Logic-based Modeling Research Articles

Assessing the dynamics of forest ecosystem services to define forest use suitability: a case study of Pinus sylvestris in Spain

Adopting a multi-criteria approach in forest management is essential for preserving or improving specific benefits while minimizing negative environmental impacts. Determining the appropriate long-term management approach for a forest requires considering heterogeneous environmental and social factors, as well as changes in forest characteristics over time. Conducting a strategic assessment of forest use suitability (FUS), namely productive, protective, conservation-oriented, social and multifunctional, at the national level, taking into account the dynamics in the provision of forest ecosystem services and the trade-offs between FUS alternatives, can guide the development of customized management strategies and policies that align with the specific requirements and conditions of the forest. In this study, we evaluate the supply and simulation-based changes over time of diverse ecosystem services of Pinus sylvestris stands in Spain and utilize a decision model to determine the most suitable FUS alternative that enhances the provision of these services. The assignment of the most appropriate FUS alternative aims to help in decision-making processes and in the selection of the most adequate management strategies. To achieve this, we utilize the last version of ecosystem management decision support (EMDS) system, a spatially focused decision support tool capable of generating precise results for multi-criteria assessment. Participatory planning actions based on Delphi principles and Analytic Hierarchy Process (AHP) analysis were applied and combined with geospatial logic-based modelling. According to the results, the dominant FUS is protective, followed by productive alternative, exhibiting high levels of multifunctionality.

Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.Graphical abstract

FUZZY LOGIC-BASED MODELING OF A CENTRIFUGAL BLOOD PUMP PERFORMANCE VIA EXPERIMENTAL DATA OF NEWTONIAN AND NON-NEWTONIAN FLUIDS

Using fuzzy logic methods, some complex experiments that are not possible due to critical limitations can be simulated in a short time. In this study, experimental data of Newtonian 40% aqueous glycerin solution (GS) and non-Newtonian 600[Formula: see text]ppm aqueous xanthan gum solution (XGS) working fluids were used to model the hydraulic performance of a centrifugal blood pump. A novel fuzzy logic-based model (FLM) for modeling the hydraulic performance of the pump model is proposed. In the proposed model, there are two inputs which are flow rate and impeller rotational speed and one output which is head pressure. In FLM, the range for flow rate is 1–7.8[Formula: see text]L/min in GS and 1–8[Formula: see text]L/min in XGS, and for head pressure 50–245[Formula: see text]mmHg in GS and 50–215[Formula: see text]mmHg in XGS. In addition, impeller rotational speed range is 2700–3600[Formula: see text]rpm for both fluids. The estimated results with FLM were validated with the experimental results and it was seen that the FLM was compatible with the experimental results with an accuracy of 96.25%. These results imply that the developed FLM is acceptable and can be used to assist in determining the performance of blood pumps.

Fuzzy logic-based modeling method for regional multi-ship collision risk assessment considering impacts of ship crossing angle and navigational environment

The ship crossing angle and navigational environment have significant impact on the precisely modeling of regional ship collision risk, but the existed studies seldom consider this problem. In order to realize the perception of regional multi-ship collision risk, this paper proposes a fuzzy logic-based modelling method for regional multi-ship collision risk assessment considering impact of ship crossing angle and navigational environment. The method incorporates the impact factors of DCPA, TCPA, ship crossing angle and navigational environment in multi-ship encounter situation by using fuzzy logic theory. We use Analytic Hierarchy Process (AHP) to obtain the risk weights of different ships versus single ships, and then obtain the value of multi-ship collision risk. In order to verify the validity of the model, data from the Taiwan Strait is used for verification, and the results show that this method can realize the early warning of regional multi-ship collision risk. The method can provide an important basis for maritime collision risk monitoring and ship navigation risk assessment for maritime administration departments and shore-based center of maritime autonomous ships.

Fuzzy logic-based modeling and analysis of SBCNC-60 machine for turning operation of surface finish and MRR output

On the everlasting demand for better accuracy, high speed, and the inevitable approach for the high-quality surface finish as the basic requirements in the process industry, there felt the requirement to develop models which are reliable for predicting surface roughness (SR) as it is having a crucial role in the process industries. In this paper, SBCNC-60 of HMT make used to study the purpose of machining, while cutting speed (CS), feed rate (FR), and the depth of cut (DoC) were considered as parameters for machining of P8 material. Turning experiments data is studied by keeping two parameters constant at the mid-level out of three parameters. An artificial intelligence technique named fuzzy was engaged in working out for surface roughness and material removal rate (MRR) to design the models of reliable nature for the predictions. The accurate prediction performance of the fuzzy logic model was then better analyzed by calculating MAPE, RMSE, MAD, and correlation coefficient between experimental values and fuzzy logic predictions. MAPE, RMSE, MAD, and correlation coefficient calculated 2.66%, 8.20, 6.44, and 0.98 for MRR and 4.19%,1.16, 0.86 and 0.90 for SR, respectively. Hence, the proposed fuzzy logic rules efficiently predict the SR and MRR on P8 material with higher accuracy and computational cost.

Boolean function metrics can assist modelers to check and choose logical rules

Computational models of biological processes provide one of the most powerful methods for a detailed analysis of the mechanisms that drive the behavior of complex systems. Logic-based modeling has enhanced our understanding and interpretation of those systems. Defining rules that determine how the output activity of biological entities is regulated by their respective inputs has proven to be challenging. Partly this is because of the inherent noise in data that allows multiple model parameterizations to fit the experimental observations, but some of it is also due to the fact that models become increasingly larger, making the use of automated tools to assemble the underlying rules indispensable. We present several Boolean function metrics that provide modelers with the appropriate framework to analyze the impact of a particular model parameterization. We demonstrate the link between a semantic characterization of a Boolean function and its consistency with the model’s underlying regulatory structure. We further define the properties that outline such consistency and show that several of the Boolean functions under study violate them, questioning their biological plausibility and subsequent use. We also illustrate that regulatory functions can have major differences with regard to their asymptotic output behavior, with some of them being biased towards specific Boolean outcomes when others are dependent on the ratio between activating and inhibitory regulators. Application results show that in a specific signaling cancer network, the function bias can be used to guide the choice of logical operators for a model that matches data observations. Moreover, graph analysis indicates that commonly used Boolean functions become more biased with increasing numbers of regulators, supporting the idea that rule specification can effectively determine regulatory outcome despite the complex dynamics of biological networks.

Fuzzy Logic-Based Modeling Method for Regional Multi-Ship Collision Risk Assessment Considering Impact of Ship Crossing Angle and Navigational Environment

Fuzzy Logic-Based Modeling Method for Regional Multi-Ship Collision Risk Assessment Considering Impact of Ship Crossing Angle and Navigational Environment

DynASP2.5: Dynamic Programming on Tree Decompositions in Action

Efficient exact parameterized algorithms are an active research area. Such algorithms exhibit a broad interest in the theoretical community. In the last few years, implementations for computing various parameters (parameter detection) have been established in parameterized challenges, such as treewidth, treedepth, hypertree width, feedback vertex set, or vertex cover. In theory, instances, for which the considered parameter is small, can be solved fast (problem evaluation), i.e., the runtime is bounded exponential in the parameter. While such favorable theoretical guarantees exists, it is often unclear whether one can successfully implement these algorithms under practical considerations. In other words, can we design and construct implementations of parameterized algorithms such that they perform similar or even better than well-established problem solvers on instances where the parameter is small. Indeed, we can build an implementation that performs well under the theoretical assumptions. However, it could also well be that an existing solver implicitly takes advantage of a structure, which is often claimed for solvers that build on Sat-solving. In this paper, we consider finding one solution to instances of answer set programming (ASP), which is a logic-based declarative modeling and solving framework. Solutions for ASP instances are so-called answer sets. Interestingly, the problem of deciding whether an instance has an answer set is already located on the second level of the polynomial hierarchy. An ASP solver that employs treewidth as parameter and runs dynamic programming on tree decompositions is DynASP2. Empirical experiments show that this solver is fast on instances of small treewidth and can outperform modern ASP when one counts answer sets. It remains open, whether one can improve the solver such that it also finds one answer set fast and shows competitive behavior to modern ASP solvers on instances of low treewidth. Unfortunately, theoretical models of modern ASP solvers already indicate that these solvers can solve instances of low treewidth fast, since they are based on Sat-solving algorithms. In this paper, we improve DynASP2 and construct the solver DynASP2.5, which uses a different approach. The new solver shows competitive behavior to state-of-the-art ASP solvers even for finding just one solution. We present empirical experiments where one can see that our new implementation solves ASP instances, which encode the Steiner tree problem on graphs with low treewidth, fast. Our implementation is based on a novel approach that we call multi-pass dynamic programming (M-DPSINC). In the paper, we describe the underlying concepts of our implementation (DynASP2.5) and we argue why the techniques still yield correct algorithms.

Retraction Note: Analytical investigations and fuzzy logic-based modeling of the impact resistance of aluminum-epoxy laminated composites

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11431-021-1775-1

The fuzzy logic-based modeling of a micro-scale sloped solar chimney power plant

The energy demand of world is increasing worldwide because of increasing population and developing technology. The use of environmentally friendly renewable resources is very important in providing the increasing energy needs. In the renewable energy sources, the solar energy has a strategic importance because of its huge potential and unlimited. The production of electrical energy by solar chimney power plants is one of the reliable and profitable methods. Fuzzy logic-based approaches are commonly used for modeling different systems in many fields. Also, a renewable energy system can be modelled by fuzzy definitions. In this way, it can provide efficiently and quickly theoretical estimates of systems with productive simulations. In this study, using the experimental data obtained from the micro-scale sloped solar chimney power plant in carried on scientific research project by authors, the obtaining and verifying a fuzzy logic-based model (FLBM) that can calculate the change in air velocity at turbine according to the change of radiation and temperature is presented. The air velocity at the turbine inlet is the considerable variable determining the electricity generation in a solar chimney. Thus, the output of the model is determined as this air velocity. In changes in the radiation and temperature values are defined as inputs. A two input-one output fuzzy model is obtained, in which the inference method is designed in the form of Mamdani and the membership functions in the form of the triangle, making inferences according to the rule base determined by the experience achieved from the experimentally studies. In order to investigate the accuracy of the FLBM, the simulation results and the data get from experimental setup in April 2019 are compared and evaluated. The validation of the FLBM compared to the experimental system is investigated using different error evaluation criteria. It is proved that the results of FLBM and experimental data are realized at a high rate (95.95 %) close to each other and similarly.

Application of artificial intelligence to maximize methane production from waste paper

This article proposes a methodology based on artificial intelligence to enhance methane production from waste paper. The proposed methodology combines fuzzy logic-based modelling and modern optimization. Firstly, a robust Adaptive Network-based Fuzzy Inference System model of methane production process through fuzzy logic modelling is created using experimental datasets. Second, a particle swarm optimizer was used to obtain the optimal process conditions. During the optimization procedure, the beating time and feedstock/inoculum ratio are employed as decision variables in order to maximize methane production. The obtained resulted from the proposed methodology are compared with those obtained by response surface methodology. The results of the comparison confirmed the superiority of the proposed methodology. The fuzzy model shows a better fitting to the experimental data compared to ANOVA. The fuzzy model showed a higher coefficient of determination and a lower value of root mean squared errors compared to ANOVA. Moreover, the proposed strategy, that is, modelling and optimization, is an effective method for increasing the biomethane yield at extended range conditions.

О завершаемости транзакций над семантическими документными моделями

We consider the framework of Document Modeling, which lays the formal basis for representing the document lifecycle in Business Process Management systems. We formulate document models in the scope of the logic-based Semantic Modeling language and study the question whether transactions given by a document model terminate on any input. We show that in general this problem is undecidable and formulate sufficient conditions, which guarantee decidability and polynomial boundedness of effects of transactions.

Robotic welding using fuzzy logic to predict penetration for an oil pipeline weldment

Welding oil pipeline is one of the most difficult process with high quality requirements. Normally, a high skilled welder with long time experience is needed. Presently, robotic technology is sophisticated and advanced in several engineering applications. This paper proposes a robotic application for oil pipeline using fuzzy logic-based modelling to predict a depth penetration. All parameters are defined in fuzzy input and output. Fuzzy rules are created for inference reasoning. Fuzzy output carries out solutions for effective decision making to select optimization parameters as well as directly linked to a welding robot. The most critical aspect for an oil pipe welding is to get a proper penetration which is can not visually see but can infer from the actual operating process and predict by fuzzy.

Fuzzy logic-based modeling and analysis of surface roughness, electrostatic charge, and material removal rate in ultrahigh precision diamond turning of rigid contact lens polymer

Due to increasing demand for high accuracy and high-quality surface finish in optical industry, contact lens manufacturing requires reliable models for predicting surface roughness (Ra) which plays a very important role in the optical manufacturing industry. In this study, a Nanoform 250 ultra-grind turning machine was used for machining, while cutting speed, feed rate, and the depth of cut (with values selected to cover a wide range based on the literature) were considered as the machining parameters for a diamond turned rigid polymethylmethacrylate (PMMA) contact lens polymer. Turning experiments were designed and conducted according to Box–Behnken design which is a response surface methodology technique. Fuzzy logic-based artificial intelligence method was employed to develop an electrostatic charge (ESC), Ra, and material removal rate (MRR) prediction models. The accuracy and predictive ability of the fuzzy logic model was then judged by considering an average percentage error between experimental values and fuzzy logic predictions. Further, a comparative evaluation of experiments and fuzzy logic approach showed that the average errors of ESC, Ra, and MRR using fuzzy logic system were in tandem with experimental results. Hence, the developed fuzzy logic rules can be effectively utilized to predict the ESC, Ra, and MRR of a rigid PMMA contact lens polymers in automated optical manufacturing environments for high accuracy and computational cost.

Fuzzy logic-based modelling of yield strength of as-cast A356 alloy

Uncertain and imprecise data are inherent to many domains, e.g. casting lightweight components. Fuzzy logic offers a way to handle such data, which makes it possible to create predictive models even with small and imprecise data sets. Modelling of cast components under fatigue load leads to understanding of material behaviour on component level. Such understanding is important for the design for minimum warranty risk and maximum weight reduction of lightweight cast components. This paper contributes with a fuzzy logic-based approach to model fatigue-related mechanical properties of as-cast components, which has not been fully addressed by the current research. Two fuzzy logic models are constructed to map yield strength to the chemical composition and the rate of solidification of castings for two A356 alloys. Artificial neural networks are created for the same data sets and then compared to the fuzzy logic approach. The comparison shows that although the neural networks yield similar prediction accuracy, they are less suitable for the domain because they are opaque models. The prediction errors exhibited by the fuzzy logic models are 3.53% for the model and 3.19% for the second, which is the same error level as reported in related work. An examination of prediction errors indicated that these are affected by parameters of the membership functions of the fuzzy logic model.

Disease-modifying effects of allergen-nanoparticle conjugates – A study in human alveolar co-culture models mimicking the type 2 pre-inflamed state

This paper presents a life history-oriented modeling framework to investigate residential location decisions as a two-tier process of location search and location choice. In the first tier, a stress-based location search model is developed by assuming that households search for a new location due to continual stress at different life-domains. The search model adopts a fuzzy logic-based modeling method that mimics the inter-dependencies between push and pull factors. In the second tier, a location choice model is developed that accommodates how location decisions interact with life-cycle events at different life-domains. The model utilizes a latent segmentation-based logit modeling technique to address the panel effect of the households’ housing career. The model results suggest that households in general show preference for larger lots, and locations closer to work place, transit stop, and health service. Location choice is found to be significantly influenced by the life-cycle events as well as the lead and lagged effects. For example, the birth of a child magnifies the need of larger lots. The life-history effects, however vary across two segments. Suburbanite households in segment two prefer larger lots following a job change; whereas, urbanite households in segment one show a negative relationship. The adjustment period for a job change is longer than that of addition of a new job. A longer adjustment time is also found in the case of the first time vehicle purchase than acquisition of a vehicle. Presence of children influences suburbanite households to reside closer to work place. Urbanite households with children prefer to live closer to school.

Life history-oriented residential location choice model: A stress-based two-tier panel modeling approach

This paper presents a life history-oriented modeling framework to investigate residential location decisions as a two-tier process of location search and location choice. In the first tier, a stress-based location search model is developed by assuming that households search for a new location due to continual stress at different life-domains. The search model adopts a fuzzy logic-based modeling method that mimics the inter-dependencies between push and pull factors. In the second tier, a location choice model is developed that accommodates how location decisions interact with life-cycle events at different life-domains. The model utilizes a latent segmentation-based logit modeling technique to address the panel effect of the households’ housing career. The model results suggest that households in general show preference for larger lots, and locations closer to work place, transit stop, and health service. Location choice is found to be significantly influenced by the life-cycle events as well as the lead and lagged effects. For example, the birth of a child magnifies the need of larger lots. The life-history effects, however vary across two segments. Suburbanite households in segment two prefer larger lots following a job change; whereas, urbanite households in segment one show a negative relationship. The adjustment period for a job change is longer than that of addition of a new job. A longer adjustment time is also found in the case of the first time vehicle purchase than acquisition of a vehicle. Presence of children influences suburbanite households to reside closer to work place. Urbanite households with children prefer to live closer to school.

Abstract A44: A landscape of pharmacogenomic interactions in cancer

Abstract Systematic studies of cancer genomes are providing unprecedented insights into the molecular nature of human cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here we report how cancer-driving alterations identified in 11,289 tumors from 29 tissues (integrating mutations, copy-number alterations, methylation and gene expression) correlate with response to 265 compounds profiled in 1,001 human cancer cell-lines. We find that cell-lines faithfully recapitulate oncogenic aberrations identified in tumors, and that many of these associate with drug sensitivity or resistance. Logic-based modeling uncovers combinations of aberrations that specifically sensitize to drugs, while machine-learning demonstrates the redundancy of different molecular data types in predicting drug response. Our comprehensive analysis and associated datasets are rich resources to identify novel therapeutic options for selected cancer sub-populations. Citation Format: Francesco Iorio, Theo Knijnenburg, Daniel vis, Graham Bignell, Michael Menden, Lodewyk Wessels, Julio Saez-Rodriguez, Ultan McDermott, Mathew Garnett. A landscape of pharmacogenomic interactions in cancer. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Targeting the Vulnerabilities of Cancer; May 16-19, 2016; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(1_Suppl):Abstract nr A44.

A Landscape of Pharmacogenomic Interactions in Cancer

SummarySystematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.

Oncoguide System: A computerized interactive assistance system for the diagnosis and treatment of CML/MPN and MDS D.

e18545 Background: The project aims to develop and implement a computerized interactive assistance system for the diagnosis and treatment of CML / MPN and MDS. The system is based on modulated, established medical guidelines with reference to the individual patient information. The user is navigated through the complex recommendations of current guidelines and decision trees of the CML/MPN and MDS, similar to a car navigation system. Methods: The medical user is hereby offered a systematic decision support. In addition, the decisions are documented and visualized in an intuitive user interface. The expert system is also able to point to currently recruiting therapy studies. Technical implementation and advantages of this solution: From a technical perspective, the knowledge-based system is implemented as a client-server architecture. The server acts as a central data storage, for example, in the form of a database. As a client, for example, Internet browsers can be used. So it is possible to centrally store the accumulated knowledge and to present the current research results to each user. The advantage of such a technical solution compared to guidelines-books is rapidly adaptable knowledge on new research results, as well as the implementation of a self-learning system. The system design has to pay particular attention to an intuitive user interface. On the server's side, the knowledge of guidelines and interviews with experts have to be formalized in an appropriate manner. There are approaches based on an ontological or logic-based modeling. Results: On the client's side the system suggests the user appropriate decisions for the diagnosis and further treatment of diseases. The underlying methodology is based on approaches from artificial intelligence, such as the Bayesian inference or machine learning methods. . Conclusions: The presented computerized interactive assistance system could help to increase the accuracy of diagnosis, treatment and follow up of CML/MPN and MDS