Model Construction Process Research Articles

A Study on the Construction of Visitors’ Cultural Memories in a Global Context

Abstract: This study investigates empirically the way in which visitors construct their (cross) cultural memories during the process of cultural representation, for measuring the success of Chinese culture "going global". We propose a framework that incorporates the reception theory and communicative theory. And we have conducted a survey of questionnaire and interview of the real visitors in Chinese museums to collect their opinions on cultural presentation and other related material, such as expectation and aesthetic gap. By analyzing the process of Chinese and international visitors’ interaction with the text symbols of Chinese museums, we find that (cross) there are some similarities and differences in cultural memories, a collection of visitors’ values, beliefs and mindsets, which are shaped or influenced in their interactions with the text symbols. And the (cross) cultural memories are constructed in visitors’ continuous interaction with the text symbols transmitted by Chinese cultural institutions, and different constructions may redefine or shake the thoughts and value systems of visitors to some extent, which is an ongoing and open process. The description of the model of construction process will contribute to the effective dissemination of culture for institutions.

Developing a CDY Model for Grapes and Experimentally Validating it with an Android App that Focuses on Agro-climatic and Disease Prevention Aspects

Introduction: Crop development and yield are both influenced by the weather. This study has developed and analytically resolved a general agro-climatic model for grapes. Methods: In the field of mathematical biology, researchers, professors, and academics will find this model useful. To create the final version of the model for yield prediction, the CDY model and asymptotic analyses have been performed. Climate, disease, and grape production have been taken into consideration as dependent characteristics during the model construction process. The frequency of infection, the occurrence of disease, seasonality, and the elimination of grape output throughout each harvest cycle have been viewed as distinct qualities. Moreover, the model has been examined, and field-level data have been used to estimate the parameters collected between 2016-2021 from the nearby villages of GRS and Theni. Results: A description of this model’s stability analysis has also been provided. An association has been determined between the numerical validity and stability of the given analytical solution analyses. In addition, the developed Android mobile app for grapes has been validated using the proposed model under the climatic scenario. Conclusion: It is advised to apply the created model to estimate grape yield based on the findings obtained. A useful technique for forecasting crop yield has thus been proposed in this study.

Stress field analysis: Construction of site descriptive model of underground research site in KAERI

This study aims to integrate geological investigation data and construct a rock mechanical site descriptive model relative to site selection research for deep geological disposal of spent nuclear fuel. As preliminary research for the model construction process, a sub-divided numerical domain was introduced by adopting existing fracture zone in the site region of an underground research facility in KAERI. The spatial properties of the domain were decided based on the rock classification information of core specimens obtained from several boreholes. Stress field estimation modeling was performed using the constructed domain and the results were compared with the in-situ state of the field test. When the depth-dependent stress ratio was assigned to the model condition, it was confirmed that an appropriate stress state was ensured with the similarity of the in-situ stress along the depths. Additionally, uncertainties of the input properties in the stress estimation model were also assessed. The effect of uncertainties could be considered through a sensitivity analysis of rock mass and fracture zone properties to identify stress dependence on mechanical properties. Significant stress distribution was not indicated with a variation in the rock mass properties. However, weak fracture zone modeling induced a slightly disturbed stress in a shallow depth range. These results indicated that the uncertainty of the rock mechanical site descriptive model could be increased due to the fracture zone properties. This study is a case of prior research in Korea on the site evaluation model that is already applied in leading foreign countries related to site selection research for nuclear fuel disposal. The applicability based on the achievement of this study can be improved through consideration of derived equivalent properties of fractured rock. And more reliable results can be succeeded by assigning properly defined boundary conditions for the simulation of various geological scenarios.

Genetic programming-based predictive model for the Cr removal effect of in-situ electrokinetic remediation in contaminated soil

Soil electrokinetic remediation is an emerging and efficient in-situ remediation technology for reducing environmental risks. Promoting the dissolution and migration of Cr in soil under the electric field is crucial to decrease soil toxicity and ecological influences. However, it is difficult to establish strong relationships between soil treatment and impact factors and to quantify their contributions. Machine learning can help establish pollutant migration models, but it is challenging to derive predictive formulas to improve remediation efficiency, describe the predictive model construction process, and reflect the importance of the predictors for better regulation. Therefore, this paper established a predictive model for the electrokinetic remediation of Cr-contaminated soil using genetic programming (GP) after determining the characteristic parameters which influenced the remediation effect, described the model's adaptive optimization process through the algorithm's function, and identified the sensitivity factors affecting the Cr removal effect. Results showed that the Cr(VI) and total Cr concentrations predicted by GP were in satisfactory agreement with the experimental values, 92% of the training data and 90% of the validation data achieved errors within 1%, and could fully reflect the target ions’ content variation in different soil layers. By substituting the above prediction formulas into Sobol sensitivity analysis, it was determined that conductivity, pH, current, and moisture content dramatically affected the Cr content variation in distinguished regions. For overall contaminated area, the system current and soil pH were the most sensitive factors for Cr(VI) and total Cr contents. Remediation efforts throughout the contaminated area should focus on the role of current versus soil pH. GP and sensitivity analysis can provide decision support and operational guidance for in-situ soil electrokinetic treatment by establishing a remediation effect prediction model, expediting the development and innovation of electrokinetic technology.

Constructing shadowed set based on game analysis of uncertainty and decision cost

As a three-way approximation scheme to deal with fuzzy set, shadowed set approximately divides fuzzy objects with uncertainty into elevated, reduced, and shadow areas by a pair of decision thresholds α,β, thus effectively reducing the uncertainty of information. However, the current construction of shadowed set is all based on a single principle, lacking the comprehensive consideration of multiple principles, and these construction principles focus on the rationality of semantic interpretation of model construction process and threshold determination, and lack of consideration of the validity of partition results. To resolve these issues, this paper proposes a new shadowed set model through the game analysis between uncertainty and decision cost (UC-GTSS). First, Combining shadowed set construction with game theory, UC-GTSS is proposed based on game competition mechanism. Through the game analysis of uncertainty and decision cost, the optimal α,β is found, and a game payoff fusion algorithm based on Topsis is proposed. Second, the definition of UC-GTSS model expression, game player, strategy, payoff function, equilibrium analysis, optimization α,β determination and the influence of model parameters are analyzed and discussed. Third, the UC-GTSS model is extended and discussed based on the validity of the partition results, such as the game between approximate classification accuracy and coverage, the game between the Gini coefficient of the partition areas. Finally, through the analysis of algorithms, instances and data experiments, the construction process, validity, and rationality of UC-GTSS and its extended model are demonstrated.

Automatic Screen‐out of Ir(III) Complex Emitters by Combined Machine Learning and Computational Analysis

AbstractThe organic light‐emitting diode (OLED) has gained widespread commercial use, yet there is a continuous need to identify innovative emitters that offer higher efficiency and a broader color gamut. To effectively screen out promising OLED molecules that are yet to be synthesized, representation learning aided high throughput virtual screening (HTVS) over millions of Ir(III) complexes, which are prototypical types of phosphorescent OLED material constructed via a random combination of 278 reported ligands. This study successfully screens out a decent amount of promising candidates for both display and lighting purposes, which are worth further experimental investigation. The high efficiency and accuracy of this model are largely attributed to the pioneering attempt of using representation learning to organic luminescent molecules, which is initiated by a pre‐training procedure with over 1.6 million 3D molecular structures and frontier orbital energies predicted via semi‐empirical methods, followed by a fine‐tuning scheme via the quantum mechanical computed properties over around 1500 candidates. Such workflow enables an effective model construction process that is otherwise hindered by the scarcity of labeled data and can be straightforwardly extended to the discovery of other novel materials.

Does information sharing affect the regulation of street vending: An analysis based on the dynamic tripartite evolutionary game in social networks

Street vending enormously contributes to the economies in the global south, but its high probability of illegal operation, also has generated many spin-off problems. Moreover, both the government's attitude to street vending regulation and the willingness of public supervision oscillates. How these stakeholders interact in the street vending remain controversial and is worth further researching. We verify the conditions required in the model construction process through text and correlation analysis, and use them to construct a dynamic evolutionary model in social network. The strategic interactions between three groups: governors, street vendors and citizens are studied, where governors can adjust their regulation strength, vendors select their operating strategies, citizens choose the supervision strategies, and they can move and learn freely in networks. Research results show that it is difficult to achieve the optimal equilibrium of strict regulation by governors, high-quality production by street vendors, and active supervision by citizens in situations of complete information. This ultimately hinders the high-quality development of street vending. In situations with partial information, the behavior of all agents in regulation is more proactive, reflecting the promoting effect of asymmetric information in the group on the regulation of street vending. This work provides decision-making support for the municipal government.

Process Discovery Techniques Recommendation Framework

In a competitive environment, organizations need to continuously understand, analyze and improve the behavior of processes to maintain their position in the market. Process mining is a set of techniques that allows organizations to have an X-ray view of their processes by extracting process related knowledge from the information recorded in today’s process aware information systems such as ‘Enterprise Resource Planning’ systems, ‘Business Process Management’ systems, ‘Supply Chain Management’ systems, etc. One of the major categories of process mining techniques is the process of discovery. This later allows for automatically constructing process models just from the information stored in the system representing the real behavior of the process discovered. Many process discovery algorithms have been proposed today which made users and businesses, in front of many techniques, unable to choose or decide the appropriate mining algorithm for their business processes. Moreover, existing evaluation and recommendation frameworks have several important drawbacks. This paper proposes a new framework for recommending the most suitable process discovery technique to a given process taking into consideration the limitations of existing frameworks.

Prediction and Conservation Recommendations for the Effects of Diversified Drought on Plant Community Species

Based on the analysis of plant community data and climate data, this paper established a plant community competition prediction model that includes weather and environmental factors by utilizing species competition theory and the Lotka-Volterra model. During the model construction process, the actual data was pre-processed and summarized for analysis, which ultimately determined the parameters of species quantity, growth rate, and precipitation, improving the accuracy and reliability of the model. Meanwhile, using the Lotka-Volterra model, a relationship model between biomass and time was established, and through the simulated annealing algorithm, two optimal species quantity changes were obtained. Finally, this paper optimized the multi-factor plant community Lotka-Volterra model through the genetic algorithm and obtained the optimal solution: the plant community can achieve the most beneficial competitive state with four species. This further proves the rationality and robustness of the model and also provides new methods and ideas for the management and protection of ecosystems. In summary, the results of this paper are of great significance for the stability and sustainable development of ecosystems and provide useful references for solving the problem of plant community competition and ecosystem stability.

Analysis of crystal structure transition of polycrystalline 3C-SiC in nanocrystalline grinding based on molecular dynamics simulation

In order to study the grain/grain boundary crossing mechanism of polycrystalline 3C-SiC materials under nano-grinding, molecular dynamics(MD) method was used to simulate the grinding process of polycrystalline 3C-SiC materials under diamond abrasive particles. Through the coupling method of Voronoi construction of polycrystalline 3C-SiC workpiece model and MD construction process, the nano-grinding process of polycrystalline 3C-SiC material was established, the potential function field parameters were optimized, and the residual stress in polycrystalline 3C-SiC was eliminated. The energy transition equation is modified to reduce the time for the system to enter dynamic equilibrium. The crystal structure and dislocation generation of polycrystalline 3C-SiC were analyzed by post-processing methods such as defect analysis and crystal structure recognition. The results found that: there are cubic lattice, simple cubic lattice, hexagonal lattice and graphene mechanism type atoms at the grain boundary. During the machining process, the cubic lattice type atoms are converted to other types atoms, and other types structure atoms are converted to each other. The internal structure atoms of grains are converted to the grain boundary structure atoms during the machining process, and the crystal atoms near the grain boundary have low structural stability. The grinding force mainly acts on the forward direction and just below the grinding grain, and also has a small amount of distribution at the grain boundary, but it is relatively weak. The dislocation caused by grinding mainly occurs at grain boundaries. The resulting debris was monolithic, and a few were granular. The interconversion between grains and different structural atoms at the grain boundary is mainly from the crystal lattice atoms in grains to other atoms at the grain boundary. The force is transferred to the grain through the grain boundary, which hinders the transboundary transfer of force and protects the grains. The shearing force plays a dominant role in the grinding process.

Prediction and Analysis of Chlorophyll-a Concentration in the Western Waters of Hong Kong Based on BP Neural Network

The Chlorophyll-a (Chl-a) concentration is an important indicator of water environmental conditions; thus, the simultaneous monitoring of large-area water bodies can be realized through the remote sensing-based retrieval of Chl-a concentrations. Together with hyperspectral remote sensing data, a BP neural network model was used to invert chlorophyll-a concentration, with remote sensing reflectance as the input factor. Given the presence of many bands in the hyperspectral data, selecting an appropriate band reflectance as the input factor is crucial to improving inversion accuracy. In this study, a Pearson correlation analysis method was proposed to select bands. A normality test was performed on the reflectance of each band of the Zhuhai-1 hyperspectral remote sensing data, and the significance index was p < 0.05. The absolute kurtosis value was less than 10, and the absolute skewness value was less than 3, indicating that the Pearson method was applicable. Pearson correlation analysis was utilised to calculate the correlation coefficient between the reflectance data and chlorophyll-a concentration. Five reflectance data with high correlation were selected as the input factors, and chlorophyll-a concentration was adopted as the output factor. An error backpropagation network model was constructed to predict chlorophyll-a concentration, and a Garson function was added to clarify the connection weights of the input factors in the model construction process. Model 12 was determined as the optimal model on the basis of the criteria of the coefficient of determination, the average relative variance, and the minimum mean square error. The chlorophyll-a concentration was predicted for July and November 2020 in the study area, and the results showed that the predicted values had a small error compared with the measured values. The root-mean-square error and mean relative error of the chlorophyll-a concentration predicted and measured values were 2.12 μg/L and 9.66%, respectively. Significant spatial differences in the Chl-a concentration were observed in the study area due to the influence of islands and land; the Chl-a concentration in July was generally higher than that in November. The results of these studies provide a reference for monitoring the water environment in the study area.

Revolutionizing steel building project cost overrun risk assessment by Bayesian network

PurposeIn recent years, cost overrun becomes a common problem in steel building construction projects. The average percentage can vary widely depending on the project type, size, complexity and location. The steel structure change ratio in Taiwan is from 1 to 18% in statistics. The contractors always put every possible effort into preventing or mitigating project cost overruns, and one of the approaches is an accurate cost overrun risk estimate. Traditional project cost overrun risk assessment models mainly focus on macro-level evaluation and may not function well for the project-specific level (micro-level). This study creates a network-like connection model between the outcome (i.e. cost overrun risk) and the associated root causes in which the project status evaluation checklists of design, manufacturing, construction and interfaces are used to evaluate the checklists' influences through the Bayesian network (BN) composed by intermediate causes.Design/methodology/approachDue to the constraint of data availability, BN nodes, relationships and conditional probabilities are defined to establish a BN-based steel building project cost overrun assessment model following the knowledge of experts. Because of the complexity of the BN, the construction of the BN structure is first to build BN's fault tree (FT) hierarchy. And then, basic BN framework is constructed by the transformation of the FT hierarchy. Furthermore, some worthwhile additional arcs among BN nodes are inserted if necessary. Furthermore, conditional probability tables (CPTs) among BN nodes are explored by experts following the concept of the ranked node. Finally, the BN-based model was validated against the final cost analysis reports of 15 steel building projects done in Taiwan and both were highly consistent. The overall BN-based model construction process consists of three steps: (1) FT construction and BN framework transformation, (2) CPT computation and (3) model validation.FindingsThis study established a network-like bridge model between the outcome (i.e. cost overrun risk) and the root causes in a network of which cost influences are evaluated through the project-specific status evaluation checklists of design, manufacturing, construction and interfaces. This study overcame several limitations of the previous cost overrun risk assessment models: (1) few past research support assessment of cost overrun based on real-time project-owned data and (2) the traditional causal models inadequately depict interdependencies among influence factors of cost overrun at the network. The main influence factors of the cost overrun risk at the steel building projects in Taiwan were also examined using sensitivity analysis. The main root causes of cost overrun in steel building projects are design management and interface integration.Originality/valueThe proposed model belongs to the project-specific causal assessment model using real-time project-owned status checklist data as input. Such a model was seldom surveyed in the past due to the complicated interdependence among causes in the network. For practical use, a convenient and simple regression equation was also developed to forecast the cost overrun risk of the steel building project based on the root causes as input. Based on the analysis of cost overrun risk and significant influence factors, proper tailor-made preventive strategies are established to reduce the occurrence of cost overrun at the project.

Application of Internet big data analysis technology based on deep learning in tourism marketing performance evaluation

Marketing through the network is the basic marketing method in the information age. It has certain marketing value, and its position has gradually become stable. It has become the mainstream marketing method, especially praised by the tourism industry, which has injected fresh blood into this field. As the development of tourism market is dynamic, businesses will consider the needs of tourists when paying attention to its development process, so as to design performance evaluation criteria for tourism marketing. Under this research background, by integrating big data analysis technology and deep learning technology, this paper has successfully developed an Internet marketing performance evaluation system for the tourism market. This system can effectively evaluate the actual development of the tourism industry, and the system design comprehensively considers the 360° performance appraisal and balanced integral models. Based on the actual tourism data of a certain place, this study summarizes some additional evaluation indicators, thus improving the evaluation model and laying the foundation for the introduction of data and model construction process of AHP. Applying the principle of maximum membership, the experimental results demonstrate that a scenic spot with poor performance evaluation results has the highest membership. This highlights the practical application value of the Internet marketing performance evaluation system for the tourism market, which effectively analyzes market performance. This paper improves the performance evaluation system by introducing in-depth learning and big data analysis technology into the tourism market performance analysis.

JSUM: A Multitask Learning Speech Recognition Model for Jointly Supervised and Unsupervised Learning

In recent years, the end-to-end speech recognition model has emerged as a popular alternative to the traditional Deep Neural Network—Hidden Markov Model (DNN-HMM). This approach maps acoustic features directly onto text sequences via a single network architecture, significantly streamlining the model construction process. However, the training of end-to-end speech recognition models typically necessitates a significant quantity of supervised data to achieve good performance, which poses a challenge in low-resource conditions. The use of unsupervised representation significantly reduces this necessity. Recent research has focused on end-to-end techniques employing joint Connectionist Temporal Classification (CTC) and attention mechanisms, with some also concentrating on unsupervised presentation learning. This paper proposes a joint supervised and unsupervised multi-task learning model (JSUM). Our approach leverages the unsupervised pre-trained wav2vec 2.0 model as a shared encoder that integrates the joint CTC-Attention network and the generative adversarial network into a unified end-to-end architecture. Our method provides a new low-resource language speech recognition solution that optimally utilizes supervised and unsupervised datasets by combining CTC, attention, and generative adversarial losses. Furthermore, our proposed approach is suitable for both monolingual and cross-lingual scenarios.

Procedural Modeling Method of Pipe Network Based on Spline Grammar

The shape grammar is usually used to describe the three-dimensional model with certain regularity, but in the process of constructing the three-dimensional model of industrial pipeline network or urban underground pipeline network, due to the complex wiring characteristics of the pipeline, the pipeline with different changing trajectories is often unable to be described by fixed shape. Therefore, the construction process for 3D pipe networks often requires art developers to spend a lot of time and effort to develop models. Arbitrarily shaped pipes can be effectively described in procedural modeling construction processes by incorporating splines that are inherently variable in nature and combining them as terminator shapes into shape grammar. Based on this theory, this paper proposes that the procedural modeling method based on spline grammar can efficiently complete the procedural construction of pipeline networks, and this method can quickly complete the splicing of pipeline networks. Experimental results show that our method is suitable for pipe network construction projects in different fields.

The use of INTEGRO GIS in the integrated geophysical study
 of the deep structure of oil and gas prospective regions (part 2)

This article continues a description of the methodological and technological aspects of the constructing physical-geological models of oil and gas promising regions with a help of the INTEGRO GIS tools. The methods are described on the materials of studying the deep structure of the Yenisei-Khatanga regional trough (YKRT).
 The first part of the article covered the stages of analysis of the spatial distributions of potential fields and integrated regional profile geophysical data. The present, second, part of the paper describes the process of the YKRT three-dimensional density model construction, which is based on the results of the previous analysis.

A machine learning model to predict efficacy of neoadjuvant therapy in breast cancer based on dynamic changes in systemic immunity

Neoadjuvant therapy (NAT) has been widely implemented as an essential treatment to improve therapeutic efficacy in patients with locally-advanced cancer to reduce tumor burden and prolong survival, particularly for human epidermal growth receptor 2-positive and triple-negative breast cancer. The role of peripheral immune components in predicting therapeutic responses has received limited attention. Herein we determined the relationship between dynamic changes in peripheral immune indices and therapeutic responses during NAT administration. Peripheral immune index data were collected from 134 patients before and after NAT. Logistic regression and machine learning algorithms were applied to the feature selection and model construction processes, respectively. Peripheral immune status with a greater number of CD3+ T cells before and after NAT, and a greater number of CD8+ T cells, fewer CD4+ T cells, and fewer NK cells after NAT was significantly related to a pathological complete response (P < 0.05). The post-NAT NK cell-to-pre-NAT NK cell ratio was negatively correlated with the response to NAT (HR = 0.13, P = 0.008). Based on the results of logistic regression, 14 reliable features (P < 0.05) were selected to construct the machine learning model. The random forest model exhibited the best power to predict efficacy of NAT among 10 machine learning model approaches (AUC = 0.733). Statistically significant relationships between several specific immune indices and the efficacy of NAT were revealed. A random forest model based on dynamic changes in peripheral immune indices showed robust performance in predicting NAT efficacy.

Point pattern analysis and classification on compact two-point homogeneous spaces evolving time.

This paper introduces a new modeling framework for the statistical analysis of point patterns on a manifold defined by a connected and compact two-point homogeneous space, including the special case of the sphere. The presented approach is based on temporal Cox processes driven by a -valued log-intensity. Different aggregation schemes on the manifold of the spatiotemporal point-referenced data are implemented in terms of the time-varying discrete Jacobi polynomial transform of the log-risk process. The n-dimensional microscale point pattern evolution in time at different manifold spatial scales is then characterized from such a transform. The simulation study undertaken illustrates the construction of spherical point process models displaying aggregation at low Legendre polynomial transform frequencies (large scale), while regularity is observed at high frequencies (small scale). K-function analysis supports these results under temporal short, intermediate and long range dependence of the log-risk process.

Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm

Production forecasting using numerical simulation has become a standard in the oil and gas industry. The model construction process requires an explicit definition of multiple uncertain parameters; thus, the outcome of the modelling is also uncertain. For the reservoirs with production data, the uncertainty can be reduced by history-matching. However, the manual matching procedure is time-consuming and usually generates one deterministic realization. Due to the ill-posed nature of the calibration process, the uncertainty cannot be captured sufficiently with only one simulation model. In this paper, the uncertainty quantification process carried out for a gas-condensate reservoir is described. The ensemble-based uncertainty approach was used with the ES-MDA algorithm, conditioning the models to the observed data. Along with the results, the author described the solutions proposed to improve the algorithm’s efficiency and to analyze the factors controlling modelling uncertainty. As a part of the calibration process, various geological hypotheses regarding the presence of an active aquifer were verified, leading to important observations about the drive mechanism of the analyzed reservoir.

Image life trails based on contrast reduction models for face counter-spoofing

Natural face images are both content and context-rich, in the sense that they carry significant immersive information via depth cues embedded in the form of self-shadows or a space varying blur. Images of planar face prints, on the other hand, tend to have lower contrast and also suppressed depth cues. In this work, a solution is proposed, to detect planar print spoofing by enhancing self-shadow patterns present in face images. This process is facilitated and siphoned via the application of a non-linear iterative functional map, which is used to produce a contrast reductionist image sequence, termed as an image life trail. Subsequent images in this trail tend to have lower contrast in relation to the previous iteration. Differences taken across this image sequence help in bringing out the self-shadows already present in the original image. The proposed solution has two fronts: (i) a calibration and customization heavy 2-class client specific model construction process, based on self-shadow statistics, in which the model has to be trained with respect to samples from the new environment, and (ii) a subject independent and virtually environment independent model building procedure using random scans and Fourier descriptors, which can be cross-ported and applied to new environments without prior training. For the first case, where calibration and customization is required, overall mean error rate for the calibration-set (reduced CASIA dataset) was found to be 0.3106%, and the error rates for other datasets such OULU-NPU and CASIA-SURF were 1.1928% and 2.2462% respectively. For the second case, which involved building a 1-class and 2-class model using CASIA alone and testing completely on OULU, the error rates were 5.86% and 2.34% respectively, comparable to the customized solution for OULU-NPU.