Process Model Research Articles

Prompt engineering techniques for semantic enhancement in business process models

PurposeThe term knowledge refers to the part of the world investigated by a specific discipline and that includes a specific taxonomy, vocabulary, concepts, theories, research methods and standards of justification. Our approach uses domain knowledge to improve the quality of business process models (BPMs) by exploiting the domain knowledge provided by large language models (LLMs). Among these models, ChatGPT stands out as a notable example of an LLM capable of providing in-depth domain knowledge. The lack of coverage presents a limitation in each approach, as it hinders the ability to fully capture and represent the domain’s knowledge. To solve such limitations, we aim to exploit GPT-3.5 knowledge. Our approach does not ask GPT-3.5 to create a visual representation; instead, it needs to suggest missing concepts, thus helping the modeler improve his/her model. The GPT-3.5 may need to refine its suggestions based on feedback from the modeler.Design/methodology/approachWe initiate our semantic quality enhancement process of a BPM by first extracting crucial elements including pools, lanes, activities and artifacts, along with their corresponding relationships such as lanes being associated with pools, activities belonging to each lane and artifacts associated with each activity. These data are systematically gathered and structured into ArrayLists, a form of organized collection that allows for efficient data manipulation and retrieval. Once we have this structured data, our methodology involves creating a series of prompts based on each data element. We adopt three approaches to prompting: zero-shot, few-shot and chain of thoughts (CoT) prompts. Each type of prompting is specifically designed to interact with the OpenAI language model in a unique way, aiming to elicit a diverse array of suggestions. As we apply these prompting techniques, the OpenAI model processes each prompt and returns a list of suggestions tailored to that specific element of the BPM. Our approach operates independently of any specific notation and offers semi-automation, allowing modelers to select from a range of suggested options.FindingsThis study demonstrates the significant potential of prompt engineering techniques in enhancing the semantic quality of BPMs when integrated with LLMs like ChatGPT. Our analysis of model activity richness and model artifact richness across different prompt techniques and input configurations reveals that carefully tailored prompts can lead to more complete BPMs. This research is a step forward for further exploration into the optimization of LLMs in BPM development.Research limitations/implicationsThe limitation is the domain ontology that we are relying on to evaluate the semantic completeness of the new BPM. In our future work, the modeler will have the option to ask for synonyms, hyponyms, hypernyms or keywords. This feature will facilitate the replacement of existing concepts to improve not only the completeness of the BPM but also the clarity and specificity of concepts in BPMs.Practical implicationsTo demonstrate our methodology, we take the “Hospitalization” process as an illustrative example. In the scope of our research, we have presented a select set of instructions pertinent to the “chain of thought” and “few-shot prompting.” Due to constraints in presentation and the extensive nature of the instructions, we have not included every detail within the body of this paper. However, they can be found in the previous GitHub link. Two appendices are given at the end. Appendix 1 describes the different prompt instructions. Appendix 2 presents the application of the instructions in our example.Originality/valueIn our research, we rely on the domain application knowledge provided by ChatGPT-3 to enhance the semantic quality of BPMs. Typically, the semantic quality of BPMs may suffer due to the modeler's lack of domain knowledge. To address this issue, our approach employs three prompt engineering methods designed to extract accurate domain knowledge. By utilizing these methods, we can identify and propose missing concepts, such as activities and artifacts. This not only ensures a more comprehensive representation of the business process but also contributes to the overall improvement of the model's semantic quality, leading to more effective and accurate business process management.

Complex system for analysis and monitoring of technological processes based on tomography

The main objective was to build a mobile impedance/capacitance tomograph for flow analysis and process optimization. The product responds to the identified needs of target customers who operate in process monitoring, control, and optimization of manufacturing processes. They need information on data processing and analysis and spatial analysis of technological processes. The designed solution's target market is the food, chemical, pharmaceutical, petrochemical, and mining industries. The paper presents the device's design and an application for data collection and visualization using tomography. Tomography is a technique for imaging a cross-section of an object based on measurements taken from the outside. Tikhonov regularisation reconstructions were used to reconstruct tomographic images. Test measurements were taken to confirm the correct operation of the completed system. Keywords: tomography, production analysis, optimization, process management, process modeling, process control.

Characterization of damage behavior and failure mechanisms of the bonded‐bolted hybrid three‐bolt joint in composite laminates

AbstractIn this paper, the mechanical properties, damage modes, and bolt load distribution of the bonded‐bolted hybrid three‐bolt joint were investigated by a combination of experiments and numerical simulations. Acoustic emission (AE) was used to monitor the tensile process of the connection in real time, and the K‐means clustering algorithm was used to realize the cluster analysis of the damage modes. A numerical simulation model of the tensile process of the joint was established using ABAQUS simulation software. The progressive damage evolution of composite laminates, and adhesive layer was characterized by using the VUMAT subroutine, the B‐K failure criterion, and cohesive element with zero thickness, respectively. The results show that four main damage modes occur in composite laminates during tension: matrix cracking, delamination, fiber pull‐out, and fiber breakage, with corresponding peak frequency ranges of (0–80 kHz), (80–210 kHz), (210–330 kHz), and (330–375 kHz), respectively. The adhesive layer between the laminates mainly exhibits peeling failure because of the secondary bending effect. In the load‐bearing process, the adhesive layer was loaded first and failed, and the bolt loading was delayed. Meanwhile, the results of bolt load distribution showed that the bolts and the adhesive layer at both ends carried greater loads.Highlights Damage modes and damage evolution of the bonded‐bolted hybrid joint in tension were analyzed by combining acoustic emission technology and numerical simulation. The simulation of delamination damage in composite laminates was carried out by inserting zero‐thickness cohesive elements into the laminates, and the delamination damage between the carbon fiber layup and the adjacent glass fiber layup was analyzed. The effect of the inclusion of the adhesive layer on the mechanical properties of the joint and the distribution of the bolt load was analyzed.

Conceptual design of furfural extraction, oxidative upgrading and product recovery: COSMO-RS-based process-level solvent screening

Liquid phase oxidation of furfural using hydrogen peroxide offers a promising route for bio-based C4 furanones and diacids; however, only dilute water-based process designs have been previously suggested that have limited techno-economic potential. In this study, a conceptual process design is presented, where aqueous furfural is extracted using an organic solvent, coupled with peroxide oxidation and product recovery in the presence of the solvent. To address the problem of solvent selection, the COSMO-RS-based solvent screening framework is applied, where quantum mechanics-based thermodynamics are utilized in pinch-based process models. About 2500 solvent candidates were identified as feasible. Focusing on a set of 400 solvent candidates revealed energy consumption values (Qreb,tot/ṁprod recov) between approximately 2 MWh/tonne and 33 MWh/tonne, signifying the potential of the solvent-based process in outperforming the reference aqueous process (49.4 MWh/tonne). The study provides potential solvent candidates and future directions to consider in more costly computational and experimental efforts.

Physical symptoms in prolonged grief disorder: a case report.

Prolonged grief disorder (PGD) was added as a new disorder to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR) in 2022. PGD is defined as an intense yearning or longing for the deceased and preoccupation with thoughts or memories of the deceased. The official diagnostic criteria for PGD do not include physical symptoms, but it is sometimes associated with somatic symptoms. We present the case of a patient suffering from facsimile illness who lost her husband due to the deterioration of a brain tumor. She suffers from similar physical symptoms (severe headache reminiscent of a brain tumor, and hypertension) as her deceased husband. We focused on the dual process model in which grief exposure and behavioral activation began, ensuring an oscillation between loss-oriented grief (e.g., crying, feeling a continuing bond) and restoration-oriented grief (e.g., attending to life changes, new roles/identities/relationships). Outpatient psychotherapy based on the dual-process model of grief improved her physical symptoms. This case report highlights the physical symptoms experienced by those bereaved by a deceased loved one. Exposure therapy and behavioral activation approaches based on the dual-process model of grief response were effective in reducing the physical symptoms of facsimile illness. Physical symptoms in deceased family members triggered by bereavement are often overlooked and need to be recognized.

Mapping socio-economic status using mixed data: a hierarchical Bayesian approach

Abstract We propose a Bayesian hierarchical model to estimate a socio-economic status (SES) index based on mixed dichotomous and continuous variables. In particular, we extend Quinn’s ([2004]. Bayesian factor analysis for mixed ordinal and continuous responses. Political Analysis, 12(4), 338–353. https://doi.org/10.1093/pan/mph022) and Schliep and Hoeting’s ([2013]. Multilevel latent Gaussian process model for mixed discrete and continuous multivariate response data. Journal of Agricultural, Biological, and Environmental Statistics, 18(4), 492–513. https://doi.org/10.1007/s13253-013-0136-z) factor analysis models for mixed dichotomous and continuous variables by allowing a spatial hierarchical structure of key parameters of the model. Unlike most SES assessment models proposed in the literature, the hierarchical nature of this model enables the use of census observations at the household level without needing to aggregate any information a priori. Therefore, it better accommodates the variability of the SES between census tracts and the number of households per area. The proposed model is used in the estimation of a socio-economic index using 10% of the 2010 Ghana census in the Greater Accra Metropolitan area. Out of the 20 observed variables, the number of people per room, access to water piping and flushable toilets differentiated high and low SES areas the best.

How do organizations refine their post‐merger integration routines over consecutive acquisitions?

AbstractThe post‐merger integration (PMI) phase of mergers and acquisitions (M&A) can determine M&A success. Little is known about how organizations refine their PMI routines with accumulated M&A experience. This study proposes a process model of PMI‐routine development, where the outcomes of routines enacted in earlier acquisitions shape routines in subsequent acquisitions. We test the model using 312 instances of routine implementation, across five consecutive acquisitions by the same acquiring company, and make two key scholarly contributions. First, we advance prior research by identifying how a routine's level of complexity moderates the association between routine adherence and routine outcomes. Second, we elucidate the effect of learning from failure—uncovering a learning process with performance feedback as input. Specifically, negative routine outcomes at the focal acquisition strengthened the positive effect of the improvisation (low adherence) enacted in the focal acquisition on the subsequent modifications to the codified routine, which are likely to improve its performance in future acquisitions.

Application and Optimization of Rheological Mathematical Models in Preparation and Injection Process of Polymer Flooding

The impact of shear on the properties of polymer solutions is not fully accounted for in the simulation of conventional rheological models. To optimize the application of these mathematical models, the shear rheological characteristics of polyacrylamide at various concentrations were investigated under different shear rates and shear modes. The results indicate that pseudoplastic fluid polymers exhibit two distinct rheological characteristics within their "shear thinning" rate range. The critical shear rate at this threshold signifies the point at which shear stress begins to disrupt the structure of the polymer solution, resulting in a rheological curve that transitions from high to low shear rates without reverting back to a low-to-high state. The concentration of the solution has minimal effect on the critical shear rate of the polymer, which is primarily determined by the inherent properties of the polymer itself. The application of rheological models during preparation and injection processes can elucidate the effects of shear on polymers by analyzing changes in apparent viscosity. Furthermore, the rheological model following supercritical shear rates requires modifications to the consistency coefficient (K) and flow index (n) based on shear rheological data obtained from high to low shear rates.

Simulation of UV curing of photosensitive resins with phase change materials

Based on the mechanism of the resin polymerization reaction and free radical diffusion under UV irradiation, here, a mathematical model of the curing process was established for the mixture of photosensitive resin (PAA) and polyethylene glycol (PEG, as the phase change material), and the factor influencing the mechanical performance of the cured samples was analyzed. The yield stress (σs) of the pure resin sample can reach 50 MPa when the monolayer curing depth is 5 mm. σs decreases with the increase in the PEG content and monolayer curing depth. With increasing the monolayer exposure time, the yield stress increases and then decreases, and there is an optimum value. The material has the maximum stress at the monolayer exposure time of 13.5 s when the resin content is 50 %.

Numerical Modeling and Optimization of a Quasi-Resonant Inverter-Based Induction Heating Process of a Magnetic Gear

Numerical Modeling and Optimization of a Quasi-Resonant Inverter-Based Induction Heating Process of a Magnetic Gear

Remaining Useful Life Prediction for Power Storage Electronic Components Based on Fractional Weibull Process and Shock Poisson Model

For lithium-ion batteries and supercapacitors in hybrid power storage facilities, both steady degradation and random shock contribute to their failure. To this end, in this paper, we propose to introduce the degradation-threshold-shock (DTS) model for their remaining useful life (RUL) prediction. Non-homogeneous compound Poisson process (NHCP) is proposed to simulate the shock effect in the DTS model. Considering the long-range dependence and heavy-tailed characteristics of the degradation process, fractional Weibull process (fWp) is employed in the diffusion term of the stochastic degradation model. Furthermore, the drift and diffusion coefficients are constantly updated to describe the environmental interference. Prior to the model training, steady degradation and shock data must be separated, based on the three-sigma principle. Degradation data for the lithium-ion batteries (LIBs) and ultracapacitors are employed for model verification under different operation protocols in the power system. Recent deep learning models and stochastic process-based methods are utilized for model comparison, and the proposed model shows higher prediction accuracy.

Unconfined gravity current interactions with orthogonal topography: Implications for combined‐flow processes and the depositional record

ABSTRACTTurbidity current behaviour is affected by interactions with seafloor topography. Changes in flow dynamics will depend on the orientation and gradient of the topography, and the magnitude and rheology of the incoming flow. A better understanding of how unconfined turbidity currents interact with topography will improve interpretations of the stratigraphic record, and is addressed herein using three‐dimensional flume tank experiments with unconfined saline density currents that enter a horizontal basin before interacting with a ramp orientated perpendicular to flow direction. The incoming flow parameters remained constant, whilst the slope angle was independently varied. On a 20° slope, superelevation of the flow and flow stripping of the upper, dilute region of the flow occurred high on the slope surface. This resulted in a strongly divergent flow and the generation of complex multidirectional flows (i.e. combined flows). The superelevation and extent of flow stripping decreased as the slope angle increased. At 30° and 40°, flow reflection and deflection, respectively, are the dominant flow process at the base of slope, with the reflected or deflected flow interacting with the parental flow, and generating combined flows. Thus, complicated patterns of flow direction and behaviour are documented even on encountering simple, planar topographies orientated perpendicular to flow direction. Combined flows in deep‐water settings have been linked to the interaction of turbidity currents with topography and the formation of internal waves with a dominant oscillatory flow component. Here, combined flow occurs in the absence of an oscillatory component. A new process model for the formation and distribution of hummock‐like bedforms in deep‐marine systems is introduced. This bedform model is coupled to a new understanding of the mechanics of onlap styles (draping versus abrupt pinchout) to produce a spatial model of gravity‐current interaction, and deposition, on slopes to support palaeogeographical reconstructions.

Estimating the scale-dependent influence of natural terrestrial corridors on the positioning of settlements: A multi-scale study of Roman forts in Wales

Natural terrestrial corridors have been shown to have influenced the positioning of past settlements. The scale at which this pattern-process relationship operates is often un-estimated and thus remains unclear. This paper proposes the comparison of multiple point process models as an approach for estimating the optimal scale at which this relationship is strongest. With this approach, it is revealed that the positioning of Roman forts used during the conquest of Wales was most influenced by natural terrestrial corridors at a scale of 1,100m. At this scale, the Roman army stationed at these forts could control natural corridors – both via on-the-ground response as well as through overseeing movements by the native communities of Wales. Comparing multiple scenarios, it is also shown that the control of river-systems did not influence the positioning of Roman forts at the expense of controlling these natural terrestrial corridors used by those on foot. With archaeological interpretations susceptible to change as a result of the scale at which this pattern-process relationship is measured, the estimation of the optimal scale is pivotal for non-biased inferences on the processes thought to have influenced the positioning of settlements in the past.

Thermodynamic modeling and optimal short-term scheduling of a green H2-fueled combined heat and power generation system for industrial zones

This paper presents a novel green combined heat and power generation system for industrial applications. It composes of a hydrogen-fueled combined cycle power plant and a battery energy storage. Firstly, the hydrogen-air rich mixture enters the combustion chamber of the gas turbine cycle for generating electricity, while recovering the heating energy from the exhausted flue gases for driving a steam turbine cycle. The heating energy extracted from the condenser heat exchanger is also used to supply the heating demand of the end-user. Moreover, a comprehensive thermodynamic model and analysis of H2 combustion process is provided to estimate the adiabatic flame temperature based on enthalpies of reactants and products. The optimum operating point of the cogeneration system is found by solving a mixed-integer nonlinear problem using MATLAB and GAMS over a 24-h study horizon aiming to minimize its daily operation cost considering all technical constraints of system components and load-generation balance criterion. It is found that the energy efficiency of the hydrogen-fueled combined cycle changes from 46 to 58 % while generating 11.331–12.982 MW electricity and 4.436–4.984 MW heat. Two cases are studied without and with participation of battery in energy procurement strategy. It is demonstrated that the objective function of the optimization problem improves from 18 $ cost in case 1 to 29 $ revenue in case 2.

Performance optimization of latent heat storage device based on surrogate-assisted multi-objective evolutionary algorithm and CFD method

The design of fin structures and the formulation of heat conductivity enhancers have always been critical factors influencing the performance of latent heat storage devices. However, achieving the optimal parameters of fin structures and the fraction of heat conductivity enhancers when adjusting multiple parameters simultaneously remains a time-consuming and labor-intensive task. In this paper, a combination of machine learning methods and computational fluid dynamics (CFD) was employed to optimize the parameters of fin structures and the mass fraction of expanded graphite with minor computational resources and time costs, thereby obtaining a set of Pareto optimal latent heat storage (LHS) devices that simultaneously considers energy storage power and energy storage capacity. The combination essentially is a surrogate assistant multi-objective evolutionary optimization algorithm, which utilizes batch recommendation strategies to recommend multiple high-quality candidate solutions that achieve a balance between two conflicting optimization objectives in CFD simulations. This method significantly reduces the number of model iterations and saves the time and computational resources required for multi-objective optimization. The results showed that the proposed algorithm only required 80 cases of CFD calculations to obtain two well-fitted Gaussian process models, leading to a 91 % improvement in computational efficiency. According to the recommendations from the algorithm, the LHS device with 78 fins of 2.0 mm thickness and 20 % mass fraction of expanded graphite could achieve the optimal trade-off of energy storage power and energy storage capacity. Compared to the prototype, the energy storage power and total energy storage capacity increased by 46.7 % and 22.1 %, respectively. The method proposed in this study can provide guidance for the optimal configuration design of LHS devices.

The development and evaluation of a parameter-free circuit-based model of bipolar membrane electrodialysis for process design and optimisation

Bipolar membrane electrodialysis (BPMED) is an emerging electromembrane technology which has the potential to replace existing pH manipulation process units among others and take advantage of the benefits posed by process electrification. The development of robust and flexible process models of BPMED for design and optimisation is paramount in derisking potential instillations and improving commercial viability. Herein, a circuit-based model of BPMED is presented which avoids reliance on empirical fitting parameters and training data. The resulting model is flexible enough that extension to account for added complexities may be readily adopted. The mass transfer and electrical resistance of six different domains (three membranes and three streams) were computed by applying fundamental laws such as Ohm’s law and Faraday’s first law. Acid-base reactions and their effect releasing current within the membranes were also considered. Furthermore, the stack model can be readily embedded in a broader process model. To this end, the stack model is applied to a recirculating-batch experiment using a delayed differential material balance to account for dead-time within the tubing and measurement flow-cells. Two orthogonal methods of experimental validation were conducted to assess the performance of the model over a range of concentrations and applied voltages. These involved running a recirculating-batch experiment and collecting current–voltage polarisation data, respectively, and both showed good agreement with the model predictions. Overall, a robust model of BPMED has been produced which is able to accurately predict system performance and will prove useful for the design and optimisation of industrial systems.

Adsorption dynamics of Eriochrome Black dye removal using raw and ultrasonicated Pithecellobium seed biomass: ANN modeling and mechanisms

Utilization of Ultrasonic modified Pithecellobium dulce seed biomass, proved the efficacy in Eriochrome black dye remediation. The study investigates the sorption dynamics of Eriochrome Black (EB) dye removal using raw and ultrasonicated Pithecellobium seed biomass combined with Artificial Neural Network (ANN) modeling for the elucidation of underlying mechanisms. To improve the adsorption performance, Pithecellobium dulce biomass has been subjected to ultrasonication. The characterization of modified seed adsorbent revealed the adsorptive properties of biomass for the removal process. Varying impacts of dye adsorption were studied revealing the optimal parameters to be pH of 5, temperature of 303 K, contact time of 40 min-Ultrasonicated Pithecellobium dulce (UPDB) biomass; 80 min – Raw Pithecellobium dulce biomass (RPDB), and dose of 5 g/L and 2.5 g/L for RPDB and UPDB respectively. Isotherm and Kinetic analysis revealed the Freundlich and pseudo-first order models to be best fitting for the current system. Ultrasonicated seed biomass exhibited enhanced adsorption capacity of 126.9 mg/g compared to 83.8 mg/g for raw seed biomass. Thermodynamic investigation infers the spontaneity and exothermic nature of adsorption process. The ANN model for the prediction of eriochrome black dye adsorption onto ultrasonic modified Pithecellobium dulce seed biomass exhibited better correlation coefficient of 0.9559 indicating the better prediction of trained model for dye removal process. Thus, ANN model provides a better prediction of the relation between operational factors and dye removal.

Development Augmented Reality of CNC Lathe G-Code Programming

In the realm of education, the most recent novel methods, such as augmented reality (AR), are becoming increasingly popular since students can apply the learning circumstances seen in virtual form to the real-world environment. The study's goal is to create an AR application for G code programming on CNC Lathe machines for the Machining course at Batu Pahat Vocational College. The ADDIE model was employed as the process model in this development. The phases of this paradigm are as follows: analysis, design, development, implementation, and assessment. SolidWorks, Blender, and Blippar are used to create AR apps. Product functionality was evaluated by five experts in the fields of machining and multimedia. The data is collected using an expert confirmation form and analysed using the frequency of acceptance and expert feedback. The study's findings suggest that the built application achieved its purpose. Overall, this AR application for G code programming was created with vocational college students in mind and is suited for use as a supplementary reference that can be used in theoretical or practical lessons.

Does the Dual Representation System of Player Agents in International Football Benefit Players? An Economic Analysis

It is common in international football for the same agent to be paid both commission fees by the purchasing club for negotiating a player salary and a transfer fee. This dual representation creates a potential conflict of interest for the agent. The agent may not negotiate the highest salary for the player if a higher salary reduces the likelihood of a successful transfer by lowering the resources available for the transfer fee. We construct a gamble model of the transfer process and simulate the ex-ante agent commissions rates, expected player salary, transfer fee and club profit under the assumption of dual representation where the buying club pays all agent commissions. The assumption that the quality of the agent increases with the commission rate is essential to the model. Both the player and the club are better off when the club pays both commissions, however the agent is worse off.

Electrochemical performance monitoring and degradation modeling method for organic coating systems: Integrating three-phase Wiener process and kinetic models

The deterioration of organic anticorrosive coating systems not only diminishes the lifetime of underlying metals but also incurs substantial economic losses and the risk of unforeseen catastrophes. Thus, rigorously monitoring and accurately predicting the lifetime of organic anticorrosive coatings are crucial. In this paper, we introduce a new approach for monitoring the electrochemical performance and predicting the durability of organic coatings. Initially, we propose a model grounded in degradation mechanisms that merges an aging kinetic model of the coating with the three-phase Wiener process, accurately representing the degradation trajectory and its inherent uncertainties. We then apply change point detection techniques, utilizing the t distribution test and the Schwarz Information Criterion, to delineate the three distinct phases of coating degradation. By defining new failure criteria based on degradation thresholds and stages, we develop a reliability model for coating systems and apply the Fiducial Inference-Monte Carlo method for numerical solutions and interval estimations. To corroborate the efficacy of our methodology, we designed electrochemical sensors for coatings and executed accelerated degradation experiments in the laboratory. The model parameters derived from the initial three sets of test data successfully predict the behavior of the fourth data set, verifying the effectiveness of the proposed method.