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.

A method to interpret fracture aperture of rock slope using adaptive shape and unmanned aerial vehicle multi-angle nap-of-the-object photogrammetry

The aperture of natural rock fractures significantly affects the deformation and strength properties of rock masses,as well as the hydrodynamic properties of fractured rock masses.The conventional measurement methods are inadequate for collecting data on high-steep rock slopes in complex mountainous regions.This study establishes a high-resolution three-dimensional model of a rock slope using unmanned aerial vehicle(UAV)multi-angle nap-of-the-object photogrammetry to obtain edge feature points of fractures.Fracture opening morphology is characterized using coordinate projection and transformation.Fracture central axis is determined using vertical measuring lines,allowing for the interpretation of aperture of adaptive fracture shape.The feasibility and reliability of the new method are verified at a construction site of a railway in southeast Tibet,China.The study shows that the fracture aperture has a significant interval effect and size effect.The optimal sampling length for fractures is approximately 0.5e1 m,and the optimal aperture interpretation results can be achieved when the measuring line spacing is 1%of the sampling length.Tensile fractures in the study area generally have larger apertures than shear fractures,and their tendency to increase with slope height is also greater than that of shear fractures.The aperture of tensile fractures is generally positively correlated with their trace length,while the correlation between the aperture of shear fractures and their trace length appears to be weak.Fractures of different orientations exhibit certain differences in their distribution of aperture,but generally follow the forms of normal,log-normal,and gamma distributions.This study provides essential data support for rock and slope stability evaluation,which is of significant practical importance.

Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems

Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications.Although there is an extensive literature on qualitative properties such as safety and liveness,there is still a lack of quantitative and uncertain property verifications for these systems.In uncertain environments,agents must make judicious decisions based on subjective epistemic.To verify epistemic and measurable properties in multi-agent systems,this paper extends fuzzy computation tree logic by introducing epistemic modalities and proposing a new Fuzzy Computation Tree Logic of Knowledge(FCTLK).We represent fuzzy multi-agent systems as distributed knowledge bases with fuzzy epistemic interpreted systems.In addition,we provide a transformation algorithm from fuzzy epistemic interpreted systems to fuzzy Kripke structures,as well as transformation rules from FCTLK formulas to Fuzzy Computation Tree Logic(FCTL)formulas.Accordingly,we transform the FCTLK model checking problem into the FCTL model checking.This enables the verification of FCTLK formulas by using the fuzzy model checking algorithm of FCTL without additional computational overheads.Finally,we present correctness proofs and complexity analyses of the proposed algorithms.Additionally,we further illustrate the practical application of our approach through an example of a train control system.

Identification and distribution of 13003 landslides in the northwest margin of Qinghai-Tibet Plateau based on human-computer interaction remote sensing interpretation

The periphery of the Qinghai-Tibet Plateau is renowned for its susceptibility to landslides.However,the northwestern margin of this region,characterised by limited human activities and challenging transportation,remains insufficiently explored concerning landslide occurrence and dispersion.With the planning and construction of the Xinjiang-Tibet Railway,a comprehensive investigation into disastrous landslides in this area is essential for effective disaster preparedness and mitigation strategies.By using the human-computer interaction interpretation approach,the authors established a landslide database encompassing 13003 landslides,collectively spanning an area of 3351.24 km^(2)(36°N-40°N,73°E-78°E).The database incorporates diverse topographical and environmental parameters,including regional elevation,slope angle,slope aspect,distance to faults,distance to roads,distance to rivers,annual precipitation,and stratum.The statistical characteristics of number and area of landslides,landslide number density(LND),and landslide area percentage(LAP)are analyzed.The authors found that a predominant concentration of landslide origins within high slope angle regions,with the highest incidence observed in intervals characterised by average slopes of 20°to 30°,maximum slope angle above 80°,along with orientations towards the north(N),northeast(NE),and southwest(SW).Additionally,elevations above 4.5 km,distance to rivers below 1 km,rainfall between 20-30 mm and 30-40 mm emerge as particularly susceptible to landslide development.The study area’s geological composition primarily comprises Mesozoic and Upper Paleozoic outcrops.Both fault and human engineering activities have different degrees of influence on landslide development.Furthermore,the significance of the landslide database,the relationship between landslide distribution and environmental factors,and the geometric and morphological characteristics of landslides are discussed.The landslide H/L ratios in the study area are mainly concentrated between 0.4 and 0.64.It means the landslides mobility in the region is relatively low,and the authors speculate that landslides in this region more possibly triggered by earthquakes or located in meizoseismal area.

Simultaneous Interpreting Output Assessment of Earnings Conference Call: Case Analysis of Tencent 2022 Q3 Result Announcement

Quality in simultaneous interpreting is a frequently discussed concept. In the enterprise setting, earnings conference call remains a rarely explored field. This thesis offers a descriptive study on assessing interpreting quality from perspectives of fidelity, fluency, and appropriacy. As the corpus, Tencent 2022 Third Quarter Result Announcement provides an ideal transcript to the author to conduct its analysis. Interpreting is frequently done without bearing in mind the multitude of factors that can affect the quality of interpreting. Drawing a conclusion that the interpreter does make a lot of omissions, pauses and hesitations posing a negative effect on the fidelity, fluency and accuracy of the interpreting, the present author suggests that more preparation should be done for improving performance, such as terminologies, company background information, a reasonable speech rate, good image and acoustic quality, and so on.

Effects and Causes of VR-Supported Interpreting Learning Environment on the Interpretation Classroom Anxiety of Student Interpreter

Interpreting activity is considered a high-anxiety activity due to its immediacy, multitasking, complexity of cognitive processing, and uncertainty of cognitive processing. Research has shown that interpreting anxiety, as the biggest emotional obstacle in the interpreting process, is the main emotional factor that leads to individual differences in interpreting. Students often claim to have fear or anxiety behaviors in interpreting exams, interpreting competitions, and interpreting classes. However, the research on interpreting teaching attaches importance to the cultivation of language knowledge, cultural knowledge, and interpreting skills, and does not pay enough attention to emotional factors such as motivation and anxiety in interpreting learning, which makes it difficult for the cultivated interpreters to meet the requirements of professional practice. In recent years, virtual reality technology (VR) has been gradually applied in the field of foreign language and interpreting teaching for creating a real, interactive and experiential language learning environment. Situated Learning Theory stresses that the fundamental mechanism for learning to take place is for individuals to participate in the real context in which knowledge is generated, and to realize the construction of knowledge through the interaction with the community of practice and the environment. Virtual reality technology can satisfy the needs of language learners for real contexts by providing learners with immersive, imaginative and interactive scenario simulations, and has a certain positive effect on alleviating learning anxiety. Therefore, relying on the virtual simulation course “United Nations Kubuqi International Desert Ecological Science and Technology Innovation International Volunteer Language Service Practical Training System”, this paper adopts a combination of quantitative and qualitative analyses to investigate the interpretation anxiety level of the interpreter trainees and the factors affecting them in the VR situation to help them discover effective responses to interpreter anxiety.

Preliminary report of coseismic surface rupture(part)of Türkiye's M_(W)7.8 earthquake by remote sensing interpretation

Both M_(W) 7.8 and M_(W) 7.5 earthquakes occurred in southeastern Türkiye on February 6,2023,resulting in numerous buildings collapsing and serious casualties.Understanding the distribution of coseismic surface ruptures and secondary disasters surrounding the epicentral area is important for post-earthquake emergency and disaster assessments.High-resolution Maxar and GF-2 satellite data were used after the events to extract the location of the rupture surrounding the first epicentral area.The results show that the length of the interpreted surface rupture zone(part of)is approximately 75 km,with a coseismic sinistral dislocation of 2-3 m near the epicenter;however,this reduced to zero at the tip of the southwest section of the East Anatolia Fault Zone.Moreover,dense soil liquefaction pits were triggered along the rupture trace.These events are in the western region of the Eurasian Seismic Belt and result from the subduction and collision of the Arabian and African Plates toward the Eurasian Plate.The western region of the Chinese mainland and its adjacent areas are in the eastern section of the Eurasian Seismic Belt,where seismic activity is controlled by the collision of the Indian and Eurasian Plates.Both China and Türkiye have independent tectonic histories.

Problems of Music Interpretation and Its Expression: On the Example of the Violin

Music is essentially a sonic phenomenon, an intangible art that we experience through our auditory organs. It is written as a physical note on paper. It is the result of harmony and composition of music that expresses the meaning of music created from the author’s mind, thinking, skills, and feelings, or composition or work. If a composition is not interpreted by a musician and set to music, it is nothing more than a notation and notation written down on paper. The main process of conveying the composition to the listeners through the musician’s interpretation of the music is somewhat overlooked. Therefore, in this research, the musician’s thinking and editing method, and how to express it through interpretation, were the doctoral dissertation and articles of researchers such as have been studied in comparison.

Structural Analysis of the Factors Influencing the Financing of Forestry Enterprises Based on Interpretive Structural Modeling(ISM)

Through the collection of related literature,we point out the six major factors influencing China's forestry enterprises' financing: insufficient national support; regulations and institutional environmental factors; narrow channels of financing; inappropriate existing mortgagebacked approach; forestry production characteristics; forestry enterprises' defects. Then,we use interpretive structural modeling( ISM) from System Engineering to analyze the structure of the six factors and set up ladder-type structure. We put three factors including forestry production characteristics,shortcomings of forestry enterprises and regulatory,institutional and environmental factors as basic factors and put other three factors as important factors. From the perspective of the government and enterprises,we put forward some personal advices and ideas based on the basic factors and important factors to ease the financing difficulties of forestry enterprises.

Why caution should be applied when interpreting and promoting findings from Mendelian randomisation studies

Introduction In their article entitled‘Investigating genetic causal relationships between blood pressure and anxiety,depressive symptoms,neuroticism and subjective well-being’,Cai and colleagues1 presented the results of a two-sample Mendelian randomisation2(MR)study examining associations between blood pressure traits(systolic,diastolic,hypertension and pulse pressure)and psychological traits(anxiety,depression,neuroticism and subjective well-being).After correction for multiple testing.

A Novel Belief Rule-Based Fault Diagnosis Method with Interpretability

Fault diagnosis plays an irreplaceable role in the normal operation of equipment.A fault diagnosis model is often required to be interpretable for increasing the trust between humans and the model.Due to the understandable knowledge expression and transparent reasoning process,the belief rule base(BRB)has extensive applications as an interpretable expert system in fault diagnosis.Optimization is an effective means to weaken the subjectivity of experts in BRB,where the interpretability of BRB may be weakened.Hence,to obtain a credible result,the weakening factors of interpretability in the BRB-based fault diagnosis model are firstly analyzed,which are manifested in deviation from the initial judgement of experts and over-optimization of parameters.For these two factors,three indexes are proposed,namely the consistency index of rules,consistency index of the rule base and over-optimization index,tomeasure the interpretability of the optimizedmodel.Considering both the accuracy and interpretability of amodel,an improved coordinate ascent(I-CA)algorithmis proposed to fine-tune the parameters of the fault diagnosis model based on BRB.In I-CA,the algorithm combined with the advance and retreat method and the golden section method is employed to be one-dimensional search algorithm.Furthermore,the random optimization sequence and adaptive step size are proposed to improve the accuracy of the model.Finally,a case study of fault diagnosis in aerospace relays based on BRB is carried out to verify the effectiveness of the proposed method.

An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research.The safety criteria for medical imaging are highly stringent,and models are required for an explanation.However,existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs.Thus,the interpretability of CNNs has come into the spotlight.Since medical imaging data are limited,many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public Image Net datasets by the transfer learning method.Unfortunately,this generates many unreliable parameters and makes it difficult to generate plausible explanations from these models.In this study,we trained from scratch rather than relying on transfer learning,creating a novel interpretable approach for autonomously segmenting the left ventricle with a cardiac MRI.Our enhanced GPU training system implemented interpretable global average pooling for graphics using deep learning.The deep learning tasks were simplified.Simplification included data management,neural network architecture,and training.Our system monitored and analyzed the gradient changes of different layers with dynamic visualizations in real-time and selected the optimal deployment model.Our results demonstrated that the proposed method was feasible and efficient:the Dice coefficient reached 94.48%,and the accuracy reached 99.7%.It was found that no current transfer learning models could perform comparably to the ImageNet transfer learning architectures.This model is lightweight and more convenient to deploy on mobile devices than transfer learning models.

Research on SAP Business One Implementation Risk Factors with Interpretive Structural Model

The possible risk factors during SAP Business One implementation were studied with depth interview. The results are then adjusted by experts. 20 categories of risk factors that are totally 49 factors were found. Based on the risk factors during the SAP Business One implementation, questionnaire was used to study the key risk factors of SAP Business One implementation. Results illustrate ten key risk factors, these are risk of senior managers leadership, risk of project management, risk of process improvement, risk of implementation team organization, risk of process analysis, risk of based data, risk of personnel coordination, risk of change management, risk of secondary development, and risk of data import. Focus on the key risks of SAP Business One implementation, the interpretative structural modeling approach is used to study the relationship between these factors and establish a seven-level hierarchical structure. The study illustrates that the structure is olive-like, in which the risk of data import is on the top, and the risk of senior managers is on the bottom. They are the most important risk factors.

Application of Interpretive Theory to Business Interpretation

Interpretive theory brings forward three phases of interpretation: understanding, deverberlization and re-expression. It needs linguistic knowledge and non-linguistic knowledge. This essay discusses application of interpretive theory to business interpretation from the perspective of theory and practice.

On Teaching of Interpreting from Interpretive Theory

This paper aims to explore teaching of interpreting nowadays by starting from the interpretive theory and its characteristics. The author believes that the theory is mainly based on the study of interpretation practice, whose core content, namely,"deverbalization"has made great strides and breakthroughs in the theory of translation; when we examine translation, or rather interpretation once again from the bi-perspective of language and culture, we will have come across new thoughts in terms of translation as well as teaching of interpreting.

The Application of the Interpretive Theory of Translation

The interpretive theory of translation(ITT) is a school of theory originated in the late 1960 s in France,focusing on the discussion of the theory and teaching of interpreting and non-literary translation. ITT believes that what the translator should convey is not the meaning of linguistic notation,but the non-verbal sense. In this paper,the author is going to briefly introduce ITT and analyze several examples to show different situations where ITT is either useful or unsuitable.

RMA-CNN:A Residual Mixed Domain Attention CNN for Bearings Fault Diagnosis and Its Time-Frequency Domain Interpretability

Early fault diagnosis of bearings is crucial for ensuring safe and reliable operations.Convolutional neural networks(CNNs)have achieved significant breakthroughs in machinery fault diagnosis.However,complex and varying working conditions can lead to inter-class similarity and intra-class variability in datasets,making it more challenging for CNNs to learn discriminative features.Furthermore,CNNs are often considered“black boxes”and lack sufficient interpretability in the fault diagnosis field.To address these issues,this paper introduces a residual mixed domain attention CNN method,referred to as RMA-CNN.This method comprises multiple residual mixed domain attention modules(RMAMs),each employing one attention mechanism to emphasize meaningful features in both time and channel domains.This significantly enhances the network’s ability to learn fault-related features.Moreover,we conduct an in-depth analysis of the inherent feature learning mechanism of the attention module RMAM to improve the interpretability of CNNs in fault diagnosis applications.Experiments conducted on two datasets—a high-speed aeronautical bearing dataset and a motor bearing dataset—demonstrate that the RMA-CNN achieves remarkable results in diagnostic tasks.

Enhancing Emergency ECG Interpretation: A Practical Checklist Developed from a Study Assessing Young Physicians’ Diagnostic Abilities

Effective interpretation of electrocardiograms (ECGs) is a critical skill for healthcare professionals, especially in emergency settings. This study assessed the diagnostic abilities of 200 final-year medical students and young doctors in ECG interpretation. Subsequently, based on the study findings, a practical checklist was developed, and a comprehensive book entitled “Critical ECG in Emergencies” was authored by the researchers. The checklist provides cognitive aids and serves as a systematic guide to enhance ECG interpretation in urgent situations. The study revealed a need for improved confidence in interpreting critical ECGs among the participants. The checklist offers promise for reducing diagnostic errors and improving patient outcomes. Further research is required to evaluate its impact on daily practice and patient care, while the book provides valuable resources for healthcare professionals in emergency medicine.

A New Prediction System Based on Self-Growth Belief Rule Base with Interpretability Constraints

Prediction systems are an important aspect of intelligent decisions.In engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accuracy of the model.The belief rule base(BRB)can implement nonlinear modeling and express a variety of uncertain information,including fuzziness,ignorance,randomness,etc.However,the BRB system also has two main problems:Firstly,modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy.Secondly,interpretability is not considered in the optimization process of current research,resulting in the destruction of the interpretability of BRB.To balance the accuracy and interpretability of the model,a self-growth belief rule basewith interpretability constraints(SBRB-I)is proposed.The reasoning process of the SBRB-I model is based on the evidence reasoning(ER)approach.Moreover,the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system.A case study showed that the accuracy and interpretability of the model could be guaranteed.The SBRB-I model has good application prospects in prediction systems.

Classification and structural characteristics of amorphous materials based on interpretable deep learning

Defining the structure characteristics of amorphous materials is one of the fundamental problems that need to be solved urgently in complex materials because of their complex structure and long-range disorder.In this study,we develop an interpretable deep learning model capable of accurately classifying amorphous configurations and characterizing their structural properties.The results demonstrate that the multi-dimensional hybrid convolutional neural network can classify the two-dimensional(2D)liquids and amorphous solids of molecular dynamics simulation.The classification process does not make a priori assumptions on the amorphous particle environment,and the accuracy is 92.75%,which is better than other convolutional neural networks.Moreover,our model utilizes the gradient-weighted activation-like mapping method,which generates activation-like heat maps that can precisely identify important structures in the amorphous configuration maps.We obtain an order parameter from the heatmap and conduct finite scale analysis of this parameter.Our findings demonstrate that the order parameter effectively captures the amorphous phase transition process across various systems.These results hold significant scientific implications for the study of amorphous structural characteristics via deep learning.