control policies targeting sulfur dioxide and PM2.5, then volatile organic compounds and nitrogen oxides, changed the PM2.5 chemical composition which resulted in an increased linear slope that indicates a weaker O3 control effect than occurred for PM2.5. These policies also enhanced the relative PM2.5 suppression effect as shown by an increase in the power function coefficient. Model simulations suggest that regional equal percentage emission reductions for Beijing and other Chinese megacities will be necessary to avoid further increase in the O3/PM2.5 linear slope and continuing occurrences of high levels of ozone.

Abstract Atmospheric particulate matter (PM) has a significant impact on both the natural environment and human health. Iron is one of the most abundant elements in the earth’s crust, playing an important role in geochemical processes, and is also an important trace component in atmospheric PM. In recent years, with the rapid development of non-traditional (metal) stable isotope technologies, new solutions and methods for the source apportionments of heavy metal elements have been put forward. Stable iron isotope analysis has become an effective tool to trace iron in atmospheric particles. This review paper briefly summarizes the recent progress of atmospheric iron isotope geochemistry. We show that some of the major natural and anthropogenic PM sources have different iron isotopic compositions. A Bayesian isotopic mixing model MixSIAR was used to quantitatively re-evaluate the contributions of different sources to iron in both urban and marine aerosols based on iron isotopic data in the literature. The results highlight the value of stable iron isotope analyses as an effective tool in the source apportionment of atmospheric aerosols.

Abstract Size distribution of aerosol iron (Fe) concentration and solubility determines their environmental and health impacts but they are rarely reported. In this study, size-resolved aerosol particles were collected in Hangzhou, a Chinese megacity upwind of the Asian outflow to the Pacific Ocean. Total and dissolved Fe collected under non-haze, haze, and dust conditions were determined. Results show that total Fe concentrations displayed a unimodal distribution peaking at 3.2–5.6 μm under non-haze and dust conditions, but a bimodal distribution under the haze condition peaking at 3.2–5.6 μm and 0.32–0.56 μm. Dissolved Fe under the non-haze, haze, and dust conditions all displayed bimodal size distributions with one peak in the fine-mode (0.056–1.0 μm) fraction and one peak in the coarse-mode (>1.0 μm) fraction. Fe enrichment factor in fine-mode particles under the haze condition is up to 17.8, suggesting a potential contribution from anthropogenic Fe. Fe solubility in total suspended particles under the haze condition (3.8%) is significantly higher than those under non-haze (2.6%) and particularly dust (0.5%) conditions. Furthermore, Fe solubilities in fine-mode particles under non-haze (10.8%) and haze (10.9%) conditions were much higher than those (1.5% and 2.4%) in coarse-mode particles. We found that Fe solubility was correlated positively with aerosol acidity and negatively with particle size from 0.32 to 5.6 μm. Individual particle analysis further reveals that nanosized iron oxides were internally mixed with acidic sulfates/nitrates under the haze condition. Our results indicate that the strong size dependence of aerosol Fe solubility is associated with the differences in sources of aerosol Fe and its acidic processing.

Redistribution of black carbon from the inorganic core to the organic coating of aged aerosols due to liquid-liquid phase separation can reduce radiative absorption by up to 34%, according to field observations and laboratory experiments.

The intervention time of stability control system is determined by stability judgment, which is the basis of vehicle stability control. According to the different working conditions of the vehicle, we construct the phase plane of the vehicle's sideslip angle and sideslip angular velocity, and establish the sample dataset of the stable region of the different phase planes. To reduce the complexity of phase plane stable region division and avoid large amount of data, we established the support vector regression (SVR) model, and realized the automatic regression of dynamic stable region. The testing of the test set shows that the model established in this paper has strong generalization ability. We designed a direct yaw-moment control (DYC) stability controller based on linear time-varying model predictive control (LTV-MPC). The influence of key factors such as centroid position and road adhesion coefficient on the stable region is analyzed through phase diagram. The effectiveness of the stability judgment and control algorithm is verified by simulation tests.

The task of fine-grained named entity recognition is to locate entities in text and classify them into predefined fine-grained categories. At present, Chinese fine-grained NER only uses the pretrained language model to encode the characters in the sentence and lacks the ability to extract the deep semantic, sequence, and position information. The sequence annotation method is character-based and lacks the processing of entity boundaries. Fine-grained entity categories have a high degree of similarity, which makes it difficult to distinguish similar categories. To solve the above problems, this paper constructs the BILTAR deep semantic extraction module and adds the GlobalPointer module to improve the accuracy of Chinese fine-grained named entity recognition. The BILTAR module is used to extract deep semantic features from the coding information of pretrained language models and use higher-quality features to improve the model performance. In the GlobalPointer module, the model first adds the rotation position encoding information to the feature vector, using the position information to achieve data enhancement. Finally, the model considers all possible entity boundaries through the GlobalPointer module and calculates the scores for all possible entity boundaries in each category. In this paper, all possible entity boundaries in the text are considered by the above method, and the accuracy of entity recognition is improved. In this paper, the corresponding experiments were carried out on CLUENER 2020 and the micro Chinese fine-grained NER dataset, and the F1 scores of the model in this paper reached 80.848% and 75.751%, respectively. In ablation experiments, the proposed method outperforms the most advanced baseline model and improves the performance of the basic model.

City gas explosion accidents have occurred frequently in recent years, leading to major injuries and losses. An effective emergency response process is necessary to deal with city gas accidents by preventing them from expanding and reducing losses. However, emergency response is a complex process that includes many contributing factors, such as personnel, departments, and materials. If one or more of these factors fails, it will affect the implementation of the emergency response. Therefore, more efforts should be made on the systematic procedures and methods addressing risks in emergency response process. From the view of Process Hazard Analysis (PHA), the emergency response process can be seen as a work process with many operation nodes. In this sense, PHA methods such as HAZOP can be used for the risk analysis of the emergency response process. First, the emergency response process of a city gas explosion accident is broken down into procedure-based operations. Then HAZOP is used to comprehensively review the process and obtain deviations, causes, and consequences, which are considered as risk factors. Finally, Bayesian networks are used to characterize the inter relationships of various factors and quantify the risk of the emergency response process. Furthermore, the most critical events can be identified based on the sensitivity analysis. The results show that firefighters' operations, management of the emergency command center, and environmental conditions on site are critical factors during city gas explosion accidents. The method proposed can help identify potential risks of the emergency response process systematically and prevent accident expansion.

BackgroundSocial anxiety disorder (SAD) is characterized by abnormal fear to social cues. Although unisensory processing to social stimuli associated with social anxiety (SA) has been well described, how multisensory processing relates to SA is still open to clarification. Using electroencephalography (EEG) measurement, we investigated the neural correlates of multisensory processing and related temporal dynamics in social anxiety disorder (SAD).MethodsTwenty-five SAD participants and 23 healthy control (HC) participants were presented with angry and neutral faces, voices and their combinations with congruent emotions and they completed an emotional categorization task.ResultsWe found that face-voice combinations facilitated auditory processing in multiple stages indicated by the acceleration of auditory N1 latency, attenuation of auditory N1 and P250 amplitudes, and decrease of theta power. In addition, bimodal inputs elicited cross-modal integrative activity which is indicated by the enhancement of visual P1, N170, and P3/LPP amplitudes and superadditive response of P1 and P3/LPP. More importantly, excessively greater integrative activity (at P3/LPP amplitude) was found in SAD participants, and this abnormal integrative activity in both early and late temporal stages was related to the larger interpretation bias of miscategorizing neutral face-voice combinations as angry.ConclusionThe study revealed that neural correlates of multisensory processing was aberrant in SAD and it was related to the interpretation bias to multimodal social cues in multiple processing stages. Our findings suggest that deficit in multisensory processing might be an important factor in the psychopathology of SA.

Considering the complexity of entity pair relations and the information contained in the target neighborhood in few-shot knowledge graphs (KG), existing few-shot KG completion methods generally suffer from insufficient relation representation learning capabilities and neglecting the contextual semantics of entities. To tackle the above problems, we propose a Few-shot Relation Learning-based Knowledge Graph Completion model (FRL-KGC). First, a gating mechanism is introduced during the aggregation of higher-order neighborhoods of entities in formation, enriching the central entity representation while reducing the adverse effects of noisy neighbors. Second, during the relation representation learning stage, a more accurate relation representation is learned by using the correlation between entity pairs in the reference set. Finally, an LSTM structure is incorporated into the Transformer learner to enhance its ability to learn the contextual semantics of entities and relations and predict new factual knowledge. We conducted comparative experiments on the publicly available NELL-One and Wiki-One datasets, comparing FRL-KGC with six few-shot knowledge graph completion models and five traditional knowledge graph completion models for five-shot link prediction. The results showed that FRL-KGC outperformed all comparison models in terms of MRR, Hits@10, Hits@5, and Hits@1 metrics.

Link prediction is a crucial problem in the analysis of graph-structured data, and graph neural networks (GNNs) have proven to be effective in addressing this problem. However, the computational and temporal costs associated with large-scale graphs remain a concern. This study introduces a novel method for link prediction called Sparse Subgraph Prediction Based on Adaptive Attention (SSP-AA). The method generates sparse subgraphs and utilizes Graph SAmple and aggreGatE (GraphSAGE) for prediction, aiming to reduce computation and time costs while providing a foundation for future exploration of large-scale graphs. Certain key issues in GraphSAGE are addressed by integrating an adaptive attention mechanism and a jumping knowledge module into the model. To address the issue of adaptive weight distribution in GraphSAGE, an aggregation function is employed, which is based on the attention mechanism. This modification enables the model to distribute weights adaptively among neighboring nodes, significantly improving its ability to capture node relationships. Furthermore, to tackle the common issue of over-smoothing in GNNs, a jumping knowledge module is integrated, enabling information sharing across different layers and providing the model with the flexibility to select the appropriate representation depth based on the specific situation. By enhancing the quality of node representations, SSP-AA further boosts the performance of GraphSAGE in various prediction tasks involving graph-structured data.

ObjectiveThe retrospective study aimed to explore the difference in mood outcomes and cognitive function between high-frequency repetitive transcranial magnetic stimulation (HF-rTMS) over dorsolateral prefrontal cortex (DLPFC) and electroconvulsive therapy in major depression disorder (MDD) patients and to examine the improvement of HF-rTMS on cognitive impairment evoked by electroconvulsive therapy (ECT).Materials and methodsA total of 116 participants with MDD, who completed a 4-week follow-up assessment, were enrolled. The cohort consisted of 26 cases classed as control, 46 participants administrated with HF-rTMS (HF-rTMS group), 22 patients treated with ECT (ECT group), and 23 cases treated with HF-rTMS and ECT at the course of hospitalization (HF-rTMS + ECT group). Medication was kept constant as well in all participants. The 17-item Hamilton Depression Rating Scale for Depression (HAMD-17) and 14-item Hamilton Anxiety Rating Scale (HAMA-14) were used to assess depression and anxiety, respectively. Montreal Cognitive Assessment (MoCA) was to elevate cognitive function.ResultsNo statistical significance was found for baseline in sociodemographic, characteristics of depression, anxiety and cognition, and psychopharmaceutic dosages among control, HF-rTMS, ECT, and HF-rTMS + ECT groups (p > 0.05). Compared with baseline level, total scores of HAMD-17 and HAMA-14 significantly decreased at the end of 4 weeks after treatment (p < 0.001). Furthermore, the decline in scores of HAMD-17 and its sleep disorder and retardation factors from baseline to post-treatment was greater in HF-rTMS, ECT, and HF-rTMS + ECT group than in control (p < 0.05), and there was a significant difference between control and HF-rTMS group in the decline of psychological factor of HAMA-14 (p < 0.01). ECT treatment evoked total score of MoCA to decrease significantly at the end of 4-week after intervention (p < 0.001), and the decline in scores of MoCA and its delayed recall and language performances from baseline to post-treatment was greater in ECT than control, HF-rTMS, and HF-rTMS + ECT (p < 0.05).ConclusionHigh-frequency repetitive transcranial magnetic stimulation improved psychological anxiety and ameliorated the cognition impairment evoked by ECT though it had the same anti-depressant efficacy as ECT.

ObjectiveIn this study, we sought to explore the effectiveness of bilateral repetitive transcranial magnetic stimulation (rTMS) over the dorsolateral prefrontal cortex (DLPFC) on depressive symptoms and dysfunction of hypothalamic–pituitary–adrenal (HPA) axis in patients with major depressive disorder (MDD).Materials and methodsOne hundred and thirty-six adults with MDD were administrated drugs combined with 3 weeks of active rTMS (n = 68) or sham (n = 68) treatment. The 17-item Hamilton Depression Rating Scale for Depression (HAMD-17) was to elevate depression severity at baseline and weeks 4. To test the influence of rTMS on the HPA axis, plasma adrenocorticotropic hormone (ACTH) and serum cortisol (COR) were detected in pre- and post-treatment.ResultsNo statistical significance was found for the baseline of sociodemographic, characteristics of depression, and psychopharmaceutical dosages between sham and rTMS groups (p > 0.05). There was a significant difference in the HAMD-17 total score between the two groups at end of 4 weeks after treatment (p < 0.05). Compared to the sham group, the rTMS group demonstrated a more significant score reduction of HAMD-17 and sleep disorder factor (HAMD-SLD) including sleep onset latency, middle awakening, and early awakening items at end of 4-week after treatment (p < 0.05). Furthermore, total score reduction of HAMD-17 was correlated with a decrease in plasma ACTH, not in COR, by rTMS stimulation (p < 0.05).ConclusionBilateral rTMS for 3 weeks palliated depression via improvement of sleep disorder, and plasma ACTH is a predictor for the efficacy of rTMS, especially in male patients with MDD.

Sulfate aerosols are an important component of wintertime haze events in China, but their production mechanisms are not well known. Here, the authors show that transition metal-catalyzed oxidation of SO2 on aerosol surfaces could be the dominant sulfate formation pathway in Northern China.

Abstract Metal halide perovskite scintillators encounter unprecedented opportunities in indirect ionizing radiation detection due to their high quantum yields. However, the long scintillation lifetime of microseconds upon irradiation, known as the afterglow phenomenon, obviously limits their fast development. Here, a new type of hybrid X‐ray detector wafer combining direct methylamine lead iodide (MAPbI3) semiconductor and indirect zero‐dimensional cesium copper iodide (Cs3Cu2I5) scintillator through low‐cost fast tableting processes is reported. Due to the fast energy transfer from Cs3Cu2I5 to MAPbI3, the device response time to X‐rays is dramatically reduced by nearly 30 times to 36.6 ns, which enables fast X‐ray detection capability by a large area detector arrays within 1 s. Moreover, Cs3Cu2I5 exists at the grain boundaries of MAPbI3 crystals, and blocks the paths of mobile ions of perovskite, leading to the lowest detectable dose rate of hybrid X‐ray detector is thus reduced by 1.5 times compared with control MAPbI3 direct‐type semiconductor, and 10 times compared with the Cs3Cu2I5 indirect‐type scintillator. The direct/indirect hybrid wafer also exhibits improved operation stability at ambient conditions without any encapsulation. This new kind of hybrid X‐ray detectors provides strong competitiveness by combining the advantages of both direct perovskite semiconductors and indirect perovskite scintillators for next‐generation products.

Time series appear in many scientific fields and are an important type of data. The use of time series analysis techniques is an essential means of discovering the knowledge hidden in this type of data. In recent years, many scholars have achieved fruitful results in the study of time series. A statistical analysis of 120,000 literatures published between 2017 and 2021 reveals that the topical research about time series is mostly focused on their classification and prediction. Therefore, in this study, we focus on analyzing the technical development routes of time series classification and prediction algorithms. 87 literatures with high relevance and high citation are selected for analysis, aiming to provide a more comprehensive reference base for interested researchers. For time series classification, it is divided into supervised methods, semi-supervised methods, and early classification of time series, which are key extensions of time series classification tasks. For time series prediction, from classical statistical methods, to neural network methods, and then to fuzzy modeling and transfer learning methods, the performance and applications of these different methods are discussed. We hope this article can help aid the understanding of the current development status and discover possible future research directions, such as exploring interpretability of time series analysis and online learning modeling.

Particles with internal inclusions or cores are ubiquitous in the atmosphere. One example is dust particles coated with water-soluble aerosols such as sulfate or nitrate. For these particles, the dust non-sphericity and the core-shell structure of the particle both fundamentally impact the optical properties. However, it is challenging to obtain an accurate and comprehensive parameterization of the optical properties of coated atmospheric particles. Recent studies have found that super-spheroids could provide a robust model for representing the non-sphericity of irregular particles, such as dust, sea salt, and ice particles. This important finding warrants further investigations on coated super-spheroids for the development of mixed-aerosol models. In this study, we developed a GPU version of the invariant imbedding T-matrix program (GPU-IITM) to improve the computational efficiency. This version allows for efficient computation of the optical properties of coated super-spheroids with extensive shape parameters and refractive indices. The GPU-IITM is 8–25 times more efficient than the conventional CPU-IITM, depending on the size parameter of the coated super-spheroids. The single-scattering properties of coated super-spheroids computed from the GPU-IITM allows for close examination of the optical differences between homogeneous and inhomogeneous particles. As an example, we present the linear depolarization ratios (LDRs) of coated super-spheroids, which have implications in active lidar remote-sensing research. We found that the LDR decreases obviously as the real part of the shell’s refractive index increases from 1.3 to 1.6. Greater LDRs occur for particles with a thin coating, while a rounder shape of the particle decreases the LDRs. However, special attention should be given to nearly spherical particles, for which the LDRs could be enhanced. In comparison to homogeneous particles, the degree of enhancement decreases or increases depending on whether the shell’s refractive index is smaller or larger than the core refractive index.

Abstract Background Acyl-CoA-binding proteins (ACBPs) possess a conserved acyl-CoA-binding (ACB) domain that facilitates binding to acyl-CoA esters and trafficking in eukaryotic cells. Although the various functions of ACBP have been characterized in several plant species, their structure, molecular evolution, expression profile, and function have not been fully elucidated in Zea mays L. Results Genome-wide analysis identified nine ZmACBP genes in Z. mays, which could be divided into four distinct classes (class I, class II, class III, and class IV) via construction of a phylogenetic tree that included 48 ACBP genes from six different plant species. Transient expression of a ZmACBP-GFP fusion protein in tobacco (Nicotiana tabacum) epidermal cells revealed that ZmACBPs localized to multiple different locations. Analyses of expression profiles revealed that ZmACBPs exhibited temporal and spatial expression changes during abiotic and biotic stresses. Eight of the nine ZmACBP genes were also found to have significant association with agronomic traits in a panel of 500 maize inbred lines. The heterologous constitutive expression of ZmACBP1 and ZmACBP3 in Arabidopsis enhanced the resistance of these plants to salinity and drought stress, possibly through alterations in the level of lipid metabolic and stress-responsive genes. Conclusion The ACBP gene family was highly conserved across different plant species. ZmACBP genes had clear tissue and organ expression specificity and were responsive to both biotic and abiotic stresses, suggesting their roles in plant growth and stress resistance.

Abstract Ag2W2O7 nanoribbons were synthesized by hydrothermal method and then Ag/Ag2W2O7 NRs were developed by in situ chemical‐reduction with NaBH4. The samples were characterized by X‐ray diffraction (XRD), scanning electron microscopy, and UV–vis spectroscopy, high‐resolution transmission electron microscope (HRTEM), and Raman spectrum. In addition, methylene blue and Rhodamine B were selected as probe molecules to explore surface‐enhanced Raman scattering effect of Ag/Ag2W2O7 NRs. It was found that the detection of limit and enhancement factor for methylene blue is 8.91 × 10−13 m and 2.78 × 107, whereas the counterpart for Rhodamine B is 8.71 × 10−12 m and 4.34 × 106 on the Ag/Ag2W2O7 NRs substrate. Relative standard deviation was not exceed 19% when methylene blue concentration reached 1.0 × 10−6 m, it can be seen that the substrate has good uniformity, strength stability and high repeatability, which can be applied to the detection of organic dyes and additives.

Abstract Nitrogen-containing species are major components in atmospheric aerosols. However, little is known about the sources of N-containing aerosols over high mountainous regions, especially for organic nitrogen (ON). This study aims to reveal the emission sources of both inorganic and organic nitrogen in tropospheric aerosols atop Mt. Tai, China, and to improve our understanding of the N cycle imbalance in the North China Plain (NCP). Total suspended particle (TSP) samples were collected on a daytime/nighttime basis in spring 2017 and were investigated for the concentrations and stable N isotopic compositions of total nitrogen, NH4 +, NO3 − and ON. Our results show that the concentrations of N-containing compounds were higher in daytime than nighttime, mainly resulting from mountain–valley breezes and the changes of planetary boundary layer height. However, no significant day/nighttime changes were found for their corresponding δ15N values, indicating similar contributions from different N sources between day and night. The MixSIAR Bayesian stable isotope mixing model results suggest that the most important emission source of NH3 for aerosol NH4 + was agriculture, followed by fossil fuel-related sources, human waste and biomass burning. Aerosol NO3 − was mainly formed from combustion and mobile emitted NO x . Interestingly, the isotopes of ON suggest that ON were very likely firstly of primary origin. Our study reveals the characteristics of reactive N emission sources and helps understand the regional transport of tropospheric N-containing aerosols in the NCP.

Natural hazards have caused damages to buildings and infrastructures and economic losses in many countries. Immediate emergency response requires accurate damage detection. In recent studies, advances in deep learning approaches have put forward the development of data-driven damage detection. However, these proposed approaches can successfully classify damaged samples into different damage states without quantifying them. Therefore, we propose a deep learning-based framework for damage detection, which comprises signal processing and damage recognition modules. The signal processing module is introduced to denoise the noisy signals collected from sensors and process the denoised signals via fast Fourier transform. The damage recognition module is characterized by a novel network with the architecture of encoder-decoder-encoder. We train the network with only undamaged samples to capture the pattern in the undamaged state. In the testing stage, given an unknown damage state, we feed damaged samples into the trained network to quantify it by outputting the corresponding probability of damage. We conduct experiments to verify the validity and feasibility of the proposed framework with a dataset of a building subjected to excitations. Our approach can map samples from various damage states to a specific probability of damage, which increases as the intensity of excitation increases.

The complete mitochondrial genomes (mitogenomes) of beetles in the tribe Scolytoplatypodini (genus Scolytoplatypus) were sequenced and annotated. These included Scolytoplatypus raja (15,324 bp), Scolytoplatypus sinensis (15,394 bp), Scolytoplatypus skyliuae (15,167 bp), and Scolytoplatypus wugongshanensis (15,267 bp). The four mitogenomes contained 37 typical genes, including 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), and 2 ribosomal RNA genes (rRNAs). The gene orientation and arrangement of the four mitogenomes were similar to other Coleoptera mitogenomes. PCGs mostly started with ATN and terminated with TAA. The Ka/Ks ratio of 13 PCGs in the four species revealed that cox1 had the slowest evolutionary rate and atp8 and nad6 had a higher evolutionary rate. All tRNAs had typical cloverleaf secondary structures, but trnS1 lacked dihydrouridine arm. Partial tRNAs lost the discriminator nucleotide. The trnY did not possess the discriminator nucleotide and also lost three bases, showing a special amino-acyl arm. Bayesian inference (BI) and maximum likelihood (ML) methods were conducted for phylogenetic analyses using 13 PCGs. Scolytoplatypodini was clustered with Hylurgini and Hylastini, and the monophyly of Scolytoplatypodini was supported. The four newly sequenced mitogenomes increase understanding of the evolutionary relationships of Scolytoplatypodini and other Scolytinae species.

Abstract In this paper, the gravitational deflection of a relativistic massive neutral particle in the Schwarzschild-de Sitter spacetime is studied via the Rindler–Ishak method in the weak-field limit. When the initial velocity $$v_0$$ v 0 of the particle tends to the speed of light, the result is consistent with that obtained in the previous work for the light-bending case. Our result is reduced to the Schwarzschild deflection angle of massive particles up to the second order, if the contributions from the cosmological constant $$\varLambda $$ Λ are dropped. The observable correctional effects due to the deviation of $$v_0$$ v 0 from light speed on the $$\varLambda $$ Λ -induced contributions to the deflection angle of light are also analyzed.

Abstract The gravitational shift of electromagnetic frequency in the strong field limit is usually investigated under the common scenario, where the light receiver is far away from the central body while the emitter is in the strong-field region of the lens. In this paper, the gravitational frequency shift of light caused by a Reissner–Nordström (RN) black hole is studied numerically in the traditional strong-field scenario, as well as in the scenario where both the light emission and reception events happen in the strong-field region of the black hole. In order to obtain the numerical results of the gravitational redshift, we first derive the exact null equations of motion in the RN geometry in harmonic coordinates. For a given light observer, a new numerical technique is proposed in the integration of the geodesic equations to determine the spatial position of the emitter, considering the fact that their spatial positions are not always known simultaneously. Our work might be helpful to the related observations for probing strong gravity.

Face alignment is a common technology in face recognition and face verification field. Previous works mostly pay attention to improving the accuracy of prediction and ignored the practicability of the method. In this paper, we aim at providing a two-stage face alignment network for mobile platform. Firstly, the network was trained with residual label which is the difference between ground truth and mean shape. Secondly, the input data in the second stage is composed of the original data and generated heatmap which enriched the data types. Finally, a new loss function is used to enhance the convergence of local region. Experimental results show that our method not only provides high precision but also improve the real-time processing performance on the mobile platforms.

Since generative adversarial network (GAN) was first proposed, the processing of face images, especially the research of facial attribute editing, has attracted much interest. It not only can alleviate the problems associated with data deficiency, but also has great applications in the field of entertainment. However, existing approaches have limited scalability in the processing of newly-added face attributes, and the quality of generated images is poor. To solve these problems, FEGAN is proposed in this paper to achieve the accurate editing of multi-attribute faces by modifying feature vectors in the latent space. Firstly, a trained generator is used, which greatly reduces the training difficulty of GANs, and the inverse of the generator is used to establish the unique correspondence between the input image and the latent code. Secondly, a linear guide is applied to the latent code, and thus the same distribution as the target image in the latent space is assured. Finally, a generator is used to generate a face image from the guided latent code. The proposed method is utilized for a large number of attribute editing experiments, and the results show that FEGAN can flexibly perform accurate attribute editing while guaranteeing that other areas are not changed. Both qualitative and quantitative results demonstrate its advantages over existing methods.

In order to improve the accuracy of capsule network in disentangled representation, and further expand its application in computer vision, a novel BDARS_CapsNet (bi-directional attention routing sausage capsule network) architecture is proposed in this paper. Firstly, the bi-directional routing, namely bottom-up and top-down attention is used to achieve information feed-forward and feedback mechanism, which contributes to describing the attributes of object entity more accurately and completely. Secondly, inspired by the concept of covering learning, the sausage measure model is introduced into the network. The sausage model measures both the similarities and differences of the capsules and projects them into a more complex curved surface, which makes it possible to approximate any nonlinear function with arbitrary precision and preserving the local responsiveness of capsule entity to the maximum. Finally, the BDARS_CapsNet combines the CNN (Convolutional Neural Network), bi-directional attention routing, and sausage measure into capsule network modeling, and makes full use of high-level category information and low-level vision information; as a result, the reconstruction and classification accuracy is accordingly improved. Experiments demonstrate the effectiveness of proposed information routing, sausage measure, and new framework. Furthermore, the proposed BDARS_CapsNet provides a foundation for future research on disentangled representation learning.

Traditional visual simultaneous localization and mapping (SLAM) systems mostly based on small-area static environments. In recent years, some studies focused on combining semantic information with visual SLAM. However, most of them are hard to obtain better performance in the large-scale dynamic environment. And the accuracy, rapidity of the system still needs to strengthen. In this paper, we develop a more efficient semantic SLAM system in the two-wheeled mobile robot by using semantic segmentation to recognize people, chairs, and other objects in every keyframe. With a preliminary understanding of the environment, fusing the RGB-D camera and encoders information, to localization and creating a dense colored octree map without dynamic objects. Besides, for the incomplete identification of movable objects, we used image processing algorithms to enhance the semantic segmentation effect. In the proposed method, enhanced semantic segmentation in keyframes dramatically increases the efficiency of the system. Moreover, fusing the different sensors can highly raise localization accuracy. We conducted experiments on various datasets and in some real environments and compared them with DRE-SLAM, DS-SLAM, to evaluate the performance of the proposed approach. The results suggest we significantly improve the processing efficiency, robustness, and quality of the map.

In order to solve the problems in fuzzy computation tree logic model checking with cost operator, we propose a fuzzy decision process computation tree logic model checking method with cost. Firstly, we introduce a fuzzy decision process model with cost, which can not only describe the uncertain choice and transition possibility of systems, but also quantitatively describe the cost of the systems. Secondly, under the model of the fuzzy decision process with cost, we give the syntax and semantics of the fuzzy computation tree logic with cost operators. Thirdly, we study the problem of computation tree logic model checking for fuzzy decision process with cost, and give its matrix calculation method and algorithm. We use the example of medical expert systems to illustrate the method and model checking algorithm.

Reliability is of great significance in ensuring the safe operation of modern industry, which mainly relies on data analysis and life tests. However, as the life of mechanical systems becomes increasingly longer with the rapid development of the manufacturing industry, the collection of historical failure data becomes progressively more time-consuming. In this paper, a few-shot reliability assessment approach is proposed in order to overcome the dependence on historical data. Firstly, the vibration response of a bearing was illustrated. Then, based on a vibration response analysis, a morphological component analysis (MCA) method based on sparse representation theory was used to decompose vibration signals and extract impulse signals. After the impulse components’ reconstruction, their statistical indexes were utilized as the input observation vector of a Mixture of Gaussians Hidden Markov Model (MoG-HMM) for a reliability estimation. Finally, the experimental dataset of an aerospace bearing was analyzed via the proposed method. The comparison results illustrate the effectiveness of the proposed method of a few-shot reliability assessment.

Presently, finger-vein recognition is a new research direction in the field of biometric recognition. The Gabor filter has been extensively used for finger-vein recognition; however, its parameters are difficult to adjust. To solve this problem, an adaptive-learning Gabor filter is presented herein. We combine convolutional neural networks with a Gabor filter to calculate the gradient of the Gabor-filter parameters, based on the objective function, and to then optimize its parameters via back-propagation. The parameter θ of Gabor filter can be trained to the same angle as the vein texture of finger vein image. The parameter σ of Gabor filter has a certain relation with λ, and the parameter λ of Gabor filter can converge to the optimal value well. Using this method, we not only select appropriate and effective Gabor filter parameters to design the filter banks, we also consider the relationship between those parameters. Finally, we perform experiments on four public finger-vein datasets. Experimental results demonstrate that our method outperforms state-of-the-art methods in finger-vein classification.

Recently, deep learning techniques have been successfully used for bearing remaining useful life (RUL) prediction. However, the degradation pattern of bearings can be much different from each other, which leads to the trained model usually not being able to work well for RUL prediction of a new bearing. As a method that can adapt a model trained on source datasets to a different but relative unlabeled target dataset, transfer learning shows the potential to solve this problem. Therefore, we propose a two-stage transfer regression (TR)-based bearing RUL prediction method. Firstly, the incipient fault point (IFP) is detected by a convolutional neural network (CNN) classifier to identity the start time of degradation stage and label the training samples. Then, a transfer regression CNN with multiloss is constructed for RUL prediction, including regression loss, classification loss, maximum mean discrepancy (MMD) and regularization loss, which can not only extract fault information from fault classification loss for RUL prediction, but also minimize the probability distribution distance, thus helping the method to be trained in a domain-invariant way via the transfer regression algorithm. Finally, real data collected from run-to-failure bearing experiments are analyzed by the TR-based CNN method. The results and comparisons with state-of-the-art methods demonstrate the superiority and reliable performance of the proposed method for bearing RUL prediction.

Brephallus Wang et al., gen. n. is established as a genus distinct from Pseudophoraspis Kirby, 1903 because of the lack of a well-developed apical outgrowth on sclerite L2D and substantial genetic differences. Two species are transferred to the new genus from Pseudophoraspis, i.e., Brephallus fruhstorferi (Shelford, 1910), comb. n. and Brephallus tramlapensis (Anisyutkin, 1999), comb. n. We provide a detailed generic diagnosis of Brephallus Wang et al., gen. n. Based on COI data, males, females and nymphs of three Pseudophoraspis species (P. clavellata Wang et al., 2013, P. recurvata Wang et al., 2013 and P. kabakovi Anisyutkin, 1999) were successfully matched. The former two are sexually dimorphic with macropterous males and micropterous females. Photos of the species from China are presented.

As a fundamental assumption in simultaneous localization and mapping, the static scenes hypothesis can be hardly fulfilled in applications of indoor/outdoor navigation or localization. Recent works about simultaneous localization and mapping in dynamic scenes commonly use heavy pixel-level segmentation net to distinguish dynamic objects, which brings enormous calculations and limits the real-time performance of the system. That restricts the application of simultaneous localization and mapping on the mobile terminal. In this article, we present a lightweight system for monocular simultaneous localization and mapping in dynamic scenes, which can run in real time on central processing unit (CPU) and generate a semantic probability map. The pixel-wise semantic segmentation net is replaced with a lightweight object detection net combined with three-dimensional segmentation based on motion clustering. And a framework integrated with an improved weighted-random sample consensus solver is proposed to jointly solve the camera pose and perform three-dimensional object segmentation, which enables high accuracy and efficiency. Besides, the prior information of the generated map and the object detection results is introduced for better estimation. The experiments on the public data set, and in the real-world demonstrate that our method obtains an outstanding improvement in both accuracy and speed compared to state-of-the-art methods.

Summary: Easy regenerability of core components such as electrode and electrolyte is highly required in advanced electrochemical devices. This work reports a reliable, regenerable, and stretchable hydrogel electrolyte based on ionic bonds between polyacrylic acid (PAA) and polyallylamine (PAH). PAA-PAH electrolyte (1M LiCl addition) exhibits high ionic conductivity (0.050 S·cm-1) and excellent mechanical property (fracture strain of 1,688%). Notably, the electrolyte can be regenerated to any desired shape under mild conditions and remains 96% and 90% of the initial ionic conductivity after the first and second regeneration, respectively. PAA-PAH/LiCl-based supercapacitor exhibits nearly 100% capacitance retention upon rolling, stretching, and 5,000 charge-discharge cycles, whereas the regenerated device holds 97.6% capacitance of the initial device and 90.9% after 5,000 cycles. This low-cost, high-efficiency, and regenerable hydrogel electrolyte reveals very promising use in solid-state/flexible supercapacitors and possibly becomes a standard commercial hydrogel electrolyte for sustainable electrochemical energy devices.

Inconsistent results exist regarding the treatment effectiveness of immediate versus deferred percutaneous coronary intervention (PCI) in patients with acute coronary syndrome (ACS). This meta-analysis aimed to evaluate the efficacy and safety of immediate versus deferred PCI in ACS patients. PubMed, EMBASE, and Cochrane Library electronic databases were systematically searched from their inception up to August 2019. Random-effects models were employed to calculate pooled relative risks (RRs) and weight mean differences (WMDs) with 95% confidence intervals (CIs). A total of 10 randomized controlled trials (RCTs) that recruited 3350 patients were selected for inclusion in the final meta-analysis. Four trials included patients with non-ST elevation ACS (NSTEACS), whereas the remaining six trials included patients with ST elevation myocardial infarction (STEMI). There were no significant differences between immediate versus deferred PCI for the risk of major adverse cardiovascular events (NSTEACS patients: RR, 0.76, 95%CI, 0.33-1.75, P = 0.513; STEMI patients: RR, 1.24, 95%CI, 0.80-1.92, P = 0.335), myocardial infarction (NSTEACS patients: RR, 0.88, 95%CI, 0.27-2.81, P = 0.826; STEMI patients: RR, 0.86, 95%CI, 0.43-1.74, P = 0.678), all-cause mortality (NSTEACS patients: RR, 0.85, 95%CI, 0.38-1.88, P = 0.686; STEMI patients: RR, 1.16, 95%CI, 0.82-1.66, P = 0.407), target vessel revascularisation (NSTEACS patients: RR, 1.26, 95%CI, 0.29-5.43, P = 0.756; STEMI patients: RR, 1.01, 95%CI, 0.51-1.97, P = 0.988), or major bleeding (NSTEACS patients: RR, 0.99, 95%CI, 0.64-1.54, P = 0.972; STEMI patients: RR, 0.90, 95%CI, 0.45-1.77, P = 0.753). Although patients who underwent immediate PCI may experience increased incidences of cardiac death (RR, 1.19, 95%CI, 0.69-2.07, P = 0.525) and no or slow reflow (RR, 1.60, 95%CI, 0.91-2.84, P = 0.105), these increases were not statistically significant. We noted that immediate versus deferred PCI was associated with a reduced incidence of myocardial brush grade 3 (RR, 0.70, 95%CI, 0.56-0.88, P = 0.002); however, no significant differences were observed between immediate and deferred PCI for TIMI III flow (RR, 0.98, 95%CI, 0.93-1.03, P = 0.453), complete ST-segment resolution (RR, 0.93, 95%CI, 0.75-1.17, P = 0.548), and ejection fraction (WMD, -1.05, 95%CI, -2.58 to 0.49, P = 0.182). The findings of this study suggested that deferred PCI did not yield significant benefits for clinical endpoints. Further large-scale RCTs should be conducted to verify the findings of this study.

With the rapid development of the Web, a large number of electronic resources have been generated. Currently, XML has been an important tool for data representation and exchange over the Web. The incompleteness of information in the real-world is inherent. To deal with imprecise and uncertain data, fuzzy XML and fuzzy ontology modeling recently receive more attention. In order to represent the fuzzy information, we concentrate on fuzzy information modeling in a fuzzy XML model and fuzzy OWL 2 ontology in this paper. Furthermore, we propose an approach of transforming fuzzy OWL 2 ontologies (including structures and instances) into fuzzy XML models. Then we prove that the sementics of this transformation approach are preserved and propose a transforming example to explain the transforming process. This paper provides a new approach for the fuzzy XML modeling and fuzzy XML mapping based on the fuzzy OWL 2 ontologies.