Process Model Research Articles

The phases of self in transformative experiences: Insights from astronauts, econauts and psychonauts

In the transformation age, individuals pursue experiences that offer awe-inducing moments and long-lasting transformation. Grounded in a psychological and phenomenological approach, this research aims to map out the holistic process a person undergoes in transformative tourism experiences by analysing some of the world's most transformational settings, namely, outer space, natural environments, and psychedelic journeys. The experiences of astronauts, econauts, and psychonauts are explored and synthesised into a multi-phasic transformative experience process model from initiation to integration - through the stages of the varying self. This study contributes novel theoretical and practical insights to tourism on how to design transformative offers and guide experiences to integration for a long-lasting positive impact on an individual's life.

3D Modelling and Simulation Based on Kinematic Analysis for Hippotherapy

The engineering perspective on the physiology and anatomy of living things has led to the emergence of the science of biomechanics. Developing computer technologies have made it possible to examine the behaviour of living things in more detail and to develop technologies that enable kinetic and kinematic analysis of movements to imitate real limbs. In this study, the natural gaits of horses (Walk, Canter and Trot) were analysed with image processing techniques, kinematic analyses were made and three-dimensional solid models were created. Giving movement ability to the extracted solid models was carried out with the simulation software prepared on the Matlab-SimMechanic platform. By comparing the findings obtained as a result of image processing and simulation, the success of transferring the movements to a developed system was evaluated. After mathematical equations were created in the MATLAB environment, the equations were tested on the developed system and their performance rates were determined. With the developed system, joint angles, ability to reach the correct position and movement ability were analysed and it was shown how the mathematical equations required to perform the movements could be derived. As a result, a design process model for movement analysis, simulation and control of different living groups is presented with the equations obtained by formulating the information obtained from the developed system.

3D printing slicing algorithm and partition scanning strategy for big data

3D printing technology is a popular new technology that combines information technology with mechanical engineering and material science, and is being widely used in various fields. From the computer point of view, the basic technology of 3D printing is to slice and scan each part of the 3D model. The slicing algorithm used directly affects the speed of slicing and the effect of post-printing, and the result of slicing needs to be optimized. This paper analyzes the slicing algorithm and partition scanning strategy related technologies of 3D printing models and solves the practical problems of models in the 3D printing process. This paper first studies the slicing algorithm in the 3D printing process, then obtains and intercepts the cross-sectional contour, and finally performs mosaic and partition scanning on the sliced polygon, and uses experiments to verify the effectiveness of the research. The experimental results show that compared with other 3D printing methods, the printing method in this paper reduces the slicing time by 23.61%, and the fitting error range and the fluctuation range of the residual height of the fusion volume are smaller.

Probabilistic physics-integrated neural differentiable modeling for isothermal chemical vapor infiltration process

Chemical vapor infiltration (CVI) is a widely adopted manufacturing technique used in producing carbon-carbon and carbon-silicon carbide composites. These materials are especially valued in the aerospace and automotive industries for their robust strength and lightweight characteristics. The densification process during CVI critically influences the final performance, quality, and consistency of these composite materials. Experimentally optimizing the CVI processes is challenging due to the long experimental time and large optimization space. To address these challenges, this work takes a modeling-centric approach. Due to the complexities and limited experimental data of the isothermal CVI densification process, we have developed a data-driven predictive model using the physics-integrated neural differentiable (PiNDiff) modeling framework. An uncertainty quantification feature has been embedded within the PiNDiff method, bolstering the model’s reliability and robustness. Through comprehensive numerical experiments involving both synthetic and real-world manufacturing data, the proposed method showcases its capability in modeling densification during the CVI process. This research highlights the potential of the PiNDiff framework as an instrumental tool for advancing our understanding, simulation, and optimization of the CVI manufacturing process, particularly when faced with sparse data and an incomplete description of the underlying physics.

Development of a web-based patient decision aid for myopia laser correction method

BackgroundIn the context of healthcare centered on the patient, Patient Decision Aids (PtDAs) acts as an essential instrument, promoting shared decision-making (SDM). Considering the prevalent occurrence of myopia, the objective of this study is to furnish exhaustive and easily comprehensible information to assist patients in making well-informed decisions about their options for myopia laser correction.MethodThe research team developed a decision guide for myopia patients considering laser correction, aiming to facilitate informed decisions. The study followed the first four stages of the IPDAS process model: “scope/scoping,” “design,” “prototype development,” and “alpha testing.” Ten semi-structured interviews with patients (n = 6) and corneal specialist ophthalmologists (n = 4) were conducted to understand the challenges in selecting a laser correction method. Online meetings with 4 corneal specialists were held to discuss challenging cases. A comparison table of harms and benefits was created. The initial prototype was developed and uploaded on the internet portal. User feedback on software and text aspects was incorporated into the final web software, which was reviewed by a health education expert for user-friendliness and effectiveness.ResultEducational needs assessment revealed concerns such as pain, daily life activities, return to work, the potential need for glasses (‘number return’), eye prescription stability, and possible complications. These shaped the decision aid tool’s content. Expert consensus was achieved in several areas, with some items added or extended. In areas lacking consensus, comments were added for clarity. Five clients assessed the web app (PDAIN), rating it 46/50 in user-centricity, 47/50 in usability, and 45/50 in accuracy and reliability, totaling 138/150. Post-piloting, software errors were documented and rectified. During the trial phase, five myopic users interacted with the software, leading to modifications. User feedback indicated the tool effectively enhanced understanding and influenced decision-making.ConclusionPDAIN, serves as a facilitative tool in the process of selecting a corneal laser correction method for myopic patients. It enabling Nearsighted patients to make informed decisions.

Development of a multi-layer green-ampt infiltration based modeling framework for hydrologic process simulation and design of various low impact development measures for mitigating urban flooding

ABSTRACT Low Impact Development (LID) measures have proven to be effective in reducing the runoff volume and the peak of runoff at small spatial scales. Assessment of the hydrological impacts of LID measures at river basin scales is currently limited by the absence of adequate physically-based process models that can simulate LID processes at such scales. In this study, an attempt has been made to develop LID modules to simulate the water balance and hydrological processes based on the Multi-Layer Green-Ampt (MLGA) infiltration method, which is an advancement to the existing LID modeling techniques. The performance of the MLGA-based LID modules was benchmarked with the process-intensive numerical tool HYDRUS 1D for about 5000 soil samples across the soil textural classes. The results indicated that the MLGA-based model simulates the hydrological processes in close agreement with HYDRUS for soils with moderate-to-high hydraulic conductivity values, which are amenable for LID implementation.

Process Model of Talent Management and Enterprise Competitiveness in Bosnia and Herzegovina

Talent management is an essential area within human resource management and has been increasingly receiving attention over the past several decades. The focus of talent management is on the most crucial employees within an enterprise. Therefore, it is vital to have a specialized and tailored management system for them to maximize business results. This paper addresses the connection between talent management and enterprise competitiveness. It aims to examine the relationship between these two variables within the business environment of Bosnia and Herzegovina. This paper significantly contributes to both theory and practice because it proposes a new, more comprehensive process model of talent management based on a detailed analysis and synthesis of all available scientific and research works. Following this, the paper tests the proposed model in practice and measures its success by examining enterprise competitiveness. The research was conducted on 101 service enterprises in Bosnia and Herzegovina in the second quarter of 2023. Managers of service enterprises involved in human resource management were surveyed. The questionnaire was formulated based on a combination of existing research in the specified fields. The data were subjected to correlation and regression analysis, and the research results were presented according to the previously set objectives and hypotheses. The research results showed that talent management is a significant predictor of competitive advantage. Additionally, a positive impact on competitiveness was confirmed for each individual group of talent management activities presented in the proposed process model.

The relationship between Entrepreneurial Education and the Sustainable Development of SMEs in China: the mediating effect of Innovation Capability

This study explores the relationship between entrepreneurship education, innovation capabilities and the sustainable development of small and medium-sized enterprises in China, especially the mediating effect of innovation capabilities. By analyzing questionnaire data from SMEs in different regions of China, this study uses SPSS 27 and Process Model 4 to verify the impact of entrepreneurship education on the sustainable development of SMEs and the mediating effect of innovation capabilities. The results show that entrepreneurship education significantly positively predicts innovation ability (β=0.691, p<0.001), and indirectly promotes the sustainable development of small and medium-sized enterprises through innovation ability. The indirect effect accounts for 78.6% of the total effect, confirming the partial mediation of innovation ability. effect. In addition, the direct effect (β=0.152, p<0.001) also shows that entrepreneurship education has a direct positive impact on SMEs. These findings highlight the importance of entrepreneurship education in promoting corporate innovation capabilities and sustainable growth and provide policy makers with policy recommendations to enhance the innovation capabilities and sustainable development of small and medium-sized enterprises.

Contextual Knowledge Framework in CAD Education from the Perspectives of Practicing Engineers

The contemporary industry landscape requires engineering graduates to have expertise in utilizing advanced Computer-Aided Design (CAD) tools. However, prevailing practices reveal a lack of contextual knowledge among Mechanical Engineering undergraduates and fresh graduate engineers in effectively employing 3D CAD modelling software for optimal product design development. Consequently, this study was undertaken to address this issue, with a specific focus on the integration of contextual knowledge into 3D CAD modelling. The study aims to elucidate the fundamental elements of contextual knowledge regularly employed by practicing engineers in their daily design endeavours, specifically in 3D CAD modelling. A transcendental phenomenology approach was used, and four engineers from the engineering department of a shipbuilding company in Peninsular Malaysia were purposefully selected as respondents. The analysis yielded three recurring themes central to the application of contextual knowledge in visualizing and presenting models through 3D CAD modelling: Realization, Design Intention, and Normalization. These elements are pivotal in assisting engineers in contextualizing their design work during the modelling and presentation stages of the new product development process.

Structural insight into piezo-solvatochromism of Reichardt's dye.

To date, accurate modelling of the solvation process is challenging, often over-simplifying the solvent-solute interactions. The interplay between the molecular arrangement associated with the solvation process and crystal nucleation has been investigated by analysis of the piezo-solvatochromic behaviour of Reichardt's dye, ET(1), in methanol, ethanol and acetone under high pressure. High-pressure single-crystal X-ray diffraction and UV-Vis spectroscopy reveal the impact of solute-solvent interactions on the optical properties of ET(1). The study underscores the intricate relationship between solvent properties, molecular conformation and crystal packing. The connection between liquid and solid phases emphasizes the capabilities of high-pressure methods for expanding the field of crystal engineering. The high-pressure environment allowed the determination of the crystal structures reported here that are built from organic molecules fourfold solvated with ethanol or methanol: ET(1)·4CH3OH and ET(1)·4C2H5OH·H2O. The observed piezo-solvatochromic effects highlight the potential of ET(1) in nonlinear optoelectronics and expand the application of solvatochromic chemical indicators to pressure sensors.

An improved multiphase chemistry mechanism for methylamines: significant dimethylamine cloud production

Monomethylamine (MMA), dimethylamine (DMA), and trimethylamine (TMA) are important compounds for atmospheric key processes, e.g., new particle formation (NPF). A description of their multiphase chemical processing within atmospheric models is incomplete, but mandatory to describe their atmospheric budgets. In this study, a detailed multiphase chemistry mechanism was developed and first process model investigations were performed. The simulations focused on pristine marine conditions, where open research questions exist regarding ambient gas-phase concentrations of methylamines, particularly with regards to unexpectedly high DMA levels. The simulations reveal that TMA oxidation in cloud droplets results into DMA formation with a yield of around 87%, a missing DMA production pathway in current models. Also, it is demonstrated that about 21% and 69% of the respective DMA and TMA gas-phase oxidation proceed via autoxidation yielding HOOCH2NHCHO and HOOCH2N(CHO)CH2OOH, respectively. The discussed processes should be included into atmospheric models for advanced predictions of NPF and climate impacts.

Artificial Intelligence Modeling of the Heterogeneous Gas Quenching Process for Steel Batches Based on Numerical Simulations and Experiments

High-pressure gas quenching is widely used in the metals industry during the heat treatment processing of steel specimens to improve their material properties. In a gas quenching process, a preheated austenised metal specimen is rapidly cooled with a gas such as nitrogen, helium, etc. The resulting microstructure relies on the temporal and spatial thermal history during the quenching. As a result, the corresponding material properties such as hardness are achieved. Challenges reside with the selection of the proper process parameters. This research focuses on the heat treatment of steel sample batches. The gas quenching process is fundamentally investigated in experiments and numerical simulations. Experiments are carried out to determine the heat transfer coefficient and the cooling curves as well as the local flow fields. Quenched samples are analyzed to derive the material hardness. CFD and FEM models numerically determine the conjugate heat transfer, flow behavior, cooling curve, and material hardness. In a novel approach, the experimental and simulation results are adopted to train artificial neural networks (ANNs), which allow us to predict the required process parameters for a targeted material property. The steels 42CrMo4 (1.7225) and 100Cr6 (1.3505) are investigated, nitrogen is the quenching gas, and geometries such as a disc, disc with a hole and ring are considered for batch series production.

An improved genetic algorithm based on reinforcement learning for aircraft assembly scheduling problem

The assembly work of aircraft occupies a significant portion of the time in aircraft manufacturing. The aircraft assembly operations are highly challenging in the generation of production plans due to multiple constraints in the scheduling process. In addressing the aircraft assembly scheduling problem with the objective of minimizing the duration of assembly operations, a mathematical model has been established. This model incorporates characteristics such as job sequencing constraints, resource constraints, spatial constraints, and others, the problem-solving process is divided into two stages: determining the priority sequence of tasks and establishing the start times for each task. Proposing an improved genetic algorithm based on Q-learning, referred to as QIGA (Q-learning-based Improved Genetic Algorithm), utilizing Q-learning to dynamically adjust the crossover and mutation probability parameters during each iteration to enhance the algorithm’s convergence speed and search capabilities. Constructing a Markov Decision Process (MDP) model to dynamically adjust the crossover and mutation probability in the problem, proposing a state set partition rule based on the population’s fitness values, setting the action set to represent crossover and mutation probabilities in different intervals. Additionally, designing reward rules based on the population’s performance. Finally, the effectiveness of the proposed algorithm is verified with a real case containing 44 assembly operations, and the results show that the algorithm can solve the aircraft assembly scheduling problem well. Validating the feasibility and optimization effectiveness of the algorithm through various aircraft assembly scheduling cases of different scales. The experimental results indicate that, compared to traditional meta-heuristic algorithms, the proposed algorithm demonstrates superior optimization performance in aircraft assembly scheduling problems. It effectively reduces the total duration of aircraft assembly operations.

METHODOLOGY OF COGNITIVE-DISCURSIVE MODELLING OF LITERARY TRANSLATION (case study of Ukrainian retranslations of W. Shakespeare’s tragedies of the 19th–21st centuries)

The article provides theoretical and methodological substantiation for the step-by-step construction of a cognitive-discursive model of the diachronic plurality in translation of a time-remote original work, which appears as a tool for revealing the internal mechanisms of translating a literary work and determining the criteria for the adequacy of the original and translation. This is a case study of W. Shakespeare’s tragedies as time-remote original works of England of the late 16th – early 17th centuries and their Ukrainian retranslations of the 19th–21st centuries. The algorithm of the complex methodology of cognitive-discursive modelling of literary translation, which incorporates the methods of discourse analysis and cognitive translation studies, involves five stages of construction of three modules: discursive, cognitive, and retranslation. The construction of a discursive module, the aim of which is to reveal the objective and subjective factors that influence the process and result of the translator’s interpretation of a time-remote original work, involves the construction of three components – extralingual, lingual, and interpretation, which takes place in the first three stages. The fourth stage is the building of a cognitive module, which reveals the internal mechanisms of the translation process caused by cognitive consonance (harmonious identity of the mental processes of the author and the translator) or cognitive dissonance (epistemological, ideological, and cultural-aesthetic). Cognitive consonance and cognitive dissonance result from the similarities and differences in the contexts of original creation and retranslations, which are previously defined in the discursive module. The translator’s decision to choose one or another translation strategy and local tactic in conditions of cognitive consonance or cognitive dissonance determines different degrees of cognitive proximity of the original and the translation: cognitive equivalence, cognitive analogy, and cognitive variance. The fifth stage is the construction of a retranslation module, which affirms that the translator’s choice of strategy (modernization or archaization, domestication or foreignization) and tactic (reproductive or adaptive) of transformational translation is conditioned by cognitive consonance or dissonance. The relevance of the research is determined by its appeal to the leading cognitive-discursive paradigm of modern translation studies and by the tendency of cognitive translation studies for the translation process modelling, according to which not only the translation result is the subject of study, but also the prerequisites that determine the translator’s approach to the reproduction of the original text in one way or another. Analysis and comparison of retranslations of a time-remote original text in the course of modelling the process of literary translation allows following the influence of discursive and cognitive factors on the process and result of translation. The creation of the cognitive-discursive model of the diachronic plurality in translation of the time-remote original text explains the translation decisions regarding the choice of the general strategy and local tactic of retranslations and, respectively, the diachronic plurality in translations of a time-remote original text.

Review and perspective of dry etching and deposition process modeling of Si and Si dielectric films for advanced CMOS device applications

Abstract Dry etching and deposition of Si and Si dielectric films are critical processes for achieving of high performance in advanced complementary metal-oxide-semiconductor (CMOS) devices. To accurately predict and control fluctuations in these process properties during mass production, it is essential that dry process simulation technology considers various factors. These include fluctuations in plasma-chamber wall interaction, effects of by-products on critical dimensions, Si recess dependence on wafer open area ratios and local pattern structures, the time-dependent distribution of plasma-induced damage associated with feature-scale profiles, and film properties such as density, permeability, and adhesion. Addressing these considerations can overcome issues with conventional simulations that lack the accuracy required for mass production. This paper reviews these advanced simulation technologies and discusses the perspective of fusion physical models with machine learning, incorporating real-time monitoring in manufacturing equipment, known as process informatics. This approach is anticipated to usher in the era of full digital twins.

Immigration status microaggressions: A moderated mediation analysis of cultural stress, fear, internalization, and psychological stress among Latinx and Asian college students.

Latinx and Asian people are experiencing an elevated rate of immigration status-related oppression-both systematically and individually-despite actual nationality, and this type of cultural stressor has seen a rampant increase recently in the United States. We aimed to assess the relation and effect of immigration status microaggressions on psychological stress and some mechanisms connected to these experiences. Using a sample of Latinx and Asian college students (N = 776), we unpack the relationships between individual cultural stressors, such as immigration status microaggressions, and psychological stress, by exploring their mediating relation with internalized racism (Mediator 1), and fear of foreign objectification (Mediator 2), using Hayes's (2012) PROCESS Model 6-serial mediation. Furthermore, we expanded on this model, highlighting differences between Latinx and Asian participants (moderator) using a moderated mediation. Findings suggest a full serial mediation. Specifically, the psychological stress associated with immigration status microaggressions was mediated by internalized racism and fear of foreign objectification. Results also highlighted that Latinx participants, compared to Asian ones, showed a significant positive association between immigration status microaggressions with internalized racism and fear of foreign objectification. Furthermore, a significant interaction for Latinx who experience more fear of foreign objectification was positively associated with psychological stress. Indirect effects for each group are discussed. Our study is one of the first to explore cultural stress in the form of immigration status microaggressions in connection with more general forms of psychological stress and internalizing processes for two groups historically persecuted around immigration in the United States. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Mule deer mortality in the northern Great Plains in a landscape altered by oil and natural gas extraction

AbstractA worldwide increasing demand for renewable and non‐renewable energy resources has been ongoing since the mid‐1970s and is projected to increase for the next 2 decades. The effects of oil and natural gas development on wildlife mortality risk may play an important role in mule deer (Odocoileus hemionus) population dynamics. We evaluated the potential effects of oil and natural gas development on mortality risk of mule deer in western North Dakota and eastern Montana, USA. We assessed adult and juvenile female mule deer mortality risk with Poisson point process models using 265 deer fitted with global positioning system (GPS) radio‐collars that were deployed from 2013–2016. Mortality covariates included proportion of area disturbed by oil and natural gas development, distance to oil and natural gas development, distance to roads, temperature, snow depth, normalized difference vegetation index (NDVI), and age of deer. During the study there was no effect of oil and natural gas development or roads on mule deer mortality, though <1% of all deer locations were within 500 m of active drilling rigs. Mule deer mortality was greatest in winter and spring, and positively related to temperature during these seasons. Estimated annual adult survival probability was 0.79 (95% CI = 0.71–0.85). Given the strong influence of season and temperature variables on mortality risk, weather had the strongest influence on mule deer mortality during this study. Although we did not detect an effect of energy development on mule deer mortality, effects on space use resulting from development could influence deer dynamics in the region through displacement and could occur over longer time scales than we evaluated. This study can be used in pre‐development planning in a risk assessment framework to minimize effects of development on mule deer.

A Comprehensive Decision-Making Approach for Strategic Product Module Planning in Mass Customization

This paper explores the integrated optimization of complex coupled industrial manufacturing systems and production strategies based on user customization needs. Two optimization metrics are considered: one is whether the production process of engineering manufacturing is simplified, and the other is whether it is based on the customization requirements of the customer. These two metrics are interrelated, and cases may even be conflicting. Considering the interdependence between engineering manufacturing and user requirements, this paper develops an integrated customized modular engineering manufacturing process to minimize production and maintenance costs and improve efficiency while meeting user customization requirements. This paper takes expert evaluation as an important decision indicator and optimizes the production process strategy on this basis. Finally, a case study is given to illustrate the applicability of the proposed process model.

Exploring microfine magnesite based on thermal decomposition and sintering kinetics: A new direction in the one-step preparation of high-density sintered magnesia

The thermal decomposition and sintering kinetics of microfine magnesite were investigated to inaugurate a new One-step process for preparing high-density sintered magnesia. The optimal parameters for the discharging particle size of magnesite through ball milling were determined using an orthogonal experiment and Three-Way Measures ANOVA. The model for the thermal decomposition process of microfine magnesite was established through TGA-DSC and nonlinear regression analysis. Meanwhile, by describing the sintering process of microfine magnesite in detail through microstructure and CTE, the kinetic mechanisms of grain growth at different sintering stages were obtained. The results revealed that increasing the ratio of ball to powder can quickly obtain microfine magnesite which the bulk density reaches 3.45 g·cm−3 after One-step sintering. The thermal decomposition process required an activation energy of only 124.98 kJ·mol−1, which is much lower than existing technology and followed the Two-dimensional phase boundary reaction mechanism R2. Additionally, the densification process was dominated alternatively by grain boundary diffusion and volume diffusion. This paper provided a detailed theoretical basis and leads a new direction in the One-step preparation of high-density sintered magnesia.

PM2.5 concentration prediction based on EEMD-ALSTM.

The concentration prediction of PM2.5 plays a vital role in controlling the air and improving the environment. This paper proposes a prediction model (namely EEMD-ALSTM) based on Ensemble Empirical Mode Decomposition (EEMD), Attention Mechanism and Long Short-Term Memory network (LSTM). Through the combination of decomposition and LSTM, attention mechanism is introduced to realize the prediction of PM2.5 concentration. The advantage of EEMD-ALSTM model is that it decomposes and combines the original data using the method of ensemble empirical mode decomposition, reduces the high nonlinearity of the original data, and Specially reintroduction the attention mechanism, which enhances the extraction and retention of data features by the model. Through experimental comparison, it was found that the EEMD-ALSTM model reduced its MAE and RMSE by about 15% while maintaining the same R2 correlation coefficient, and the stability of the model in the prediction process was also improved significantly.