Process Model Research Articles

Foreign market expansion of ecosystems: a process model

ABSTRACT Ecosystems of actors that need to interact to create value for end users are becoming an integral part of firms’ strategic realities as they reach new markets. However, the phenomenon of ecosystem internationalisation has not been explored in comparison to its practical prevalence. We conceptualise this phenomenon as agent-led structuration on new markets. We build on the structural ecosystem literature and literature on internationalisation challenges to create our recursive process model of ecosystem creation on foreign markets. Creating ecosystems on foreign markets necessitates new ecosystem structures that are adjusted to value propositions by orchestrators through interaction with foreign market actors. The model explains how ecosystem orchestrators drive the international expansion of ecosystems through blueprinting and aligning mechanisms and how these mechanisms help manage liabilities of foreignness. The model accounts for the agency of involved actors, and with the help of the bottleneck concept bridges international business and ecosystem literatures.

Artificial intelligence and human integration: a conceptual exploration of its influence on work processes and workplace learning

ABSTRACT This paper examines the influence of Artificial Intelligence (AI) on job and organizational processes, drawing on sociotechnical systems (STS) theory. It discusses the emergence of AI and its implications for HRD on individual, group, and organizational learning. AI has changed the trajectory of work processes and outcomes, paving the way for the future of work. An AI-human integration process model is proposed to reinforce its influence on job design in response to AI applications in the workplace. Learning is experienced between humans and between humans and AI. AI’s cognitive intelligence is capable of learning from each other through a process of deep learning. Humans, too, can learn from AI to modify their response towards working with AI-enabled applications. The model illustrates the relationships between individuals, work processes, and sociotechnical systems through multilevel interactions. Of relevance to HRD are two critical processes, namely learning and innovating, that facilitate the continuation of AI-human integration, enabling employees to embrace AI as an integral part of their work life. The paper offers several implications for HRD research and practice, recognizing opportunities for workplace learning and the potential constraints and risks arising from AI adoption that are applicable globally and internationally.

Defining a feature-level digital twin process model by extracting machining features from MBD models for intelligent process planning

Defining a feature-level digital twin process model by extracting machining features from MBD models for intelligent process planning

Exploring the Potential of Artificial Intelligence as a Facilitating Tool for Formulation Development in Fluidized Bed Processor: a Comprehensive Review.

This in-depth study looks into how artificial intelligence (AI) could be used to make formulation development easier in fluidized bed processes (FBP). FBP is complex and involves numerous variables, making optimization challenging. Various AI techniques have addressed this challenge, including machine learning, neural networks, genetic algorithms, and fuzzy logic. By integrating AI with experimental design, process modeling, and optimization strategies, intelligent systems for FBP can be developed. The advantages of AI in this context include improved process understanding, reduced time and cost, enhanced product quality, and robust formulation optimization. However, data availability, model interpretability, and regulatory compliance challenges must be addressed. Case studies demonstrate successful applications of AI in decision-making, process outcome prediction, and scale-up. AI can improve efficiency, quality, and cost-effectiveness in significant ways. Still, it is important to think carefully about data quality, how easy it is to understand, and how to follow the rules. Future research should focus on fully harnessing the potential of AI to advance formulation development in FBP.

A DEA Game Cross-Efficiency Model with Loss Aversion for Contractor Selection

Evaluating and selecting appropriate contractors is critical to the success of specific construction projects in the building industry. Existing approaches for addressing this problem are unsatisfactory due to the ignorance of the multi-dimensional nature of the evaluation process and inappropriate consideration of existent risks. This study presents a DEA game cross-efficiency model with loss aversion for evaluating and selecting specific contractors. The competitiveness of the evaluation process is modeled using game theory with respect to the adoption of the cross-efficiency model. The attitude of the decision maker toward risks is tackled with the use of loss aversion, which is a phenomenon formalized in prospect theory. As a result, the proposed approach can adequately screen available contractors through prequalification and adequately consider the attitude of the decision maker toward risks, leading to effective decisions being made. An example is presented to demonstrate the applicability of the proposed model in evaluating and selecting appropriate contractors for specific construction projects. The results show that the proposed model is effective and efficient in producing a unique solution for contractor selection through appropriate modeling of the multi-dimensional contractor selection process and adequate consideration of the competition between the contractors and the attitude of the decision maker toward risks in practical situations.

Web-based diagnostic platform for microorganism-induced deterioration on paper-based cultural relics with iterative training from human feedback

PurposePaper-based artifacts hold significant cultural and social values. However, paper is intrinsically fragile to microorganisms, such as mold, due to its cellulose composition, which can serve as a microorganisms’ nutrient source. Mold not only can damage papers’ structural integrity and pose significant challenges to conservation works but also may subject individuals attending the contaminated artifacts to health risks. Current approaches for strain identification usually require extensive training, prolonged time for analysis, expensive operation costs, and higher risks of secondary damage due to sampling. Thus, in current conservation practices with mold-contaminated artifacts, little pre-screening or strain identification was performed before mold removal, and the cleaning techniques are usually broad-spectrum rather than strain-specific. With deep learning showing promising applications across various domains, this study investigated the feasibility of using a convolutional neural network (CNN) for fast in-situ recognition and classification of mold on paper.MethodsMolds were first non-invasively sampled from ancient Xuan Paper-based Chinese books from the Qing and Ming dynasties. Strains were identified using molecular biology methods and the four most prevalent strains were inoculated on Xuan paper to create mockups for image collection. Microscopic images of the molds as well as their stains situated on paper were collected using a compound microscope and commercial microscope lens for cell phone cameras, which were then used for training CNN models with a transfer learning scheme to perform the classification of mold. To enable involvement and contribution from the research community, a web interface that actuates the process while providing interactive features for users to learn about the information of the classified strain was constructed. Moreover, a feedback functionality in the web interface was embedded for catching potential classification errors, adding additional training images, or introducing new strains, all to refine the generalizability and robustness of the model.Results & ConclusionIn the study, we have constructed a suite of high-confidence classification CNN models for the diagnostic process for mold contamination in conservation. At the same time, a web interface was constructed that allows recurrently refining the model with human feedback through engaging the research community. Overall, the proposed framework opens new avenues for effective and timely identification of mold, thus enabling proactive and targeted mold remediation strategies in conservation.

Optimal operation of reverse osmosis desalination process with deep reinforcement learning methods

AbstractThe reverse osmosis (RO) process is a well-established desalination technology, wherein energy-efficient techniques and advanced process control methods significantly reduce production costs. This study proposes an optimal real-time management method to minimize the total daily operation cost of an RO desalination plant, integrating a storage tank system to meet varying daily freshwater demand. Utilizing the dynamic model of the RO process, a cascade structure with two reinforcement learning (RL) agents, namely the deep deterministic policy gradient (DDPG) and deep Q-Network (DQN), is developed to optimize the operation of the RO plant. The DDPG agent, manipulating the high-pressure pump, controls the permeate flow rate to track a reference setpoint value. Simultaneously, the DQN agent selects the optimal setpoint value and communicates it to the DDPG controller to minimize the plant’s operation cost. Monitoring storage tanks, permeate flow rates, and water demand enables the DQN agent to determine the required amount of permeate water, optimizing water quality and energy consumption. Additionally, the DQN agent monitors the storage tank’s water level to prevent overflow or underflow of permeate water. Simulation results demonstrate the effectiveness and practicality of the designed RL agents.

Dynamic Home Care Routing and Scheduling with Uncertain Number of Visits per Referral

Despite the rapid growth of the home care industry, research on the scheduling and routing of home care visits in the presence of uncertainty is still limited. This paper investigates a dynamic version of this problem in which the number of referrals and their required number of visits are uncertain. We develop a Markov decision process (MDP) model for the single-nurse problem to minimize the expected weighted sum of the rejection, diversion, overtime, and travel time costs. Because optimally solving the MDP is intractable, we employ an approximate linear program (ALP) to obtain a feasible policy. The typical ALP approach can only solve very small-scale instances of the problem. We derive an intuitively explainable closed-form solution for the optimal ALP parameters in a special case of the problem. Inspired by this form, we provide two heuristic reduction techniques for the ALP model in the general problem to solve large-scale instances in an acceptable time. Numerical results show that the ALP policy outperforms a myopic policy that reflects current practice, and is better than a scenario-based policy in most instances considered. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2018-05225 and RGPIN-2020-210524] and by the Telfer School of Management SMRG Postdoctoral Research Fellowship Support [Grant 2020]. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/trsc.2023.0120 .

Peridynamic numerical investigation of asymmetric strain-controlled fatigue behaviour using the kinetic theory of fracture

Numerical fatigue process modelling is complex and still an open task. Discontinuity caused by fatigue cracks requires special finite element techniques based on additional parameters, the selection of which has a strong effect on the simulation results. Moreover, the calculation of fatigue life according to empirical material coefficients (e.g., Paris law) does not explain the process, and coefficients should be set from experimental testing, which is not always possible. A new nonlocal continuum mechanics formulation without spatial derivative of coordinates, namely, peridynamics (PD), which was created 20 y ago, provides new opportunities for modelling discontinuities, such as fatigue cracks. The fatigue process can be better described by using the atomistic approach-based kinetic theory of fracture (KTF), which includes the process temperature, maximum and minimum stresses, and loading frequency in its differential fatigue damage equation. Standard 316L stainless steel specimens are tested, and then the KTF-PD fatigue simulation is run in this study. In-house MATLAB code, calibrated from the material S‒N curve, is used for the KTF-PD simulation. A novel KTF equation based on the cycle stress‒strain hysteresis loop is proposed and applied to predict fatigue life. The simulation results are compared with the experimental results, and good agreement is observed for both symmetric and asymmetric cyclic loading.

Leaders' individualized consideration, team commitment and patient loyalty: The role of social and task‐related contexts

AbstractDrawing from transformational leadership (TFL) theory and research on contextual leadership, we examined a conditional process model of leadership in nursing teams to predict patient loyalty. Using TFL's individualized consideration dimension as a salient facet of the construct in care services, we first posited that nurses' team affective commitment would partially mediate the impact of nurse leadership. We further conceptualized nurse–physician collaboration, organizational formalization and task feedback as discrete contexts that may curb the influence of head nurses' individualized consideration. In a three‐wave, multisource and multilevel study, we surveyed 654 nurses and 1770 patients from 91 hospital units. We found that team‐level head nurses' individualized consideration positively and partially related to patient loyalty through nurses' team commitment and that higher levels of nurse–physician collaboration, organizational formalization and task feedback were associated with reduced influence of individualized consideration on team commitment and patient loyalty. We discuss the implications of these findings for advancing theory and research on TFL and contextual factors of leadership.

Investigation of Micropattern Forming Process on Zr‐4 Alloy Surface Considering Plowing Effects

Micropattern forming process is an efficient and low‐cost process for fabricating multifunctional surfaces in electronics, energy, and biomedical products, but this process of creating microdimples on the surface of zirconium alloys has not been investigated. Currently, there are few models describing the micropattern forming process, and these existing models have not considered the effect of roughness on forming. The influence of roughness on micropattern forming process is not to be neglected due to the size effect, especially the plowing effects caused by asperities in surface roughness during sliding process. In this article, a micropattern forming process model is developed by combining a rough plane indentation model and a plowing effects model, and the experimental study of the forming process is conducted, which can quantitatively evaluate the effects of different process parameters and different roughness of forming die on forming process. The applicability range of the developed model is obtained, and the model is capable of predicting the forming process with various process parameters and surface roughness of forming die by comparing the experiment results with the model results, which is of great significance to the design of the forming die and the improvement of the forming process.

The protective role of positive body image on the associations between body objectification, disordered eating symptoms, and depression among Korean women

ABSTRACT Despite a significant amount of research theoretically demonstrating the benefits of positive body image, limited studies have empirically examined its protective role over women’s body concerns and behaviors. Based on the objectification theory, this study explored whether the associations between body objectification, disordered eating symptoms, and depression are moderated by positive body image. We considered four specific constructs of positive body image using the Body Positive Scale (BP Scale) which includes BP-Feeling, BP-Acceptance-even if, BP-Response, and BP-Conceptualization. We conducted moderated mediation analysis using PROCESS model on data from an online survey completed by 400 Korean women ages 20–64 years. The results showed that the associations between body objectification and disordered eating symptoms were not significant at high levels of BP-Feeling and BP-Acceptance-even if. The results also revealed that indirect associations between body objectification and depression via disordered eating symptoms were not significant at high levels of BP-Feeling and BP-Acceptance-even if. While the overall results suggest the protective role of positive body image against disordered eating and depression, this study further provides specific information on positive body image that should be included in the prevention and treatment of disordered eating.

Post-combustion carbon capture process modeling, simulation, and assessment of synergistic effect of solvents

Post-combustion carbon capture appears to be a promising solution to reduce the emission of carbon dioxide (CO2) from power plants that generate electricity using either coal or natural gas. In addition, carbon capture process efficiency, capacity, and energy consumption have become challenging against the performance of the capture process. However, synergistic effect due to solvents blend has gained attention to reduce the process energy consumption and enhance process efficiency. In this study, blends of methyldiethanolamine (MDEA) and piperazine (PZ) at different concentrations were investigated using a validated post-combustion capture process model using Aspen HYSYS. Results were compared with 30 wt% monoethanolamine (MEA) as reference case. The effective process variables are concentration of solvents, the amount of water and solvent in the makeup section, viscosity of solvent, energy consumed in different process stages, and the amount of lean solvent flow rate. These variables were studied against fixed process variables using rate-based model. The study shows that using (43 wt% MDEA/7 wt% PZ) for post-combustion carbon capture needs 2.53 MJ/kgCO2 regeneration energy for 88.5% process efficiency compared to 4.003 MJ/kgco2 for 30 wt% MEA without the need for any process modifications. In addition, it was found that solvents synergistic effect contributes to resolving the drawbacks of post-combustion capture that will enable the high utilization of the process and contribution to reduce the consequences effect of climate change. Therefore, the study will help policymakers, industries and encourage researchers towards the large-scale commissioning of blended solvent -based post-combustion capture process.

BASES OF THE METHOD OF PHYSICAL MODELING OF THE PROCESS OF CRUSHING OF MUNICIPAL SOLID WASTE

A large number of studies are aimed at increasing the energy efficiency of grinding processes of various solid materials, while maintaining the values ​​of other important indicators, such as material consumption, productivity, etc. Based on this trend, a new method for physically modeling the process of grinding municipal solid waste (MSW) was proposed for the first time. Existing physical modeling techniques are designed for homogeneous and isotropic materials (for example, soil, crushed stone, snow, coal, etc.). The strength properties of solid waste vary widely due to the significant heterogeneity of their components. Consequently, when crushing solid waste, traditional crusher designs have low efficiency in terms of energy intensity, material intensity and product quality. The purpose of this work is to develop a new technique for physical modeling of the grinding process, based on the main principles of similarity theory and modeling, considering the properties of waste heterogeneity. As a result of the research, a block diagram of the physical modeling methodology for the interaction of the working bodies of impact crushing machines with solid waste was developed. A list of tasks for the modeling process and similarity criteria have been determined based on the development of rheological models of the “working body - municipal solid waste” system and the laws of mechanics that characterize the waste grinding process. Based on the developed similarity criteria, scale equations for the grinding process are substantiated and formulas are derived for determining the expected parameters of the original based on the parameters measured on the model. The developed methodology makes it possible to create a crusher design with improved energy efficiency indicators with the least material and labor costs.

Investigation of the transient change of the cutting force during the milling of C45 and X5CrNi18-10 steel taking into account the dynamics of the electro-mechanical measuring system

AbstractThe possible methods of determining the cutting force—due to the great practical importance of knowing the force—have long been and are still intensively studied areas of cutting theory. In milling, cutting is a series of successive short material removals. During force measurement in such cases, the transient process that can be detected at the beginning of the process can influence the nature of the entire cutting cycle. The present study reports on the research, the aim of which was to explore the dynamic process of cutting force measurement in intermittent cutting. The measurements were made during face milling of C-steel and highly alloyed steel (X5CrNi 18–10). The milling head contained a single insert, with which the disturbing interaction of the multiple cutting edges was eliminated. As a result of the work, a method suitable for examining the close relationship between the initial transient nature of the milling cycle and the decay, as well as a mathematical model for the decay process, was developed. The model was validated with cutting experiments and measurements. The achieved results can be used to better understand the process of chip removal and to determine the actual power requirements of milling works.

Evaluation and instantiation of a generic process model for early-stage sustainable product-service system design within three manufacturing companies

Although product-service systems (PSS) have been in focus in literature and industry for some years, no generically agreed or applied PSS design process exists thus far, in contrast to the neighbouring fields of product development or service design, respectively. This paper presents the evaluation and instantiation of a recently proposed generic process model (GPM) for early-stage sustainable PSS design in capital goods manufacturing companies. The model was applied in three case studies with an aim to quantitatively and qualitatively evaluate the GPM concerning eight criteria, and study its instantiation process actions to a company-specific model while simultaneously identifying and proposing solutions for challenges to instantiation. Based on observation and feedback received from the companies, the GPM shows promise for widespread applicability, aided by proposed guidelines for instantiating the GPM to company-specific contexts, as also described in this paper. These guidelines aim to facilitate the translation of the GPM into pragmatic models for practitioners in the field of PSS design, thus increasing its chances of successful implementation as well as impact.

Setting the tone to get their way: An attention‐based approach to how narcissistic CEOs influence the board of directors to take more risk

AbstractResearch SummaryUpper echelons research has generated insights into the organizational consequences of CEO narcissism. However, fewer studies have empirically attended to the mechanisms through which these consequences occur. Using the attention‐based view, we introduce a process model examining how CEO narcissism is linked to corporate risk‐taking through the board of director discussion tone of risk‐taking during board meetings. We further note that narcissistic CEOs have an increased ability to do so when they are appointed to be board chair. We find strong support for each of our hypotheses by utilizing a unique data set of corporate board meeting transcripts encompassing 88 public firms and 197 CEOs over 20 years. Our results suggest that narcissistic CEOs are adept at controlling the attentional foci of boards of directors to get their way.Managerial SummaryOur study offers an explanation as to how CEO narcissism influences firm risk‐taking behavior. Specifically, we demonstrate that narcissistic CEOs are prone to drive board discussions about risk‐taking to hold a positive tone—especially when they also serve as board chair—thereby enabling them to allocate increased resources toward risk‐taking strategies. Through an extensive analysis of board meeting transcripts spanning two decades across 88 companies, we illustrate how narcissistic CEOs wield substantial influence in molding board conversations to mirror their own pro‐risk inclinations. This insight further considers the importance of understanding CEO behavior in guiding risk management strategies in the future.

Parametric Dynamic Simulation and Bayesian Design Optimization of a Front-Loading Washing Machine

Abstract Objective This paper presents the dynamic modeling and design optimization of a fully parametrized front-loading washing machine. Methods A thorough mathematical analysis is performed to capture the effect of nine design variables (including rotation speed, spring stiffness, weight balancer mass density, damping coefficient, spring and damper angles) on vibrational characteristics of the washing machine. The parametric simulation reveals a complex relationship between the vibrational dynamics and design variables, entailing a computationally efficient optimization approach. A Bayesian Optimization (BO) framework is developed, in which a Gaussian process model and Genetic Algorithm (GA) are utilized along with carefully selected acquisition functions to enable adaptive sampling and search for optimal design values. The objective function is to minimize the maximum amplitude of the tub displacement inside the washing machine body. Two case studies are performed to consider a different number of design variables and choices of various infill methods. Results Results show that the fully parametrized front-loading washing machine model is applicable for parametric simulation and design optimization. The proposed BO is able to successfully find a set of design variable values corresponding to the lowest maximum displacement for vibration reduction. A comparison analysis is also carried out to exhibit the difference in optimization convergence and computational cost caused by the choice of acquisition functions. Conclusion It is shown that the fully parametrized washing machine model yields a lower maximum displacement than the model with fewer design variables. Of the two acquisition functions studied for optimization, the Expected Improvement function converges more rapidly than the Lower Bound criterion.

Psychological interventions to promote self-forgiveness: a systematic review

BackgroundBeing able to forgive one’s own wrongdoings improves the health and well-being of a person. People find it difficult to forgive themselves due to different reasons. It is essential to enhance the ability to accept one’s deeds and thereby enhance self-forgiveness. The current systematic review’s objective is to comprehend the application and efficiency of numerous interventions that improve self-forgiveness.MethodsThe search was done on electronic databases such as PubMed, ERIC, Web of Science, PsycNet, Science Direct, and Google Scholar. The initial search yielded 399 articles. After the duplicate removal, 19 articles met the eligibility criteria. Two studies were identified through related references. Thus, 21 articles were finalized for review. The study adhered to the PRISMA recommendations for systematic reviews.ResultsThe 21 finalized articles varied in method, participants, research design, duration, measurement tools used, and observed outcomes. Thirteen of the 21 finalized articles followed interventions specifically designed to promote self-forgiveness. Interventions are seen to be applied at both individual and group levels.ConclusionThe review categorizes the interventions into self-directed and group. The self-directed interventions, notably those based on Enright’s process model, demonstrate its efficiency in nurturing self-forgiveness. Self-forgiveness interventions are also found to be effective in promoting other positive psychological and clinical variables. Further implications and future research avenues are outlined.

Physical Analysis and Mathematical Modeling of the Hydrogen Storage Process in the MmNi4.2Mn0.8 Compound

The results of an experimental and mathematical study into the MmNi4.2Mn0.8 compound’s hydrogen storage properties are presented in the present research. Plotting and discussion of the experimental isotherms (P-C-T) for different starting temperatures (288 K, 298 K, 308 K, and 318 K) were carried out first. Then, the enthalpy and entropy of formation (ΔH0, ΔS0) were deduced from the plot of van’t Hoff. Following that, the P-C-T were contrasted with a mathematical model developed via statistical physics modeling. The steric and energetic parameters, such as the number of the receiving sites (n1, n2), their densities (Nm1, Nm2), and the energy parameters (P1, P2) of the system, were calculated thanks to the excellent agreement between the numerical and experimental results. Therefore, plotting and discussing these parameters in relation to temperature preceded their application in determining the amount of hydrogen in each type of site per unit of metal ([H/M]1, [H/M]2) as well as for the entire system [H/M] versus temperature and pressure besides the absorption energies associated with each kind of site (ΔE1, ΔE2) and the thermodynamic functions (free energy, Gibbs energy, and entropy) that control the absorption reaction.