Generic Process Model Research Articles

SrBERT: automatic article classification model for systematic review using BERT

BackgroundSystematic reviews (SRs) are recognized as reliable evidence, which enables evidence-based medicine to be applied to clinical practice. However, owing to the significant efforts required for an SR, its creation is time-consuming, which often leads to out-of-date results. To support SR tasks, tools for automating these SR tasks have been considered; however, applying a general natural language processing model to domain-specific articles and insufficient text data for training poses challenges.MethodsThe research objective is to automate the classification of included articles using the Bidirectional Encoder Representations from Transformers (BERT) algorithm. In particular, srBERT models based on the BERT algorithm are pre-trained using abstracts of articles from two types of datasets, and the resulting model is then fine-tuned using the article titles. The performances of our proposed models are compared with those of existing general machine-learning models.ResultsOur results indicate that the proposed srBERTmy model, pre-trained with abstracts of articles and a generated vocabulary, achieved state-of-the-art performance in both classification and relation-extraction tasks; for the first task, it achieved an accuracy of 94.35% (89.38%), F1 score of 66.12 (78.64), and area under the receiver operating characteristic curve of 0.77 (0.9) on the original and (generated) datasets, respectively. In the second task, the model achieved an accuracy of 93.5% with a loss of 27%, thereby outperforming the other evaluated models, including the original BERT model.ConclusionsOur research shows the possibility of automatic article classification using machine-learning approaches to support SR tasks and its broad applicability. However, because the performance of our model depends on the size and class ratio of the training dataset, it is important to secure a dataset of sufficient quality, which may pose challenges.

Semisupervised Association Learning Based on Partial Differential Equations for Sparse Representation of Image Class Attributes

Semisupervised learning is an idea that addresses how to use a large number of unlabeled samples and a limited number of labeled samples to learn decision knowledge together. In this paper, we propose a multitask multiview semisupervised learning model based on partial differential equation random field and Hilbert independent standard probability image genus attribute model, i.e., shared semantics. In the framework of the image-like genus attribute model, data from different data sources are generated by their shared hidden space representation. Different from the traditional model, this paper uses the Hilbert independence criterion to inscribe the shared relationship of hidden expressions. Meanwhile, to exploit the correlations between labels in the label space as well, this paper uses the partial differential equation random field to inscribe the correlations between different kinds of labels in the label space and the correlations between hidden features and labels. Using the variational expectation-maximization algorithm, the whole generative process model can be inferred. To verify the effectiveness of the model, two artificial datasets and three real datasets are tested in this paper, and the experimental results verify the effectiveness of the algorithm in the paper. On the one hand, it not only improves the classification accuracy of the multiclassification problem and the multilabel problem; it also outputs the association structure between different kinds of labels and between hidden features and labels.

COMPUTER SIMULATION OF PARTICLE VELOCITY IN A CYCLONE

The article sets the goal of determining and calculating the speed of accelerated movement of particles in the gas-dust flow of an air cyclone, depending on the size of the cyclone, the physical properties of the particle and the gas-dust flow. A method for constructing a computer model of the motion of a particle in a roaring gas-dust flow of a cyclone is presented. The sequence of formalization of the computer model of the process of sedimentation of particles of a gas-dust flow of a cyclone unit is based on the theoretical foundations of aerodynamics and touches on the most significant aspects of the development of a cyclone. Attention paid to the analysis of the dynamics of particle motion in a centrifugal field, accelerated motion and a realistic estimate of the particle velocity. The mutual influences of five forces are considered. This is the centrifugal force, the force of resistance of the gas-dust flow, the forces of gravity, the Archimedean force and the force of inertia, acting on the accelerating motion of the particle in the flow. On the basis of the block principle, a general computer model of the cycloning process is built, which makes it possible to calculate the uneven velocity of a particle in a gas-dust flow. Analytical methods for evaluating the acceleration parameters and the main methods of numerical analysis of the dynamics of particle motion, shows that the main advantage of up to 0.8 seconds is the accelerated motion of a particle in a gas-dust flow under the given conditions. During this time, most of the particles reach the cyclone wall. This shows that the determination of the accelerating motion of a particle plays an important role in cyclones.

A Generic Process Model for Sociocultural Data Collection and Decision Making in Facility Programming

This is the third paper in a trilogy of papers that started with developing the philosophical and theoretical foundations for the study of sociospatial interactions. In the second paper, we created a guiding framework for facilities programming information collection and decision making. In this paper, we use that guiding framework and develop a process model for the programming process. Our methodology involves systems theory, activity theory, and task analysis. We present a generic model of the data collection process in facilities programming. This model delineates process steps, their sequence, and their content. The intent of this model is to provide structure and content suggestions for developing project-specific facilities programing research designs for information collection, processing, and programmatic decision-making. It is a plan for action, articulated and explicated so that professional programmers can use it as a template for organizing their work. This process model is tentative and general, intended as a guide for creating customized process designs for each programming project, considering the specifics of the building type and the programming situation.

An integrated degradation modeling framework considering model uncertainty and calibration

General path models and stochastic process models are two widely applied categories of probabilistic degradation models. The former explains the randomness of degradation data as normal distributed errors with zero mean. The latter describes degradation with stochastic processes such as Wiener process, Gamma process and Inverse Gaussian process. For general path models, a limitation is the assumption of normally distributed errors. For stochastic process models, model uncertainty with respect to the available stochastic processes should be considered, but the widely-applied model selection methods in consideration of model uncertainty are unable to warn when all the candidate models fit data poorly. Therefore, an integrated degradation modeling framework based on wavelet density estimation is proposed, which can calibrate the distribution of errors for general path models and deal with model uncertainty for stochastic process models. The proposed framework can select the best stochastic process if certain stochastic processes fit the degradation data well. Otherwise, all the candidate stochastic processes can be calibrated, which overcomes the drawback of model selection methods. The effectiveness and feasibility of the proposed framework are illustrated through a case study and a numerical example.

Computing in the statistical office

Computing with data is at the hart of the activities statistical office. Yet, the area of technical computing often falls between the two stools of data analysts and IT developers. In this paper we analyze the importance of computational skills in the Generic Statistical Business Process Model. Next, we give an overview of computational topics that are of importance to the statistical office. Many of these skills turn out to be of highly technical nature. After this, we try to provoke a wider discussion on the role of technical computing by 1) introducing the role of the Research Software Engineer into the field of official statistics and 2) propose a six semester bachelor’s curriculum in official statistics.

Trace Clustering: A Preprocessing Method to Improve the Performance of Process Discovery

The information system collects a large number of business process event logs, and process discovery aims to discover process models from the event logs. Many process discovery methods have been proposed, but most of them still have problems when processing event logs, such as low mining efficiency and poor process model quality. The trace clustering method allows to decompose original log to effectively solve these problems. There are many existing trace clustering methods, such as clustering based on vector space approaches, context-aware trace clustering, model-based sequence clustering, etc. The clustering effects obtained by different trace clustering methods are often different. Therefore, this paper proposes a preprocessing method to improve the performance of process discovery, called as trace clustering. Firstly, the event log is decomposed into a set of sub-logs by trace clustering method, Secondly, the sub-logs generate process models respectively by the process mining method. The experimental analysis on the datasets shows that the method proposed not only effectively improves the time performance of process discovery, but also improves the quality of the process model.

EMX: Overcoming Silicon Chip EM Simulation Challenges for Passive Circuit Analysis and Model Development

Silicon-chips present several challenges for traditional electromagnetic (EM) tools. In this paper we review how the Cadence EMX Planar 3D Solver has been designed to overcome these issues. Specifically, we discuss necessary features for PDK support: meshing requirements, solver options, and ease-of-use enhancements in the Cadence Virtuoso design environment. EMX can be used as part of scalable model generation process for a variety of typical components, for example inductors, transformers and baluns. These scalable models can then be used for synthesis of optimal components that meet the user-supplied specifications.

Securing IoT Devices: A Robust and Efficient Deep Learning with a Mixed Batch Adversarial Generation Process for CAPTCHA Security Verification

The Internet of Things environment (e.g., smart phones, smart televisions, and smart watches) ensures that the end user experience is easy, by connecting lives on web services via the internet. Integrating Internet of Things devices poses ethical risks related to data security, privacy, reliability and management, data mining, and knowledge exchange. An adversarial machine learning attack is a good practice to adopt, to strengthen the security of text-based CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), to withstand against malicious attacks from computer hackers, to protect Internet of Things devices and the end user’s privacy. The goal of this current study is to perform security vulnerability verification on adversarial text-based CAPTCHA, based on attacker–defender scenarios. Therefore, this study proposed computation-efficient deep learning with a mixed batch adversarial generation process model, which attempted to break the transferability attack, and mitigate the problem of catastrophic forgetting in the context of adversarial attack defense. After performing K-fold cross-validation, experimental results showed that the proposed defense model achieved mean accuracies in the range of 82–84% among three gradient-based adversarial attack datasets.

An Integrated Approach for Discovering Process Models According to Business Process Types

Process discovery technique aims at automatically generating a process model that accurately describes a Business Process (BP) based on event data. Related discovery algorithms consider recorded events are only resulting from an operational BP type. While the management community defines three BP types, which are: Management, Support and Operational. They distinguish each BP type by different proprieties like the main business process objective as domain knowledge. This puts forward the lack of process discovery technique in obtaining process models according to business process types (Management and Support). In this paper, we demonstrate that business process types can guide the process discovery technique in generating process models. A special interest is given to the use of process mining to deal with this challenge.

Virtual Risk Assessment for the Deployment of Autonomous Shuttles

In recent years, trials of autonomous shuttle vehicles have been conducted worldwide. Currently, there exists no generalized process model for deployment and continuous operation of shuttles. Shuttle suppliers use their own developed procedures, making it difficult for the relevant stakeholders (e.g., public authorities) to assess the risk of potential shuttle deployment. The Digibus® Austria flagship project, among other goals, develops an approach for the virtual risk assessment of identified critical spots along proposed shuttle paths. Embedded into the deployment process, this serves as a significant body of evidence for safety assurance in shuttle deployment. Conducted simulation studies optimizing the shuttle’s trajectory for concrete maneuvers, along with derived requirements for the associated virtual environment, are part of the first noteworthy outcomes. Concretely, the developed virtual environment is integrated in the framework used for virtual validation. The framework is then used for a detailed evaluation of a right-turn maneuver, analyzing possible shuttle trajectories. Considerable differences in sensor coverage at the shuttle’s stopping point can be shown. Conclusively, by utilizing the shuttle’s restricted operational domain, the proposed virtual risk assessment is considered the first step toward a general procedure for the safety assurance of automated vehicles.

Analysis of the vaporization processes in the vehicle fuel tank. New equation for determining the vapor amount

Introduction (problem statement and relevance). Hydrocarbon emissions from vaporizationtank fuel contribute significantly to the total emissions of hazardous substances from vehicles equipped with spark ignition engines. To meet the established standards for limiting hydrocarbon emissions caused by evaporation, all modern vehicles use fuel vapor recovery systems, the optimal parameters of which require the availability and application of mathematical models and methods for their determination.The purpose of the research was to develop a model of vapor generation processes in the car fuel tank and a methodology for determining the main quantitative parameters of the vapor-air mixture.Methodology and research methods. The analysis of the processes of vapor generation in the fuel tank was carried out. It was shown that the mass of hydrocarbons generated in the steam space was directly proportional to its volume and did not depend on the amount of fuel in the tank.Scientific novelty and results. New analytical dependences of the vaporization amount on the saturated vapor pressure, barometric pressure, initial fuel temperature and fuel heating during parking have been obtained.Practical significance. A formula was obtained to estimate the temperature of gasoline boiling starting in the tank, depending on the altitude above sea level and the volatility of gasoline, determined by the pressure of saturated vapors. Using the new equations, the vaporization analysis in real situations (parking, idling, refueling, explosive concentration of vapors) was carried out.

Optimization-Based Business Process Model Matching

The rapid increase in generation of business process models in the industry has raised the demand on the development of process model matching approaches. In this paper, we introduce a novel optimization-based business process model matching approach which can flexibly incorporate both the behavioral and label information of processes for the identification of correspondences between activities. Given two business process models, we achieve our goal by defining an integer linear program which maximizes the label similarities among process activities and the behavioral similarity between the process models. Our approach enables the user to determine the importance of the local label-based similarities and the global behavioral similarity of the models by offering the utilization of a predefined weighting parameter, allowing for flexibility. Moreover, extensive experimental evaluation performed on three real-world datasets points out the high accuracy of our proposal, outperforming the state of the art.

Delegation and the regulation of U.S. financial markets

We analyze the institutional determinants of U.S. financial market regulation with a general model of the policy-making process in which legislators delegate authority to regulate financial risk at both the firm and systemic levels. The model explains changes in U.S. financial regulation leading up to the financial crisis. We test the predictions of the general model with a novel, comprehensive data set of financial regulatory laws enacted specifically between 1950 and 2009. The theoretical and empirical analysis finds that economic and political factors impact Congress’ decision to delegate regulatory authority to executive agencies, which in turn impacts the stringency of financial market regulation, and our estimation results indicate that political factors may have been stronger and resulted in inefficiencies.

A General Compact Pinned Photodiode Model Capable of Miniature PPD Modeling

This article presents a simple yet powerful general pinning process model for 3-D pinned photodiodes (PPDs), which can be used to model PPDs with small dimensions (miniature PPDs). For the development of a pinning model, capable of dealing with different PPD shapes and dimensions, the charge population in the space charge region (SCR) is calculated. Moreover, to improve the model’s accuracy, the PPD inner region depletion mechanism is investigated, and the inner SCR width is extracted analytically. The model’s application in analyzing PPDs is verified with six previously published experimental results.

PIN68 Estimating Effects in Observational Real-World Data, From Target Trials to Targeted Learning: Example of Treating COVID-Hospitalized Patients

When estimating treatment effects using real-world data, we risk introducing bias when declaring (specifying) a particular data generation process (DGP) model as if it were known. For this study, we will evaluate treatment effect estimates published from randomized control trials and compare these to estimates obtained by applying data-adaptive methods, particularly those offered by targeted learning (TL), on a cohort of COVID-hospitalized patients.

Interactive Process Automation based on lightweight object detection in manufacturing processes

Interactive Process Automation refers to the idea of supporting the interaction of humans in processes through physical objects. This is particularly promising for human/cobot collaboration tasks where the communication is fuzzy. A typical example is a picking and placing scenario. Here, a “picking area” can serve as a user interface, i.e., objects are freely placed in a defined area, and then identified and transferred to specific positions, where deterministic processes can use them. If, for example, object A is placed at position posA by the human, automatically, the robot is instructed to pick A and place it at position posB on a tray. Realizing Interactive Process Automation for picking and placing tasks in manufacturing processes requires (i) a lightweight and flexible object detection approach and (ii) a human–machine interface design for Interactive Process Automation. This work proposes (i) an object detection approach that works solely based on synthetic training data. The object detection is embedded into (ii) generic process models that are implemented based on an existing manufacturing orchestration framework and a camera-equipped cobot. The approach is prototypically implemented and evaluated based on several experiments including a pick and place cobot station.

Generating a Model to Predict Secondary School Students at Risk in Mathematics

Mathematical courses aid individuals to deal with any problem that they might encounter in their life on the ground that Mathematics familiarize them with a problem-solving process. Additionally, people who have stood out in their profession are deemed to be those who have mastered their mathematical skills. Though, a lot of students encounter insurmountable difficulties and as a consequence they fail their Mathematical courses. That holds true particularly on the case of secondary school students. Thereby, controlling the risk of students’ failure in Mathematics is of utmost importance. This paper proposes a way to predict secondary school students’ critical performance in Mathematics through the generation of a potent model by means of a proper analysis of students’ engagement data. A discriminant function analysis has been carried out on the respective data and the generated linear discriminant functions constitute the prediction model. The prediction model generation process is demonstrated through a case study on a specific Mathematical course delivered at a Greek private secondary school. The research outcome is very promising given that our model could potentially be used to develop an early warning system for secondary school students at risk in Mathematics.

A scalable approach for developing clinical risk prediction applications in different hospitals

ObjectiveMachine learning (ML) algorithms are now widely used in predicting acute events for clinical applications. While most of such prediction applications are developed to predict the risk of a particular acute event at one hospital, few efforts have been made in extending the developed solutions to other events or to different hospitals. We provide a scalable solution to extend the process of clinical risk prediction model development of multiple diseases and their deployment in different Electronic Health Records (EHR) systems. Materials and methodsWe defined a generic process for clinical risk prediction model development. A calibration tool has been created to automate the model generation process. We applied the model calibration process at four hospitals, and generated risk prediction models for delirium, sepsis and acute kidney injury (AKI) respectively at each of these hospitals. ResultsThe delirium risk prediction models have on average an area under the receiver-operating characteristic curve (AUROC) of 0.82 at admission and 0.95 at discharge on the test datasets of the four hospitals. The sepsis models have on average an AUROC of 0.88 and 0.95, and the AKI models have on average an AUROC of 0.85 and 0.92, at the day of admission and discharge respectively. DiscussionThe scalability discussed in this paper is based on building common data representations (syntactic interoperability) between EHRs stored in different hospitals. Semantic interoperability, a more challenging requirement that different EHRs share the same meaning of data, e.g. a same lab coding system, is not mandated with our approach. ConclusionsOur study describes a method to develop and deploy clinical risk prediction models in a scalable way. We demonstrate its feasibility by developing risk prediction models for three diseases across four hospitals.

Development of General data-based Process Models for Self-Pierce Riveting

The determination of ideal process parameters for mechanical joining processes such as self-pierce riveting currently requires a comprehensive understanding of the process, the availability of the materials to be joined and the corresponding system technology. General process models can simplify the use of these joining technologies, accelerate development cycles and thereby reduce the effort for implementation into production. In this paper, the development of general data-based process models for the mechanical joining method self-pierce riveting with semi-tubular rivet is described. Extensive experimental and numerical investigations with more than 2300 joint combinations for steel and aluminum sheets with tensile strengths between 240 - 1020 MPa were generated for the building of the models. Based on these results, different meta-models are fused into general data-based process models for the self-pierce riveting process in order to show the general relationships between material properties, process parameters and joining results. The paper discusses the acquisition of the experimental and numerical data, the statistical methods for evaluation and the application of the data-based process models.