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Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

The purpose of this study is to improve the mechanical property and processing performance and reduce the cost of the polylacticacid/polybutyleneadipate-co-terephthalate(PLA/PBAT) composites, the calcium carbonate (CaCO₃) and compatibilizer styrene-maleicanhydride copolymer (SMA-2025) were added to the PLA/PBAT system, and the effect of CaCO₃ and SMA-2025 on the morphology, structure, mechanical property, thermal property, thermalstability and shape memory property of the CaCO₃/PLA/PBAT composites were studied and discussed.

The CaCO₃/PLA/PBAT shape memory composites were prepared via melt-blending and hot-pressing methods, and the effect of CaCO₃ and SMA-2025 on the property of the composites was investigated via scanning electron microscope, universal testing instrument, Fourier transform infrared spectroscopy, differential scanning calorimetry, thermogravimetric analysis and DMA, respectively.

The interface property, mechanical property, thermal stability, shape memory recovery ratios and recovery stresses, and processing performance of the CaCO₃/PLA/PBAT shape memory composites were significantly improved by adding of CaCO₃ and SMA-2025. Moreover, the CaCO₃/PLA/PBAT composites have good blowing film processing performance.

This study will provide a reference for the research, processing and application of the high-performance CaCO₃/PLA/PBAT shape memory composites.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

This study investigates how virtual reality (VR) travel attributes (e.g. sense and quality of information) influence spectators' flow experience, how emotion and past experience affect enjoyment and examines the impact of flow experience and enjoyment on satisfaction and booking or visiting intention.

The VR tour stimuli were fabricated using scenic views from the National Aquarium in the USA. Participants were equipped with Matterport VR and audio headsets and started their virtual travel. Once the participants completed their VR tours, they were asked to complete the questionnaire. Using the stimulus-organism-response theory, 303 valid responses were analyzed using partial least square structural equation modeling (PLS-SEM).

The results showed that the sense and quality of information in VR travel positively and significantly impacted the flow experience. Moreover, emotions and past experiences positively and significantly influenced the enjoyment of VR travel. Similarly, flow experience and enjoyment positively and significantly affect satisfaction. However, satisfaction with VR-related tourism experiences negatively affects users' bookings and visiting intentions.

This study concludes that, from Thai tourists' perspectives, virtual travel should be used as a solution only during the pandemic because, in the long term it can cause a loss to the business chain in the tourism industry.

To the best of the authors' knowledge, no prior research has examined the influence of past experiences and emotions on satisfaction with VR travel.

The study aims to explore the mediating role of flow at work and moderating role of creative self-efficacy in the relationship between authentic leadership and innovative work behavior.

The study collected data from 315 employees working in the service sector through survey design. Data analysis was done through structural equation modeling.

The findings revealed that authentic leadership is related to flow at work and innovative work behavior. Flow at work was also found to be related to innovative work behavior. Flow at work was found to mediate the relationship between authentic leadership and innovative work behavior. The study also confirmed the moderating role of creative self-efficacy in the relationship between authentic leadership and innovative work behavior.

The study unearthed the previously unexplored mediating role of flow at work linking authentic leadership to innovative work behavior. Additionally, it is the first study that explicated how creative self-efficacy moderates the relationship between authentic leadership and innovative work behavior; the relationship between the two is stronger when creative self-efficacy is high.

The main purpose of this study resides essentially in the development of a new tool to quantify the biomass in the bioreactor operating under steady state conditions.

Modeling is the most relevant tool for understanding the functioning of some complex processes such as biological wastewater treatment. A steady state model equation of activated sludge model 1 (ASM1) was developed, especially for autotrophic biomass (XBA) and for oxygen uptake rate (OUR). Furthermore, a respirometric measurement, under steady state and endogenous conditions, was used as a new tool for quantifying the viable biomass concentration in the bioreactor.

The developed steady state equations simplified the sensitivity analysis and allowed the autotrophic biomass (XBA) quantification. Indeed, the XBA concentration was approximately 212 mg COD/L and 454 mgCOD/L for SRT, equal to 20 and 40 d, respectively. Under the steady state condition, monitoring of endogenous OUR permitted biomass quantification in the bioreactor. Comparing XBA obtained by the steady state equation and respirometric tool indicated a percentage deviation of about 3 to 13%. Modeling bioreactor using GPS-X showed an excellent agreement between simulation and experimental measurements concerning the XBA evolution.

These results confirmed the importance of respirometric measurements as a simple and available tool for quantifying biomass.

This study aims to investigate the influence of economic policy uncertainty (EPU) and geopolitical risk (GPR) on the relationship between internal cash flow and external financing in an emerging market, Saudi Arabia. It also examines the role of asset tangibility and financial crisis in establishing this relationship.

The sample was taken from non-financial sector companies listed on the Saudi Stock Exchange between 2002 and 2019. The data were analyzed using panel data regression analysis, including ordinary least squares and fixed effects model. The author addresses potential endogeneity through the generalized method of moments.

This study found that both EPU and GPR reduce the sensitivity of external financing to internal cash flow. This implies that firms depend more on internally generated funds during periods of increased EPU and GPR. Besides, this study found that the influence of EPU and GPR on the sensitivity of external financing to internal cash flow is more (less) negative for more tangible firms (during the financial crisis period). This result implies that Saudi firms boasting a higher level of tangibility are more flexible when it comes to seeking external financing. However, the presence of uncertainty during the crisis period makes the external financing costly, and therefore, firms will be less likely to raise funds from external sources.

This study has important implications for managers, policymakers and regulators. First, the paper findings provide insights for corporate decision-makers in helping them to focus on internal funds to finance their investment during uncertain times. Second, the findings help managers to understand the role of asset tangibility in raising external funding when firms face financial constraints due to uncertainty. Third, this study also helps corporates to focus on internal funds to finance their investment during the crisis period because EPU and GPR increase the cost of external finance. Finally, the results provide guidelines for policymakers and regulators to make appropriate policy measures to increase the easy availability of external finance during periods of increased EPU and GPR.

This paper is the first to shed light on the impact of internal funds on external financing while paying close attention to the role of EPU and GPR.

Temperature anomalies in the upper troposphere have become a reality as a result of global warming, which has a noticeable impact on aircraft performance. The purpose of this study is to investigate the total air temperature (TAT) anomaly observed during the cruise level and its impact on engine parameter variations.

Empirical methodology is used in this study, and it is based on measurements and observations of anomalous phenomena on the tropopause. The primary data were taken from the Boeing 747-8F's enhanced flight data recorder, which refers to the quantitative method, while the qualitative method is based on a literature review and interviews. The GEnx Integrated Vehicle Health Management system was used for the study's evaluation of engine performance to support the complete range of operational priorities throughout the entire engine lifecycle.

The study's findings indicate that TAT and SAT anomalies, which occur between 270- and 320-feet flight level, have a substantial impact on aircraft performance at cruise altitude and, as a result, on engine parameters, specifically an increase in fuel consumption and engine exhaust gas temperature values. The TAT and Ram Rise anomalies were the focus of the atmospheric deviations, which were assessed as major departures from the International Civil Aviation Organizations–defined International Standard Atmosphere, which is obvious on a positive tendency and so goes against the norms.

Necessary fixed flight parameters gathered from the aircraft's enhanced airborne flight recorder (EAFR) via Aeronautical Radio Incorporated (ARINC) 664 Part 7 at a certain velocity and altitude interfacing with the diagnostic program direct parameter display (DPD), allow for analysis of aircraft performance in a real-time frame. Thus, processed data transmits to the ground maintenance infrastructure for future evaluation and for proper maintenance solutions.

A real-time analysis of aircraft performance is possible using the diagnostic program DPD in conjunction with necessary fixed flight parameters obtained from the aircraft's EAFR via ARINC 664 Part 7 at a specific speed and altitude. Thus, processed data is transmitted to the ground infrastructure for maintenance to be evaluated in the future and to find the best maintenance fixes.

Social media has emerged as a prominent platform for marketers and brands to disseminate brand-related information. This study aims to investigate the impact of color congruence between themes and background on marketing effectiveness, focusing specifically on Instagram.

A laboratory experiment was conducted to investigate how color congruence between themes and background in brand posts influences flow and aesthetic experience, subsequently affecting marketing communication parameters such as brand attitude, visit intention, and eWOM on Instagram. Moreover, Adidas Originals was selected as the focal brand, and blue and white color was chosen as the primary color palette for the experimental material.

This study demonstrates that color congruence, regardless of brand layout or post, significantly influences flow and aesthetic experience, subsequently affecting marketing effectiveness.

This study contributes to the theoretical understanding of congruence theory and social media marketing, providing valuable insights for brands to enhance their communication through photographs and effectively manage their official Instagram accounts.

This study aims to integrate the social cognitive theory and flow theory to examine how e-skills self-efficacy (ESSE) and trust-building self-efficacy (TBSE) impact work-related flow in remote work. It also explores how work-related flow (WOLF) mediates the connection between these factors and employee resilience (ER) in the context of the growing shift toward remote work.

The study tested an empirical mediation model with 535 remote workers from Indian organizations that adopted remote work either before or during the pandemic. Hypotheses were tested using Hayes’ PROCESS macro.

The study confirmed the direct influence of ESSE and TBSE on WOLF. Moreover, a mediation analysis demonstrated that WOLF significantly mediates the association linking ESSE, TBSE and ER.

This study, evaluating experienced remote employees in the quaternary sector, offers data-driven insights for informed organizational decisions. These findings aims to guide HR and training departments in formulating recruitment and training strategies, emphasizing digital skill development to enhance knowledge and skills effectively.

This study highlights the need for remote workers and organizations to be aware of the challenges of remote work. It is among the first to explore the link between ESSE, TBSE and ER, addressing psychological factor, i.e. WOLF, often overlooked in organizational contexts.