With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration wi...With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.展开更多
In this paper,there are discussed the informational functions of the living structures,analyzing the properties of the simplest eukaryotic cell as an example of a structural unit of the living unicellular and multicel...In this paper,there are discussed the informational functions of the living structures,analyzing the properties of the simplest eukaryotic cell as an example of a structural unit of the living unicellular and multicellular systems.The initiation of this analysis starts from an older example of an imaginary mechanism,particularly that described by the Maxwell’s demon experiment,which along the history of the information development concepts accompanied the philosophic vision on the structuration of matter and of the living entities,showing that these are actually the result of the intervention of information on the matter available substrate.Particularly,it is shown that the deoxyribonucleic acid(DNA)structure is appropriate to store a large quantity of structural information,allowing the transfer of this information by transcription and translation mechanisms to proteins,which act as(re)structuration/transmission informational agents,or the generation of a new cellular daughter structure by a replication process.On the basis of the theory of information in communication channels,applicable also in biological systems,it was discussed the followed line for the evaluation of the quantity of structural information in various cells,demonstrating the evolution of organism complexity by the increase of the structural information quantity from unicellular(bacterium)to human cell.Applying a natural strategy of entropy lowering mainly by heat elimination,folding protein structuration and compartmentalization on the evolutionary scale,the living structures act as dynamic entities assuring their self-organizational structure by a permanent change of matter,energy and information with the environment in an efficient way,following a negative entropic process by internal structuration,similarly with Maxwell’s demon work.It is shown that to assure such a communication with external and internal intracellular structure,it was necessary the development of an own info-operational system of communication and decision,in which the operational“Yes/No”decisional binary(Bit)unit is essential.These revolutionary results show that the cell unit complies with the similar informational functions like the multicellular structure of the human body,organized in seven-type informational components,allowing the informational modeling of the activity of the living biologic structures and the opening of a shortcutting way to mimic the biologic functions in artificial cells.展开更多
Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological conditions.Inversion of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from...Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological conditions.Inversion of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from canopy observations,and adding prior information has been effective in alleviating the“ill-posed”problem,a major challenge in model inversion.Canopy structure parameters,such as leaf area index(LAI)and average leaf inclination angle(ALA),can serve as prior information for leaf pigment retrie-val.Using canopy spectra simulated from the PROSAIL model,we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll(C _(ab))and car-otenoid(C_(ar)).The retrieval accuracies of the two pigments were increased by use of the priors of LAI(RMSE of C_(ab) from 7.67 to 6.32μg cm^(-2),C_(ar) from 2.41 to 2.28μg cm^(-2))and ALA(RMSE of C_(ab) from 7.67 to 5.72μg cm^(-2),C_(ar) from 2.41 to 2.23μg cm^(-2)).However,this improvement deteriorated with an increase of additive and multiplicative uncertainties,and when 40% and 20% noise was added to LAI and ALA respectively,these priors ceased to increase retrieval accuracy.Validation using an experimental winter wheat dataset also showed that compared with C_(ar),the estimation accuracy of C_(ab) increased more or deteriorated less with uncertainty in prior canopy structure.This study demonstrates possible limita-tions of using prior information in RTM inversions for retrieval of leaf biochemistry,when large uncer-tainties are present.展开更多
Based on the Nansha coral islets and reef's time-space attributes,and the intension and extension of the remote sensing information, the concept model and concept system of coral islets and reef are proposed.Then ...Based on the Nansha coral islets and reef's time-space attributes,and the intension and extension of the remote sensing information, the concept model and concept system of coral islets and reef are proposed.Then twin-tree remote sensing information model for different kinds of reef is constructed by using abstracted islets and reef's primitive, and the structure recognition system for coral islets and reef type is developed.展开更多
The tracking of maneuvering targets in radar networking scenarios is studied in this paper.For the interacting multiple model algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a ...The tracking of maneuvering targets in radar networking scenarios is studied in this paper.For the interacting multiple model algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a mismatch between the model set and the target motion mode,which causes the reduction on tracking accuracy.An adaptive grid-expected-mode augmentation variable structure multiple model algorithm is proposed.The adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model set.Furthermore,combining with the unscented information filtering,and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy,a distributed target tracking algorithm based on the posterior information of the information matrix is proposed.For the problem of filtering divergence while target is leaving radar surveillance area,a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence.展开更多
This research pioneers the integration of geographic information systems(GIS)and 3D modeling within a virtual reality(VR)framework to assess the viability and planning of a 20 MW hybrid wind-solarphotovoltaic(PV)syste...This research pioneers the integration of geographic information systems(GIS)and 3D modeling within a virtual reality(VR)framework to assess the viability and planning of a 20 MW hybrid wind-solarphotovoltaic(PV)system connected to the local grid.The study focuses on Dakhla,Morocco,a region with vast untapped renewable energy potential.By leveraging GIS,we are innovatively analyzing geographical and environmental factors that influence optimal site selection and system design.The incorporation of VR technologies offers an unprecedented level of realism and immersion,allowing stakeholders to virtually experience the project's impact and design in a dynamic,interactive environment.This novel methodology includes extensive data collection,advanced modeling,and simulations,ensuring that the hybrid system is precisely tailored to the unique climatic and environmental conditions of Dakhla.Our analysis reveals that the region possesses a photovoltaic solar potential of approximately2400 k Wh/m^(2) per year,with an average annual wind power density of about 434 W/m^(2) at an 80-meter hub height.Productivity simulations indicate that the 20 MW hybrid system could generate approximately 60 GWh of energy per year and 1369 GWh over its 25-year lifespan.To validate these findings,we employed the System Advisor Model(SAM)software and the Global Solar Photovoltaic Atlas platform.This comprehensive and interdisciplinary approach not only provides a robust assessment of the system's feasibility but also offers valuable insights into its potential socio-economic and environmental impact.展开更多
With the purpose to smooth the way of a correct understanding of information concepts and their evolution,in this paper,is discussed the evolution and development of the concept of information in biological systems,sh...With the purpose to smooth the way of a correct understanding of information concepts and their evolution,in this paper,is discussed the evolution and development of the concept of information in biological systems,showing that this concept was intuitively perceived even since ancient times by our predecessors,and described according to their language level of that times,but the crystallization of the real meaning of information is an achievement of our nowadays,by successive contribution of various scientific branches and personalities of the scientific community of the world,leading to a modern description/modeling of reality,in which information plays a fundamental role.It is shown that our reality can be understood as a contribution of matter/energy/information and represented/discussed as the model of the Universal Triangle of Reality(UTR),where various previous models can be suggestively inserted,as a function of their basic concern.The modern concepts on information starting from a theoretic experiment which would infringe the thermodynamics laws and reaching the theory of information and modern philosophic concepts on the world structuration allow us to show that information is a fundamental component of the material world and of the biological structures,in correlation with the structuration/destructuration processes of matter,involving absorption/release of information.Based on these concepts,is discussed the functionality of the biologic structures and is presented the informational model of the human body and living structures,as a general model of info-organization on the entire biological scale,showing that a rudimentary proto-consciousness should be operative even at the low-scale biological systems,because they work on the same principles,like the most developed bio-systems.The operability of biologic structures as informational devices is also pointed out.展开更多
Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for ...Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for comprehensively obtaining the porosity. Deep learning methods provide an intelligent approach to suppress the ambiguity of the conventional inversion method. However, under the trace-bytrace inversion strategy, there is a lack of constraints from geological structural information, resulting in poor lateral continuity of prediction results. In addition, the heterogeneity and the sedimentary variability of subsurface media also lead to uncertainty in intelligent prediction. To achieve fine prediction of porosity, we consider the lateral continuity and variability and propose an improved structural modeling deep learning porosity prediction method. First, we combine well data, waveform attributes, and structural information as constraints to model geophysical parameters, constructing a high-quality training dataset with sedimentary facies-controlled significance. Subsequently, we introduce a gated axial attention mechanism to enhance the features of dataset and design a bidirectional closed-loop network system constrained by inversion and forward processes. The constraint coefficient is adaptively adjusted by the petrophysical information contained between the porosity and impedance in the study area. We demonstrate the effectiveness of the adaptive coefficient through numerical experiments.Finally, we compare the performance differences between the proposed method and conventional deep learning methods using data from two study areas. The proposed method achieves better consistency with the logging porosity, demonstrating the superiority of the proposed method.展开更多
The bolted flange structure finds significant applications in fields such as aerospace,shipbuilding,and pipeline transportation.The investigation of its dynamic characteristics has consistently been a focal point for ...The bolted flange structure finds significant applications in fields such as aerospace,shipbuilding,and pipeline transportation.The investigation of its dynamic characteristics has consistently been a focal point for researchers;however,there remains a deficiency in the development of robust analytical models.This paper introduces a novel analytical model based on the finite element methods and the Timoshenko beam theory to accurately simulate the bolted flange structure.The stiffness,mass,damping,and inertia matrices of the rotor system are individually derived,and the dynamic equation is subsequently formulated.The model’s validity and accuracy are validated through both the experimental testing and the finite element analysis.This study aims to elucidate the relationship between the external loads and the influence of the geometric configuration on the stiffness and contact behavior of the bolted flange structure,thereby enabling a thorough and precise prediction of the static and dynamic load transfer pathways,as well as the distribution of vibrational energy within the structure,while also facilitating the incorporation of friction and slip effects.Simultaneously,this work provides a foundational framework for the optimization design of bolted flange structures,addressing the factors such as the number,size,and geometric distribution of bolts.展开更多
To ensure the operational safety of railways in the landslide-prone areas of mountainous regions,a large-scale model test and numerical simulation were conducted to study the bending moment distribution,internal force...To ensure the operational safety of railways in the landslide-prone areas of mountainous regions,a large-scale model test and numerical simulation were conducted to study the bending moment distribution,internal force distribution,deformation development,and crack propagation characteristics of a framed anti-sliding structure(FAS)under landslide thrust up to the point of failure.Results show that the maximum bending moment and its increase rate in the fore pile are greater than those in the rear pile,with the maximum bending moment of the fore pile approximately 1.1 times that of the rear pile.When the FAS fails,the displacement at the top of the fore pile is significantly greater,about 1.27 times that of the rear pile in the experiment.Major cracks develop at locations corresponding to the peak bending moments.Small transverse cracks initially appear on the upper surface at the intersection between the primary beam and rear pile and then spread to the side of the structure.At the failure stage,major cracks are observed at the pil-beam intersections and near the anchor points.Strengthening flexural stiffness at intersections where major cracks occur can improve the overall thrust-deformation coordination of the FAS,thereby maximizing its performance.展开更多
Dear Editor,This letter deals with automatically constructing an OPC UA information model(IM)aimed at enhancing data interoperability among heterogeneous system components within manufacturing automation systems.Empow...Dear Editor,This letter deals with automatically constructing an OPC UA information model(IM)aimed at enhancing data interoperability among heterogeneous system components within manufacturing automation systems.Empowered by the large language model(LLM),we propose a novel multi-agent collaborative framework to streamline the end-to-end OPC UA IM modeling process.Each agent is equipped with meticulously engineered prompt templates,augmenting their capacity to execute specific tasks.We conduct modeling experiments using real textual data to demonstrate the effectiveness of the proposed method,improving modeling efficiency and reducing the labor workload.展开更多
Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and ...Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and events—while also involving reasoning tasks like personnel classification,relationship judgment,and implicit inference.Moreover,utilizing models for extracting information from police incident data poses a significant challenge—data scarcity,which limits the effectiveness of traditional rule-based and machine-learning methods.To address these,we propose TIPS.In collaboration with public security experts,we used de-identified police incident data to create templates that enable large language models(LLMs)to populate data slots and generate simulated data,enhancing data density and diversity.We then designed schemas to efficiently manage complex extraction and reasoning tasks,constructing a high-quality dataset and fine-tuning multiple open-source LLMs.Experiments showed that the fine-tuned ChatGLM-4-9B model achieved an F1 score of 87.14%,nearly 30%higher than the base model,significantly reducing error rates.Manual corrections further improved performance by 9.39%.This study demonstrates that combining largescale pre-trained models with limited high-quality domain-specific data can greatly enhance information extraction in low-resource environments,offering a new approach for intelligent public security applications.展开更多
The management of large-scale architectural engineering projects(e.g.,airports,hospitals)is plagued by information silos,cost overruns,and scheduling delays.While building information modeling(BIM)has improved 3D desi...The management of large-scale architectural engineering projects(e.g.,airports,hospitals)is plagued by information silos,cost overruns,and scheduling delays.While building information modeling(BIM)has improved 3D design coordination,its static nature limits its utility in real-time construction management and operational phases.This paper proposes a novel synergistic framework that integrates the static,deep data of BIM with the dynamic,real-time capabilities of digital twin(DT)technology.The framework establishes a closed-loop data flow from design(BIM)to construction(IoT,drones,BIM 360)to operation(DT platform).We detail the technological stack required,including IoT sensors,cloud computing,and AI-driven analytics.The application of this framework is illustrated through a simulated case study of a mega-terminal airport construction project,demonstrating potential reductions in rework by 15%,improvement in labor productivity by 10%,and enhanced predictive maintenance capabilities.This research contributes to the field of construction engineering by providing a practical model for achieving full lifecycle digitalization and intelligent project management.展开更多
Background:Acquiring relevant information about procurement targets is fundamental to procuring medical devices.Although traditional Natural Language Processing(NLP)and Machine Learning(ML)methods have improved inform...Background:Acquiring relevant information about procurement targets is fundamental to procuring medical devices.Although traditional Natural Language Processing(NLP)and Machine Learning(ML)methods have improved information retrieval efficiency to a certain extent,they exhibit significant limitations in adaptability and accuracy when dealing with procurement documents characterized by diverse formats and a high degree of unstructured content.The emergence of Large Language Models(LLMs)offers new possibilities for efficient procurement information processing and extraction.Methods:This study collected procurement transaction documents from public procurement websites,and proposed a procurement Information Extraction(IE)method based on LLMs.Unlike traditional approaches,this study systematically explores the applicability of LLMs in both structured and unstructured entities in procurement documents,addressing the challenges posed by format variability and content complexity.Furthermore,an optimized prompt framework tailored for procurement document extraction tasks is developed to enhance the accuracy and robustness of IE.The aim is to process and extract key information from medical device procurement quickly and accurately,meeting stakeholders'demands for precision and timeliness in information retrieval.Results:Experimental results demonstrate that,compared to traditional methods,the proposed approach achieves an F1 Score of 0.9698,representing a 4.85%improvement over the best baseline model.Moreover,both recall and precision rates are close to 97%,significantly outperforming other models and exhibiting exceptional overall recognition capabilities.Notably,further analysis reveals that the proposed method consistently maintains high performance across both structured and unstructured entities in procurement documents while balancing recall and precision effectively,demonstrating its adaptability in handling varying document formats.The results of ablation experiments validate the effectiveness of the proposed prompting strategy.Conclusion:Additionally,this study explores the challenges and potential improvements of the proposed method in IE tasks and provides insights into its feasibility for real-world deployment and application directions,further clarifying its adaptability and value.This method not only exhibits significant advantages in medical device procurement but also holds promise for providing new approaches to information processing and decision support in various domains.展开更多
The acetylpolyamine oxidase(APAO),spermine oxidase(SMO),and spermidine/spermine N1-acetyltransferase(SSAT)are pivotal enzymes in polyamine metabolism,exerting direct influence on polyamine homeostasis regulation.Dysfu...The acetylpolyamine oxidase(APAO),spermine oxidase(SMO),and spermidine/spermine N1-acetyltransferase(SSAT)are pivotal enzymes in polyamine metabolism,exerting direct influence on polyamine homeostasis regulation.Dysfunctions in these enzymes are intricately linked to inflammatory diseases and cancers.Establishing their three-dimensional structures is essential for exploring enzymatic catalytic mechanisms and designing inhibitors at the atomic level.This article primarily assesses the precision of AlphaFold2 and molecular dynamics simulations in determining the three-dimensional structures of these enzymes,utilizing protein conformation rationality assessment,residue correlation matrix,and other techniques.This provides robust models for subsequent polyamine catabolic metabolism calculations and offers valuable insights for modeling proteins that have yet to acquire crystal structures.展开更多
Product information model for welding structure plays an important role for the integration of welding CAD/CAPP/CAM. However, existing CAD modeling systems are not capable of providing enough information for subsequen...Product information model for welding structure plays an important role for the integration of welding CAD/CAPP/CAM. However, existing CAD modeling systems are not capable of providing enough information for subsequent manufacturing activities such as CAPP and CAM. A new design approach using feature technique and object oriented programming method is put forward in this paper in order to create the product information model of welding structure. With this approach, the product information model is able to effectively support computer aided welding process planning, fixturing, assembling, path planning of welding robot and other manufacturing activities. The feature classification and representing scheme of welding structure are discussed. A prototype system is developed based on feature and object oriented programming. Its structure and functions are given in detail.展开更多
Beach groynes are structures for erosion protection along sandy coasts near inlets and can reduce the coastal erosion substantially,but open groynes cannot stop erosion completely because sand can be removed from the ...Beach groynes are structures for erosion protection along sandy coasts near inlets and can reduce the coastal erosion substantially,but open groynes cannot stop erosion completely because sand can be removed from the groyne compartments by cross-shore processes.Beach groynes should be designed with sufficient bypassing of sand to minimise erosion.Regular beach maintenance is required to keep a sufficient beach width for recreational purposes.The effectiveness of groyne compartments can be significantly improved by using T-head groynes or by using a submerged sill or breakwater in between the groynes.An economic evaluation shows that the beach maintenance costs over 50 years may be substantially higher than the construction costs of a submerged breakwater.An important parameter to be studied is the longshore transport,which requires detailed information of the wave climate,preferably based on measured data(offshore buoys)in combination with deep water wave modelling.Various models have been used to determine the net longshore sand transport and coastline changes.The design of groynes to reduce coastal erosion is illustrated by three field cases(Atlantic coast near Soulac,France;Lagos coast,Nigeria and Black Sea coast,Romania).These example cases show that beach groynes are effective structures,but sufficient bypassing of longshore sand transport is essential to minimise erosion.Regular beach fills in the groyne compartments may be required at high-energy(exposed)coasts.A submerged or emerged breakwater can be built between the groynes to protect the beach in the groyne compartments against erosion by cross-shore processes.展开更多
With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration predict...With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration prediction system is of great scientific and practical significance for accurate and reliable predictions.This paper proposes a combination of pointinterval prediction system for pollutant concentration prediction by leveraging neural network,meta-heuristic optimization algorithm,and fuzzy theory.Fuzzy information granulation technology is used in data preprocessing to transform numerical sequences into fuzzy particles for comprehensive feature extraction.The golden Jackal optimization algorithm is employed in the optimization stage to fine-tune model hyperparameters.In the prediction stage,an ensemble learning method combines training results frommultiplemodels to obtain final point predictions while also utilizing quantile regression and kernel density estimation methods for interval predictions on the test set.Experimental results demonstrate that the combined model achieves a high goodness of fit coefficient of determination(R^(2))at 99.3% and a maximum difference between prediction accuracy mean absolute percentage error(MAPE)and benchmark model at 12.6%.This suggests that the integrated learning system proposed in this paper can provide more accurate deterministic predictions as well as reliable uncertainty analysis compared to traditionalmodels,offering practical reference for air quality early warning.展开更多
Accurate prediction of coal reservoir permeability is crucial for engineering applications,including coal mining,coalbed methane(CBM)extraction,and carbon storage in deep unmineable coal seams.Owing to the inherent he...Accurate prediction of coal reservoir permeability is crucial for engineering applications,including coal mining,coalbed methane(CBM)extraction,and carbon storage in deep unmineable coal seams.Owing to the inherent heterogeneity and complex internal structure of coal,a well-established method for predicting permeability based on microscopic fracture structures remains elusive.This paper presents a novel integrated approach that leverages the intrinsic relationship between microscopic fracture structure and permeability to construct a predictive model for coal permeability.The proposed framework encompasses data generation through the integration of three-dimensional(3D)digital core analysis and numerical simulations,followed by data-driven modeling via machine learning(ML)techniques.Key data-driven strategies,including feature selection and hyperparameter tuning,are employed to improve model performance.We propose and evaluate twelve data-driven models,including multilayer perceptron(MLP),random forest(RF),and hybrid methods.The results demonstrate that the ML model based on the RF algorithm achieves the highest accuracy and best generalization capability in predicting permeability.This method enables rapid estimation of coal permeability by inputting two-dimensional(2D)computed tomography images or parameters of the microscopic fracture structure,thereby providing an accurate and efficient means of permeability prediction.展开更多
Existing sandstone rock structure evaluation methods rely on visual inspection,with low efficiency,semi-quantitative analysis of roundness,and inability to perform classified statistics in particle size analysis.This ...Existing sandstone rock structure evaluation methods rely on visual inspection,with low efficiency,semi-quantitative analysis of roundness,and inability to perform classified statistics in particle size analysis.This study presents an intelligent evaluation method for sandstone rock structure based on the Segment Anything Model(SAM).By developing a lightweight SAM fine-tuning method with rank-decomposition matrix adapters,a multispectral rock particle segmentation model named CoreSAM is constructed,which achieves rock particle edge extraction and type identification.Building upon this,we propose a comprehensive quantitative evaluation system for rock structure,assessing parameters including particle size,sorting,roundness,particle contact and cementation types.The experimental results demonstrate that CoreSAM outperforms existing methods in rock particle segmentation accuracy while showing excellent generalization across different image types such as CT scans and core photographs.The proposed method enables full-sample,classified particle size analysis and quantitative characterization of parameters like roundness,advancing reservoir evaluation towards more precise,quantitative,intuitive,and comprehensive development.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.42330510)。
文摘With the development of smart cities and smart technologies,parks,as functional units of the city,are facing smart transformation.The development of smart parks can help address challenges of technology integration within urban spaces and serve as testbeds for exploring smart city planning and governance models.Information models facilitate the effective integration of technology into space.Building Information Modeling(BIM)and City Information Modeling(CIM)have been widely used in urban construction.However,the existing information models have limitations in the application of the park,so it is necessary to develop an information model suitable for the park.This paper first traces the evolution of park smart transformation,reviews the global landscape of smart park development,and identifies key trends and persistent challenges.Addressing the particularities of parks,the concept of Park Information Modeling(PIM)is proposed.PIM leverages smart technologies such as artificial intelligence,digital twins,and collaborative sensing to help form a‘space-technology-system’smart structure,enabling systematic management of diverse park spaces,addressing the deficiency in park-level information models,and aiming to achieve scale articulation between BIM and CIM.Finally,through a detailed top-level design application case study of the Nanjing Smart Education Park in China,this paper illustrates the translation process of the PIM concept into practice,showcasing its potential to provide smart management tools for park managers and enhance services for park stakeholders,although further empirical validation is required.
文摘In this paper,there are discussed the informational functions of the living structures,analyzing the properties of the simplest eukaryotic cell as an example of a structural unit of the living unicellular and multicellular systems.The initiation of this analysis starts from an older example of an imaginary mechanism,particularly that described by the Maxwell’s demon experiment,which along the history of the information development concepts accompanied the philosophic vision on the structuration of matter and of the living entities,showing that these are actually the result of the intervention of information on the matter available substrate.Particularly,it is shown that the deoxyribonucleic acid(DNA)structure is appropriate to store a large quantity of structural information,allowing the transfer of this information by transcription and translation mechanisms to proteins,which act as(re)structuration/transmission informational agents,or the generation of a new cellular daughter structure by a replication process.On the basis of the theory of information in communication channels,applicable also in biological systems,it was discussed the followed line for the evaluation of the quantity of structural information in various cells,demonstrating the evolution of organism complexity by the increase of the structural information quantity from unicellular(bacterium)to human cell.Applying a natural strategy of entropy lowering mainly by heat elimination,folding protein structuration and compartmentalization on the evolutionary scale,the living structures act as dynamic entities assuring their self-organizational structure by a permanent change of matter,energy and information with the environment in an efficient way,following a negative entropic process by internal structuration,similarly with Maxwell’s demon work.It is shown that to assure such a communication with external and internal intracellular structure,it was necessary the development of an own info-operational system of communication and decision,in which the operational“Yes/No”decisional binary(Bit)unit is essential.These revolutionary results show that the cell unit complies with the similar informational functions like the multicellular structure of the human body,organized in seven-type informational components,allowing the informational modeling of the activity of the living biologic structures and the opening of a shortcutting way to mimic the biologic functions in artificial cells.
基金supported by the National Natural Science Foundation of China (41975044)the Open Research Fund of the State Laboratory of Information Engineering in Surveying,Mapping,Remote Sensing,Wuhan University (20R02)+2 种基金the Fundamental Research Funds for the Central Universities,China University of Geosciences (Wuhan)(111-G1323520290)funded by SNSA (Dnr 96/16)the EU-Aid funded CASSECS Project。
文摘Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological conditions.Inversion of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from canopy observations,and adding prior information has been effective in alleviating the“ill-posed”problem,a major challenge in model inversion.Canopy structure parameters,such as leaf area index(LAI)and average leaf inclination angle(ALA),can serve as prior information for leaf pigment retrie-val.Using canopy spectra simulated from the PROSAIL model,we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll(C _(ab))and car-otenoid(C_(ar)).The retrieval accuracies of the two pigments were increased by use of the priors of LAI(RMSE of C_(ab) from 7.67 to 6.32μg cm^(-2),C_(ar) from 2.41 to 2.28μg cm^(-2))and ALA(RMSE of C_(ab) from 7.67 to 5.72μg cm^(-2),C_(ar) from 2.41 to 2.23μg cm^(-2)).However,this improvement deteriorated with an increase of additive and multiplicative uncertainties,and when 40% and 20% noise was added to LAI and ALA respectively,these priors ceased to increase retrieval accuracy.Validation using an experimental winter wheat dataset also showed that compared with C_(ar),the estimation accuracy of C_(ab) increased more or deteriorated less with uncertainty in prior canopy structure.This study demonstrates possible limita-tions of using prior information in RTM inversions for retrieval of leaf biochemistry,when large uncer-tainties are present.
文摘Based on the Nansha coral islets and reef's time-space attributes,and the intension and extension of the remote sensing information, the concept model and concept system of coral islets and reef are proposed.Then twin-tree remote sensing information model for different kinds of reef is constructed by using abstracted islets and reef's primitive, and the structure recognition system for coral islets and reef type is developed.
基金the Joint Fund of Advanced Aerospace Manufacturing Technology Research(No.2017-JCJQ-ZQ-031)。
文摘The tracking of maneuvering targets in radar networking scenarios is studied in this paper.For the interacting multiple model algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a mismatch between the model set and the target motion mode,which causes the reduction on tracking accuracy.An adaptive grid-expected-mode augmentation variable structure multiple model algorithm is proposed.The adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model set.Furthermore,combining with the unscented information filtering,and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy,a distributed target tracking algorithm based on the posterior information of the information matrix is proposed.For the problem of filtering divergence while target is leaving radar surveillance area,a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence.
文摘This research pioneers the integration of geographic information systems(GIS)and 3D modeling within a virtual reality(VR)framework to assess the viability and planning of a 20 MW hybrid wind-solarphotovoltaic(PV)system connected to the local grid.The study focuses on Dakhla,Morocco,a region with vast untapped renewable energy potential.By leveraging GIS,we are innovatively analyzing geographical and environmental factors that influence optimal site selection and system design.The incorporation of VR technologies offers an unprecedented level of realism and immersion,allowing stakeholders to virtually experience the project's impact and design in a dynamic,interactive environment.This novel methodology includes extensive data collection,advanced modeling,and simulations,ensuring that the hybrid system is precisely tailored to the unique climatic and environmental conditions of Dakhla.Our analysis reveals that the region possesses a photovoltaic solar potential of approximately2400 k Wh/m^(2) per year,with an average annual wind power density of about 434 W/m^(2) at an 80-meter hub height.Productivity simulations indicate that the 20 MW hybrid system could generate approximately 60 GWh of energy per year and 1369 GWh over its 25-year lifespan.To validate these findings,we employed the System Advisor Model(SAM)software and the Global Solar Photovoltaic Atlas platform.This comprehensive and interdisciplinary approach not only provides a robust assessment of the system's feasibility but also offers valuable insights into its potential socio-economic and environmental impact.
文摘With the purpose to smooth the way of a correct understanding of information concepts and their evolution,in this paper,is discussed the evolution and development of the concept of information in biological systems,showing that this concept was intuitively perceived even since ancient times by our predecessors,and described according to their language level of that times,but the crystallization of the real meaning of information is an achievement of our nowadays,by successive contribution of various scientific branches and personalities of the scientific community of the world,leading to a modern description/modeling of reality,in which information plays a fundamental role.It is shown that our reality can be understood as a contribution of matter/energy/information and represented/discussed as the model of the Universal Triangle of Reality(UTR),where various previous models can be suggestively inserted,as a function of their basic concern.The modern concepts on information starting from a theoretic experiment which would infringe the thermodynamics laws and reaching the theory of information and modern philosophic concepts on the world structuration allow us to show that information is a fundamental component of the material world and of the biological structures,in correlation with the structuration/destructuration processes of matter,involving absorption/release of information.Based on these concepts,is discussed the functionality of the biologic structures and is presented the informational model of the human body and living structures,as a general model of info-organization on the entire biological scale,showing that a rudimentary proto-consciousness should be operative even at the low-scale biological systems,because they work on the same principles,like the most developed bio-systems.The operability of biologic structures as informational devices is also pointed out.
基金the support of Research Program of Fine Exploration and Surrounding Rock Classification Technology for Deep Buried Long Tunnels Driven by Horizontal Directional Drilling and Magnetotelluric Methods Based on Deep Learning under Grant E202408010the Sichuan Science and Technology Program under Grant 2024NSFSC1984 and Grant 2024NSFSC1990。
文摘Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for comprehensively obtaining the porosity. Deep learning methods provide an intelligent approach to suppress the ambiguity of the conventional inversion method. However, under the trace-bytrace inversion strategy, there is a lack of constraints from geological structural information, resulting in poor lateral continuity of prediction results. In addition, the heterogeneity and the sedimentary variability of subsurface media also lead to uncertainty in intelligent prediction. To achieve fine prediction of porosity, we consider the lateral continuity and variability and propose an improved structural modeling deep learning porosity prediction method. First, we combine well data, waveform attributes, and structural information as constraints to model geophysical parameters, constructing a high-quality training dataset with sedimentary facies-controlled significance. Subsequently, we introduce a gated axial attention mechanism to enhance the features of dataset and design a bidirectional closed-loop network system constrained by inversion and forward processes. The constraint coefficient is adaptively adjusted by the petrophysical information contained between the porosity and impedance in the study area. We demonstrate the effectiveness of the adaptive coefficient through numerical experiments.Finally, we compare the performance differences between the proposed method and conventional deep learning methods using data from two study areas. The proposed method achieves better consistency with the logging porosity, demonstrating the superiority of the proposed method.
基金Project supported by the National Defense Technology Foundation Under the State Administration of Science,Technology,and Industry for National Defense of China(No.JSZL2022213A001)the Special Funds for Basic Research in Central Universities of China(No.HYGJZN202322)。
文摘The bolted flange structure finds significant applications in fields such as aerospace,shipbuilding,and pipeline transportation.The investigation of its dynamic characteristics has consistently been a focal point for researchers;however,there remains a deficiency in the development of robust analytical models.This paper introduces a novel analytical model based on the finite element methods and the Timoshenko beam theory to accurately simulate the bolted flange structure.The stiffness,mass,damping,and inertia matrices of the rotor system are individually derived,and the dynamic equation is subsequently formulated.The model’s validity and accuracy are validated through both the experimental testing and the finite element analysis.This study aims to elucidate the relationship between the external loads and the influence of the geometric configuration on the stiffness and contact behavior of the bolted flange structure,thereby enabling a thorough and precise prediction of the static and dynamic load transfer pathways,as well as the distribution of vibrational energy within the structure,while also facilitating the incorporation of friction and slip effects.Simultaneously,this work provides a foundational framework for the optimization design of bolted flange structures,addressing the factors such as the number,size,and geometric distribution of bolts.
基金The National Natural Science Foundation of China(No.52078427).
文摘To ensure the operational safety of railways in the landslide-prone areas of mountainous regions,a large-scale model test and numerical simulation were conducted to study the bending moment distribution,internal force distribution,deformation development,and crack propagation characteristics of a framed anti-sliding structure(FAS)under landslide thrust up to the point of failure.Results show that the maximum bending moment and its increase rate in the fore pile are greater than those in the rear pile,with the maximum bending moment of the fore pile approximately 1.1 times that of the rear pile.When the FAS fails,the displacement at the top of the fore pile is significantly greater,about 1.27 times that of the rear pile in the experiment.Major cracks develop at locations corresponding to the peak bending moments.Small transverse cracks initially appear on the upper surface at the intersection between the primary beam and rear pile and then spread to the side of the structure.At the failure stage,major cracks are observed at the pil-beam intersections and near the anchor points.Strengthening flexural stiffness at intersections where major cracks occur can improve the overall thrust-deformation coordination of the FAS,thereby maximizing its performance.
基金supported supported by the Fundamental Research Funds for the Central Universities(226-2024-00004)the National Natural Science Foundation of China(U23 A20326)Key Research and Development Program of Zhejiang Province(2025C01061).
文摘Dear Editor,This letter deals with automatically constructing an OPC UA information model(IM)aimed at enhancing data interoperability among heterogeneous system components within manufacturing automation systems.Empowered by the large language model(LLM),we propose a novel multi-agent collaborative framework to streamline the end-to-end OPC UA IM modeling process.Each agent is equipped with meticulously engineered prompt templates,augmenting their capacity to execute specific tasks.We conduct modeling experiments using real textual data to demonstrate the effectiveness of the proposed method,improving modeling efficiency and reducing the labor workload.
文摘Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and events—while also involving reasoning tasks like personnel classification,relationship judgment,and implicit inference.Moreover,utilizing models for extracting information from police incident data poses a significant challenge—data scarcity,which limits the effectiveness of traditional rule-based and machine-learning methods.To address these,we propose TIPS.In collaboration with public security experts,we used de-identified police incident data to create templates that enable large language models(LLMs)to populate data slots and generate simulated data,enhancing data density and diversity.We then designed schemas to efficiently manage complex extraction and reasoning tasks,constructing a high-quality dataset and fine-tuning multiple open-source LLMs.Experiments showed that the fine-tuned ChatGLM-4-9B model achieved an F1 score of 87.14%,nearly 30%higher than the base model,significantly reducing error rates.Manual corrections further improved performance by 9.39%.This study demonstrates that combining largescale pre-trained models with limited high-quality domain-specific data can greatly enhance information extraction in low-resource environments,offering a new approach for intelligent public security applications.
文摘The management of large-scale architectural engineering projects(e.g.,airports,hospitals)is plagued by information silos,cost overruns,and scheduling delays.While building information modeling(BIM)has improved 3D design coordination,its static nature limits its utility in real-time construction management and operational phases.This paper proposes a novel synergistic framework that integrates the static,deep data of BIM with the dynamic,real-time capabilities of digital twin(DT)technology.The framework establishes a closed-loop data flow from design(BIM)to construction(IoT,drones,BIM 360)to operation(DT platform).We detail the technological stack required,including IoT sensors,cloud computing,and AI-driven analytics.The application of this framework is illustrated through a simulated case study of a mega-terminal airport construction project,demonstrating potential reductions in rework by 15%,improvement in labor productivity by 10%,and enhanced predictive maintenance capabilities.This research contributes to the field of construction engineering by providing a practical model for achieving full lifecycle digitalization and intelligent project management.
文摘Background:Acquiring relevant information about procurement targets is fundamental to procuring medical devices.Although traditional Natural Language Processing(NLP)and Machine Learning(ML)methods have improved information retrieval efficiency to a certain extent,they exhibit significant limitations in adaptability and accuracy when dealing with procurement documents characterized by diverse formats and a high degree of unstructured content.The emergence of Large Language Models(LLMs)offers new possibilities for efficient procurement information processing and extraction.Methods:This study collected procurement transaction documents from public procurement websites,and proposed a procurement Information Extraction(IE)method based on LLMs.Unlike traditional approaches,this study systematically explores the applicability of LLMs in both structured and unstructured entities in procurement documents,addressing the challenges posed by format variability and content complexity.Furthermore,an optimized prompt framework tailored for procurement document extraction tasks is developed to enhance the accuracy and robustness of IE.The aim is to process and extract key information from medical device procurement quickly and accurately,meeting stakeholders'demands for precision and timeliness in information retrieval.Results:Experimental results demonstrate that,compared to traditional methods,the proposed approach achieves an F1 Score of 0.9698,representing a 4.85%improvement over the best baseline model.Moreover,both recall and precision rates are close to 97%,significantly outperforming other models and exhibiting exceptional overall recognition capabilities.Notably,further analysis reveals that the proposed method consistently maintains high performance across both structured and unstructured entities in procurement documents while balancing recall and precision effectively,demonstrating its adaptability in handling varying document formats.The results of ablation experiments validate the effectiveness of the proposed prompting strategy.Conclusion:Additionally,this study explores the challenges and potential improvements of the proposed method in IE tasks and provides insights into its feasibility for real-world deployment and application directions,further clarifying its adaptability and value.This method not only exhibits significant advantages in medical device procurement but also holds promise for providing new approaches to information processing and decision support in various domains.
基金National Natural Science Foundation of China(22073023)Natural Science Foundation of Henan Province(242300421134)+1 种基金the Young Backbone Teacher in Colleges and Universities of Henan Province(2021GGJS020)Foundation of State Key Laboratory of Antiviral Drugs。
文摘The acetylpolyamine oxidase(APAO),spermine oxidase(SMO),and spermidine/spermine N1-acetyltransferase(SSAT)are pivotal enzymes in polyamine metabolism,exerting direct influence on polyamine homeostasis regulation.Dysfunctions in these enzymes are intricately linked to inflammatory diseases and cancers.Establishing their three-dimensional structures is essential for exploring enzymatic catalytic mechanisms and designing inhibitors at the atomic level.This article primarily assesses the precision of AlphaFold2 and molecular dynamics simulations in determining the three-dimensional structures of these enzymes,utilizing protein conformation rationality assessment,residue correlation matrix,and other techniques.This provides robust models for subsequent polyamine catabolic metabolism calculations and offers valuable insights for modeling proteins that have yet to acquire crystal structures.
文摘Product information model for welding structure plays an important role for the integration of welding CAD/CAPP/CAM. However, existing CAD modeling systems are not capable of providing enough information for subsequent manufacturing activities such as CAPP and CAM. A new design approach using feature technique and object oriented programming method is put forward in this paper in order to create the product information model of welding structure. With this approach, the product information model is able to effectively support computer aided welding process planning, fixturing, assembling, path planning of welding robot and other manufacturing activities. The feature classification and representing scheme of welding structure are discussed. A prototype system is developed based on feature and object oriented programming. Its structure and functions are given in detail.
文摘Beach groynes are structures for erosion protection along sandy coasts near inlets and can reduce the coastal erosion substantially,but open groynes cannot stop erosion completely because sand can be removed from the groyne compartments by cross-shore processes.Beach groynes should be designed with sufficient bypassing of sand to minimise erosion.Regular beach maintenance is required to keep a sufficient beach width for recreational purposes.The effectiveness of groyne compartments can be significantly improved by using T-head groynes or by using a submerged sill or breakwater in between the groynes.An economic evaluation shows that the beach maintenance costs over 50 years may be substantially higher than the construction costs of a submerged breakwater.An important parameter to be studied is the longshore transport,which requires detailed information of the wave climate,preferably based on measured data(offshore buoys)in combination with deep water wave modelling.Various models have been used to determine the net longshore sand transport and coastline changes.The design of groynes to reduce coastal erosion is illustrated by three field cases(Atlantic coast near Soulac,France;Lagos coast,Nigeria and Black Sea coast,Romania).These example cases show that beach groynes are effective structures,but sufficient bypassing of longshore sand transport is essential to minimise erosion.Regular beach fills in the groyne compartments may be required at high-energy(exposed)coasts.A submerged or emerged breakwater can be built between the groynes to protect the beach in the groyne compartments against erosion by cross-shore processes.
基金supported by General Scientific Research Funding of the Science and Technology Development Fund(FDCT)in Macao(No.0150/2022/A)the Faculty Research Grants of Macao University of Science and Technology(No.FRG-22-074-FIE).
文摘With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration prediction system is of great scientific and practical significance for accurate and reliable predictions.This paper proposes a combination of pointinterval prediction system for pollutant concentration prediction by leveraging neural network,meta-heuristic optimization algorithm,and fuzzy theory.Fuzzy information granulation technology is used in data preprocessing to transform numerical sequences into fuzzy particles for comprehensive feature extraction.The golden Jackal optimization algorithm is employed in the optimization stage to fine-tune model hyperparameters.In the prediction stage,an ensemble learning method combines training results frommultiplemodels to obtain final point predictions while also utilizing quantile regression and kernel density estimation methods for interval predictions on the test set.Experimental results demonstrate that the combined model achieves a high goodness of fit coefficient of determination(R^(2))at 99.3% and a maximum difference between prediction accuracy mean absolute percentage error(MAPE)and benchmark model at 12.6%.This suggests that the integrated learning system proposed in this paper can provide more accurate deterministic predictions as well as reliable uncertainty analysis compared to traditionalmodels,offering practical reference for air quality early warning.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(Grant No.LY23E040001)Fundamental Research Funding Project of Zhejiang Province,China(Project Category A,Grant No.2022YW06)National Key R&D Program of China(Grant No.2023YFF0614902).
文摘Accurate prediction of coal reservoir permeability is crucial for engineering applications,including coal mining,coalbed methane(CBM)extraction,and carbon storage in deep unmineable coal seams.Owing to the inherent heterogeneity and complex internal structure of coal,a well-established method for predicting permeability based on microscopic fracture structures remains elusive.This paper presents a novel integrated approach that leverages the intrinsic relationship between microscopic fracture structure and permeability to construct a predictive model for coal permeability.The proposed framework encompasses data generation through the integration of three-dimensional(3D)digital core analysis and numerical simulations,followed by data-driven modeling via machine learning(ML)techniques.Key data-driven strategies,including feature selection and hyperparameter tuning,are employed to improve model performance.We propose and evaluate twelve data-driven models,including multilayer perceptron(MLP),random forest(RF),and hybrid methods.The results demonstrate that the ML model based on the RF algorithm achieves the highest accuracy and best generalization capability in predicting permeability.This method enables rapid estimation of coal permeability by inputting two-dimensional(2D)computed tomography images or parameters of the microscopic fracture structure,thereby providing an accurate and efficient means of permeability prediction.
基金Supported by the National Natural Science Foundation of China(42372175,72088101)PetroChina Science and Technology Project of(2023DJ84)Basic Research Cooperation Project between China National Petroleum Corporation and Peking University.
文摘Existing sandstone rock structure evaluation methods rely on visual inspection,with low efficiency,semi-quantitative analysis of roundness,and inability to perform classified statistics in particle size analysis.This study presents an intelligent evaluation method for sandstone rock structure based on the Segment Anything Model(SAM).By developing a lightweight SAM fine-tuning method with rank-decomposition matrix adapters,a multispectral rock particle segmentation model named CoreSAM is constructed,which achieves rock particle edge extraction and type identification.Building upon this,we propose a comprehensive quantitative evaluation system for rock structure,assessing parameters including particle size,sorting,roundness,particle contact and cementation types.The experimental results demonstrate that CoreSAM outperforms existing methods in rock particle segmentation accuracy while showing excellent generalization across different image types such as CT scans and core photographs.The proposed method enables full-sample,classified particle size analysis and quantitative characterization of parameters like roundness,advancing reservoir evaluation towards more precise,quantitative,intuitive,and comprehensive development.
