Process Model Research Articles

The role of financial literacy in enhancing firm’s sustainable performance through Fintech adoption: a moderated mediation analysis

Purpose This study aims to explore the intricate relationship between financial literacy, digital transformation, Fintech adoption, competitiveness and sustainable firm performance, particularly focusing on how financial literacy empowers firms in the evolving digital landscape. Leveraging technological innovation systems (TIS) and resource-based view (RBV), this research suggests a model that incorporates these concepts, focusing on the moderating role of financial literacy in essential interactions. Design/methodology/approach This study employed a survey-based methodology, collecting data from employees across five major Pakistani banks. The survey yielded 426 responses, from which 387 valid ones were selected for analysis. The analysis utilized partial least squares-structural equation modeling (PLS-SEM), complemented by the Hayes Process Model for moderated mediation analysis. This approach ensured robust examination of the relationships between the constructs of the proposed model. Findings The study's findings validate that digital transformation significantly enhances sustainable performance, with Fintech adoption and competitiveness acting as crucial mediators. Financial literacy is highlighted as a key moderator, influencing the effects of digital transformation on Fintech adoption and competitiveness, although its direct impact on sustainable performance is less pronounced. This comprehensive analysis underscores the complex interplay among these factors in driving sustainable performance in the banking sector. Originality/value This research enriches the theoretical and practical comprehension of how digital transformation and Fintech integration, underpinned by financial literacy, bolster sustainable business outcomes. It sheds light on the synergy between technology, strategy and organizational success, offering key insights for the banking industry's navigation through the digital era's challenges.

Association of frailty with workplace social activity, physical activity, and well-being among older employees: a moderated mediation in two income-variant samples

BackgroundResearch suggests that frailty is associated with lower physical activity and well-being in old age, but social activities at work may facilitate physical activity and its positive effect on well-being among older employees with frailty. This study, therefore, ascertained whether there is a moderated mediation of the association of frailty, Workplace Social Activity (WSA), and well-being by Physical Activity (PA).MethodsThe study adopted a cross-sectional design with relevant sensitivity analyses for confounding. The participants were within two Ghanaian samples with different income levels (low-income, n = 897, and higher income, n = 530). The minimum samples were calculated, and the statistical models were tested with Haye’s Process Model through structural equation modelling.ResultsFrailty was negatively associated with PA, and this relationship was moderated by WSA in both samples. Higher frailty was directly and indirectly associated with lower well-being in the higher-income sample but only indirectly associated with lower well-being in the low-income sample. The mediation of PA in the frailty-well-being relationship is partial in the higher-income sample but complete in the low-income sample. There was evidence of moderated mediation in both samples.ConclusionWSA may reduce the strength of the negative association of frailty with PA and well-being among older employees in both samples. Workplace interventions aimed at enhancing WSA may encourage PA and enhance well-being among older employees with frailty.

CFD simulation of a Rushton turbine stirred-tank using open-source software with critical evaluation of MRF-based rotation modeling

AbstractA critical evaluation of the impact of the Multiple Reference Frame (MRF) technique on steady RANS simulations of a Rushton turbine stirred-tank is presented. The analysis, based on the open-source software OpenFOAM, is focused on the choice of the diameter and thickness of the MRF region and on their effect on the predicted velocity field and mixing times in the tank. Five diameters of the MRF region are compared for the same operating conditions of the turbine, showing limited differences in velocity profiles, which are found in general good agreement with available experimental data. Significant differences are nonetheless found in the predicted levels of turbulence intensity within the tank, with a considerable amount of artificially generated turbulence at the boundary of the MRF region for the largest diameters. The impact of the different predictions of the turbulent field on the modeling of the mixing process in the tank is evaluated by simulating the release of a passive scalar, using the frozen-flow field hypothesis. The results show changes in mixing times up to a factor of three when comparing MRF regions of different sizes. Thus, the present investigation highlights the importance of assessing the effect of the MRF zone size on numerical results as a standard practice in RANS based simulations of stirred-tanks.

P-220 Two strategies for reducing cryoprotectant toxicity: recent trends in oocyte, embryo and tissue cryopreservation

Abstract Study question In this study, we demonstrate two methods to reduce the toxicity of cryoprotectants. Both have been tested in cells and tissues Summary answer Employing both the theoretical model of the incubation process and an ultra-rapid rewarming based on high-intensity focused ultrasound, it is possible to reduce this toxicity What is known already Cryoprotectants used in assisted reproduction for the cryopreservation of gametes, embryos, or ovarian or testicular tissue are based on DMSO, ethylene glycol, glycerol, etc. ... Minimizing exposure to these substances is always desirable. In this work, we show how, by modeling the hydraulic permeability of the embryo membrane and by the practical application of high-intensity focused ultrasound, it is possible to drastically reduce the biological material’s exposure to these agents. The former achieves a 90% reduction in incubation times in the cryoprotectant. The latter significantly increases the speed of rewarming, thereby reducing the amount of cryoprotectant needed for tissue recovery. Study design, size, duration The system was modeled using computer simulations based on MATLAB algorithms. It has been verified that in 1 minute, 90% of the cryoprotectant penetrates the embryo. The results have been experimentally validated in human embryos and oocytes. Regarding rewarming, High-Intensity Focused Ultrasound (HIFU) has achieved rewarming rates two orders of magnitude higher than standard rewarming. This has enabled the recovery of tissue samples that otherwise would not have been viable. Participants/materials, setting, methods For this study, donated oocytes and embryos were used. The MATLAB programming language was utilized for computer simulations. For ultrasonic rewarming, a piezoelectric excited at a frequency of 1.2 MHz and 60VDC was used. Standard vitrification was compared with our toxicity reduced protocol in the case of oocytes and embryos, Standard slow freezing was used in the case of rewarming with high intensity focused ultrasound. Main results and the role of chance The application of mathematical modeling, together with the experimental resultas on oocytes and embryos, demonstrate that is it possible to reduce in 90% the toxicity of the cryoprotectant in a typical vitrification protocol. Also, the application of High Intensity Focused Ultrasound reaches warming rates that are two orders of magnitud higher than the conventional rewarming in a water bath. This finding represent a significative improvement of the present cryopreservation protocols. The success of both strategies is demonstrated, both in reducing incubation time and the superiority of rewarming using ultrasound. The study is generally applicable to oocytes, sperm, embryos, ovarian and testicular tissue. Limitations, reasons for caution No limitations. Wider implications of the findings We believe that these findings will soon be adopted by centers where the cryopreservation of biological material is necessary. Trial registration number NOT APPLICABLE

Modelling and Optimization of Residential Electricity Load under Stochastic Demand

The paper considers a modelling framework for a set of households in residential areas; using electricity as a form of energy for domestic consumption. Considering the demand and availability of units for electricity consumption, optimal decisions for electricity load allocation are paramount to sustain energy management. We formulate this problem as a stochastic decision-making process model where electricity demand is characterized by Markovian demand. The loading and operational framework is governed by the demand and supply phenomena; where shortage costs are realized when demand exceeds supply. Empirical data for electricity consumption was collected from fifty households in two residential areas within the suburbs of Kampala in Uganda. Data collection was made at hourly intervals over a period of four months. The major problem focussed on determining an optimal electricity loading decision in order to minimize consumption costs as demand changes from one state to another. Considering a multi-period planning horizon, an optimal decision was determined for loading or not loading additional electricity units using Markov decision process approach. The model was tested; whose results demonstrated the existence of an optimal state-dependent decision and consumption costs considering the case study used in this study. The proposed model can be cost-effective for managers in the electricity industry. Improved efficiency and utilization of resources for the distribution systems of electricity to residential areas was realised; with subsequent enhanced reliability of service to essential customers of the energy market.

Abrasive Waterjet Machining.

The abrasive waterjet machining process was introduced in the 1980s as a new cutting tool; the process has the ability to cut almost any material. Currently, the AWJ process is used in many world-class factories, producing parts for use in daily life. A description of this process and its influencing parameters are first presented in this paper, along with process models for the AWJ tool itself and also for the jet-material interaction. The AWJ material removal process occurs through the high-velocity impact of abrasive particles, whose tips micromachine the material at the microscopic scale, with no thermal or mechanical adverse effects. The macro-characteristics of the cut surface, such as its taper, trailback, and waviness, are discussed, along with methods of improving the geometrical accuracy of the cut parts using these attributes. For example, dynamic angular compensation is used to correct for the taper and undercut in shape cutting. The surface finish is controlled by the cutting speed, hydraulic, and abrasive parameters using software and process models built into the controllers of CNC machines. In addition to shape cutting, edge trimming is presented, with a focus on the carbon fiber composites used in aircraft and automotive structures, where special AWJ tools and manipulators are used. Examples of the precision cutting of microelectronic and solar cell parts are discussed to describe the special techniques that are used, such as machine vision and vacuum-assist, which have been found to be essential to the integrity and accuracy of cut parts. The use of the AWJ machining process was extended to other applications, such as drilling, boring, milling, turning, and surface modification, which are presented in this paper as actual industrial applications. To demonstrate the versatility of the AWJ machining process, the data in this paper were selected to cover a wide range of materials, such as metal, glass, composites, and ceramics, and also a wide range of thicknesses, from 1 mm to 600 mm. The trends of Industry 4.0 and 5.0, AI, and IoT are also presented.

A Data-driven Approach for Forecasting Current Direction with a Hybrid Model of Empirical Mode Decomposition and Warped Gaussian Process

Abstract Ocean current forecasting is essential for tidal renewable energy generation and operation. However, comprehensive studies and an efficient approach for forecasting the current direction at multiple points along the water depth are still lacking. In this study, a data-driven approach was developed to attain short-term prediction in the current direction with reasonable uncertainty quantification. The developed approach employed empirical mode decomposition (EMD) and the warped Gaussian process (WGP) in the forecasting process. The ocean current data were measured by a seabed-mounted acoustic Doppler current profiler (ADCP) in the Haitian Strait and were used to illustrate the developed approach. The measured current direction data were preprocessed with the average shifting method to obtain the principal and random components for the improvement of the forecasting accuracy. The random components were decomposed into intrinsic mode functions (IMFs) and residuals. The principal components, IMFs and residuals of the current direction were then forecasted by the WGP approach. The forecasting performance of the developed approach was investigated through comparisons with those of single standard GP, single WGP and EMD+GP models. The effects of the kernel function and training input on the forecasting efficiency and precision were investigated. The extrapolation performances of the proposed model for a 1-step predication and multistep ahead prediction were also examined.

Adaptability Analysis of Integrated Project Delivery Method in Large- and Medium-Sized Engineering Projects: A FAHP-Based Modeling Solution

With the emerging large- and medium-sized engineering projects, prominent project delivery methods make sense in terms of cost, risk, management, and schedule. Among these, the Integrated Project Delivery (IPD) method stands out due to its adaptability for growing scale and complexity projects. This study compares the IPD method with other methods, emphasizing its benefits in large- and medium-sized projects and introducing the Fuzzy Analytic Hierarchy Process (FAHP) model to analyze IPD’s adaptability quantitatively. By conducting a matrix calculation of eighteen second-level indicators, this study derived weight values for four first-level indicators: Cost control, Risk control, Management control, and Schedule control. These first-level indicators were then used to formulate the total evaluation index calculation. Based on this foundation, we verified the calculations using a case study in Fujian. Implementing the IPD method led to a lower cost than the Owner’s Representative method and a one-year schedule acceleration. The FAHP model introduced in this study offers a novel and objective approach for adaptability analysis of the IPD method in large- and medium-sized engineering projects, coupling decision theory into project management.

Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM

ABSTRACT Ecosystem process modeling is vital for a broad range of applications. It requires a key metric to characterize vegetation canopies: the leaf area index (LAI). Many land surface models utilize satellite-derived LAI for modeling ecosystem processes. The use of satellite-derived LAI constrains the models for predicting ecosystem processes when observational data are unavailable. Enriching prognostic models for predicting the LAI is favorable to land surface studies for forecasting vegetation-related processes. An attention-enhanced long and short memory (AELSTM) model was developed to predict the vegetation LAI time series based on climatic data. The AELSTM outperformed comparative machine learning models when assessed using satellite and flux tower data. The LAI predicted by AELSTM are temporally and spatially consistent with satellite-derived LAI. The R2 values attained 0.86–0.93 across biomes. AELSTM can be coupled with the land surface models to predict gross primary productivity. Our proposed model was demonstrated to be effective in predicting the LAI time series under different Shared Socioeconomic Pathways in CMIP6. Our study demonstrated that deep learning approaches are capable of modeling and characterizing the spatial and temporal patterns of key vegetation variables such as LAI. Moreover, the study has provided references for vegetation processes in land surface studies.

Validation of point process predictions with proper scoring rules

AbstractWe introduce a class of proper scoring rules for evaluating spatial point process forecasts based on summary statistics. These scoring rules rely on Monte Carlo approximations of expectations and can therefore easily be evaluated for any point process model that can be simulated. In this regard, they are more flexible than the commonly used logarithmic score and other existing proper scores for point process predictions. The scoring rules allow for evaluating the calibration of a model to specific aspects of a point process, such as its spatial distribution or tendency toward clustering. Using simulations, we analyze the sensitivity of our scoring rules to different aspects of the forecasts and compare it to the logarithmic score. Applications to earthquake occurrences in northern California, United States and the spatial distribution of Pacific silver firs in Findley Lake Reserve in Washington highlight the usefulness of our scores for scientific model selection.

Discovering chiral auxetic structures with near-zero Poisson's ratio using an active learning strategy

In this paper, a set of hexachiral auxetic structural designs with near zero Poisson's ratio (ZPR) characteristics is discovered via the combination of machine learning and experimentally validated finite element simulation. An active learning-enhanced Gaussian process model is utilized to generate multiple designs with near-ZPR properties and discover the boundary of the positive and negative Poisson's ratio. The results show that active learning successfully constructs a probabilistic estimation of the ZPR boundary. A comprehensive analysis of the identified ZPR contour is performed to extract crucial design insights. The findings indicate that the near-ZPR characteristic can be attained through various combinations of geometric parameters. This offers users the flexibility to select the configuration that best aligns with their specific requirements. Additionally, an investigation of the various ZPR configurations that have been discovered is carried out to understand the mechanism that yields near-ZPR property. One discovered near-ZPR design was subsequently fabricated using 3D printing for validation purposes. The experimental outcomes demonstrated a good agreement with the numerical predictions, underscoring the effectiveness of the active learning strategy in uncovering designs that closely approach ZPR conditions.

Rigorous modelling and optimization of hybrid absorption-adsorption separation process of H2/CH4 using ZIF-8/glycol/water slurry

Efficient separation of H2/CH4 is vitally important for the hydrogen energy system. To guide the scale-up of the separation technique using the ZIF slurry, the rigorous modelling and optimization of the separation process of H2/CH4 using ZIF-8/glycol/water slurry is conducted. A rigorous phase equilibrium model is proposed to describe the hybrid absorption-adsorption with liquid film permeation. Next, the MESH (Material balance, phase Equilibrium, Summation, Heat balance) equations for mass transfer units, modelling and multi-objective optimization of the full separation process are established. The effect of operation pressure and gas-slurry volume ratio on product purity, recovery ratio and unit energy consumption are investigated. Results show that the calculated solubilities of pure/binary components are in good agreement with the experiment data. The feed gas (H2/CH4: 70: 30) can be enriched to be (H2/CH4: 99: 1) with the H2 recovery ratio of 99.5 % and the total unit energy consumption reaches 0.2492 kW·h/Nm3 (feed gas).

Isobutyric and n-butyric acid production from H2 and CO2 by Clostridium luticellarii in a hollow fiber membrane biofilm reactor: Reactor operation and process modelling

Isobutyric and n-butyric acid production from H2 and CO2 by Clostridium luticellarii in a hollow fiber membrane biofilm reactor: Reactor operation and process modelling

Finite element modeling and simulation of IN738 laser powder bed fusion process assisted via machine learning

Finite element modeling and simulation are considered one of the effective methods for optimizing additive manufacturing process windows, which can better capture metallurgical features under the interaction of multiple physical fields. However, correctly selecting or obtaining heat source parameters, boundary conditions, and thermophysical parameters is the biggest challenge of finite element modeling. In this work, the laser semi-ellipsoid heat source parameters and powder thermophysical parameters of the laser powder bed fusion process was effectively determined by machine learning method, and an accurate laser powder bed fusion process model of IN738 alloy was established. The simulation error of the size of single-track molten pool under different process conditions is less than 2.0%. The simulation results of stress field by multi-layer and multi-track under different process conditions are consistent with the micro crack and density test results of deposited samples. The relevant research results provide a new idea for correctly establishing the model of additive manufacturing process and realizing reliable finite element simulation.

Mathematical modeling and performance evaluations of a wood drying process using photovoltaic thermal and double-pass solar air collectors

This research conducts a novel and complete 4E assessments and introduces an innovative method for examining solar wood drying systems. The software TRNSYS and MATLAB programming are combined to simulate and create a numerical program. This novel combination allows for a thorough examination that goes beyond conventional assessments of wood drying. The aim is to evaluate the effectiveness of a solar wood dryer using two different collectors, a double-pass and a photovoltaic thermal collectors, in terms of four key aspects: energy, exergy, economic, and environmental. Further, the drying process was investigated under various climates in three African cities (Errachidia, Dakar, and Nouakchott). The moisture content of wood was reduced from 74% to 12% in the three cities. Elaborate simulations were performed and the results were confirmed by experimental validation, using methods including Mean Absolute Error, Mean Relative Error, and Root Mean Square Error.The primary findings of this study are the following: In the double-pass dryer, the drying time required in Errachidia, Dakar, and Nouakchott was 139h, 189h, and 170h, respectively. Whereas in the photovoltaic thermal dryer, it needed 192h in Errachidia, 210h in Dakar, and 189h in Nouakchott. Further, using photovoltaic thermal, the dryer exhibited superior exergy efficiency compared to the use of double pass collector. Photovoltaic thermal collector reduces yearly CO2 emissions by an average of 54.87, 66.2, and 44.11 tonnesCO2 in Errachidia, Dakar, and Nouakchott, respectively. In contrast, double pass collector reduces CO2 emissions by an average of 38.49, 57.9, and 38.56 tonnesCO2 in Errachidia, Dakar, and Nouakchott, respectively, throughout the year. Further, the solar dryer, using double pass collector, has a shorter payback time than using photovoltaic thermal collector, indicating a quicker economic return.

Study on the influence of selective laser melting temperature on the melting bath

Abstract The high-temperature gradient and high-frequency thermal cycling during the selective laser melting process will seriously affect the melting bath morphology and dimensions, which in turn affects the quality of formed parts. In this paper, a numerical model of the SLM process is established to emulate the temperature field and investigate temperature variation of single-layer and multi-channel models, as well as the influence of disparate laser power, hatch spacing, and scanning speed on melting baths. The simulation results show that the temperature gradient at the front end of the melting bath is small due to physical phase transition, and the temperature gradient nearer to the end of the melting bath is bigger. The midpoint of the second melting channel has the highest peak temperature due to heat conduction and accumulation; the impact of each technological parameter on the melting bath is in the following order: laser power > scanning speed > hatch spacing. Therefore, it is clear that the temperature distribution will provide a theoretical foundation for regulating the stress state of molded parts and ensuring the surface quality and mechanical capacity of the processed component.

Imprints of new physics operators in the semileptonic [formula omitted] process in SMEFT approach

At present, there are several measurements of B decays that exhibit discrepancies with the predictions of the Standard Model, and suggest the presence of new physics in b→s and b→c(u) quark level transitions. Motivated by the prospects of the ongoing high-luminosity B factories, we study the exclusive B→a1(1260)ℓ−ν¯ℓ process within the Standard Model Effective Field Theory (SMEFT) formalism, to understand the sensitivity of various new physics operators. The new physics parameters are constrained by using the experimental branching fractions of the (semi)leptonic B→ℓν¯ and B→(π,ρ,ω)ℓν¯ processes (where ℓ=e,μ,τ) which undergo b→uℓν¯ quark level transitions. We then perform a comprehensive angular analysis of the exclusive B→a1(1260)ℓ−ν¯ℓ process in the Standard Model and in the presence of various new physics operators. We also provide the predictions and comment on various observables, such as branching ratio, forward-backward asymmetry, and the test of lepton flavor non universality of the B→a1(1260)ℓ−ν¯ℓ channel.

Novel calibration design improves knowledge transfer across products for the characterization of pharmaceutical bioprocesses.

Modern machine learning has the potential to fundamentally change the way bioprocesses are developed. In particular, horizontal knowledge transfer methods, which seek to exploit data from historical processes to facilitate process development for a new product, provide an opportunity to rethink current workflows. In this work, we first assess the potential of two knowledge transfer approaches, meta learning and one-hot encoding, in combination with Gaussian process (GP) models. We compare their performance with GPs trained only on data of the new process, that is,local models. Using simulated mammalian cell culture data, we observe that both knowledge transfer approaches exhibit test set errors that are approximately halved compared to those of the local models when two, four, or eight experiments of the new product are used for training. Subsequently, we address the question whether experiments for a new product could be designed more effectively by exploiting existing knowledge. In particular, we suggest to specifically design a few runs for the novel product to calibrate knowledge transfer models, a task that we coin calibration design. We propose a customized objective function to identify a set of calibration design runs, which exploits differences in the process evolution of historical products. In two simulated case studies, we observed that training with calibration designs yields similar test set errors compared to common design of experiments approaches. However, the former requires approximately four times fewer experiments. Overall, the results suggest that process development could be significantly streamlined when systematically carrying knowledge from one product to the next.

Irregular extended target tracking with unknown measurement noise covariance

To address the challenge of tracking irregular extended target while adapting to unknown measurement noise, this paper presents a novel tracking algorithm for irregular extended target. Considering the flexibility of the Gaussian process (GP) model for representing shape, in the proposed algorithm, we utilize the GP model to delineate the radial function of the tracked extended target’s contour. Then, we incorporate the GP model into the variational Bayesian technique to formulate the posterior distribution for both the unknown measurement noise and the shape state of the tracked target. Subsequently, the square-root cubature Kalman filter is employed to tackle the nonlinearity filtering in the problem of irregular extended target tracking (ETT), enabling simultaneous estimation of intricate shape details and the unknown measurement noise covariance. Through extensive experiments using both simulated and real-time lidar data, the proposed algorithm demonstrates superior performance compared to the existing ETT algorithms for extended target tracking in both accuracy and robustness.

Longitudinal Relationship Between Psychological Capital and Anxiety in College Students: The Mediating Effect of Emotion Reregulation Strategy and Moderating Effect of Parent-Child Relationship.

Based on Conservation of resources theory, process model of emotion regulation and attachment theory, the present study examined how psychological capital affects anxiety through the mediation of emotion regulation strategies and explored the moderating role of parent-child relationship. Using a longitudinal study method, 962 college students were surveyed twice at one-year intervals. (1) College students' emotion regulation strategies (including cognitive reappraisal and expressive suppression) partially mediated psychological capital and anxiety mood; (2) parent-child relationship moderated the pathways of psychological capital and expressive suppression on anxiety, respectively. College students with higher levels of parent-child relationship had stronger predictive effects of psychological capital and weaker predictive effects of expressive suppression on anxiety mood. The research findings clarify the combined effects of emotion regulation and parent-child relationships on anxiety among college students, providing valuable reference for the design and implementation of interventions to promote individual psychological well-being.