This paper develops a comprehensive computational modeling and simulation framework based on Complex Adaptive Systems(CAS)theory to unveil the underlying mechanisms of self-organization,nonlinear evolution,and emergen...This paper develops a comprehensive computational modeling and simulation framework based on Complex Adaptive Systems(CAS)theory to unveil the underlying mechanisms of self-organization,nonlinear evolution,and emergence in social systems.By integrating mathematical models,agent-based modeling,network dynamic analysis,and hybrid modeling approaches,the study applies CAS theory to case studies in economic markets,political decision-making,and social interactions.The experimental results demonstrate that local interactions among individual agents can give rise to complex global phenomena,such as market fluctuations,opinion polarization,and sudden outbreaks of social movements.This framework not only provides a more robust explanation for the nonlinear dynamics and abrupt transitions that traditional models often fail to capture,but also offers valuable decision-support tools for public policy formulation,social governance,and risk management.Emphasizing the importance of interdisciplinary approaches,this work outlines future research directions in high-performance computing,artificial intelligence,and real-time data integration to further advance the theoretical and practical applications of CAS in the social sciences.展开更多
Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate...Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.展开更多
Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to i...Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism.Specifically,based on complex adaptive system theory and the basic stimulus-response model,we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape.The results indicate the following:(i)The dynamic interaction is“stimulus-reaction-selection”,which promotes the dominant technology’s formation.(ii)The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms.(iii)The dominant technology’s formation in the high-tech industry is influenced by learning ability,the number of adopting users and adaptability.Therein,a“critical scale”of learning ability exists to promote the formation of leading technology:a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape.There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology’s formation.(iv)The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry,and different elements have different effects.This study promotes research on the formation mechanism of dominant technology in the high-tech industry,presents new perspectives and methods for researchers,and provides essential enlightenment for managers to formulate technology strategies.展开更多
To investigate the long-term stability of deep rocks,a three-dimensional(3D)time-dependent model that accounts for excavation-induced damage and complex stress state is developed.This model comprises three main compon...To investigate the long-term stability of deep rocks,a three-dimensional(3D)time-dependent model that accounts for excavation-induced damage and complex stress state is developed.This model comprises three main components:a 3D viscoplastic isotropic constitutive relation that considers excavation damage and complex stress state,a quantitative relationship between critical irreversible deformation and complex stress state,and evolution characteristics of strength parameters.The proposed model is implemented in a self-developed numerical code,i.e.CASRock.The reliability of the model is validated through experiments.It is indicated that the time-dependent fracturing potential index(xTFPI)at a given time during the attenuation creep stage shows a negative correlation with the extent of excavationinduced damage.The time-dependent fracturing process of rock demonstrates a distinct interval effect of the intermediate principal stress,thereby highlighting the 3D stress-dependent characteristic of the model.Finally,the influence of excavation-induced damage and intermediate principal stress on the time-dependent fracturing characteristics of the surrounding rocks around the tunnel is discussed.展开更多
Triaxial compression tests were conducted on the alfalfa root-loess complex at different growthperiods obtained through artificial planting.The research focused on analyzing the time variation law of the shear strengt...Triaxial compression tests were conducted on the alfalfa root-loess complex at different growthperiods obtained through artificial planting.The research focused on analyzing the time variation law of the shear strength index and deformation index of the alfalfa root-loess complex under dry-wet cycles.Additionally,the time effect of the shear strength index of the alfalfa root-loess complex under dry-wet cycles was analyzed and its prediction model was proposed.The results show that the PG-DWC(dry-wet cycle caused by plant water management during plant growth period)causes the peak strength of plain soil to change in a"V"shape with the increase of growth period,and the peak strength of alfalfa root-loess complex is higher than that of plain soil at the same growth period.The deterioration of the peak strength of alfalfa root-loess complex in the same growth period is aggravated with the increase of drying and wetting cycles.Compared with the 0 days growth period,the effective cohesion of alfalfa root-loess complex under different dry-wet cycles maximum increase rate is at the 180 days,which are 33.88%,46.05%,30.12%and 216.02%,respectively.When the number of dry-wet cycles is constant,the effective cohesion of the alfalfa root-loess complex overall increases with the growth period.However,it gradually decreases comparedwith the previous growth period,and the minimum increase rate are all at the 180 days.For the same growth period,the effective cohesion of the alfalfa root-loess complex decreases with the increase of the number of dry-wet cycles.This indicates that EC-DWC(the dry-wet cycles caused by extreme natural conditions such as continuous rain)have a detrimental effect on the time effect of the shear strength of the alfalfa root-loess complex.Finally,based on the formula of total deterioration,a prediction model for the shear strength of the alfalfa root-loess complex under dry-wet cycles was proposed,which exhibits high prediction accuracy.The research results provide useful guidance for the understanding of mechanical behavior and structural damage evolution of root-soil composite.展开更多
Recent advances in hydrocarbon exploration have been made in the Member Deng-2 marginal microbial mound-bank complex reservoirs of the Dengying Formation in the western Sichuan Basin, SW China,where the depositional p...Recent advances in hydrocarbon exploration have been made in the Member Deng-2 marginal microbial mound-bank complex reservoirs of the Dengying Formation in the western Sichuan Basin, SW China,where the depositional process is regarded confusing. The microfacies, construction types, and depositional model of the Member Deng-2 marginal microbial mound-bank complex have been investigated using unmanned aerial vehicle photography, outcrop section investigation, thin section identification,and seismic reflections in the southwestern Sichuan Basin. The microbialite lithologic textures in this region include thrombolite, dendrolite, stromatolite, fenestral stromatolite, spongiostromata stone,oncolite, aggregated grainstone, and botryoidal grapestone. Based on the comprehensive analysis of“depositional fabrics-lithology-microfacies”, an association between a fore mound, mound framework,and back mound subfacies has been proposed based on water depth, current direction, energy level and lithologic assemblages. The microfacies of the mound base, mound core, mound flank, mound cap, and mound flat could be recognized among the mound framework subfacies. Two construction types of marginal microbial mound-bank complex have been determined based on deposition location, mound scale, migration direction, and sedimentary facies association. Type Jinkouhe microbial mound constructions(TJMMCs) develop along the windward margin owing to their proximity to the seaward subfacies fore mound, with a northeastwardly migrated microbial mound on top of the mud mound,exhibiting the characteristics of large-sized mounds and small-sized banks in the surrounding area. Type E'bian microbial mound constructions(TEMMCs) primarily occur on the leeward margin, resulting from the presence of onshore back mound subfacies, with the smaller southwestward migrated microbial mounds existing on a thicker microbial flat. The platform margin microbial mound depositional model can be correlated with certain lateral comparison profile and seismic reflection structures in the 2D seismic section, which can provide references for future worldwide exploration. Microbial mounds with larger buildups and thicker vertical reservoirs are typically targeted on the windward margin, while small-sized microbial mounds and flats with better lateral connections are typically focused on the leeward margin.展开更多
The parameters that describe the complex degree of a certain casting are of some uncertainty. Therefore, a new method based on the fuzzy theory to classify the complex degree of castings has been presented in this pap...The parameters that describe the complex degree of a certain casting are of some uncertainty. Therefore, a new method based on the fuzzy theory to classify the complex degree of castings has been presented in this paper. The feasibility of fuzzy theory in describing the complex degree of castings has been discussed and the procedure of this method has been specified by analyzing the complex degrees of some typical castings. The element factors that influence the casting complexity, have been summarized, which include the wall-thickness and the number of transition surface, etc. The results show that it is reasonable and practicable to classify the complex degree of castings with the fuzzy theory.展开更多
In this paper, we use the cellular automation model to imitate earthquake process and draw some conclusionsof general applicability. First, it is confirmed that earthquake process has some ordering characters, and it ...In this paper, we use the cellular automation model to imitate earthquake process and draw some conclusionsof general applicability. First, it is confirmed that earthquake process has some ordering characters, and it isshown that both the existence and their mutual arrangement of faults could obviously influence the overallcharacters of earthquake process. Then the characters of each stage of model evolution are explained withself-organized critical state theory. Finally, earthquake sequences produced by the models are analysed interms pf algorithmic complexity and the result shows that AC-values of algorithmic complexity could be usedto study earthquake process and evolution.展开更多
Analyzes the shortcomings of the classic capital market theories based on EMH and discloses the complexity essence of the capital market. Considering the capital market a complicated, interactive and adaptable dynamic...Analyzes the shortcomings of the classic capital market theories based on EMH and discloses the complexity essence of the capital market. Considering the capital market a complicated, interactive and adaptable dynamic system, with complexity science as the method for researching the operation law of the capital market, this paper constructs a nonlinear logical model to analyze the applied realm, focal point and interrelationship of such theories as dissipative structure theory, chaos theory, fractal theory, synergetics theory, catastrophe theory and scale theory, and summarizes and discusses the achievements and problems of each theory. Based on the research, the paper foretells the developing direction of eomplexity science in a capital market.展开更多
This paper researched the traffic of optical networks in time-space complexity,proposed a novel traf-fic model for complex optical networks based on traffic grooming,designed a traffic generator GTS(gener-ator based o...This paper researched the traffic of optical networks in time-space complexity,proposed a novel traf-fic model for complex optical networks based on traffic grooming,designed a traffic generator GTS(gener-ator based on time and space)with 'centralized+distributed' idea,and then made a simulation in Clanguage.Experiments results show that GTS can produce the virtual network topology which can changedynamically with the characteristic of scaling-free network.GTS can also groom the different traffic andtrigger them under real-time or scheduling mechanisms,generating different optical connections.Thistraffic model is convenient for the simulation of optical networks considering the traffic complexity.展开更多
The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical r...The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical recession for the third and fourth quarters of the year 2023. This study assessed the efficacy of the Generalised Additive Model for Location, Scale and Shape (GAMLSS) as a flexible distributional regression with smoothing additive terms in forecasting the UK economic growth in-sample and out-of-sample over the conventional Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM). The aim was to investigate the effectiveness and efficiency of GAMLSS models using a machine learning framework over the conventional time series econometric models by a rolling window. It is quantitative research which adopts a dataset obtained from the Office for National Statistics, covering 105 monthly observations of major economic indicators in the UK from January 2015 to September 2023. It consists of eleven variables, which include economic growth (Econ), consumer price index (CPI), inflation (Infl), manufacturing (Manuf), electricity and gas (ElGas), construction (Const), industries (Ind), wholesale and retail (WRet), real estate (REst), education (Edu) and health (Health). All computations and graphics in this study are obtained using R software version 4.4.1. The study revealed that GAMLSS models demonstrate superior outperformance in forecast accuracy over the ARDL and ECM models. Unlike other models used in the literature, the GAMLSS models were able to forecast both the future economic growth and the future distribution of the growth, thereby contributing to the empirical literature. The study identified manufacturing, electricity and gas, construction, industries, wholesale and retail, real estate, education, and health as key drivers of UK economic growth.展开更多
The detection of early gastric cancer that often develops asymptomatically is crucial for improving patient survival.The photodynamic diagnosis(PDD)of gastric cancer using 5-aminolevulinic acid/protoporphyrin IX(5-ALA...The detection of early gastric cancer that often develops asymptomatically is crucial for improving patient survival.The photodynamic diagnosis(PDD)of gastric cancer using 5-aminolevulinic acid/protoporphyrin IX(5-ALA/PpIX)has been reported in several studies.However,the selectivity of PDD of gastric tumor is poor with often false-positive results that require the development of new methods to improve PDD for early gastric cancer.Therefore,a measure of the complexity of gastric microcirculation(multi-scale entropy,MSE)and the detrendedfluctuation analysis(DFA)were applied as additional tools to detect early gastric cancer in rats.In this experimental study,we used our original model of metastatic adenocarcinoma in the stomach of a rat.To induce a gastric tumor,we used a long-term combination(for 9 months,which is 1/2 of the life of rats)of two natural factors,such as chronic stress(overpopulation being typical for modern cities)and the daily presence of nitrites in food and drinks,which are common ingredients added to processed meat andfish to help preserve food.Our results clearly show that both methods,namely,PDD using 5-ALA/PpIX and complexity/correlation analysis,can detect early gastric cancer,which was confirmed by histological analysis.Pre-cancerous areas in the stomach were detected as an intermediatefluorescent signal or MSE level between normal and malignant lesions of the stomach.However,in some cases,PDD with 5-ALA/PpIX produced a false-positivefluorescence of exogenousfluorophores due to its accumulation in benign and inflammatory areas of the mucosa.This fact indicates that the PDD itself is not sufficient for the correct diagnosis of gastric cancer,and the use of additional characteristics,e.g.,complexity measures or scaling exponents,can significantly improve the diagnostic accuracy of PDD of gastric cancer that should be confirmed in further clinical studies and applications.展开更多
Independent cascade(IC)models,by simulating how one node can activate another,are important tools for studying the dynamics of information spreading in complex networks.However,traditional algorithms for the IC model ...Independent cascade(IC)models,by simulating how one node can activate another,are important tools for studying the dynamics of information spreading in complex networks.However,traditional algorithms for the IC model implementation face significant efficiency bottlenecks when dealing with large-scale networks and multi-round simulations.To settle this problem,this study introduces a GPU-based parallel independent cascade(GPIC)algorithm,featuring an optimized representation of the network data structure and parallel task scheduling strategies.Specifically,for this GPIC algorithm,we propose a network data structure tailored for GPU processing,thereby enhancing the computational efficiency and the scalability of the IC model.In addition,we design a parallel framework that utilizes the full potential of GPU's parallel processing capabilities,thereby augmenting the computational efficiency.The results from our simulation experiments demonstrate that GPIC not only preserves accuracy but also significantly boosts efficiency,achieving a speedup factor of 129 when compared to the baseline IC method.Our experiments also reveal that when using GPIC for the independent cascade simulation,100-200 simulation rounds are sufficient for higher-cost studies,while high precision studies benefit from 500 rounds to ensure reliable results,providing empirical guidance for applying this new algorithm to practical research.展开更多
A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,...A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and environment.The science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such challenges.We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling CAS.Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.展开更多
This paper addresses urban sustainability challenges amid global urbanization, emphasizing the need for innova tive approaches aligned with the Sustainable Development Goals. While traditional tools and linear models ...This paper addresses urban sustainability challenges amid global urbanization, emphasizing the need for innova tive approaches aligned with the Sustainable Development Goals. While traditional tools and linear models offer insights, they fall short in presenting a holistic view of complex urban challenges. System dynamics (SD) models that are often utilized to provide holistic, systematic understanding of a research subject, like the urban system, emerge as valuable tools, but data scarcity and theoretical inadequacy pose challenges. The research reviews relevant papers on recent SD model applications in urban sustainability since 2018, categorizing them based on nine key indicators. Among the reviewed papers, data limitations and model assumptions were identified as ma jor challenges in applying SD models to urban sustainability. This led to exploring the transformative potential of big data analytics, a rare approach in this field as identified by this study, to enhance SD models’ empirical foundation. Integrating big data could provide data-driven calibration, potentially improving predictive accuracy and reducing reliance on simplified assumptions. The paper concludes by advocating for new approaches that reduce assumptions and promote real-time applicable models, contributing to a comprehensive understanding of urban sustainability through the synergy of big data and SD models.展开更多
Large-scale 3D physical models of complex structures can be used to simulate hydrocarbon exploration areas. The high-fidelity simulation of actual structures poses challenges to model building and quality control. Suc...Large-scale 3D physical models of complex structures can be used to simulate hydrocarbon exploration areas. The high-fidelity simulation of actual structures poses challenges to model building and quality control. Such models can be used to collect wideazimuth, multi-azimuth, and full-azimuth seismic data that can be used to verify various 3D processing and interpretation methods. Faced with nonideal imaging problems owing to the extensive complex surface conditions and subsurface structures in the oil-rich foreland basins of western China, we designed and built the KS physical model based on the complex subsurface structure. This is the largest and most complex 3D physical model built to date. The physical modeling technology advancements mainly involve 1） the model design method, 2） the model casting flow, and 3） data acquisition. A 3D velocity model of the physical model was obtained for the first time, and the model building precision was quantitatively analyzed. The absolute error was less than 3 mm, which satisfies the experimental requirements. The 3D velocity model obtained from 3D measurements of the model layers is the basis for testing various imaging methods. Furthermore, the model is considered a standard in seismic physical modeling technology.展开更多
Six model compounds have been synthesized and used for probing the structural features of the Mn cluster in oxygen_evolving complex (OEC) of photosystem Ⅱ (PSⅡ). The model compounds contain Mn 2(μ_O) 2 and μ_O_...Six model compounds have been synthesized and used for probing the structural features of the Mn cluster in oxygen_evolving complex (OEC) of photosystem Ⅱ (PSⅡ). The model compounds contain Mn 2(μ_O) 2 and μ_O_μ_carboxylato di_manganese structural units, which offer Mn—Mn, Mn……Mn, and Mn—O(N) structural parameters consistent with the corresponding data of the OEC in PSⅡ, implying that the Mn cluster in OEC may possess similar structural features. Two model compounds containing halide anion have been used for discussing the binding of Cl - to Mn in PSⅡ. It is suggested that in the five S states, ligand exchange would lead to the ligation of chloride to Mn in the S states with Mn of higher valence.展开更多
A synthesis of the petrological characters of granulite facies rocks that contain equilibrium sapphirine ＋ quartz assemblage from two localities （Tonagh Island （TI） and Priestley Peak （PP）） in the Napier Comple...A synthesis of the petrological characters of granulite facies rocks that contain equilibrium sapphirine ＋ quartz assemblage from two localities （Tonagh Island （TI） and Priestley Peak （PP）） in the Napier Complex,East Antarctica,provides unequivocal evidence for extreme crustal metamorphism possibly associated with the collisional orogeny during Neoarchean.The reaction microstructures associated with sapphirine ＋ quartz vary among the samples,probably suggesting different tectonic conditions during the metamorphic evolution.Sapphirine and quartz in TI sample were probably in equilibrium at the peak stage,but now separated by corona of Grt ＋ Sil ＋ Opx suggesting near isobaric cooling after the peak metamorphism,whereas the Spr ＋ Qtz ＋ Sil ＋ Crd ＋ Spl assemblage replaces garnet in PP sample suggesting post-peak decompression.The application of mineral equilibrium modeling in NCKFMASHTO system demonstrated that Spr ＋ Qtz stability is lowered down to 930 ℃ due to small Fe3＋ contents in the rocks （mole Fe2O3/（FeO ＋ Fe2O3） =0.02）.The TI sample yields a peak p-T range of 950-1100 ℃ and 7.5-11 kbar,followed by cooling toward a retrograde stage of 800-950 ℃ and 8-10 kbar,possibly along a counterclockwise p-T path.In contrast,the peak condition of the PP sample shows 1000-1050 ℃ and ＞12 kbar,which was followed by the formation ofSpr ＋ Qtz corona around garnet at 930-970 ℃ and 6.7-7.7 kbar,suggesting decompression possibly along a clockwise p-T trajectory.Such contrasting p-T paths are consistent with a recent model on the structural framework of the Napier Complex that correlates the two areas to different crustal blocks.The different p-T paths obtained from the two localities might reflect the difference in the tectonic framework of these rocks within a complex Neoarchean subduction/collision belt.展开更多
The results from a hybrid approach that combines a mesoscale meteorological model with a diagnostic model to produce high-resolution wind fields in complex coastal topography are evaluated.The diagnostic wind model(Ca...The results from a hybrid approach that combines a mesoscale meteorological model with a diagnostic model to produce high-resolution wind fields in complex coastal topography are evaluated.The diagnostic wind model(California Meteorological Model,CALMET) with 100-m horizontal spacing was driven with outputs from the Weather Research and Forecasting(WRF) model to obtain near-surface winds for the 1-year period from 12 September 2003 to 11 September 2004.Results were compared with wind observations at four sites.Traditional statistical scores,including correlation coefficients,standard deviations(SDs) and mean absolute errors(MAEs),indicate that the wind estimates from the WRF/CALMET modeling system are produced reasonably well.The correlation coefficients are relatively large,ranging from 0.5 to 0.7 for the zonal wind component and from 0.75 to 0.85 for the meridional wind component.MAEs for wind speed range from 1.5 to 2.0 m s-1 at 10 meters above ground level(AGL) and from 2.0 to 2.5 m s-1 at 60 m AGL.MAEs for wind direction range from 30 to 40 degrees at both levels.A spectral decomposition of the time series of wind speed shows positive impacts of CALMET in improving the mesoscale winds.Moreover,combining the CALMET model with WRF significantly improves the spatial variability of the simulated wind fields.It can be concluded that the WRF/CALMET modeling system is capable of providing a detailed near-surface wind field,but the physics in the diagnostic CALMET model needs to be further improved.展开更多
Fault diagnosis is an important measure to ensure the safety of production, and all kinds of fault diagnosis methods are of importance in actual production process. However, the complexity and uncertainty of productio...Fault diagnosis is an important measure to ensure the safety of production, and all kinds of fault diagnosis methods are of importance in actual production process. However, the complexity and uncertainty of production process often lead to the changes of data distribution and the emergence of new fault classes, and the number of the new fault classes is unpredictable. The reconstruction of the fault diagnosis model and the identification of new fault classes have become core issues under the circumstances. This paper presents a fault diagnosis method based on model transfer learning and the main contributions of the paper are as follows: 1) An incremental model transfer fault diagnosis method is proposed to reconstruct the new process diagnosis model. 2) Breaking the limit of existing method that the new process can only have one more class of faults than the old process, this method can identify M faults more in the new process with the thought of incremental learning. 3) The method offers a solution to a series of problems caused by the increase of fault classes. Experiments based on Tennessee-Eastman process and ore grinding classification process demonstrate the effectiveness and the feasibility of the method.展开更多
文摘This paper develops a comprehensive computational modeling and simulation framework based on Complex Adaptive Systems(CAS)theory to unveil the underlying mechanisms of self-organization,nonlinear evolution,and emergence in social systems.By integrating mathematical models,agent-based modeling,network dynamic analysis,and hybrid modeling approaches,the study applies CAS theory to case studies in economic markets,political decision-making,and social interactions.The experimental results demonstrate that local interactions among individual agents can give rise to complex global phenomena,such as market fluctuations,opinion polarization,and sudden outbreaks of social movements.This framework not only provides a more robust explanation for the nonlinear dynamics and abrupt transitions that traditional models often fail to capture,but also offers valuable decision-support tools for public policy formulation,social governance,and risk management.Emphasizing the importance of interdisciplinary approaches,this work outlines future research directions in high-performance computing,artificial intelligence,and real-time data integration to further advance the theoretical and practical applications of CAS in the social sciences.
文摘Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.
基金supported by the Shanghai Philosophy and Social Science Foundation(2022ECK004)Shanghai Soft Science Research Project(23692123400)。
文摘Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism.Specifically,based on complex adaptive system theory and the basic stimulus-response model,we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape.The results indicate the following:(i)The dynamic interaction is“stimulus-reaction-selection”,which promotes the dominant technology’s formation.(ii)The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms.(iii)The dominant technology’s formation in the high-tech industry is influenced by learning ability,the number of adopting users and adaptability.Therein,a“critical scale”of learning ability exists to promote the formation of leading technology:a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape.There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology’s formation.(iv)The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry,and different elements have different effects.This study promotes research on the formation mechanism of dominant technology in the high-tech industry,presents new perspectives and methods for researchers,and provides essential enlightenment for managers to formulate technology strategies.
基金supported by the National Natural Science Foundation of China(Grant No.52125903)the China Postdoctoral Science Foundation(Grant No.2023M730367)the Fundamental Research Funds for Central Public Welfare Research Institutes of China(Grant No.CKSF2023323/YT).
文摘To investigate the long-term stability of deep rocks,a three-dimensional(3D)time-dependent model that accounts for excavation-induced damage and complex stress state is developed.This model comprises three main components:a 3D viscoplastic isotropic constitutive relation that considers excavation damage and complex stress state,a quantitative relationship between critical irreversible deformation and complex stress state,and evolution characteristics of strength parameters.The proposed model is implemented in a self-developed numerical code,i.e.CASRock.The reliability of the model is validated through experiments.It is indicated that the time-dependent fracturing potential index(xTFPI)at a given time during the attenuation creep stage shows a negative correlation with the extent of excavationinduced damage.The time-dependent fracturing process of rock demonstrates a distinct interval effect of the intermediate principal stress,thereby highlighting the 3D stress-dependent characteristic of the model.Finally,the influence of excavation-induced damage and intermediate principal stress on the time-dependent fracturing characteristics of the surrounding rocks around the tunnel is discussed.
基金received the Key Research and Development Project of Ningxia Hui Autonomous Region(2022BEG03052,2023BEG02072).
文摘Triaxial compression tests were conducted on the alfalfa root-loess complex at different growthperiods obtained through artificial planting.The research focused on analyzing the time variation law of the shear strength index and deformation index of the alfalfa root-loess complex under dry-wet cycles.Additionally,the time effect of the shear strength index of the alfalfa root-loess complex under dry-wet cycles was analyzed and its prediction model was proposed.The results show that the PG-DWC(dry-wet cycle caused by plant water management during plant growth period)causes the peak strength of plain soil to change in a"V"shape with the increase of growth period,and the peak strength of alfalfa root-loess complex is higher than that of plain soil at the same growth period.The deterioration of the peak strength of alfalfa root-loess complex in the same growth period is aggravated with the increase of drying and wetting cycles.Compared with the 0 days growth period,the effective cohesion of alfalfa root-loess complex under different dry-wet cycles maximum increase rate is at the 180 days,which are 33.88%,46.05%,30.12%and 216.02%,respectively.When the number of dry-wet cycles is constant,the effective cohesion of the alfalfa root-loess complex overall increases with the growth period.However,it gradually decreases comparedwith the previous growth period,and the minimum increase rate are all at the 180 days.For the same growth period,the effective cohesion of the alfalfa root-loess complex decreases with the increase of the number of dry-wet cycles.This indicates that EC-DWC(the dry-wet cycles caused by extreme natural conditions such as continuous rain)have a detrimental effect on the time effect of the shear strength of the alfalfa root-loess complex.Finally,based on the formula of total deterioration,a prediction model for the shear strength of the alfalfa root-loess complex under dry-wet cycles was proposed,which exhibits high prediction accuracy.The research results provide useful guidance for the understanding of mechanical behavior and structural damage evolution of root-soil composite.
基金jointly funded by projects supported by the National Natural Science Foundation of China(Grant No.41872150)the Joint Funds of the National Natural Science Foundation of China(Grant No.U19B6003)Major Scientific and Technological Projects of CNPC during the 13th five-year plan(No.2019A-02-10)。
文摘Recent advances in hydrocarbon exploration have been made in the Member Deng-2 marginal microbial mound-bank complex reservoirs of the Dengying Formation in the western Sichuan Basin, SW China,where the depositional process is regarded confusing. The microfacies, construction types, and depositional model of the Member Deng-2 marginal microbial mound-bank complex have been investigated using unmanned aerial vehicle photography, outcrop section investigation, thin section identification,and seismic reflections in the southwestern Sichuan Basin. The microbialite lithologic textures in this region include thrombolite, dendrolite, stromatolite, fenestral stromatolite, spongiostromata stone,oncolite, aggregated grainstone, and botryoidal grapestone. Based on the comprehensive analysis of“depositional fabrics-lithology-microfacies”, an association between a fore mound, mound framework,and back mound subfacies has been proposed based on water depth, current direction, energy level and lithologic assemblages. The microfacies of the mound base, mound core, mound flank, mound cap, and mound flat could be recognized among the mound framework subfacies. Two construction types of marginal microbial mound-bank complex have been determined based on deposition location, mound scale, migration direction, and sedimentary facies association. Type Jinkouhe microbial mound constructions(TJMMCs) develop along the windward margin owing to their proximity to the seaward subfacies fore mound, with a northeastwardly migrated microbial mound on top of the mud mound,exhibiting the characteristics of large-sized mounds and small-sized banks in the surrounding area. Type E'bian microbial mound constructions(TEMMCs) primarily occur on the leeward margin, resulting from the presence of onshore back mound subfacies, with the smaller southwestward migrated microbial mounds existing on a thicker microbial flat. The platform margin microbial mound depositional model can be correlated with certain lateral comparison profile and seismic reflection structures in the 2D seismic section, which can provide references for future worldwide exploration. Microbial mounds with larger buildups and thicker vertical reservoirs are typically targeted on the windward margin, while small-sized microbial mounds and flats with better lateral connections are typically focused on the leeward margin.
基金Sponsored by the National Natural Science Foundation of China(Grant No.50775050)the State Key Laboratory of Solidification Processing in NWPU(Grant No.200702)
文摘The parameters that describe the complex degree of a certain casting are of some uncertainty. Therefore, a new method based on the fuzzy theory to classify the complex degree of castings has been presented in this paper. The feasibility of fuzzy theory in describing the complex degree of castings has been discussed and the procedure of this method has been specified by analyzing the complex degrees of some typical castings. The element factors that influence the casting complexity, have been summarized, which include the wall-thickness and the number of transition surface, etc. The results show that it is reasonable and practicable to classify the complex degree of castings with the fuzzy theory.
文摘In this paper, we use the cellular automation model to imitate earthquake process and draw some conclusionsof general applicability. First, it is confirmed that earthquake process has some ordering characters, and it isshown that both the existence and their mutual arrangement of faults could obviously influence the overallcharacters of earthquake process. Then the characters of each stage of model evolution are explained withself-organized critical state theory. Finally, earthquake sequences produced by the models are analysed interms pf algorithmic complexity and the result shows that AC-values of algorithmic complexity could be usedto study earthquake process and evolution.
文摘Analyzes the shortcomings of the classic capital market theories based on EMH and discloses the complexity essence of the capital market. Considering the capital market a complicated, interactive and adaptable dynamic system, with complexity science as the method for researching the operation law of the capital market, this paper constructs a nonlinear logical model to analyze the applied realm, focal point and interrelationship of such theories as dissipative structure theory, chaos theory, fractal theory, synergetics theory, catastrophe theory and scale theory, and summarizes and discusses the achievements and problems of each theory. Based on the research, the paper foretells the developing direction of eomplexity science in a capital market.
基金Supported by the High Technology Research and Development Programme of China (No. 2008AA01A328)the National Natural Science Foundation of China (No. 60772022)+2 种基金the Program for New Century Excellent Talents in University (No. NCET-05-0112)the Program for Changjiang Scholars and Innovative Research Team in University of MOE, China (No. IRT0609)111 Project (No. B07005)
文摘This paper researched the traffic of optical networks in time-space complexity,proposed a novel traf-fic model for complex optical networks based on traffic grooming,designed a traffic generator GTS(gener-ator based on time and space)with 'centralized+distributed' idea,and then made a simulation in Clanguage.Experiments results show that GTS can produce the virtual network topology which can changedynamically with the characteristic of scaling-free network.GTS can also groom the different traffic andtrigger them under real-time or scheduling mechanisms,generating different optical connections.Thistraffic model is convenient for the simulation of optical networks considering the traffic complexity.
文摘The UK’s economic growth has witnessed instability over these years. While some sectors recorded positive performances, some recorded negative performances, and these unstable economic performances led to technical recession for the third and fourth quarters of the year 2023. This study assessed the efficacy of the Generalised Additive Model for Location, Scale and Shape (GAMLSS) as a flexible distributional regression with smoothing additive terms in forecasting the UK economic growth in-sample and out-of-sample over the conventional Autoregressive Distributed Lag (ARDL) and Error Correction Model (ECM). The aim was to investigate the effectiveness and efficiency of GAMLSS models using a machine learning framework over the conventional time series econometric models by a rolling window. It is quantitative research which adopts a dataset obtained from the Office for National Statistics, covering 105 monthly observations of major economic indicators in the UK from January 2015 to September 2023. It consists of eleven variables, which include economic growth (Econ), consumer price index (CPI), inflation (Infl), manufacturing (Manuf), electricity and gas (ElGas), construction (Const), industries (Ind), wholesale and retail (WRet), real estate (REst), education (Edu) and health (Health). All computations and graphics in this study are obtained using R software version 4.4.1. The study revealed that GAMLSS models demonstrate superior outperformance in forecast accuracy over the ARDL and ECM models. Unlike other models used in the literature, the GAMLSS models were able to forecast both the future economic growth and the future distribution of the growth, thereby contributing to the empirical literature. The study identified manufacturing, electricity and gas, construction, industries, wholesale and retail, real estate, education, and health as key drivers of UK economic growth.
基金This collaborative work was supported in the frames of Russian Science Foundation project#18-15-00139\Optical technologies for early diagnostics of stomach cancer."Fluorescence measurements were made using spectrometric system purchased in the frames of Bulgarian NSF-MES project#DFNIB02/9/2014\Development of biophotonics methods as a basis of oncology theranostics."。
文摘The detection of early gastric cancer that often develops asymptomatically is crucial for improving patient survival.The photodynamic diagnosis(PDD)of gastric cancer using 5-aminolevulinic acid/protoporphyrin IX(5-ALA/PpIX)has been reported in several studies.However,the selectivity of PDD of gastric tumor is poor with often false-positive results that require the development of new methods to improve PDD for early gastric cancer.Therefore,a measure of the complexity of gastric microcirculation(multi-scale entropy,MSE)and the detrendedfluctuation analysis(DFA)were applied as additional tools to detect early gastric cancer in rats.In this experimental study,we used our original model of metastatic adenocarcinoma in the stomach of a rat.To induce a gastric tumor,we used a long-term combination(for 9 months,which is 1/2 of the life of rats)of two natural factors,such as chronic stress(overpopulation being typical for modern cities)and the daily presence of nitrites in food and drinks,which are common ingredients added to processed meat andfish to help preserve food.Our results clearly show that both methods,namely,PDD using 5-ALA/PpIX and complexity/correlation analysis,can detect early gastric cancer,which was confirmed by histological analysis.Pre-cancerous areas in the stomach were detected as an intermediatefluorescent signal or MSE level between normal and malignant lesions of the stomach.However,in some cases,PDD with 5-ALA/PpIX produced a false-positivefluorescence of exogenousfluorophores due to its accumulation in benign and inflammatory areas of the mucosa.This fact indicates that the PDD itself is not sufficient for the correct diagnosis of gastric cancer,and the use of additional characteristics,e.g.,complexity measures or scaling exponents,can significantly improve the diagnostic accuracy of PDD of gastric cancer that should be confirmed in further clinical studies and applications.
基金support from the National Natural Science Foundation of China(Grant No.T2293771)the STI 2030-Major Projects(Grant No.2022ZD0211400)the Sichuan Province Outstanding Young Scientists Foundation(Grant No.2023NSFSC1919)。
文摘Independent cascade(IC)models,by simulating how one node can activate another,are important tools for studying the dynamics of information spreading in complex networks.However,traditional algorithms for the IC model implementation face significant efficiency bottlenecks when dealing with large-scale networks and multi-round simulations.To settle this problem,this study introduces a GPU-based parallel independent cascade(GPIC)algorithm,featuring an optimized representation of the network data structure and parallel task scheduling strategies.Specifically,for this GPIC algorithm,we propose a network data structure tailored for GPU processing,thereby enhancing the computational efficiency and the scalability of the IC model.In addition,we design a parallel framework that utilizes the full potential of GPU's parallel processing capabilities,thereby augmenting the computational efficiency.The results from our simulation experiments demonstrate that GPIC not only preserves accuracy but also significantly boosts efficiency,achieving a speedup factor of 129 when compared to the baseline IC method.Our experiments also reveal that when using GPIC for the independent cascade simulation,100-200 simulation rounds are sufficient for higher-cost studies,while high precision studies benefit from 500 rounds to ensure reliable results,providing empirical guidance for applying this new algorithm to practical research.
基金The National Science Foundation funded this research under the Dy-namics of Coupled Natural and Human Systems program(Grants No.DEB-1212183 and BCS-1826839)support from San Diego State University and Auburn University.
文摘A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and environment.The science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such challenges.We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling CAS.Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.
基金sponsored by the U.S.Department of Housing and Urban Development(Grant No.NJLTS0027-22)The opinions expressed in this study are the authors alone,and do not represent the U.S.Depart-ment of HUD’s opinions.
文摘This paper addresses urban sustainability challenges amid global urbanization, emphasizing the need for innova tive approaches aligned with the Sustainable Development Goals. While traditional tools and linear models offer insights, they fall short in presenting a holistic view of complex urban challenges. System dynamics (SD) models that are often utilized to provide holistic, systematic understanding of a research subject, like the urban system, emerge as valuable tools, but data scarcity and theoretical inadequacy pose challenges. The research reviews relevant papers on recent SD model applications in urban sustainability since 2018, categorizing them based on nine key indicators. Among the reviewed papers, data limitations and model assumptions were identified as ma jor challenges in applying SD models to urban sustainability. This led to exploring the transformative potential of big data analytics, a rare approach in this field as identified by this study, to enhance SD models’ empirical foundation. Integrating big data could provide data-driven calibration, potentially improving predictive accuracy and reducing reliance on simplified assumptions. The paper concludes by advocating for new approaches that reduce assumptions and promote real-time applicable models, contributing to a comprehensive understanding of urban sustainability through the synergy of big data and SD models.
基金sponsored by National Science and Technology Major Project(2011ZX05046-001)
文摘Large-scale 3D physical models of complex structures can be used to simulate hydrocarbon exploration areas. The high-fidelity simulation of actual structures poses challenges to model building and quality control. Such models can be used to collect wideazimuth, multi-azimuth, and full-azimuth seismic data that can be used to verify various 3D processing and interpretation methods. Faced with nonideal imaging problems owing to the extensive complex surface conditions and subsurface structures in the oil-rich foreland basins of western China, we designed and built the KS physical model based on the complex subsurface structure. This is the largest and most complex 3D physical model built to date. The physical modeling technology advancements mainly involve 1） the model design method, 2） the model casting flow, and 3） data acquisition. A 3D velocity model of the physical model was obtained for the first time, and the model building precision was quantitatively analyzed. The absolute error was less than 3 mm, which satisfies the experimental requirements. The 3D velocity model obtained from 3D measurements of the model layers is the basis for testing various imaging methods. Furthermore, the model is considered a standard in seismic physical modeling technology.
基金The State Key Basic Research and Development Plan(G1998010100)the National Natural Science Foundation of China(29733090,29973047,39970177)
文摘Six model compounds have been synthesized and used for probing the structural features of the Mn cluster in oxygen_evolving complex (OEC) of photosystem Ⅱ (PSⅡ). The model compounds contain Mn 2(μ_O) 2 and μ_O_μ_carboxylato di_manganese structural units, which offer Mn—Mn, Mn……Mn, and Mn—O(N) structural parameters consistent with the corresponding data of the OEC in PSⅡ, implying that the Mn cluster in OEC may possess similar structural features. Two model compounds containing halide anion have been used for discussing the binding of Cl - to Mn in PSⅡ. It is suggested that in the five S states, ligand exchange would lead to the ligation of chloride to Mn in the S states with Mn of higher valence.
基金Partial funding for this project was produced a Grant-in-Aid for Scientifc Research (B) from Japan Society for the Promotion of Science (JSPS) to Tsunogae(Nos.20340148,22403017)a Grant-in-Aid for JSPS Fellows to Shimizu (No.23-311)
文摘A synthesis of the petrological characters of granulite facies rocks that contain equilibrium sapphirine ＋ quartz assemblage from two localities （Tonagh Island （TI） and Priestley Peak （PP）） in the Napier Complex,East Antarctica,provides unequivocal evidence for extreme crustal metamorphism possibly associated with the collisional orogeny during Neoarchean.The reaction microstructures associated with sapphirine ＋ quartz vary among the samples,probably suggesting different tectonic conditions during the metamorphic evolution.Sapphirine and quartz in TI sample were probably in equilibrium at the peak stage,but now separated by corona of Grt ＋ Sil ＋ Opx suggesting near isobaric cooling after the peak metamorphism,whereas the Spr ＋ Qtz ＋ Sil ＋ Crd ＋ Spl assemblage replaces garnet in PP sample suggesting post-peak decompression.The application of mineral equilibrium modeling in NCKFMASHTO system demonstrated that Spr ＋ Qtz stability is lowered down to 930 ℃ due to small Fe3＋ contents in the rocks （mole Fe2O3/（FeO ＋ Fe2O3） =0.02）.The TI sample yields a peak p-T range of 950-1100 ℃ and 7.5-11 kbar,followed by cooling toward a retrograde stage of 800-950 ℃ and 8-10 kbar,possibly along a counterclockwise p-T path.In contrast,the peak condition of the PP sample shows 1000-1050 ℃ and ＞12 kbar,which was followed by the formation ofSpr ＋ Qtz corona around garnet at 930-970 ℃ and 6.7-7.7 kbar,suggesting decompression possibly along a clockwise p-T trajectory.Such contrasting p-T paths are consistent with a recent model on the structural framework of the Napier Complex that correlates the two areas to different crustal blocks.The different p-T paths obtained from the two localities might reflect the difference in the tectonic framework of these rocks within a complex Neoarchean subduction/collision belt.
基金National Public Benefit Research Foundation of China (2008416048GYHY201006035)
文摘The results from a hybrid approach that combines a mesoscale meteorological model with a diagnostic model to produce high-resolution wind fields in complex coastal topography are evaluated.The diagnostic wind model(California Meteorological Model,CALMET) with 100-m horizontal spacing was driven with outputs from the Weather Research and Forecasting(WRF) model to obtain near-surface winds for the 1-year period from 12 September 2003 to 11 September 2004.Results were compared with wind observations at four sites.Traditional statistical scores,including correlation coefficients,standard deviations(SDs) and mean absolute errors(MAEs),indicate that the wind estimates from the WRF/CALMET modeling system are produced reasonably well.The correlation coefficients are relatively large,ranging from 0.5 to 0.7 for the zonal wind component and from 0.75 to 0.85 for the meridional wind component.MAEs for wind speed range from 1.5 to 2.0 m s-1 at 10 meters above ground level(AGL) and from 2.0 to 2.5 m s-1 at 60 m AGL.MAEs for wind direction range from 30 to 40 degrees at both levels.A spectral decomposition of the time series of wind speed shows positive impacts of CALMET in improving the mesoscale winds.Moreover,combining the CALMET model with WRF significantly improves the spatial variability of the simulated wind fields.It can be concluded that the WRF/CALMET modeling system is capable of providing a detailed near-surface wind field,but the physics in the diagnostic CALMET model needs to be further improved.
文摘Fault diagnosis is an important measure to ensure the safety of production, and all kinds of fault diagnosis methods are of importance in actual production process. However, the complexity and uncertainty of production process often lead to the changes of data distribution and the emergence of new fault classes, and the number of the new fault classes is unpredictable. The reconstruction of the fault diagnosis model and the identification of new fault classes have become core issues under the circumstances. This paper presents a fault diagnosis method based on model transfer learning and the main contributions of the paper are as follows: 1) An incremental model transfer fault diagnosis method is proposed to reconstruct the new process diagnosis model. 2) Breaking the limit of existing method that the new process can only have one more class of faults than the old process, this method can identify M faults more in the new process with the thought of incremental learning. 3) The method offers a solution to a series of problems caused by the increase of fault classes. Experiments based on Tennessee-Eastman process and ore grinding classification process demonstrate the effectiveness and the feasibility of the method.
