Model Coefficients Research Articles

Numerical model of asphaltene deposition in vertical wellbores: Considerations of particle shape and drag force

Asphaltene deposition and plugging are common phenomena during crude oil production, significantly reducing production efficiency. Understanding the deposition behavior of asphaltene particles in vertical wellbores, particularly in relation to drag force interactions and settlement velocity, is imperative to mitigate these issues. Existing empirical models often fail due to their inherent inaccuracies and neglect of asphaltene particles' irregular shapes. This work established a quantitative method to describe irregular geometries of asphaltene particles. Shape coefficient, ∅, and shape coefficient influence factor, θ, are used to assess the impact of particle shape on the drag force coefficient and settling velocity. Experimental results from sedimentation experiments reveal particles with smaller shape coefficients encounter greater flow resistance and higher drag force coefficients. Moreover, the impact of shape coefficient on the drag coefficient gradually decreases as the Reynolds number increases. Based on these experimental findings, distinct models for the drag force coefficient and settling velocity of asphaltene particles are established, achieving a reduction in average error ranging between 2 % ∼ 25 % compared to existing models.

Gender differences in the effects of nostalgia on conspicuous consumption and impulsive buying

PurposeThe current study investigated the relationship between nostalgia, conspicuous consumption, and impulse buying and compared gender differences in the effects of nostalgia on conspicuous consumption and impulse buying.Design/methodology/approachA total of 307 college students in western Singapore were recruited. Confirmatory factor analysis and structural equation modeling were conducted to assess the psychometric properties of the scales and estimate model path coefficients. A multi-group analysis was conducted to test if the path coefficients in the research model varied across genders.FindingsThis study found that nostalgia had a positive influence on consumers’ conspicuous consumption, which in turn had a positive impact on their impulse buying of sport products. The results of the multi-group analysis also revealed significant differences across gender groups in that the impacts of nostalgia on conspicuous consumption and impulse buying were stronger for male consumers.Originality/valueThis study forges new ground by investigating the intricate dynamics among nostalgia, conspicuous consumption, and impulse buying in the context of sport. By delving into the relationships between them and exploring gender disparities in their effects, this study enhances our understanding of the pivotal role nostalgia plays in shaping consumer behavior in sport. In addition, the identification of gender-specific patterns underscores the significance of tailored marketing approaches for effectively engaging both male and female consumers. Overall, this study presents fresh perspectives that can inform the development of targeted marketing strategies for sport marketers and retailers aiming to optimize their offerings and promotional endeavors.

A simplified analytical formula for the coefficient of 3rd-order Hermite moment model and its application

Based on the 3rd-order softened Hermite nonlinear equations, a new simplified formula of the Hermite moment model is proposed. According to the wind tunnel pressure test data of a flat roof, the new and existing simplified formulas are used to calculate the Hermite parameters and peak factors respectively. The calculation accuracies of different simplified formulas are compared and analyzed to verify the applicability of the new simplified formula. The results show that the new simplified formula has higher accuracy for the peak factor calculation of high-order softened time histories, and the relative error is kept within 15%. And the applicability for mild non-Gaussian time histories is relatively better. For low-order softened time histories, the calculation errors of the new simplified formula are relatively large for the Hermite parameters, but the calculation accuracy of the peak factor is kept within the applicable range. Specifically, the calculation errors of the negative peak factors distributed only in the 1st-order softened applicable range are less than 20%, and the calculation errors of the negative peak factors distributed only in the 2nd-order softened applicable range are less than 15%. Finally, the new simplified formula shows good applicability to the extreme value analysis of the high-order and low-order softened wind pressure time histories.

Laboratory measurements of water saturation effects on the acoustic velocity and attenuation of sand packs in the 1–20 kHz frequency range

AbstractWe present novel experimental measurements of acoustic velocity and attenuation in unconsolidated sand with water saturation within the sonic (well‐log analogue) frequency range of 1–20 kHz. The measurements were conducted on jacketed sand packs with 0.5‐m length and 0.069‐m diameter using a bespoke acoustic pulse tube (a water‐filled, stainless steel, thick‐walled tube) under 10 MPa of hydrostatic confining pressure and 0.1 MPa of atmospheric pore pressure. We assess the fluid distribution effect on our measurements through an effective medium rock physics model, using uniform and patchy saturation approaches. Our velocity and attenuation (Q−1) are accurate to ±2.4% and ±5.8%, respectively, based on comparisons with a theoretical transmission coefficient model. Velocity decreases with increasing water saturation up to ∼75% and then increases up to the maximum saturation. The velocity profiles across all four samples show similar values with small differences observed around 70%–90% water saturation, then converging again at maximum saturation. In contrast, the attenuation increases at low saturation, followed by a slight decrease towards maximum saturation. Velocity increases with frequency across all samples, which contrasts with the complex frequency‐dependent pattern of attenuation. These results provide valuable insights into understanding elastic wave measurements over a broad frequency spectrum, particularly in the sonic range.

Addressing the implementation challenge of risk prediction model due to missing risk factors: The submodel approximation approach.

Clinical prediction models have been widely acknowledged as informative tools providing evidence-based support for clinical decision making. However, prediction models are often underused in clinical practice due to many reasons including missing information upon real-time risk calculation in electronic health records (EHR) system. Existing literature to address this challenge focuses on statistical comparison of various approaches while overlooking the feasibility of their implementation in EHR. In this article, we propose a novel and feasible submodel approach to address this challenge for prediction models developed using the model approximation (also termed "preconditioning") method. The proposed submodel coefficients are equivalent to the corresponding original prediction model coefficients plus a correction factor. Comprehensive simulations were conducted to assess the performance of the proposed method and compared with the existing "one-step-sweep" approach as well as the imputation approach. In general, the simulation results show the preconditioning-based submodel approach is robust to various heterogeneity scenarios and is comparable to the imputation-based approach, while the "one-step-sweep" approach is less robust under certain heterogeneity scenarios. The proposed method was applied to facilitate real-time implementation of a prediction model to identify emergency department patients with acute heart failure who can be safely discharged home.

Reliability of a Photo-Based Modified Foot Posture Index (MFPI) in Quantifying Severity of Foot Deformity in Children With Cerebral Palsy.

Children with cerebral palsy (CP) have high rates of foot deformity. Accurate assessment of foot morphology is crucial for therapeutic planning and outcome evaluation. This study aims to evaluate the reliability of a novel photo-based Modified Foot Posture Index (MFPI) in the evaluation of foot deformity in children with CP. Thirteen orthopaedic surgeons with neuromuscular clinical focus from 12 institutions evaluated standardized standing foot photographs of 20 children with CP, scoring foot morphology using the MFPI. Raters scored the standardized photographs based on five standard parameters. Two parameters assessed the hindfoot: curvature above and below the malleoli and calcaneal inversion/eversion. Three parameters assessed the midfoot and forefoot: talonavicular congruence, medial arch height, and forefoot abduction/adduction. Summary MFPI scores range from -10 to +10, where positive numbers connote planovalgus, whereas negative numbers connote a tendency toward cavovarus. Intra- and interrater reliability were calculated using a 2-way mixed model of the intraclass correlation coefficient (ICC) set to absolute agreement. Feet spanned the spectrum of potential pathology assessable by the MFPI, including no deformity, mild, moderate, and severe planovalgus or cavovarus deformities. All scored variables showed high intrarater reliability with ICCs from 0.891 to 1. ICCs for interrater reliability ranged from 0.965 to 0.984. Hindfoot total score had an ICC of 0.979, with a 95% CI, 0.968-0.988 (P<0.001). The forefoot total score had an ICC of 0.984 (95% CI, 0.976-0.991, P<0.001). Mean total score by the MFPI was 3.67 with an ICC of 0.982 (95% CI, 0.972-0.990, P<0.001). The photo-based MFPI demonstrates high intra- and interrater reliability in assessing foot deformities in children with CP. Its noninvasive nature and ease of use make it a promising tool for both clinical and research settings. MFPI should be considered as part of standard outcomes scores in studies regarding the treatment of CP-associated foot deformities. Level V.

Research on power operation typical characteristic state of proton exchange membrane fuel cell based on principal component analysis

The stability, reliability, and lifespan of proton exchange membrane fuel cells (PEMFCs) are closely related to power dynamic response. Therefore, it is necessary to study in depth the relationship between the power operation state and typical characteristics. In this paper, the fuel cell system (FCS) power dynamic response based on constant power change amplitude and various power change amplitude is analyzed. Then, three typical power characteristics of the FCS, namely, low single-cell voltage, drastic voltage uniformity change, voltage undershoot and voltage overshoot, are investigated. Furthermore, to solve the high-dimensional datasets problem, a quick evaluation method based on data dimensionality reduction (DDR) is proposed. The innovation of this method is based on principal component analysis to extract typical characteristic relationships of power operation levels. The results demonstrate that the extracted new one-dimensional (1-D) eigenvectors index exhibits a positive correlation with power levels. Meanwhile, the power operation state empirical model is established through data regression. The Pearson coefficients of the empirical model are all greater than 0.9981. Thus, the new index and proposed method offer novel insights for improving the power adaptive control of fuel cells and quickly assessing their health state.

Design and research of face gear central limited slip differential and analysis of its influence on vehicle performance

In this paper, a new design scheme of face gear central limited-slip differential (F-LSD) is proposed, which aims to unveil the technical veil of central limited-slip differential and fill the research blank of face gear application in the field of central limited-slip differential. Through the analysis of structural principle and motion characteristics, the dynamic equilibrium equation and locking coefficient model of F-LSD are established. The Simulink simulation model based on F-LSD dynamics and the torque distribution model of the whole transmission system are established. A 7-DOF vehicle dynamics model was established, and the performance of F-LSD vehicle in four conditions of acceleration, climbing, chassis twist sine wave and double line change was studied by using simulation software. At the same time, four groups of four-wheel drive vehicles with different central differential speeds were set as the control test group. The simulation results show that F-LSD system can effectively improve the vehicle's dynamic performance and extrication performance, and will not reduce the vehicle's handling stability, which provides a reference for the practical application of the face gear central limited slip differential.

Advanced regression model fitting to experimental results in case of the effect of grinding conditions on a cutting force with the planer technical knives

The use of well-fit models enables users to make better decisions and draw accurate insights, so in this article a multi-step procedure for establishing regression functions for experimental data is presented. This was inspired by the need to determine well-fitted models for the results of the study of the relationship between the input factors of the grinding process of planer knives and the measured grinding power, as well as in relation to the knife cutting force. The sharpening process was carried out under various conditions, making it possible to assess their influence on the results and select the most favorable technological parameters to prepare the knives for operation. The equations adjusted by Excel or other basic methods require additional verification if the mathematical formula and values of the model coefficients correspond to the data and the physical meaning. For this purpose, one-, two- and three-factor analyses were performed to eliminate redundant factors and introduce significant interactions between input parameters. Additional analyses of variance and Tukey, Levene and Brown-Forsythe tests were used to determine the general form of the model. Detailed form of nonlinear models was worked out by comparing the distribution of residuals, looking for outliers and evidence of the presence or absence of collinearity, as well as by determining the values of additional model quality indicators, such as Akaike Information Criterion, Cook’s distance and Variance Inflation Factor. The models presented include a categorization variable (grinding type) and are presented graphically in the form of surface plots. With reference to the technological processes studied, a discussion of the physical significance of the developed models and the influence of the input factors on the model output value was conducted. The analyzes additionally include an evaluation of the model factors using prediction slice plots.

Subgrain Size Modeling and Substructure Evolution in an AA1050 Aluminum Alloy during High-Temperature Compression.

For materials with high stacking fault energy (SFE), such as aluminum alloys, dynamic recovery (DRV) and dynamic recrystallization (DRX) are essential softening mechanisms during plastic deformation, which lead to the continuous generation and refinement of newborn subgrains (2° ˂ misorientation angle ˂ 15°). The present work investigates the influence of compression parameters on the evolution of the substructures for a 1050 aluminum alloy at elevated temperatures. The alloy microstructure was investigated under deformation temperatures ranging from 300 °C to 500 °C and strain rates from 0.01 to 0.1 s-1, respectively. A well-defined substructure and subsequent subgrain refinement provided indication of the evolution laws of the substructure under high-temperature compression. Corresponding experimental data on the average subgrain size under various compression conditions were obtained. Two different independent average subgrain size evolution models (empirical and substructure-based) were used and applied with several internal state variables. The substructure model employed physical variables to simulate subgrain refinement and thermal coarsening during deformation, incorporating a corresponding dislocation density evolution model. The correlation coefficient (R) and root mean square error (RMSE) of the substructure-based model were calculated to be 0.98 and 5.7%, respectively. These models can provide good estimates of the average subgrain size, with both predictions and experiments reproducing the expected subgrain size evolution using physically meaningful variables during continuous deformation.

Enhancing water security: Statistical measurement and spatiotemporal analysis

Water security is essential for ensuring energy security, sustainable development, and human survival. However, due to a series of challenges, including rising water demand, environmental pollution, and water resource shortages, the global water security situation remains concerning and poses a threat to global sustainable development. To assess water security in China, this study uses data from 30 provinces in China from 2012 to 2021. A comprehensive evaluation method was applied to determine the level of water resource security in China. The Dagum Gini coefficient, Moran index, and spatial model were used to clarify regional differentiation characteristics and the driving factors. The results indicate that while China's water resource security is relatively low, it has shown steady improvement in recent years. Significant regional disparities exist in water resource security across China, with notable spatial characteristics, and socio-economic factors are the primary causes of these differences. Based on the above research, we put forward policy recommendations from the aspects of water resources management, public participation and inter-regional water resources cooperation, to provide reference for water resources security in developing countries.

Numerical and sensitivity analysis of hydraulic characteristics of triangular labyrinth side weir

Triangular labyrinth side weirs have significantly more discharge capacity than traditional nonlinear weirs, and the complex hydraulic parameter interaction mechanism has been the research focus. This study used Computational Fluid Dynamics (CFD) to analyze the side weir's flow characteristics. Then, the Bayesian optimization algorithm and Extreme Learning Machine (BELM) developed a prediction model for the side weir's discharge coefficient. Finally, Sobol's method performed a sensitivity analysis for hydraulic parameters. The results show that the main channel's streamline is evenly distributed and begins to shift when it is close to the side weir. The overflow front is increasing and secondary flow also increases. BELM's Mean Absolute Percentage Error and Root Mean Square Error are 8.793 % and 0.455 in the testing stage, respectively, declined by about 56.24 % and 32.29 % compared with ELM; Froude number Fr, weir crest angle θ and the ratio of overflow front length to weir head l/h1 are the most critical hydraulic parameters affecting the discharge coefficient, the global sensitivity coefficients are 0.4393, 0.4218 and 0.4152, respectively.

Enhanced Tailings Dam Beach Line Indicator Observation and Stability Numerical Analysis: An Approach Integrating UAV Photogrammetry and CNNs

Tailings ponds are recognized as significant sources of potential man-made debris flow and major environmental disasters. Recent frequent tailings dam failures and growing trends in fine tailings outputs underscore the critical need for innovative monitoring and safety management techniques. Here, we propose an approach that integrates UAV photogrammetry with convolutional neural networks (CNNs) to extract beach line indicators (BLIs) and conduct enhanced dam safety evaluations. The significance of real 3D geometry construction in numerical analysis is investigated. The results demonstrate that the optimized You Only Look At CoefficienTs (YOLACT) model outperforms in recognizing the beach boundary line, achieving a mean Intersection over Union (mIoU) of 72.63% and a mean Pixel Accuracy (mPA) of 76.2%. This approach shows promise for future integration with autonomously charging UAVs, enabling comprehensive coverage and automated monitoring of BLIs. Additionally, the anti-slide and seepage stability evaluations are impacted by the geometry shape and water condition configuration. The proposed approach provides more conservative seepage calculations, suggesting that simplified 2D modeling may underestimate tailings dam stability, potentially affecting dam designs and regulatory decisions. Multiple numerical methods are suggested for cross-validation. This approach is crucial for balancing safety regulations with economic feasibility, helping to prevent excessive and unsustainable burdens on enterprises and advancing towards the goal of zero harm to people and the environment in tailings management.

Exploring the Online Leadership Effect on Blended Learning in Educational Institutions: Post-COVID-19 Learning Context

Background: The COVID-19 pandemic has necessitated the transition of administrative and instructional work in schools to an online format. This shift has prompted school administrators to embrace online leadership as a suitable approach during and after the pandemic. Blended learning has been recommended as a sustainable and well-suited solution for educational institutions in the post-pandemic period. Aims: to investigate the impact between online leadership and the adoption of blended learning in educational settings. Sample(s): A total of 385 instructors participated in this study. The participants were surveyed using Google Forms to gather their perspectives. Methods: Structural equation modeling (SEM) and path coefficients analysis were employed to analyze the survey data and examine the attitudes of instructors toward online leadership and blended learning. Results: The findings indicated that online leadership (OL) exhibited a higher likelihood of being adopted for blended learning (BL) implementation, as revealed by the study results (59%). Instructors demonstrated increased flexibility and readiness to utilize online leadership methods for career advancement in the post-pandemic era. However, it should be noted that the adoption of online leadership measures was primarily driven by the necessity (41%) to maintain continuity in educational practices during the pandemic. Conclusions: These findings shed light on the significance of online leadership in facilitating the integration of blended learning in schools. The study highlights the adaptability of instructors and their recognition of the potential benefits associated with online leadership practices. The results emphasize the importance of online leadership as a strategic approach for educational institutions in navigating the challenges brought about by the COVID-19 pandemic and beyond.

A novel framework for predicting the burst pressure of energy pipelines with clustered corrosion defects

The accurate prediction of failure pressures in pipelines containing multiple defects is important for assessing the integrity and reliability of corroded pipelines. First, the effect of the remaining defects on the failure pressure of the pipeline containing triple defects was investigated by developing the finite element (FE) model of pipelines containing triple defects. Then, to integrate machine learning method and finite element models to establish a prediction model for the interaction coefficients corresponding to combined corrosion defects. Subsequently, a framework for predicting the burst pressure of pipelines appliable to the scenario of group corrosion defects was proposed by integrating the interacting coefficient and the failure pressure of single defect. Finally, the accuracy of the model was demonstrated by the evaluation indicators and bursting experimental data of corroded pipeline containing cluster defects. The framework is expected to provide a maintenance foundation for the integrity assessment of pipelines with clustered corrosion defects.

PM2.5 estimation and its relationship with NO2 and SO2 in China from 2016 to 2020

ABSTRACT The distribution of surface particulate matter (PM2.5) exhibits significant spatiotemporal patterns, especially with a spatial clustering effect. Therefore, resolving spatial characteristics is essential in the modeling process. This study proposes a space–time varying coefficients (STVC-LG) model by considering spatial effects from the perspective of eigenvector spatial filtering and incorporating spatial effects and temporal features into the LightGBM model to estimate ground PM2.5. A site-based cross-validation result of the estimated daily 1 km PM2.5 concentration in China from 2016 to 2020 shows that the proposed model offers high accuracy, an R2 of 0.88, and an RMSE of 12.21 μg/m3 (17% and 32% enhanced compared to the original LightGBM model). It uses a series of spatial eigenvectors to describe different spatial patterns. It then constructs spatial interaction terms by combining the eigenvectors and influencing variables, allowing the values of the variable to vary spatially with the eigenvectors. The obtained PM2.5, with a decreasing trend, had a clear distributional consistency with NO2 and SO2 according to the co-occurrence distribution maps, particularly in winter. In areas where high concentrations co-occur, both NO2 and SO2 concentrations are Granger-causing PM2.5 concentrations from a statistical perspective.

Characteristics of hollow micron zero valent iron and its transport properties in groundwater: Effect of key engineering parameters and retention mechanism

In this study, a hollow micron zero-valent iron (H-mZVI) was synthesized, and its transport and retention property in saturated porous media was determined via a series of column experiments. Furthermore, the maximum migration distance (Lmax) and sedimentation rate coefficient (Kdep) models of H-mZVI in saturated porous media were established using statistical methods. The results revealed a distinct hollow structure in H-mZVI, with a density of 1.03±0.03 g/cm3, significantly lower than solid micron zero-valent iron (4.57±0.15 g/cm3). FTIR and XRD analyses indicated no formation of new functional groups on H-mZVI's surface, with iron being the main component. The column experiment demonstrated that the Lmax of H-mZVI in saturated porous media was 4.15 times that of solid micron zero-valent iron (mZVI) under the same conditions. The prediction model of Lmax aligned with the linear model, where Lmax correlated positively with particle size, injection velocity, and H-mZVI concentration, but inversely with ionic strength. Medium particle size and injection velocity were the main engineering parameters to control H-mZVI. The prediction model of Kdep accorded with the quadratic model, and an interaction was observed between medium particle size and injection velocity, which jointly affected the deposition rate of H-mZVI. Moreover, the single particle capture coefficient (η0) was hereby calculated and analyzed using the T-E theory. Interception primarily governed the precipitation of H-mZVI in saturated porous media, with gravity sedimentation contributing minimally to η0.

Migration characteristics of bubbles moving in gas–liquid countercurrent two-phase flow in an inclined pipe

While exploring and developing oil and gas, well kick and overflow accidents are inevitable. In such an accident, if the drilling bit is not at the bottom of the well, the drilling tool is blocked, or the reservoir contains toxic gases such as hydrogen sulfide, the traditional technique for well control is no longer suitable, and the bullheading method must be adopted to kill the well. However, during bullheading, the flow patterns of gas and liquid are intricate, making gas velocity hard to predict. To solve these problems, a set of visual simulation experimental device for directional well bullheading was built to find out the effects of wellbore inclination, liquid viscosity, gas and liquid velocities, and bubble size on bubble migration characteristics in gas–liquid countercurrent (gas flowing in opposite direction to the liquid). Prediction models of bubble distribution coefficient and rising velocity considering liquid viscosity, bubble size, and wellbore inclination angle were worked out. The experimental results show that small bubbles are mainly located in the center of the wellbore and large bubbles tend to approach the wellbore wall in gas–liquid countercurrent. Increasing wellbore inclination, viscosity, and the velocity of the liquid flowing against the bubbles results in a gradual decrease in Taylor bubble migration speed, which promotes the bubbles' pressing-back, as inhibiting the upward movement of large bubbles is essential for effective bullheading operations. The prediction models of distribution coefficient and bubble migration velocity have an error of less than 10% and 9. 78%, respectively.

Experimental and theoretical study on the ceasing motion of a droplet manipulated by air-blowing nozzle

In this study, the dynamic process of a droplet moving with a substrate until blocked by air flow is investigated experimentally and theoretically. A sequence of experiments has been conducted to investigate the impacts of wetting properties, droplet volumes, air flow velocities, and droplet velocities. The substrate is driven by a linear motion motor to ensure the droplet moves at a certain velocity alongside the substrate. The air flow that is vertically injected from the nozzles toward the substrate is known as an impinging jet. After the air flow impacts the substrate, it will blow horizontally. When the direction of air flow is opposite to that of the droplet movement, a force will be exerted on the surface of the droplet. This action incurs the deformation of the droplet and the cessation of its movement, eventually resulting in an equilibrium state. The droplet shape and motion processes are recorded by a high-speed camera. A mathematical model considering the effect of droplet contact angle, droplet size, droplet moving velocity, and air flow velocity is established in the state of equilibrium. Correlation factors are used in the model for the drag coefficient and air average velocity acting on the droplet. It is found that the air flow rate required to stop the motion of the droplet increases with the droplet moving velocity and the droplet size but reduces with the increase in the static contact angle. The mathematical model, when equipped with suitable correlation factors, exhibits good agreement with experimental data and could potentially be utilized as a predictor of critical velocity for the cessation of the droplet motion.

Group Inverse-Gamma Gamma Shrinkage for Sparse Linear Models with Block-Correlated Regressors

Heavy-tailed continuous shrinkage priors, such as the horseshoe prior, are widely used for sparse estimation problems. However, there is limited work extending these priors to predictors with grouping structures. Of particular interest in this article, is regression coefficient estimation where pockets of high collinearity in the covariate space are contained within known covariate groupings. To assuage variance inflation due to multicollinearity we propose the group inverse-gamma gamma (GIGG) prior, a heavy-tailed prior that can trade-off between local and group shrinkage in a data adaptive fashion. A special case of the GIGG prior is the group horseshoe prior, whose shrinkage profile is correlated within-group such that the regression coefficients marginally have exact horseshoe regularization. We show posterior consistency for regression coefficients in linear regression models and posterior concentration results for mean parameters in sparse normal means models. The full conditional distributions corresponding to GIGG regression can be derived in closed form, leading to straightforward posterior computation. We show that GIGG regression results in low mean-squared error across a wide range of correlation structures and within-group signal densities via simulation. We apply GIGG regression to data from the National Health and Nutrition Examination Survey for associating environmental exposures with liver functionality.