Structural transition and migration of incoherent twin boundary in diamond.

Grain boundaries (GBs), with their diversity in both structure and structural transitions, play an essential role in tailoring the properties of polycrystalline materials1-5. As a unique GB subset, {112} incoherent twin boundaries (ITBs) are ubiquitous in nanotwinned, face-centred cubic materials6-9. Although multiple ITB configurations and transitions have been reported7,10, their transition mechanisms and impacts on mechanical properties remain largely unexplored, especially in regard to covalent materials. Here we report atomic observations of six ITB configurations and structural transitions in diamond at room temperature, showing a dislocation-mediated mechanism different from metallic systems11,12. The dominant ITBs are asymmetric and less mobile, contributing strongly to continuous hardening in nanotwinned diamond13. The potential driving forces of ITB activities are discussed. Our findings shed new light on GB behaviour in diamond and covalent materials, pointing to a new strategy for development of high-performance, nanotwinned materials.

Stability of Quadruple Hydrogen Bonds in an Ionic Liquid Environment.

Hydrogen bonds (H-bonds) are highly sensitive to the surrounding environments owing to their dipolar nature, with polar solvents kown to significantly weaken H-bonds. Herein, the stability of the H-bonding motif ureidopyrimidinone (UPy) is investigated, embedded into a highly polar polymeric ionic liquid (PIL) consisting of pendant pyrrolidinium bis(trifluoromethylsulfonyl)imide (IL) moieties, to study the influence of such ionic environments on the UPy H-bonds. The content of the surrounding IL is changed by addition of an additional low molecular weight IL to further boost the IL content around the UPy moieties in molar ratios of UPy/IL ranging from 1/4 up to 1/113, thereby promoting the polar microenvironment around the UPy-H-bonds. Variable-temperature solid-state MAS NMR spectroscopy and FT-IR spectroscopy demonstrate that the UPy H-bonds are largely present as (UPy-) dimers, but sensitive to elevated temperatures (>70 °C). Subsequent rheology and DSC studies reveal that the ILs only solvate the polymeric chains but do not interfere with the UPy-dimer H-bonds, thus accounting for their high stability and applicability in many material systems.

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.

BACKGROUND: The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas. METHODS: This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data. RESULTS: We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617]. CONCLUSIONS: Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

[Research progress of traditional Chinese medicines as quorum sensing inhibitors in collaboration with antibiotics to inhibit drug-resistant bacteria].

Quorum sensing system regulates the expression of genes related to bacterial growth, metabolism and other behaviors by sensing bacterial density, and controls the unified action of the entire bacterial population. This mechanism can ensure the normal secretion of bacterial metabolites and the stability of the biofilm microenvironment, providing protection for the formation of biofilms and the normal growth and reproduction of bacteria. Traditional Chinese medicine, capable of quorum sensing inhibition, can inhibit the formation of bacterial biofilms, reduce bacterial resistance, and enhance the anti-infection ability of antibiotics when combined with antibiotics. In recent years, the combination of traditional Chinese and Western medicine in the treatment of drug-resistant bacterial infections has become a research hotspot. Starting with the associations between quorum sensing, biofilm and drug-resistant bacteria, this paper reviews the relevant studies about the combined application of traditional Chinese medicines as quorum sensing inhibitors with antibiotics in the treatment of drug-resistant bacteria. This review is expected to provide ideas for the development of new clinical treatment methods and novel anti-infection drugs.

Prediction of human thermal comfort preference based on supervised learning.

Human thermal comfort is relevant to human life comfort and plays a pivotal role in occupational health and thermal safety. To ensure that intelligent temperature-controlled equipment can deliver a sense of cosiness to people while improving its energy efficiency, we designed a smart decision-making system that sets the thermal comfort adjustment preference as a label, reflecting both the human body's thermal feeling and its acceptance of the thermal environment. By training a series of supervised learning models underpinned by environmental and human features, the most appropriate adjustment mode in the current environment was predicted. To bring this design into reality, we tried six supervised learning models, and then, by comparison and evaluation, we identified that the Deep Forest's performance was the best. The model takes into account objective environmental factors and human body parameters. In this way, it can achieve high accuracy in application and good simulation and prediction results. The results can provide feasible references for feature selection and model selection in further research with the aim of testing thermal comfort adjustment preference. The model can provide recommendations for the thermal comfort preference in a specific place at a particular time, as well as guidance on human thermal comfort preference and thermal safety precautions in specific occupational groups.

Effects of different extreme cold exposure on heart rate variability.

Frequent extreme cold events in recent years have brought serious threats to outdoor workers and rescuers. Changes in ambient temperature are associated with altered cardiac autonomic function. The study aims to investigate heart rate variability (HRV) and its relationship to other physiological parameters under extreme cold exposures. Twelve males underwent a 30-min preconditioning phase in a neutral environment followed by a 30-min cold exposure (-5, -10, -15, and -20 °C). Time-domain indexes(meanRR, SDNN, RMSSD, and pNN50), frequency domain indexes [Log(HF), Log(LF), and low frequency/high frequency (LF/HF)], parasympathetic nervous system (PNS), and sympathetic nervous system (SNS) were analysed. Results showed all HRV indexes of four cold exposures were significant. The decrease in temperature was accompanied by progressive PNS activation with SNS retraction. SDNN was the most sensitive HRV index and had good linear relationships with blood pressure, pulse, and hand temperature. The results are significant for formulating safety protection strategies for workers in extremely cold environments.Practitioner Summary: This study investigated heart rate variability (HRV) in 12 males during a 30-min cold exposure (-5, -10, -15, and -20 °C). Results showed all HRV indexes of four cold exposures were significant. The decrease in temperature was accompanied by progressive PNS activation with SNS retraction. SDNN was the most sensitive HRV index and had good linear relationships with blood pressure, pulse, and hand temperature.

[Research on batch-to-batch quality control of compound Danshen extract based on physical characterization and multivariate statistical analysis].

Physical attributes of Chinese herbal extracts are determined by their chemical components, and the physical and chemical attributes jointly affect the preparation process performance and the final product quality. Therefore, in order to improve the quality control of Chinese herbal extracts, we should comprehensively study the batch-to-batch consistency of physical and chemical attributes as well as the correlations between them. This paper first explored the physical attributes affecting the preparation process performance of the compound Danshen extract and developed a method for characterizing the texture attributes. With such main chemical components as water, phenolic acids, saponins, and saccharides and texture, rheology, and other physical attributes taken into consideration, the batch-to-batch quality fluctuation of products from different production lines and time was analyzed by principal components analysis(PCA). Finally, the correlation and partial least squares(PLS) analysis was conducted, and the regression equation was established. The fitting result of the PLS model for dynamic viscosity was satisfying(R~2Y=0.857, Q~2=0.793), suggesting that the chemical components could be adjusted by the component transfer rate in the extraction process, the impurity removal rate in the alcohol precipitation process, and the water retention rate of the concentration process to meet the control of the extract dynamic viscosity. This study clarified the correlations between physical and chemical attributes of the compound Danshen extract and established a method for controlling its physical attributes based on process regulation, which would provide reference for improving the quality control of Chinese herbal extracts.

Calibration and location analysis of a heterogeneous binocular stereo vision system.

In the dairy farming industry, we can obtain the temperature, color, and location information of dairy cows by patrol inspection robot so as to monitor the health status and abnormal behaviors of dairy cows. We build and calibrate a heterogeneous binocular stereo vision (HBSV) system comprising a high-definition color camera and infrared thermal camera and mount it on a patrol inspection robot. First, based on the traditional chessboard, an easy-to-make calibration board for the HBSV system is designed. Second, an accurate locating and sorting algorithm for the calibration points of the calibration board is designed. Then, the cameras are calibrated and the HBSV system is stereo-calibrated. Finally, target locating is achieved based on the above calibration results and Yolo target detection technology. In this paper, several experiments are carried out from many aspects. The target locating average error of HBSV system is 3.11%, which satisfies the needs of the dairy farming environment. The video's FPS captured by using HBSV is 7.3, which is 78% higher than that by using binocular stereo vision system and infrared thermal camera. The results show that the HBSV system has application value to a certain degree.

Comparing C2=O and C2=S Barbiturates: Different Hydrogen-Bonding Patterns of Thiobarbiturates in Solution and the Solid State.

Carbonyl-centered hydrogen bonds with various strength and geometries are often exploited in materials to embed dynamic and adaptive properties, with the use of thiocarbonyl groups as hydrogen-bonding acceptors remaining only scarcely investigated. We herein report a comparative study of C2=O and C2=S barbiturates in view of their differing hydrogen bonds, using the 5,5-disubstituted barbiturate B and the thiobarbiturate TB as model compounds. Owing to the different hydrogen-bonding strength and geometries of C2=O vs. C2=S, we postulate the formation of different hydrogen-bonding patterns in C2=S in comparison to the C2=O in conventional barbiturates. To study differences in their association in solution, we conducted concentration- and temperature-dependent NMR experiments to compare their association constants, Gibbs free energy of association ∆Gassn., and the coalescence behavior of the N-H‧‧‧S=C bonded assemblies. In Langmuir films, the introduction of C2=S suppressed 2D crystallization when comparing B and TB using Brewster angle microscopy, also revealing a significant deviation in morphology. When embedded into a hydrophobic polymer such as polyisobutylene, a largely different rheological behavior was observed for the barbiturate-bearing PB compared to the thiobarbiturate-bearing PTB polymers, indicative of a stronger hydrogen bonding in the thioanalogue PTB. We therefore prove that H-bonds, when affixed to a polymer, here the thiobarbiturate moieties in PTB, can reinforce the nonpolar PIB matrix even better, thus indicating the formation of stronger H-bonds among the thiobarbiturates in polymers in contrast to the effects observed in solution.

A Hyperspectral Image Classification Method Based on Multi-Discriminator Generative Adversarial Networks.

Hyperspectral remote sensing images (HSIs) have great research and application value. At present, deep learning has become an important method for studying image processing. The Generative Adversarial Network (GAN) model is a typical network of deep learning developed in recent years and the GAN model can also be used to classify HSIs. However, there are still some problems in the classification of HSIs. On the one hand, due to the existence of different objects with the same spectrum phenomenon, if only according to the original GAN model to generate samples from spectral samples, it will produce the wrong detailed characteristic information. On the other hand, the gradient disappears in the original GAN model and the scoring ability of a single discriminator limits the quality of the generated samples. In order to solve the above problems, we introduce the scoring mechanism of multi-discriminator collaboration and complete semi-supervised classification on three hyperspectral data sets. Compared with the original GAN model with a single discriminator, the adjusted criterion is more rigorous and accurate and the generated samples can show more accurate characteristics. Aiming at the pattern collapse and diversity deficiency of the original GAN generated by single discriminator, this paper proposes a multi-discriminator generative adversarial networks (MDGANs) and studies the influence of the number of discriminators on the classification results. The experimental results show that the introduction of multi-discriminator improves the judgment ability of the model, ensures the effect of generating samples, solves the problem of noise in generating spectral samples and can improve the classification effect of HSIs. At the same time, the number of discriminators has different effects on different data sets.

Dimension Reduction for Hyperspectral Remote Sensor Data Based on Multi-Objective Particle Swarm Optimization Algorithm and Game Theory.

Information entropy and interclass separability are adopted as the evaluation criteria of dimension reduction for hyperspectral remote sensor data. However, it is rather single-faceted to simply use either information entropy or interclass separability as evaluation criteria, and will lead to a single-target problem. In this case, the chosen optimal band combination may be unfavorable for the improvement of follow-up classification accuracy. Thus, in this work, inter-band correlation is considered as the premise, and information entropy and interclass separability are synthesized as the evaluation criterion of dimension reduction. The multi-objective particle swarm optimization algorithm is easy to implement and characterized by rapid convergence. It is adopted to search for the optimal band combination. In addition, game theory is also introduced to dimension reduction to coordinate potential conflicts when both information entropy and interclass separability are used to search for the optimal band combination. Experimental results reveal that compared with the dimensionality reduction method, which only uses information entropy or Bhattacharyya distance as the evaluation criterion, and the method combining multiple criterions into one by weighting, the proposed method achieves global optimum more easily, and then obtains a better band combination and possess higher classification accuracy.

Deep Belief Network for Spectral⁻Spatial Classification of Hyperspectral Remote Sensor Data.

With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.

Safe and Robust Mobile Robot Navigation in Uneven Indoor Environments.

Complex environments pose great challenges for autonomous mobile robot navigation. In this study, we address the problem of autonomous navigation in 3D environments with staircases and slopes. An integrated system for safe mobile robot navigation in 3D complex environments is presented and both the perception and navigation capabilities are incorporated into the modular and reusable framework. Firstly, to distinguish the slope from the staircase in the environment, the robot builds a 3D OctoMap of the environment with a novel Simultaneously Localization and Mapping (SLAM) framework using the information of wheel odometry, a 2D laser scanner, and an RGB-D camera. Then, we introduce the traversable map, which is generated by the multi-layer 2D maps extracted from the 3D OctoMap. This traversable map serves as the input for autonomous navigation when the robot faces slopes and staircases. Moreover, to enable robust robot navigation in 3D environments, a novel camera re-localization method based on regression forest towards stable 3D localization is incorporated into this framework. In addition, we utilize a variable step size Rapidly-exploring Random Tree (RRT) method which can adjust the exploring step size automatically without tuning this parameter manually according to the environment, so that the navigation efficiency is improved. The experiments are conducted in different kinds of environments and the output results demonstrate that the proposed system enables the robot to navigate efficiently and robustly in complex 3D environments.

Detection and Tracking of Moving Targets for Thermal Infrared Video Sequences.

The joint detection and tracking of multiple targets from raw thermal infrared (TIR) image observations plays a significant role in the video surveillance field, and it has extensive applied foreground and practical value. In this paper, a novel multiple-target track-before-detect (TBD) method, which is based on background subtraction within the framework of labeled random finite sets (RFS) is presented. First, a background subtraction method based on a random selection strategy is exploited to obtain the foreground probability map from a TIR sequence. Second, in the foreground probability map, the probability of each pixel belonging to a target is calculated by non-overlapping multi-target likelihood. Finally, a δ generalized labeled multi-Bernoulli ( δ -GLMB) filter is employed to produce the states of multi-target along with their labels. Unlike other RFS-based filters, the proposed approach describes the target state by a pixel set instead of a single point. To meet the requirement of factual application, some extra procedures, including pixel sampling and update, target merging and splitting, and new birth target initialization, are incorporated into the algorithm. The experimental results show that the proposed method performs better in multi-target detection than six compared methods. Also, the method is effective for the continuous tracking of multi-targets.

Grey Model Optimized by Particle Swarm Optimization for Data Analysis and Application of Multi-Sensors.

Data on the effective operation of new pumping station is scarce, and the unit structure is complex, as the temperature changes of different parts of the unit are coupled with multiple factors. The multivariable grey system prediction model can effectively predict the multiple parameter change of a nonlinear system model by using a small amount of data, but the value of its q parameters greatly influences the prediction accuracy of the model. Therefore, the particle swarm optimization algorithm is used to optimize the q parameters and the multi-sensor temperature data of a pumping station unit is processed. Then, the change trends of the temperature data are analyzed and predicted. Comparing the results with the unoptimized multi-variable grey model and the BP neural network prediction method trained under insufficient data conditions, it is proved that the relative error of the multi-variable grey model after optimizing the q parameters is smaller.

Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network.

In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes it more difficult to extract image features with differences, and image detail features are easily lost. This result seriously affects the accuracy of image classification. Thus, a new overlapping pooling method is proposed, where maximum pooling is used in an improved convolutional neural network to avoid the fuzziness of average pooling. The step size used is smaller than the size of the pooling kernel to achieve overlapping and coverage between the outputs of the pooling layer. The dataset selected for this experiment was the Indian Pines dataset, collected by the airborne visible/infrared imaging spectrometer (AVIRIS) sensor. Experimental results show that using the improved convolutional neural network for remote sensing image classification can effectively improve the details of the image and obtain a high classification accuracy.

[Study on perceived fatigue evaluating model during simulated load carriage].

OBJECTIVE: To establish a perceived fatigue evaluating model during simulated load carriage that is based on objective variables through analyzing the characteristics and trends of shoulder force, shoulder pressure, waist pressure, back pressure, and perceived fatigue, and to provide an analytical technique for research on load carriage. METHODS: A 50-min simulated walking (at a speed of 5 km/h and a slope of 0%) experiment including 14 healthy male adults was conducted under four levels of backpack payloads (25, 29, 34, 37 kg). Shoulder force and trunk pressure were sampled simultaneously and analyzed with time- and frequency- domain methods. Multivariable linear regression was used to build a perceived fatigue evaluating model during load carriage. RESULTS: The perceived fatigue evaluating model based on shoulder force, trunk pressure distribution ratio, load, and body mass index (BMI) was established. Its adjusted determination coefficient (aR2) was 0.709 and the absolute percentage error (APE) at the end of the experiment was less than 20%. The goodness of fit of the model based on frequency-domain independent variables was much higher compared with the model based on time-domain independent variables. The addition of BMI that represents the individual differences to the model obviously improved the goodness of fit. CONCLUSION: The perceived fatigue evaluating model established in this study does not rely on the physiological changes of individuals, and thus can be used to establish an evaluation system for human load carriage with dummy as a substitution for human in experiments and to provide a scientific basis for efficient human load carriage.

A generalized Udwadia-Kalaba control design for uncertain belt conveyor systems with inequality constraints.

Uncertainty can lead to jitter or overshoot in mechanical systems, necessitating the design of multiple constraints to stabilize them. This paper proposes a control structure based on the generalized Udwadia-Kalaba equation to address these constraints simultaneously. An uncertain dynamical model is developed, incorporating both equality and inequality constraints. By integrating diffeomorphism theory, a robust control strategy is designed to ensure compliance with these constraints. Utilizing the Lyapunov approach, the uniform boundedness and uniform ultimate boundedness of the dynamical system are demonstrated. Finally, the feasibility of the proposed control method is validated through its application to a belt conveyor system.

Kirigami-Triggered Spoof Plasmonic Interconnects for Radiofrequency Elastronics.

The flexible and conformal interconnects for electronic systems as a potential signal transmission device have great prospects in body-worn or wearable applications. High-efficiency wave propagation and conformal structure deformation around human body at radio communication are still confronted with huge challenges due to the lack of methods to control the wave propagation and achieve the deformable structure simultaneously. Here, inspired by the kirigami technology, a new paradigm to construct spoof plasmonic interconnects (SPIs) that support radiofrequency (RF) surface plasmonic transmission is proposed, together with high elasticity, strong robustness, and multifunction performance. Leveraging the strong field-confinement characteristic of spoof surface plasmons polaritons, the Type-I SPI opens its high-efficiency transmission band after stretching from a simply connected metallic surface. Meanwhile, the broadband transmission of the kirigami-based SPI exhibits strong robustness and excellent stability undergoing complex deformations, i.e., bending, twisting, and stretching. In addition, the prepared Type-II SPI consisting of 2 different subunit cells can achieve band-stop transmission characteristics, with its center frequency dynamically tunable by stretching the buckled structure. Experimental measurements verify the on-off switching performance in kirigami interconnects triggered by stretching. Overcoming the mechanical limitation of rigid structure with kirigami technology, the designer SPIs exhibit high stretchability through out-of-plane structure deformation. Such kirigami-based interconnects can improve the elastic functionality of wearable RF electronics and offer high compatibility to large body motion in future body network systems.

[Study on real-time wearable monitoring system for human heat and cold stresses].

In order to study the way of evaluating human performance under heat and cold stresses, we developed a wearable physiological monitoring system-intelligent belt system, capable of providing real-time, continuous and dynamic monitoring of multiple physiological parameters. The system has following features: multiuser communication, high integration, strong environment adaptability, dynamic features and real time physiological monitoring ability. The system uses sensing belts and elastic belts to acquire physiological parameters, uses WIFI to build wireless network monitoring for multiuser, and uses Delphi to develop data processing software capable of real-time viewing, storagng, processing, and alerting. With four different intensity-activity trials on six subjects and compared with standard laboratory human physiological acquisition instruments, the system was proved to be able to acquire accu-rate physiological parameters such as ECG, respiration, multi-point body temperatures, and body movement. The system worked steadily and reliably. This wearable real-time monitoring system for human heat and cold stresses can solve the problem facing our country that human heat stress and cold stress monitoring technology is insufficient, provide new methods and new ways for monitoring and evaluation of human heat and cold stresses under real task or stress environment, and provide technical platform for the study on human ergonomics.