Symbolic Model Research Articles

Probabilistic model checking of cancer metabolism

Cancer cell metabolism is often deregulated as a result of adaption to meeting energy and biosynthesis demands of rapid growth or direct mutation of key metabolic enzymes. Better understanding of such deregulation can provide new insights on targetable vulnerabilities, but is complicated by the difficulty in probing cell metabolism at different levels of resolution and under different experimental conditions. We construct computational models of glucose and glutamine metabolism with focus on the effect of IDH1/2-mutations in cancer using a combination of experimental metabolic flux data and patient-derived gene expression data. Our models demonstrate the potential of computational exploration to reveal biologic behavior: they show that an exogenously-mutated IDH1 experimental model utilizes glutamine as an alternative carbon source for lactate production under hypoxia, but does not fully-recapitulate the patient phenotype under normoxia. We also demonstrate the utility of using gene expression data as a proxy for relative differences in metabolic activity. We use the approach of probabilistic model checking and the freely-available Probabilistic Symbolic Model Checker to construct and reason about model behavior.

An innovative joint-space explicit dynamics symbolic computation model for closed-chain mechanisms

In this article, an innovative joint-space explicit dynamics symbolic computation model for closed-chain mechanisms is proposed, which greatly reduces the symbolic and numerical complexity. This study is developed based on our previous work on the explicit dynamics model of tree-chain. The closed-chain mechanism problem is decoupled into a tree-chain problem by using the method of simplified system dynamics, and then the kinematic constraint equation and constraint force equation of the closed-chain are derived based on Axis-Invariant, which greatly simplifies the establishment and solution of the kinematic constraint equation and constraint force vector. The correctness and universality of the proposed method are verified by two common closed-chain mechanisms by comparison using ADAMS software. The advantage of the proposed method lies in the fact that the modeling and solution complexity can be greatly reduced, which is analyzed by comparison with Stefan Staicu's method.

Exploratory research on influential factors of China's sulfur dioxide emission based on symbolic regression.

The amount of China's sulfur dioxide emission remains significantly large in recent years. To further reduce sulfur dioxide emission, the key is to find out the leading factors affecting sulfur dioxide emission and then take measures to control it accordingly. In order to investigate the influential factors of sulfur dioxide emission of various provinces, the data of sulfur dioxide emission of 30 provinces in China from 2001 to 2020 were collected. We established the symbolic regression model to explore the relationship between the GDP (x1), total population (x2), total energy consumption (x3), thermal power installed capacity (x4), and sulfur dioxide emission (dependent variable) for each province. The results show that the amount of China's total sulfur dioxide emission and sulfur dioxide emission in most provinces meet the environmental Kuznets curve (EKC). The influential degree of the factors affecting China's sulfur dioxide emission are GDP, total energy consumption, thermal power installed capacity, and total population. The provinces with the primary factor of GDP have the lowest average total energy consumption and average thermal power installed capacity, and their average sulfur dioxide emissions are also relatively low. The provinces with the primary factor of GDP do not show obvious geographical characteristics, but the provinces with the primary factor of total energy consumption are all distributed in southern China. Based on the research results, some control measures are also put forward.

Experimental validation ofnon‐Newtonianstratifiedco‐extrusionprediction models using a digital process twin

AbstractIn the co‐extrusion of plastics, pressure‐throughput behavior, layer distribution, and residence time are crucial parameters, modeling of which contributes to manufacturing high‐quality products at optimized process efficiency and significantly shortens development times for die systems. In previous work, we have presented symbolic regression models to predicting the (i) pressure‐throughput behavior, (ii) position of the interface, (iii) interfacial shear stress, (iv) ratio of volume flow rates, and (v) interfacial flow velocity for isothermal two‐layer co‐extrusion flows through rectangular ducts. These regression models are mathematically simple and capable of capturing the shear‐thinning nature of polymer melts without the need for numerical methods. Here, we present an experimental study validating the proposed models against co‐extrusion process data and comparing them to existing theories. To this end, a two‐layer co‐extrusion demonstration die instrumented with an optical coherence tomography sensor for detecting the interfacial position was used. To accurately set up and evaluate the die flows, the overall co‐extrusion process was represented by means of a digital process twin. Industrially relevant combinations of materials were tested under a wide range of processing conditions. Comparisons of pressure losses and interfacial positions to the predictions showed excellent agreement and the results outperformed the concept of representative viscosity.

Learning-based symbolic abstractions for nonlinear control systems

Symbolic models or abstractions are known to be powerful tools for the control design of cyber–physical systems (CPSs) with logic specifications. In this paper, we investigate a novel learning-based approach to the construction of symbolic models for nonlinear control systems. In particular, the symbolic model is constructed based on learning the un-modeled part of the dynamics from training data based on state-space exploration, and the concept of an alternating simulation relation that represents behavioral relationships with respect to the original control system. Moreover, we aim at achieving safe exploration, meaning that the trajectory of the system is guaranteed to be in a safe region for all times while collecting the training data. In addition, we provide some techniques to reduce the computational load, in terms of memory and computation time,of constructing the symbolic models and the safety controller synthesis, so as to make our approach practical. Finally, a numerical simulation illustrates the effectiveness of the proposed approach.

Patterns Of Cultural Communication In Selling Activities In The Limbung Traditional Market, Gowa Regency

This research started from the finding of communication phenomena in traditional markets. In the traditional market the process of buying and selling transactions with interpersonal communication (face to face) between the seller and the buyer. Bargaining in buying and selling in traditional markets is common in the process of transactions (payments) or even without transactions because there is no agreement between the seller and the buyer. The process of buying and selling transactions begins with greetings, the process of bargaining until the transaction is colored by conversations between the seller and the buyer that even leaves the main topic. The process in these stages forms a communication pattern. The formulation of the problem to be answered in this study is how the pattern of communication between sellers and buyers in buying and selling transactions in traditional markets is. This study examines the communication patterns of sellers and buyers in the transaction process in traditional markets. The focus in this research is the communication component in buying and selling transactions in traditional markets. Using the ethnographic method of communication, with participant observation and in-depth interviews with five seller informants and five buyer informants who were selected using a purposive sampling technique with predetermined criteria. The results of this study indicate that sellers and buyers in transactions use dyadic communication patterns, there is a symbolic interactionism model communication process between the seller and the buyer, the unique communication component is found in verbal (language) and non-verbal messages (clothing, touch, body language) and effects and context of communication. The effect of communication in the process of buying and selling transactions that can give birth to intimacy between the seller and the buyer. The context of communication is not only about the topic of buying and selling transactions, but also about other topics such as personal gossip, economics, education, religion, culture, social and even politics. This research requires further research with a different paradigm so that it can be further developed in an ethnographic communication research.

Color as a pictorial principle in the artistic language of Chinese painting: approaches and models of representation

The purpose of the article is to study the approaches and models of color as a pictorial principle in modern Chinese painting.
 The article is devoted to the actual problem of the study of the artistic language of painting. In Chinese art history, it has several forms of representation. However, one of the key themes is the role of color as a central pictorial principle in contemporary fine art.
 The study of the role of color in painting is based on the analysis of the formal and compositional-plastic features of artwork. At the same time, color is an important factor in the interpretation of artistic signs and symbols of a particular author. The selection of color from and a number of other elements of the artistic language is associated with the influence of the traditional artistic system in the art of China on the practice of modernization. In many professional studies, we can come across an opinion about the practical significance of tonal and color solutions for substantiating the artistic imagery of a work and its ideological and aesthetic meaning.
 The methodology. For the analysis of sources we used the method of comparative analysis and the method of typology. Visual sources are analyzed with the use of methodological, formal, compositional and figurative-stylistic analysis.
 The results. In modern Chinese studies, the problem of color is a completely independent area, which is of universal importance for the analysis of the evolution of fine art. As a visual principle, the subject matter is considered within two approaches: 1) figurative-plastic, where the central place is occupied by tonal and color features in the context of the search for form and composition; 2) emotional-figurative, where the central place is occupied by the perception of color and the interpretation of the signs and symbols of the work.
 Within the framework of the first approach, three research models dominate: 1) figurative and artistic model, which is characterized by the ability of color to change within the framework of the author’s style idea; 2) the symbolic model focuses the artist’s attention on cultural and historical references; 3) the decorative-figurative model positions the problem of color as a matter of color morphology, as well as the use of traditional decor techniques along with oil painting methods.
 Within the framework of the second approach, the sign-emotive and semantic-emotive models are the main ones. The first model represents color as a factor of the author’s sign space. The second model is a tool for studying color as a part of a sign artistic language. This artistic language regulates the specific meaning of the aesthetic essence of color (for example, red in traditional symbolism is a sign of power). In this model, the semiotic feature of color is enhanced by the emotional nature of painting techniques.
 The scientific novelty. The article substantiates the concept of color in painting as the main pictorial principle in the contemporary fine arts of China. The research approaches of Chinese art critics are summarized.

A symbolic interactionist model of interpreter-facilitated communication—Key communication issues in face-to-face interpreting

Communication has been conceptualized and studied in a wide range of disciplines. However, very few communication theories or models have explicitly incorporated interpreting as an indispensable process to achieve communicative goals in intercultural and interlinguistic settings where communicative parties do not share a common language. By the same token, despite a strong emphasis of interpreting as “a communicative pas de trois”, there is much remaining to be explored in how existing communication theories and models could be drawn on and adapted to shed light on the key communication issues in interpreting studies. In view of such a distinct gap attributed to a striking lack of attention from both communication and interpreting scholars, as highlighted in this special issue, the author develops a symbolic interactionist model of interpreter-facilitated communication with a focus on exploring how an interpreter's identification with self-meanings and role management, which is key to their intrapersonal covert rehearsal process, impact on their interpreting decisions and behaviors. Through one-to-one interviews with three professional interpreters from the National Register of Public Service Interpreters, it is found that the interpreters' identification with particular self-meanings at the intrapersonal level, which gives rise to identity integration, identity accumulation and disidentification strategies, has impacted on how they managed various challenges at the interpersonal level, such as the impossibility of the neutrality expectation, dealing with inappropriate non-interpreting demands from communicative parties, and resolving identity conflicts linked to communicative contexts.

Reasoning and interaction for social artificial intelligence

Current work on multi-agent systems at King’s College London is extensive, though largely based in two research groups within the Department of Informatics: the Distributed Artificial Intelligence (DAI) thematic group and the Reasoning & Planning (RAP) thematic group. DAI combines AI expertise with political and economic theories and data, to explore social and technological contexts of interacting intelligent entities. It develops computational models for analysing social, political and economic phenomena to improve the effectiveness and fairness of policies and regulations, and combines intelligent agent systems, software engineering, norms, trust and reputation, agent-based simulation, communication and provenance of data, knowledge engineering, crowd computing and semantic technologies, and algorithmic game theory and computational social choice, to address problems arising in autonomous systems, financial markets, privacy and security, urban living and health. RAP conducts research in symbolic models for reasoning involving argumentation, knowledge representation, planning, and other related areas, including development of logical models of argumentation-based reasoning and decision-making, and their usage for explainable AI and integration of machine and human reasoning, as well as combining planning and argumentation methodologies for strategic argumentation.

High-throughput informed machine learning models for ultrastrong B-N solids

High-throughput (HT) computations and machine learning (ML) algorithms are two fundamental approaches in data-driven paradigms to predict various properties of solids due to their efficiency in data creation and model construction, which however are usually used individually and lack generalization and flexibility. In this paper, we propose a scheme combining HT computations for the efficient creation of consistent data and ML algorithms for the fast construction of surrogate models to screen B-N solids' stability and mechanical properties at ambient and high pressures. Employing HT computations, a standardized database of formation enthalpy, elasticity and ideal strength of thousands of B-N structures with high precision is first established. Then several ML models are comparatively built employing the XGBoost approach with the consideration of four descriptors, i.e., sine matrix, Ewald sum matrix, SOAP, and MBTR. Our results suggest the MBTR provides more accurate estimates of various physical properties except for bulk modulus, which is evaluated with greater precision by the Ewald sum matrix. To further improve the model interpretability, based on the brittleness/ductility criterion of materials, a symbolic model with strong physical significance is successfully built for the ideal strength of the B-N solids through the key descriptors screened by the ML methods, showing great accuracy. Our research demonstrates the possibility of building high-efficient ML models and compact symbolic physical models by incorporating consistent data through HT computations for high accuracy in predicting the thermodynamic and mechanical properties of strong solids with high precision, providing a pathway for inverse design of novel materials.

Research on the Creation of Chinese National Cultural Identity Symbols Based on Visual Images

Cultural symbol generation has always been a challenging task to achieve symbols that can represent national culture and promote people’s identification with Chinese culture. In this paper, we combine generative adversarial network (GAN) to propose a symbolic generation model of Chinese national cultural identity based on visual images. First, combining pattern search regular terms with generator cross-loss functions based on GAN generative adversarial networks to improve the pattern collapse phenomenon of generative adversarial networks. Second, the normal convolutional layer of the generator in the network structure is replaced with a deep-space separable convolution to improve the real-time performance of the model by reducing the model parameters. Through extensive testing on real datasets, the results show that the model in this paper can generate higher performance ethnic culture symbols while maintaining better temporal performance, which has some practical application value.

Symbolic Model of New Media Art Expression Based on Artificial Intelligence Big Data

With the rapid development of China’s economy and the continuous optimization and improvement of Internet information technology, a new thing, new media, has emerged. On this basis, a kind of artistic behavior of creating, carrying, spreading, and appreciating is also called new media art. New media art has developed for a long time in foreign countries, but its development and popularization are relatively late in China, and it is still in a stage of improvement. As far as visual communication design is concerned, the emergence and development of new media have strongly promoted the development of its work and the dissemination of various works. Developing visual communication design based on new media art has become a key issue for all visual communication designers. Nowadays, deep learning is widely used in different fields in different ways, and image art representation is a hot research field in computer vision. Therefore, this paper mainly studies and analyzes the symbolism of new media art expression forms and proposes a new media art symbolism model based on artificial intelligence big data. In order to provide a method of real-time representation of art images, the deep learning method is used to model the artistic expression of images, and spatial reorganization of content features and style features is carried out. This paper designs simulation experiments to verify the effectiveness of the model, which can provide some help for the development and improvement of new media art in China. We compared the results of our model with a number of other models to test its effectiveness. We achieved a matching accuracy of 98.4% which is higher than or equal to the results of the existing models.

A formal verification approach for composite smart contracts security using FSM

Many of today's companies use Smart Contracts to represent and execute their business processes. Smart contracts are self-executed programs running over blockchain. In this context, composite smart contracts are used to represent collaborative business processes. A composite smart contract is a smart contract that needs to execute other contracts using external calls to achieve its tasks. Composite smart contracts, through the use of external calls and the execution of other smart contracts that might belong to other owners or companies, bring many challenges with regard to security requirements. As a result, special efforts must be done to ensure composite smart contracts security verification. In this paper, we propose a novel approach to verify the security and the correctness of the composite smart contracts written in solidity in Ethereum blockchain. This approach is based on the finite state machine models and model checking method for modeling and verifying the composite smart contracts respectively. We consider seven security properties as well as the security issues that depend on the contract context to be checked in the composite smart contract. For this, we provide two different yet complementary types of verification. The first type of verification is applied to all smart contracts with properties called in our approach “standard properties” that represent the generic ones, while the second type considers the context-dependent properties that we called “specific properties” varying from one smart contract to another. Finally, we express all properties using computation tree logic formulae and we use the nuXmv symbolic model checker to verify the model against all properties. This approach is validated using a different set of solidity smart contracts.

Nursing Care According to Riehl’s Symbolic Interaction Model in a Patient Receiving ECMO Treatment Due to ARDS Developing After H1N1 Diagnosis: Case Report

Nursing Care According to Riehl’s Symbolic Interaction Model in a Patient Receiving ECMO Treatment Due to ARDS Developing After H1N1 Diagnosis: Case Report

Determination of Health Index of Insulating Oil of In-Service Transformer & Reactors Based on IFT Predicted by Multigene Symbolic Regression

This article aims at developing a multigene symbolic regression model of interfacial tension (IFT) for determining the health index of insulating oil. The oil parameters considered for modeling of IFT are break down voltage, resistivity, acidity, tan delta and water. The unique study undertaken in this article attempts to develop a multigene symbolic regression model for generating and evaluating the relationship between IFT and other oil parameters. Data utilized in this study involves actual oil testing parameters of in-service transformers and reactors of age more than 15 years. A sensitivity analysis has been carried out to investigate the relative contribution of input variables to the model output. The result of sensitivity analysis has further been used to determine the weight of parameters for calculating the health index of insulating oil. Finally, the health index of 400 in-service transformer and reactors are evaluated and analyzed. This research will help in undertaking decisions for the replacement of oil based on health index, thereby increasing the life span of transformer and reactors. The health index so obtained will act as a reference for planning necessary maintenance activities of the equipment and thereby saving unprecedented failure of the oil-filled transformer and reactors.

Symbols and mental programs: a hypothesis about human singularity.

Natural language is often seen as the single factor that explains the cognitive singularity of the human species. Instead, we propose that humans possess multiple internal languages of thought, akin to computer languages, which encode and compress structures in various domains (mathematics, music, shape…). These languages rely on cortical circuits distinct from classical language areas. Each is characterized by: (i) the discretization of a domain using a small set of symbols, and (ii) their recursive composition into mental programs that encode nested repetitions with variations. In various tasks of elementary shape or sequence perception, minimum description length in the proposed languages captures human behavior and brain activity, whereas non-human primate data are captured by simpler nonsymbolic models. Our research argues in favor of discrete symbolic models of human thought.

Symbolic Control Design of an Artificial Pancreas for Type-2 Diabetes

Diabetes is a widespread disease characterized by chronic hyperglycemia so that diabetic individuals usually require the administration of exogenous insulin for survival. As a consequence, in the context of the so-called artificial pancreas, many glucose control methods have been presented in the last few years. In this work, we focus on type-2 diabetes and propose a novel model-based glucose control technique based on the use of symbolic models, which are finite approximations of complex dynamical systems. This framework allows taking into account nonlinearities and delays in the dynamics, uncertainties, and input bounds, as well as nonidealities coming from the interaction between physical plant and digital environment. The methodology is extensively validated over a virtual patient model, broadly accepted as a substitute to animal trials in the preclinical testing of closed-loop glucose control strategies. The results show the effectiveness and the robustness of the approach.

Towards post-quantum security for cyber-physical systems: Integrating PQC into industrial M2M communication1

The threat of a cryptographically relevant quantum computer contributes to an increasing interest in the field of post-quantum cryptography (PQC). Compared to existing research efforts regarding the integration of PQC into the Transport Layer Security (TLS) protocol, industrial communication protocols have so far been neglected. Since industrial cyber-physical systems (CPS) are typically deployed for decades, protection against such long-term threats is needed. In this work, we propose two novel solutions for the integration of post-quantum (PQ) primitives (digital signatures and key establishment) into the industrial protocol Open Platform Communications Unified Architecture (OPC UA): a hybrid solution combining conventional cryptography with PQC and a solution solely based on PQC. Both approaches provide mutual authentication between client and server and are realized with certificates fully compliant to the X.509 standard. We implement the two solutions and measure and evaluate their performance across three different security levels. All selected algorithms (Kyber, Dilithium, and Falcon) are candidates for standardization by the National Institute of Standards and Technology (NIST). We show that Falcon is a suitable option – especially – when using floating-point hardware provided by our ARM-based evaluation platform. Our proposed hybrid solution provides PQ security for early adopters but comes with additional performance and communication requirements. Our solution solely based on PQC shows superior performance across all evaluated security levels in terms of handshake duration compared to conventional OPC UA but comes at the cost of increased handshake sizes. In addition to our performance evaluation, we provide a proof of security in the symbolic model for our two PQC-based variants of OPC UA. For this proof, we use the cryptographic protocol verifier ProVerif and formally verify confidentiality and authentication properties of our quantum-resistant variants.

A novel genetic expression programming assisted calibration strategy for discrete element models of composite joints with ductile adhesives

Discrete element (DE) model has a great feasibility in modelling the microstructural behaviours of adhesive composite joints. However, it demands a sophisticated calibration process to determine microscale bond parameters, which involves massive efforts in both experimental and numerical investigations. This work developed a novel calibration strategy based on DE models and genetic expression programming (GEP) approach for predicting the behaviours of hybrid composite joints encompassing the material nonlinearity, large ductile deformation and multiple fracture modes. In the developed strategy, both the bulk and interlaminar-like properties of ductile adhesives were concerned to suit various joint configurations. GEP modelling was performed based on the datasets from virtual DE experiments. Symbolic regression models were subsequently developed to facilitate the parameters determination. A series lab tests were conducted to validate the numerical results. It shows that the calibrated DE model can adaptively simulate the featured behaviours of both the ductile adhesive and composite joints with different configurations well in most examined occasions. Therefore, it could be suggested to generalize the developed strategy in the development of other DE models for saving the massive efforts in the calibration process of microstructural parameters.

Discovering sparse interpretable dynamics from partial observations

Identifying the governing equations of a nonlinear dynamical system is key to both understanding the physical features of the system and constructing an accurate model of the dynamics that generalizes well beyond the available data. Achieving this kind of interpretable system identification is even more difficult for partially observed systems. We propose a machine learning framework for discovering the governing equations of a dynamical system using only partial observations, combining an encoder for state reconstruction with a sparse symbolic model. The entire architecture is trained end-to-end by matching the higher-order symbolic time derivatives of the sparse symbolic model with finite difference estimates from the data. Our tests show that this method can successfully reconstruct the full system state and identify the equations of motion governing the underlying dynamics for a variety of ordinary differential equation (ODE) and partial differential equation (PDE) systems.