Process Model Research Articles

Pathways to persuasion: The impact of social media influencers’ self-disclosure and follower size on persuasion outcomes

A 2 (influencer type: nano with 5000 followers vs mega with 1.1 million followers) × 2 (influencer self-disclosure: low depth vs high depth) between-subjects online experiment tested the different pathways social media influencers take to achieve persuasion outcomes in one model. Participants viewed an Instagram influencer’s profile page with either 5000 or 1.1 million followers, and a post from the influencer with either low or high depth self-disclosure. Participants then answered questions regarding social attraction, parasocial relationship, perceived expertise, persuasion outcomes, covariates, and demographic information. Results showed three serial pathways facilitate persuasion: two pathways through self-disclosure depth by way of social attraction and parasocial relationship development and another pathway through follower size by way of perceived expertise. This research advances understanding of the influencing process by using theory and empirical precedent to derive and test an integrated model of the influencing process.

A review of AI and machine learning contribution in business process management (process enhancement and process improvement approaches)

PurposeThe significance of business processes has fostered a close collaboration between academia and industry. Moreover, the business landscape has witnessed continuous transformation, closely intertwined with technological advancements. Our main goal is to offer researchers and process analysts insights into the latest developments concerning artificial intelligence (AI) and machine learning (ML) to optimize their processes in an organization and identify research gaps and future directions in the field.Design/methodology/approachIn this study, we perform a systematic review of academic literature to investigate the integration of AI/ML in business process management (BPM). We categorize the literature according to the BPM life-cycle and employ bibliometric and objective-oriented methodology to analyze related papers.FindingsIn business process management and process map, AI/ML has made significant improvements using operational data on process metrics. These developments involve two distinct stages: (1) process enhancement, which emphasizes analyzing process information and adding descriptions to process models and (2) process improvement, which focuses on redesigning processes based on insights derived from analysis.Research limitations/implicationsWhile this review paper serves to provide an overview of different approaches for addressing process-related challenges, it does not delve deeply into the intricacies of fine-grained technical details of each method. This work focuses on recent papers conducted between 2010 and 2024.Originality/valueThis work addresses a significant gap by employing a pioneering approach to introduce challenges in BPM alongside AI/ML techniques and integrated tools. Hence, it offers comprehensive guidelines that elucidate the alignment between ML methods and solutions to current challenges across the BPM life-cycle, including process enhancement and process improvement. Additionally, by detailing various aspects of the life-cycle phases and highlighting ML technique characteristics, this research demonstrates potential approaches for future exploration, thereby enhancing applicability for both process analysts and researchers in this context.

Development and verification of material models in modeling of wave strain hardening and additive synthesis processes

BACKGROUND: The creation of competitive machine parts capable of withstanding standard and increased operational loads is an urgent task in mechanical engineering. Developing additive synthesis technologies together with strengthening technologies make it possible to create such products with high load-bearing capacity. However, to improve the efficiency of these technologies, it is necessary to create theoretical models of the processes taking place. The article presents the results of the first stage of creating complex theoretical models of the combined 3DMP process and wave strain hardening (WSH) required for designing technological processes for manufacturing engine parts and brake systems of automotive equipment. . AIMS: Creation and assessment of the adequacy of material models used in finite element modeling of additive synthesis processes with subsequent hardening. MATERIALS AND METHODS: Theoretical models of the material were created in the ANSYS software package, which allows for multidisciplinary calculations. The experimental data required for preparing the models were obtained by testing tensile samples manufactured using standardized methods. The hardness of materials was studied using a KB 30S automatic hardness tester. The adequacy of additive synthesis modeling was assessed based on the distribution of temperature fields. The adequacy of material models for the VDU process was assessed based on the sizes of individual plastic indentations and the distribution diagrams of the depth and degree of hardening in the surface layer. RESULTS: Theoretical models of the following materials were developed: steel, stainless steel, bronze alloy, titanium alloy, aluminum alloy. The theoretical data obtained from the modeling results have a high level of significance. The studies were conducted for various thermal (in the range from +20ºС to +800ºС) and deformation modes. Graphical results of theoretical and experimental studies allow us to obtain a qualitative assessment of the processes under study with the required accuracy. CONCLUSIONS: As a result of the assessment of the adequacy of the developed models, it was established that the discrepancy between the empirical and theoretical data does not exceed 7.4%. The obtained models of materials are statistically significant and can be correctly applied in further studies.

Redescription mining-based business process deviance analysis

AbstractBusiness processes often deviate from their expected or desired behavior. Such deviations can be either positive or negative, depending on whether or not they lead to better process performance. Deviance mining addresses the problem of identifying such deviations and explaining why a process deviates. In this paper, we propose a novel approach to identify and explain the causes of deviant process executions based on the technique of redescription mining, which extracts knowledge in the form of logical rules. By analyzing, comparing, and filtering these rules, the reasons for the deviant behaviors of a business process are identified both in general and for particular process instances. Afterward, the results of this analysis are transformed into a concise and well-readable natural language text that can be used by business analysts and process owners to optimize processes in a reasoned manner. We evaluate our approach from different angles using four process models and provide some advice for further optimization.

Side-by-Side Economic Process Model for the Comparison and Evaluation of Magnetic Bead-Based Processes and Legacy Process for the Manufacturing of Monoclonal Antibodies

This study models two alternative downstream processes based on magnetic separation with the objective of understanding the economic feasibility of these processes compared to the traditional mAb process. The key focus lies in the economic understanding of the cell harvest and capture steps in the models. Here, the models revealed that integrating cell removal and product capture in a single operation is the main factor driving the unified productivity between USP and the magnetic bead-based processes. This results in significant economic benefits, such as savings in both the cost of goods per gram of mAb and fixed costs, as well as increasing annual facility output. The predicted savings potential approaches 38% for COGs, 17% for capital investment, and 40% for annual facility output. For mammalian cell-based manufacturing, the magnetic separation-based DSP provides a highly valuable option due to its integration of several individual unit operations compared to the traditional process both in reducing process time and cost and accommodating higher demands.

Precise large deviations of the net loss process in a non-standard two-dimensional risk model

Abstract This paper investigates the precise large deviations of the net loss process in a two-dimensional risk model with consistently varying tails and dependence structures, and gives some asymptotic formulas which hold uniformly for all x varying in t-intervals. The study is among the initial efforts to analyze potential risk via large deviation results for the net loss process of the two-dimensional risk model, and can provide a novel insight to assess the operation risk in a long run by fully considering the premium income factors of the insurance company.

Kinetic Features of Degradation of R-Loops by RNase H1 from Escherichia coli

R-loops can act as replication fork barriers, creating transcription–replication collisions and inducing replication stress by arresting DNA synthesis, thereby possibly causing aberrant processing and the formation of DNA strand breaks. RNase H1 (RH1) is one of the enzymes that participates in R-loop degradation by cleaving the RNA strand within a hybrid RNA–DNA duplex. In this study, the kinetic features of the interaction of RH1 from Escherichia coli with R-loops of various structures were investigated. It was found that the values of the dissociation constants Kd were minimal for complexes of RH1 with model R-loops containing a 10–11-nt RNA–DNA hybrid part, indicating effective binding. Analysis of the kinetics of RNA degradation in the R-loops by RH1 revealed that the rate-limiting step of the process was catalytic-complex formation. In the presence of RNA polymerase, the R-loops containing a ≤16-nt RNA–DNA hybrid part were efficiently protected from cleavage by RH1. In contrast, R-loops containing longer RNA–DNA hybrid parts, as a model of an abnormal transcription process, were not protected by RNA polymerase and were effectively digested by RH1.

Origin of Intercrystalline Brine Formation in the Balun Mahai Basin, Qaidam: Constraints from Geochemistry and H-O-Sr Isotopes

The Balun Mahai Basin (BLMH), located in the northern Qaidam Basin (QB), is endowed with substantial brine resources; however, the genetic mechanisms and potential of these brine resources remain inadequately understood. This study investigated the intercrystalline brine (inter-brine) in BLMH, performing a comprehensive geochemical analysis of elemental compositions and H-O-Sr isotopes. It evaluated the water source, solute origin, evolutionary process, and genetic model associated with this brine. Moreover, a mass balance equation based on the 87Sr/86Sr isotopic ratio was developed to quantitatively evaluate the contributions of Ca-Cl water and river water to the inter-brine in the study area. The results suggest that the hydrochemical type of inter-brine in the north part of BLMH is Cl-SO4-type and in the south part is Ca-Cl-type. The solutes in brine are mainly derived from the dissolution of minerals such as halite, sylvite, and gypsum. The hydrochemical process of brine is controlled by evaporation concentration, water–rock interaction, and ion exchange interaction. Hydrogen and oxygen isotopes suggest that the inter-brine originates from atmospheric precipitation or ice melt water and has experienced intense evaporation concentration and water–rock interaction. The strontium isotopes suggest that the inter-brine was affected by the recharge and mixing of Ca-Cl water and river water, which controlled the spatial distribution and formation of brine hydrochemical types. The analysis of ionic ratios suggest that the inter-brine is derived from salt dissolution and filtration, characterized by poor sealing and short sealing time in the salt-bearing formation. The differences in hydrochemical types and spatial distribution between the north and the south are fundamentally related to the replenishment and mixing of these two sources, which can be summarized as mixed origin model of “dissolution and filtration replenishment + deep replenishment” in BLMH.

Transforming carbon-intensive coal-fired power plants into negative emission technologies via biomass-fired calcium looping retrofit

Abstract Calcium looping is a promising CO2 capture technology due to reduced energy and economic penalties compared to mature solvent scrubbing technologies. It also can enable negative CO2 emissions when biomass is used to drive the sorbent regeneration process in the calciner. However, the trade-off between the energy, economic and environmental performance under different biomass co-firing fractions in the calciner and process operating scenarios has not yet been well understood. This study examined the potential for transforming a 580 MWel coal-fired power plant into a negative CO2 emitter via retrofit of calcium looping with biomass co-firing in the calciner. High-fidelity process models were developed in Aspen Plus and used to analyse the effect of biomass co-firing fraction, CO2 capture rate in the carbonator, and the fraction of flue gas fed to the carbonator on the techno-economic performance indicators. The results revealed that co-firing 30% biomass with coal in the calciner was sufficient for the retrofitted process to achieve negative CO2 emissions (−3.9 gCO2/kWelh). In this scenario, the levelized cost of electricity was 5% lower (81.1 €/MWelh) than that in the reference retrofit scenario without biomass co-firing (85.4 €/MWelh) at a carbon tax of 100 €/tCO2. Further improvement to the techno-economic performance was achieved by reducing the amount of CO2 captured in the carbonator by reducing either the CO2 capture rate (81.1 €/tCO2) or the amount of flue gas processed (80.4 €/tCO2). Although this was achieved at the expense of the increased specific CO2 emissions to 65.2 gCO2/kWelh and 109.0 gCO2/kWelh, respectively, the net specific emissions were still about 90% lower than those of the unabated host plant (792.3 gCO2/kWelh). This study demonstrated that depending on design priorities, biomass co-firing in the calciner can transform the existing coal-fired power plants into negative CO2 emission technologies or improve the process techno-economic viability.

The relationship between illness perception and vision-related quality of life among Chinese patients with diabetic retinopathy: the mediating role of resignation coping style.

Maladaptive illness perception and coping strategies have been linked to illness-related quality of life in patients with chronic diseases. However, the impact of illness perception and coping strategies on vision-related quality of life (VRQOL) in patients with diabetic retinopathy (DR), a significant microvascular complication of diabetes mellitus, remains unclear. This study aims to evaluate the associations among illness perception, coping styles, and VRQOL in Chinese patients with DR, and to explore the mediating role of coping styles in the relationship between illness perception and VRQOL. This cross-sectional study included 303 adult Chinese with DR recruited from a general Grade-A tertiary hospital in Hunan, China. All participants completed clinical and demographic questionnaires. The Brief Illness Perception Questionnaire(BIPQ), the National Eye Institute 25-Item Visual Function Questionnaire(NEI-VFQ-25), and the Medical Coping Modes Questionnaire were respectively adopted to assess the illness perception, VRQOL and coping styles. The BIPQ total scores were converted to logit scores using Rasch analysis. The NEI-VFQ-25 composite scores were converted to estimated person measures based on Rasch analysis and the method of successive dichotomizations. The relationships among these variables were assessed through independent-sample t-test, one-way analysis of variance, correlation analyses and multiple linear regression analysis, while SPSS Process model and the bootstrap analysis were utilized to evaluate the mediating effect. Illness perception was positively correlated with resignation coping style (r = 0.33, P < 0.001) and negatively correlated with VRQOL (r = -0.43, P < 0.001). Resignation coping style was negatively correlated with VRQOL (r = -0.38, P < 0.001). Illness perception significantly predicted resignation coping style (t = 5.91, P < 0.001) and resignation coping style significantly predicted VRQOL (t = -5.30, P < 0.001). The mediating effect through resignation coping was - 0.49 (95% CI: -0.75 to -0.27), accounting for 21.08% of the total effect of illness perception on VRQOL. The study revealed that the resignation coping style partially mediated the relationship between illness perception and VRQOL among DR patients. Specifically, DR patients may accept their worsening circumstances without attempting to change them due to their perception that their illness is threatening and beyond control. This resignation coping strategy could, in turn, lead to poorer VRQOL. These findings suggest that interventions targeting the improvement of illness perception and coping strategies could enhance the VRQOL of DR patients.

Integrative Review-Based Conceptual Modeling: An Agent-Based Modeling Synthesis of Dynamic Energy Tariff Research and Models

Adoption of dynamic energy tariffs by households is crucial for the transition to carbon-neutral energy systems. Influencing the adoption patterns of these tariffs necessitates an examination of the drivers, decision components, and contextual factors influencing household decisions. Few computational models address this comprehensively, often omitting non-financial decision variables. Moreover, methodologically robust integrative reviews on this topic are scarce. To address this gap, this paper develops a concept-centered integrative review methodology aimed at deriving computer models for socio-techno-economic simulations of household adoption of sustainable technologies. The methodology encompasses five sequential phases: Setup, Literature Search, Analysis, Synthesis and Conceptual Model, and Discussion. To illustrate the methodology, it is applied to the case of household adoption of dynamic energy tariffs, resulting in an abstract conceptual model adaptable to local contexts. The review reveals a lack of consensus on modeled tariffs but highlights the significance of tariff complexity, relative advantage, household heterogeneity, and various agent properties. It also identifies potential improvements in model fundamentals, particularly spatial modeling. The developed process model focuses on the stages ‘knowledge’, ‘decision’, and ‘reevaluation’. The article contributes by presenting a comprehensive review scheme and delivering a concept-centered integrative review along with an explicit conceptual model derived from it.

An extended class of multivariate counting processes and its main properties

Abstract In this paper, a new multivariate counting process model (called Multivariate Poisson Generalized Gamma Process) is developed and its main properties are studied. Some basic stochastic properties of the number of events in the new multivariate counting process are initially derived. It is shown that this new multivariate counting process model includes the multivariate generalized Pólya process as a special case. The dependence structure of the multivariate counting process model is discussed. Some results on multivariate stochastic comparisons are also obtained.

Application of physical and mathematical modeling in the educational process by the example of power plant simulators

This article discusses the application of digital twin technology in thermal power engineering. Special attention is paid to the influence of digital models of the existing equipment of the powerplant on the educational process in higher educational institutions and professional development of service personnel. The article summarizes the results of the introduction of power plant simulators into the curriculum, the methodology of laboratory work and the organization of training sessions. It is also shown that the introduction of physical and mathematical modeling in education and practice contributes to improving the safety and efficiency of work in the energy sector. The results of the introduction of plant simulators into the curriculum are summarized. The software of computer equipment provided for classes at a higher educational institution is considered as a tool for practicing operation skills and creating digital models. When working in a software package, the user is given the opportunity to control units of equipment individually, collectively as a whole unit, or create computational models consisting of simple blocks of equipment. The proposed mutual cooperation between software developers and universities can lead to the creation of digital models to enable retraining of specialists in the workplace, as well as increasing the level of knowledge of graduates. The result of the introduction of a virtual testing ground in a higher educational institution in order to verify the knowledge of students is considered. Thus, the introduction of digital twins into the educational process and practice of work in the thermal power industry is a promising direction for the development of the industry. It contributes to improving the level of training of specialists, ensuring the safety and efficiency of equipment operation, as well as reducing the risks of emergency situations

Machining Characteristics During Short Hole Drilling of Titanium Alloy Ti10V2Fe3Al

The single-phase titanium ß-alloy Ti10V2Fe3Al (Ti-1023) has been widely used in the aerospace industry due to its unique mechanical properties, which include high fatigue strength and fracture toughness, as well as high corrosion resistance. On the other hand, these unique properties significantly hinder the cutting processes of this material, especially those characterized by a closed machining process area, such as drilling. This paper is devoted to the study of the short hole drilling process of the above-mentioned titanium alloy using direct measurements and numerical modeling. Measurements of the cutting force components in the drilling process and determination of the resultant cutting force and total cutting power were performed. The macro- and microstructure of chips generated during drilling were analyzed, and the dependence of the chip compression ratio and the distance between neighboring segments of serrated chips on cutting speed and drill feed was determined. Experimental studies were supplemented by determining the temperature on the lateral clearance face of the drill’s outer cutting insert in dependence on the cutting modes. For the modeling of the drilling process using the finite element model, the parameters of the triad of component submodels of the numerical model were determined: the machined material model, the model of contact interaction between the tool and the machined material, and the fracture model of the machined material. The determination of these parameters was performed through the DOE sensitivity analysis. The target values for performing this analysis were the total cutting power and the distance between neighboring chip segments. The maximum deviation between the simulated and experimentally determined values of the resulting cutting force is no more than 25%. At the same time, the maximum deviation between the measured values of the temperature on the lateral clearance face of the drill’s outer cutting insert and the corresponding simulated values is 26.1%.

The mediating effect of digital literacy and self-regulation on the relationship between emotional intelligence and academic stress among university students: a cross-sectional study.

Academic stress is a prevalent issue affecting students' well-being and academic achievement in today's challenging educational environment. Furthermore, combining digital literacy and self-regulation enhances emotional intelligence, creating a holistic "digitally regulated emotional intelligence" strategy to reduce academic stress effectively. This study emphasizes the significance of developing these abilities in educational settings to prepare students for success in a complex and technology-driven world. To assess emotional intelligence and academic stress among healthcare students and investigate the mediating role of students' digital literacy and self-regulation. A cross-sectional study was conducted to examine correlations and describe quantitative data. Data on two hundred forty students were collected from Fatima College of Health Sciences in the 2023-24 academic year. Pearson's correlation coefficient was applied to assess the associations between variables, and univariate linear regression was employed to investigate the connections between emotional intelligence and pertinent variables. The Hayes Process Model 4 macro was used to study how students' digital literacy and self-regulation mediate the relationship between emotional intelligence and academic stress. Emotional intelligence, digital literacy, and self-regulation were high. Furthermore, the academic stress level was moderate. The mediation analysis revealed that the direct effect of emotional intelligence on academic stress in the presence of the mediators was significant (t = 3.830, p < 0.001). Hence, digital literacy partially mediated the relationship between emotional intelligence and academic stress. On the other hand, self-regulation had no mediation effect on the relationship above. The study noted significant connections between studyattributes, mainly showing the substantial relationship between emotional intelligence and academic stress. Higher emotional intelligence did not consistently reduce stress levels for moderately stressed students, suggesting that other factors may influence their stress levels. Furthermore, digital literacy mediated between emotional intelligence and academic stress, implying that proficient use of online resources could help reduce stress in students with advanced digital skills. Finally, self-regulation did not act as a mediator in the relationship between emotional intelligence and academic stress.

Systematic Review on Additive Friction Stir Deposition: Materials, Processes, Monitoring and Modelling

Emerging solid-state additive manufacturing (AM) technologies have recently garnered significant interest because they can prevent the defects that other metal AM processes may have due to sintering or melting. Additive friction stir deposition (AFSD), also known as MELD, is a solid-state AM technology that utilises bar feedstocks as the input material and frictional–deformational heat as the energy source. AFSD offers high deposition rates and is a promising technique for achieving defect-free material properties like wrought aluminium, magnesium, steel, and titanium alloys. While it offers benefits in terms of productivity and material properties, its low technology readiness level prevents widespread adoption. Academics and engineers are conducting research across various subfields to better understand the process parameters, material properties, process monitoring, and modelling of the AFSD technology. Yet, it is also crucial to compile and compare the research findings from past studies on this new technology to gain a comprehensive understanding and pinpoint future research paths. This paper aims to present a comprehensive review of AFSD focusing on process parameters, material properties, monitoring, and modelling. In addition to examining data from existing studies, this paper identifies areas where research is lacking and suggests paths for future research efforts.

Religiously Integrated Forgiveness Therapy: A Psychotherapeutic Tool for Depression and Pain Among Pakistani Patients with Chronic Widespread Pain.

Individuals experiencing chronic widespread musculoskeletal pain (CWP) frequently have comorbid depression. The presence of depression and pain-related outcomes in these patients are found to be positively correlated with inability to forgive or let go of interpersonal hurts and maltreatments. So for these patients, the current study aimed to apply a religiously integrated forgiveness intervention and to find out its efficacy in lessening depression and pain. The first part of research consisted of developing a religiously integrated forgiveness intervention by incorporating Islamic principles in Enright's process model of forgiveness. The other half was a randomized-control design where Depression Subscale of Depression Anxiety Stress Scale Urdu version as reported by Zafar (Urdu translations of the DASS, 2014) and Heartland Forgiveness Scale Urdu version (Butt in Interdiscip J Contemp Res Bus 4:769-792, 2012) were applied on a total of 272 adults (122 male and 150 female). After screening, 60 individuals (18 males and 39 females) were selected on the basis of high and low score on depression and forgiveness scales, respectively. Their ages ranged from 26 to 45years. Selected individuals were randomly assigned to be a part of either treatment or control group. Religiously integrated forgiveness therapy (RIFT) was applied on 30 individuals in comparison to the 30 participants from control group. Results indicated that in comparison with control group, from pretest to posttest participants in forgiveness intervention group scored lower on depression as well as pain and scored higher on forgiveness (p>.001). It was found that the religiously integrated model of forgiveness therapy was efficacious in enhancing forgiveness and in reducing depression among individuals with chronic widespread musculoskeletal pain.

On Process Discovery Experimentation

Process mining aims to derive insights into business processes from event logs recorded from information systems. Process discovery algorithms construct process models that describe the executed process. With the increasing availability of large-scale event logs, process discovery has shifted towards a data-oriented research discipline, aiming to design algorithms that are applicable and useful in practice. This shift has revealed a fundamental problem in process discovery research: Currently, contributions can only be considered in isolation. Researchers conduct experiments to show that they move the field forward, but due to a lack of reliability and validity, the individual contributions are hard to generalise. In this paper, we argue that one reason for these problems is the lack of conventions or standards for experimental design in process discovery. Hence, we propose “process discovery engineering”: a research methodology for process discovery, consisting of a shared terminology and a checklist for conducting experiments. We demonstrate its applicability by means of an example experimental evaluation of process discovery algorithms and discuss the implications of the methodology on the field. This paper is not meant to be prescriptive, but to invite and encourage the community to contribute to this discussion to advance the field as a whole.

Wear Calculation for a Shaft Lip Seal under Randomly Changing Temperature and Load

A stochastic model of the wear process of a shaft lip seal that takes into account random changes in the temperature and external load was described. The results of a numerical analysis of the process of seal wear in relation to the conditions of operation in open space at near-Earth orbital stations are presented. The importance of taking into account random changes in temperature and external load for predicting the seal durability was evaluated.

Mass transport modelling of two partially miscible, multicomponent fluids in nanoporous media

High-pressure fluid transport in nanoporous media such as shale formations requires further understanding because conventional continuum approaches become inadequate due to their ultralow permeability and complexity of transport mechanisms. We propose a species-based approach for modelling two partially miscible, multicomponent fluids in nanoporous media – one that does not rely on conventional bulk fluid transport frameworks but on species movement. We develop a numerical model for species transport of partially miscible, non-ideal fluid mixtures using the chemical potential gradient as the driving force. The model considers the binary friction concept to include the friction between fluid molecules as well as between fluid molecules and pore walls, and incorporates the key multicomponent transport mechanisms – Knudsen, viscous and molecular diffusion. Under single-phase conditions, the system under consideration is quantified by introducing multicomponent Sherwood number (Sh), Péclet number (Pe) and fluid–solid friction modulus (φ). Despite the complexity of fluid transport in nanopores, the steady-state single-phase transport results reveal the contribution of diffusion in nanopores, where all parameters collapse on a set of master curves for the multicomponent Sh with a dependence on multicomponent Pe and φ. Unsteady state, two-phase transport modelling of the codiffusion process shows that light and intermediate alkanes are produced much higher than heavy alkanes when the vapour phase appears. We demonstrate that the pressure gradient is also crucial in promoting CO2 and alkane mixing during counterdiffusion processes. These results stress the need for a paradigm shift from classical bulk flow modelling to species-based transport modelling in nanoporous media.