Process Model Research Articles

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

The mediating role of emotional regulation and emotional expression in the relationship between autistic traits and empathy in Saudi students

The aim was to investigate the mediating role of emotional regulation and emotional expression in the relationship between autistic traits and empathy in Saudi students. Participants were undergraduate students at Umm Al-Qura University. A total of 398 questionnaires were sent out, and 260 valid questionnaires were received. Descriptive statistics and correlation analysis were conducted on the variables hypothesized in the study; on this basis, the structural process model modeling method was used to examine the impact of autistic trait empathy after controlling for factors unrelated to age and gender. This study investigated the relationship between empathy, emotional expression, cognitive reappraisal, and autistic traits in Saudi students. A statistical analysis to test the posited hypotheses was undertaken using IBM SPSS Statistics, version 26. Key descriptive statistics were undertaken using the software, making it possible to study the distributions of successive measured variables. The results found that autistic traits can directly and negatively predict empathy, and also through negative emotional expression and cognitive reappraisal can indirectly predict empathy, and can even predict empathy through the chain mediation of negative emotional expression and positive emotional expression to cognitive reappraisal. However, autistic traits did not predict empathy through positive emotional expression.

Artificial Neural Network and Gaussian Approach to Predict Rotor-Airframe Acoustic Waveforms

An artificial neural network-based surrogate model and Gaussian process model were developed to predict the acoustic interaction for a fixed-pitch rotor in proximity to a downstream cylindrical airframe typical of small unmanned aerial system platforms. The models were trained to predict the acoustic waveform under representative hover conditions as a function of rotational speed, airframe proximity, and observer angle. Training data were acquired in an anechoic chamber on both isolated rotors and rotor–airframe configurations. The acoustic amplitude and phase of the revolution-averaged interaction were predicted, which required up to 25 harmonics to capture the impulse event caused by the blade’s approach and departure from the airframe. Prediction performance showed, on average, that the artificial neural network models could estimate the acoustic amplitude and phase over the relevant harmonics for unseen conditions with 86% and 75% accuracy, respectively. This enables a time-domain reconstruction of the waveform for the range of geometric and flow parameters tested. In contrast, the Gaussian process matched the amplitude but underpredicted the phase for unseen conditions at 86% and 45% accuracy, respectively.

Detection of E. coli using bacteriophage T7 and analysis of excitation‑emission matrix fluorescence spectroscopy

Conventional detection methods require the isolation and enrichment of bacteria, followed by molecular, biochemical, or culture-based analysis. To address some of the limitations of conventional methods, this study develops a machine learning (ML) approach to analyze the excitation-emission matrix (EEM) fluorescence data generated based on bacteriophage T7 and E. coli interactions for in-situ detection of live bacteria in the presence of fresh produce homogenate. We trained classification models using various ML algorithms based on the 3-D EEM data generated with bacteria and their interactions with a T7 phage. These ML algorithms, including linear Support Vector Classifier and Random Forest, demonstrate high accuracy (>0.85) for detecting E. coli at 102 CFU/ml concentration within 6 hours. Additionally, these ML models can differentiate among different E. coli concentration levels. For example, the Gaussian Process model achieved an accuracy of 92% in detecting different concentration levels of live E. coli. Application of these ML methods to detect E. coli in spinach homogenate yielded an accuracy of 89% using the linear-SVC model. Furthermore, feature selection techniques were employed to reduce the dimensionality of the data, revealing that only six features were necessary for achieving classification accuracy (>0.85) of spinach homogenate samples containing 102 CFU/ml of E. coli. These findings highlight the potential of this novel bacterial detection methodologies, offering rapid, specific, and efficient solutions for applications in food safety and environmental monitoring.

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.

Improving communication to increase uptake of high-risk breast cancer prevention appointments: Building a better letter

ObjectiveMailed letters to women identified as being at high-risk for developing breast cancer were not having the desired effect for encouraging appointments with prevention-focused providers at a large Midwest healthcare system. A partnership with communication scholars sought to revise the letter to increase awareness, intentions, and appointments. MethodsGuided by the Extended Parallel Process Model, survey responses were collected from letter recipients over the course of two years, both pre and post letter revision. Appointments attributed to letters were also tracked. ResultsRecipients of the revised letter had increased knowledge regarding the length of prevention appointments and indicated greater self-efficacy and intentions to make and attend appointments compared to those who received the non-revised letter. A greater percentage who received the revised letter also made appointments. ConclusionPartnering with communication scholars helped with improving a letter mailed to thousands of patients each year. Finding ways to increase response-efficacy of breast cancer prevention activities within communications may assist in increasing appointments. InnovationCross-disciplinary partnerships across the medical and social sciences – while not quick or simple - are essential for finding ways to improve patient wellbeing and hopefully reducing the prevalence of preventable diseases in the future.

Location-aware business process modeling and execution

Location-aware business process modeling and execution

Grief Responses During the COVID-19 Pandemic: A Qualitative Study in Spain

IntroductionThe recent COVID-19 pandemic led to a rise in the number of people bereaved by the death of a loved one. There are many pandemic-related stressors that may have further complicated grief in these people. The aim of this research was to conduct an in-depth the experience of illness and death during the pandemic, as well as obstacles to and factors facilitating grief in people who had lost a loved one during this period, whether due to COVID-19 or to natural or sudden causes. MethodThe sample consisted of 42 participants who had lost loved ones to COVID-19 or other causes (both natural and sudden). An inductive approach using grounded theory with open, axial and selective coding was used to analyse the semi-structured open-ended interviews. ResultsThe resulting theory was structured around a central category: “Processes of Loss and Grief in Pandemics”. The other relevant and related categories were: disease processes during the pandemic, emotional responses to the loss, factors facilitating the grieving process and obstacles to the grieving process. The various analytical categories were then further classified with reference to the Dual Process Model. ConclusionThis study highlights the main features of grief during the pandemic, including the impact of restrictive measures on disease processes; the funeral rituals performed and subsequent coping; responses such as intense shock, anger, fear or loneliness; and the way in which factors facilitating the grief process were adapted to the circumstances.

Evaluation method of carbon-fiber-reinforced polymer material anti-radiation performance based on synergistic effect model

Aiming at the high computational costs of the current multi-source X-ray radiation numerical simulations, a multi-source X-ray evaluation method based on a synergistic effect model is studied. First, based on the advantage of the low computational cost of single-source X-ray radiation numerical simulations, a hierarchical Gaussian process model is used to construct a superimposed single-source numerical simulation data fusion model. Second, by constructing composite correlation functions, the data fusion ability of the Gaussian process model is improved. The unknown parameters in the data fusion evaluation model are solved with the help of the Markov chain Monte Carlo method and a nonlinear optimization algorithm. Based on the obtained sampling values of the unknown parameters, an approximate solution method for the estimated value of the superimposed single-source X-ray radiation numerical simulations with unknown input conditions is given. Then a synergistic effect model is constructed based on the established linear regression surrogate model. The synergistic evaluation of multi-source X-ray radiation tests based on single-source X-ray radiation numerical simulation superimposed test data is studied. Finally, carbon-fiber-reinforced polymer experiments show the proposed method in better than existing Kriging methods, for prediction of multi-source X-ray radiation data.

A Dirichlet Process Model for Directional-linear Data with Application to Bloodstain Pattern Analysis

Directional data require specialized models because of the non-Euclidean nature of their domain. When a directional variable is observed jointly with linear variables, modeling their dependence adds an additional layer of complexity. A Bayesian nonparametric approach is introduced to analyze directional-linear data. Firstly, the projected normal distribution is extended to model the joint distribution of linear variables and a directional variable with arbitrary dimension projected from a higher-dimensional augmented multivariate normal distribution. The new distribution is called the semi-projected normal distribution (SPN) and can be used as the mixture distribution in a Dirichlet process model to obtain a more flexible class of models for directional-linear data. Then, a conditional inverse-Wishart distribution is proposed as part of the prior distribution to address an identifiability issue inherited from the projected normal and preserve conjugacy with the SPN. The SPN mixture model shows superior performance in clustering on synthetic data compared to the semi-wrapped Gaussian model. The experiments show the ability of the SPN mixture model to characterize bloodstain patterns. A hierarchical Dirichlet process model with the SPN distribution is built to estimate the likelihood of bloodstain patterns under a posited causal mechanism for use in a likelihood ratio approach to the analysis of forensic bloodstain pattern evidence.

Data-aware process models: From soundness checking to repair

Data-aware process models: From soundness checking to repair

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.

Development of mathematical models of power consumption at coal plants

The object of research is coal enterprises where powerful electrical equipment is used (underground installations up to 3000 kW, tunneling complexes of 500–1500 kW, technological complexes of 6–10/0.4 kV, etc.). The deficit of generated capacities, caused by growing energy consumption, can be reduced by regulating power consumption modes. The relevance of this issue is determined by the need to conduct a research of the electricity consumption system, to determine the qualitative and quantitative characteristics of electricity consumption using mathematical models. The lack of mathematical models makes it difficult to analyze energy intensity and consumption modes of each technological operation in the overall balance of electricity consumption of coal enterprises. The article considers the structure and classification of the main technological groups of energy consumers, the development of mathematical models for each type of load modes, as well as a generalized model of electricity consumption of coal enterprises, with the use of mathematical apparatus of probability theory and mathematical statistics. As a result of the work, mathematical models of the electricity consumption process for the main technological groups and for models of daily electricity consumption of coal enterprises as a whole were developed, and it was also established that technological objects of electricity consumption are divided into three different values, in terms of power consumption modes: constant, uniform and pulse. For each class of consumers their statistical characteristics were obtained. The work results can be applied for managing power consumption modes of coal enterprises

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.

Evaluating FAIR Principles Compliance in the Capes Open Data Portal

The article examines the Open Government Data (OGD) movement, emphasizing applying the FAIR (Findable, Accessible, Interoperable, Reusable) principles to government data, using Brazil's Capes open data portal as a case study. The study underscores the significance of Open Government Data (OGD) in promoting transparency and democratizing access to information within the public sector. It addresses issues such as typographical errors, the absence of standardization, duplicated data, and emphasizes the necessity for a process model that integrates quality control mechanisms The paper also explores the economic and political potential of OGD, analyzing the challenge of managing open government data with high quality and interoperability. The study's results on the Capes portal show mixed compliance with the FAIR principles, with recommendations for future improvements.

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.