Process Model Research Articles

Morphological changes and color development during cookie baking-Kinetic, heat, and mass transfer considerations.

Color and shape are important quality attributes in baked goods, particularly cookies. Composition and processing conditions determine and influence color development and morphological changes in these baked goods. The objective of this study was to systematically evaluate the evolution of color and shape during baking to determine useful correlations that can be implemented during the assessment and modeling of the baking process. Cookies (AACC-I standard protocol 10-53.01) were baked at 185, 205, and 225°C. Moisture content, water activity, surface temperature, characteristic dimensions (radius and thickness), and color indexes (lightness, redness, blueness, and browning index [BI]) were monitored at different locations on the cookie surface and baking times. Relationships among the tested conditions were explored using correlation analysis. The cookies' dimensions and color indexes were strongly correlated with changes in moisture content over time, and those relationships were characterized using empirical models. The temperature dependence of the kinetic parameters of the changes in lightness and BI was also described and deemed independent of the location on the cookie surface. This study provides insights into the influence of heat and mass transfer on the physical and physicochemical changes of cookies during baking. The kinetic and secondary models developed in this study can serve as important components for establishing a comprehensive approach for coupling heat transfer, mass transfer, and reaction kinetics to estimate and optimize cookie-baking processes. PRACTICAL APPLICATION: The findings from this study provide valuable information for better understanding the morphological changes and color developments during the cookie-baking process. The quantitative data and models generated in this study will allow identifying baking conditions for better quality development.

Bivariate tempered space-fractional Poisson process and shock models

Abstract We introduce a bivariate tempered space-fractional Poisson process (BTSFPP) by time-changing the bivariate Poisson process with an independent tempered $\alpha$ -stable subordinator. We study its distributional properties and its connection to differential equations. The Lévy measure for the BTSFPP is also derived. A bivariate competing risks and shock model based on the BTSFPP for predicting the failure times of items that undergo two random shocks is also explored. The system is supposed to break when the sum of two types of shock reaches a certain random threshold. Various results related to reliability, such as reliability function, hazard rates, failure density, and the probability that failure occurs due to a certain type of shock, are studied. We show that for a general Lévy subordinator, the failure time of the system is exponentially distributed with mean depending on the Laplace exponent of the Lévy subordinator when the threshold has a geometric distribution. Some special cases and several typical examples are also demonstrated.

The Effects of Childhood Maternal Attachment on the Parenting Behavior of Mothers with Preschoolers: The Mediating Effects of Adult Attachment and Marital Satisfaction

The aim of this study was to explore the mediating effects of adult attachment and marital satisfaction on the relationship between childhood maternal attachment and parenting behavior. A total of 372 mothers of preschoolers completed questionnaires on childhood maternal attachment, adult attachment, marital satisfaction, and parenting behavior. Adult attachment was analyzed by distinguishing between attachment anxiety and attachment avoidance. Parenting behavior was categorized into positive and negative behaviors. For the main analyses, Preacher and Hayes’s PROCESS macro program was used to examine serial mediating effects. The results revealed that attachment anxiety mediated the effects of mothers’ childhood attachment experiences on both positive and negative parenting behavior. However, attachment avoidance only mediated the effects on positive parenting behavior. Marital satisfaction mediated the effects of mothers’ childhood attachment experiences on both positive and negative parenting behavior. Notably, the sequential mediating effects of attachment anxiety and marital satisfaction were not significant for either positive or negative parenting behavior. By contrast, the sequential mediating effects of attachment avoidance and marital satisfaction were significant for both positive and negative parenting behavior. These findings elucidate the predictive factors for parenting behavior within a process model framework, providing valuable insights for parental education and counseling aimed at enhancing mothers' parenting practices.

Process modelling for the production of hydrogen-based direct reduced iron in shaft furnaces using different ore grades

This process modelling study explored the behaviour of hydrogen-based direct reduced iron (DRI) manufacturing in a shaft furnace. Various performance parameters such as metallisation ratio (MR), consumption of hydrogen per tonne of DRI, production of by-products, reactor energy demand and total energy demands for the process have been analysed with respect to temperature, ore grade (gangue content), and reactant conditions. The HSC Chemistry (H: enthalpy, S: entropy and C: heat capacity) SIM (simulation) module was employed for the modelling coupled with the Gibbs energy minimisation calculation. The shaft furnace was divided into three zones to model the three-step reduction of iron ore in a counterflow arrangement. Results show that temperature and hydrogen supply have a significant effect on the metallisation of DRI. Increasing temperature and hydrogen flow rate were predicted to increase the MR or reducibility. A full metallisation can be achieved with hydrogen supply of 130, 110, 100 and 90 kg/tDRI at 700, 800, 900 and 1000°C, respectively, using the best-grade ore (Fe 69 wt.%, gangue 5.2 wt.%). However, the hydrogen consumption in full metallisation was calculated to be 54 kg/tDRI (tonne of DRI). At full MR, the reactor energy consumption (supplementary electrical energy) was calculated to be 0.56 to 0.59 MWh/t Fefeed using the reactor temperature from 700 to 1000°C. Ore grade or gangue content has a significant impact on reactor energy demand. For example, at 900°C, the top-grade ore was calculated to consume 0.69 MWh/tDRI compared to 0.88 MWh/tDRI using the lowest grade ore. A certain percentage of CO (i.e. 15%) blended with hydrogen was predicted to be beneficial for metallisation, hydrogen consumption, and overall energy demand. However, increasing CO would increase CO2 emissions significantly.

Investigation of the efficacy of Alhagi maurorum plant powder for Janus Green B dye removal from wastewater

The growth of industrial activities, has led to a significant increase in the influx of color pollutants into the environment. Phytoremediation can play a crucial role in enhancing wastewater quality. Accordingly, this study sought to evaluate the effectiveness of Alhagi maurorum plant powder in removing Janus Green B (JGB) dye from aqueous solutions. The adsorbent’s properties were characterized through Fourier-transform infrared spectroscopy. The study examined various parameters, including initial dye concentration (20–110 mg/L), adsorbent dosage (0.002–0.02 g), solution pH (2–10), and contact time (5–50 min). The experiments revealed that the maximum dye removal efficiency, 99.51%, was achieved under optimal conditions: pH 7, a contact time of 20 min, an adsorbent dosage of 0.01 g, and an initial dye concentration of 90 mg/L. The adsorption of JGB onto the adsorbent followed the Langmuir isotherm model, with a maximum adsorption capacity of 90.909 mg/g. The kinetic results supported a pseudo-second-order model for the adsorption process, with an R 2 value of 0.9999. The calculated Gibbs free energy changes (ΔG°) at temperatures of 288, 298, 308, 318, and 328 K were found to be −5354.28, −5993.61, −6439.66, −7026.51, and −7932.05 kJ/mol, respectively, indicating the spontaneity of the adsorption process.

Challenges, Coping, and Personal Strengths during COVID-19: Lessons from the Experiences of at-Risk Young Adults in Israel

The COVID-19 pandemic significantly endangers the development of at-risk young adults across various life domains, with potential enduring consequences. Despite prior reporting on the pandemic’s adverse effects on these individuals, there is a lack of the empirical literature regarding their resilience. Building upon the stress process model (Pearlin, 1978), we explored the challenges of 20 at-risk young adults (aged 18–25 years) and the strategies and resources they employed to cope with these challenges during this period. Thematic analysis revealed that these young adults struggled with worsened family relationships, loneliness, mental health issues, and setbacks in their quest for independence and autonomy. However, resources and character strengths that facilitated coping strategies during this period also emerged, specifically the motivation to become a role model within their family, personal abilities, and the use of formal and informal support. Theoretical and practical implications are discussed.

A simultaneous optimization of the seawater bittern and waste seashell recovery process for CO2 and SOx utilization

This study proposes a novel integration of seawater bittern and waste seashell recovery processes for CO2 and SOx utilization. Furthermore, simultaneous optimization of waste seashells and Ca(OH)2 separated from seawater bittern as an SOx absorbent was conducted to derive the blending ratio using a genetic algorithm (GA). The GA-based optimization process consists of four steps: (1) data generation using process model and data preprocessing, (2) DNN-based surrogate model development to predict the gypsum purity, CaCO3 yield, and CO2 emissions, (3) mathematical model development for TAC calculation, (4) GA-based optimization to derive the cost-optimal blending ratio and TAC. As a result, the derived cost-optimal blending ratios were scallop shells (0.03 %), cockle shells (0.03 %), clam shells (13.66 %), oyster shells (40.31 %), mussel shells (28.80 %), and Ca(OH)2 (17.18 %), reducing the TAC by up to 10.88 % compared to conventional CO2 and SOx capture processes. At the optimal blending ratio, the recycling efficiency of each seashell in the Republic of Korea was achieved from a minimum of 2.72 % to a maximum of 99.15 % annually. Therefore, this work makes a significant contribution to the chemical engineering field, providing an environmentally friendly, economical process and an efficient simultaneous optimization framework applied to the proposed model.

High‐Dissolution Mechanism of Ti–48Al–2Cr–2Nb Alloy in Electrochemical Machining from a Nonlinear Dynamic Perspective

High‐rate dissolution process of Ti–48Al–2Cr–2Nb alloy in electrochemical machining exhibits strong nonlinear dynamic characteristics, but the quantitative relationship between these nonlinear characteristics and surface topography, as well as the electro‐dissolution mechanism, is unknown. Therefore, the fractal and recursive features of dissolution behavior and quantitative characterization of the dissolved surface topography were investigated. Additionally, an electro‐dissolution mechanism model in electrochemical machining process was presented. Applying a higher potential of 11 V can enhance the stability of the electrochemical system, resulting in a smoother and uniform surface. The saturation correlation dimension, surface roughness, and integral area of lacunarity initially decrease before increasing with higher electrolytic pressure, while laminarity and determinism show an initial increase followed by a decrease. These five characteristic parameters reach their optimal values at an electrolyte pressure of 0.4 MPa, indicating that achieving a stable and deterministic electrochemical system is crucial for obtaining a uniform smooth surface. These nonlinear dynamic characteristic parameters can be considered as in‐situ monitoring parameters for surface quality in ECM. The anodic dissolution process comprises three stages: initial passive film dissolution and breakdown, rapid dissolving with surface coarsening, and stable dissolving with polishing.

The economic production quantity model for an imperfect production process by control chart design

The Economic Production Quantity (EPQ) plays a crucial role in the manufacturing process. The traditional EPQ model operates under the assumption that Non-Conforming (NC) items are not produced, and the process remains under control. However, real-world scenarios often involve the production of NC products, leading to process deviations. This study introduces a model that optimizes the design of a control chart to incorporate NC products into the EPQ model. The model integrates inventory and quality-related costs to minimize the expected total cost by determining EPQ, sample size, and control limit. The solution methodology employs direct search techniques to solve this problem. The sensitivity analysis results demonstrate the significant cost reduction achieved by the proposed model compared to the classical EPQ model. The numerical examples presented in the study show an average cost reduction of approximately 21 percent, highlighting the potential effectiveness of the model in cost reduction efforts.

Sensitivity of transport object control quality for measuring the inaccuracy of state variables

This work analyzes the sensitivity functions and optimum control of a transport and logistics process model. It explains the fundamental model of controlling safe ship movement as a differential game, and optimizing control algorithms through multi-matrix game and multi-stage positioning game. The sensitivity features for controlling safe ship in actual collision scenario are described in relation to inaccurate information of process position and variations in its varables, based on the determination of computer simulation algorithms in Matlab/Simulink software.

Internet addiction and suicidal ideation in Chinese college students: The role of meaning in life and social support

Worldwide, internet addiction and suicidal ideation have become serious issues in public health. However, little is known about the underlying mechanisms in the relationship between internet addiction and suicidal ideation. This study aimed to investigate the relationship between internet addiction and suicidal ideation, and the role of meaning in life (MIL) and social support in this relationship. A total of 714 (female= 53.78%; mean age = 18.29 years, SD = 0.68 years) completed internet addiction, suicidal ideation, MIL, and social support measures. Results from PROCESS Model analysis showed higher internet addiction to be associated with a higher risk for suicidal ideation. This association was fully mediated by MIL so that internet addiction predicts suicidal ideation by reducing MIL. Social support moderated the effect of loss of MIL on suicidal ideation; namely, a lower sense of MIL was associated with higher suicidal ideation at low levels of social support, while high social support eliminated the effect of meaninglessness on suicidal ideation among college students. These findings imply a need for intervention in order to prevent suicidal ideation in college students in the digital era.

Gaussian processes modeling for the prediction of polymeric nanoparticle formulation design to enhance encapsulation efficiency and therapeutic efficacy.

Conventional drugs have been facing various drug delivery obstacles, including first-pass metabolism for oral medications, drug degradation by cellular enzymes, off-target effects, and cytotoxicity of healthy cells. Nanoparticles (NP) application in drug delivery can compensate for these drawbacks to a great extent. NPs can be fabricated using different materials and structures to achieve desired therapeutic effects. For each type of NP material, its physicochemical properties determine compatibility with specific drugs and other supplemental compositions. The optimized material selection becomes prominent in NP development to improve NP performances. Due to the nature of NP fabrication, the process is long and expensive. To accelerate NP composition optimization, machine learning (ML) techniques are among the most promising methods for efficient data predictions and optimizations.As a proof-of concept, we created Gaussian Process (GP) models to make predictions for drug encapsulation efficiency (EE%) and therapeutic efficacy of 32 poly (lactic-co-glycolic acid) (PLGA) NPs that are formed with materials with different physicochemical properties. Two model drugs, doxorubicin (DOX) and docetaxel (DTX) were loaded separately. The IC50 values for the various NPs formulations were evaluated using the OVCAR3 epithelial ovarian cancer cell line. EE% GP model has the highest prediction accuracy with the lowest normalized root-mean-squared-error (RMSE) of 0.187. The DOX and DTX IC50 GP models have normalized RMSEs of 0.296 and 0.206, respectively, which are higher than that of the EE% GP model.

Use of Risk Management to Support Business Sustainability in the Automotive Industry

Today’s companies operate in a dynamic, constantly evolving, and highly competitive environment. The globalization of markets has significantly changed the economy, where companies operate within increasingly complex supply chains. The ever-increasing expectations of customers and company stakeholders, as well as the need to incorporate a comprehensive approach to the life cycle of manufactured products in corporate strategies, expose companies to a whole range of risks. The research was based on the need of organizations operating in the (automotive) industry to manage the dynamics of the business environment. This was accomplished using an appropriate model that, through its universality, would help to ensure the effective risk management in a holistic approach, protecting their performance and meeting the needs of the relevant stakeholders. The main idea of the research was to create and implement a dynamic model of risk management in the environment of a production organization based on the use of available methods such as SWOT, PESTLE, brainstorming, affinity diagrams, risk matrix, SIPOC diagram, risk, and the results of questionnaire surveys. The research was conducted in two directions: (1) designing the structure of the dynamic risk management model for the strategic management process; and (2) verifying the effectiveness of the proposed model in specific cases and evaluating the technical and economic benefits. To support the dynamics of the model, three basic management tools have been proposed: process review, internal audit, and management review, enriched with features that support the concept of risk-based thinking.

A Multiscale Parallel Pedestrian Recognition Algorithm Based on YOLOv5

Mainstream pedestrian recognition algorithms have problems such as low accuracy and insufficient real-time performance. In this study, we developed an improved pedestrian recognition algorithm named YOLO-MSP (multiscale parallel) based on residual network ideas, and we improved the network architecture based on YOLOv5s. Three pooling layers were used in parallel in the MSP module to output multiscale features and improve the accuracy of the model while ensuring real-time performance. The Swin Transformer module was also introduced into the network, which improved the efficiency of the model in image processing by avoiding global calculations. The CBAM (Convolutional Block Attention Module) attention mechanism was added to the C3 module, and this new module was named the CBAMC3 module, which improved model efficiency while ensuring the model was lightweight. The WMD-IOU (weighted multidimensional IOU) loss function proposed in this study used the shape change between the recognition frame and the real frame as a parameter to calculate the loss of the recognition frame shape, which could guide the model to better learn the shape and size of the target and optimize recognition performance. Comparative experiments using the INRIA public data set showed that the proposed YOLO-MSP algorithm outperformed state-of-the-art pedestrian recognition methods in accuracy and speed.

Hydrogen bonding heterogeneity correlates with protein folding transition state passage time as revealed by data sonification

Protein-protein and protein-water hydrogen bonding interactions play essential roles in the way a protein passes through the transition state during folding or unfolding, but the large number of these interactions in molecular dynamics (MD) simulations makes them difficult to analyze. Here, we introduce a state space representation and associated "rarity" measure to identify and quantify transition state passage (transit) events. Applying this representation to a long MD simulation trajectory that captured multiple folding and unfolding events of the GTT WW domain, a small protein often used as a model for the folding process, we identified three transition categories: Highway (faster), Meander (slower), and Ambiguous (intermediate). We developed data sonification and visualization tools to analyze hydrogen bond dynamics before, during, and after these transition events. By means of these tools, we were able to identify characteristic hydrogen bonding patterns associated with "Highway" versus "Meander" versus "Ambiguous" transitions and to design algorithms that can identify these same folding pathways and critical protein-water interactions directly from the data. Highly cooperative hydrogen bonding can either slow down or speed up transit. Furthermore, an analysis of protein-water hydrogen bond dynamics at the surface of WW domain shows an increase in hydrogen bond lifetime from folded to unfolded conformations with Ambiguous transitions as an outlier. In summary, hydrogen bond dynamics provide a direct window into the heterogeneity of transits, which can vary widely in duration (by a factor of 10) due to a complex energy landscape.

Process-Driven Layout Optimization of a Portable Hybrid Manufacturing Robotic Cell Structure

Hybrid manufacturing combines manufacturing processes (typically additive manufacturing and machining) exploiting the benefits of each and enabling repair scenarios. Such an approach can be integrated with a robot, and if made portable, can form a flexible machine tool that can be easily transported anywhere to enable repairs in the field. However, the design of the load-bearing structure determines its transportability, and its stiffness plays a crucial functional role under dynamic loads and affects the product quality. Finding the right balance between weight and stiffness requires accurate boundary conditions and an effective design. In this work, a method is proposed towards process-driven optimization of a portable manufacturing cell structure. The dynamic cutting forces are determined through a machining process model and, via a simplified model of the robot arm, the forces at the base of the robot are estimated. Since the frame consists of beams, the layout optimization method is applied, using the estimated process forces as boundary conditions to optimize the arrangement of beams. The proposed method achieved 0.05 mm displacement in the load-bearing structure under milling and an acceptable accuracy of the position of a hole’s center during drilling, while the overall weight reduced by 17.6%.

Bayesian Optimization for Efficient Prediction of Gas Uptake in Nanoporous Materials.

The discovery and optimization of novel nanoporous materials (NPMs) such as Metal-Organic Frameworks (MOFs) are crucial for addressing global challenges. Traditional experimental approaches for optimizing these materials are time-consuming and resource-intensive. This research paper presents a strategy using Bayesian optimization (BO) to efficiently navigate the NPMs for gas storage. For a MOF dataset drawn from 19 different sources, we present a quantitative evaluation of BO. In our study, we employed machine learning (ML) techniques to conduct regression analysis on many models. Following this, we identified the three ML models that exhibited the highest accuracy, which were subsequently chosen as surrogates in our investigation, includingthe conventional Gaussian Process (GP) model. We found that GP with expected improvement (EI) as the acquisition function but without a gamma prior which is standard in Bayesian Optimization python library outperforms other surrogate models. Additionally, it should be noted that while the machine learning model that exhibits superior performance in predicting the target variable may be considered the best choice, it may not necessarily serve as the most suitable surrogate model for BO. This observation has significant importance and warrants further investigation. This comprehensive framework accelerates the pace of materials discovery.

Gasification process modelling and optimization using Gaussian process regression and hybrid population-based algorithms

Gasification process modelling and optimization using Gaussian process regression and hybrid population-based algorithms

Development and Validation of a 1D Dynamic Model of an Injection Moulding Process and Design of a Model-Based Nozzle Pressure Controller.

A 1D model describing the dynamics of an injection moulding machine and the injection process is presented. The model describes an injection cylinder actuated by a dual-pump electro-hydraulic speed-variable drive and the filling, holding and cooling phases of the injection moulding process utilising amorphous polymers. The model is suggested as the foundation for the design of model-based pressure controllers of, e.g., the nozzle pressure. The focus is on using material, mould and machine properties to construct the model, making it possible to analyse and design the dynamic system prior to manufacturing hardware or conducting experiments. Both the presented model and the developed controller show good agreement with experimental results. The proposed method is general in nature and enables the design, analysis and evaluation of the machine, material and mould dynamics for controller design based solely on the physical properties of the system.

Support for Family Caregivers: Implications of Work Strain and its Intersections with Formal and Informal Help.

This study seeks to assess whether and to what extent caregiver work strain is ameliorated by the presence of additional family caregivers and formal service use. Building on the stress process model and stress-appraisal moderations, we examine how formal and informal support varies in associations with caregiver distress for men and women. This study utilizes data provided by the National Study of Caregiving (NSOC), which is linked with care-recipient information from the National Health and Aging Trends Study (NHATS). Using panel methods for the pooled waves, we estimated caregiver outcomes of emotional well-being on the intersection of experiences of work strain and 1) the number of additional caregivers and 2) utilization of six different types of formal support. Additional informal caregivers for each respective care recipient are associated with lower levels of distress, while utilization of formal services (paid help and Medicaid funding) is positively associated with caregiver distress. Informal support can offset the impact of work strain, but interactions are only evident for women caregivers. The findings suggest that informal support, exemplified by the number of additional caregivers, corresponds with reduced emotional distress among employed caregivers and can mitigate the negative impacts of work strain. However, positive associations of formal support and male and female caregiver distress suggest that the context of formal services may offer limited or untimely support. This study is expected to broaden our understanding of informal caregiving in later life and provide practical implications on how to sustain informal care.