Process Model Research Articles

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

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.

Study on heat storage characteristics and carbon optimisation in the sintering process

In response to the phenomenon of automatic heat storage during the sintering process of iron ore, heat balance analysis concerning the main chemical reactions and physical heat carried by gas and solid materials was conducted. A unit heat storage model for the sintering process was established. Further, a mathematical model to solve the heat transfer characteristics between different sintering layer units was proposed and established based on temperature–time curves of the gas and solid phases. Using these models, the heat storage of each layer unit was calculated under the condition of uniform carbon allocation. Subsequently, the optimisation of carbon allocation at each layer unit for maximising the utilisation of heat storage was investigated. The heat required for sintering by the layer unit was taken as the constraint, and the maximum utilisation of heat stored in each layer unit was taken as the goal to optimise the carbon allocation in the height direction. The calculation results show that as the unit thickness is 100 mm and the total thickness of the layer is 600 mm, the heat transfer ratio of the upper unit to the lower unit is 34.22%, 30.29%, 14.74%, 9.49%, and 1.17%, respectively. The heat storage and available heat storage rate increase with the increase of layer height, with heat storage ranging from 0.69 × 104 to 5.14 × 104 KJ/t and heat storage rate ranging from 11.63% to 49.37%. After optimising carbon allocation, the unit achieved a maximum carbon reduction of 60.04%, resulting in an overall reduction in carbon allocation of 46.20%. This adjustment aligns more closely with the industrial processes.

Mental health and well-being in prisons and places of detention.

This study aims to address the critical mental health challenges faced by individuals in prisons and places of detention. By introducing and validating a novel conceptual framework that integrates social determinants of health with the stress process model, this study aims to provide actionable insights for improving mental health care in correctional settings. The research seeks to inform policymakers, prison administrators and mental health professionals about effective interventions and systemic reforms that can reduce recidivism, enhance rehabilitation and promote a more humane and just criminal justice system. This study uses a multi-method research design integrating a systematic literature review, qualitative interviews, multi-criteria evaluation (MCE) and a small-scale empirical study within a correctional facility. The systematic review follows PRISMA guidelines, analysing peer-reviewed articles, government reports and policy documents. Semi-structured interviews with 25 mental health professionals provide practical insights, whereas the MCE assesses intervention effectiveness based on expert consensus. The empirical study involves baseline assessments, intervention implementation and follow-up evaluations using validated tools, providing robust data to validate the proposed conceptual framework that integrates social determinants of health with the stress process model. This study finds that mental health disorders are significantly more prevalent in prison populations than in the general population, with interventions like cognitive behavioural therapy (CBT), peer support programs and trauma-informed care showing considerable effectiveness in reducing symptoms. Emerging technologies such as AI and telemedicine present promising avenues for enhancing care but require careful ethical consideration. The research also highlights the critical role of social determinants and the importance of systemic reforms, such as reducing solitary confinement and integrating prison health care with broader public health systems, in improving inmate mental health and reducing recidivism. This study's quasi-experimental design, while robust, may limit the generalizability of the findings to other correctional settings because of specific facility characteristics. Additionally, the sample size for qualitative interviews, although diverse, could have been larger to provide more nuanced insights, particularly for underrepresented groups. This study highlights the need for more longitudinal research to assess the long-term effects of interventions and the scalability of approaches across different cultural and legal contexts. Future studies should address these limitations by using more diverse sampling, larger sample sizes and extended follow-up periods. This study provides actionable insights for policymakers and prison administrators, emphasizing the need to integrate evidence-based mental health interventions, such as CBT and trauma-informed care, into prison reform strategies. It highlights the importance of training correctional staff in mental health first aid and the potential of emerging technologies like AI and telemedicine to enhance care delivery in resource-limited settings. Implementing these recommendations could significantly improve inmate mental health outcomes, reduce recidivism rates and promote a more humane and effective correctional system, with broader benefits for public health and social equity. Improving mental health care in prisons has significant social implications, particularly in promoting social justice and reducing inequalities. Effective mental health interventions can help break the cycle of incarceration and recidivism, particularly in marginalized communities disproportionately affected by the criminal justice system. By addressing the mental health needs of incarcerated individuals, society can foster safer communities, reduce crime rates and alleviate the economic burden on the criminal justice and health-care systems. Furthermore, enhancing mental health care in prisons reflects a commitment to human dignity, fairness and the rehabilitation of offenders, contributing to a more just and compassionate society. This study introduces a novel conceptual framework that integrates social determinants of health with the stress process model, specifically tailored for correctional facilities. It provides original empirical data from a real-world prison setting, offering new insights into the effectiveness of mental health interventions, such as CBT and trauma-informed care. The study's value lies in its comprehensive approach, combining quantitative and qualitative methods, and its focus on underrepresented populations in correctional settings. By addressing significant gaps in the literature, this research offers practical recommendations for policymakers and practitioners, contributing to improved mental health outcomes and reduced recidivism.

Optimizing Colorectal Screening in Portugal with Process Mining

Abstract Background Efficient healthcare screening programs management influences patient outcomes. Process mining has emerged as a powerful tool to enhance these programs by refining complex workflows and pinpointing operational deviations. Objectives This study utilizes process mining techniques to analyze the colorectal cancer screening program in Northern Portugal, focusing on detecting deviations and performance bottlenecks in process models. Methods This screening program started in March 2018, but process mining was only used on data posterior to 2020. Over 1 million case observations from administrative data were included in the process model analysis, involving more than six key process activities and numerous resources. We used new software and statistical tools for process mining to rapidly compute observations and filter for different operational needs and monitor interventions. Results The discovery analysis identified deviations, such as unexpected performance issues and irregular activity sequences. Although over 100 traces were identified, fewer than five traces accounted for more than 90% of all case paths, indicating the most common traces that have a true impact in participation rates, referral rates, and treatment rates. Notable differences in resource performance during specific periods, such as COVID-19 or seasonal events, were also observed. These findings provided insights for adjusting resource allocation and improving coordination, with impact in minimizing seasonal phenomena or peak production periods. Lessons Process mining offers actionable insights for effective health program coordination, enabling the monitoring and timely response to process deviations. With key areas for action identified in a more timely and precise manner, we can enhance efficiency, and expand population coverage and treatment referral. Future initiatives will focus on further process optimization and national coverage expansion using these management tools. Key messages • Process mining revolutionizes healthcare by enabling the strategic management of complex oncological screening program workflows. • Comprehensive data analysis across the patient pathway enhances program efficiency and expands treatment access.

A Multi-level Reference Model and a Dedicated Method for Cyber-Security by Design

AbstractThe increased reliance of organizations on information technology inherently increases their vulnerability to cyber-security attacks. As a response, a host of cyber-security approaches exists. While useful, these approaches exhibit shortcomings such as an inclination to be fragmented, not accounting for up-to-date organizational data, focusing on singular vulnerabilities only, and being reactive, i.e., focusing on patching up vulnerabilities in current systems. The paper presents and evaluates a modeling method aiming to address those shortcomings and to support security by design with a focus on the electricity sector. The proposed modeling method encompasses a multi-level reference model reconstructing and integrating existing initiatives and supporting top-down and bottom-up analyses. Compared to earlier work, the paper contributes (1) a process model for cyber-security by design, which proactively considers security as a first-class citizen during the design process, (2) a complete coverage of the multi-level model, in terms of three views complementing the introduced process model, (3) an elaborated evaluation, in terms of reporting on an additional design science cycle.

A method for interactive collaboration between different actors: theory of change workshops

Abstract It is well recognised that the involvement of different actors in health research is crucial to the success of projects and interventions, as it increases the likelihood that they will be relevant, impactful and applicable in practice. However, researchers and policy makers often lack tools for the participatory involvement of actors. One way of actively involving actors is the Theory of Change (ToC) method. A ToC is a model of a change process that describes how and why an intervention leads to the intended change or outcome in a given context. It describes the path along which change is expected to occur and provides a framework for investigating whether and how change takes place. It is a systematic methodology that can be used both prospectively and retrospectively for the planning, implementation and evaluation of interventions. The approach recognises that changes in socio-ecological systems are complex and challenging and that the causal processes are often non-linear and intertwined. A ToC workshop begins with the formulation of a clear vision for the desired change and the long-term goal of the intervention. Starting from the long-term goal, the necessary medium-term and short-term goals are then defined in a backward mapping process. Next, indicators, activities and assumptions are defined for each intermediate goal. The results of a ToC workshop can be visualised in a ToC map, which shows the causal pathways, relationships, and connections between the different components of the ToC. This visual representation helps the actors to understand the logic, connections, and assumptions underlying the ToC. This in turn facilitates a shared understanding, collaboration, and ownership among all actors involved. Overall, successful involvement of actors using the ToC method can not only enrich the research process, but ultimately also increase the health benefits of interventions and health policies.

Parent's Perspectives on Boarding Schools in Primary Education Settings: The Case of Std Vi and Vii in Magharibi B District Unguja

This paper is set to determine the Parents' perspectives on boarding schools in Zanzibar. The study is expected to provide insights into the Parents' perspectives and experiences regarding boarding schools in primary school settings, especially for STD VI and VII. The study will shed light on the benefits, challenges, and potential outcomes associated with boarding in primary school settings from different parents. The study utilized the Social Learning Theory developed by Albert Bandura, which is a psychological framework that emphasizes the role of observation, imitation, and modeling in the learning process. This theory highlighted the impact of models in shaping behavior, both positive and negative, and demonstrated how children could imitate aggressive behavior they witnessed in adults. In this study, data was gathered directly from primary sources through various methods such as interviews, Questionnaires, and group discussions in order to give chances for any respondent to give out his or her view about boarding schools in primary education settings in confidence and freeway. The findings indicate that parents prefer boarding schools as they provide a conducive learning environment, help in students' social development, and safety in boarding schools is well-managed. Also, boarding schools have adequate resources and facilities and communication between parents and boarding schools is effective. The study concludes that parents hold varied perspectives on boarding schools, emphasizing their perceived benefits such as conducive learning environments, social development opportunities, and well-managed safety protocols. These positive views are complemented by concerns about resource adequacy, communication effectiveness, and the rigidity of the boarding school environment

The relationship between familial factors and youth mental health outcomes in Korean American families: The mediation effects of youth's negative emotionality.

Depressive symptoms are one of the mental health problems among Korean American (KA) youth. Although several studies examined mental distress among KA youth, few have examined the associations between different familial factors, negative emotionality, and depressive symptoms among them, especially using longitudinal data. Drawing on the Triadic Model of Family Process (TMFP), this study examined the longitudinal associations between Korean-specific aspects of familial factors and depressive symptoms among KA youth and the mediation role of negative emotionality in the relationships. Using KA youth and parent data from the Midwest Longitudinal Study of Asian American Families (MLSAAF) project, path analyses revealed that family enmeshment and youth-reported intergenerational conflict were associated with youth depressive symptoms, with negative emotionality serving as a mediator. The findings suggest that interventions aimed at reducing negative emotionality and improving parent-youth relationship quality and family boundaries could be helpful in reducing youth depressive symptoms.

Toward a theory of resource flow based on guanxi: Key stakeholders and dynamic process model

Toward a theory of resource flow based on guanxi: Key stakeholders and dynamic process model

A Strengths‐Based Human Resource Management Approach to Neurodiversity: A Multi‐Actor Qualitative Study

ABSTRACTAlthough the attention for neurodiversity in human resource management (HRM) is growing, neurodivergent individuals are still primarily supported from a deficit‐oriented paradigm, which points towards individuals' deviation from neurotypical norms. Following the HRM process model, our study explored to what extent a strengths‐based HRM approach to the identification, use, and development of strengths of neurodivergent groups is intended, implemented, and perceived in organizations. Thirty participants were interviewed, including HRM professionals (n = 15), supervisors of neurodivergent employees (n = 4), and neurodivergent employees (n = 11). Our findings show that there is significant potential in embracing the strengths‐based approach to promote neurodiversity‐inclusion, for instance with the use of job crafting practices or (awareness) training to promote strengths use. Still, the acknowledgement of neurodivergent individuals' strengths in the workplace depends on the integration of the strengths‐based approach into a supportive framework of HR practices related to strengths identification, use, and development. Here, particular attention should be dedicated to strengths development for neurodivergent employees (e.g., optimally balancing strengths use). By adopting the strengths‐based HRM approach to neurodiversity as a means of challenging the ableist norms of organizations, we add to the HRM literature by contributing to the discussion on how both research and organizations can optimally support an increasingly diverse workforce by focusing on individual strengths.

A general Bayesian algorithm for the autonomous alignment of beamlines.

Autonomous methods to align beamlines can decrease the amount of time spent on diagnostics, and also uncover better global optima leading to better beam quality. The alignment of these beamlines is a high-dimensional expensive-to-sample optimization problem involving the simultaneous treatment of many optical elements with correlated and nonlinear dynamics. Bayesian optimization is a strategy of efficient global optimization that has proved successful in similar regimes in a wide variety of beamline alignment applications, though it has typically been implemented for particular beamlines and optimization tasks. In this paper, we present a basic formulation of Bayesian inference and Gaussian process models as they relate to multi-objective Bayesian optimization, as well as the practical challenges presented by beamline alignment. We show that the same general implementation of Bayesian optimization with special consideration for beamline alignment can quickly learn the dynamics of particular beamlines in an online fashion through hyperparameter fitting with no prior information. We present the implementation of a concise software framework for beamline alignment and test it on four different optimization problems for experiments on X-ray beamlines at the National Synchrotron Light Source II and the Advanced Light Source, and an electron beam at the Accelerator Test Facility, along with benchmarking on a simulated digital twin. We discuss new applications of the framework, and the potential for a unified approach to beamline alignment at synchrotron facilities.

Selection processes, transportability, and failure time analysis in life history studies.

In life history analysis of data from cohort studies, it is important to address the process by which participants are identified and selected. Many health studies select or enrol individuals based on whether they have experienced certain health related events, for example, disease diagnosis or some complication from disease. Standard methods of analysis rely on assumptions concerning the independence of selection and a person's prospective life history process, given their prior history. Violations of such assumptions are common, however, and can bias estimation of process features. This has implications for the internal and external validity of cohort studies, and for the transportabilty of results to a population. In this paper, we study failure time analysis by proposing a joint model for the cohort selection process and the failure process of interest. This allows us to address both independence assumptions and the transportability of study results. It is shown that transportability cannot be guaranteed in the absence of auxiliary information on the population. Conditions that produce dependent selection and types of auxiliary data are discussed and illustrated in numerical studies. The proposed framework is applied to a study of the risk of psoriatic arthritis in persons with psoriasis.

Neural Network Architectures and Magnetic Hysteresis: Overview and Comparisons

The development of innovative materials, based on the modern technologies and processes, is the key factor to improve the energetic sustainability and reduce the environmental impact of electrical equipment. In particular, the modeling of magnetic hysteresis is crucial for the design and construction of electrical and electronic devices. In recent years, additive manufacturing techniques are playing a decisive role in the project and production of magnetic elements and circuits for applications in various engineering fields. To this aim, the use of the deep learning paradigm, integrated with the most common models of the magnetic hysteresis process, has become increasingly present in recent years. The intent of this paper is to provide the features of a wide range of deep learning tools to be applied to magnetic hysteresis context and beyond. The possibilities of building neural networks in hybrid form are innumerable, so it is not plausible to illustrate them in a single paper, but in the present context, several neural networks used in the scientific literature, integrated with various hysteretic mathematical models, including the well-known Preisach model, are compared. It is shown that this hybrid approach not only improves the modeling of hysteresis by significantly reducing computational time and efforts, but also offers new perspectives for the analysis and prediction of the behavior of magnetic materials, with significant implications for the production of advanced devices.

Influence of Process Parameters on Flatness During Single-Track Laser Cladding

During the laser cladding process, poor flatness of the cladding track can cause the surface structure to be uneven or corrugated, affecting the geometrical accuracy of the workpiece. Adjusting process parameters is an effective way to achieve high cladding track flatness. This study established a mesoscale model of the laser cladding process for CoCrMoSi powder to simulate the formation of a single cladding track. Subsequently, the formation mechanism of cladding track flatness was revealed by analyzing the flow within the molten pool and the solidification behavior of the molten pool edge. The influences of laser power, scanning speed, and powder feeding rate on flatness were determined through simulations and physical experiments. Finally, a parameter window of flatness was established using simulation and experimental results. The window indicates that high flatness is achieved with a high scanning speed (v > 260 mm/min), high laser power (P > 2300 W), and low powder feed rate (Pf < 5.5 g/min). The accuracy of the numerical model was verified by comparing the simulated results with the experimental measurements.

Identifying At-Risk Factors of a College Course in Blended Mode and a Case Study

A framework was proposed to identify the at-risk factors of college courses in blended mode, offering suggestions for continuous improvement. An indicator system concerning teaching quality characteristics was constructed based on context, input, process, and product (CIPP) model. Subsequently, the group Analytic Hierarchy Process (AHP) algorithm was employed to assign weights to each indicator. Notably, the approach for assessing the consistency of the judgment matrix was improved, with a focus on preserving the initial judgments of the experts to the greatest extent. Finally, results were computed using a fuzzy comprehensive evaluation. Continuous improvements were suggested to enhance the teaching quality of the course and improve student learning outcomes. A typical case study was presented to illustrate the detailed process of the proposed framework. The evaluation results demonstrate its potential as a new tool for understanding blended teaching mode and enhancing learning outcomes across various college courses.

Cultural appreciation and appropriation in the crafting of the new Peruvian cuisine

Elite craftspeople, as specialist producers with privileged access and resources, can elevate the perceived status of craft elements such as materials, traditions, and techniques regarded as lowbrow due to their association with marginalized groups. While they strive to emphasize cultural appreciation, they often face accusations of cultural appropriation, which, if unaddressed, can undermine the perceptions of authenticity crucial for achieving higher cultural status. However, this tension between appreciation and appropriation overlooks the complex interplay of authenticity types that can structure the actions of elite craftspeople. Drawing on a qualitative study of new Peruvian cuisine, we examine how local elite chefs elevate marginalized craft elements through authenticity claims and develop a dual process model of cultural appreciation and appropriation. In doing so we identify how the elevation of marginalized craft elements involves, and even necessitates, aspects of appreciation and appropriation, especially when power dynamics are at play. We provide insights into the interplay between authenticity as connection and conformity, the agency in neo-craftwork, and claims of craftwashing.

Analyzing and forecasting air pollution concentration in the capital and Southern Thailand using a lag-dependent Gaussian process model.

The air pollution problem has now amassed worldwide attention due to its multifaceted harm to human health. Exploring the concentration of air pollution and improving forecast have important consideration worldwide. In this research, we analyze the air pollution concentration of Southern Thailand and compare it with the central region. Also, we proposed a methodology based on the lag-dependent Gaussian process (LDGP), a Bayesian non-parametric machine learning model, with a stable optimization approach, which is a cluster-based multi-starter technique based on the Nelder-Mead optimizer. This model also provides the confidence band for forecasted values. We also used autoregressive deep neural network (AR-DNN), autoregressive random forest (AR-RF), gradient boosting (GB), and K-nearest neighbors (KNN) models. A comparison of the proposed methodology was performed on the daily air pollution data collected from the southern provinces and also from the capital of Thailand from 1 January 2018 to 31 December 2022. We used well-established performance evaluation measures to compare the performance of the models. To evaluate the bias due to overfit, we performed a tenfold cross-validation for all the pollutants in each region and compared the models to choose the best one. Moreover, we explored the concentration of air pollution in these regions. Results of descriptive analysis revealed that Bangkok had a much higher concentration of air pollution as compared to the southern region. However, the southern region had higher exposure to PM air pollutants as per WHO recommendations and also had higher exposure to O3 and CO levels. The proposed LDGP model outperformed the other machine learning models for forecasting all air pollutants. Hence, it is recommended to be used by experts for further research and studies with different kernel functions. This research is also expected to contribute to local government planning and prevention and worldwide use of the same methodology for the sustainability of public health.

When Time Is the Enemy: An Initial Test of the Process Model of Patience.

The process model of patience attempts to reconcile disparate approaches to understanding patience. This investigation provides an initial test of the tenets of this new theoretical model, which positions impatience as a discrete emotion and patience as a targeted form of emotion regulation. In three studies with diverse samples (N = 1,401; data collected 2022-2023), participants responded to hypothetical scenarios designed to tap into familiar experiences of impatience. Regarding impatience, findings support our claim that impatience arises in response to the perception that a delay is unreasonable or unfair, and situational and intrapersonal characteristics emerged as predictors of impatience. Regarding patience, findings were consistent with the conceptualization of patience as driven more by intrapersonal than situational factors and revealed a set of individual differences that predicted patience. This investigation lends support to the process model of patience as a viable approach, generative of testable research questions, with implications for well-being.