Visual Analysis of the Application and Development Trend of Cinnamon in the Field of Traditional Chinese Medicine Based on CiteSpace

[Objectives]This study was conducted to explore the application and development trend of Chinese medicinal material cinnamon in the field of traditional Chinese medicine.[Methods]Articles published from 2008 to 2023 were exported using"cinnamon"as the subject word in the Chinese database of CNKI.Knowledge graphs were drawn by CiteSpace software on the number of articles published,the institutions publishing articles,and keyword clustering,and the data were sorted by Excel.Combined with the extracted information,the application of cinnamon in traditional Chinese medicine and integrated traditional Chinese and Western medicine was analyzed,and its development trend was discussed and prospected,providing further reference for researchers.[Results]The number of articles published showed an overall upward trend and maintained a high number of articles.In the analysis of the journals publishing articles,the journal with the largest number of articles was West China Journal of Pharmaceutical Sciences,which had certain representativeness.In the analysis of institutions publishing articles,the institution with the largest number of articles was Beijing University of Chinese Medicine,and most institutions had little cooperation.Four categories was obtained in keyword clustering,respectively,general research,component identification,production process and product development of cinnamon."Glycyrrhizin"was the keyword with the earliest burst time,and the hot words that have received much attention in recent years are"medication law"and"data mining".[Conclusions]The application of cinnamon in the field of traditional Chinese medicine is mainly to treat diseases and as raw materials for traditional Chinese medicine products.The development trend is"quality control"and"product research and development".Further research and development of cinnamon in traditional Chinese medicine need to promote the participation of more institutions to participate,and cooperation and communication between institutions should be strengthened to promote the deep integration of production,research and academia.

Application of Isogeometric Analysis Method in Three-Dimensional Gear Contact Analysis

Gears are pivotal in mechanical drives,and gear contact analysis is a typically difficult problem to solve.Emerging isogeometric analysis(IGA)methods have developed new ideas to solve this problem.In this paper,a threedimensional body parametric gear model of IGA is established,and a theoretical formula is derived to realize single-tooth contact analysis.Results were benchmarked against those obtained from commercial software utilizing the finite element analysis(FEA)method to validate the accuracy of our approach.Our findings indicate that the IGA-based contact algorithmsuccessfullymet theHertz contact test.When juxtaposed with the FEA approach,the IGAmethod demonstrated fewer node degrees of freedomand reduced computational units,all whilemaintaining comparable accuracy.Notably,the IGA method appeared to exhibit consistency in analysis accuracy irrespective of computational unit density,and also significantlymitigated non-physical oscillations in contact stress across the tooth width.This underscores the prowess of IGA in contact analysis.In conclusion,IGA emerges as a potent tool for addressing contact analysis challenges and holds significant promise for 3D gear modeling,simulation,and optimization of various mechanical components.

Analysis on Demonstration Application of Silicon Fertilizer in Field Cultivation of Rice

[Objectives]This study was conducted to investigate the scientific application of silicon fertilizer in rice cultivation,one of the staple crops.[Methods]In 2022,Yandu District carried out a special experiment and field demonstration study on the effects of foliar application of Zhengda water-soluble silicon fertilizer on rice production.[Results]The preliminary results showed that①Zhengda water-soluble silicon fertilizer could effectively improve the growth and development of rice and improve the population quality.The peak number of tillers,productive tiller percentage,number of effective panicles and number of effective grains per panicle increased by 6.7%,5.8%,5.5%,and 1.2%,respectively.②The yield and processing quality were improved.After applying silicon fertilizer,the yield per unit area increased by about 6.8%,and the unpolished rice yield,milled rice yield and head rice yield increased by 0.7%,1.94%and 2.15%respectively.[Conclusions]The demonstration application of silicon fertilizer in field cultivation of rice in Yandu District further proves previous research conclusions and has important practical significance.

Analysis of application range of simplified models for field to thermo-field to thermionic emission processes from the cathode

Electron emission plays a dominant role in plasma-cathode interactions and is a key factor in many plasma phenomena and industrial applications.It is necessary to illustrate the various electron emission mechanisms and the corresponding applicable description models to evaluate their impacts on discharge properties.In this study,detailed expressions of the simplified formulas valid for field emission to thermo-field emission to thermionic emission typically used in the numerical simulation are proposed,and the corresponding application ranges are determined in the framework of the Murphy-Good theory,which is commonly regarded as the general model and to be accurate in the full range of conditions of the validity of the theory.Dimensionless parameterization was used to evaluate the emission current density of the Murphy-Good formula,and a deviation factor was defined to obtain the application ranges for different work functions(2.5‒5 eV),cathode temperatures(300‒6000 K),and emitted electric fields(10^(5) to 10^(10) V·m^(-1)).The deviation factor was shown to be a nonmonotonic function of the three parameters.A comparative study of particle number densities in atmospheric gas discharge with a tungsten cathode was performed based on the one-dimensional implicit particle-in-cell(PIC)with the Monte Carlo collision(MCC)method according to the aforementioned application ranges.It was found that small differences in emission current density can lead to variations in the distributions of particle number density due to changes in the collisional environment.This study provides a theoretical basis for selecting emission models for subsequent numerical simulations.

An Empirical Application of User-Guided Program Analysis

Although static program analysis methods are frequently employed to enhance software quality,their efficiency in commercial settings is limited by their high false positive rate.The EUGENE tool can effectively lower the false positive rate.However,in continuous integration(CI)environments,the code is always changing,and user feedback from one version of the software cannot be applied to a subsequent version.Additionally,people find it difficult to distinguish between true positives and false positives in the analytical output.In this study,we developed the EUGENE-CI technique to address the CI problem and the EUGENE-rank lightweight heuristic algorithm to rate the reports of the analysis output in accordance with the likelihood that they are true positives.On the three projects ethereum,go-cloud,and kubernetes,we assessed our methodologies.According to the trial findings,EUGENE-CI may drastically reduce false positives while EUGENE-rank can make it much easier for users to identify the real positives among a vast number of reports.We paired our techniques with GoInsight~1 and discovered a vulnerability.We also offered a patch to the community.

Simplified quantitative analysis method and its application in the insitu synthesized copper-based azide chips

Copper-based azide(Cu(N_(3))2 or CuN_(3),CA)chips synthesized by in-situ azide reaction and utilized in miniaturized explosive systems has become a hot research topic in recent years.However,the advantages of in-situ synthesis method,including small size and low dosage,bring about difficulties in quantitative analysis and differences in ignition capabilities of CA chips.The aim of present work is to develop a simplified quantitative analysis method for accurate and safe analysis of components in CA chips to evaluate and investigate the corresponding ignition ability.In this work,Cu(N_(3))2 and CuN_(3)components in CA chips were separated through dissolution and distillation by utilizing the difference in solubility and corresponding content was obtained by measuring N_(3)-concentration through spectrophotometry.The spectrophotometry method was optimized by studying influencing factors and the recovery rate of different separation methods was studied,ensuring the accuracy and reproducibility of test results.The optimized method is linear in range from 1.0-25.0 mg/L,with a correlation coefficient R^(2)=0.9998,which meets the requirements of CA chips with a milligram-level content test.Compared with the existing ICP method,component analysis results of CA chips obtained by spectrophotometry are closer to real component content in samples and have satisfactory accuracy.Moreover,as its application in miniaturized explosive systems,the ignition ability of CA chips with different component contents for direct ink writing CL-20 and the corresponding mechanism was studied.This study provided a basis and idea for the design and performance evaluation of CA chips in miniaturized explosive systems.

Artificial intelligence applications in ophthalmic optical coherence tomography:a 12-year bibliometric analysis

AIM:To explore the current application and research frontiers of global ophthalmic optical coherence tomography(OCT)imaging artificial intelligence(AI)research.METHODS:The citation data were downloaded from the Web of Science Core Collection database(WoSCC)to evaluate the articles in application of AI in ophthalmic OCT published from January 1,2012 to December 31,2023.This information was analyzed using CiteSpace 6.2.R2 Advanced software,and high-impact articles were analyzed.RESULTS:In general,877 articles from 65 countries were studied and analyzed,of which 261 were published by the United States and 252 by China.The centrality of the United States is 0.33,the H index is 38,and the H index of two institutions in England reaches 20.Ophthalmology,computer science,and AI are the main disciplines involved.

On the Application of Mixed Models of Probability and Convex Set for Time-Variant Reliability Analysis

In time-variant reliability problems,there are a lot of uncertain variables from different sources.Therefore,it is important to consider these uncertainties in engineering.In addition,time-variant reliability problems typically involve a complexmultilevel nested optimization problem,which can result in an enormous amount of computation.To this end,this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model.In this method,the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a timeindependent reliability problem.Further,to solve the double nested optimization problem in hybrid reliability calculation,an efficient iterative scheme is designed in standard uncertainty space to determine the most probable point(MPP).The limit state function is linearized at these points,and an innovative random variable is defined to solve the equivalent static reliability analysis model.The effectiveness of the proposed method is verified by two benchmark numerical examples and a practical engineering problem.

Digital Application Objectives and Benefit Analysis of BIM Technology in Large-Scale Comprehensive Development Projects

This paper discusses the digital application and benefit analysis of building information model(BIM)technology in the large-scale comprehensive development project of the Guangxi headquarters base.The project covers a total area of 92,100 square meters,with a total construction area of 379,700 square meters,including a variety of architectural forms.Through three-dimensional modeling and simulation analysis,BIM technology significantly enhances the design quality and efficiency,shortens the design cycle by about 20%,and promotes the collaboration and integration of project management,improving the management efficiency by about 25%.During the construction phase,the collision detection and four-dimensional visual management functions of BIM technology have improved construction efficiency by about 15%and saved the cost by about 10%.In addition,BIM technology has promoted green building and sustainable development,achieved the dual improvement of technical and economic indicators and social and economic benefits,set an example for enterprises in digital transformation,and opened up new market businesses.

Survey and Application Prospect Analysis of Spontaneous Plants in Urban Park Lawn in Hangzhou

The spontaneous plants in the lawn not only improve the biodiversity of the lawn,but also provide food and shelter for insects,amphibians and birds,as well as pleasant sensory experience for people.The spontaneous plants in eight different types of parks in Hangzhou were investigated in detail by field survey.A total of 89 species of 27 families of spontaneous plants were found in Hangzhou urban parks,of which Asteraceae,Fabaceae and Poaceae were the dominant families.The life forms are mainly perennial herbs,and the growth forms are mostly erect.According to the screening criteria,20 spontaneous plants,such as Ranunculus sieboldii,Taraxacum mongolicum and Corydalis decumbens,were recommended for potential exploitation.According to the spontaneous plants of lawn,three application models of flower lawn,semi-natural succession of lawn and natural grassland were put forward.

Application of near-infrared spectroscopy for fast germplasm analysis and classification in multi-environment using intact-seed peanut(Arachis hypogaea L.)

Peanut is a worldwide oilseed crop and the need to assess germplasm in a non-destructive manner is important for seed nutritional breeding.In this study,Near Infrared Spectroscopy(NIRS)was applied to rapidly assess germplasm variability from whole seed of 699 samples,field-collected and assembled in four genetic and environmentbased sets:one set of 300 varieties of a core-collection and three sets of 133 genotypes of an interspecific population,evaluated in three environments in a large spatial scale of two countries,Mbalmayo and Bafia in Cameroon and Nioro in Senegal,under rainfed conditions.NIR elemental spectra were gathered on six subsets of seeds of each sample,after three rotation scans,with a spectral resolution of 16 cm-1over the spectral range of867 nm to 2530 nm.Spectra were then processed by principal component analysis(PCA)coupled with Partial least squares-discriminant analysis(PLS-DA).As results,a huge variability was found between varieties and genotypes for all NIR wavelength within and between environments.The magnitude of genetic variation was particularly observed at 11 relevant wavelengths such as 1723 nm,usually related to oil content and fatty acid composition.PCA yielded the most chemical attributes in three significant PCs(i.e.,eigenvalues>10),which together captured 93%of the total variation,revealing genetic and environment structure of varieties and genotypes into four clusters,corresponding to the four samples sets.The pattern of genetic variability of the interspecific population covers,remarkably half of spectrum of the core-collection,turning out to be the largest.Interestingly,a PLS-DA model was developed and a strong accuracy of 99.6%was achieved for the four sets,aiming to classify each seed sample according to environment origin.The confusion matrix achieved for the two sets of Bafia and Nioro showed 100%of instances classified correctly with 100%at both sensitivity and specificity,confirming that their seed quality was different from each other and all other samples.Overall,NIRS chemometrics is useful to assess and distinguish seeds from different environments and highlights the value of the interspecific population and core-collection,as a source of nutritional diversity,to support the breeding efforts.

Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors.For example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long time.Such data,accumulated over time,is redundant and consumes a lot ofmemory in its raw form.We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks.To this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal data.Assuming that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save space.When modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker decomposition.The factor matrices and core tensor of the decomposed results can then be used for further data analysis.We compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker decomposition.We show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction error.We applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to 2022.We were able to model the spatial and temporal air quality trends.We were also able to verify unusual events,such as chronic ozone alerts and large fire events.

Application of modified discontinuous deformation analysis to dynamic modelling of the Baige landslide in the Jinsha River

Accurate dynamic modeling of landslides could help understand the movement mechanisms and guide disaster mitigation and prevention.Discontinuous deformation analysis(DDA)is an effective approach for investigating landslides.However,DDA fails to accurately capture the degradation in shear strength of rock joints commonly observed in high-speed landslides.In this study,DDA is modified by incorporating simplified joint shear strength degradation.Based on the modified DDA,the kinematics of the Baige landslide that occurred along the Jinsha River in China on 10 October 2018 are reproduced.The violent starting velocity of the landslide is considered explicitly.Three cases with different violent starting velocities are investigated to show their effect on the landslide movement process.Subsequently,the landslide movement process and the final accumulation characteristics are analyzed from multiple perspectives.The results show that the violent starting velocity affects the landslide motion characteristics,which is found to be about 4 m/s in the Baige landslide.The movement process of the Baige landslide involves four stages:initiation,high-speed sliding,impact-climbing,low-speed motion and accumulation.The accumulation states of sliding masses in different zones are different,which essentially corresponds to reality.The research results suggest that the modified DDA is applicable to similar high-level rock landslides.

Bunch-length measurement at a bunch-by-bunch rate based on time–frequency-domain joint analysis techniques and its application

This paper presents a new technique for measuring the bunch length of a high-energy electron beam at a bunch-by-bunch rate in storage rings.This technique uses the time–frequency-domain joint analysis of the bunch signal to obtain bunch-by-bunch and turn-by-turn longitudinal parameters,such as bunch length and synchronous phase.The bunch signal is obtained using a button electrode with a bandwidth of several gigahertz.The data acquisition device was a high-speed digital oscilloscope with a sampling rate of more than 10 GS/s,and the single-shot sampling data buffer covered thousands of turns.The bunch-length and synchronous phase information were extracted via offline calculations using Python scripts.The calibration coefficient of the system was determined using a commercial streak camera.Moreover,this technique was tested on two different storage rings and successfully captured various longitudinal transient processes during the harmonic cavity debugging process at the Shanghai Synchrotron Radiation Facility(SSRF),and longitudinal instabilities were observed during the single-bunch accumulation process at Hefei Light Source(HLS).For Gaussian-distribution bunches,the uncertainty of the bunch phase obtained using this technique was better than 0.2 ps,and the bunch-length uncertainty was better than 1 ps.The dynamic range exceeded 10 ms.This technology is a powerful and versatile beam diagnostic tool that can be conveniently deployed in high-energy electron storage rings.

Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.

Visual Analysis of Application and Development Trend of Agarwood Based on CiteSpace

The Chinese traditional medicine,agarwood,is a commonly used medicine for regulating qi,which has many clinical applications because of its unique curative effect.In this study,CiteSpace software was used to visually analyze the application and development trend of agarwood in the literature from CNKI website in the period of 2007-2022.The analysis results showed that the research on agarwood has basically formed core groups of authors,and universities and their affiliated hospitals are main publishing institutions.Moreover,in this study,several research directions of agarwood were also summarized,including clinical research,chemical composition,structural identification and quality standards,showing that agarwood has rich and flexible application prospects in many aspects.On this basis,several suggestions were put forward:strengthening the cooperation between universities and research institutes and building a scientific research cooperation community,and promoting the combination of clinical research and laboratory research.

Analysis of the Application Effect and Value of Dydrogesterone in The Treatment of Preeclampsia for Fetal Preservation

Objective: To analyze the application effect and value of dydrogesterone in the fertility preservation treatment of preeclampsia. Methods: Forty cases of patients with preeclampsia admitted to our hospital between January 2023 and January 2024 were divided randomly into a control group and an observation group of 20 cases each. The control group applied progesterone to preserve the fetus, and the observation group applied dydrogesterone. The symptom relief time, hormone levels before and after treatment, as well as adverse drug reactions, and the effect of fetal preservation between the two groups were compared. Results: The time to relieve vaginal bleeding, abdominal pain. and lumbago in the observation group was shorter than that in the control group (P < 0.05). After treatment, the progesterone levels and incidence of adverse drug reactions in the observation group were lower than those in the control group (P < 0.05). The success rate of fertility preservation in the observation group was higher than that in the control group (P < 0.05). Conclusion: In the treatment of fetal preservation of preeclampsia, the application of dydrogesterone positively alleviated vaginal bleeding, abdominal pain, and lumbago, with mild adverse reactions and a good effect on fetal preservation.

Research and Analysis on the Application of Strategic Performance Management in Enterprises

In today’s competitive business environment,strategic performance management(SPM)is crucial for continuous growth and innovation.Therefore,it is important to realize the advantages of SPM in enterprise applications.This paper analyzes the theoretical basis,implementation framework,and application effects of SPM in enterprises by examining actual cases from different industries.It reveals the core elements of an effective performance management system,including clear goal setting,reasonable performance indicators,and periodic performance evaluations.Additionally,the paper examines the impact of China’s specific economic policy environment on the implementation of SPM and proposes strategies to optimize performance management practices,ultimately promoting the achievement of strategic goals.This paper provides specific and personalized practical guidance for enterprises.

Analysis of the Design and Application of a Novel CT Secondary Cable Line Calibrator

With the rapid development of electrical power systems,ensuring the accuracy and reliability of power transmis-sion has become particularly crucial.The secondary cable line calibrator for current transformers(CT)plays an essential role in calibrating electrical power systems.It is not only related to the safe operation of the system but also directly im-pacts the accuracy of energy metering.This study aims to design and analyze an efficient CT secondary cable line calibra-tor to explore its application effects in the power system.By thoroughly analyzing the characteristics of CT secondary ca-ble lines and the design requirements of the calibrator,this paper proposes an innovative design scheme for the calibrator.This device demonstrates significant effects in enhancing the accuracy and stability of power system calibration,providing robust technical support for the optimization and upgrade of the power system.This research not only offers a theoretical basis and practical guidance for the design and application of CT secondary cable line calibrators but also contributes new ideas and methods for the precise calibration and efficient management of the power system.

Regression analysis and its application to oil and gas exploration:A case study of hydrocarbon loss recovery and porosity prediction,China

In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging.Through a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological datasets.Our linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct advantages.The Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of multicollinearity.The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and comprehensibility.On the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative approaches.Gradient Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk assessment.The Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend shifts.Our analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring its utility in resource quantification refinement.Meanwhile,Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A,evidencing its aptness for intricate geological structures.This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector.The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction,evaluation,and recovery.