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.

High-precision frequency attenuation analysis and its application

Based on seismic attenuation theory in a fluid-filled porous medium, we improve conventional methods of low-frequency shadow analysis （LFSA） and energy absorption analysis （EAA） and propose a high-precision frequency attenuation analysis technology. First, we introduce the method of three-parameter wavelet transform and the time-frequency focused criterion and develop a high-precision time-frequency analysis method based on an adaptive three-parameter wavelet transform, which has high time-frequency resolution with benefit to LFSA and can obtain a single-peaked spectrum with narrow side-lobes with benefit to EAA. Second, we correctly compute absorption coefficient by curve fitting based on the nonlinear Nelder-Mead algorithm and effectively improve EAA precision. Practical application results show that the proposed frequency attenuation analysis technology integrated with LFSA and EAA can effectively predict favorable zones of carbonate oolitic reservoir. Furthermore, reservoir prediction results based on LFSA correspond with EAA. The new technology can effectively improve reservoir prediction reliability and reduce exploration risk.

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.

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.

Selection and application of wavelet transform in high-frequency sequence stratigraphy analysis of coarse-grained sediment in rift basin

Wavelet transformation is a widely used method in high-frequency sequence stratigraphic analysis.However, the application is problematic since different wavelets always return the same sequence analysis results. To address this issue, we applied five commonly used wavelets to theoretical sequence models to document some application criteria. Five gradual scale-change sequence models were simplified from the glutenite succession deposition by gravity flows to form the fining-upwards cycle sequences(FUCS) and coarsening-upwards cycle sequences(CUCS). After conducting theoretical sequence model tests, the optimal wavelet(sym4) was selected and successfully used with actual data to identify the sequence boundaries. We also proposed a new method to optimize the scale of continuous wavelet transformation(CWT) for sequence boundary determination. We found that the balloon-like marks in scalograms of db4, sym4, and coif4 wavelet determine, respectively, the fourth-order sequence boundary, the thick succession sequence boundaries in FUCS, and the thick succession sequence in FUCS and CUCS. Comparing the sequence identification results shows that the asymmetric wavelets had an advantage in high-frequency sequence boundary determination and sedimentary cycle discrimination through the amplitude trend of the coefficient, in which the sym4 wavelet is the most effective. In conclusion, the asymmetry of wavelets is the first selection principle, of which asymmetric wavelets are more sensitive to sediment deposition by flood flows. The match of the wavelet between the sequence is the second selection principle, in which the correlation of time-frequency impacts the accuracy of sequence surface localization. However, the waveform of the wavelet is a visual and abstract parameter for sequence boundary detection. The appropriate wavelet for lacustrine sequence analysis is the asymmetric wavelet with a weak number of side lobes. The depositional flows, depositional process,and autogenic are three sedimentary factors that influence the sequence analysis results.

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.

Progress in the Application of Mendelian Randomization Analysis in Allergic Rhinitis

Allergic rhinitis (AR) is a non-infectious chronic inflammatory disease of the nasal mucosa mediated mainly by immunoglobulin E (IgE) in atopic individuals after exposure to allergens, with the typical symptoms of paroxysmal sneezing, watery runny nose, itchy nose and nasal congestion. Mendelian randomization (MR), an innovative epidemiological approach that uses common genetic variants as instrumental variables for exposure, thus enabling prediction of their causal relationship with outcomes, has been widely used in recent years in studies related to AR. This paper provides a review of the method and its progress in the field of allergic rhinitis research.

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.

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.

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.

Construction and preliminary application of large language model for reservoir performance analysis

A large language model(LLM)is constructed to address the sophisticated demands of data retrieval and analysis,detailed well profiling,computation of key technical indicators,and the solutions to complex problems in reservoir performance analysis(RPA).The LLM is constructed for RPA scenarios with incremental pre-training,fine-tuning,and functional subsystems coupling.Functional subsystem and efficient coupling methods are proposed based on named entity recognition(NER),tool invocation,and Text-to-SQL construction,all aimed at resolving pivotal challenges in developing the specific application of LLMs for RDA.This study conducted a detailed accuracy test on feature extraction models,tool classification models,data retrieval models and analysis recommendation models.The results indicate that these models have demonstrated good performance in various key aspects of reservoir dynamic analysis.The research takes some injection and production well groups in the PK3 Block of the Daqing Oilfield as an example for testing.Testing results show that our model has significant potential and practical value in assisting reservoir engineers with RDA.The research results provide a powerful support to the application of LLM in reservoir performance analysis.

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.

Data Visualization in Big Data Analysis: Applications and Future Trends

The advent of the big data era has made data visualization a crucial tool for enhancing the efficiency and insights of data analysis. This theoretical research delves into the current applications and potential future trends of data visualization in big data analysis. The article first systematically reviews the theoretical foundations and technological evolution of data visualization, and thoroughly analyzes the challenges faced by visualization in the big data environment, such as massive data processing, real-time visualization requirements, and multi-dimensional data display. Through extensive literature research, it explores innovative application cases and theoretical models of data visualization in multiple fields including business intelligence, scientific research, and public decision-making. The study reveals that interactive visualization, real-time visualization, and immersive visualization technologies may become the main directions for future development and analyzes the potential of these technologies in enhancing user experience and data comprehension. The paper also delves into the theoretical potential of artificial intelligence technology in enhancing data visualization capabilities, such as automated chart generation, intelligent recommendation of visualization schemes, and adaptive visualization interfaces. The research also focuses on the role of data visualization in promoting interdisciplinary collaboration and data democratization. Finally, the paper proposes theoretical suggestions for promoting data visualization technology innovation and application popularization, including strengthening visualization literacy education, developing standardized visualization frameworks, and promoting open-source sharing of visualization tools. This study provides a comprehensive theoretical perspective for understanding the importance of data visualization in the big data era and its future development directions.

On stability analysis of nonlinear ADRC-based control system with application to inverted pendulum problems

This paper mainly focuses on stability analysis of the nonlinear active disturbance rejection control(ADRC)-based control system and its applicability to real world engineering problems.Firstly,the nonlinear ADRC(NLADRC)-based control system is transformed into a multi-input multi-output(MIMO)Lurie-like system,then sufficient condition for absolute stability based on linear matrix inequality(LMI)is proposed.Since the absolute stability is a kind of global stability,Lyapunov stability is further considered.The local asymptotical stability can be deter-mined by whether a matrix is Hurwitz or not.Using the inverted pendulum as an example,the proposed methods are verified by simulation and experiment,which show the valuable guidance for engineers to design and analyze the NL ADRC-based control system.

The MDOF equivalent linear system and its applications in seismic analysis and design of framed structures

This paper reviews the applications of the multi degree-of-freedom(MDOF)equivalent linear system in seismic analysis and design of planar steel and reinforced concrete framed structures.An equivalent MDOF linear structure,analogous to the original MDOF nonlinear structure,is constructed,which has the same mass and elastic stiffness as the original structure and modal damping ratios that account for the effects of geometrical and material nonlinearities.The equivalence implies a balance between the viscous damping work of the equivalent linear structure and that of the nonlinearities in the original nonlinear structure.This work balance is established with the aid of a transfer function in the frequency domain.Thus,equivalent modal damping ratios can be explicitly determined in terms of the period and deformation levels of the structure as well as the soil types.Use of these equivalent modal damping ratios can help address a variety of seismic analysis and design problems associated with planar steel and reinforced concrete framed structures in a rational and accurate manner.These include force-based seismic design with the aid of acceleration response spectra characterized by high amounts of damping,improved direct displacement-based seismic design and the development of advanced seismic intensity measures.The equivalent modal damping ratios are also utilized in the context of linear modal analysis for the definition and construction of the MDOF response spectrum.Furthermore,the equivalent modal damping ratios are employed in a seismic retrofit method for steel-framed structures with viscous dampers.Finally,it is demonstrated that modal behavior(or strength reduction)factors can be easily constructed based on these modal damping ratios for a more rational and accurate force-based seismic design,including the determination of inelastic displacement profiles.

Normative Analysis and Strategy Research on Harmonized Development of Modern Industrialization, Urbanization, Agricultural Modernization and IT Application

Targeting modernization, we should keep to the Chinese-style path of car- rying out industrialization in a new way and advancing IT application, urbanization, and agricultural modernization. In the research, the scientific concepts of IT applica- tion, urbanization, agricultural modernization and industrialization were illustrated and the interrelations were analyzed, with measures on harmonized development pro- posed based on existing problems.