Application of STEEP and Interpretive Structural Modeling in the Design Imagery of Taiwan Public Ceramic Relief Murals

Ceramic relief mural is a contemporary landscape art that is carefully designed based on human nature,culture,and architectural wall space,combined with social customs,visual sensibility,and art.It may also become the main axis of ceramic art in the future.Taiwan public ceramic relief murals(PCRM)are most distinctive with the PCRM pioneered by Pan-Hsiung Chu of Meinong Kiln in 1987.In addition to breaking through the limitations of traditional public ceramic murals,Chu leveraged local culture and sensibility.The theme of art gives PCRM its unique style and innovative value throughout the Taiwan region.This study mainly analyzes and understands the design image of public ceramic murals,taking Taiwan PCRM’s design and creation as the scope,and applies STEEP analysis,that is,the social,technological,economic,ecological,and political-legal environments are analyzed as core factors;eight main important factors in the artistic design image of ceramic murals are evaluated.Then,interpretive structural modeling(ISM)is used to establish five levels,analyze the four main problems in the main core factor area and the four main target results in the affected factor area;and analyze the problem points and target points as well as their causal relationships.It is expected to sort out the relationship between these factors,obtain the hierarchical relationship of each factor,and provide a reference basis and research methods.

An Auto-Grading Oriented Approach for Off-Line Handwritten Organic Cyclic Compound Structure Formulas Recognition

Auto-grading,as an instruction tool,could reduce teachers’workload,provide students with instant feedback and support highly personalized learning.Therefore,this topic attracts considerable attentions from researchers recently.To realize the automatic grading of handwritten chemistry assignments,the problem of chemical notations recognition should be solved first.The recent handwritten chemical notations recognition solutions belonging to the end-to-end trainable category suffered fromthe problem of lacking the accurate alignment information between the input and output.They serve the aim of reading notations into electrical devices to better prepare relevant edocuments instead of auto-grading handwritten assignments.To tackle this limitation to enable the auto-grading of handwritten chemistry assignments at a fine-grained level.In this work,we propose a component-detectionbased approach for recognizing off-line handwritten Organic Cyclic Compound Structure Formulas(OCCSFs).Specifically,we define different components of OCCSFs as objects(including graphical objects and text objects),and adopt the deep learning detector to detect them.Then,regarding the detected text objects,we introduce an improved attention-based encoder-decoder model for text recognition.Finally,with these detection results and the geometric relationships of detected objects,this article designs a holistic algorithm for interpreting the spatial structure of handwritten OCCSFs.The proposedmethod is evaluated on a self-collected data set consisting of 3000 samples and achieves promising results.

Assessment of electrostatic discharge sensitivity of nitrogen-rich heterocyclic energetic compounds and their salts as high energy-density dangerous compounds:A study of structural variables

Nitrogen-rich heterocyclic energetic compounds(NRHECs)and their salts have witnessed widespread synthesis in recent years.The substantial energy-density content within these compounds can lead to potentially dangerous explosive reactions when subjected to external stimuli such as electrical discharge.Therefore,developing a reliable model for predicting their electrostatic discharge sensitivity(ESD)becomes imperative.This study proposes a novel and straightforward model based on the presence of specific groups(-NH_(2) or-NH-,-N=N^(+)-O^(-)and-NNO_(2),-ONO_(2) or-NO_(2))under certain conditions to assess the ESD of NRHECs and their salts,employing interpretable structural parameters.Utilizing a comprehensive dataset comprising 54 ESD measurements of NRHECs and their salts,divided into 49/5 training/test sets,the model achieves promising results.The Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Maximum Error for the training set are reported as 0.16 J,0.12 J,and 0.5 J,respectively.Notably,the ratios RMSE(training)/RMSE(test),MAE(training)/MAE(test),and Max Error(training)/Max Error(test)are all greater than 1.0,indicating the robust predictive capabilities of the model.The presented model demonstrates its efficacy in providing a reliable assessment of ESD for the targeted NRHECs and their salts,without the need for intricate computer codes or expert involvement.

An Evaluation of the Critical Success Factors in Sustainable Food Supply Chains in Developing Countries

Food is one of the biggest industries in developed and underdeveloped countries. Supply chain sustainability is essential in established and emerging economies because of the rising acceptance of cost-based outsourcing and the growing technological, social, and environmental concerns. The food business faces serious sustainability and growth challenges in developing countries. A comprehensive analysis of the critical success factors (CSFs) influencing the performance outcome and the sustainable supply chain management (SSCM) process. A theoretical framework is established to explain how they are used to examine the organizational aspect of the food supply chain life cycle analysis. This study examined the CSFs and revealed the relationships between them using a methodology that included a review of literature, interpretative structural modeling (ISM), and cross-impact matrix multiplication applied in classification (MICMAC) tool analysis of soil liquefaction factors. The findings of this research demonstrate that the quality and safety of food are important factors and have a direct effect on other factors. To make sustainable food supply chain management more adequate, legislators, managers, and experts need to pay attention to this factor. In this work. It also shows that companies aiming to create a sustainable business model must make sustainability a fundamental tenet of their organization. Practitioners and managers may devise effective long-term plans for establishing a sustainable food supply chain utilizing the recommended methodology.

Correction of seismic attribute-based smallstructure prediction errors using GPR data-a case study of the Shuguang Coal Mine,Shanxi

Small structures in coal mine working face is one of the main hidden dangers of safe and effi cient production in coal mine.Currently,seismic exploration is often used as the main method for detecting such structures.However,limited by the accuracy of seismic data processing and interpretation,the interpreted location of small structures is often deviated.Ground-penetrating radar(GPR)can detect small structures accurately,but the exploration depth is shallow.The combination of the two methods can improve the exploration accuracy of small structures in coal mine.Aiming at the 1226#working face of Shuguang coal mine,we propose a method of seismic-attributes based small-structure prediction error correction using GPR data.First,we extract the coherence,curvature,and dip attributes from seismic data,that are sensitive to small structures,then by considering factors such as the eff ective detection range of GPR and detection environment,we select two structures from the prediction results of seismic attributes for GPR detection.Finally,based on the relationship between the positions of small structures predicted by the two methods,we use statistical methods to determine the overall off set distance and azimuth of the small structures in the entire study area and use the results as a standard for correcting each structure position.The results show that the GPR data can be used to correct the horizontal position errors of small structures predicted by seismic attribute analysis.The accuracy of the prediction results is greatly improved,with the error controlled within 5 m and reduced by more than 80%.Therefore,the feasibility of the method proposed in this study is verified.

Regional metallogenic structure based on aeromagnetic data in northern Chile

Chile is a very important country that forms part of the Andean metallogenic belts. The Atacama and Domeyko fault systems in northern Chile control the tectonic- magmatic activities that migrate eastward and the types of mineral resources. In this paper, we processed and interpreted aeromagnetic data from northern Chile using reduction to pole, upward field continuation, the second derivative calculation in the vertical direction, inclination angle calculation, and analytical signal amplitude analysis. We revealed the locations and planar distribution characteristics of the regional deep faults along the NNE and NS directions. Furthermore, we observed that the major reasons for the formation of the tectonic-magmatic rocks belts were the nearly parallel deep faults distributed from west to east and multiple magmatic activities along these faults. We ascertained the locations of volcanic mechanisms and the relationships between them using these regional deep faults. We deduced the spatial distributions of the basic-intermediate, basic, and acidic igneous rocks, intrusive rocks, and sedimentary sequences. We showed the linear positive magnetic anomalies and magnetic anomaly gradient zones by slowly varying the background, negative magnetic anomaly field, which indicated the presence of strong magmatic activities in these regional deep faults; it also revealed the favorable areas of copper and polymetallic mineralization. This study provides some basic information for further research on the geology, structural characteristics, and mineral resource prospecting in northern Chile.

Systematic rationalization approach for multivariate correlated alarms based on interpretive structural modeling and Likert scale

Alarm flood is one of the main problems in the alarm systems of industrial process. Alarm root-cause analysis and alarm prioritization are good for alarm flood reduction. This paper proposes a systematic rationalization method for multivariate correlated alarms to realize the root cause analysis and alarm prioritization. An information fusion based interpretive structural model is constructed according to the data-driven partial correlation coefficient calculation and process knowledge modification. This hierarchical multi-layer model is helpful in abnormality propagation path identification and root-cause analysis. Revised Likert scale method is adopted to determine the alarm priority and reduce the blindness of alarm handling. As a case study, the Tennessee Eastman process is utilized to show the effectiveness and validity of proposed approach. Alarm system performance comparison shows that our rationalization methodology can reduce the alarm flood to some extent and improve the performance.

Analysis on the Structure of Influencing Factors of Sustainable Supply Chain Implementation of Water Diversion Project

The systematic analysis of the hierarchical relationship among the factors affecting the sustainable supply chain implementation of water diversion projects has theoretical value and practical significance for the sustainable development of large-scale water diversion projects. Through the investigation of relevant literature, books, web pages, materials, and discussions with relevant experts and scholars, a total of 23 factors influencing the sustainable supply chain implementation of water diversion projects were identified. Then using ISM (Interpretative Structural Modeling Method) to analyze the causality of each factor, a multi-level hierarchical structure model was obtained. The results showed that: 1) The surface-level influencing factors of the sustainable supply chain implementation of the water diversion project mainly included 8 factors such as water-saving awareness and water-saving intensity in the diversion area, water quality, water pollution and other disasters, effective incentive mechanisms, etc., and surface-level influencing factors were directly related to the sustainable supply chain implementation of water diversion projects. 2) The indirect influencing factors of the sustainable supply chain of water diversion projects included 12 factors such as the water quality and quantity guarantee rate of the supply chain, the government’s enforcement of laws and regulations, water distribution, ecological compensation, and compensation mechanisms for residents in the water source area. Indirect influencing factor scan acts directly on the direct influencing factors, and intervening in the factors that can be controlled by humans is one of the important ways to improve the sustainable operation of water diversion projects. 3) The fundamental influencing factors for the sustainable supply chain implementation of water diversion projects included three factors: Resettlement policy, government financial support, and sound laws and regulations. Deep influencing factors had multi-channel influence and controllability, and intervening in them was the main means to improve the sustainable operation of water diversion projects.

A Semi-automatic Method Based on Statistic for Mandarin Semantic Structures Extraction in Specific Domains

This paper proposed a new method of semi-automatic extraction for semantic structures from unlabelled corpora in specific domains. The approach is statistical in nature. The extracted structures can be used for shallow parsing and semantic labeling. By iteratively extracting new words and clustering words, we get an inital semantic lexicon that groups words of the same semantic meaning together as a class. After that, a bootstrapping algorithm is adopted to extract semantic structures. Then the semantic structures are used to extract new

A hybrid approach for evaluating CPT-based seismic soil liquefaction potential using Bayesian belief networks

Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.

Analysis of Ecosystem Degradation Factors in Yuanmou Arid-Hot Valleys Based on Interpretative Structural Model

For ecological restoration and reconstruction of the degraded area, it is an important premise to correctly understand the degradation factors of the ecosystem in the arid-hot valleys. The factors including vegetation degradation, land degradation, arid climate, policy failure, forest fire, rapid population growth, excessive deforestation, overgrazing, steep slope reclamation, economic poverty, engineering construction, lithology, slope, low cultural level, geological hazards, biological disaster, soil properties etc, were selected to study the Yuanmou arid-hot valleys. Based on the interpretative structural model （ISM）, it has found out that the degradation factors of the Yuanmou arid-hot valleys were not at the same level but in a multilevel hierarchical system with internal relations, which pointed out that the degradation mode of the arid-hot valleys was ＂straight （appearance）-penetrating-background＂. Such researches have important directive significance for the restoration and reconstruction of the arid-hot valleys ecosystem.

Fault detection of large-scale process control system with higher-order statistical and interpretative structural model

Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model(ISM) and HOS is proposed:(1) the adjacency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method; and(4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.

ISM-based Correlation Analysis of Development Constraints of Forest Resource-exhausted Cities

Forest resource-exhausted cities have to face with various constraints in the acceleration of its urbanization.This paper analyzed major development constraints of these cities,such as unitary economic structure,weakened forest ecological functions,and geographical barriers,and applied ISM method(Interpretive Structural Modeling) to analyze the correlation among the constraints,and gave suggestions for promoting the development of forest resource-exhausted cities.

Knowledge-Based Multifaceted Modeling Methodology for Open Complex Giant Systems

In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.

Application examples of 3D visual interpretation technology in Dagang Oilfield

3D visualization technology is a new seismic interpretation technology that is completely different from traditional seismic data interpretation technologies. This method takes geologic bodies or any 3D region of interest as a unit and integrates the points, lines, and planes of the bodies to make a 3D visual interpretation. This technology can be integrated with the coherence technique to interpret complex faults. Spatial domain automatic horizon tracing can accurately determine structural features. Target sculpting can determine the spatial dimensions and depth extent of perspective 3D lithology bodies using inversion data. I applied this technology to the interpretation of the complex block structures in the Guanxi area of Dagang Oilfield and the prediction of subtle reservoirs in the western slope of the Qinan sag with good results. This study indicated that the visual interpretation technology can be widely and effectively applied in oil field development and to improve the exploration success ratio.

Is Ziarat a Potential Site for Conventional or Unconventional Energy Resources?

A structural interpretation of the Ziarat block in the Balochistan region （a part of the Suleiman Fold and Thrust Belt） has been carried out using seismic and seismological data. Seismic data consists of nine 2.5D pre-stack migrated seismic lines, whereas the seismological data covers the Fault Plane Solution and source parameters. Structural interpretation describes two broad fault sets of fore and back thrusts in the study area that have resulted in the development of pop-up structures, accountable for the structural traps and seismicity pattern in terms of seismic hazard. Seismic interpretation includes time and depth contour maps of the Dungan Formation and Ranikot group, while seismological interpretation includes Fault Plane Solution, that is correlated with a geological and structural map of the area for the interpretation of the nature of the subsurface faults. Principal stresses are also estimated for the Ranikot group and Dungan Formation. In order to calculate anisotropic elastic properties, the parameters of the rock strength of the formations are first determined from seismic data, along with the dominant stresses （vertical, minimum horizontal, and maximum horizontal）. The differential ratio of the maximum and minimum horizontal stresses is obtained to indicate optimal zones for hydraulic fracturing, and to assess the potential for geothermal energy reservoir prospect generation. The stress maps indicate high values towards the deeper part of the horizon, and low towards the shallower part, attributed to the lithological and structural variation in the area. Outcomes of structural interpretation indicate a good correlation of structure and tectonics from both seismological and seismic methods.

Energy consumption hierarchical analysis based on interpretative structural model for ethylene production

Interpretative structural model(ISM) can transform a multivariate problem into several sub-variable problems to analyze a complex industrial structure in a more efficient way by building a multi-level hierarchical structure model. To build an ISM of a production system, the partial correlation coefficient method is proposed to obtain the adjacency matrix, which can be transformed to ISM. According to estimation of correlation coefficient, the result can give actual variable correlations and eliminate effects of intermediate variables. Furthermore, this paper proposes an effective approach using ISM to analyze the main factors and basic mechanisms that affect the energy consumption in an ethylene production system. The case study shows that the proposed energy consumption analysis method is valid and efficient in improvement of energy efficiency in ethylene production.

Structural interpretation for varied gold lodes in Jiudian gold field,Shandong

The gold deposits in the Jiudian gold field in the southwestern part of the Jiaodong terrane are much similar, but less attention-paid ones than those in the northwest part of the same terrane. The ore-controlling faults in the Jiudian gold field could be classified into three types: fluid transporting; fluid dispatching and fluid precipitating. The fluid precipitating faults are usually small and low-order ones, and could be fatherly classified into three types based on their orientation: NE-, NNE-and NW oriented, and they are of quite different features. The Jiudian gold field can be divided into three sectors based on the distance from the major fault zone-The Zhaoping fault zone. The present paper mainly deals with the features of these differently oriented ore-controlling faults and of sectors and presents a tentative structural model for them.

Hydrocarbon Play Assessment of “Oswil” Field, Onshore Niger Delta Region

Hydrocarbon play assessment of any field involves the evaluation of the production capacity of hydrocarbon reservoir unit in the field.This involves detail study of the reservoir petrophysical properties and geological interpretation of structures suitable for hydrocarbon accumulation in the field as observed from seismic reflection images.This study details the assessment of hydrocarbon play in OSWIL field onshore in Niger Delta,with the intent of appraising its productivity using a combination of seismic,well logs,petrophysical parameters and volumetric estimation using proven techniques which involves an integrated methodology.Two reservoir windows“R1”and“R2”were defined from five wells OSWIL-02,04,06,07 and 12.The top and base of each reservoir window was delineated from the wells.Structural interpretation for inline 6975 revealed two horizons(X and Y)and eight faults labelled(F1,F2,F6,F8,F10,F16,F17 and F18).Five faults(F1,F6,F10,F17 and F18)were identified as synthetic faults and dip basin wards while three faults(F2,F8 and F16)were identified as antithetic faults and dips landwards.Time-depth structural map at top of reservoirs R1 and R2 revealed structural highs and closures.These observations are characteristics of growth structures(faults)which depicts the tectonic style of the Niger Delta.Results of petrophysical evaluation for reservoirs“R1”and“R2”across the five wells were analysed.For reservoir“R1”effective porosity values of 27%,26%,23%,20%and 22%were obtained for wells OSWIL-04,12,07,06 and 02 respectively with an average of 23.6%,while for reservoir“R2”effective porosity values of 26%,22%,21%,24%and 23%for wells OSWIL-04,12,07,06 and 02 were obtained respectively with an average of 23.2%.This porosity values correspond with the already established porosity range of 28-32%within the Agbada formation of the Niger Delta.Permeability index of the order(K>100mD)were obtained for both reservoirs across the five wells and is rated very good.Hydrocarbon saturation(Shc)across the five wells averages at 61.6%for reservoir“R1”and 67.4%for reservoir“R2”.Result of petrophysical model for porosity,permeability and water saturation reveal that the reservoir system in R1 and R2 is fault assisted and fluid flow within both reservoirs is aided by presence of effective porosity and faulting.Volumetric estimation for both reservoirs showed that reservoir R1 contains an estimate of 455×106 STB of hydrocarbon in place,while reservoir R2 contains an estimate of 683×106 STB of hydrocarbon in place.These findings impact positively on hydrocarbon production in the field and affirm that the two reservoirs R1 and R2 are highly prospective.

COMPLEXITY OF SYSTEM MAINTAINABILITY ANALYSIS BASED ON THE INTERPRETIVE STRUCTURAL MODELING METHODOLOGY： TRANSDISCIPLINARY APPROACH

This paper outlines a diagnostic approach to quantify the maintainability of a Commercial off-the-Shelf （COTS）-based system by analyzing the complexity of the deployment of the system components. Interpretive Structural Modeling （ISM） is used to demonstrate how ISM supports in identifying and understanding interdependencies among COTS components and how they affect the complexity of the maintenance of the COTS Based System （CBS）. Through ISM analysis we have determined which components in the CBS contribute most significantly to the complexity of the system. With the ISM, architects, system integrators, and system maintainers can isolate the COTS products that cause the most complexity, and therefore cause the most effort to maintain, and take precautions to only change those products when necessary or during major maintenance efforts. The analysis also clearly shows the components that can be easily replaced or upgraded with very little impact on the rest of the system.