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Liquid chromatography-mass spectrometry （LC-MS） has become one of the most important analytical techniques in modern times. It combines the high-efficiency separation capabilities of chromatography with the powerful structural identification capabilities of mass spectrometry， offering high sensitivity and excellent specificity. It has been widely applied in qualitative and quantitative analysis of complex systems. This review introduces the fundamental principles， instrument structure， and analytical methods of LC-MS， and discusses the applications of LC coupled with low-resolution mass spectrometry （LRMS） and high-resolution mass spectrometry （HRMS） in areas such as food safety， nutritional component analysis， omics analysis （e.g.， proteomics， metabolomics）， and environmental pollutant analysis. The latest advancements in LC-MS， including two-dimensional chromatography （2D-LC） and ion mobility separation （IMS） technologies， are also discussed.
In order to accurately identify the key factors influencing the safety risks in chemical enterprises and quantitatively evaluate the impact of these key factors on the safety risks， 551 typical case investigation reports of safety production accidents in national chemical enterprises over the past 10 years were analyzed， and 15 key factors affecting safety production risks were extracted. By combining fault tree analysis （FTA）， Bayesian network （BN） and bow-tie analysis （BTA）， a risk analysis framework system integrating multiple methods （FTA-BN-BTA） was constructed， and a safety risk traceability model was established. This model was employed to conduct an empirical analysis of the safety accidents in a domestic chemical enterprise. The results show that four key factors， namely non-standard hot work operations， non-standard confined space operations， untimely emergency response， and leakage from process equipment， have the greatest impact on production safety in this chemical enterprise. The results are consistent with the actual situation in the chemical enterprise. The FTA-BN-BTA safety risk traceability model integrates the advantages of the three analytical methods. The resulting three-in-one enterprise safety risk management closed-loop model of “causal analysis-probability calculation-barrier optimization” can address the deficiencies of traditional methods in the dimension of “structure-probability-control”. These results can provide theoretical guidance for the classification and control of safety risks， as well as the governance of safety hazards， in other chemical enterprises.
The rapid urban development and economic growth of Lhasa pose potential risks to its ecological environment. In order to promote the high-quality， coordinated development of the economy and ecological environment in Lhasa， the ecological sensitivity of Lhasa was evaluated based on the analytic hierarchy process （AHP）-entropy weight coupling method. The elevation， slope degree， slope direction， water buffer zone， vegetation coverage， land use type and average annual rainfall were selected as ecological sensitivity index factors. The weighted values for each index were determined by using the AHP-entropy weight coupling method. It was found that vegetation coverage and average annual rainfall in Lhasa have a significant impact on ecological sensitivity， with weights of 0.266 and 0.213， respectively. Single-factor ecological sensitivity and comprehensive ecological sensitivity analyses were conducted using ArcGIS software. The results indicated that the overall ecological sensitivity of Lhasa is relatively high. The area of highly sensitive and extremely sensitive regions accounts for 41.31%， mainly distributed in the northeastern part of Dangxiong county， the Namtso area， and the vegetation-covered areas of Mozhugongka county， Linzhou county， Nimu county and Qushui county. The area of the moderately sensitive zone accounts for 27.96%， mainly distributed in the human settlement areas of the Lhasa river basin and in the shrubbery and grassland areas with moderate vegetation coverage. The total area proportion of extremely and relatively low sensitivity regions is 30.73%， mainly distributed in the high mountains in the northwest of Lhasa， areas of high human activity， and permanent glacier and snow-covered zones. In the future， Lhasa should enhance overall environmental protection and the rational utilization of land resources. Special attention should be paid to water ecology， vegetation protection and rainfall in highly sensitive areas. At the same time， the impact of regional development on permanent glaciers and snow-covered areas should be taken into consideration.
To date， the simulation of hydrogen-blended natural gas has been unable to effectively balance the two parameters of calculation accuracy and simulation speed， which is not conducive to guiding the rapid decision-making of on-site operation plans. To address this issue， decoupling simulations were conducted on the component tracking equation， hydraulic equation and thermal equation. The differences between the coupling model and the TGNET model， as well as between the coupling model and the decoupling model， were analyzed. The optimal time step and spatial step were determined. Single instruction multiple data （SIMD） parallel computing technology was introduced. The rapid processing of the intermediate parameter-solving process was then implemented using the AVX2 instruction set， and the calculation and analysis were carried out with actual cases. The results of the coupling model are in high agreement with those of the TGNET software. The computing times of the two models are 8 237 ms and 8 359 ms， respectively， and they show consistency and compatibility. When calculating the decoupling model， it is more appropriate to use a time step of 90 seconds and a spatial step of 1 km. Compared with the coupling model， the speedup ratio with this combination is 2.89. Compared with the maximum pressure-maximum flow boundary condition， the errors in pressure， temperature and hydrogen molar fraction under the maximum flow-maximum flow boundary condition all increase. It is recommended to give priority to using this boundary condition in applications such as pipeline network planning and design， operation peak shaving， sudden gas consumption and gas supply safety redundancy guarantee. Compared with the decoupling model， the computational speedup ratio after introducing SIMD instructions is 2.68， which is lower than the upper limit of the theoretical speedup ratio. These results can provide a theoretical basis and practical reference for the simulation of large-scale hydrogen-blended natural gas pipeline networks.
Trans-ferulic acid （TFA） is a natural phenolic acid compound with anti-inflammatory， antioxidant and anti-tumor activities. Its efficient separation in complex biological fermentation broths is challenging. In this study， sulfonated graphene oxide （SGO）/molecularly imprinted polymers （MIP） composite membranes were prepared. Specific recognition sites were constructed using bifunctional monomers （methacrylic acid and acrylamide）， and the membrane pore structure was regulated by non-solvent induced phase separation （NIPS）， which enabled efficient separation of TFA. The results show that the SGO/MIP composite membrane prepared with mass ratio of SGO∶MIP∶polyvinylidene fluoride （PVDF）∶polyethylene glycol 2000 （pore-forming agent） of 0.9∶0.225∶12∶0.72 has an adsorption capacity of 2 249.31 mg/m2 for TFA in the fermentation broth at 25 ℃， and a membrane flux of 53.94 L/（m²·h）， indicating that it maintains good permeation performance while efficiently retaining target molecules. After gradient elution with 10%-60% （volume fraction） ethanol and recrystallization， the purity of TFA was 96.4% and the recovery rate was 87.17%. This study shows that the selective adsorption capacity of target molecules is significantly improved by introducing SGO and bifunctional monomer strategies， which provides a new idea for the large-scale separation of natural products.
To address environmental concerns caused by traditional fossil-based elastomers， the development of bio-based biodegradable polyester elastomers has become a focus of current research. In this study， glycerol-based polyester elastomers with controllable crosslinking networks were synthesized via optimized melt polycondensation using bio-based diacids， diols， and glycerol. By controlling the degree of esterification between dicarboxylic acids and diols when introducing glycerol， an enhancement in the elasticity of polyester cross-linked elastomers was achieved （with a gel content of approximately 31% and a sol molecular weight ranging from 2.84 to 7.76 kg/mol）. Differential scanning calorimetry （DSC） confirmed the completely amorphous nature of the elastomers， with glass transition temperatures （T g） ranging from -49 to -47 ℃. In scaled-up 5 L reactor experiments， high-elasticity material was successfully prepared （gel content： 33.53%， sol molecular weight： 7.25 kg/mol） by optimizing the process parameters. This work not only provides a new route for developing environmentally friendly chewing gum but also establishes valuable insights for future engineering applications of bio-based elastomers.
High-efficiency， low-energy-consumption electrostatic fog water-harvesting technology can alleviate drinking water shortages in high-fog and water-scarce regions. Through systematic investigation of key factors， including material properties and structural arrangements of collection surfaces and emission terminals， various materials were selected to construct uniform planar collection surfaces. A multi-parameter analysis was conducted to examine their impact on electrostatic water collection efficiency. The results reveal that the hydrophilicity/hydrophobicity， resistance， and conductivity of collection surfaces are not the dominant factors. Instead， collection efficiency primarily depends on the magnitude of corona-induced air currents， exhibiting an exponential relationship. Within the initial current range， efficiency increases rapidly with increasing current， but shows slower growth beyond a threshold value. The type of tip of electrostatic emitters affect collection efficiency by influencing electric field uniformity， with carbon fiber emitters demonstrating superior performance compared to copper needle emitters. Collection distance modifies efficiency through its dual effects on air current magnitude and electric field uniformity， while maintaining consistent exponential correlation with current for the same emitter material. Hydrophilic insulating surfaces exhibit high collection efficiency， whereas hydrophobic insulating plastic surfaces perform poorly. Notably， hydrophobic polytetrafluoroethylene （PTFE） microporous membranes achieve efficient fog collection by creating conductive pathways. Clarification of these fundamental factors provides valuable guidance for the future design of safer， more energy-efficient， and more portable electrostatic fog water-harvesting devices.
This study was inspired by the exceptional mechanical properties of diamond crystals. To meet the requirements for the specific stiffness and strength of satellite load⁃bearing panels， a lattice structure mimicking the single crystal structure of diamond has been designed. This lattice structure was employed in satellite load⁃bearing panels. The design and constituent elements of the lattice structure were first introduced. Subsequently， theoretical mathematical models for the relative density， in⁃plane compressive stiffness， and bending stiffness of a single lattice cell were established. Finite element simulations using Abaqus software were then conducted to analyze the in⁃plane compression and three⁃point bending of the satellite load⁃bearing panels filled with the designed lattice structure. Experimental tests were also performed to evaluate the mechanical responses of the panels under these two typical conditions. Finally， the study compared theoretical predictions， simulation analyses， and experimental results for in⁃plane compressive modulus and bending modulus， showing a high degree of agreement among the three， thereby verifying the accuracy of the theoretical mathematical models and finite element models. The results demonstrate that satellite load⁃bearing panels filled with the lattice structure meet the requirements for strength and stiffness， providing a novel and effective approach for the design of primary load⁃bearing structures in satellites.
Deletion variation is an important structural variation associated with various diseases such as cancer， Alzheimer’s disease， and autism. Due to the complexity of deletion variation and alignment information， existing methods often misclassify a significant number of non⁃deletion variations as deletion variations， leading to high false‑positive rates in detection results and posing challenges for downstream analysis. To address this issue， this paper proposes a Transformer⁃based deletion variation detection method for third⁃generation sequencing data， called TDD （Transformer deletion detection）. Candidate variation sites are first extracted by analyzing the CIGAR field in the BAM files and the spacing between adjacent reads. Then， the variation intervals are divided into continuous sub⁃intervals， and feature matrices are constructed for each sub⁃interval. Subsequently， the feature matrices are encoded using the Encoder module in Transformer and passed to the classification layers to determine the presence of variation. Finally， breakpoint estimation and merging of variation sub⁃intervals are performed to obtain the final set of deletion variations. Experimental results show that the proposed method achieves higher F1 scores than mainstream tools， reduces false positives in detection results， and thereby improves the detection of deletions.
The state of charge （SOC） of lithium batteries is one of the core state parameters of Battery Management Systems （BMS）， and accurate estimation of the battery SOC is of great significance for the development of electric vehicles. Traditional methods rely heavily on the accuracy of battery models and struggle to adapt to the highly nonlinear and time-dependent characteristics of batteries. With the development of deep learning theory， estimation methods based on neural networks have been widely applied. This paper proposes an improved sparrow search algorithm （ISSA） model， which combines chaotic mapping， sine⁃cosine algorithm， and firefly disturbance methods to optimize the backpropagation （BP） neural network （ISSA⁃BP） for high-precision SOC estimation. The model was validated using the public experimental dataset from the University of Maryland， which includes various complex operating conditions and different temperatures. The prediction results were evaluated from the perspectives of mean absolute error， mean square error， and root mean square error. The results show that the ISSA⁃BP model can control the SOC estimation error within 2% under various conditions and temperatures， demonstrating better accuracy compared to single neural network models， as well as excellent robustness and generalization ability.
To meet the requirements of positioning accuracy in the pose recognition process of the SLAM （simultaneous localization and mapping） algorithm based on point-line features， an improved EPLF-VINS （monocular visual-inertial SLAM with efficient point-line flow features） algorithm has been proposed. The influence of gradient threshold parameters on the EDLines （line segment detection by edge drawing） line segment extraction algorithm was first analyzed. Secondly， after forward optical flow tracing of point features， reverse optical flow tracing was used to eliminate the wrong tracking points， thereby improving the accuracy of optical flow tracing. Then， an adaptive adjustment algorithm was fused at the line segment extraction of the EPLF-VINS algorithm， and the gradient threshold parameters were adjusted in real time by calculating the success rate of point feature optical flow tracing after reverse optical flow tracing. This allows dynamic adjustment of line segment extraction based on changes in the environment， and results in a better balance of calculation cost and positioning accuracy. Finally， based on the Robot Operating System （ROS） platform， the trajectory accuracy and efficiency of the improved EPLF-VINS algorithm and the comparison algorithm in the EuRoc and TUM-VI datasets were analyzed. The results show that the trajectory curve generated by the improved EPLF-VINS algorithm more closely matches the real trajectory， and has higher positioning accuracy while maintaining real-time performance.
Component segmentation and determination of coke optical microstructure can effectively enhance the application of coke in industrial production and hence contribute to the achievement of China’s “dual carbon” goals. With the rapid development of artificial intelligence technology， current segmentation and recognition methods for coke optical microstructure primarily rely on deep learning-based semantic segmentation models. However， the segmentation accuracy of existing models is often limited by the quantity and quality of data samples， resulting in low performance. To solve this problem， this paper proposes a few-shot coke optical microstructure segmentation method using Artificial Intelligence Generated Content （AIGC）. In the first step， an initial dataset of 12 base images is expanded into a dataset of 3 000 images using AIGC techniques. Secondly， to improve the segmentation accuracy of coke optical microstructures， Local Contrastive Learning is incorporated into the MoCov3 framework， and a Semantic Controller module is integrated into the backbone networks. This enhances the extraction of useful features and generates the MoCov3-CD semantic segmentation model. Finally， we conduct contrast and ablation experiments on the constructed dataset to evaluate and analyze the MoCov3-CD model. Experimental results show that using the dataset expanded through AIGC， the MoCov3-CD semantic segmentation model achieves a pixel accuracy （PA） of 88.03% and a mean Intersection over Union （mIoU） of 68.59%. Compared with other state-of-the-art semantic segmentation models， our model achieves results closer to those of fully supervised segmentation models. Specifically， its PA and mIoU are 2.68% and 3.72% higher than those of self-supervised segmentation models on average， and 1.8% and 2.42% higher than those o nf semi-supervised segmentation models on average.
Cuprate superconductors have wide application prospects and rich physical mechanisms， attracting extensive research. However， exploring the microscopic mechanism of high-temperature superconductivity is challenging. To understand how magnetic field-induced spin fluctuations affect high-temperature superconductivity， we have investigated the Raman scattering spectra of electron-doped cuprates under external magnetic fields based on the t-t´-J model. In order to discuss the effects of different dopant concentrations and different temperatures， we calculated the Raman scattering spectra at dopant concentrations of 0.17 and 0.165 and temperatures of 0.002 J and 0.007 J. Our calculations show that with the increase of the external magnetic field， the peak intensities of B1g and B2g decrease significantly at different dopant concentrations and temperatures and the peak positions shift slightly to the left. This indicates that the superconducting energy gap decreases with increasing external magnetic field. In addition， the higher the temperature， the slower the rate at which the peak intensity decreases with increasing magnetic field. Our calculation results are qualitatively consistent with experiments， and provide a theoretical explanation for the Raman scattering spectra of electron-doped copper oxide superconductors under an external magnetic field.
In 1983， Rotenberg firstly proposed the Rotenberg model， which has attracted wide attention. For Rotenberg model， many previous studies have been carried out， but the time delay of cell division was not considered in previous studies. In order to describe cell proliferation better and more accurately， on the basis of previous studies， we consider the time delay of cell division， make some modifications to the original Rotenberg model， and prove that transport operator AH can generate a C 0 semigroup. The spectrum of the corresponding transport operator is analyzed， and it is only consists of finite discrete eigenvalues with finite algebraic multiplicity in the region （ ）.
The definition of an exponential B-spline function and its basic derivative formats on a rectangular uniform grid were given. For a given Burgers equation， the exponential B-spline scheme was used for discretization in the space direction and the Crank-Nicolson scheme combined with the Hermite compact scheme was used for discretization in the time direction. The numerical solution was obtained， and the stability and convergence of this scheme were proved. The errors and convergence orders of the numerical solution and the influence of different parameter values on the precision of the numerical solution were studied experimentally.
