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ABSTRACTCoxiella burnetii (C. burnetii) is the causative agent of Q fever, a zoonotic disease. Intracellular replication of C. burnetii requires the maturation of a phagolysosome-like compartment known as the replication permissive Coxiella-containing vacuole (CCV). Effector proteins secreted by the Dot/Icm secretion system are indispensable for maturation of a single large CCV by facilitating the fusion of promiscuous vesicles. However, the mechanisms of CCV maintenance and evasion of host cell clearance remain to be defined. Here, we show that C. burnetii secreted Coxiella vacuolar protein E (CvpE) contributes to CCV biogenesis by inducing lysosome-like vacuole (LLV) enlargement. LLV fission by tubulation and autolysosome degradation is impaired in CvpE-expressing cells. Subsequently, we found that CvpE suppresses lysosomal Ca2+ channel transient receptor potential channel mucolipin 1 (TRPML1) activity in an indirect manner, in which CvpE binds phosphatidylinositol 3-phosphate [PI(3)P] and perturbs PIKfyve activity in lysosomes. Finally, the agonist of TRPML1, ML-SA5, inhibits CCV biogenesis and C. burnetii replication. These results provide insight into the mechanisms of CCV maintenance by CvpE and suggest that the agonist of TRPML1 can be a novel potential treatment that does not rely on antibiotics for Q fever by enhancing Coxiella-containing vacuoles (CCVs) fission.

ABSTRACTRickettsia rickettsii (R. rickettsii), the causative agent of Rocky Mountain spotted fever (RMSF), is the most pathogenic member among Rickettsia spp. Previous studies have shown that tripartite motif-containing 56 (TRIM56) E3 ligase-induced ubiquitination of STING is important for cytosolic DNA sensing and type I interferon production to induce anti-DNA viral immunity, but whether it affects intracellular replication of R. rickettsii remains uncharacterized. Here, we investigated the effect of TRIM56 on HeLa and THP-1 cells infected with R. rickettsii. We found that the expression of TRIM56 was upregulated in the R. rickettsii-infected cells, and the overexpression of TRIM56 inhibited the intracellular replication of R. rickettsii, while R. rickettsii replication was enhanced in the TRIM56-silenced host cells with the reduced phosphorylation of IRF3 and STING and the increased production of interferon-β. In addition, the mutation of the TRIM56 E3 ligase catalytic site impairs the inhibitory function against R. rickettsii in HeLa cells. Altogether, our study discovers that TRIM56 is a host restriction factor of R. rickettsii by regulating the cGAS-STING-mediated signaling pathway. This study gives new evidence for the role of TRIM56 in the innate immune response against intracellular bacterial infection and provides new therapeutic targets for RMSF.IMPORTANCEGiven that Rickettsia rickettsii (R. rickettsii) is the most pathogenic member within the Rickettsia genus and serves as the causative agent of Rocky Mountain spotted fever, there is a growing need to explore host targets. In this study, we examined the impact of host TRIM56 on R. rickettsii infection in HeLa and THP-1 cells. We observed a significant upregulation of TRIM56 expression in R. rickettsii-infected cells. Remarkably, the overexpression of TRIM56 inhibited the intracellular replication of R. rickettsii, while silencing TRIM56 enhanced bacterial replication accompanied by reduced phosphorylation of IRF3 and STING, along with increased interferon-β production. Notably, the mutation of the TRIM56’s E3 ligase catalytic site did not impede R. rickettsii replication in HeLa cells. Collectively, our findings provide novel insights into the role of TRIM56 as a host restriction factor against R. rickettsii through the modulation of the cGAS-STING signaling pathway.

In the field of ultrasonic-assisted micro-forming, in addition to acoustic softening, impact effects also play a significant role, especially in terms of influencing the deformation behavior of surfaces, such as by generating more deformation on surface asperity. In this study, to understand the mechanisms involved in the effect of an impact, ultrasonic-assisted micro-forging tests were conducted on commercially pure copper, pure aluminum, and pure titanium. A method that can measure the increment in the temperature during ultrasonic vibration was developed. As a result, changes in the surface temperature of the material under the impact effect and acoustic softening were measured. It is indicated that, during ultrasonic vibration, the heat generated through acoustic softening is very limited and the main heat increase occurs after the impact effect. Once the impact effect occurs, the surface temperature increases with increasing amplitude. Nevertheless, for materials with different crystal structures, the influences of the impact effect are also different. The surfaces of copper and aluminum soften, creating more surface deformation, but the exact opposite effect is seen on a titanium surface. Observing the evolution of the microstructure on the material surface with EBSD demonstrates that the impact effect on FCC materials can reach deeper below the surface in terms of temperature diffusion compared to titanium. Meanwhile, the impact effect in the case of titanium causes the regeneration of twinning, which is reduced under the influence of the acoustic softening effect, consequently resulting in strain hardening.

Coxiella burnetii is an obligate intracellular bacterium that causes Q fever, a zoonotic disease typically manifests as a severe flu-illness. After invading into the host cells, C. burnetii delivers effectors to regulate the vesicle trafficking and fusion events to form a large and mature Coxiella-containing vacuole (CCV), providing sufficient space and nutrition for its intracellular growth and proliferation. Lysosomal trafficking regulator (LYST) is a member of the Beige and Chediak-Higashi syndrome (BEACH) family, which regulates the transport of vesicles to lysosomes and regulates TLR signaling pathway, but the effect of LYST on C. burnetii infection is unclear. In this study, a series of experiments has been conducted to investigate the influence of LYST on intracellular growth of C. burnetii. Our results showed that lyst transcription was up-regulated in the host cells after C. burnetii infection, but there is no significant change in lyst expression level after infection with the Dot/Icm type IV secretion system (T4SS) mutant strain, while CCVs expansion and significantly increasing load of C. burnetii appeared in the host cells with a silenced lyst gene, suggesting LYST inhibits the intracellular proliferation of C. burnetii by reducing CCVs size. Then, the size of CCVs and the load of C. burnetii in the HeLa cells pretreated with E-64d were significantly decreased. In addition, the level of iNOS was decreased significantly in LYST knockout THP-1 cells, which was conducive to the intracellular replication of C. burnetii. This data is consistent with the phenotype of L-NMMA-treated THP-1 cells infected with C. burnetii. Our results revealed that the upregulation of lyst transcription after infection is due to effector secretion of C. burnetii and LYST inhibit the intracellular replication of C. burnetii by reducing the size of CCVs and inducing nos2 expression.

Chlamydia trachomatis, one species of Chlamydia spp., has the greatest impact on human health and is the main cause of bacterial sexually transmitted diseases and preventable blindness among all Chamydia spp. species. The obligate intracellular parasitism and unique biphasic developmental cycle of C. trachomatis are the main barriers for the development of tools of genetic manipulation. The past decade has witnessed significant gains in genetic manipulation of C. trachomatis, including chemical mutagenesis, group II intron-based targeted gene knockout, fluorescence-reported allelic exchange mutagenesis (FRAEM), CRISPR interference (CRISPRi) and the recently developed transposon mutagenesis. In this review, we discuss the current status of genetic manipulations of C. trachomatis and highlights new challenges in the nascent field of Chlamydia genetics.

Silicon carbide (SiC) ceramic material has become the most promising third-generation semiconductor material for its excellent mechanical properties at room temperature and high temperature. However, SiC ceramic machining has serious tool wear, low machining efficiency, poor machining quality and other disadvantages due to its high hardness and high wear resistance, which limits the promotion and application of such materials. In this paper, comparison experiments of longitudinal torsional ultrasonic vibration grinding (LTUVG) and common grinding (CG) of SiC ceramics were conducted, and the longitudinal torsional ultrasonic vibration grinding SiC ceramics cutting force model was developed. In addition, the effects of ultrasonic machining parameters on cutting forces, machining quality and subsurface cracking were investigated, and the main factors and optimal parameters affecting the cutting force improvement rate were obtained by orthogonal tests. The results showed that the maximum improvement of cutting force, surface roughness and subsurface crack fracture depth by longitudinal torsional ultrasonic vibrations were 82.59%, 22.78% and 30.75%, respectively. A longitudinal torsional ultrasonic vibrations cutting force prediction model containing the parameters of tool, material properties and ultrasound was established by the removal characteristics of SiC ceramic material, ultrasonic grinding principle and brittle fracture theory. And the predicted results were in good agreement with the experimental results, and the maximum error was less than 15%. The optimum process parameters for cutting force reduction were a spindle speed of 22,000 rpm, a feed rate of 600 mm/min and a depth of cut of 0.011 mm.

Chlamydia psittaci is the causative agent of psittacosis, a worldwide zoonotic disease. A rapid, specific, and sensitive diagnostic assay would be benefit for C. psittaci infection control. In this study, an assay combining recombinase-aided amplification and a lateral flow strip (RAA-LF) for the detection of active C. psittaci infection was developed. The RAA-LF assay targeted the CPSIT_RS02830 gene of C. psittaci and could be accomplished in 15 min at a single temperature (39°C). The analytical sensitivity of the assay was as low as 1 × 100 copies/μl and no cross-reaction with some other intracellular pathogens was observed. Moreover, all feces samples from mice infected with C. psittaci at day-1 post-infection were positive in the RAA-LF assay. In conclusion, the RAA-LF assay provides a convenient, rapid, specific and sensitive method for detection of active C. psittaci infection and it is also suitable for C. psittaci detection in field.

Hyalomma marginatum is an important arthropod vector in the transmission of various zoonoses. The aim of this study was to identify the tick-borne pathogens (TBPs) maintained in Hy. marginatum in Tibet and to estimate the risk of human tick-borne diseases. Adult Hy. marginatum ticks (n = 14) feeding on yaks were collected. The individual DNA samples of these ticks were sequenced with metagenomic next-generation sequencing to survey the presence of TBPs. TBPs in individual ticks were identified with nested polymerase chain reaction (PCR) combined with DNA sequencing. The presence of Rickettsia , Anaplasma , and Ehrlichia in individual ticks was indicated by the taxonomic profiles at the genus level, but only Rickettsia aeschlimannii (100%, 13/13) was further detected in the ticks by nested PCR. This study provides information on the microbial communities of Hy. marginatum in Tibet, China, and provides the first report of R. aeschlimannii found in Hy. marginatum in Tibet. The results of this study indicated that yaks in Tibet are exposed to R. aeschlimannii .

Background and objective It has been demonstrated that epidermal growth factor receptor (EGFR) signal pathway had an important role in the oncogenesis and development of non-small cell lung cancer (NSCLC). Small-molecule tyrosine kinase inhibitors (TKIs) that target at the kinase domain of EGFR are recently developed target therapy reagents for treatment of NSCLC patients. Previous studies revealed that different patient groups response differently to EGFR-TKI, which is based on the EGFR gene status. The aim of this research was to define the clinicopathologic features associated with the gene amplification and mutation status of the EGFR gene in NSCLC patients and determine the most likely population to benefit from TKI treatment. Methods 187 of NSCLC cases were collected. The mutation status of EGFR exon 19 and 21 were determined by Real-time PCR, as well as the gene amplification status of EGFR gene by FISH. The relationship between EGFR mutation and gene amplification and the clinical pathologic features were analyzed with χ2 test. Results EGFR gene amplifications were identified in 89 of 187 samples (47.6%). EGFR gene amplification was not associated with age, gender, pathological type, smoking status and metestasis status (P>0.05). 20.3% (38/187) of NSCLC patients had EGFR gene mutation. EGFR gene mutation rates were significantly higher in the female (32.3% vs 14.4% male), non-smoker (38.2% vs 10.1% smoker) and patients with adenocarinoma (35.5% vs 9.9% non-adenocarcinoma) (P0.05). The patients with EGFR gene mutation had a better response to TKIs therapy than those without. Conclusion The EGFR gene mutation rate is different in the patients with different lung cancers. EGFR gene mutation occurs more frequently in female, non-smoker and patients with adenocarcinoma. Although there wasn’t a significant relationship between EGFR gene amplication and the clinicopathologic features, EGFR gene amplication may correlate with the prognosis of lung cancer patients.

Abstract Colon adenocarcinoma (COAD) is one of the most common types of cancer. The interconnection between non-apoptotic cell death and COAD has not been adequately addressed. In our study, an integrative bioinformatics analysis was performed to explore non-apoptotic cell death-related biomarkers in COAD. ENO2 was determined as a potent biomarker for prognosis, drug response, immunity, and immunotherapy prediction. We used EdU and RT-qPCR assays to test our hypothesis and investigate how the ENO2 gene may influence or regulate cancer-related processes. ENO2 was expected to be a potential target in COAD.

To accurately explore the numerical vibration response and heat transfer characteristics of heat exchanger with fluid-solid coupling in the actual working environment. According to the finite volume method and Euler-Lagrange method, the three-dimensional model and mathematical model of coupled vibration are founded. The 0.5 m/s working condition is taken as the experimental object to verify the rationality of the coupling calculation model. Then, bidirectional and unidirectional fluid-solid coupling simulation methods are chosen to resolve the vibration acceleration and heat transfer characteristics of heat exchange tubes at different speeds, and the effects of the two methods on the vibration response of heat exchange tubes are contrasted. Results indicate that under bidirectional coupling, the heat transfer characteristics of the heat exchange tube will experience significant fluctuations due to the vibration of the tube bundle. The shell-side domain has more prominent influence on the vibration of the heat exchange tube when the water velocity is greater than 0.5 m/s. Typical digital features of vibration acceleration and vibration frequency are improved by 110.03%–140.47% and 99.71%–99.88% respectively. The research not only provides a valid analysis approach for fluid-solid coupling vibration of heat exchanger, but also gives theoretical guidance to its vibration optimization design.

This article aimed to find effective modeling methods to predict the extraction rate of safflower seed oil by subcritical CO2 extraction, and optimize its extraction process conditions. Based on single-factor experiments, Box-Behnken experimental design was adopted to study the effects of extraction pressure, separation temperature, and extraction time on the extraction rate of safflower seed oil. Response surface methodology (RSM) and artificial neural network (ANN) were used to model and analyze the same experiment. The process conditions were optimized using RSM numerical optimization, ANN, and the combination of artificial neural network and genetic algorithm (ANN-GA). The results showed that both RSM and ANN models could accurately predict the extraction rate. However, by comparing the determination coefficient (R2), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) values of the two models, it was concluded that the ANN model (R2=0.9966) had a better predictive effect than the RSM model (R2=0.9950). The optimal extraction conditions and extraction rate determined by ANN-GA were as follows: Extraction pressure of 19.04 MPa, separation temperature of 55.50 °C, extraction time of 134.98 min, and extraction rate of 23.52%. The study showed that both RSM and ANN methods could be used for modeling and optimization of subcritical CO2 extraction of safflower seed oil, but ANN had better prediction accuracy and fitting ability.

Abstract Random bit generators are critical for information security, cryptography, stochastic modeling, and simulations. Speed and scalability are key challenges faced by current physical random bit generation. Herein, we propose a massively parallel scheme for ultrafast random bit generation towards rates of order 100 terabit per second based on a single micro-ring resonator. A modulation-instability-driven chaotic comb in a micro-ring resonator enables the simultaneous generation of hundreds of independent and unbiased random bit streams. A proof-of-concept experiment demonstrates that using our method, random bit streams beyond 2 terabit per second can be successfully generated with only 7 comb lines. This bit rate can be easily enhanced by further increasing the number of comb lines used. Our approach provides a chip-scale solution to random bit generation for secure communication and high-performance computation, and offers superhigh speed and large scalability.

Abstract Supramolecular chirality typically originates from either chiral molecular building blocks or external chiral stimuli. Generating chirality in achiral systems in the absence of a chiral input, however, is non-trivial and necessitates spontaneous mirror symmetry breaking. Achiral nematic lyotropic chromonic liquid crystals have been reported to break mirror symmetry under strong surface or geometric constraints. Here we describe a previously unrecognised mechanism for creating chiral structures by subjecting the material to a pressure-driven flow in a microfluidic cell. The chirality arises from a periodic double-twist configuration of the liquid crystal and manifests as a striking stripe pattern. We show that the mirror symmetry breaking is triggered at regions of flow-induced biaxial-splay configurations of the director field, which are unstable to small perturbations and evolve into lower energy structures. The simplicity of this unique pathway to mirror symmetry breaking can shed light on the requirements for forming macroscopic chiral structures.

Due to the complex intersection and control of multiple structural systems, the hydrogeological conditions of the Laiyuan Basin in China are complex. The depth of research on the relationship between geological structure and groundwater migration needs to be improved. The supply relationship of each aquifer is still uncertain. This paper systematically conducts research on the characteristics of hydrogen and oxygen isotopes, and combines magnetotelluric impedance tensor decomposition and two-dimensional fine inversion technology to carry out fine exploration of the strata and structures in the Laiyuan Basin, as well as comprehensive characteristics of groundwater migration and replenishment. The results indicate the following: (i) The hydrogen and oxygen values all fall near the local meteoric water line, indicating that precipitation is the main groundwater recharge source. (ii) The excess deuterium decreased gradually from karst mountain to basin, and karst water and pore water experienced different flow processes. (iii) The structure characteristics of three main runoff channels are described by MT fine processing and inversion techniques. Finally, it is concluded that limestone water moved from the recharge to the discharge area, mixed with the deep dolomite water along the fault under the control of fault F2, and eventually rose to the surface of the unconsolidated sediment blocked by fault F1 to emerge into an ascending spring.

It is vital to analyze ship collision risk for preventing collisions and improving safety at sea. This paper takes Ningbo-Zhoushan Port, a typical complex navigable water, as the research object. Firstly, a probabilistic conflict detection method based on an AIS data-driven dynamic ship domain model is proposed to achieve effective ship conflict detection under uncertain environments. Then, a ship group identification method is proposed, which can extract the ship groups with conflict correlation and space compactness. Finally, according to the characteristics of ship traffic in complex navigable waters, the dynamic calculation of ship collision risk is carried out from individual, regional, and local multi-scale perspectives. The experimental results show that the proposed method can detect the collision risk in a timely, reliable, and effective manner under complex dynamic conditions. As such, they provide valuable insights into ship collision risk prediction and the development of risk mitigation measures.

The Soil Plant Analysis Development (SPAD) is a vital index for evaluating crop nutritional status and serves as an essential parameter characterizing the reproductive growth status of winter wheat. Non-destructive and accurate monitorin3g of winter wheat SPAD plays a crucial role in guiding precise management of crop nutrition. In recent years, the spectral saturation problem occurring in the later stage of crop growth has become a major factor restricting the accuracy of SPAD estimation. Therefore, the purpose of this study is to use features selection strategy to optimize sensitive remote sensing information, combined with features fusion strategy to integrate multiple characteristic features, in order to improve the accuracy of estimating wheat SPAD. This study conducted field experiments of winter wheat with different varieties and nitrogen treatments, utilized UAV multispectral sensors to obtain canopy images of winter wheat during the heading, flowering, and late filling stages, extracted spectral features and texture features from multispectral images, and employed features selection strategy (Boruta and Recursive Feature Elimination) to prioritize sensitive remote sensing features. The features fusion strategy and the Support Vector Machine Regression algorithm are applied to construct the SPAD estimation model for winter wheat. The results showed that the spectral features of NIR band combined with other bands can fully capture the spectral differences of winter wheat SPAD during the reproductive growth stage, and texture features of the red and NIR band are more sensitive to SPAD. During the heading, flowering, and late filling stages, the stability and estimation accuracy of the SPAD model constructed using both features selection strategy and features fusion strategy are superior to models using only a single feature strategy or no strategy. The enhancement of model accuracy by this method becomes more significant, with the greatest improvement observed during the late filling stage, with R2 increasing by 0.092-0.202, root mean squared error (RMSE) decreasing by 0.076-4.916, and ratio of performance to deviation (RPD) increasing by 0.237-0.960. In conclusion, this method has excellent application potential in estimating SPAD during the later stages of crop growth, providing theoretical basis and technical support for precision nutrient management of field crops.

Aboveground biomass (AGB) is regarded as a critical variable in monitoring crop growth and yield. The use of hyperspectral remote sensing has emerged as a viable method for the rapid and precise monitoring of AGB. Due to the extensive dimensionality and volume of hyperspectral data, it is crucial to effectively reduce data dimensionality and select sensitive spectral features to enhance the accuracy of rice AGB estimation models. At present, derivative transform and feature selection algorithms have become important means to solve this problem. However, few studies have systematically evaluated the impact of derivative spectrum combined with feature selection algorithm on rice AGB estimation. To this end, at the Xiaogang Village (Chuzhou City, China) Experimental Base in 2020, this study used an ASD FieldSpec handheld 2 ground spectrometer (Analytical Spectroscopy Devices, Boulder, Colorado, USA) to obtain canopy spectral data at the critical growth stage (tillering, jointing, booting, heading, and maturity stages) of rice, and evaluated the performance of the recursive feature elimination (RFE) and Boruta feature selection algorithm through partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM) and ridge regression (RR). Moreover, we analyzed the importance of the optimal derivative spectrum. The findings indicate that (1) as the growth stage progresses, the correlation between rice canopy spectrum and AGB shows a trend from high to low, among which the first derivative spectrum (FD) has the strongest correlation with AGB. (2) The number of feature bands selected by the Boruta algorithm is 19~35, which has a good dimensionality reduction effect. (3) The combination of FD-Boruta-PCR (FB-PCR) demonstrated the best performance in estimating rice AGB, with an increase in R² of approximately 10% ~ 20% and a decrease in RMSE of approximately 0.08% ~ 14%. (4) The best estimation stage is the booting stage, with R2 values between 0.60 and 0.74 and RMSE values between 1288.23 and 1554.82 kg/hm2. This study confirms the accuracy of hyperspectral remote sensing in estimating vegetation biomass and further explores the theoretical foundation and future direction for monitoring rice growth dynamics.

Abstract To address the problem of conventional approaches for mechanical property determination requiring destructive sampling, which may be unsuitable for in-service structures, the authors proposed a method for determining the quasi-static fracture toughness and impact absorbed energy of ductile metals from spherical indentation tests (SITs). The stress status and damage mechanism of SIT, mode I fracture, Charpy impact tests, and related tests were first investigated through finite element (FE) calculations and scanning electron microscopy (SEM) observations, respectively. It was found that the damage mechanism of SITs is different from that of mode I fractures, while mode I fractures and Charpy impact tests share the same damage mechanism. Considering the difference between SIT and mode I fractures, uniaxial tension and pure shear were introduced to correlate SIT with mode I fractures. Based on this, the widely used critical indentation energy (CIE) model for fracture toughness determination using SITs was modified. The quasi-static fracture toughness determined from the modified CIE model was used to evaluate the impact absorbed energy using the dynamic fracture toughness and energy for crack initiation. The effectiveness of the newly proposed method was verified through experiments on four types of steels: Q345R, SA508-3, 18MnMoNbR, and S30408.

Sea Surface Temperature (SST) is of great importance to study several major phenomena due to ocean interactions with other earth systems. Previous studies on SST based on statistical inference methods were less accurate for longer prediction lengths. A considerable number of studies in recent years involve machine learning for SST modeling. These models were able to mitigate this problem to some length by modeling SST patterns and trends. Sequence analysis by decomposition is used for SST forecasting in several studies. Ensemble Empirical Mode Decomposition (EEMD) has been proven in previous studies as a useful method for this. The application of EEMD in spatiotemporal modeling has been introduced as Multidimensional EEMD (MEEMD). The aim of this study is to employ fast MEEMD methods to decompose the SST spatiotemporal dataset and apply a Convolutional Long Short-Term Memory (ConvLSTM)-based model to model and forecast SST. The results show that the fast MEEMD method is capable of enhancing spatiotemporal SST modeling compared to the Linear Inverse Model (LIM) and ConvLSTM model without decomposition. The model was further validated by making predictions from April to May 2023 and comparing them to original SST values. There was a high consistency between predicted and real SST values.

In the assessment of the seismic performance of ultrahigh-arch dams, challenges such as complex structural models, extensive degrees of freedom, and extended computation times are commonly encountered. Consequently, there is a pressing need to develop a more streamlined and effective approach for investigating the seismic behavior of ultrahigh-arch dams. The endurance time method, a novel approach in seismic analysis, is increasingly favored for the dynamic analysis of large structures. In this study, the dynamic damage characteristics of an ultrahigh-arch dam are analyzed based on the endurance time method, and the seismic fragility analysis is evaluated. Utilizing a case study of an ultrahigh-arch dam in southwest China, a comprehensive three-dimensional nonlinear model encompassing dam, bedrock, and water interactions is developed. This model incorporates considerations of dam and bedrock damage, cracking, dynamic behavior of contraction joints, and the interaction between the dam and reservoir water. The nonlinear least-squares function is used to generate three endurance time acceleration functions (ETAFs) based on the response spectra of the hydraulic structure seismic design standard. The dynamic analysis of the above model is carried out by using the endurance time method and the incremental dynamic analysis (IDA) method. The results indicate that the endurance time method can well reflect the fragility of ultrahigh-arch dams under different intensities of ground motions, with significant computational efficiency.

With the development of rice varieties and mechanized planting technology, reliable and efficient nitrogen and planting density status diagnosis and recommendation methods have become critical to the success of precise nitrogen and planting density management in crops. In this study, we combined population structure, plant shape characteristics, environmental weather conditions, and management information data using a machine learning model to simulate the responses of the yield and nitrogen nutrition index and developed an ensemble learning model-based nitrogen and planting density recommendation strategy for different varieties of rice types. In the third stage, the NNI and yield prediction effect of the ensemble learning model was more significantly improved than that of the other two stages. The scenario analysis results show that the optimal yields and nitrogen nutrition indices were obtained with a density and nitrogen amount of 100.1 × 104 plant/ha and 161.05 kg·ha−1 for the large-spike type variety of rice, 75.08 × 104 plant/ha and 159.52 kg·ha−1 for the intermediate type variety of rice, and 75.08 × 104 plant/ha and 133.47 kg·ha−1 for the panicle number type variety of rice, respectively. These results provide a scientific basis for the nitrogen application and planting density for a high yield and nitrogen nutrition index of rice in northeast China.

Leaf nitrogen concentration (LNC) is a primary indicator of crop nitrogen status, closely related to the growth and development dynamics of crops. Accurate and efficient monitoring of LNC is significant for precision field crop management and enhancing crop productivity. However, the biochemical properties and canopy structure of wheat change across different growth stages, leading to variations in spectral responses that significantly impact the estimation of wheat LNC. This study aims to investigate the construction of feature combination indices (FCIs) sensitive to LNC across multiple wheat growth stages, using remote sensing data to develop an LNC estimation model that is suitable for multiple growth stages. The research employs UAV multispectral remote sensing technology to acquire canopy imagery of wheat during the early (Jointing stage and Booting stage) and late (Early filling and Late filling stages) in 2021 and 2022, extracting spectral band reflectance and texture metrics. Initially, twelve sensitive spectral feature combination indices (SFCIs) were constructed using spectral band information. Subsequently, sensitive texture feature combination indices (TFCIs) were created using texture metrics as an alternative to spectral bands. Machine learning algorithms, including partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and Gaussian process regression (GPR), were used to integrate spectral and texture information, enhancing the estimation performance of wheat LNC across growth stages. Results show that the combination of Red, Red edge, and Near-infrared bands, along with texture metrics such as Mean, Correlation, Contrast, and Dissimilarity, has significant potential for LNC estimation. The constructed SFCIs and TFCIs both enhanced the responsiveness to LNC across multiple growth stages. Additionally, a sensitive index, the Modified Vegetation Index (MVI), demonstrated significant improvement over NDVI, correcting the over-saturation concerns of NDVI in time-series analysis and displaying outstanding potential for LNC estimation. Spectral information outperforms texture information in estimation capability, and their integration, particularly with SVR, achieves the highest precision (coefficient of determination (R2) = 0.786, root mean square error (RMSE) = 0.589%, and relative prediction deviation (RPD) = 2.162). In conclusion, the sensitive FCIs developed in this study improve LNC estimation performance across multiple growth stages, enabling precise monitoring of wheat LNC. This research provides insights and technical support for the construction of sensitive indices and the precise management of nitrogen nutrition status in field crops.

Topological defects play a key role in the structures and dynamics of liquid crystals and other ordered systems. There is a recent interest in studying defects in different biological systems with distinct textures. However, a robust method to directly recognize defects and extract their structural features from various traditional and nontraditional nematic systems remains challenging to date. Here we present a machine learning solution, termed machine eye for defects (MED), for automated defect analysis in images with diverse nematic textures. MED seamlessly integrates state-of-the-art object detection networks, segment anything model, and vision transformer algorithms with tailored computer vision techniques. We show that MED can accurately identify the positions, winding numbers, and orientations of ±1/2 defects across distinct cellular contours, sparse vector fields of nematic directors, actin filaments, microtubules, and simulation images of Gay-Berne particles. MED performs faster than conventional defect detection methods and can achieve over 90% accuracy on recognizing ±1/2 defects and their orientations from vector fields and experimental tissue images. We further demonstrate that MED can identify defect types that are not included in the training data, such as giant-core defects and defects with higher winding numbers. Remarkably, MED provides correct structural information about ±1 defects, i.e., the phase angle for +1 defects and the orientation angle for −1 defects. As such, MED stands poised to transform studies of diverse ordered systems by providing automated, rapid, accurate, and insightful defect analysis.

To enhance the comprehensive performance of solid oxide fuel cells (SOFCs) ferritic stainless steel (FSS) interconnectors, a novel approach involving composite electrodeposition and thermal conversion is proposed to prepare Ni-doped Co-Mn composite spinel protective coatings on FSS surfaces. The process involves the composite electrodeposition of a Ni-doped Co-Mn precursor coating, followed by thermal conversion to obtain the Co-Mn-Ni composite spinel coating. Crofer 22H was used as the substrate and orthogonal experiments were designed to investigate the influences of deposition solution pH, stirring rate, cathode current density, and the element content of Mn and Ni on the surface morphology and properties of the composite coatings, respectively. The characterization of the prepared coatings was conducted through macroscopic and microscopic morphology observations of the component surface, energy dispersive spectroscopy (EDS) analysis, and area specific resistance (ASR) testing, etc. Finally, the optimized composite electrodeposition parameters and the Mn-Ni content ratio in the solution were obtained. Experimental results indicated that the composite spinel coating prepared with the optimized process parameters exhibited excellent adhesion to the substrate, and the diffusion and migration of Cr element has been effectively inhibited. Compared with the substrate, the ASR of the coated components has also been decreased simultaneously, which provided an effective method for the surface modification of SOFC FSS interconnectors.

In this study, molecular dynamics simulation was used to explore the interaction characteristics of palmitic acid and CO2, and the effects of temperature and pressure on the solubility of palmitic acid in CO2 were investigated. In the range of 293–353 K and 5–30 MPa, the snapshot of palmitic acid distribution in CO2 shows that the molecular chain of palmitic acid in high-density CO2 system is more straight and more dispersed than that in low-density CO2 system. The radial distribution function further clearly shows that the solubility of palmitic acid in CO2 decreases with the increase of temperature and increases with the increase of pressure, which is consistent with the fatty acid solubility data reported in the literature and the setting rules of supercritical CO2 extraction process conditions. As the temperature decreases and the pressure increases, the interaction energy between palmitic acid and CO2 increases, which is conducive to overcoming the intermolecular force of palmitic acid and promoting dissolution. The solubility parameters of palmitic acid and CO2 can better reflect the trend of palmitic acid solubility changing with temperature and pressure, which can play a guiding role in the determination of process conditions and even the development of new processes.

Background: Epidemiological studies show that outdoor artificial light at night (ALAN) is linked to metabolic hazards, but its association with metabolic syndrome (MetS) remains unclear. We aimed to investigate the association of outdoor ALAN with MetS in middle-aged and elderly Chinese. Methods: From 2017–2020, we conducted a cross-sectional study in a total of 109,452 participants living in ten cities of eastern China. MetS was defined by fasting blood glucose (FG), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), blood pressure (BP), and waist circumference (WC). In 2021, we followed up 4395 participants without MetS at the baseline. Each participant's five-year average exposure to outdoor ALAN, as well as their exposure to green space type, were measured through matching to their address. Generalized linear models were used to assess the associations of outdoor ALAN with MetS. Stratified analyses were performed by sex, age, region, physical activity, and exposure to green space. Results: In the cross-sectional study, compared to the first quantile (Q1) of outdoor ALAN exposure, the odds ratios (ORs) of MetS were 1.156 [95 % confidence interval (CI): 1.111–1.203] and 1.073 (95 %CI: 1.021–1.128) respectively in the third and fourth quantiles (Q3, Q4) of outdoor ALAN exposure. The follow-up study found that, compared to the first quantile (Q1) of outdoor ALAN exposure, the OR of MetS in Q4 of ALAN exposure was 1.204 (95 %CI: 1.019–1.422). Adverse associations of ALAN with MetS components, including high FG, high TG, and obesity, were also found. Greater associations of ALAN with MetS were found in males, the elderly, urban residents, those with low frequency of physical activity, and those living in areas with low levels of grass cover and tree cover. Conclusions: Outdoor ALAN exposure is associated with an increased MetS risk, especially in males, the elderly, urban residents, those lacking physical activity, and those living in lower levels of grass cover and tree cover.

A simulation framework based on meshless method has become an alternative numerical tool to model many deformation problems in geotechnical engineering. Large deformations in the failure zone can alter porosity, permeability etc., which in turn can affect the process of the failure. In this paper, a two-phase model in the framework of Smooth Particle Hydrodynamics (SPH) is introduced to model the interaction between water and soil through drag forces according to Darcy’s law. Changes in soil porosity and associated permeability are automatically adjusted within this framework. Firstly, two different problems i.e., flow through porous media and fluidized bed problem, are investigated to examine the suitability, and stability of the proposed SPH method. Then, the stability analysis of a soil slope under different water level conditions is performed with the strength reduction technique, and the groundwater effect of the slope is simulated. It is found that owing to the negative impact of seepage on soil slope, the horizontal displacement of the slope can be significantly larger. Afterwards, the influence of the variable permeabilities on the slope failure is investigated. The simulation results show that the change in permeability has a slight effect on the slope. Although the calculated safety factor does not change, the sliding distance differs by about 10%. The initial porosity has a large negative influence on the stability of the slope. The developed SPH model has been shown to be a valuable and effective tool for modelling complex problems that are challenging to be addressed with traditional approaches.

The application of deep learning methods in civil engineering has gained significant attention, but its usage in studying chloride penetration in concrete is still in its early stages. This research paper focuses on predicting and analyzing chloride profiles using deep learning methods based on measured data from concrete exposed for 600 days in a coastal environment. The study reveals that Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) models exhibit rapid convergence during the training stage, but fail to achieve satisfactory accuracy when predicting chloride profiles. Additionally, the Gate Recurrent Unit (GRU) model proves to be more efficient than the Long Short-Term Memory (LSTM) model, but its prediction accuracy falls short compared to LSTM for further predictions. However, by optimizing the LSTM model through parameters such as the dropout layer, hidden units, iteration times, and initial learning rate, significant improvements are achieved. The mean absolute error (MAE), determinable coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) values are reported as 0.0271, 0.9752, 0.0357, and 5.41%, respectively. Furthermore, the study successfully predicts desirable chloride profiles of concrete specimens at 720 days using the optimized LSTM model.

The near-infrared (NIR) image obtained by an NIR camera is a grayscale image that is inconsistent with the human visual spectrum. It can be difficult to perceive the details of a scene from an NIR scene; thus, a method is required to convert them to visible images, providing color and texture information. In addition, a camera produces so much video data that it increases the pressure on the cloud server. Image processing can be done on an edge device, but the computing resources of edge devices are limited, and their power consumption constraints need to be considered. Graphics Processing Unit (GPU)-based NVIDIA Jetson embedded systems offer a considerable advantage over Central Processing Unit (CPU)-based embedded devices in inference speed. For this study, we designed an evaluation system that uses image quality, resource occupancy, and energy consumption metrics to verify the performance of different NIR image colorization methods on low-power NVIDIA Jetson embedded systems for practical applications. The performance of 11 image colorization methods on NIR image datasets was tested on three different configurations of NVIDIA Jetson boards. The experimental results indicate that the Pix2Pix method performs best, with a rate of 27 frames per second on the Jetson Xavier NX. This performance is sufficient to meet the requirements of real-time NIR image colorization.

We assessed the performance of state-of-the-art coupled models in reproducing the equatorial undercurrent (EUC) in the Indian Ocean based on the outputs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and compared with the Phase 5 (CMIP5) models. Our results showed that the CMIP6 models reproduced the boreal winter–spring Indian Ocean EUC more realistically than the CMIP5 models, although both generations of models underestimated the strength of the Indian Ocean EUC compared with the observations. This underestimation of the Indian Ocean EUC can be attributed to the excessively strong and westward-extended cold tongue in the equatorial Pacific. In the CMIP models, a stronger winter-mean cold tongue favors a stronger zonal sea surface temperature gradient, which forces a strong easterly wind bias over the equatorial western Pacific. This, in turn, contributes to an acceleration of the Walker circulation. This enhanced Walker circulation over the Indo-Pacific Ocean directly causes a lower level westerly wind bias over the equatorial Indian Ocean and drives a shallow west–deep east thermocline tilt bias, ultimately leading to an excessively weak EUC in the Indian Ocean via wind-induced thermocline processes. Compared with the CMIP5 models, the overall improvement in the strength of the winter–spring Indian Ocean EUC in the CMIP6 models can be traced back to the improvement in the degree of the strong and westward-extended cold tongue bias. Our results suggest that efforts should be made to reduce the bias in the mean-state equatorial Pacific sea surface temperature to further improve the simulation and projection of the atmospheric and oceanic circulations in the Indian Ocean.

Novel Fe-Zn alloys were prepared by pulse electrodeposition to solve the low corrosion rate of iron-based biodegradable materials for vascular stents. The effects of frequency and current density on the composition, microstructure, hardness, and corrosion characteristics of alloys were studied. The results manifested that all the Fe-Zn alloys prepared under the conditions studied here were supersaturated solid solutions. The composition and microstructure of the alloys exhibited a strong dependence on the frequency and current density. When the pulse frequency increased from 50 to 1000 Hz, the zinc content in alloy decreased from 7.2 wt.% to 2.1 wt.% with the grain coarsening. Besides, the low-zinc alloy had low hardness due to its slight lattice distortion and grain boundary strengthening. When the peak current density increased from 10 to 13 A/dm2, the oxygen content in alloy increased from 0.1 wt.% to 1.4 wt.%, and the microstructure changed from coarse columnar grain to fine nanocrystalline with the hardness of the alloy increasing sharply. Electrochemical and immersion tests in simulated body fluid (SBF) demonstrated that the corrosion rates of Fe-Zn alloys were higher than that of pure iron. The alloy containing 4.6% Zn was more prone to corrosion (0.267 mm·y−1 in immersion test) than other alloys while keeping relatively low hardness. The intrinsic causes of zinc to the accelerated corrosion of electrodeposited ferroalloy should be attributed to the decrease of corrosion potential, the increase of local corrosion, and the weak protection barrier caused by the corrosion products.

In contrast to the marine environment, coastal regions encompass substantial saline soils characterized by complex corrosive chemical compositions. This poses notable challenges to the durability of concrete structures erected in coastal dredger fill silty soil environments. This research undertook concrete chloride corrosion assessments in both a dredger fill silty soil environment and a simulated solution environment. The findings demonstrated a progressive escalation in the free chloride concentration within concrete specimens, as the exposure duration was extended from 60 to 120 d, and discernible convection zones were observed with depths ranging from 6 to 8 mm. The investigation revealed a diminishing trend in the apparent chloride diffusion coefficient, corresponding to the elongation of exposure time and the augmentation of burial depth. Paradoxically, the burial depth and exposure duration exhibited converse effects on the apparent surface chloride concentration. Empirical formulations were derived to express the apparent surface chloride concentrations and apparent chloride diffusion coefficients as dependent on the exposure time and burial depth variables. These models exhibited an excellent goodness of fit, reaching up to 0.96. Notably, concrete specimens interred at a depth of 0.0 m displayed a favorable likeness to the simulated solution environment throughout the 60 d exposure period.

Due to the high energy efficiency per bit and high sensitivity, Return-to-zero differential-phase-shift-keying (RZ-DPSK) is perfectly suitable for free-space laser communications. However, the conventional generation method of RZ-DPSK optical signal requires two modulators, which is costly, bulky, and heavy, significantly hindering the application of RZ-DPSK in size, weight, and power (SWaP)-constrained satellite platforms. In this paper, we propose and experimentally demonstrate a modulator-free variable multi-rate RZ-DPSK free-space optical (FSO) communication system based on chirp-managed laser (CML). Based on the proposed scheme, an FSO outfield experiment over 1 km has been successfully undertaken, achieving receiving sensitivities of −48.9 dBm and −45.6 dBm at 2.5 Gbps and 5 Gbps, with bit error rate (BER) of 1 × 10−3 without forward error correction (FEC), respectively. The performance of the proposed system is also investigated by studying the eye diagrams under two different test conditions of back-to-back transmission and 1-km free space transmission. In addition to the small size, lightweight and low cost, the proposed scheme shows great potential for a variety of FSO communication applications ranging from Cube-Star to larger satellite laser communication platforms.

Magnetorheological elastomer (MRE) has been demonstrated to be effective in structural vibration control because of controllable stiffness and damping properties with the effect of external magnetic fields. To achieve a high performance of MRE device-based vibration control, a robust and accurate model is necessary to describe nonlinear dynamics of MRE device. This article aims at realising this target via nonlinear modeling of an innovative MRE device, i.e. MRE vibration isolator. First, the field-dependent properties of MRE isolator were analysed based on experimental data of the isolator in various dynamic tests. Then, a phenomenal model was developed to account for these unique characteristics of MRE-based device. Moreover, an improved PSO algorithm was designed to estimate model parameters. Based on identification results, a generalised model was proposed to clarify the field-dependent properties of the isolator due to varied currents, which was then validated by random and earthquake-excited test data. Based on the proposed model, a frequency control strategy was designed for semi-active control of MRE devices-incorporated smart structure for vibration suppression. Finally, using a three-storey frame model and four benchmark earthquakes, a numerical study was conducted to validate the performance of control strategy based on the generalised current-dependent model with satisfactory results.

The capability of characterizing arbitrary and non-repetitive emission spectra with a high resolution in real-time is of great merit in various research fields. Optical frequency combs provide precise and stable frequency grids for the measurement of a single spectral line with high accuracy. Particularly, dual-comb spectroscopy enables spectral measurement with a large bandwidth spanning tens of nanometers, but it is limited to measuring absorption spectra and has to trade-off spectral resolution vs the acquisition frame rate set by the repetition rate. Here, to alleviate these restrictions, we propose and demonstrate time-resolved spectroscopy for an emission spectrum based on hybrid asynchronous optical sampling, which features a spectral resolution of 0.63 pm, a frame rate of 1 MHz, and a measurement bandwidth of 13.6 nm, simultaneously. A mode-locked fiber comb with a repetition frequency of f1 is harnessed to interrogate emission spectral features with high resolution via optical Fourier transform achieved using a time-lens. Subsequently, a soliton microcomb of repetition frequency f2s ≈ 1000f1 serving as a probe pulse implements hybrid asynchronous optical sampling, thus significantly increasing the acquisition rate by nearly 3 orders of magnitude. As a proof-of-concept demonstration, the frequency trajectory of a rapidly scanning laser with a linear chirp of 6.2 THz/s is tracked. We believe that chip-scale microcombs will make the fast and high-resolution emission spectroscopy presented here a powerful tool for widespread applications.

Some studies have concentrated on the adverse effects of unreasonable tasks on work engagement. So far, however, the underlying mechanisms and boundary conditions of the relationship have not been adequately discussed. Based on the cognitive-affective systems theory and the job demands-resources model, this study constructs a chain mediation model in which unreasonable tasks influence work engagement through work alienation and negative affect and explores the moderating role of supervisor support in the model. An analysis of 427 questionnaires from multiple types of organizations shows that: Unreasonable tasks have a negative impact on work engagement; work alienation and negative affect play both separate and chain mediating roles in the negative effect of unreasonable tasks on work engagement, and supervisor support negatively moderates chain mediation by moderating the positive effect of unreasonable tasks on work alienation. This study re-investigates the relationship between unreasonable tasks and work engagement from cognitive, affective, and resource perspectives, which could be a valuable addition to established research and provide suggestions and assistance for management practice.

In the treatment of drinking water, the ibuprofen (IBP) disinfection by-products, toxicity, and its impact on drinking water safety have caused widespread attention in domestic and overseas research areas. We studied the removal efficiency of IBP under the following conditions: combination of good catalytic activity of a silicate-based microfiltration membrane with the strong oxidizing ability of ozone in the continuous flow experiment mode and various influencing factors. This research revealed that with the increase of pH and hydraulic retention time, the removal efficiency of IBP exhibited an increasing trend; with the increase of alkalinity and humic acid concentration in water, the removal efficiency of IBP was obviously inhibited. Free radical inhibitors and electron spin resonance (ESR) analysis demonstrated that hydroxyl radical (∙OH) is an important active species during the reaction of ozone-catalyzed IBP with the silicate-based microfiltration membrane.

There is evidence that the interannual relationship between El Niño events and the following Indian summer monsoon rainfall (ISMR) has weakened with the more frequent occurrence of central Pacific (CP) El Niño events. We revisited the following ISMR responses to the two different types of El Niño events using observations and reanalysis datasets. Our results show that the ISMR anomalies associated with eastern Pacific (EP) and CP El Niño events are different, with decreased (increased) rainfall in early summer (June–July) following EP (CP) El Niño events. This is primarily attributed to the different responses to anomalous warming of the sea surface temperature (SST) in the northern Indian Ocean (NIO), which is characterized by double peaks in the warming SST during EP El Niño events, but only one peak during CP El Niño events. For EP El Niño events, the second SST warming peak in early summer contributes to the lower level antisymmetric wind pattern over the tropical Indian Ocean (TIO), which delays the onset of the Indian summer monsoon (ISM) and decreases the supply of moisture to India, implying a decrease in the ISMR. By contrast, for CP El Niño events, the cooling SST over the western TIO directly induces a significantly positive meridional SST gradient and drives the lower level southwesterly wind anomalies, resulting in an eastward shift in the decreased antisymmetric winds over TIO and the early onset of ISM. These circulation features are associated with anomalous upper-level divergence over TIO and sinking over India, jointly leading to the excess ISMR in early summer. These results suggest that, in addition to the key role of the warming of the NIO SST, cooling of the SST over the western TIO during CP El Niño events should be considered carefully in understanding the El Niño–ISMR relationship.

Self-oscillation and bifurcation as many-body dynamics solutions in a high-Q microresonator have induced substantial interest in nonlinear optics and ultrafast science. Strong mode coupling between clockwise (CW) wave and counterclockwise (CCW) wave induces mode-splitting and optical self-oscillation in the optical cavity. This study experimentally demonstrates the self-oscillation microcomb formation in a microresonator with strong backward Rayleigh scattering. When a pump laser sweeps across a resonance, both spontaneous symmetry breaking (SSB) and self-oscillation phenomenon are observed. The breathing soliton and stable soliton state can switch to each other through careful tuning of the pump detuning. Our experiments provide a reliable scheme for breather soliton microcomb generation. Meanwhile, the rich physics process enhances the comprehension of nonlinear optics in a cavity.

Here the authors demonstrate an on-demand generation of perfect soliton crystal using synthesized potential field. The individual solitons can also be controlled, for example oscillate around their equilibrium position, by the external field.

In self-driving cars, object detection algorithms are becoming increasingly important, and the accurate and fast recognition of objects is critical to realize autonomous driving. The existing detection algorithms are not ideal for the detection of small objects. This paper proposes a YOLOX-based network model for multi-scale object detection tasks in complex scenes. This method adds a CBAM-G module to the backbone of the original network, which performs grouping operations on CBAM. It changes the height and width of the convolution kernel of the spatial attention module to 7 × 1 to improve the ability of the model to extract prominent features. We proposed an object-contextual feature fusion module, which can provide more semantic information and improve the perception of multi-scale objects. Finally, we considered the problem of fewer samples and less loss of small objects and introduced a scaling factor that could increase the loss of small objects to improve the detection ability of small objects. We validated the effectiveness of the proposed method on the KITTI dataset, and the mAP value was 2.46% higher than the original model. Experimental comparisons showed that our model achieved superior detection performance compared to other models.

Dissipative Kerr solitons in microcavity systems exhibit remarkable nonlinear dynamics. The real-time measurement of soliton motion facilitates the comprehensive understanding of soliton physics. In this Letter, an all-optical radio frequency (RF) spectrum analyzer (named f-LISA) is used to characterize various stable soliton states and to track relative soliton motion in real time. By applying an inverse Fourier transform to the broadband RF spectrum, the autocorrelation traces are obtained with a temporal resolution of 373 fs and an ultrahigh frame rate of 20.6 MHz. We successfully characterize not only the stable single soliton state but also the stable multi-soliton states with different azimuthal angles between adjacent solitons. Furthermore, the dynamics of soliton switching from four-soliton state to single soliton state is observed in a temporal window of 60 µs. It is believed that the proposed scheme provides an alternative way to visualize the multi-soliton trajectories and enable the study of the soliton dynamics in integrated microcavities.