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In this paper, we apply the variational approach for solving the time-space fractal Bogoyavlenskii equation. By the two-scale fractal complex transformation, the fractal modification of Bogoyarlenskii equation is equivalently rewritten as the original Bogoyavlenskii equation. By variational principle, different type solutions including fractal bright soliton solution, fractal bright-like soliton solution, fractal kinky-bright soliton solution and fractal periodic wave solution are provided, which are not touched in the existing literature. Numerical results are presented to show the propagation behaviours.

The early combustion stage in coalfield fire areas (CFA) is the initial phase of coal fire development and spread, making the effective identification of the combustion state crucial. To effectively identify the early combustion state of CFA, this study investigates the CFA in Xinjiang, China. Based on the abnormal characteristics of shallow soil and surface space, a data-driven evaluation method for identifying early combustion states of CFA is proposed. Construct a soil-gas dual-layer early combustion state assessment model of CFA. The evaluation model was applied and validated in the coal fire areas of Xinjiang, China.The results show that there are obvious differences in the spatial distribution of auto-ignition gas, temperature, humidity, surface vegetation coverage and other indicators in the shallow soil and surface space of the coal fire area. The early combustion states of coal fires in areas A, B, and C in the survey area are all in the combustion stage, the combustion state in area D is in the oxidation stage. The accuracy of the evaluation method is bidirectionally verified by the radon measurement method and the infrared thermal imaging detection method. It provides a theoretical basis for controlling the development and spread of coalfield fire areas.

Radiometers and Wearable biosensors, as vital parts of the realm of quantum medicine, are becoming popular for their ability to continuously and immediately provide physiological data through single-cell spectroscopy, brain imaging, and noninvasive monitoring of biochemical markers in various neuroimaging and biofluids such as sweat, tears, and interstitial fluid. Various biosensing, microfluidic sampling, and transport technologies have been combined, made smaller, and incorporated with flexible materials to improve ease of use and comfort. Enhancing the understanding of the connections between noninvasive biofluids and blood analyte levels is crucial for improving the reliability of wearable biosensors. This review discusses the noninvasive monitoring of biomarkers such as hormones and metabolites utilizing electrochemical and optical biosensors, single-cell spectroscopy, and brain imaging. Increasing the number of biomarkers for monitoring will need further on-body bio affinity testing and various sensing devices. Large-scale validation studies with many participants are necessary to use wearable biosensors in therapeutic settings. Wearable biosensor technology's ability to quickly and accurately detect real-time physiological data in therapy might significantly impact our daily routines

Centrifugal pumps are important equipment in industrial production, and their safe and reliable operation is of great significance to water supply and industrial safety. During the use of centrifugal pumps, faults such as bearing damage, blade wear, shaft imbalance, shaft misalignment and water hammer often occur. Among them, although water hammer faults occur at a low frequency, they are difficult to monitor and pose significant risks to valve and pipeline interfaces. This article analyzes the causes, mechanisms and phenomena of water hammer faults in centrifugal pumps, designs a monitoring method to effectively monitor the vibration signal of the centrifugal pumps, extracts vibration characteristics to determine and record water hammer events, designs monitoring and diagnostic models for the edge layer and server side, and establishes an experimental verification testing system. The test results show that, under the conditions of simulating water hammer faults, after high-pass filtering of the collected vibration data, the kurtosis index, pulse index and margin index all exceed twice the threshold, and both sensors emit water hammer alarms. The designed data acquisition method can capture water hammer signals in a timely manner, and the analysis model can automatically identify water hammer faults based on existing fault knowledge and rules. This fully demonstrates the scientific and effective nature of the proposed centrifugal pump fault monitoring method and system, which is of great significance for ensuring the safe operation and improving the design of centrifugal pumps.

The power production, fuel consumption and revenue of any gas turbine (GT) power plant are greatly affected by variations in ambient conditions (entering air into the compressor). This paper simulates and reports the power output and efficiency of a gas turbine-based power plant (SGT-750 SIEMENSE) using around 8000 h-by-hour yearly weather data of two regions of Australia (north and south) without pre-cooling, with sole M−cycle pre-cooling and finally with an innovative hybrid pre-cooler. The hybrid pre-cooler consists of a Maisotsenko indirect evaporative cooler (M−cycle) and an absorption chiller (ABC). Briefly, the results indicate that power raise of the GT plant using the M−cycle IEC alone is about 2 % in temperate regions and 4 % in hot regions, while it would be about 4 % and 6 % respectively if the hybrid pre-cooler is used. Reducing the inlet air temperature by 8–10 °C increases the generated power of a GT with pre-cooler by 8–9 %.

The application of tumor antigen-based immunotherapy is hindered by the rarity of validated immunogenic peptides. In this study, we aimed to investigate the potential of circular RNAs (circRNAs) as a novel source of tumor antigen peptides in hepatobiliary tumor organoids. Using RNA-sequencing (RNA-seq) with an algorithm-based score tool, 3950 translated tumor-specific circRNAs were predicted to generate 18 971 antigen peptides in 27 organoids. In view of the antigen landscape, 11 amino acid length (mer) peptides and human leukocyte antigen (HLA)-A binding peptides harbored the highest immunogenicity-related scores. In three out of five analyzed organoids, 13 predicted antigen peptides were directly confirmed as HLA-A, -B, and -C (HLA-ABC) binding peptides with mass spectrometry (MS)-based immunopeptidomics. CircRNA-derived tumor-specific peptides presented by the HLA-ABC molecules stimulated cluster of differentiation 8 (CD8) T cells to exhibit increased CD107a interferon γ (IFNγ) co-expressions and IFNγ secretion in flow cytometry and enzyme-linked immunosorbent assay (ELISA). Cytotoxic T cell activity targeting the organoids, induced by the immunogenic circRNA-derived peptides, was verified in a killing assay. Notably, the antigen peptide YGFNEILKK from circTBC1D15 was not only recognized as an HLA-ABC-presented peptide of the organoids but also drastically reduced the tumor organoid survival rate. Our findings highlight a crucial subset for generating tumor antigens, which has implications for targeting tumor-specific circRNAs in cancers.

In order to solve the problem that the transient response of ZPW-2000A track circuit cannot be calculated directly in time domain, the modified nodal admittance method (MNA) combined with Q-D method is proposed to analyze the transient response of ZPW-2000A track circuit. Firstly, the hybrid model of traction network lines and ZPW-2000A track circuit is established. Secondly, using this model and Kirchhoff’s law, the MNA time domain equation is obtained. Through Laplace transform and decoupling, the complex frequency domain solution of the rail surface voltage at the receiving end of ZPW-2000A track circuit is obtained. Then, the numerical time domain solution of rail surface voltage is obtained by using Fourier transform and Q-D algorithm, and a simulation model is built to verify the correctness of the proposed method. Furthermore, the variation laws of the rail surface voltage at the receiving end affected by frequency, transmission distance and ballast resistance are analyzed. Finally, when the train enters and leaves the track section, there is a transient process, which can provide a reference for the train occupancy inspection and when the air core coil is in fault state, its transient response is different from that in normal state, which provides a reference for fault detection of track circuit.

This study exhibits the possibility of using an artificial neural network (ANN) to model the mechanical behavior of wire arc additive manufacturing (WAAM) for Inconel 625. For this reason, tensile tests of Inconel 625 superalloy as-built (AB) samples and samples after heat treatment at 1200 °C (HT-1200) by WAAM were performed. For the HT-1200 samples, the yield stress decreased, and the elongation increased significantly due to grain refinement and the formation of annealed twins. A new hybrid model combining a swarm intelligence optimization algorithm with a back propagation neural network (BPNN) was developed to simulate the flow behavior of the superalloy. Compared with other hybrid BPNN models that have been reported, the proposed BPNN model is in better agreement with the experimental data and provides a better description of the flow stress of the Inconel 625 superalloy. The excellent predictive ability of the model may be attributed to the optimization of the weights and thresholds of the BPNN, which obtains the optimal global solution in the search space more efficiently.

The intricate geological conditions of reservoir banks render them highly susceptible to destabilization and damage from fluctuations in water levels. The study area, the Cheyipin section of the Huangdeng Hydroelectric Station, is characterized by numerous ancient landslides of varying scales and ages. In June 2019, during the reservoir filling process of the Huangdeng Hydroelectric Station, a large-scale reactivation of ancient landslides occurred in this area, posing severe threats to riverside infrastructure and human safety, including ground cracking, house cracking, foundation settlement, and road collapse. The reactivation mechanism of ancient landslides at reservoir banks is highly complex due to fluid dynamics. This study conducted field investigations in the Cheyipin landslide area, monitored surface and subsurface deformations using GNSS and inclinometers, and analyzed the distribution characteristics, destruction features, and reactivation mechanisms of the landslides through correlation analysis and numerical calculations. The results indicate that the instability pattern of the slopes manifests as traction-type sliding failure. The slopes do not slide along the ancient sliding surface but along a newly formed arcuate sliding surface, with the direct impact area mainly concentrated near the waterline. The stability of the slopes in this project is closely related to the reservoir water level. It can be assumed that the lowering of the reservoir water level triggered the reactivation of the ancient landslides in the Cheyipin section, while the influence of rainfall can be ignored. To prevent the reactivation of ancient landslides, attention should be focused on the changes in reservoir water level, avoiding rapid adjustments in water level during the initial lowering and final raising of the water level.

Antibiotics cannot be effectively removed by traditional wastewater treatment processes, and have become widespread pollutants in various environments. In this study, a Z-type heterojunction photo-catalyst Pg-C3N4 (PCN)/Nitrogen doped biochar (N-Biochar)/BiVO4 (NCBN) for the degradation of norfloxacin (NOR) was prepared by the hydrothermal method. The specific surface area of the NCBN (42.88 m2/g) was further improved compared to BiVO4 (4.528 m2/g). The photo-catalytic performance of the catalyst was investigated, and the N-Biochar acted as a charge transfer channel to promote carrier separation and form Z-type heterojunctions. Moreover, the NCBN exhibited excellent performance (92.5%) in removing NOR, which maintained 70% degradation after four cycles. The main active substance of the NCBN was •O2−, and the possible degradation pathways are provided. This work will provide a theoretical basis for the construction of heterojunction photo-catalysts.

The dynamic vibration characteristics of multibody system in axial piston pump is studied based on a lumped parameter model. The model is used to predict the vibration responses of the rotor assembly and the separator assembly based on Runge-Kutta method. The built model is verified by comparing the theoretical frequencies and experimental results. The developed model is used to investigate the effect of operating circumstances and coefficients of stiffness and damping on the amplitude-frequency vibration response and phase trajectory diagram. The results demonstrated that the frequency simulated by the model is similar to that measured by the experiment, and the error is 8.85%. The frequency of the harmonic is related to the excitation force period and the rotational speed of the axial piston pump. Higher harmonic vibration frequencies are integer multiples of the pump rotation frequency and the inherent flow pulsation frequency. The frequency of pistons impulse is twice that of the rotation. The frequency diagram and phase trajectory diagram are affected by various operating conditions as well as stiffness and damping factors.

Transmission tower re-identification refers to the recognition of the location and identity of transmission towers, facilitating the rapid localization of transmission towers during power system inspection. Although there are established methods for the defect detection of transmission towers and accessories (such as crossarms and insulators), there is a lack of automated methods for transmission tower identity matching. This paper proposes an identity-matching method for transmission towers that integrates machine vision and deep learning. Initially, the method requires the creation of a template library. Firstly, the YOLOv8 object detection algorithm is employed to extract the transmission tower images, which are then mapped into a d-dimensional feature vector through a matching network. During the training process of the matching network, a strategy for the online generation of triplet samples is introduced. Secondly, a template library is built upon these d-dimensional feature vectors, which forms the basis of transmission tower re-identification. Subsequently, our method re-identifies the input images. Firstly, we propose that the YOLOv5n-conv head detects and crops the transmission towers in images. Secondly, images without transmission towers are skipped; for those with transmission towers, The matching network maps transmission tower instances into feature vectors. Ultimately, transmission tower re-identification is realized by comparing feature vectors with those in the template library using Euclidean distance. Concurrently, it can be combined with GPS information to narrow down the comparison range. Experiments show that the YOLOv5n-conv head model achieved a mean Average Precision at an Intersection Over Union threshold of 0.5 (mAP@0.5) score of 0.974 in transmission tower detection, reducing the detection speed by 2.4 ms compared to the original YOLOv5n. Integrating the online triplet sample generation into the matching network training with Inception-ResNet-v1 (d = 128) as the backbone enhanced the network’s rank-1 performance by 3.86%.

Bearing failures often result from compound faults, where the characteristics of these compound faults span across multiple domains. To tackle the challenge of extracting features from compound faults, this paper proposes a novel fault detection method based on the Legendre multiwavelet transform (LMWT) combined with envelope spectrum analysis. Additionally, to address the issue of identifying suitable wavelet decomposition coefficients, this paper introduces the concept of relative energy ratio. This ratio assists in identifying the most sensitive wavelet coefficients associated with fault frequency bands. To assess the performance of the proposed method, the results obtained from the LMWT method are compared with those derived from the empirical wavelet transform (EWT) method using different datasets. Experimental findings demonstrate that the proposed method exhibits more effective frequency spectrum segmentation and superior detection performance across various experimental conditions.

Real-time estimation of thelength of mixed oil in a multi-product pipeline is a critical task during batch transportation. In previous studies, various predictive models have been built while they merely depended on a single predictive model to fulfill the regression work, and model performance severely deteriorated with the presence of outliers. The Student’s t mixture regression (SMR) model can identify multimode characteristics and reduce the impact of outliers. However, ignorance of physics knowledge and the simplistic assumption of a linear relationship between variables in the SMR may lead to unsatisfactory performance. In addition, the possible singularity problem can make the SMR fails to work. Motivated by resolving these issues, this paper proposes a physics-informed SMR modeling method by integrating the physics knowledge and the SMR to develop a robust hybrid predictive model for predicting the mixed oil length in a multi-product pipeline. Case studies are carried out on the measured dataset to demonstrate the effectiveness and advantages of the proposed new modeling method compared to the model entirely based on the SMR method and two state-of-the-art predictive models.

Abstract In order to obtain the quantitative relationship between multisource data from road landscape and driving visual comfort in two‐lane mountainous road, we collected the driver's eye movement data and road environment data through real vehicle tests. The threshold segmentation method and the target landscape area ratio method are used to process the visual image, and the spatial enclosure degree is used to express the driver's sense of spatial closure. In addition, an HSV color model is established to characterize the driver's visual perception of the mountain highway landscape environment. Combining the target landscape area ratio of road landscape with the amount of color information, the calculation method of road landscape information is obtained. According to the characteristics of normal distribution of pupil area ratio, five grades of driver's visual comfort level are proposed, and the regression relationship between comfort level and landscape information is established. The study found that there is a significant negative correlation between the amount of road landscape information and the driver's visual comfort. When the amount of road landscape information is less than 0.338, the driving comfort is higher, which is conducive to driving safety. However, when the amount of landscape information in the road area exceeds 0.644, the driving comfort decreases significantly, which is detrimental to driving safety.

Smart surfaces with controllable friction have generated considerable attention lately. However, most composites prepared with traditional fillers cannot achieve “real-time” friction conversion. Herein, a new smart surface was designed to achieve different friction coefficients (0.65 and 0.12). Different coefficients of friction were reversibly and precisely controlled via heating. Via friction and heating, 1H,1H,2H,2H-perfluorohexyl hexadecane (PHHD), a kind of phase-change material—paraffin wax—was released from the microcapsules, and a stable and complete film was formed. It changed the interface from “solid-solid” to “solid-liquid” in a dry friction state. The composite contains microcapsules that prevent phase separation between PHHD and matrix, which enables the composite to have a long service time and switchable friction performance. In addition, this composite can maintain its extraordinary ability even in harsh environments like UV irradiation. By demonstrating switchable friction based on changes in the interactions between contact interfaces, this work provides a new principle for designing smart tribological composites.

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease. Providing trustworthy AD progression predictions for at-risk individuals contributes to early identification of AD patients and holds significant value in discovering effective treatments and empowering the patient in taking proactive care. Recently, although numerous disease progression models based on machine learning have emerged, they often focus solely on enhancing predictive accuracy and ignore the measurement of result reliability. Consequently, this oversight adversely affects the recognition and acceptance of these models in clinical applications. To address these problems, we propose a multi-task evidential sequence learning model for the trustworthy prediction of disease progression. Specifically, we incorporate evidential deep learning into the multi-task learning framework based on recurrent neural networks. We simultaneously perform AD clinical diagnosis and cognitive score predictions while quantifying the uncertainty of each prediction without incurring additional computational costs by leveraging the Dirichlet and Normal-Inverse-Gamma distributions. Moreover, an adaptive weighting scheme is introduced to automatically balance between tasks for more effective training. Finally, experimental results on the TADPOLE dataset validate that our model not only has a comparable predictive performance to similar models but also offers reliable quantification of prediction uncertainties, providing a crucial supplementary factor for risk-sensitive AD progression prediction applications.

Water inrush has become one of the bottlenecks restricting tunnel construction. Among various advanced forecasting techniques, the direct current method is more cost-effective and sensitive to water-bearing structures. It has been widely used in exploring water inrush disasters in practical engineering. Although traditional resistivity linear inversion methods are reasonably practical, they usually suffer from volume effects and cannot accurately locate the location and morphology of water-bearing bodies. Therefore, nonlinear techniques such as deep learning have recently become popular to directly approximate the inversion function by learning the mapping of apparent resistivity data to the geoelectric model. This work presents a novel deep learning-based electrical approach that combines resistivity and polarizability to estimate water-bearing location and morphology. Specifically, we design an encoder-decoder network. A shared encoder extracts features from the input data, two encoders output resistivity, and polarizability models, respectively, and fine-tuned collinear regularization for both outputs reduces solutions’ multiplicity. Compared with traditional linear inversion methods and independent parameter inversion, our proposed joint inversion method shows superiority in locating and delineating anomalous bodies.

Intrahepatic cholangiocarcinoma (ICC) is the second most common liver cancer. Chemotherapy remains the main therapeutic strategy for advanced ICC patients, but chemosensitivity varies individually. Here, we applied cytometry by time-of-flight (CyTOF) to establish the immune profile of peripheral blood mononuclear cells (PBMCs) on the single-cell level at indicated time points before, during, and after chemotherapy. Multiplex immunofluorescence staining was applied to examine the spatial distribution of certain immune clusters. Tissue microarrays (TMAs) were used for prognostic evaluation. A total of 20 ICC patients treated with gemcitabine (GEM) were enrolled in our study, including eight cases with good response (R) and 12 cases with non-response (NR). Tremendous changes in PBMC composition, including an increased level of CD4/CD8 double-positive T cells (DPT), were observed after chemotherapy. Patients with higher level of CD4+CD45RO+CXCR3+ T cells before treatment had a favorable response to chemotherapy. Our study identified a positive correlation between the percentage of T cell subpopulations and clinical response after chemotherapy, which suggests that it is practical to predict the potential response before treatment by evaluating the proportions of the cell population in PBMCs.

Currently, research on the edge effect issue in the micro-arc oxidation process primarily focuses on investigating process conditions and enhancing additives. However, some scholars have utilized finite element analysis software to simulate the edge effect during the simulation process, overlooking the investigation of the morphology of the auxiliary cathode. This study analyzes the growth characteristics of the oxide film on aluminum alloy 2A12 during micro-arc oxidation. Additionally, the inter-electrode discharge electric field is simulated using the finite element analysis method. The auxiliary cathode is optimized to mitigate the influence of the edge effect on the film layer. The findings indicate that employing a cylindrical shape as the auxiliary cathode instead of a rectangular groove leads to an increased thickness of the micro-arc oxidation film. However, it also results in an augmented length of the film layer affected by the edge effect at both ends of the workpiece. Decreasing the distance between the auxiliary cathode and the workpiece surface leads to a higher thickness of the obtained micro-arc oxidation film. Decreasing the length of the auxiliary cathode results in a reduction in both the thickness of the film layer on the workpiece surface and the area affected by the edge effect. Increasing the eccentric cone ratio of the auxiliary cathode enhances the uniformity of the micro-arc oxidation film layer. In this study, we present a novel auxiliary cathode model that incorporates a smaller cylindrical shell at the center and eccentric cone shells on each side. This model has the potential to enhance the optimization rate of the micro-arc oxidation film layer on cylindrical workpieces by 17.77%.

To solve the problem of “under-maintenance” and “over-maintenance” in the daily maintenance of equipment, the predictive maintenance method based on the running state of equipment has shown great advantages, and fault prediction is an important part of predictive maintenance. First, the spectrum information of the equipment is extracted by the Hilbert–full-vector spectrum as the input of fault prediction. Compared with the traditional spectrum, this spectrum information fuses the signals of two sensors in the same section of the device, which can reflect the actual operational state of the device more comprehensively. Then, the temporal convolutional network is used to predict the amplitudes of different feature frequencies, and the double-layer attention mechanism is introduced to mine the correlation between the corresponding amplitudes of different feature frequencies and between the data at different historical moments, to highlight the more important influencing factors. In this way, the prediction accuracy of the model for the amplitude corresponding to the feature frequency of concern is improved. Finally, experimental verification is carried out on the XJTU-SY dataset. The results show that the TCDAN model proposed in this paper is significantly superior to TCN, GRU, BiLSTM, and LSTM, which can provide a more effective decision-making basis for the predictive maintenance of equipment.

The fabrication of high-performance copper alloys by selective laser melting (SLM) is challenging, and establishing relationships between the process parameters and microstructures is necessary. In this study, Cu–Cr–Nb–Ti alloy is manufactured by SLM, and the microstructures of the alloy are investigated by X-ray diffraction (XRD), scanning electron microscope (SEM), and electron backscatter diffraction (EBSD). The effects of processing parameters such as laser power and scanning speed on the relative density, defects, microstructures, mechanical properties, and electrical conductivity of the Cu–Cr–Nb–Ti alloy are studied. The optimal processing window for fabricating Cu–Cr–Nb–Ti alloy by SLM is determined. Face-centered cubic (FCC) Cu diffraction peaks shifting to small angles are observed, and there are no diffraction peaks related to the second phase. The grains of XY planes have a bimodal distribution with an average grain size of 24–55 μm. Fine second phases with sizes of less than 50 nm are obtained. The microhardness, tensile strength, and elongation of the Cu–Cr–Nb–Ti alloy manufactured using the optimum processing parameters, laser power of 325 W and scanning speed of 800 mm/s, are 139 HV0.2, 416 MPa, and 27.8%, respectively, and the electrical conductivity is 15.6% IACS (International Annealed Copper Standard). This study provides a feasible scheme for preparing copper alloys with excellent performance and complex geometries.

Dense and enhanced mechanical properties (Zr, Ti)(C, N)-based composites were fabricated using ZrC, TiC0.5N0.5, and Si powders as the raw powders by reactive hot-pressing at 1500–1700 °C. At the low sintering temperature, both (Zr, Ti)(C, N) and (Ti, Zr)(C, N) solid solutions were formed in the composites by adjusting the ratio of ZrC to TiC0.5N0.5. During the sintering process, the Si added at a rate of 5 mol% reacted with ZrC and TiC0.5N0.5 to generate SiC. With the increase in Si addition, it was found that the residual β-ZrSi was formed, which greatly reduced the flexural strength of composites but improved their toughness. The reaction and solid-solution-driven inter-diffusion processes enhanced mass transfer and promote densification. The solid solution strengthening and grain refinement improved the mechanical properties. The ZrC–47.5 mol% TiC0.5N0.5–5 mol% Si (raw powder) composite possessed excellent comprehensive performance. Its flexural strength, Vickers hardness, and fracture toughness were 508 ± 33 MPa, 24.5 ± 0.7 GPa, and 3.8 ± 0.1 MPa·m1/2, respectively. These reached or exceeded the performance of most (Zr, Ti)(C, N) ceramics reported in previous studies. The lattice distortion, abundant grain boundaries, and fine-grained microstructure may make it possible for the material to be resistant to radiation.

High-quality NdCrSb3 single crystals are grown using a Sn-flux method, for electronic transport and magnetic structure study. Ferromagnetic ordering of the Nd3+ and Cr3+ magnetic sublattices are observed at different temperatures and along different crystallographic axes. Due to the Dzyaloshinskii–Moriya interaction between the two magnetic sublattices, the Cr moments rotate from the b axis to the a axis upon cooling, resulting in a spin reorientation (SR) transition. The SR transition is reflected by the temperature-dependent magnetization curves, e.g., the Cr moments rotate from the b axis to the a axis with cooling from 20 to 9 K, leading to a decrease in the b-axis magnetization f and an increase in the a-axis magnetization. Our elastic neutron scattering along the a axis shows decreasing intensity of magnetic (300) peak upon cooling from 20 K, supporting the SR transition. Although the magnetization of two magnetic sublattices favours different crystallographic axes and shows significant anisotropy in magnetic and transport behaviours, their moments are all aligned to the field direction at sufficiently large fields (30 T). Moreover, the magnetic structure within the SR transition region is relatively fragile, which results in negative magnetoresistance by applying magnetic fields along either a or b axis. The metallic NdCrSb3 single crystal with two ferromagnetic sublattices is an ideal system to study the magnetic interactions, as well as their influences on the electronic transport properties.

As sensor parameters and atmospheric conditions create uncertainties for at−sensor radiation detection, radiometric consistency among satellite images is difficult to achieve. Relative radiometric normalization is a method that can improve multi−image consistency with accurate pseudo−invariant features (PIFs), especially over large areas or long time series satellite images. Although there are algorithms that manually or automatically select PIFs, the spatial mismatch of satellite images can affect PIF extraction, particularly with artificial pixels. To alleviate this problem, we proposed to use Landsat−8 OLI as the reference image and Sentinel−2A as the subject image, to apply pseudo−invariant features−based algorithms with polygon features through the single−band and multiple−band regression. Compared to pseudo−invariant point features, hyperspectral library, and histogram matching approaches, the results demonstrate the superiority of the proposed algorithms with correlation coefficients of 0.9948 and 0.9945, and an RMSE of 0.0097 and 0.0095 with multiple− and single−band regression, respectively. We also found more accurate linear fitting and better shape matching through band scattering and reflectance frequency analysis. The proposed algorithms are a significant improvement in radiometric normalization, within artificial pixels, achieving spectral signature consistency.

To better understand the shallow sea geological information and avoid the risk caused by potential geo-disasters, the efficient offshore geological exploration methods are required. Better detection resolution can be obtained by using a spark source. As the foundation, the observation system plays an important role for geological detection. The influence of the variation of the parameters of the observation system on the detection accuracy is analysed theoretically. Then, the numerical simulations based on the finite difference method was applied, and imaging characteristics of observation system with different parameter were studied. For submarine acoustic exploration, the spark source with frequency over 200 Hz can obtain the clear reflections for geological interpretation; besides, the receiver array with the interval of 5 m–10 m helps to obtain better wave signals when the buried depth of a geological body is less than 300 m, and the width is more than 10 m. Based on numerical simulations, the observation system was optimized and designed. The results of numerical examples show that the accurate position information of the structure can be obtained by using the observation system proposed in this paper. Different imaging performances are obtained by adjusting the parameters of the observation system. On this basis, combined with directivity analysis, the optimal observation system parameters are proposed. Finally, the proposed observation system is used to image the fault model. The research results of this paper can provide reference for the observation system design in similar projects.

In order to solve the problem that the traditional computer-aided teaching system is affected by communication technology, which leads to the inability to interact between teachers and students, the author proposes a research on a computer teaching system based on the Internet of Things and machine learning. The hardware structure is designed according to the functions of each module of the system, in which the student learning module is composed of a teaching coordination agent and a number of other agents, responsible for the presentation of specific teaching materials, problem solving, knowledge sharing through a collaborative mechanism, and providing personalized teaching basis for the system. The teacher’s teaching module mainly provides students with corresponding teaching strategies according to the learning requirements and uses its own reasoning mechanism to provide intelligent guidance to the problems encountered in the teaching process; the assessment module uses assessment rules to analyze student responses, comprehensive assessment of students’ learning behaviors, attitudes, effects, and abilities. The software function is designed with SQL Server 2000 as the database server, in the case of determining the data attributes, the data online evaluation is carried out, and the distance teaching is completed by combining the network technology. The experimental results show that when the time is 20s, the teaching efficiency of the traditional system is 61%, and the teaching efficiency of the artificial intelligence system is 91%. As a result, the teaching efficiency of the system based on the Internet of Things and machine learning is high, and it can provide equipment support for students’ learning.

The utility pole inclination angle is an important parameter for determining pole health conditions. Without depth information, the angle cannot be estimated from a 2D image, and without large labeled reference pole data, it is time consuming to locate the pole in the 3D point cloud. Therefore, this paper proposes a method that processes the pole data from the 2D image and 3D point cloud to automatically measure the pole inclination angle. Firstly, the mask of the pole skeleton is obtained from an improved Mask R-CNN. Secondly, the pole point cloud is extracted from a PointNet that deals with the generated frustum from the pole skeleton mask and depth map fusion. Finally, the angle is calculated by fitting the central axis of the pole cloud data. ApolloSpace open dataset and laboratory data are used for evaluation. The experimental results show that the AP75 of improved Mask R-CNN is 58.15%, the accuracy of PointNet is 92.4%, the average error of pole inclination is 0.66°, and the variance is 0.12°. It is proved that the method can effectively realize the automatic measurement of pole inclination.

Novel near-infrared (NIR) phosphors are in demand for light-emitting diode (LED) devices to extend their suitability for new applications and, in turn, support the sustainable and healthy development of the LED industry. The Cr3+ has been used as an activator in the development of new NIR phosphors. However, one main obstacle for the Cr3+-activated phosphors is the low luminescence efficiency due to the spin-forbidden d-d transition of Cr3+. The rare-earth (RE) huntite minerals that crystallize in the form of REM3(BO3)4 (M = Al, Sc, Cr, Fe, Ga) have a large family of members, including the rare-earth scandium borates of RESc3(BO3)4. Interestingly, in our research, we found that the luminescence efficiency of Cr3+ in the CeSc3(BO3)4 host, whose quantum yield was measured at 56%, is several times higher than that in GdSc3(BO3)4, TbSc3(BO3)4, and LuSc3(BO3)4 hosts. Hereby, the energy conversion and transfer in the luminescence of CeSc3(BO3)4:Cr3+ phosphor were examined. The Stokes shift of electron energy conversion within the Cr3+ 4T2g level for the emission at 818 nm and excitation at 625 nm in CeSc3(BO3)4 host was evaluated to be 3775.1 cm−1, and the super-large splitting energy of the 2F5/2 and 2F72 sub-states of the Ce3+ 4f1 state, about 3000 cm−1, was found in CeSc3(BO3)4 host. The typical electronic thermal vibration peaks were observed in the excitation spectra of CeSc3(BO3)4:Cr3+. On this basis, the smallest phonon energy, around 347.7 cm−1, of the CeSc3(BO3)4 host was estimated. Finally, the energy transfer that is responsible for the far higher photoluminescence of Cr3+ in CeSc3(BO3)4 than in other hosts was proven through the way of Ce3+ emission and Cr3+ reabsorption.

White ultra-high-performance concrete (WUHPC) performed outstanding mechanical, durability, and aesthetical properties, which was preferred in infrastructure to avoid the secondary painting, decrease the maintenance, and prolong the service life. Supplementary cementitious materials (SCMs) were often used in WUHPC to reduce the environment impacts and material costs. In this study, limestone powder (LP), metakaolin (MK), and silica fume (SF) were used as SCMs to largely substitute white Portland cement (WPC) to prepare WUHPC, the effects of substituted ratio on flowability, strength, and whiteness were studied, and the hydration products were also analyzed by quantitative-XRD method and SEM. The whiteness was calculated in chromatic space CIELAB by measuring tristimulus values of L, a*, and b*, and the controlled factor on whiteness was also investigated. As the results, the WUHPC with compressive strength exceeded 150 MPa and whiteness over 90 was prepared with WPC substitution of 35~65%. The SF improved the flowability and strength about 10% due to its filling and ball effect, while the irregular particle sharp and non-uniform size distribution of MK caused the reversed development. The increased dosage of raw materials with higher L value, such as LP and MK, made the WUHPC whiter. The hydration products with varied SCMs ratio were in the same category by different content. It was supposed that CaCO3 and C-S-H gel in hydration products caused higher whiteness, while C3S, CaMg(CO3)2, and SiO2 were against the whiteness. The results proved that with a large fraction of SCMs, the WUHPC with high strength and good appearance were prepared, and the whiteness of WUHPC were both controlled by the raw materials and the content of hydration products.

The aerodynamic drag for speed skating helmets with surface structures was investigated in this work by using numerical and experimental methods. Computational fluid dynamic (CFD) research was performed to analyze the detail of the flow field around the helmets. The simplified helmet models, with riblet and bump surface structures, were analyzed using the CFD simulations. The pressure distribution and velocity field around the helmets were obtained through the CFD analysis. The CFD results showed that the boundary layer separation position was obviously delayed, and the pressure changed to a higher value at the back area for structured helmets. Therefore, the aerodynamic drag for the structured helmet was lower than that of the original model. According to the CFD results, three types of helmets, with the of riblet and bump surface structure printed on the helmets by using flexible film, were tested in a wind tunnel. A full-scaled skater mannequin of half a body was used in the experiment to simulate the actual skating process. Compared with the original helmet, a drag reduction rate of 7% was achieved for the helmet with the bump at the middle region in the wind tunnel experiment, at the average speed in competitions for skaters.

There are many issues affecting multi-hole parallel tunneling and long tunnel ventilation, such as insufficient fresh air supplied at the tunnel face, pollution by contaminated winds, and uncoordinated work among multiple fans. In view of these issues, this paper uses the numerical simulation software Fluent combined with the orthogonal analysis test method, and, based on the actual working conditions of Yuelongmen Tunnel on the Chenglan Line, simulates the parameters of the duct, the installation position of the jet fan at the cross channel, and the combined operation status of multiple fans. The main conclusions are as follows: (1) the primary factor affecting the CO discharge from the tunnel face is the distance between the exit of the duct and the tunnel face, and the secondary factor is the diameter of the duct, while the position of the duct is the least influential factor; (2) the jet fan at the intersection of the cross channel should be set close to the pollution source in such a way as to prevent the jet from hitting the windward side of the cross channel and thereby avoid causing the contaminated wind to contaminate the adjacent tunnel; (3) in tunnels in which blasting operations do not occur, the simultaneous use of high-power air supplies and jet fans will reduce the air pressure in the tunnel, generate an air pressure difference with the adjacent tunnel, trap polluted air, and reduce the ventilation effect.

Polyurethane elastomers are anticipated to be applied in the field of cavitation erosion (CE) resistance, but their protection and damage mechanisms are not clear, which greatly restricts their further development. In this article, five polyether polyurethanes (PUx) with different crosslinking densities were prepared. Their mechanical properties, thermal properties, water absorption, surface morphology and chemical structure before and after CE tests were compared with ESEM, OM, TG-DSC, FTIR and XPS in detail. The results showed that with an increase in crosslinking density, the tensile strength of PUx increased first and then decreased, elongation at break and water absorption reduced gradually and thermal decomposition temperature and adhesion strength increased steadily. During the CE process, cavitation load aggravated the degree of microphase separation and made brittle hard segments concentrate on the coating surface; meanwhile, cavitation heat accelerated hydrolysis, pyrolysis, oxidation and the fracture of molecular chains. As a result, the mechano-thermal coupling intensified the formation and propagation of fatigue cracks, which should be the fundamental reason for the CE damage of polyurethane elastomer. PU0.4 exhibited the best CE resistance among the five coatings thanks to its good comprehensive properties and may find potential applications on the surface of hydraulic components.

This paper proposes and conceptualizes the mini indirect evaporative cooler (MIEC) and its applicability as a local cooling process of electronic units such as Graphics Processing Unit (GPU), Central Processing Unit (CPU) and so on. Water is the main coolant in both direct evaporative cooler (DEC) and indirect evaporative cooler (IEC). However, contrary to the DEC, no water (humid) is added to the product air in IEC. That is why the cooled dry air can be safely employed for cooling process of electronic units. Maisotsenko type of indirect evaporative cooler (MIEC) is able to provide sub-wet-bulb air temperature (not possible in DEC or conventional IEC). In order to reach the aim of this research and evaluate the possibility of the provided idea, an MIEC cooler is designed and fabricated by 3D printer mechanism (with heat transfer area of around 64 cm2) and its cooling characteristics are investigated through a comprehensive experimental/theoretical study under different thermal, fluid flow and geometric conditions. Finally, the required considerations in real application of MIEC including proposed structure/dimensions and economic criteria are represented and discussed. The results show that the MIEC can be employed to reduce the temperature of the electronic units and enhance their efficiency if all operation/design parameters are selected in optimum values.

Recently, many information processing applications appear on the web on the demand of user requirement. Since text is one of the most popular data formats across the web, how to measure text similarity becomes the key challenge to many web applications. Web text is often used to record events, especially for news. One text often mentions multiple events, while only the core event decides its main topic. This core event should take the important position when measuring text similarity. For this reason, this paper constructs a passage-level event connection graph to model the relations among events mentioned in one text. This graph is composed of many subgraphs formed by triggers and arguments extracted sentence by sentence. The subgraphs are connected via the overlapping arguments. In term of centrality measurement, the core event can be revealed from the graph and utilized to measure text similarity. Moreover, two improvements based on vector tunning are provided to better model the relations among events. One is to find the triggers which are semantically similar. By linking them in the event connection graph, the graph can cover the relations among events more comprehensively. The other is to apply graph embedding to integrate the global information carried by the entire event connection graph into the core event to let text similarity be partially guided by the full-text content. As shown by experimental results, after measuring text similarity from a passage-level event representation perspective, our calculation acquires superior results than unsupervised methods and even comparable results with some supervised neuron-based methods. In addition, our calculation is unsupervised and can be applied in many domains free from the preparation of training data.

The problem of stainless steel brazing is still the focus of scientific research. In this work, the Mn-based brazing filler was used to braze 1Cr18Ni9Ti and 1Cr21Ni5Ti stainless steel. The typical microstructure of the 1Cr18Ni9Ti/1Cr21Ni5Ti joint was analyzed in detail, and the interface structure of the joint was determined to be 1Cr18Ni9Ti/Mn(s, s)/1Cr21Ni5Ti. The brazing temperature and holding time were shown to have a great influence on the microstructure of the brazed joint. The tensile strength of brazed joints first increased and then decreased with the rising of the brazing temperature and the holding time. The maximum tensile strength was 566 MPa when the joints were brazed at 1125 °C for 15 min. The diffusion of Mn and Cr was an important factor affecting the quality of the joints. The diffusion distances of Mn and Cr at different brazing temperatures and holding times were measured, and the diffusion activation energy and diffusion coefficient were achieved by the Arrhenius equation.

In this work, a novel total non-ionic polystyrene-polyurethane (PS-PU) composite latex was synthesized with polymerizable polyethylene glycol ether. Contrary to traditional styrene-butyl acrylate latex (St-BA), PS-PU has a smaller size and superior dispersion stability, and it is stable in saturated Ca(OH)2 even after 72 h. In fresh-mixed mortars, PS-PU showed a little adverse effect on workability and insignificant air entrainment, with little defoamer consumption. The retardation effect of PS-PU is also much milder than traditional St-BA. As for strength, PS-PU showed a less adverse effect on early and late age compressive strength, but its effect on flexural strength is not as pronounced as St-BA at high dosages (4% and 6%). The different behavior in cementitious materials between PS-PU and St-BA can be reasoned from their different adsorption behavior and surface charge properties, as the results from characterizations suggest. The non-ionic nature of PS-PU made it less prone to destabilization and adsorption, which turned out as the aforementioned behavior in cementitious systems. The difference can further be ascribed to the difference in their polymeric structure and properties.

Metamaterials, or metasurfaces, allow the flexible and efficient manipulation of electromagnetic (EM) wave. Although the passive coding metasurfaces have achieved a great deal of functionality, they also need a complex design process. In this paper, we propose Hilbert-coding metasurfaces for flexible and convenient EM regulation by arranging Hilbert-coding metamaterial units of different orders. To demonstrate this behavior, we designed 12 metasurfaces, then fabricated and measured 6 samples. Validation results on 6 Hilbert-coding metasurfaces show the deflection angles of the four single beam patterns obtained are about 21°, 13°, 12°, and 39°, with energy values of 7.75 dB, 7.3 dB, 7.2 dB, and 7.7 dB, respectively, and the deflection angles of the dual-beam patterns are 28.5° and 20° with energy values of 10.05 dB and 11.4 dB, respectively. The results are quite consistent with the simulation data, further confirming the feasibility of our idea. In addition, there are potential applications in Wireless Communications and Radar-imaging, like EM beam scanning and EM field energy distribution control in communication and imaging scenarios.

As a core tool, anomaly detection based on a generative adversarial network (GAN) is showing its powerful potential in protecting the safe and stable operation of industrial control systems (ICS) under the Internet of Things (IoT). However, due to the long-tailed distribution of operating data in ICS, existing GAN-based anomaly detection models are prone to misjudging an unseen marginal sample as an outlier. Moreover, it is difficult to collect abnormal samples from ICS. To solve these challenges, a dual auto-encoder GAN-based anomaly detection model is proposed for the industrial control system, simply called the DAGAN model, to achieve an accurate and efficient anomaly detection without any abnormal sample. First, an “encoder–decoder–encoder” architecture is used to build a dual GAN model for learning the latent data distribution without any anomalous sample. Then, a parameter-free dynamic strategy is proposed to robustly and accurately learn the marginal distribution of the training data through dynamic interaction between two GANs. Finally, based on the learned normal distribution and marginal distribution, an optimized anomaly score is used to measure whether a sample is an outlier, thereby reducing the probability of a marginal sample being misjudged. Extensive experiments on multiple datasets demonstrate the advantages of our DAGAN model.

Unpredictable fluctuant passenger flow usually exists in urban metro operations. In this situation, traditional predetermined metro timetables cannot always meet the variation of passenger flow, and thus the service quality of the metro system could be affected profoundly. In this paper, by introducing an innovative metro operation method without timetables, we develop a nonlinear integer programming model to continually optimise the train operation to deal with detected real-time passenger flow variations. We aim to minimise the total passenger waiting time in the research time horizon under the vehicle number constraint. A modified genetic algorithm integrated with a macroscopic metro simulator is adopted to solve the proposed model. A case study based on the Beijing Metro Line 19 is implemented to provide a quantitative result for evaluating the proposed passenger flow-oriented metro operation method without timetables. Compared to traditional timetable-based metro operation, the method could significantly improve the metro operation’s flexibility and the quality of services.

Chemical corrosion has a significant impact on the properties of rock materials. To study the mechanical behavior and energy mechanism of rock under chemical corrosion, this paper took the sandstone of Haitangshan tunnel in Fuxin as the research object, used a Na2SO4 solution to simulate different chemical environments, carried out a triaxial loading test on sandstone through the MTS815.02 test system, and analyzed the mechanical parameters and energy damage evolution law of sandstone under different chemical environments. The test results showed that the basic mechanical parameters (peak strength σpk, peak strain εpk, elastic modulus E, cohesion c, and internal friction angle φ) and characteristic stress parameters (closure stress σcc, initiation stress σci, and dilatancy stress σcd) of sandstone first increased and then decreased with the increase of pH in the Na2SO4 solution, Poisson’s ratio µ showed the opposite trend, and the extreme values of all parameters were taken when pH = 7. The influence degree of different pHs on the mechanical parameters of sandstone were as follows: strong acid environment (pH ≤ 4) > strong alkali environment (pH ≥ 10) > weak acid environment (4 ≤ pH < 6) > weak alkali environment (8 ≤ pH < 10) > neutral environment (6 < pH< 8). The total energy and elastic strain energy increased first and then decreased, and the dissipated energy was the opposite. The damage variable decreased first and then increased. With the increasing concentration of the Na2SO4 solution, all the above parameters changed monotonically. Based on the energy theory, the damage evolution equation considering the effect of the Na2SO4 concentration was established. Combined with the test data, the model was verified and the result was good. Under the action of Na2SO4 corrosion, Ca2+ in calcite and Fe2+ in hematite were dissolved and precipitated. With the gradual increase of Ca2+ and Fe2+ concentration, the damage variable increased gradually. The relationship between the two ion concentrations and the damage variable approximately satisfied a linear function.

[Background] Virtual reality (VR) technology can provide unique immersive experiences for group users, and especially for analytics tasks with visual information in learning. Providing a shared control/view may improve the task performance and enhance the user experience during VR collaboration. [Objectives] Therefore, this research explores the effect of collaborative modes and user position arrangements on task performance, user engagement, and collaboration behaviors and patterns in a VR learning environment that supports immersive collaborative tasks. [Method] The study involved two collaborative modes (shared and non-shared view and control) and three position arrangements (side-by-side, corner-to-corner, and back-to-back). A user study was conducted with 30 participants divided into three groups (Single, Shared, and Non-Shared) using a VR application that allowed users to explore the structural and transformational properties of 3D geometric shapes. [Results] The results showed that the shared mode would lead to higher task performance than single users for learning analytics tasks in VR. Besides, the side-by-side position got a higher score and more favor for enhancing the collaborative experience. [Conclusion] The shared view would be more suitable for improving task performance in collaborative VR. In addition, the side-by-side position may provide a higher user experience when collaborating in learning VR. From these results, a set of guidelines for the design of collaborative visualizations for VR environments are distilled and presented at the end of the paper. All in all, although our experiment is based on a colocated setting with two users, the results are applicable to both colocated and distributed collaborative scenarios with two or more users.

The rectangular magnetoelectric (ME) composites of Metglas/PZT and Terfenol-D/PZT are prepared, and the effects of a magnetostrictive layer’s material characteristics on the magnetoimpedance of ME composite are discussed and experimentally investigated. The theoretical analyses show that the impedance is not only dependent on Young’s modulus and the magnetostrictive strain of magnetostrictive material but is also influenced by its relative permeability. Compared with Terfenol-D, Metglas possesses significantly higher magnetic permeability and larger magnetostrictive strain at quite low Hdc due to the small saturation field, resulting in the larger magnetoimpedance ratio. The experimental results demonstrate that the maximum magnetoimpedance ratios (i.e., ΔZ/Z) of Metglas/PZT composite are about 605.24% and 239.98% at the antiresonance and resonance, respectively. Specifically, the maximum ΔZ/Z of Metglas/PZT is 8.6 times as high as that of Terfenol-D/PZT at the antiresonance. Such results provide the fundamental guidance in the design and fabrication of novel multifunction devices based on the magnetoimpedance effect of ME composites.

This study investigated the flame structure and dynamics of a bluff-body flame when numerically close to blow-off conditions. This includes the impact of the hydrogen ratio on lean CH4/H2/air flame stabilization and blow-off characteristics. In this study, we assessed the impacts of four different hydrogen ratios: 0%, 30%, 60%, and 90%. Large eddy simulation (LES) was coupled with a thickened flame (TF) model to determine the turbulent combustion using a 30-species skeletal mechanism. The numerical results were progressively validated using OH-PLIF and PIV techniques. The results obtained from the numerical simulations showed minor differences with the experimental data on the velocity field and flame structure for all conditions. The presented results reveal that the flame is stabilized in higher-strain-rate spots more easily in the presence of high hydrogen ratios. Moreover, the flame location moves away from the concentrated vortex area with an increasing hydrogen ratio. The results of our blow-off investigation indicate that the blow-off sequence of a premixed bluff-body flame can be separated into two stages. The entire blow-off process becomes shorter with an increase in the hydrogen ratio. The primary reason for global extinction is a reduction in the heat release rate, and enstrophy analysis implies that blending hydrogen can reduce the enstrophy values of flames at the downstream locations. The dilatation and baroclinic torque terms decrease close to blow-off, but their decline is not significant in high-hydrogen-ratio conditions.

This study investigates the permeability characteristics of unsaturated compacted loess by focusing on the anisotropy parallel and perpendicular to the compaction. Three tests are conducted on compacted loess: permeability test, soil-water characteristic curve test, and scanning electron microscope (SEM) test. Samples are maintained and tested at different dry densities under optimal moisture conditions. Test results showed that the horizontal saturated permeability coefficient of compacted loess was larger than in the vertical direction, showing obvious anisotropy. Based on the saturated permeability coefficient, the permeability coefficient of unsaturated compacted loess can be predicted according to the soil-water characteristic curve fitted using the van Genuchten model combined with the Childs and Collis-Geroge model. The relational formula was established between the unsaturated permeability anisotropy ratio and the matrix suction through introducing a pore connectivity-tortuosity parameter, which represents the relationship between unsaturated permeability anisotropy ratio and matrix suction. The difference of pore characteristics between horizontal and vertical directions of compacted loess is the main reason for the permeability anisotropy.

Research on regulation of the immune microenvironment based on bioactive materials is important to osteogenic regeneration. Hydroxyapatite (HAP) is believed to be a promising scaffold material for dental and orthopedic implantation due to its ideal biocompatibility and high osteoconductivity. However, any severe inflammation response can lead to loosening and fall of implantation, which cause implant failures in the clinic. Morphology modification has been widely studied to regulate the host immune environment and to further promote bone regeneration. Here, we report the preparation of nHAPs, which have uniform rod-like shape and different size (200 nm and 400 nm in length). The morphology, biocompatibility, and anti-inflammatory properties were evaluated. The results showed that the 400 nm nHAPs exhibited excellent biocompatibility and osteoimmunomodulation, which can not only induce M2-phenotype macrophages (M2) polarization to decrease the production of inflammatory cytokines, but also promote the production of osteogenic factor. The reported 400 nm nHAPs are promising for osteoimmunomodulation in bone regeneration, which is beneficial for clinical application of bone defects.