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Based on X ray diffraction and Fourier transform infrared spectroscopy, the microcrystalline structure characteristics and evolution patterns of a total of 6 coal samples from the eastern Junggar, southern Junggar, Tuha, and Kubai coalfields in Xinjiang were investigated. The peak shape of 002 and 100 peaks in low-rank coal is gentle, the peak 002 becomes sharper and the “symmetry” becomes better with the increase of coal rank, and the peak height of the peak 100 gradually increases but the peak width gradually shrinks. The height of the peak 100 gradually increases but the width gradually narrows; with the increase of coal sample metamorphism, the stacking height, elongation and quantity of aromatic laminates increase gradually, the spacing of aromatic laminates decreases on the whole, and the condensation degree of aromatic structures in coal increases gradually; the peak shape of aromatic compounds and oxygen-containing functional groups is sharp, and the peak shape of aliphatic hydrocarbons and hydroxyl functional groups is gentle, which gradually widens and slows down with the rise of coal rank; with the increase of coal rank, the aromaticity, fat chain length, branched chain degree and maturity gradually decrease, while the polycondensation degree and hydrocarbon (oil) generation ability gradually increase. The evolution process of coal is characterized by the shedding of oxygen-containing functional groups, the enrichment of aliphatic hydrocarbons, and the condensation of some aromatics.

Introduction: Stroke is the second leading cause of death globally and a primary factor contributing to disability. Unilateral limb motor impairment caused by stroke is the most common scenario. The bilateral movement pattern plays a crucial role in assisting stroke survivors on the affected side to relearn lost skills. However, motion compensation often lead to decreased coordination between the limbs on both sides. Furthermore, muscle fatigue resulting from imbalanced force exertion on both sides of the limbs can also impact the rehabilitation outcomes.Method: In this study, an assessment method based on muscle synergy indicators was proposed to objectively quantify the impact of motion compensation issues on rehabilitation outcomes. Muscle synergy describes the body’s neuromuscular control mechanism, representing the coordinated activation of multiple muscles during movement. 8 post-stroke hemiplegia patients and 8 healthy subjects participated in this study. During hand-cycling tasks with different resistance levels, surface electromyography signals were synchronously collected from these participants before and after fatigue. Additionally, a simulated compensation experiment was set up for healthy participants to mimic various hemiparetic states observed in patients.Results and discussion: Synergy symmetry and synergy fusion were chosen as potential indicators for assessing motion compensation. The experimental results indicate significant differences in synergy symmetry and fusion levels between the healthy control group and the patient group (p ≤ 0.05), as well as between the healthy control group and the compensation group. Moreover, the analysis across different resistance levels showed no significant variations in the assessed indicators (p > 0.05), suggesting the utility of synergy symmetry and fusion indicators for the quantitative evaluation of compensation behaviors. Although muscle fatigue did not significantly alter the symmetry and fusion levels of bilateral synergies (p > 0.05), it did reduce the synergy repeatability across adjacent movement cycles, compromising movement stability and hindering patient recovery. Based on synergy symmetry and fusion indicators, the degree of bilateral motion compensation in patients can be quantitatively assessed, providing personalized recommendations for rehabilitation training and enhancing its effectiveness.

We present the discovery of three pulsars in the globular cluster (GC) M15 (NGC 7078) by the Five-hundred-meter Aperture Spherical Radio Telescope. PSR J2129+1210J (M15J) is a millisecond pulsar with a spin period of 11.84 ms and a dispersion measure of 66.68 pc cm ^−3 . Both PSR J2129+1210K and L (M15K and L) are long-period pulsars with spin periods of 1928 ms and 3961 ms, respectively. M15L is the GC pulsar with the longest spin period known. We update the timing solutions of M15A to M15H. As predicted by A. Ridolfi et al., the flux density of M15C keeps decreasing, and the latest detection in our data set was on 2022 December 20. We have also detected M15I’s signal for the first time since its discovery. Current timing suggests that it is an isolated pulsar.

We present the discovery of eight isolated millisecond pulsars in globular cluster (GC) NGC 6517 using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The spin periods of those pulsars (namely PSR J1801–0857K to R, or NGC 6517K to R) are all shorter than 10 ms. With these discoveries, NGC 6517 is currently the GC with the most known pulsars in the FAST sky. The largest difference in dispersion measure of the pulsars in NGC 6517 is 11.2 cm ^−3 pc, the second among all GCs. The fraction of isolated pulsars in this GC (16 of 17, 94%) is consistent with previous studies indicating an overabundance of isolated pulsars in the densest GCs, especially in those undergoing cluster core collapse. Considering the FAST GC pulsar discoveries, we modeled the GC pulsar population using the empirical Bayesian method described by Turk and Lorimer with the recent counts. Using this approach, we find that the expected number of potential pulsars in GCs seems to be correlated with the central escape velocity; hence, the GCs Liller 1, NGC 6441, M54 (NGC 6715), and ω -Cen (NGC 5139) are expected to host the largest numbers of pulsars.

We present the phase-connected timing solutions of all five pulsars in globular cluster M3 (NGC 5272), namely PSRs M3A to F (PSRs J1342+2822A to F), with the exception of PSR M3C, from FAST archival data. In these timing solutions, those of PSRs M3E and F are obtained for the first time. We find that PSRs M3E and F have low-mass companions and are in circular orbits with periods of 7.1 and 3.0 days, respectively. For PSR M3C, we have not detected its signal in all 41 observations. We found no X-ray counterparts for these pulsars in archival Chandra images in the band of 0.2–20 keV. From the autocorrelation function analysis of M3A and M3B’s dynamic spectra, the scintillation timescale ranges from 7.0 ± 0.3 to 60.0 ± 0.6 minutes, and the scintillation bandwidth ranges from 4.6 ± 0.2 to 57.1 ± 1.1 MHz. The measured scintillation bandwidths from the dynamic spectra indicate strong scintillation, and the scattering medium is anisotropic. From the secondary spectra, we captured a scintillation arc only for PSR M3B with a curvature of 649 ± 23 m ^−1 mHz ^−2 .

Abstract Accurate estimates of aerosol refractive index (RI) are critical for modeling aerosol‐radiation interaction, yet this information is limited for ambient organic aerosols, leading to large uncertainties in estimating aerosol radiative effects. We present a semi‐empirical model that predicts the real RI n of organic aerosol material from its widely measured oxygen‐to‐carbon (O:C) and hydrogen‐to‐carbon (H:C) elemental ratios. The model was based on the theoretical framework of Lorenz‐Lorentz equation and trained with n‐values at 589 nm (n589nm) of 160 pure compounds. The predictions can be expanded to predict n‐values in a wide spectrum between 300 and 1,200 nm. The model was validated with newly measured and literature datasets of n‐values for laboratory secondary organic aerosol (SOA) materials. Uncertainties of n589nm predictions for all SOA samples are within ±5%. The model suggests that n589nm‐values of organic aerosols may vary within a relatively small range for typical O:C and H:C values observed in the atmosphere.

Remote sensing images are very vulnerable to cloud interference during the imaging process. Cloud occlusion, especially thick cloud occlusion, significantly reduces the imaging quality of remote sensing images, which in turn affects a variety of subsequent tasks using the remote sensing images. The remote sensing images miss ground information due to thick cloud occlusion. The thick cloud removal method based on a temporality global–local structure is initially suggested as a solution to this problem. This method includes two stages: the global multi-temporal feature fusion (GMFF) stage and the local single-temporal information restoration (LSIR) stage. It adopts the fusion feature of global multi-temporal to restore the thick cloud occlusion information of local single temporal images. Then, the featured global–local structure is created in both two stages, fusing the global feature capture ability of Transformer with the local feature extraction ability of CNN, with the goal of effectively retaining the detailed information of the remote sensing images. Finally, the local feature extraction (LFE) module and global–local feature extraction (GLFE) module is designed according to the global–local characteristics, and the different module details are designed in this two stages. Experimental results indicate that the proposed method performs significantly better than the compared methods in the established data set for the task of multi-temporal thick cloud removal. In the four scenes, when compared to the best method CMSN, the peak signal-to-noise ratio (PSNR) index improved by 2.675, 5.2255, and 4.9823 dB in the first, second, and third temporal images, respectively. The average improvement of these three temporal images is 9.65%. In the first, second, and third temporal images, the correlation coefficient (CC) index improved by 0.016, 0.0658, and 0.0145, respectively, and the average improvement for the three temporal images is 3.35%. Structural similarity (SSIM) and root mean square (RMSE) are improved 0.33% and 34.29%, respectively. Consequently, in the field of multi-temporal cloud removal, the proposed method enhances the utilization of multi-temporal information and achieves better effectiveness of thick cloud restoration.

Self-supporting run-flat tires (SSRFTs) achieve good zero-pressure driving ability by reinforcing the sidewalls, and the structural shape of sidewall insert rubber (SIR) is critical in influencing the mechanical characteristics of SSRFTs. In this paper, an SSRFT contour model is established by combining the radial tire contour theory and the design elements of SIR. The influence of two design parameters (maximum width L and maximum thickness H) of SIR on the tire stiffness characteristics and the contact characteristics is analyzed in depth, and the accuracy of the model is verified by the tire mechanics bench test. The results show that the radial stiffness of SSRFTs is positively correlated with two design parameters; an increase in L affects the stress concentration at the end of SIR, while a change in H has a more drastic effect on the stress distribution of SIR, leading to a large change in both the location of the deformation of SIR and the maximum equivalent stress; under rated pressure conditions, when L is less than 100 mm, the overlap between SIR and the tread decreases, which in turn makes the contact characteristics of SSRFTs closer to that of a normal tire, and obtains better comfort and abrasion resistance; under zero-pressure conditions, the maximum contact stress of the tread is the smallest when the H is 8 mm, but when H is less than 6 mm, the contact characteristics appear to deteriorate uniformly, and the maximum contact stress continues to rise. The results of the research provide a reference value for the selection of the design parameters for SIR and the optimization of the dynamic performance of SSRFTs.

ObjectiveThis study aimed to investigate the application value of multi-parametric magnetic resonance imaging (MRI) in the diagnosis of iron deposition in the substantia nigra dense zone in Parkinson's disease (PD) and to evaluate the diagnostic value of the correlation among multi-parametric imaging indicators, clinical stage, and disease duration.Materials and methodsThirty-six patients with clinically confirmed PD and 36 healthy controls were enrolled. The disease course was recorded, and PD severity was graded using the Hoehn–Yahr (H–Y) scale. All subjects underwent magnetic sensitivity weighted imaging (SWI), neuromelanin magnetic resonance imaging (NM-MRI), and a T2*mapping sequence. Based on the fusion of the NM-MRI and SWI amplitude maps, phase maps, and T2*MAPPING value maps, NM-MRI was used to delineate the dense zone of the substantia nigra, which was divided into three sub-regions: upper, middle, and lower. In this way, the amplitude, phase, and R2* values of each sub-region and the average value of the sum of the three sub-regions were obtained simultaneously in the SWI amplitude, phase, and T2*MAPPING maps. The multi-parameter imaging indices were compared between the two groups, and the correlation between them and clinical indices was evaluated in the PD group.ResultsThe upper (amplitude, phase value, R2* value), middle, and lower (amplitude) right substantia nigra compact zones were significantly different between the PD and control groups. The upper (phase value, R2* value) and middle (amplitude) areas of the left substantia nigra compact zone were also significantly different between the two groups (all P < 0.05). The mean values (amplitude, phase value, R2* value) of the right substantia nigra densification zone and the mean values (phase value) of the left substantia nigra densification zone were also significantly different (all P < 0.05). Amplitudes in the middle and lower parts of the right substantia nigra dense zone were negatively correlated with the H–Y grade (middle: r = −0.475, P = 0.003; lower: r = −0.331, P = 0.049). Amplitudes of the middle and lower parts of the dense zone of the left substantia nigra were negatively correlated with the H–Y grade (middle: r = −0.342, P = 0.041; lower: r = −0.399, P = 0.016). The average amplitude of the right substantia nigra compact zone was negatively correlated with the H–Y grade (r = −0.367, P = 0.027). The average R2* value of the compact zone of the left substantia nigra was positively correlated with the H–Y grade (r = 0.345, P = 0.040).ConclusionMultiparametric MRI sequence examination has application value in the evaluation of iron deposition in the dense zone of the substantia nigra in PD. Combined with NM-MRI, fusion analysis is beneficial for accurately locating the substantia nigra compact zone and quantitatively analyzing the iron deposition in different sub-regions. Quantitative iron deposition in the middle and lower parts of the substantia nigra dense zone may become an imaging biological indicator for early diagnosis, severity evaluation, and follow-up evaluation of PD and is thus conducive for clinical diagnosis and treatment evaluation.

Thick cloud and cloud shadow often exist in optical remote sensing (RS) images, which limit the application range of RS data. To solve this challenging problem, we propose a multitemporal-based method of cloud removal to reconstruct the large cloud-cover region in RS images. The method can combine the advantages of high accuracy of deep-learning method and low computation cost of traditional method, and reduce the difficulty of establishing multi-temporal dataset to a certain extent. In the proposed method, the complex problem of thick cloud removal is decomposed into two simple processes: coarse stage and refined stage. In the stage of coarse cloud removal, it is found that the cloud-cover images are distributed more regularly in frequency domain than in the space domain. Therefore, the frequency domain residual module is introduced, and the frequency domain attention module is designed to achieve more focused fusion of three-temporal features. In the stage of refined cloud removal, the refined network is designed to retain the effective features of the coarse stage and further refine the reconstruction result based on the image self-correlation. We carried out sufficient experiments to demonstrate the superiority of the proposed method compared with four classical methods, which include WLR, STSCNN, RFRnet and PSTCR. In qualitative experiments, the proposed method has obvious advantages in information accuracy, visual naturalness and semantic rationality. In quantitative experiments, compared with the best of four comparison methods, average values of Peak Signal to Noise Ratio (PSNR) on three-temporal images are improved by 5.1934 dB, 2.7729 dB and 7.2364 dB respectively, and average values of Correlation Coefficient (CC) on three-temporal images are increased by 0.1048, 0.0110 and 0.0966 respectively. Moreover, the proposed method has an obvious advantage in computation cost because it can reconstruct multiple cloud-cover images synchronously.

Aiming at the problem of sparse measurement points due to test conditions in engineering, a smoothing method based on zero-padding in the wavenumber domain is proposed to increase data density. Firstly, the principle of data extension and smoothing is introduced. The core idea of this principle is to extend the discrete data series by zero-padding in the wavenumber domain. The conversion between the spatial and wavenumber domains is achieved using the Discrete Fourier Transform (DFT) and the Inverse Discrete Fourier Transform (IDFT). Then, two sets of two-dimensional discrete random data are extended and smoothed, respectively, and the results verify the effectiveness and feasibility of the algorithm. The method can effectively increase the density of test data in engineering tests, achieve smoothing and extend the application to areas related to data processing.

Abstract Background Endometrial cancer is a common gynecological cancer with annually increasing incidence worldwide. However, the biomarkers that provide prognosis and progression for this disease remain elusive. Results Two eligible human endometrial cancer datasets (GSE17025 and GSE25405) were selected for the study. A total of 520 differentially expressed mRNAs and 30 differentially expressed miRNAs were identified. These mRNAs were mainly enriched in cell cycle, skeletal system development, vasculature development, oocyte maturation, and oocyte meiosis signalling pathways. A total of 160 pairs of differentially expressed miRNAs and mRNAs, including 22 differentially expressed miRNAs and 71 overlapping differentially expressed mRNAs, were validated in endometrial cancer samples using starBase v2.0 project. The prognosis analysis revealed that Cyclin E1 (CCNE1, one of the 82 hub genes, which correlated with hsa-miR-195 and hsa-miR-424) was significantly linked to a worse overall survival in endometrial cancer patients. Conclusions The hub genes and differentially expressed miRNAs identified in this study might be used as prognostic biomarkers for endometrial cancer and molecular targets for its treatment.

A vision-based intelligent robotic grasping system is essential for realizing unmanned operations in industrial manufacturing, and pose estimation plays an import role in this system. In this study, deep learning was used to obtain the 6D pose of an axisymmetric body which was optimal for robotic grasping in industrial stacked scenarios. We propose a method to obtain the 6D pose of an axisymmetric body by detecting the pre-defined keypoints on the side surface. To realize this method and solve other challenges in industrial stacked scenarios, we propose a multitask real-time convolutional neural network (CNN), named Key-Yolact, which involves object detection, instance segmentation, and multiobject 2D keypoint detection. A small CNN as a decision-making subsystem was designed to score multiple predictions of Key-Yolact, and the body with the highest score is considered the best for grasping. Experiments on a self-built stacked dataset showed that Key-Yolact has a practical tradeoff between inference speed and precision. The inference speed of Key-Yolact is higher by 10 FPS, whereas its precision is decreased by only 7% when compared with the classical multitask Keypoint R-CNN. Robotic grasping experiments showed that the proposed design is effective and can be directly applied to industrial scenarios.

Abstract Background Ovarian carcinoma (OC) is a common cause of death among women with gynecological cancer. MicroRNAs (miRNAs) are believed to have vital roles in tumorigenesis of OC. Although miRNAs are broadly recognized in OC, the role of has-miR-182-5p (miR-182) in OC is still not fully elucidated. Methods We evaluated the significance of miR-182 expression in OC by using analysis of a public dataset from the Gene Expression Omnibus (GEO) database and a literature review. Furthermore, we downloaded three mRNA datasets of OC and normal ovarian tissues (NOTs), GSE14407, GSE18520 and GSE36668, from GEO to identify differentially expressed genes (DEGs). Then the targeted genes of hsa-miR-182-5p (TG_miRNA-182-5p) were predicted using miRWALK3.0. Subsequently, we analyzed the gene overlaps integrated between DEGs in OC and predicted target genes of miR-182 by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network and the prognostic effects of the hub genes were analyzed. Results A common pattern of up-regulation for miR-182 in OC was found in our review of the literature. A total of 268 DEGs, both OC-related and miR-182-related, were identified, of which 133 genes were discovered from the PPI network. A number of DEGs were enriched in extracellular matrix organization, pathways in cancer, focal adhesion, and ECM-receptor interaction. Two hub genes, MCM3 and GINS2, were significantly associated with worse overall survival of patients with OC. Furthermore, we identified covert miR-182-related genes that might participate in OC by network analysis, such as DCN, AKT3, and TIMP2. The expressions of these genes were all down-regulated and negatively correlated with miR-182 in OC. Conclusions Our study suggests that miR-182 is essential for the biological progression of OC.

Progress in wearable techniques makes the long-term daily electrocardiogram (ECG) monitoring possible. However, the long-term wearable ECGs can be significantly contaminated by various noises, which affect the detection and diagnosis of cardiovascular diseases (CVDs). The situation becomes more serious for wearable ECG screening, where the data are huge, and doctors have no way to visually check the signal quality episode-by-episode. Therefore, automatic and accurate noise rejection for the wearable big-data ECGs is craving. This paper addressed this issue and proposed a noise rejection method for wearable ECGs based on the combination of modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN). Wearable ECGs were recorded using the newly developed 12-lead Lenovo smart ECG vest with a sample rate of 500 Hz and a resolution of 16 bit. One thousand 10-s ECG segments were picked up and were manually labeled into three quality types: clinically useful segments with good signal quality (type A), clinically useful segments with poor signal quality (type B), and clinically useless segments (pure noises, type C). Each of the 1,000 10-s ECG segments were transformed into a 2-D time-frequency (T-F) image using the MFSWT, with a pixel size of 200×50. Then, the 2-D grayscale images from MFSWT were fed into a 13-layer CNN model for training the classification models. Results from the standard 5-folder cross-validation showed that the proposed combination method of MFSWT and CNN achieved a highest classification accuracy of 86.3%, which was higher than the comparable methods from continuous wavelet transform (CWT) and artificial neural networks (ANN). The combination of MFSWT and CNN also had a good calculation efficiency. This paper indicated that the combination of MFSWT and CNN is a potential method for automatic identification of noisy segments from wearable ECG recordings.

With the continuous development of economy and society, power demand forecasting has become an important task of the power industry. Accurate power demand forecasting can promote the operation and development of the power supply industry. However, since power consumption is affected by a number of factors, it is difficult to accurately predict the power demand data. With the accumulation of data in the power industry, machine learning technology has shown great potential in power demand forecasting. In this study, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM) are integrated by stacking to build an XLG-LR fusion model to predict power demand. Firstly, preprocessing was carried out on 13 months of electricity and meteorological data. Next, the hyperparameters of each model were adjusted and optimized. Secondly, based on the optimal hyperparameter configuration, a prediction model was built using the training set (70% of the data). Finally, the test set (30% of the data) was used to evaluate the performance of each model. Mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and goodness-of-fit coefficient (R^2) were utilized to analyze each model at different lengths of time, including their seasonal, weekly, and monthly forecast effect. Furthermore, the proposed fusion model was compared with other neural network models such as the GRU, LSTM and TCN models. The results showed that the XLG-LR model achieved the best prediction results at different time lengths, and at the same time consumed the least time compared to the neural network model. This method can provide a more reliable reference for the operation and dispatch of power enterprises and future power construction and planning.

As one of the largest inland megacities in Northwest (NW) China, Xi’an has been facing serious regional haze frequently, especially during winter. The composition of aerosols in Xi’an is highly complex due to its unique basinal topography and unique meteorological conditions. In this study, we characterized the morphology, size, and composition of individual aerosol particles collected during regional haze events at an urban site in Xi’an using Transmission Electron Microscopy (TEM) coupled with Energy-Dispersive X-ray Spectrometry (EDX). Six types of particles were identified based on their morphology and chemical composition, including organic (41.88%), sulfate (32.36%), soot (8.33%), mineral (7.91%), K-rich (5.13%), and fly ash particles (4.49%). These results demonstrate that the organic particles made a larger contribution to haze formation than the secondary inorganic particles during the sampling period. Size distribution and dominance suggest that organic and sulfate particles exert major control on the variation trends of particle size in haze. The coating thickness of organic-cored particles was about 369 nm and that of sulfate-cored particles was about 322 nm, implying that the organic particles were more aged than the sulfate particles. The results presented in this study provide further insights into understanding haze particle formation.

Abstract Because there is no temporal information available, rain removal with a single image is more challenging than that with a video. In this paper, we present a weighted median guided filtering method for rain removal with a single image. It consists of two filtering operations. Firstly, a weighted median filter is convoluted with an input rainy image to obtain a coarse rain-free image; then, guided filter is employed to obtain a refined rain-free image, where the coarse rain-free image is used as a guided image and convoluted with the input rainy image via guided filter. Experimental results show that the proposed method generated comparable results to the state-of-the-art algorithms with low computation cost.

Congestive heart failure (CHF) is a serious pathophysiological condition with high morbidity and mortality, which is hard to predict and diagnose in early age. Artificial intelligence and deep learning combining with cardiac rhythms and physiological time series provide a potential to help in solving it. In this paper, we proposed a novel method that combines a convolutional neural network (CNN) and a distance distribution matrix (DDM) in entropy calculation to classify CHF patients from normal subjects, and demonstrated the effectiveness of this combination. Specifically, three entropy methods were used to generate the distribution matrixes from a 300-point RR interval (i.e., the time interval between the successive cardiac cycles) time series, which are Sample entropy, fuzzy local measure entropy, and fuzzy global measure entropy. Then, three high representative CNN models, i.e., AlexNet, DenseNet, and SE_Inception_v4 were chosen to learn the pattern of the data distributions hidden in the generated distribution matrixes. All data used in our experiments were gathered from the MIT-BIH RR Interval Databases (http://www.physionet.org). A total of 29 CHF patients and 54 normal sinus rhythm subjects were included in this paper. The results showed that the combination of FuzzyGMEn-generated DDM and Inception_v4 model yielded the highest accuracy of 81.85% out of all proposed combinations.

The traditional chassis has the problems of low trafficability and poor stability under complex and changeable unstructured conditions. Thus, a wheel rail composite chassis is proposed. The chassis had a tracked travel mechanism at the front wheels and a wheeled travel mechanism at the rear wheels. This study presents the design, theoretical analysis and dynamic simulation analysis of the chassis. The maximum values of the passability of the wheel–track composite chassis that can be passed were calculated according to the relevant parameters. Furthermore, the chassis was modeled and simulated using RecurDyn to verify whether the values were reasonable. According to different values of the terrain, slope, vertical obstacle height and trench width, the change regularity of the track tension and driving torque of the chassis were obtained. The chassis is designed to improve the vehicle’s ability to operate under complex and diverse unstructured conditions.

On the research about the ultra-high frequency (UHF) re-radiation interference (RRI) from the power lines, traditional high-frequency algorithms need excessive computing resources or have a bad calculation precision. Based on the surface model of RRI, the calculation idea using large element-physical optics (LE-PO) is proposed to calculate the level of RRI. According to the assumption of LE-PO, the electric field integral equation is simplified. When the excitation source is plane wave, Linearly Phased-Rao Wilton Glisson (LP-RWG) basis functions are used to disperse the surface-induced current and the expression of scattering field from the tower of power lines was deduced. Related analysis example is used to analyse the calculation precision and the computing resource of LE-PO. As a result, the computing resources of LE-PO are much less than PO, while the calculation precision of LE-PO is the same as PO.

Understanding the compositions and evolution of atmospheric organic vapors is crucial for exploring their impact on air quality. However, the molecular and seasonal characteristics of organic vapors in urban areas, with complex anthropogenic emissions and high variability, remain inadequately understood. In this study, we conducted measurements in urban Beijing during 2021-2022 covering four seasons using a Vocus-PTR, an improved Proton Transfer Reaction-Mass Spectrometry (PTR-MS). During the measurement period, a total of 895 peaks are observed, and 543 of them can be assigned to formulas. The contribution of CxHyOz species is most significant, which compose up to 53.7% of the number and 76.0% of the mass of total organics. With enhanced sensitivity and mass resolution, various sub-ppt level species and organics with multiple oxygens (≥3) were discovered. When counting the species number, 42.2% of the organics measured are at sub-ppt level and 37.8% of the species contain 3-8 oxygens. Organic vapors with multiple oxygens mainly consist of intermediate volatility and semi-volatile compounds, and many of them are found to be the multi-generational oxidation products of various volatile organic precursors. In summer, the fast photooxidation process generates organic vapors with multiple oxygens, and leads to an increase in both their concentration and proportion. While in other seasons, the variations of organic vapors with multiple oxygens are closely correlated with those of organic vapors with 1-2 oxygens, which could be heavily influenced by primary emissions. Organic vapors with low oxygen content (≤ 2 oxygens) are comparable to the results obtained by traditional PTR-MS measurements in both urban Beijing and neighboring regions. [ABSTRACT FROM AUTHOR]

Radon observation is an important measurement item of seismic precursor network observation. The radon detector calibration is a key technical link for ensuring radon observation accuracy. At present, the radon detector calibration in seismic systems in China is faced with a series of bottleneck problems, such as aging and scrap, acquisition difficulties, high supervision costs, and transportation limitations of radon sources. As a result, a large number of radon detectors cannot be accurately calibrated regularly, seriously affecting the accuracy and reliability of radon observation data in China. To solve this problem, a new calibration method for radon detectors was established. The advantage of this method is that the dangerous radioactive substance, i.e., the radon source, can be avoided, but only “standard instruments” and water samples with certain dissolved radon concentrations can be used to realize radon detector calibration. This method avoids the risk of radioactive leakage and solves the current widespread difficulties and bottleneck of radon detector calibration in seismic systems in China. The comparison experiment with the traditional calibration method shows that the error of the calibration coefficient obtained by the new method is less than 5% compared with that by the traditional method, which meets the requirements of seismic observation systems, confirming the reliability of the new method. This new method can completely replace the traditional calibration method of using a radon source in seismic systems.

Stratospheric aerosols play important roles in Earth's radiative budget and in heterogeneous chemistry. Volcanic eruptions modulate the stratospheric aerosol layer by injecting particles and particle precursors like sulfur dioxide (SO2) into the stratosphere. Beginning on April 9th, 2021, La Soufrière erupted injecting SO2 into the tropical upper troposphere and lower stratosphere, yielding a peak SO2 loading of 0.3-0.4 Tg. The resulting volcanic aerosol plumes dispersed predominately over the northern hemisphere (NH), as indicated by the CALIOP/CALIPSO satellite observations and model simulations. From June to August 2021 and May to July 2022, the NASA ER-2 high-altitude aircraft extensively sampled the stratospheric aerosol layer over the continental United States during the Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) mission. These in situ aerosol measurements provide detailed insights into the number concentration, size distribution, and spatiotemporal variations of particles within volcanic plumes. Notably, aerosol surface area density and number density in 2021 were enhanced by a factor of 2-4 between 380-500 K potential temperature compared to the 2022 DCOTSS observations, which were minimally influenced by volcanic activity. Within the volcanic plume, the observed aerosol number density exhibited significant meridional and zonal variations while the mode and shape of aerosol size distributions did not vary. The La Soufrière eruption led to an increase in the number concentration of small particles (<400 nm), resulting in a smaller aerosol effective diameter during the summer of 2021 compared to the baseline conditions in the summer of 2022, as observed in regular ER-2 profiles over Salina, Kansas. A similar reduction in aerosol effective diameter was not observed in ER-2 profiles over Palmdale, California, possibly due to the already smaller values in that region during the limited sampling period in 2022. The La Soufrière eruption was modeled with the SOCOL-AERv2 aerosol-chemistry-climate model. The modeled aerosol enhancement aligned well with DCOTSS observations, although the direct comparison was complicated by issues related to the model's background aerosol burden. This study indicates that the La Soufrière eruption contributed at most 0.6% to Arctic and Antarctic ozone column depletion in both 2021 and 2022, which is well within the range of natural variability. The modeled top-of-atmosphere one-year global average radiative forcing was -0.08 W/m2 clear-sky and -0.04 W/m2 all-sky. The radiative effects were concentrated in the tropics and NH midlatitudes and diminished to near-baseline levels after one year. [ABSTRACT FROM AUTHOR]

Carbonaceous materials in seismic fault zones may considerably influence seismic fault slip; however, the formation mechanism of carbonaceous materials remains unclear. In this study, we proposed a novel hypothesis for the formation of carbonaceous materials in fault gouge. Thus, we conducted a CO2 hydrogenation experiment in a high-temperature reactor at a co-seismic temperature, with fault gouge formed during the Wenchuan earthquake as the catalyst. Our experimental results demonstrate that carbonaceous materials in fault zones are formed on the fault gouge during the chemical reaction process, suggesting that the carbonaceous materials are possibly generated from the catalytic hydrogenation of CO2, followed by thermal cracking of its products. The results of this study provide a theoretical basis for understanding fault behavior and earthquake physics.

Abstract Despite notable advancements in the investigation and management of lung adenocarcinoma (LUAD), the mortality rate for individuals afflicted with LUAD remains elevated, and attaining an accurate prognosis is challenging. LUAD exhibits intricate genetic and environmental components, and it is plausible that free fatty acid receptors (FFARs) may bridge the genetic and dietary aspects. The objective of this study is to ascertain whether a correlation exists between FFAR4, which functions as the primary receptor for dietary fatty acids, and various characteristics of LUAD, while also delving into the potential underlying mechanism. The findings of this study indicate a decrease in FFAR4 expression in LUAD, with a positive correlation (P

The atmospheric oxidation of dimethyl sulfide (DMS) represents a major natural source of atmospheric sulfate aerosols. However, there remain large uncertainties in our understanding of the underlying chemistry that governs the product distribution and sulfate yield from DMS oxidation. Here, chamber experiments were conducted to simulate gas-phase OH-initiated oxidation of DMS under a range of reaction conditions. Most importantly, the bimolecular lifetime (τbi) of the peroxy radical CH3SCH2OO was varied over several orders of magnitude, enabling the examination of the role of peroxy radical isomerization reactions on product formation. An array of analytical instruments was used to measure nearly all sulfur-containing species in the reaction mixture, and results were compared with a near-explicit chemical mechanism. When relative humidity was low, “sulfur closure” was achieved under both high-NO (τbi < 0.1 s) and low-NO (τbi > 10 s) conditions, though product distributions were substantially different in the two cases. Under high-NO conditions, approximately half the product sulfur was in the particle phase, as methane sulfonic acid (MSA) and sulfate, with most of the remainder as SO2 (which in the atmosphere would eventually oxidize to sulfate or be lost to deposition). Under low-NO conditions, hydroperoxymethyl thioformate (HPMTF, HOOCH2SCHO), formed from CH3SCH2OO isomerization, dominates the sulfur budget over the course of the experiment, suppressing or delaying the formation of SO2 and particulate matter. The isomerization rate constant of CH3SCH2OO at 295 K is found to be 0.13 ± 0.03 s-1, in broad agreement with other recent laboratory measurements. The rate constants for the OH oxidation of key first-generation oxidation products (HPMTF and methyl thioformate, MTF) were also determined (kOH+HPMTF = 2.1 × 10-11 cm³ molec-1 s-1, kOH+MTF = 1.35 × 10-11 cm³ molec-1 s-1). Product measurements agree reasonably well with mechanistic predictions in terms of total sulfur distribution and concentrations of most individual species, though the mechanism overpredicts sulfate and underpredicts MSA under high-NO conditions. Lastly, results from high-RH conditions suggest efficient heterogenous loss of at least some gas-phase products. [ABSTRACT FROM AUTHOR]

In biological micromanipulation, cell penetration is a typical procedure that precedes cell injection or oocyte enucleation. During this procedure, cells usually undergo significant deformation, which leads to cell damage. In this paper, we focus on modeling and evaluating the cell penetration process to reduce cell deformation and stress, thereby reducing cell damage. Initially, a finite element model (FEM) is established to simulate the cell penetration process. The effectiveness of the model is then verified through visual detection and comparison of cell deformation with experimental data. Next, various mechanical responses are analyzed, considering the influence of parameters, such as the radius and shape of the injection micropipettes, material properties, and size of the cells. Finally, the relationship between the intracellular stress and the cell penetration depth of biological cells is obtained. The evaluation results will be applied to develop optimized operation plans, enhancing the efficiency and safety of the cell penetration process.

The shear mechanical behavior of soils in the slip zone under cyclic loading is of great importance for the stability analysis of landslides. In this paper, an experimental device is developed to study the nonlinear deformation mechanism of soil in sliding zone under cyclic load. Based on field tests and model tests, the change of shear strength parameters of the soil in the slip zone with cyclic loading parameters was investigated. Based on the deformation characteristics of slip zone soils under cyclic dynamic loading, the unsteady creep transient model based on fractional derivative is established, and three deformation types of sliding zone soil are described: stable, critical and destructive. The Levenberg-Marquardt (LM) algorithm is used to fit the nonlinear curves, and the applicability of the model is also verified. This research method provides a test apparatus and a method for studying the deformation characteristics of slip zone soils under cumulative loading.

Salt stress is one of the major adverse factors affecting plant growth and crop production. Rapeseed is an important oil crop, providing high-quality edible oil for human consumption. This experiment was conducted to investigate the effects of salt stress on the phenotypic traits and physiological processes of rapeseed. The soil salinity was manipulated by setting three different levels: 0 g NaCl kg−1 soil (referred to as S0), 1.5 g NaCl kg−1 soil (referred to as S1), and 3.0 g NaCl kg−1 soil (referred to as S2). In general, the results indicated that the plant height, leaf area, and root neck diameter decreased with an increase in soil salinity. In addition, the biomass of various organs at all growth stages decreased as soil salinity increased from S0 to S2. The increasing soil salinity improved the distribution of biomass in the root and leaf at the seedling and flowering stages, indicating that rapeseed plants subjected to salt stress during the vegetative stage are capable of adapting their growth pattern to sustain their capacity for nutrient and water uptake, as well as leaf photosynthesis. However, as the soil salinity increased, there was a decrease in the distribution of biomass in the pod and seed at the maturity stage, while an increase was observed in the root and stem, suggesting that salt stress inhibited carbohydrate transport into reproductive organs. Moreover, the C and N accumulation at the flowering and maturity stages exhibited a reduction in direct correlation with the increase in soil salinity. High soil salinity resulted in a reduction in the C/N, indicating that salt stress exerted a greater adverse effect on C assimilation compared to N assimilation, leading to an increase in seed protein content and a decrease in oil content. Furthermore, as soil salinity increased from S0 to S2, the activity of superoxide dismutase (SOD) and catalase (CAT) and the content of soluble protein and sugar increased by 58.39%, 33.38%, 15.57%, and 13.88% at the seedling stage, and 38.69%, 22.85%, 12.04%, and 8.26% at the flowering stage, respectively. In summary, this study revealed that salt stress inhibited C and N assimilation, leading to a suppressed phenotype and biomass accumulation. The imbalanced C and N assimilation under salt stress contributed to the alterations in the seed oil and protein content. Rapeseed had a certain degree of salt tolerance by improving antioxidants and osmolytes.

Abstract The power quality and efficiency of the hydro-power station depend on the stable operation of the hydro-generator unit, which needs to continue to operate and it is prone to axis failure. Therefore, to adopt effective axis adjustment technology to eliminate faults. This paper proposes a new method for axis adjustment of hydro-generator unit based on an improved grey prediction model and swarms intelligence optimization neural network. First of all, it proposes a sequence acceleration translation and mean value transformation method, which is used to pre-process the axis net total swing sequence that exhibits oscillating fluctuations. It uses e 1 and e 2 factor transformation to establish an improved axis net total swing gray prediction model. Then, the advanced flamingo search algorithm is used to search the maximum value of the sine function of the net total pendulum of the axis, and the axis adjustment orientation is obtained. This method solves the problem that GM(1, 1) can only be predicted by monotone sequence in the past and the problem that the search algorithm is easy to fall into local optimum, effectively improves the calculation efficiency of axis and shorts the search time. Simulation examples show that the proposed method can significantly improve accuracy of axis adjustment. This method greatly improves the efficiency of azimuth search for axis adjustment.

A new capture vaporizer (CV) has been developed and used recently in the Aerodyne aerosol mass spectrometers (AMS) and aerosol chemical speciation monitors (ACSM) instead of the standard vaporizer (SV) to reduce the particle bounce. It is important to characterize the CV performance in different environments. In this study, we characterized specific organic aerosols (OA) from vehicle, cooking, biomass burning, and coal burning emissions by a Time-of-Flight ACSM (TOF-ACSM) with the CV. Their corresponding marker ions that have been defined in the previous SV-based analysis are still valid in the CV mass spectra. Spectra of OA from cooking and vehicle exhaust show similarities in distinct alkyl fragments but different ratios of m/z 55 and 57. Ions related to polycyclic aromatic hydrocarbons are present in the OA spectra obtained from burning lignite and bituminous coal, but not in the spectra obtained from burning anthracite. Although the relative intensities of m/z 60 and 73 are much lower in the CV spectra than in the SV spectra for biomass burning OA, they are still relatively greater compared with the spectra for other sources. Our data suggest an atmospheric background of f60 of below 0.03 % for CV. Moreover, we deployed the CV TOF-ACSM along with a SV AMS in urban Beijing during the winter of 2017 to characterize ambient OA with strong anthropogenic influences. The CV TOF-ACSM shows a collection efficiency (CE) of about unity. The CV and SV data show consistent mass concentrations of sulfate, nitrate, ammonium, and OA. Six OA factors are identified by the positive matrix factorization (PMF) analysis for both the CV and the SV data. The SV and CV PMF factors show good correlations in mass concentrations. The SV and CV factors related to coal combustion and cooking differ significantly in loadings, explained by the PMF uncertainty and the lack of understanding of the relative ionization efficiency (RIE) and CE for primary OA. The CV factors related to secondary sources show greater loadings than the SV factors, which may be associated with the changes of signal-to-noise ratios of various ions in the PMF analysis. Our results support improved mass quantification and useful source identification by the CV for ambient particles in the polluted urban environment. The difference in factor loadings between SV and CV should be considered when interpreting or comparing the PMF results among studies. [ABSTRACT FROM AUTHOR]

The power cyber–physical system (CPS) realizes the wide-area interconnection of new energy sources and multiple loads and the dynamic interaction of information and energy flows; however it faces increasingly severe security threats of false data injection attacks (FDIAs). In this context, the active defense methods against power CPS FDIAs from the multiple spatiotemporal perspective are summarized and analyzed in this paper. First, the architecture of a power CPS is abstracted and its multiple spatiotemporal coordination characteristic and FDIAs security risk are expounded upon. Then, the active defense system against FDIAs is conceived in the spatiotemporal coordinate system, and based on different spatiotemporal intersections, the six active defense methods and application examples – namely network deception, data encryption protection, vulnerability policy protection, false identification correction, traffic anomaly detection, and fault prevention control – are summarized. Finally, the limitations of existing active defense methods against FDIAs are discussed, and the linked defense methods with multiple spatiotemporal coordination are prospected.

The calvaria (top part of the skull) is made of pieces of bone as well as multiple soft tissue joints called sutures. The latter is crucial to the growth and morphogenesis of the skull, and thus a loss of calvarial sutures can lead to severe congenital defects in humans. During embryogenesis, the calvaria develops from the cranial mesenchyme covering the brain, which contains cells originating from the neural crest and the mesoderm. While the mechanism that patterns the cranial mesenchyme into bone and sutures is not well understood, function of Lmx1b, a gene encoding a LIM-domain homeodomain transcription factor, plays a key role in this process. In the current study, we investigated a difference in the function of Lmx1b in different parts of the calvaria using neural crest-specific and mesoderm-specific Lmx1b mutants. We found that Lmx1b was obligatory for development of the interfrontal suture and the anterior fontanel along the dorsal midline of the skull, but not for the posterior fontanel over the midbrain. Also, Lmx1b mutation in the neural crest-derived mesenchyme, but not the mesoderm-derived mesenchyme, had a non-cell autonomous effect on coronal suture development. Furthermore, overexpression of Lmx1b in the neural crest lineage had different effects on the position of the coronal suture on the apical part and the basal part. Other unexpected phenotypes of Lmx1b mutants led to an additional finding that the coronal suture and the sagittal suture are of dual embryonic origin. Together, our data reveal a remarkable level of regional specificity in regulation of calvarial development.

Cell models play a crucial role in analyzing the mechanical response of cells and quantifying cellular damage incurred during micromanipulation. While traditional models can capture the overall mechanical behavior of cells, they often lack the ability to discern among distinct cellular components. Consequently, by employing dissipative particle dynamics, this study constructed a triangular network-like representation of the cell membrane along with cross-linked cytoskeletal chains. The mechanical properties of both the membrane and cytoskeleton were then analyzed through a series of simulated mechanical tests, validated against real-world experiments. The investigation utilized particle-tracking rheology to monitor changes in the mean square displacements of membrane particles over time, facilitating the analysis of the membrane’s storage and loss moduli. Additionally, the cytoskeletal network’s storage and loss moduli were examined via a double-plate oscillatory shear experiment. The simulation results revealed that both the membrane and cytoskeleton exhibit viscoelastic behavior, as evidenced by the power-law dependency of their storage and loss moduli on frequency. Furthermore, indentation and microinjection simulations were conducted to examine the overall mechanical properties of cells. In the indentation experiments, an increase in the shear modulus of the membrane’s WLCs correlated with a higher Young’s modulus for the entire cell. Regarding the microinjection experiment, augmenting the microinjection speed resulted in reduced deformation of the cell at the point of membrane rupture and a lower percentage of high strain.

Abstract Taking account of the fact that the existing knowledge‐driven detection process for false data injection attacks (FDIAs) has been in a passive detection state for a long time and ignores the advantages of data‐driven active capture of features, an active and passive hybrid detection method for power cyber‐physical systems (CPS) FDIAs with improved adaptive Kalman filter (AKF) and convolutional neural networks (CNN) is proposed in this paper. First, the shortcomings of the traditional AKF algorithm in terms of filtering divergence and calculation speed are analyzed. The state estimation algorithm based on non‐negative positive‐definite adaptive Kalman filter (NDAKF) is improved, and a passive detection method of FDIAs is constructed, with similarity Euclidean distance detection and residual detection at its core. Then, combined with the advantages of gate recurrent unit (GRU) and CNN in terms of temporal memory and feature‐expression ability, an active detection method of FDIAs based on a GRU‐CNN hybrid neural network is proposed. Finally, the results of joint knowledge‐driven and data‐driven parallel detection are used to define a mixed fixed‐calculation formula, and an active and passive hybrid detection method of FDIAs is established, considering the characteristic constraints of the parallel mode.

Contraction and relaxation are important functions of cardiomyocytes, and measuring their characteristics provides an evaluation index to explore the effects of drugs on cardiomyocytes. In addition, cardiomyocytes have an innate advantage in acting as a biopower by virtue of their ability to contract and relax, which also requires the detection of cardiomyocyte actions. However, existing measurement methods, such as mechanosensor measurements and calcium concentration measurements, have high requirements for experimental equipment and operation and are challenging to perform simultaneously with other cellular manipulations. Here, we propose a simple visual detection method for cardiomyocyte contraction and relaxation. We first recorded the contraction and relaxation of cardiomyocytes under a bright-field microscope, then used the optical flow method to track the sampling points on the cardiomyocytes in the video, and obtained the frequency of cardiomyocyte contraction and relaxation by analyzing the optical flow matrices. This method does not require the use of additional equipment or additional processing of cardiomyocytes, which significantly reduces the operational difficulty of detection and provides a method to achieve real-time detection of cardiomyocyte contraction and relaxation.

Intracellular pressure, a key physical parameter of the intracellular environment, has been found to regulate multiple cell physiological activities and impact cell micromanipulation results. The intracellular pressure may reveal the mechanism of these cells’ physiological activities or improve the micro-manipulation accuracy for cells. The involvement of specialized and expensive devices and the significant damage to cell viability that the current intracellular pressure measurement methods cause significantly limit their wide applications. This paper proposes a robotic intracellular pressure measurement method using a traditional micropipette electrode system setup. First, the measured resistance of the micropipette inside the culture medium is modeled to analyze its variation trend when the pressure inside the micropipette increases. Then, the concentration of KCl solution filled inside the micropipette electrode that is suitable for intracellular pressure measurement is determined according to the tested electrode resistance–pressure relationship; 1 mol/L KCl solution is our final choice. Further, the measurement resistance of the micropipette electrode inside the cell is modeled to measure the intracellular pressure through the difference in key pressure before and after the release of the intracellular pressure. Based on the above work, a robotic measurement procedure of the intracellular pressure is established based on a traditional micropipette electrode system. The experimental results on porcine oocytes demonstrate that the proposed method can operate on cells at an average speed of 20~40 cells/day with measurement efficiency comparable to the related work. The average repeated error of the relationship between the measured electrode resistance and the pressure inside the micropipette electrode is less than 5%, and no observable intracellular pressure leakage was found during the measurement process, both guaranteeing the measurement accuracy of intracellular pressure. The measured results of the porcine oocytes are in accordance with those reported in related work. Moreover, a 90% survival rate of operated oocytes was obtained after measurement, proving limited damage to cell viability. Our method does not rely on expensive instruments and is conducive to promotion in daily laboratories.

Our research aimed to investigate the effectiveness of auditory, visual, and audiovisual warning signals for capturing the attention of the pilot, and how stimulus onset asynchronies (SOA) in audiovisual stimuli affect pilots perceiving the bimodal warning signals under different perceptual load conditions. In experiment 1 of the low perceptual load condition, participants discriminated the location (right vs. left) of visual targets preceded by five different types of warning signals. In experiment 2 of high perceptual load, participants completed the location task identical to a low load condition and a digit detection task in a rapid serial visual presentation (RSVP) stream. The main effect of warning signals in two experiments showed that visual and auditory cues presented simultaneously (AV) could effectively and efficiently arouse the attention of the pilots in high and low load conditions. Specifically, auditory (A), AV, and visual preceding auditory stimulus by 100 ms (VA100) increased the spatial orientation to a valid position in low load conditions. With the increase in visual perceptual load, auditory preceding the visual stimulus by 100 ms (AV100) and A warning signals had stronger spatial orientation. The results are expected to theoretically support the optimization design of the cockpit display interface, contributing to immediate flight crew awareness.

Building an integrated energy system (IES) is an excellent solution to environmental pollution and energy consumption currently. However, there are usually various IES configurations with different performances, and it is usually difficult for the decision-makers to select the most sustainable IES solution among multiple choices. In this paper, we attempt to design an evaluation method that considers the renewable energy and energy system model for evaluating the benefit of an IES. Firstly, this paper constructs an evaluation index system including four dimensions of technology, economy, environment, and society, based on the physical architecture of IES and its benefit influencing factors. It focuses on multiple energy sources or systems, and satisfies the overall evaluation of IES, resulting in accurate benefit evaluation results. Secondly, the subjective weight is determined by combining the triangular fuzzy number (TFN) and the analytic hierarchy process (AHP). The objective weight is determined by the entropy method. The combined weight is obtained by combining the subjective and objective weights, which solves the problem of the weight calculation error caused by the mutual influence between the indexes. Finally, the fuzzy comprehensive evaluation method is used to conduct the benefit evaluation of the IES, which solves the problem that the qualitative index cannot be calculated. This paper takes an IES project in a city in northern China as a case study. Through sensitivity analysis and comparative analysis, the reliability and feasibility of the evaluation results and proposed method are verified. The analysis results show that the model evaluation results are intuitive and consistent with reality, and the data information utilization rate is high.

To extend the shelf life and retain bioactive proteins in milk, this study utilized microfiltration (MF) combined with ultrasonication to treat skim milk and investigated its efficiency in removing bacteria and retaining bioactive proteins compared with HTST pasteurization and microfiltration alone. Results showed that microfiltration combined with ultrasonication at 1296 J/mL could completely remove the bacteria in skim milk. Ultrasonication further extended the shelf life (4 °C) of microfiltered skim milk, which could reach at least 40 days when MF was combined with ˃1296 J/mL ultrasonication. In addition, ELISA showed that HTST pasteurization significantly decreased the levels of IgG by ~30%, IgA by ~ 50%, IgM by ~60%, and lactoferrin by ~40%, whereas the activity of the enzymes lactoperoxidase and xanthine oxidase were also decreased by ~ 20%. Compared with HTST, MF alone or combined with ultrasonication retained these bioactive proteins to a larger degree. On the other hand, proteomics indicated both damage to casein micelle and fat globule structures in milk when ultrasonication at >1296 J/mL was applied, as shown by increases in caseins and milk fat globular proteins. Simultaneously, this ultrasound intensity also decreased levels of bioactive proteins, such as complement factors. Taken together, this study provided new insights that may help to implement this novel combination of non-thermal technologies for the dairy industry aimed at improving milk quality and functionality.

The early steps of embryogenesis are controlled exclusively by the quality of oocyte that linked closely to its mechanical properties. The mechanical properties of an oocyte were commonly characterized by assuming it was homogeneous such that the result deviated significantly from the true fact that it was composed of subcellular components. In this work, we accessed and characterized the subcellular components of the oocytes and developed a layered high-fidelity finite element model for describing the viscoelastic responses of an oocyte under loading. The zona pellucida (ZP) and cytoplasm were isolated from an oocyte using an in-house robotic micromanipulation platform and placed on AFM to separately characterizing their mechanical profiling by analyzing the creep behavior with the force clamping technique. The spring and damping parameters of a Kelvin–Voigt model were derived by fitting the creeping curve to the model, which were used to define the shear relaxation modulus and relaxation time of ZP or cytoplasm in the ZP and cytoplasm model. In the micropipette aspiration experiment, the model was accurate sufficiently to deliver the time-varying aspiration depth of the oocytes under the step negative pressure of a micropipette. In the micropipette microinjection experiment, the model accurately described the intracellular strain introduced by the penetration. The developed oocyte FEM model has implications for further investigating the viscoelastic responses of the oocytes under different loading settings.

In this paper, robust H∞ control for fractional-order switched systems (FOSSs) with uncertainty is studied. Firstly, the fractional-order switching law for FOSSs is proposed. Then, H∞ control for FOSSs is proven based on the switching law and linear matrix inequalities (LMIs). Moreover, H∞ control for FOSSs with a state feedback controller is extended. Furthermore, the LMI-based condition of robust H∞ control for FOSSs with uncertainty is proven. Furthermore, the condition of robust H∞ control is proposed to design the state feedback controller. Finally, four simulation examples verified the effectiveness of the proposed methods.

In the paper, H∞ and robust H∞ control for fractional order systems (FOS) with order 0<α<1 are studied. Firstly, necessary and sufficient conditions of H∞ control and state feedback controller design are proposed. Then, robust H∞ control for FOS with uncertainty is studied, and state feedback controller is designed. These conditions are based on linear matrix inequalities (LMI) and can be easily solved by the LMI toolbox. Finally, the effectiveness of these conditions is verified by two numerical examples.

Oocyte penetration is an essential step for many biological technologies, such as animal cloning, embryo microinjection, and intracytoplasmic sperm injection (ICSI). Although the success rate of robotic cell penetration is very high now, the development potential of oocytes after penetration has not been significantly improved compared with manual operation. In this paper, we optimized the oocyte penetration speed based on the intracellular strain. We firstly analyzed the intracellular strain at different penetration speeds and performed the penetration experiments on porcine oocytes. Secondly, we studied the cell development potential after penetration at different penetration speeds. The statistical results showed that the percentage of large intracellular strain decreased by 80% and the maximum and average intracellular strain decreased by 25–38% at the penetration speed of 50 μm/s compared to at 10 μm/s. Experiment results showed that the cleavage rates of the oocytes after penetration increased from 65.56% to 86.36%, as the penetration speed increased from 10 to 50 μm/s. Finally, we verified the gene expression of oocytes after penetration at different speeds. The experimental results showed that the totipotency and antiapoptotic genes of oocytes were significantly higher after penetration at the speed of 50 μm/s, which verified the effectiveness of the optimization method at the gene level.