Generic Process Model Research Articles

КЕСИПТИК ОРТО БИЛИМ БЕРҮҮНҮ ЖАӉЫЛАНТУУ ЖАНА АНЫН ОКУУ ПРОЦЕССИН БАШКАРУУСУНУН МАСЕЛЕСИ: ТЕОРИЯЛЫК АНАЛИЗ

The article reviews the current state of professional secondary education in the Kyrgyz Republic, and emphasizes that determining its development opportunities is a very urgent issue. Emphasis is placed on theoretical and methodological research in this direction, and the need to develop the principles and directions of innovations in the professional secondary education system based on theoretical research is noted. Theoretical studies, concepts, ideas conducted around the given problem are considered. The author notes that the effectiveness of comprehensive modernization of vocational secondary educational institutions is closely related to the management of the learning process in vocational secondary educational institutions. It is noted that in order to study ways of creating a general conceptual model of the learning process in a secondary professional educational institution (college, etc.), a theoretical analysis is required and the need for a theoretical-methodological base created on its basis.

Discovery of medical pathways considering complications

Many medical solutions exist in the vast medical logs, and how to use them to make precise recommendations for medical pathways has become a current research hotspot. This paper proposed an algorithmic framework that combines trace clustering, process discovery, and neural network techniques to discover models of medical pathways considering complications from medical logs. First, trace clustering divided the treatment pathways set in medical logs into multiple subsets with similar behaviours. Then multiple streamlined process models were mined by process discovery techniques. The reasonable medical pathways were extracted from each process model. A cluster of trained neural network models was used to determine the case characteristic labels and obtain the attention of events in the medical pathways. The output was finally integrated to generate a model of medical pathways considering complications. The results of the experiments using the Sepsis Cases dataset showed that the average simplicity of the generated process models was 0.695, the average accuracy of the neural network models was 93.3%, and the medical pathways model score was about 0.879.

Development of a process modeling method for supporting engineering activities based on digital triplet

“Digital Triplet” is a concept proposed to support manufacturing system engineers by combining the knowledge and know-how of expert engineers with digital technology. This study proposes a method to represent engineering processes executed by engineers in a reusable manner based on Digital Triplet. Specifically, we develop a procedure to create a "log-level description" that records an engineering process as it is and a “generalized process model” that generalizes multiple log-level descriptions, using a process modeling language named “PD3” (Process Modeling Language for Digital Triplet). In a case study, expert engineers first executed engineering processes to improve production speed on a simplified production line. Next, log-level descriptions and a generalized process model were created for the engineering processes by the proposed procedure. Furthermore, an experiment was conducted on novice engineers using the generalized process model, and the results suggested that the model is effective for education. Currently, creating log-level descriptions or generalized process models is a labor-intensive task as it is done manually based on the subjective judgment of the person representing the processes. However, we showed the application of "process mining" techniques has the potential to partially automate the task of creating the models.

REactor Safety Analysis ToolboX RESA-TX

The REactor Safety Analysis ToolboX RESA-TX is a software and data package in development that combines the automatisation of all established procedures for deterministic safety analysis (DSA), the integration of expert know-how and a large database including the most relevant information required for conducting a DSA. In the current state of the art, DSA is a complex and thus error-prone process that is highly time-consuming and repetitive. The reliability of the result is strongly dependent on the availability of plant data and expert know-how. The idea of developing the RESA-TX toolbox arose at GRS to cope with these conditions. The innovative approach proposes an automated and standardised procedure, supported by a large database of plant design characteristics, plant behaviour, regulatory rules and DSA expert knowledge incorporated within the tool. Its application allows the end user to automatically generate and verify an input deck, as well as conduct design basis accident (DBA) calculations for a certain design with highly reduced manual intervention. The databases can be extended depending on available information or other boundary conditions. A heuristic approach is integrated into the model generation process, where users often suffer from a lack of information about the facility under consideration. These heuristics can be replaced when higher information quality is available or enhanced over time, which can lead to more reliable results with increasing usage of the tool. As a result, the application of RESA-TX could highly increase the efficiency of the DSA process, reducing both repetitiveness as well as user-induced errors. This in return will lead to an improvement in the quality of the analysis and reliability of the results. In consequence, RESA-TX will allow for a DSA to be conducted more frequently in situations where time or budget was a limitation before, thereby contributing to an increase in reactor safety.

Density-guided Incremental Dominant Instance Exploration for Two-view Geometric Model Fitting.

Existing two-view multi-model fitting methods typically follow a two-step manner, i.e., model generation and selection, without considering their interaction. Therefore, in the first step, these methods have to generate a considerable number of instances in order to cover all desired ones, which not only offers no guarantees, but also introduces unnecessary expensive calculations. To address this challenge, this study presents a new algorithm, termed as D2Fitting, that incrementally explores dominant instances. Particularly, rather than viewing model generation and selection as two disjoint parts, D2Fitting fully considers their interaction, and thus performs these two subroutines alternatively under a simple yet effective optimization framework. This design can avoid generating too many redundant instances, thus reducing computational overhead and allowing the proposed D2Fitting being real-time. Meanwhile, we further design a novel density-guided sampler to sample high-quality minimal subsets during the model generation process, so as to fully exploit the spatial distribution of the input data. Also, to mitigate the influence of noise on the subsets sampled by the proposed sampler, a global-residual optimization strategy is investigated for the minimal subset refinement. With all the ingredients mentioned above, the proposed D2Fitting can accurately estimate the number and parameters of geometric models and efficiently segment the input data simultaneously. Extensive experiments on several public datasets demonstrate the significant superiority of D2Fitting over several state-of-the-arts.

Improving business process management at a small agribusiness enterprise

The purpose of the work was to generalize approaches used in creating maps of business processes at agro-industrial complex enterprises and the possibility of structural-branch formation of a model in sequential visualization of stages and all types of work for the main and auxiliary types of production for these organizations. Methods included analysis and synthesis, induction and deduction, abstract-logical and statistical methods, fundamentals of the scientific organization of labor by A.K. Gastev, E. Deming's decision-making methodology and T. Buzan's mind maps. Result and scope: the formation of a map of business processes by industry and in conjunction with all levels of management allows managers of small enterprises in the agro-industrial complex to strengthen control over the main and indirect problems, blocks of production and services, eliminate duplication in management and other operations, optimize linear-functional connections in the management structure. The use of operation modes with the creation of business cards based on achievements of management science and time management can and should be carried out on the job in the form of a continuous form of training. Scientific novelty concerned adaptation of methods and graphical techniques for working with goals, business processes, management solutions in small enterprises of the agro-industrial complex, where there is no position of a business process manager in the staff, but it is possible to use functional specialists and managers in their work to improve labor productivity and increase its efficiency by all categories of workers. Creation of a general model of business processes at an agro-industrial complex, linking all the necessary operations by industry into a single complex, creates a detailed layout of a software product for future developers of automated control systems and the introduction of digitalization, which can serve all processes in the enterprise continuously, efficiently and with better quality.

Time for a group hug? Toward a theory of shared emotional leadership in and of family business

Despite clear evidence that emotions, both individual- and group-level, matter in shaping family business processes and outcomes, there is a dearth of research on the importance of shared leadership of emotions, especially in the context of family business. Consequently, we seek to expand shared leadership theory to incorporate various aspects of emotions in the family business milieu. We introduce a generalized process model of shared leadership in family firms, then provide a brief overview of emotions. Next, operating within the larger theoretical domain of group emotion, we introduce and work toward developing a theory of shared emotional leadership in and of family business, including representative antecedents and outcomes. We subsequently lay out a research agenda to stimulate future theoretical and empirical work in the general ken.

Математична динамічна модель теплових та гідравлічних процесів в баках паливної системи літака

A generalized mathematical model of dynamic thermal processes in fuel system tanks and hydraulic processes in the supercharging and drainage system is proposed. In the model of dynamic temperature changes in the CB, the idea of forming a parametric dependence is laid down, which, due to the selection of the values of the model parameters, would take into account the influence of the main factors at different stages of the flight and allow the adjustment of the model. 9 such parameters are highlighted and illustrated how each of them separately affects the temperature change in the tank. The given calculation data show that the change in their values has a different effect on the shape of the graph of the temperature change in the CB for the analyzed flight. Therefore, due to their selection, it is possible to obtain temperature values in the CB close to the experimental ones. The found values of the coefficients change little for different variants of flights, which indicates the acceptability of model (4) - (6) for use in preliminary analysis for a number of flights based on one of them. Such a model, perhaps in a somewhat simplified form, can be used in assessing the flammability of fuel in aircraft tanks. For the model of hydraulic processes, a universal algorithm for determining the flow distribution for 10 variants of boundary conditions for the network is proposed, which describes the calculation scheme of the supercharging and drainage system, where part of the boundary conditions can be set implicitly, provided that the pressure drops on the branches of the network are linear functions of the flow rate in the corresponding branch. This model complements the general model of dynamic hydraulic processes in the supercharging and drainage system, including tanks, which was proposed by the author earlier.

Cross-Project Defect Prediction with Metrics Selection and Balancing Approach

Abstract In software development, defects influence the quality and cost in an undesirable way. Software defect prediction (SDP) is one of the techniques which improves the software quality and testing efficiency by early identification of defects(bug/fault/error). Thus, several experiments have been suggested for defect prediction (DP) techniques. Mainly DP method utilises historical project data for constructing prediction models. SDP performs well within projects until there is an adequate amount of data accessible to train the models. However, if the data are inadequate or limited for the same project, the researchers mainly use Cross-Project Defect Prediction (CPDP). CPDP is a possible alternative option that refers to anticipating defects using prediction models built on historical data from other projects. CPDP is challenging due to its data distribution and domain difference problem. The proposed framework is an effective two-stage approach for CPDP, i.e., model generation and prediction process. In model generation phase, the conglomeration of different pre-processing, including feature selection and class reweights technique, is used to improve the initial data quality. Finally, a fine-tuned efficient bagging and boosting based hybrid ensemble model is developed, which avoids model over -fitting/under-fitting and helps enhance the prediction performance. In the prediction process phase, the generated model predicts the historical data from other projects, which has defects or clean. The framework is evaluated using25 software projects obtained from public repositories. The result analysis shows that the proposed model has achieved a 0.71±0.03 f1-score, which significantly improves the state-of-the-art approaches by 23 % to 60 %.

Pore volume and surface diffusion model to characterize batch adsorption of Cu(II) over chemically modified Cucurbita moschata biosorbent: simulation using gPROMS

Abstract This work describes the successful application of the pore volume and surface diffusion (PVSD) model characterizing the batch adsorption of Cu(II) on a chemically modified Cucurbita moschata biosorbent. The PVSD model captures the convective transport of Cu(II) from the bulk solution to the biosorbent surface, followed by its surface and pore diffusion inside the biosorbent. The adsorption of Cu(II) is mimicked using the Langmuir isotherm. The algebraic, ordinary, and partial differential equations, involved in the PVSD model, are solved using the general process modeling system (gPROMS). The model simulation results, depicted by the Cu(II) concentration decay curve, show an excellent match with experimental data. The external mass transfer coefficient (≈10−3 m/s) indicated no restriction on approaching Cu(II) toward the biosorbent surface. Within the biosorbent, surface diffusion was dominant over pore volume diffusion. The statistical analysis of the PVSD model results has been done by calculating R2, Chi-square value, normalized standard deviation, p-value, and root-mean-square error. The PVSD model approach presented in this work could be beneficial to other heavy metal–biosorbent systems.

5D-QSAR studies of 1H-pyrazole derivatives as EGFR inhibitors.

Epidermal growth factor receptor (EGFR) is highlighted as a target for anticancer treatment. Several EGFR inhibitors were approved in cancer treatment. Comparatively, 5D-QSARis a new methodology which considers an ensemble of different induced-fit models. Based on 1H-pyrazole derivatives as EGFR inhibitors, a 5D-QSAR was studied in which the method of quasi-atomistic receptor surface modeling was used. The presented QSAR model showed contributions of the hydrogen bond acceptor, and hydrophobic and salt bridge fields to the activity. The QSAR model was statistically validated and also externally validated applying 19 compounds (test set) which were not included in the model generation process. The scramble tests were performed to further verify the robustness. Apart from exploration of the binding of 1H-pyrazole derivatives to the EGFR, the 5D-QSAR model can be helpful to design of new EGFR inhibitors. The five-dimensional quantitative structure-activity relationship (5D-QSAR) of 1H-pyrazole derivatives as EGFR inhibitors with quasi-atomistic receptor surface modeling approach is described.

Hierarchical Mixture-of-Experts approach for neural compact modeling of MOSFETs

With scaling, physics-based analytical MOSFET compact models are becoming more complex. Parameter extraction based on measured or simulated data consumes a significant time in the compact model generation process. To tackle this problem, ANN-based approaches have shown promising performance improvements in terms of accuracy and speed. However, most previous studies used a multilayer perceptron (MLP) architecture which commonly requires a large number of parameters and train data to guarantee accuracy. In this article, we present a Mixture-of-Experts approach to neural compact modeling. It is 78.43% more parameter-efficient and achieves higher accuracy using fewer data when compared to a conventional neural compact modeling approach. It also uses 43.8% less time to train, thus, demonstrating its computational efficiency.

Combined control of multirate impulse processes in a cognitive map of COVID-19 morbidity

In this article, a cognitive map (CM) of COVID-19 morbidity in a given region was built. A general linear impulse process (IP) model in the CM was developed and measured, and unmeasured CM node coordinates were defined. The general IP model was decomposed into interrelated subsystems with measurable and unmeasurable node coordinates. For the subsystem with measurable node coordinates, multirate sampling of coordinates was conducted, resulting in the development of discrete dynamics models for quickly and slowly measured node coordinates. External controls were selected in IP models based on the possible variation of resources of node coordinates and CM weighting coefficients. IP control laws based on the variation of CM nodes and weight were designed. As a result, recurrent procedures for control generation in closed-loop control subsystems with multirate sampling were formulated. Experimental research on the control subsystems was carried out. It confirmed high efficiency for decreasing COVID-19 morbidity.

BIAXIAL HEAT BALANCE MODEL OF SOLAR COLLECTOR

Solar collectors and thermophotoelectric systems (PV/T) are considered, which are one of the most promising systems for instilling energy. Electricity, which is vibrated by photovoltaic panels, has a great potential, but there may be technological shortcomings, which do not give maximum efficiency. Development of a general model of heat exchange processes for optimizing the design features of PV/T systems at the stages of design and variability allows us to increase the term of service of such systems and increase their efficiency. The expanded model allows you to change more practical parameters for two coordinates of a flat collector, such as to change the consumption of thermal energy, thermal support of the absorber plate, heat exchange, operating temperature, etc. The results of the model investigations correlate with the experimental data. On the basis of the proposed model, a software product for the model-bath of PV/T systems was developed and tested on the experimental results of those ready-to-wear PV/T systems. In the course of carrying out the expansions, depending on the basic parameters, the heating of heat was removed when one segment of the collector was passed by approximately 1.5 °C. The designated increase in temperature is reached at a heat transfer rate of 0.6 m/s, which is to achieve a great rate. The most optimal will be the heating of heat when passing through the collector by 5 °C, which will allow to reduce the speed of heat transfer to 0.2 m/s and significantly reduce the amount of electricity consumed by the pump. The variation of the expanded model allows to implement a wide range of optimization tasks at the stages of designing and optimizing solar collectors and PV/T systems, to take the optimal design parameters to achieve the greatest efficiency and minimum occupancy.

Generic surgical process model for minimally invasive liver treatment methods

Surgical process modelling is an innovative approach that aims to simplify the challenges involved in improving surgeries through quantitative analysis of a well-established model of surgical activities. In this paper, surgical process model strategies are applied for the analysis of different Minimally Invasive Liver Treatments (MILTs), including ablation and surgical resection of the liver lesions. Moreover, a generic surgical process model for these differences in MILTs is introduced. The generic surgical process model was established at three different granularity levels. The generic process model, encompassing thirteen phases, was verified against videos of MILT procedures and interviews with surgeons. The established model covers all the surgical and interventional activities and the connections between them and provides a foundation for extensive quantitative analysis and simulations of MILT procedures for improving computer-assisted surgery systems, surgeon training and evaluation, surgeon guidance and planning systems and evaluation of new technologies.

Precision of a Hand-Held 3D Surface Scanner in Dry and Wet Skeletal Surfaces: An Ex Vivo Study.

Three-dimensional surface scans of skeletal structures have various clinical and research applications in medicine, anthropology, and other relevant fields. The aim of this study was to test the precision of a widely used hand-held surface scanner and the associated software’s 3D model generation-error in both dry and wet skeletal surfaces. Ten human dry skulls and ten mandibles (dry and wet conditions) were scanned twice with an industrial scanner (Artec Space Spider) by one operator. Following a best-fit superimposition of corresponding surface model pairs, the mean absolute distance (MAD) between them was calculated on ten anatomical regions on the skulls and six on the mandibles. The software’s 3D model generation process was repeated for the same scan of four dry skulls and four mandibles (wet and dry conditions), and the results were compared in a similar manner. The median scanner precision was 31 μm for the skulls and 25 μm for the mandibles in dry conditions, whereas in wet conditions it was slightly lower at 40 μm for the mandibles. The 3D model generation-error was negligible (range: 5–10 μm). The Artec Space Spider scanner exhibits very high precision in the scanning of dry and wet skeletal surfaces.

Probabilistic Approach to Modelling, Identification and Prediction of Environmental Pollution

The probabilistic general model of environmental pollution process based on the semi-Markov one is developed and presented in the paper. The semi-Markov chain model approach is based on using prior information to predict the characteristic of some systems. Now, the semi-Markov process is used for the environmental pollution assessment. The methods and procedures to estimate the environmental pollution process’s basic parameters such as the vector of initial probabilities and the matrix of probabilities of transition between the process’s states as well as the methods and procedures to identify the process conditional sojourn times’ distributions at the particular environmental pollution states and their mean values are proposed and defined. Next, the formulae to predict the main characteristics of the environmental pollution process such as the limit values of transient probabilities and mean total sojourn times in the particular states in the fixed time interval are given. Finally, the application of the presented model and methods for modelling, identification and prediction of the air environmental pollution process generated by sulphur dioxide within the exemplary industrial agglomeration is proposed.

Foundations of cybercriminalistics: From general process models to case-specific concretizations in cybercrime investigations

Despite spectacular stories of successful cyber operations by law enforcement agencies, we continue to be extremely inefficient in fighting cybercrime. The research community has contributed many abstract models to guide digital forensic analyses, but these are usually too abstract to be helpful in concrete cybercrime investigations since they do not give an immediate and straightforward translation of a confronted (digital) crime scene into viable yet promising criminalistic actions. We propose a method to systematically bridge the gap between high-level process models and the demands of actual investigations. The idea is to encode phenomenon-specific knowledge of cybercrime into node-link representations, thereby literally mapping the digital crime scene in well-founded visual representations – so-called cognitive maps. These can be used to derive a prioritized plan of action for targeted acquisition and analysis of case-relevant artifacts. To illustrate our approach, we present a cognitive map for the category of botnet crime and evaluate it with the help of domain experts and by applying it to two real-world cases.

Demand response potential of industrial processes considering uncertain short‐term electricity prices

AbstractTime‐varying electricity prices on the day‐ahead and intraday market incentivize demand response of industrial processes. In prior work (Schäfer et al. AIChE J. 2020;66:1‐14), we studied the demand response potential with a generalized process model, but neglected the intraday market. Extending our prior investigation, we account for uncertain intraday prices in a mixed‐integer linear stochastic programming‐based scheduling, that is, we minimize expected cost and conditional value‐at‐risk in a bi‐objective optimization. We find that for very broad variations of the generalized process parameters, the conditional value‐at‐risk can be reduced significantly without drastically increasing the expected cost. Furthermore, simultaneously improving multiple process parameter leads to synergetic benefits. Moreover, the savings of three electrolysis processes can be more than doubled by marketing flexibility on the intraday market in addition to the day‐ahead market. Overall, our model allows for a rapid early assessment of the demand response potential considering the two markets.

Keeping synthetic patients on track: feedback mechanisms to mitigate performance drift in longitudinal health data simulation.

Synthetic data are increasingly relied upon to share electronic health record (EHR) data while maintaining patient privacy. Current simulation methods can generate longitudinal data, but the results are unreliable for several reasons. First, the synthetic data drifts from the real data distribution over time. Second, the typical approach to quality assessment, which is based on the extent to which real records can be distinguished from synthetic records using a critic model, often fails to recognize poor simulation results. In this article, we introduce a longitudinal simulation framework, called LS-EHR, which addresses these issues. LS-EHR enhances simulation through conditional fuzzing and regularization, rejection sampling, and prior knowledge embedding. We compare LS-EHR to the state-of-the-art using data from 60 000 EHRs from Vanderbilt University Medical Center (VUMC) and the All of Us Research Program. We assess discrimination between real and synthetic data over time. We evaluate the generation process and critic model using the area under the receiver operating characteristic curve (AUROC). For the critic, a higher value indicates a more robust model for quality assessment. For the generation process, a lower value indicates better synthetic data quality. The LS-EHR critic improves discrimination AUROC from 0.655 to 0.909 and 0.692 to 0.918 for VUMC and All of Us data, respectively. By using the new critic, the LS-EHR generation model reduces the AUROC from 0.909 to 0.758 and 0.918 to 0.806. LS-EHR can substantially improve the usability of simulated longitudinal EHR data.