Process Model Research Articles

The effect of gamified project-based learning with AIGC in information literacy education

ABSTRACT The emergence of artificial intelligence has necessitated a transformation in pedagogical methodologies. This study evaluated the integration of gamified project-based learning (GPBL) and artificial intelligence-generated content (AIGC) tools in enhancing information literacy education. The primary objectives were to explore the relationship among students’ learning experiences, information literacy, creativity, and engagement in an integrated learning environment. Using an explanatory sequential design with surveys and interviews, first-year university students (n = 36) from the School of Art and Design participated. Results indicated that students perceived moderately high levels of engagement(3.72) and creativity(4.11) during the GPBL activities, with positive emotions contributing significantly to motivation. Moreover, information literacy skills have substantially improved, such as in information retrieval, using AIGC tools, and understanding the ethical aspects of AI technology. A process model illustrated how positive emotions, motivation, creativity, engagement, and information literacy were linked in a GPBL-incorporating AI setting.

Process Mining, Modeling, and Management in Construction: A Critical Review of Three Decades of Research Coupled with a Current Industry Perspective

Process Mining, Modeling, and Management in Construction: A Critical Review of Three Decades of Research Coupled with a Current Industry Perspective

Using Business Process Model and Notation 2.0 to Deploy Cobots in a Manufacturing System – Case Study

Using Business Process Model and Notation 2.0 to Deploy Cobots in a Manufacturing System – Case Study

Heterogeneous graph neural network for modeling intelligent manufacturing systems

Abstract Currently, manufacturing systems have become more and more complex, often involving multiple machines, systems and processes to produce workpieces. In order to facilitate comprehensive analysis and control of manufacturing processes, the integration of connections between machine level, system level, and process level in manufacturing process modeling is needed. However, traditional graph deep learning models are unable to take into account the heterogeneity of different machines in the system when modeling manufacturing systems. To address this problem, this paper proposes a new approach: modeling manufacturing systems using the heterogeneous graph neural network sample and aggregate algorithm based on cutting-edge Bayesian neural networks and graph deep learning. This method considers the connection between different manufacturing system levels, treats machine operations as nodes, and connects different nodes through material flow and operational similarity. The effectiveness of the method is demonstrated through its application to model an aero-engine blade production line. Extensive numerical experiments show that the proposed graph modeling method is effective in expressing the heterogeneity of different machines and multi-level manufacturing processes. By integrating machine heterogeneity into the modeling of a manufacturing system, it not only facilitates comprehensive analysis and control of the manufacturing process, but also lays the foundation for cost savings and productivity improvements in the manufacturing system.

Location-aware business process modeling and execution

Location-aware business process modeling and execution

Temperature modeling of wave rotor refrigeration process based on variable time-delay compensation and SDAE-GRU

Abstract Aiming at the problem of noise and time-delay in temperature prediction modeling of large wave rotor refrigeration process, this paper proposes a novel modeling algorithm that combines the advantages of stacked denoising autoencoder (SDAE) network and Gate Recurrent Unit (GRU) algorithm and utilizes the Maximal Information Coefficient (MIC) algorithm to solve the targeting problem. The MIC algorithm is utilized to calculate the delay time of each input variable and output variable, and the input matrix is reconstructed to compensate for the time-delay, thus solving the problem afterwards. The SDAE network is utilized for denoising the input data and feature extraction to solve the problem of presence of noise in the data. The simulation results show that the proposed MIC-SDAE-GRU algorithm achieves a Root Mean Squared Error (RMSE) of 0.6432 and an R-squared (R²) value of 0.9638, outperforming traditional machine learning methods and other deep learning approaches. Specifically, compared to the standard GRU algorithm, it improves the accuracy of temperature prediction for the wave rotor refrigeration process by 24.7% and exhibits strong generalization ability across various operating conditions.

Research on Stator Sections Switching Process of High-Thrust Linear Motors

The high-thrust linear motor used for the electromagnetic launch is switched by switches composed of anti-shunt thyristors. During the switching process, different current path states will appear, which will lead to changes in motor parameters and current fluctuation. Focusing on the air-core synchronous linear motor with parallel and series power supply, this paper analyzes the change in current path caused by the change in the trigger signal of anti-parallel thyristors in the process of sectionalized stator switching. The motor circuit model of the switching process is derived. A new sectionalized stators switching method is proposed to realize the smooth switching of the sectionalized stators linear motor. Finally, the correctness of the analysis and modeling is verified by simulation, and the effectiveness of the sectionalized stators switching method is tested by using the test prototype.

Barks of Dacryodes Kukachkana presents antiinflammatory effect in rat cutaneous wounds

Background: Dacryodes kukachkana (Burseraceae) is found in the Amazon rainforest of Brazil, being the genera widely used in folk medicine for several inflammatory conditions. Objective: In this study, it was investigated the anti-inflammatory effect of the Hydroethanolic Extract of D. kukachkana (HEDk) barks in the rat model of excisional skin wounds. Methods: The wounds were induced on the dorsal region of Wistar rats and treated daily by topical application of HEDk (500 mg/100 μL), compared to the control Saline (0.9% NaCl). Clinical parameters (edema, hyperemia, exudate, hypernociception), histopathological (leukocyte infiltrate, fibroblasts, blood vessels) and oxidative stress markers were evaluated from the 2nd to the 6th day post-ulceration. Results: HEDk reduced the following parameters at the 2nd day: inflammatory exudate (0 [0-0] vs. saline: 2 [1 – 3]); wound area (36%), healing index (48.56% ± 1.886 vs. saline: 27.99% ± 2.460) and malondialdehyde (27%). HEDk also reduced leukocyte infiltrate (49 – 34%) from the 4th to the 6th days post-ulceration. HEDk increased the parameters: nociceptive threshold (31 – 73%) from the 2nd to the 4th days; blood vessels (1.9x) and reduced glutathione (32%) at the 4th day; and the number of fibroblasts (91 – 44%) at the 2nd and 6th days. Conclusion: HEDk accelerates the healing process in the rat model of excisional cutaneous wounds via attenuation of inflammatory parameters and stimulation of fibroplasia and angiogenesis.

Optimizing Thermoplastic Starch Film with Heteroscedastic Gaussian Processes in Bayesian Experimental Design Framework

The development of thermoplastic starch (TPS) films is crucial for fabricating sustainable and compostable plastics with desirable mechanical properties. However, traditional design of experiments (DOE) methods used in TPS development are often inefficient. They require extensive time and resources while frequently failing to identify optimal material formulations. As an alternative, adaptive experimental design methods based on Bayesian optimization (BO) principles have been recently proposed to streamline material development by iteratively refining experiments based on prior results. However, most implementations are not suited to manage the heteroscedastic noise inherently present in physical experiments. This work introduces a heteroscedastic Gaussian process (HGP) model within the BO framework to account for varying levels of uncertainty in the data, improve the accuracy of the predictions, and increase the overall experimental efficiency. The aim is to find the optimal TPS film composition that maximizes its elongation at break and tensile strength. To demonstrate the effectiveness of this approach, TPS films were prepared by mixing potato starch, distilled water, glycerol as a plasticizer, and acetic acid as a catalyst. After gelation, the mixture was degassed via centrifugation and molded into films, which were dried at room temperature. Tensile tests were conducted according to ASTM D638 standards. After five iterations and 30 experiments, the films containing 4.5 wt% plasticizer and 2.0 wt% starch exhibited the highest elongation at break (M = 96.7%, SD = 5.6%), while the films with 0.5 wt% plasticizer and 7.0 wt% starch demonstrated the highest tensile strength (M = 2.77 MPa, SD = 1.54 MPa). These results demonstrate the potential of the HGP model within a BO framework to improve material development efficiency and performance in TPS film and other potential material formulations.

Co-designing postpartum contraceptive services with and for immigrant women in Sweden: lessons learned from the IMPROVE-it project

Background and aimImmigrant women in many high-income countries including Sweden, report unmet need of sexual and reproductive health and rights, and face worse pregnancy outcomes and higher risk of unintended pregnancies. Postpartum contraceptive services are often inadequate to meet their needs. Co-design has shown to reduce health inequities, yet little is known about using this method for postpartum contraceptive service development and even less in collaborating with immigrant populations. The aim of this paper is to describe the co-design process and the strategies that were developed to help develop tailored and acceptable postpartum contraceptive services for immigrant women in Sweden.MethodsThe paper describes a co-design process that took place during 2022–2023, including the cyclical digital consultations with Arabic and Somali speaking immigrant women, midwives and researchers, as well as the outputs from the process. The theoretical framework for the co-design process was the ‘Double Diamond’ Design Process Model. Data analysis included qualitative content analysis.ResultsThe co-design process led to the joint development of intervention materials and strategies to improve postpartum contraceptive services. Specifically, the process revealed ideas on how to improve contraceptive counseling within three pre-identified areas of change: improve physical access to contraceptive services; improved communication strategies using visual aids and information charts; and empowerment strategies that focus on reflective practice without assumptions about what a group of women might expect. We found that participants contributed actively to the process with ideas and suggestions, and that the co-design process facilitated positive reflections on ongoing counseling practices.ConclusionThe co-design process resulted in the successful and participative development of innovative tools and activities to improve contraceptive counseling services. This approach is original because it involves both immigrant women, often left behind, and midwives delivering the services. Whilst this interplay allowed for careful refinement of services and tools by using an iterative process, it also facilitated reflective midwifery practice.

Social media‐enabled crisis response in Vietnam: A tương thân tương ái perspective

AbstractA growing amount of information systems (IS) research is examining the use of social media to enable effective crisis response. However, existing studies have overlooked the impact of indigenous concepts, which play an important role in crisis response. Our study intends to offer an indigenous perspective to this research stream by examining how social media‐enabled crisis response is enacted in Vietnam. We used COVID‐19 as the focal crisis and collected a rich set of discussion threads from social media. By analysing the data through the grounded theory method, our study identifies the indigenous theoretical concept of tương thân tương ái. We further unveil a process model consisting of six mechanisms through which tương thân tương ái inspires crisis response on social media and three roles assumed by social media during this process. Our study contributes to the literature on social media‐enabled crisis response by providing an indigenous perspective and a context‐specific explanation. It also enriches IS theory in the Association of Southeast Asian Nations (ASEAN) by investigating how the indigenous concept of tương thân tương ái influences Vietnam's crisis response on social media. Our findings also provide guidelines that can help communities beyond Vietnam to promote the values of tương thân tương ái during crisis response.

On the study of solitary wave dynamics and interaction phenomena in the ultrasound imaging modelled by the fractional nonlinear system

This research work focuses on investigating the propagation of ultrasonic waves, which propagate mechanical vibrations of molecules or particles inside materials. Ultrasound imaging is extensively used and deeply rooted in the medical field. The key technologies that form the basis for many different uses in the area include transducers, contrast agents, pulse compression, beam shaping, tissue harmonic imaging, techniques for measuring blood flow and tissue motion, and three-dimensional imaging. The third-order non-linear β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}-fractional Westervelt model has been used as a governing model in the imaging process for securing the different wave structures. The exact solutions of different types, including mixed, dark, singular, bright-dark, bright, complex and combined solitons are extracted. These solutions are obtained by using two newly introduced techniques, namely modified generalized Riccati equation mapping method and modified generalized exponential rational function method. Moreover, breather, lump and other waves are extracted by the assistance of logarithmic transformation and different test functions. The used methodologies are extremely effective and possess substantial computing capacity to effectively address the different solutions with a high level of accuracy in these systems. The techniques used are well-known for being effective, simple, and flexible enough to integrate multiple soliton systems into a unified framework. In addition, we provide 2D and 3D graphs that explain the behavior of the solution at various parameter values, under the influence of β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}-fractional derivatives. The results offered in this study may improve the comprehension of the nonlinear dynamic behavior of the specific system and confirm the efficacy of the approaches used. We expect that our approaches will be beneficial for a wide range of nonlinear models and other problems in the related fields.

Kinetic modelling of carbothermal reduction of nickel oxide (NiO): Design of Experiments (DOE) approach

ABSTRACT This study presents a mathematical model to evaluate the kinetics of the carbothermal reduction of NiO using graphite. Based on the shrinking core theory, the model was validated against experimental data obtained under both isothermal and non-isothermal conditions. The shrinking core model for isothermal and non-isothermal conditions was created using MATLAB software for the kinetic modelling of the reduction process to determine the extent of reduction and the reaction rate. The kinetics were mathematically modelled from independently measured physical and thermodynamic properties of the reaction system and experimentally measured properties. The carbothermal reduction method was effective in producing Ni metal from NiO and was considered an established basis for evaluating the modelling aspect of the solid–gas system. The experimental investigation of carbothermal reduction was conducted using a statistical design of experiments (DOE), employing a two-level factorial design (23). The experiments were performed for a range of different factors, namely reduction temperature (800-1000°C), reduction time (1-2 h), and the molar ratio of graphite to NiO (0.5-1.5) on the extent of reduction. The extent of reduction increases with increasing reduction temperature, reduction time, and molar ratio of C to NiO. The highest extent of reduction (77.9%) was achieved at a reduction temperature of 1000°C, 2 h of reduction time, and the molar ratio of C to NiO as 1.5. The X-ray diffraction (XRD) studies confirmed the formation of Ni at a lower reduction time, but the intensity of the Ni phase was higher for a longer duration of reduction. SEM-EDS analysis of the reduced products confirmed the formation of Ni in the form of elongated and larger grains, along with traces of residual or unreacted carbon. The predicted extent of reduction is then compared with the experimentally measured results. The kinetic modelling results proved that both isothermal and non-isothermal shrinking core models showed similarity and significant closeness to the experimental data. The deviation between the experimental and model results could be due to the varying geometry and porosity of the reactants and intermediate products.

Simulation and modeling of methylene blue dye removal by an agri-food waste (Peanut shells)

Our study explores the potential of utilizing peanut shells, an abundant and low-cost food waste, as a natural adsorbent for the removal of industrial dyes, specifically methylene blue. We conducted a detailed investigation of the adsorption process, which revealed that it conforms to both Langmuir and Freundlich isotherms, indicating the ability of peanut shells to adsorb methylene blue effectively. To optimize the adsorption yield, we employed a Box-Behnken experimental design. This approach allowed us to assess the impact of several key factors, including the mass of the adsorbent, dye concentration, pH, temperature, stirring rate, and the ionic strength of salts in the solution. The mathematical model and simulation of the adsorption process helped us identify the best conditions for maximizing the dye removal. Our experiments resulted in a maximum adsorption efficiency of 97%, achieved under optimal conditions: 1.5 g of adsorbent, a methylene blue concentration of 40 mg/L, pH 11, temperature of 65°C, 0 mg/L ionic strength, and a stirring rate of 165 rpm. These findings suggest that peanut shells are a viable, eco-friendly alternative for dye removal, offering a sustainable solution for wastewater treatment and contributing to waste reduction.

Modeling and Evaluation of Penetration Process Based on 3D Mechanical Simulation

In biological micromanipulation, cell penetration is a typical procedure that precedes cell injection or oocyte enucleation. During this procedure, cells usually undergo significant deformation, which leads to cell damage. In this paper, we focus on modeling and evaluating the cell penetration process to reduce cell deformation and stress, thereby reducing cell damage. Initially, a finite element model (FEM) is established to simulate the cell penetration process. The effectiveness of the model is then verified through visual detection and comparison of cell deformation with experimental data. Next, various mechanical responses are analyzed, considering the influence of parameters, such as the radius and shape of the injection micropipettes, material properties, and size of the cells. Finally, the relationship between the intracellular stress and the cell penetration depth of biological cells is obtained. The evaluation results will be applied to develop optimized operation plans, enhancing the efficiency and safety of the cell penetration process.

Human Vision as A Multi-Circuit Mathematical Model of the Automated Control System

Abstract The paper contains a proposal an original, extended mathematical model of an automatic system of human vision reaction to a forcing light pulse. A comprehensive mathematical model of the vision process was proposed in the form of an equation described in the frequency (dynamics) domain. Mathematical modelling of human senses is very important. It enables better integration of automation systems with a human cooperating with them, also as an automation system. This provides the basis for reasoning based on a mathematical model instead of intuitive reasoning about human reactions to visual stimuli. A block diagram of the proposed system with five human reaction paths is given. The following can be distinguished in the scheme: the main track consisting of: the transport delay of the eye reaction, the transport delay of the afferent nerves, the inertia of the brain with a preemptive action, the transport delay of the centrifugal nerves and the inertial and transport delay of the neuromotor system. In addition, the scheme of the system includes four tracks of negative feedback of motor and force reactions: upper eyelid, lower eyelid, pupil and lens. In the proposed model, the components of each path along with their partial mathematical models are given and discussed. For each reaction path, their overall mathematical models are also given. Taking into account the comprehensive models of all five reaction paths, a complete mathematical model of the automatic system of human reaction to a forcing light impulse is proposed. The proposed mathematical model opens up many possibilities for synchronizing it with mathematical models of many mechatronics and automation systems and their research. Optimizing the parameters of this model and its synchronization with specific models of automation systems is difficult and requires many numerical experiments. This approach enables the design of automation systems that are better synchronized with human reactions to existing stimuli and the selection of optimal parameters of their operation already in the design phase. The proposed model allows, for example, accurate determination of difficulty levels in computer games. Another example of the use of the proposed model is the study of human reactions to various situations generated virtually, for example in flight simulators and other similar ones.

Optimizing cervical cancer classification using transfer learning with deep gaussian processes and support vector machines

BackgroundCervical cancer is the fourth most frequent cancer in women worldwide. Even though cervical cancer deaths have decreased significantly in Western countries, low and middle-income countries account for nearly 90% of cervical cancer deaths. While Western countries are leveraging the powers of artificial intelligence (AI) in the health sector, most countries in sub-Saharan Africa are still lagging. In Uganda, cytologists manually analyze Pap smear images for the detection of cervical cancer, a process that is highly subjective, slow, and tedious. Machine learning (ML) algorithms have been used in the automated classification of cervical cancer. However, most of the MLs have overfitting limitations which limits their deployment, especially in the health sector where accurate predictions are needed.MethodsIn this study, we propose two kernel-based algorithms for automated detection of cervical cancer. These algorithms are (1) an optimized support vector machine (SVM), and (2) a deep Gaussian Process (DGP) model. The SVM model proposed uses an optimized radial basis kernel while the DGP model uses a hybrid kernel of periodic and local periodic kernel.ResultsExperimental results revealed accuracy of 100% and 99.48% for an optimized SVM model and DGP model respectively. Results on precision, recall, and F1 score were also reported.ConclusionsThe proposed models performed well on cervical cancer detection and classification, and therefore suitable for deployment. We plan to deploy our proposed models in a mobile application-based tool. The limitation of the study was the lack of access to high-performance computational resources.

Pipe production cost management model based on graph theory

The pipe production process is a complex dynamical system with a significant number of production operations and interconnected cost localization centres. Purpose. To develop a mathematical model of the process of handling analytical information of a pipe enterprise using graph theory for the needs of cost management at each production stage. Methodology. Technological flow charts of the production job, where the list of mandatory activities and routing of the operating process are indicated, were used to organize cost accounting by operational centres of their localization with the help of computer modelling elements, namely the graph theory. The work used a complex of research methods, including analysis and scientific synthesis of scientific and technical information; theoretical studies; methods of mathematical and computer modelling, engineering developments. Findings. The cost accounting process is represented by a directed hypergraph. Each production operation is associated with a graph vertex. Each vertex is assigned to a series of interrelated marks: type of technological operation; weight factor; the value of direct costs for the operation; the value of indirect costs for the operation; the expense factor. The initial state of the hypergraph is specified by means of its initial marking, which corresponds to the set cipher of the batch of pipes and its weight. Further execution of the marked hypergraph is performed by running the allowed vertices. Marking of the graph ends if all the information from the technological flow charts of pipe products has been used. Originality. A model of cost management by operational centres is proposed which will allow dividing the prime cost of pipes which differ according to the production technology. It is recommended to rank pipe products within each technologically similar group for an economically feasible allocation of costs between batches of pipes. The proposed process model of processing analytical information of the pipe enterprise using graph theory will allow providing data governance as for the prime cost of each individual batch of pipe products, will contribute to the improvement in the order generation procedure taking into account the acquired information, and will allow making the appropriate adjustments when making management decisions. Practical value. Calculation of expenses for production operations allows one to identify the most cost-intensive of them. Relying on the results of the calculation, the enterprise management has the opportunity to manage costs at any stage of the production process. Moreover, the model allows determining the amount of consumed metal for each production operation, which is relevant for pipe enterprises. It is appropriate to focus further scientific developments in this direction on the possibility of managing non-manufacturing cost with the help of mathematical models in economics to optimize the supply and sales activities of the enterprise.

Validated kinetic modelling of ladle furnace process for predicting inclusions in API steel grade

A kinetic model of ladle furnace (LF) process is developed in C++ format using the FactSage database and ChemApp subroutines. The model considers all the process parameters such as ferroalloy and slag dissolution, wire additions, electrical arcing, steel/slag reactions, reoxidation due to slag eye opening, heat exchange between metal/slag, and refractory dissolution. Further, a correlation for inclusion removal rate was developed as a function of argon flow rate, using discrete phase method (DPM) in Computational Fluid dynamics (CFD). Lollypop samples from steel bath and slag samples were collected from the plant for API steel grade for chemical analysis and inclusion characterisation. At LF_in, the inclusions found were mostly alumina spinel with very low amount of Mg (∼3%), followed by spinel at intermediate stage having ∼15 wt. % of Mg and after Ca treatment, inclusions transformed into CaS and Calcium Aluminate type of inclusions. Overall inclusion weight fraction varied from 136 ppm at LF_in to 93 ppm at LF_out. The model calculations were found to give very good prediction for steel and slag chemistry, and inclusion global composition as compared to the plant results. Predicted temperature was found with an accuracy of ± 5 oC, while the calculated inclusion weight fraction was having an accuracy of ±12 ppm.

Modeling the rainfall–runoff process in the Bird Creek River Basin: a comparative analysis of conceptual and artificial neural network techniques

ABSTRACT The modeling of the rainfall–runoff (RR) process is a key component for water resources projects, planning, and management for which conceptual and data-driven modeling techniques are utilized. However, these techniques in the modeling of the RR process have their own benefits, and their performance need to be explored in real basins. In this paper, five conceptual models (namely, AWBM, Sacramento, SimHyd, SMAR, and TANK) and an artificial neural network (ANN) model have been developed and their performances have been assessed using the rainfall, runoff, and other climatic data derived from the Bird Creek Basin, USA. The results obtained from the study suggest that the SimHyd performed the best among all the conceptual models during testing. The ANN model performance in simulating the RR process was found to be the best among all the models developed in this study during testing with the highest values of R = 0.941, E = 0.994 and all threshold statistics and least values of AARE = 38.9 and NRMSE = 0.031. Overall, it can be concluded that although the conceptual models are highly comprehensible, the ANN models are able to simulate the flow more accurately than any of the conceptual models developed in this study.