Process Model Research Articles

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.

Modeling of Liquefied Natural Gas Cold Power Generation for Access to the Distribution Grid

Cold energy generation is an important part of liquefied natural gas (LNG) cold energy cascade utilization, and existing studies lack a specific descriptive model for LNG cold energy transmission to the AC subgrid. Therefore, this paper proposes a descriptive model for the grid-connected process of cold energy generation at LNG stations. First, the expansion kinetic energy transfer of the intermediate work mass is derived and analyzed in the LNG unipolar Rankine cycle structure, the mathematical relationship between the turbine output mechanical power and the variation in the work mass flow rate and pressure is established, and the variations in the LNG heat exchanger temperature difference, seawater flow rate, and the turbine temperature difference in the cycle system are investigated. Secondly, based on the fifth-order equation of state of the synchronous generator, the expressions of its electromagnetic power, output AC frequency, and voltage were analyzed. Finally, the average equivalent models of the machine-side and grid-side converters are established using a direct-fed grid-connected structure, thus forming a descriptive model of the overall drive process. The ORC model is built in Aspen HYSIS to obtain the time series expression of the torque output of the turbine; based on the ORC output torque, the permanent magnet synchronous generator (PMGSG) as well as the direct-fed grid-connected structure are built in MATLAB/Simulink, and the active power and current outputs of the grid-following-type voltage vector control method and the grid-forming-type power-angle synchronous control method are also verified.

Robust malicious software detection and classification using global whale optimization algorithm with deep learning approach

Software malware detection and classification leverage sophisticated procedures and methods from the cybersecurity domain for identifying and categorizing malicious software, generally called malware. This procedure analyses code behaviour, file structures, and other features to distinguish between benign and malicious programs. Machine learning (ML) and artificial intelligence (AI) are vital in this domain, allowing the progress of dynamic and adaptive systems that identify novel and developing malware attacks. By training on massive datasets of benign and malicious instances, these systems learn patterns and signatures indicative of malware. This lets them correctly categorize and respond to potential attacks in real-time. This study presents a Global Whale Optimization Algorithm with Neutrosophic Logic for Software Malware Detection and Classification (GWOANL-SMDC) technique. The GWOANL-SMDC technique secures the software via the Android malware recognition process. Primarily, the GWOANL-SMDC technique employs the Neutrosophic Cognitive Maps (NCM) model for the feature selection process. The GWOANL-SMDC technique uses a convolutional long short-term memory (ConvLSTM) model for software malware detection. At last, the GWOA-based parameter tuning is performed to improve the performance of the ConvLSTM model. The simulation values of the GWOANL-SMDC technique are examined on the malware dataset. The obtained results ensured that the GWOANL-SMDC technique improved capability in detecting software malware.

External Corporate Governance and Corporate Misconduct: A Meta‐Analysis

ABSTRACTResearch Question/IssueExternal governance parties deter corporate misconduct through their monitoring. External monitoring increases the probability of corporate misconduct being detected and sanctioned. Current research on the relationship between external governance and corporate misconduct remains fragmented across these detection and sanction mechanisms of deterrence. This separation of mechanisms leaves us with an incomplete concept of external monitoring and obscures our understanding of what facilitates monitoring by investors, auditors, analysts, and the media.Research Findings/InsightsWe integrate the detection and sanction mechanisms of deterrence into a process model of external monitoring. Our meta‐analysis of 188 studies from 14 countries covering the period from 1970 to 2019 identifies proximity, credibility, and attention as common underlying factors that facilitate monitoring by external governance parties. Proximity is of particular relevance in deterring corporate misconduct. Ethical relativism weakens external governance parties' role in deterring corporate misconduct.Theoretical/Academic ImplicationsParties outside firm boundaries affect public perception of firms, thereby possessing a unique influence on corporate governance. We integrate this form of influence into the concept of external monitoring. Our meta‐analytic synthesis suggests a fundamental role for external governance in preventing corporate misconduct and informs on the relevance of societal values for corporate governance and corporate misconduct.

Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models

Abstract This paper addresses the critical need to quantify vehicle trajectory uncertainty in autonomous driving under environmental variability. We focus on predicting the posterior distribution of vehicle trajectories over a fixed horizon, given an initial state and a sequence of actions. We propose and compare three approaches: a probabilistic seq2seq model based on stochastic variational Gaussian processes, sequential Monte Carlo simulation with a single-step Gaussian process model, and a hybrid model that leverages the strengths of both methods. Each approach incorporates a baseline vehicle kinematics model to enhance stability and convergence. We evaluate these methods using a dataset generated from the CARLA simulator, assessing both point error metrics and probabilistic prediction metrics. This research introduces novel approaches to quantifying vehicle trajectory uncertainty through various uncertainty quantification techniques, with the goal of improving the safety and reliability of autonomous vehicle control systems.

Shifts in task absorption during decision-making episodes

In today’s rapidly evolving technological landscape, this study investigates the impact of new technology on organizational work dynamics. By integrating the Job Demands-Resources theory and the Episodic Process Model, we examine the cognitive mechanisms that influence task performance during decision-making episodes. Our research focuses on episodic job demands and resources, emphasizing the mediating role of task absorption in the relationship between these factors and task performance. Our findings reveal that episodic job resources positively affect task absorption and performance, while job demands moderate the relationship between job resources and task absorption. Employing the Beer Distribution Game alongside neuroscience-based eye-tracking techniques, we analyze visual attention dynamics during decision-making episodes, providing novel insights into the interplay between task absorption and task performance. Theoretically, our study highlights the significance of task absorption in understanding how episodic job resources impact decision-making performance. Practically, our results advocate for the implementation of decision-making-focused skills development through physiological measures such as neurofeedback training programs. This research underscores the importance of individual time management in enhancing task performance, contributing to a nuanced understanding of job resources, job demands, and task absorption in the context of technological transformation.

Susceptibility of hydraulic structures to internal erosion: modeling of suffusion process

Internal erosion is a primary cause of hydraulic structure failure, manifesting as two key phenomena: piping erosion and suffusion. These processes differ in their geometrical and hydraulic boundary conditions, resulting in varying levels of failure risk. Piping erosion is the more hazardous and rapid process, often leading to immediate failure if not promptly addressed. In contrast, suffusion gradually alters the permeability of the medium, potentially culminating in failure after a prolonged period of development. In this study, we propose a model for suffusion based on Darcy’s law, Papamichos' erosion law, and Einstein's viscosity evolution relation. The model describes the temporal evolution of the average porosity in the porous medium, the concentration of eroded particles in the fluid, and the mass of particles eroded, while highlighting the impact of hydraulic and mechanical parameters, such as the erosion coefficient and maximum porosity, on this evolution. The numerical solution is obtained using the finite difference method, with the sample discretized into elementary layers. For discretization, an explicit off-centered scheme was adopted. The simulation results reveal that suffusion is strongly influenced by soil hydraulic and mechanical parameters, particularly the erosion coefficient (λ) and final porosity (Ømax).

Characterization of physical metallurgy of quenching and partitioning steel in pulsed resistance spot welding: A simulation-aided study

This study mainly focuses on the microstructure evolution of QP980 steel during resistance spot welding and its influence on the mechanical performance of resistance spot welds which is a critical influencing factor on the quality of body-in-white at the service condition. It is observed that the thermomechanically engineered microstructure of QP980 steel changes to form metastable phases such as martensite in the fusion zone and the heat affected zone due to rapid cooling induced by the thermal cycle of the welding. A finite element modeling of the welding process was used to predict the weldment heat distribution, thermal history and microstructure evolution in different welding zones. The modeled thermal history of the weldments shows that the peak temperature in the four-pulse resistance spot welding is delayed because of pulsed welding conditions and holding times between the welding pulses. This heat management approach in pulsed welding prevents void formation. The modeled thermal history and rapid heating, and cooling conditions are discussed here to predict the microstructure evolution and transformation in the fusion and reheated zones. The modeled results were helpful in the prediction of the microstructure at different weld zones. Then the strategic links between the microstructure and mechanical performance of the welded alloy are discussed thoroughly. The microhardness profile of the weld is discussed from a microstructural point of view to disclose the physical metallurgy of the welds. Softening phenomena were not observed in the sub-critical heat affected zone.

Understanding the Integration of Building Energy Modeling into the Building Design Process: Insights from Two Collaborative Construction Projects

This research investigates the integration of building energy modeling (BEM) within collaborative construction projects to inform design decisions for achieving high-energy performance goals. The study aims to understand current practices, benefits, and challenges associated with this integration. Using an ethnographic case study approach focused on two high-energy performance social housing projects with integrated project delivery and integrated design processes, data were collected through direct observations, document analysis, and interviews with project team members. Design process modeling was utilized to dissect current practices, followed by a hybrid inductive and deductive thematic analysis to find challenges related to energy performance design in collaborative projects. Findings from this research revealed that BEM experts often operate in isolation, with late integration of energy models into design decisions. Compliance-centric BEM usage and challenges related to interoperability of design and BEM tools further compound the issue of seamless collaboration. However, the study highlights that early collaboration among project stakeholders emerges as a pivotal factor in informed design decisions, bridging the gap between energy modeling and design. This research provides valuable insights for practitioners seeking to optimize BEM in their design process, and offers support to policymakers aiming to enhance the role of BEM in projects.

Multi-agent distributed control of integrated process networks using an adaptive community detection approach

This paper focuses on developing an adaptive system decomposition approach for multi-agent distributed model predictive control (DMPC) of integrated process networks. The proposed system decomposition employs a refined spectral community detection method to construct an optimal distributed control framework based on the weighted graph representation of the state space process model. The resulting distributed architecture assigns controlled outputs and manipulated inputs to controller agents and delineates their interactions. The decomposition evolves as the process network undergoes various operating conditions, enabling adjustments in the distributed architecture and DMPC design. This adaptive architecture enhances the closed-loop performance and robustness of DMPC systems. The effectiveness of the multi-agent distributed control approach is investigated for a benchmark benzene alkylation process under two distinct operating conditions characterized by medium and low recycle ratios. Simulation results demonstrate that adaptive decompositions derived through spectral community detection, utilizing weighted graph representations, outperform the commonly employed unweighted hierarchical community detection-based system decompositions in terms of closed-loop performance and computational efficiency.

Perceived Susceptibility to and Severity of Cardiovascular Disease Is Associated With Intent to Change Behavior Among Women 25-55 Years Old.

Risk factors for cardiovascular disease (CVD) among young and middle-aged women have increased, whereas CVD knowledge and awareness remain low. The objective of this study was to describe the relationship between the stage of behavior change and awareness, knowledge, and perceptions of CVD among women 25-55 years and identify predictors of the stage of behavior change. A cross-sectional online survey of women ages 25-55 years living in the United States was conducted. Awareness was measured with the question "What is the leading cause of death for women in the United States?" Knowledge, perceptions, and the stage of behavior change were measured with the Heart Disease Fact Questionnaire, Health Beliefs Related to CVD, and Precaution Adoption Process Model instruments, respectively. Chi-square and t tests were used to determine differences between awareness, knowledge, and perceptions based on the stage of behavior change. Multiple logistic regression was used to evaluate the relationship between the stage of behavior change and awareness, knowledge, and perceptions. A total of 149 primarily minority women (n = 105) were included (mean age = 37.15 ± 7.86 years). The perception of CVD susceptibility was associated with increased intention to change behavior (odds ratio, 1.247; 95% confidence interval, 1.101-1.414; P < .001). The perception of CVD severity was associated with reduced intention to change behavior (odds ratio, 0.809; P = .004). Women who believed they were susceptible to CVD and did not perceive CVD as severe were more likely to report intent to change behavior, suggesting perception of CVD risk is more important than awareness or knowledge. Addressing misperceptions may be a strategy for primary risk reduction.