Different Kinds Of Models Research Articles

New approach to the compound energy formalism (NACEF) part I. Thermodynamic modeling based on the sublattice model

In the CALPHAD modeling, the Compound Energy Formalism (CEF) is widely used because it is a general formalism which allows to select the most appropriate model for different kind of phases (gas phase, solid and liquid solutions, interstitial solutions, intermetallic compounds, solid oxide phases, ionic melts, phases that show chemical order-disorder transformations …). It consists in dividing the phase into sublattices and the models differ by the nature of the constituents (atoms, ions, vacancies, etc.) present in the different considered sublattices where mixing may or may not occur. For many intermetallic compounds which present a crystal structure containing many crystallographic sites on which disorder occurs, the number of parameters to be evaluated which varies exponentially with the number of sublattices becomes a problem in multicomponent systems. This yields the necessity to simplify the description and reduce the number of sublattices by combining different sites in single sublattices and this leads to that different simplifications can be used to model a phase in different systems. It is quite known that the CEF approach does not allow continuous compatibility between the different simplifications which constitutes a problem for the development of multicomponent databases.In the present paper, we present a New Approach to the Compound Energy Formalism (NACEF) which is derived mathematically from the CEF and therefore retains its full potential while providing several important improvements. As example, considering the particular case of the complex σ phase, the present work shows that NACEF makes it possible to make compatible a model based on the full description (five-sublattice model) with simplifications which can be made of it. On the other hand, it is also shown that NACEF form combined to a simple Muggianu type extrapolation allows to obtain reliable estimates of the energies of ordered configurations in multicomponent systems using those for the binary configurations obtained from DFT calculations.

Prediction of Slag Viscosity Based on Machine Learning for Molten Gasification of Hazardous Wastes

Experimental data from viscosity measurements of 124 glassy slags were used to drive and develop machine learning models that could be used for direct or indirect viscosity prediction. Samples were categorized according to the content of chemical components or general competitive neural network. The direct viscosity prediction using artificial neural network models of different kinds of slag samples was established. The prediction average error and maximum absolute error in the corresponding models were significantly smaller than the artificial neural network without categorizing the samples. Moreover, the viscosity curve for each glassy slag was fitted by a general formula, and the corresponding parameters were obtained. The principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) neural network models for predicting parameters were proposed. This indirect approach was considered to successfully overcome the limitations of temperature and viscosity ranges in direct prediction while delivering smooth viscosity curves.

Mathematical optimization model on BP neural network for the preparation of C4 olefins by ethanol coupling

With industrial production, the continuous consumption of resources, people’s use, and consumption of resources make the community concerned about resources. C4 olefins are used as significant raw materials in chemical production, and the use of ethanol coupling to prepare C4 olefins will become an important route in industrial production in the future. The data of ethanol conversion and C4 olefin selectivity can reflect the utilization of resources in the reflective process.With the rapid growth of machine learning and intelligent computing, a large number and different kinds of models have been constructed for the utilization and consumption of resources, which makes it possible to optimize the process of ethanol coupling for the preparation of C4 olefins. The objective of this paper is to construct a mathematical optimization model for the preparation of C4 olefins by ethanol coupling. Firstly, the catalyst combination was split, and secondly, the prediction model for ethanol conversion and C4 selectivity was constructed based on BP neural network. Finally, the feasibility of the model was tested by determining three indicators, Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Meanwhile, the PCA algorithm was applied to rank the degree of influence of catalyst and temperature on the conversion of ethanol and C4 olefin selectivity.

Varying Infimum Gradient Descent Algorithm for Agent-Sever Systems Using Different Order Iterative Preconditioning Methods

In the traditional gradient descent (T-GD) algorithm, the convergence rate is strongly depend on the condition number of the information matrix: a larger condition number leads to a poor optimal convergence factor infimum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu _{\text{op}}$</tex-math></inline-formula> , which sets a convergence rate ceiling. That is, once the information matrix is fixed, the convergence factor of the T-GD algorithm reaches at most the infimum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu _{\text{op}}$</tex-math></inline-formula> . This article studies a varying infimum gradient descent algorithm, which can move down the infimum by using different order iterative preconditioning methods, as follows: first, for infinite iterative algorithm, the infimum becomes smaller and smaller with the increased iteration numbers; second, for finite iterative algorithm, the infimum is equal to zero, and the parameter estimates can be obtained in only one iteration; third, construct an adaptive interval between zero and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu _{\text{op}}$</tex-math></inline-formula> , which can establish a link between the least squares and T-GD algorithms. Based on the varying infimum gradient descent algorithm, researchers can adaptively choose preconditioning matrices for different kinds of models on a case by case basis. The convergence analysis and simulation examples show effectiveness of the proposed algorithms.

Animal Models of Temporomandibular Joint Osteoarthritis: Classification and Selection.

Temporomandibular joint osteoarthritis (TMJOA) is a common degenerative joint disease that can cause severe pain and dysfunction. It has a serious impact on the quality of lives of patients. Since mechanism underlying the pathogenesis of TMJOA is not fully understood, the development of effective tools for early diagnosis and disease-modifying therapies has been hindered. Animal models play a key role in understanding the pathological process of diseases and evaluating new therapeutic interventions. Although some similarities in disease processes between animals and humans are known, no one animal model is sufficient for studying all characteristics of TMJOA, as each model has different translatability to human clinical conditions. For the past 4 decades, TMJOA animal models have been studied by numerous researchers and can be broadly divided into induced, naturally occurring, and genetically modified models. The induced models can be divided into invasive models (intra-articular injection and surgical induction) or non-invasive models (mechanical loading, high-fat diet, and sleep deprivation). Different types of animal models simulate different pathological expressions of TMJOA and have their unique characteristics. Currently, mice, rats, and rabbits are commonly used in the study of TMJOA. This review sought to provide a general description of current experimental models of TMJOA and assist researchers in selecting the most appropriate models for different kinds of research.

A Domain-Specific, Model Based Systems Engineering Approach for Cyber-Physical Systems

Model Based Systems Engineering as a scientific discipline tries to address the increasing complexity of today’s cyber-physical systems by utilizing different kinds of models. In practical application, however, this approach is often constrained to SysML-based object modeling. Even though this appears to be a suitable approach for dealing with complexity, various restrictions limit stakeholder acceptance. Considering scientific discussions in the context of modeling shows two different schools of thought. On the one hand, arguments for more formalized and rigorous concepts can be found, where on the other hand, the need for more stakeholder-oriented and easier-to-understand concepts is postulated. As both are reasonable, the question of integration arises. To address this aspect, we developed the concept of Domain Specific Systems Engineering. Our research in this field lasted for nearly a decade, and different aspects have been investigated. This paper contributes a summary of the overall approach that integrates the various aspects investigated so far. Thus, the underlying concepts are explained, and the corresponding modeling stack and tool-chain are described in more detail. Further, the practical experiences from various case studies are summarized, and identified shortcomings are discussed.

The Impact of Fracture Geometries on Heterogeneity and Accuracy of Upscaled Equivalent Fracture Models

Abstract Fractures are widely distributed in the upper crust, which is important to transport processes in groundwater, geothermal, and hydrocarbon reservoirs. Little attention has been paid to the heterogeneity and accuracy of upscaled equivalent fracture models (EFMs) regarding the complex fracture geometries during numerical reservoir simulation. This paper investigates how fracture geometric properties affect the accuracy of highly heterogeneous EFMs. An ensemble of synthetic fractured porous rocks is generated stochastically with varied fracture length, fracture density, and aperture-length correlation. Each realization is upscaled to EFM with the recently developed multiple boundary method. Flow problems are solved for EFMs and discrete fracture models (DFMs) simultaneously, and their flow rates are compared. The results demonstrate that the heterogeneity of EFMs decreases with the number and length of fractures and increases with the aperture-length correlation component. The flow rates calculated by EFMs fit well with those by DFMs with a slope around 1.08 and R2 coefficient above 0.98. The accuracy of the upscaled EFM decreases when the aperture-length correlation changes from sublinear to linear. The uncertainty of upscaling accuracy decreases with dimensionless fracture density. The findings of this study can help for a better understanding of the effect of the discrete fracture geometries on the performance of upscaled EFMs and choosing the appropriate models for different kinds of fractured reservoirs.

Recognition and counting of typical apple pests based on deep learning

The recognition and counting of apple pests sampled by different sex attractants are very important and significant for pest control. Convolutional neural networks (CNNs) are common artificial intelligence algorithms widely used in image recognition and counting. However, because the sizes of different species of pests are different and the densities of pests on the sticky board are sometimes considerable, it is difficult to recognise and count them accurately and efficiently. This study proposes an improved recognition and counting approach based on deep learning with data reorganisation, termed ‘MPest-RCNN’. The contributions herein are twofold: (1) A new structure of Faster R-CNN is proposed by using ResNet101 feature extractor which has higher precision of recognition. (2) We propose a new convolutional network structure with small anchors to extract features such that the recognition accuracy is improved for small pests. We took three typical pests in apple orchards to establish an original data set using sex attractants. The proposed MPest-RCNN model solves the recognition problem of multiple types and sizes of pests by using different sex attractants. Finally, experiments are conducted, and a comparative analysis is provided for the proposed approach. The experimental results demonstrate that the precision, sensitivity, specificity, and F1-Score of the proposed approach reach 99.11%, 99.88%, 99.42%, and 99.50% respectively. In contrast with Faster R-CNN, the precision, sensitivity, and F1-Score increase by 0.31%, 7.77%, and 4.25% respectively. The comparative experimental results demonstrate that the mean average precision (mAP) of the proposed approach is higher than that of the currently used pest recognition approaches. In addition to reducing the complexity of creating multiple recognition models for different kinds of pests, the proposed model shows promise as an effective means for recognising and monitoring of other targets with similar characteristics, thereby providing theoretical support for ecological informatics in the future.

A Novel Approach for Small Object Detection in Medical Images through Deep Ensemble Convolution Neural Network

Small objects detection in medical image becomes an interesting field of research that helps the medical practitioners to focus on in-depth evaluation of diseases. The accurate localization and classification of objects face tremendous difficulty due to lower intensity of the images and distraction of pixel points that vary the decision on identifying the shape, structure etc. In many real-time cases, detection and classification of tiny objects in the medically treated images becomes mandatory. The proposed system is designed in the same criteria in which the semantic segmentation of tiny objects in the medical images is considered. The system design focused on implementing the model for different kinds of human organs such as lung and liver. The axial CT or PET images of Lung and Liver are considered as the prime input for the given system. Detection of tiny objects in the CT-PET images, segmenting it from the background and classification of segmented part as Tumor or Nodule is discussed. The preprocessed images are feature extracted after the morphology segmentation that determines the structural features of the tiny object being segmented. The feature vectors are nothing but the feature points from Kaze feature extraction and Morphology segmented image. These two inputs are fetched to the Deep ensemble Convolution neural network (DECNN) to obtain the dual classification results. Performing the quantitative measurements to evaluate the decision making system for nodule or tumor class is determined. The performance measure is done using accuracy, precision, recall and F1Score.

An efficient and scalable search engine for models

Search engines extract data from relevant sources and make them available to users via queries. A search engine typically crawls the web to gather data, analyses and indexes it and provides some query mechanism to obtain ranked results. There exist search engines for websites, images, code, etc., but the specific properties required to build a search engine for models have not been explored much. In the previous work, we presented MAR, a search engine for models which has been designed to support a query-by-example mechanism with fast response times and improved precision over simple text search engines. The goal of MAR is to assist developers in the task of finding relevant models. In this paper, we report new developments of MAR which are aimed at making it a useful and stable resource for the community. We present the crawling and analysis architecture with which we have processed about 600,000 models. The indexing process is now incremental and a new index for keyword-based search has been added. We have also added a web user interface intended to facilitate writing queries and exploring the results. Finally, we have evaluated the indexing times, the response time and search precision using different configurations. MAR has currently indexed over 500,000 valid models of different kinds, including Ecore meta-models, BPMN diagrams, UML models and Petri nets. MAR is available at http://mar-search.org.

Accelerating bioactive peptide discovery via mutual information-based meta-learning

Recently, machine learning methods have been developed to identify various peptide bio-activities. However, due to the lack of experimentally validated peptides, machine learning methods cannot provide a sufficiently trained model, easily resulting in poor generalizability. Furthermore, there is no generic computational framework to predict the bioactivities of different peptides. Thus, a natural question is whether we can use limited samples to build an effective predictive model for different kinds of peptides. To address this question, we propose Mutual Information Maximization Meta-Learning (MIMML), a novel meta-learning-based predictive model for bioactive peptide discovery. Using few samples from various functional peptides, MIMML can sufficiently learn the discriminative information amongst various functions and characterize functional differences. Experimental results show excellent performance of MIMML though using far fewer training samples as compared to the state-of-the-art methods. We also decipher the latent relationships among different kinds of functions to understand what meta-model learned to improve a specific task. In summary, this study is a pioneering work in the field of functional peptide mining and provides the first-of-its-kind solution for few-sample learning problems in biological sequence analysis, accelerating the new functional peptide discovery. The source codes and datasets are available on https://github.com/TearsWaiting/MIMML.

Modeling of a Generic Edge Computing Application Design.

Edge computing applications leverage advances in edge computing along with the latest trends of convolutional neural networks in order to achieve ultra-low latency, high-speed processing, low-power consumptions scenarios, which are necessary for deploying real-time Internet of Things deployments efficiently. As the importance of such scenarios is growing by the day, we propose to undertake two different kind of models, such as an algebraic models, with a process algebra called ACP and a coding model with a modeling language called Promela. Both approaches have been used to build models considering an edge infrastructure with a cloud backup, which has been further extended with the addition of extra fog nodes, and after having applied the proper verification techniques, they have all been duly verified. Specifically, a generic edge computing design has been specified in an algebraic manner with ACP, being followed by its corresponding algebraic verification, whereas it has also been specified by means of Promela code, which has been verified by means of the model checker Spin.

Parallel classification model of arrhythmia based on DenseNet-BiLSTM

In order to improve the classification performance of the model for different kinds of arrhythmias, a parallel classification model of arrhythmia based on DenseNet-BiLSTM is researched and proposed. Firstly, the model adopts a parallel structure. After wavelet denoising and heartbeat segmentation of ECG signals, this model can simultaneously capture the waveform features of small-scale heartbeat and large-scale heartbeat containing RR interval; Then, based on deep learning, Densely connected convolutional network (DenseNet) is applied to improve the model's ability to extract local features of ECG signals, and bidirectional long short-term memory network (BiLSTM) is introduced to improve the performance of the model in extracting time series features of ECG signals; Finally, weighted cross entropy loss function is used to alleviate the class imbalance of arrhythmia, and Softmax function is applied to achieve 4 classifications of arrhythmia. Experiments based on MIT-BIH arrhythmia database show that under the intra-patient paradigm, training time for each epoch, overall accuracy, F1 and specificity are 42 s, 99.44%, 95.89% and 99.32%, respectively; Under the inter-patient paradigm, training time for each epoch, overall accuracy, F1 and specificity are 23 s, 92.37%, 63.49% and 94.51%, respectively. Compared with other classification models, the model proposed in this paper has a good classification effect and is expected to be used in clinical auxiliary diagnosis.

Modelling Human-Fire Interactions: Combining Alternative Perspectives and Approaches

Although it has long been recognised that human activities affect fire regimes, the interactions between humans and fire are complex, imperfectly understood, constantly evolving, and lacking any kind of integrative global framework. Many different approaches are used to study human-fire interactions, but in general they have arisen in different disciplinary contexts to address highly specific questions. Models of human-fire interactions range from conceptual local models to numerical global models. However, given that each type of model is highly selective about which aspects of human-fire interactions to include, the insights gained from these models are often limited and contradictory, which can make them a poor basis for developing fire-related policy and management practices. Here, we first review different approaches to modelling human-fire interactions and then discuss ways in which these different approaches could be synthesised to provide a more holistic approach to understanding human-fire interactions. We argue that the theory underpinning many types of models was developed using only limited amounts of data and that, in an increasingly data-rich world, it is important to re-examine model assumptions in a more systematic way. All of the models are designed to have practical outcomes but are necessarily simplifications of reality and as a result of differences in focus, scale and complexity, frequently yield radically different assessments of what might happen. We argue that it should be possible to combine the strengths and benefits of different types of model through enchaining the different models, for example from global down to local scales or vice versa. There are also opportunities for explicit coupling of different kinds of model, for example including agent-based representation of human actions in a global fire model. Finally, we stress the need for co-production of models to ensure that the resulting products serve the widest possible community.

Analytical Modeling of Thermodynamic and Transport Anomalies of Water

The structures and properties of biomolecules like proteins, nucleic acids, and membranes depend on water. Water is also very important in industry. Overall, water is an unusual substance with more than 70 anomalous properties. The understanding of water is advancing significantly due to the theoretical and computational modeling. There are different kinds of models, models with fine-scale properties and increasing structural detail with increasing computational expense, and simple models, which focus on global properties of water like thermodynamics, phase diagram and are less computationally expensive. Simplified models give a better understanding of water in ways that complement more complex models. Here, we review analytical modelling of properties of water on different levels, the two- and three-dimensional Mercedes- Benz (MB) models of water and experimental water.

The knitting methods for seamless garments based on four-needle bed computerized flat machine

Seamless garments reducing the cutting and sewing processes are created using seamless knitting technology. This type of garment, being less dependent on labor, is knitted as a whole and makes a great contribution to improving production efficiency. More importantly, seamless garments are more comfortable and natural compared with those styles produced by traditional technology. However, the techniques of seamless garments are huge and complex, making the pattern design process more difficult. In addition, the seamless knitting technology has great restrictions to the styles with rich shapes due to the limitation of the devices. Therefore, how to knit more complex styles using seamless knitting technology still faces great challenges. For these reasons, this study extensively investigated the knitting principle of seamless garments and proposed six knitting models for different kinds of garments. Then the transformation methods from 3D style to 2D pattern were explored respectively based on the knitting characteristics of each model. Finally, by package programming in the SDS-ONE APEX design system, the actual knitting for six types of garments was conducted on the computerized flat knitting machine. The results showed that different types of seamless garments have significant differences in the pattern design and the approaches proposed above can make different kinds of garments using seamless knitting technology.

A New method of LncRNA classification based on ensemble learning

Long noncoding RNAs (lncRNAs), which have a length longer than 200bp (base pair), participate in various critical biological processes. Moreover, they have many similar features with another kind of RNA - coding RNA, such as long length of transcript and poly-A tail. Therefore, distinguish lncRNA and coding RNA can be one important task in bioinformatics. With the advanced and outstanding ability of machine learning, the computational method provides new insight into lncRNA classification. In this study, two feature selection methods (lasso and PCA) are applied to reduce dimension. 8 differentiated features are extracted, and lasso selection indicates better performance than the PCA method. To achieve an advanced performance of lncRNA classification, one novel ensemble learning based on primary learner and secondary learner is constructed. After comparing different kinds of models, ensemble learning achieves the most outstanding performance in AUC and accuracy within the test dataset (The median of Accuracy=0.950228, AUC=0.979664), which may shed light on the classification of lncRNA.

Overcoming the Challenges to Enhancing Experimental Plant Biology With Computational Modeling.

The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.

Measurement Noise Model for Depth Camera-Based People Tracking.

Depth cameras are widely used in people tracking applications. They typically suffer from significant range measurement noise, which causes uncertainty in the detections made of the people. The data fusion, state estimation and data association tasks require that the measurement uncertainty is modelled, especially in multi-sensor systems. Measurement noise models for different kinds of depth sensors have been proposed, however, the existing approaches require manual calibration procedures which can be impractical to conduct in real-life scenarios. In this paper, we present a new measurement noise model for depth camera-based people tracking. In our tracking solution, we utilise the so-called plan-view approach, where the 3D measurements are transformed to the floor plane, and the tracking problem is solved in 2D. We directly model the measurement noise in the plan-view domain, and the errors that originate from the imaging process and the geometric transformations of the 3D data are combined. We also present a method for directly defining the noise models from the observations. Together with our depth sensor network self-calibration routine, the approach allows fast and practical deployment of depth-based people tracking systems.

Plant Diseases Detection and Pesticide Suggestion Implementation

Plant diseases affect the life of not only farmers but also businesses which are dependent on it. Plant disease detection is a computer vision problem which tries to identify the disease splat is infected using an image of a plant leaf. Different kinds of models have been proposed to tackle this problem. This paper focuses on generating small, lightweight and accurate models with the help of deep learning and transfer learning.