Empirical Model Research Articles

Generalized Additive Spatial Smoothing (GASS): A Multiscale Regression Framework for Modeling Neighborhood Effects Across Spatial Supports

A new technique called generalized additive spatial smoothing (GASS) is introduced for modeling neighborhood effects within a regression framework. GASS has a number of desirable features, namely that it provides a data-driven mechanism for endogenously selecting neighborhoods based on a spatial scale hyperparameter. By allowing different scale hyperparameters to be selected for different relationships in the model, the technique is inherently multiscale and allows neighborhoods to vary by relationship. In addition, GASS includes a measure of uncertainty associated with each scale hyperparameter. These characteristics make it attractive for modeling phenomena where proximity might be an important aspect of a process, especially when a clear definition of proximity is not immediately available. Through multiscale data-driven spatial smoothing, GASS conducts a form of change of support and therefore also facilitates the incorporation of data from diverse sources. Finally, the technique is flexible and can be adapted and expanded with relative ease because it builds on generalized additive modeling. After providing an overview of the methodology, including a modified backfitting algorithm for calibration, a simulation experiment is described and an empirical example modeling bike-share usage is presented. The simulation results indicate that GASS can generally produce reliable results pertaining to both the regression coefficients and scale hyperparameters, and the results from the empirical example demonstrate that the GASS approach provides a better model fit and captures relationships that might otherwise be obfuscated. Overall, these results highlight the potential of the GASS framework and the importance of measuring multiscale neighborhood effects.

Influence from highways on regional economic growth – based on the trade potential in China

The question of whether the construction of the highway network is economical and can produce positive economic benefits has been a hot topic of discussion in recent years. Previous scholars have explored the impact from multiple perspectives. Our paper draws the “trade potential” model proposed by Armstrong, based on the universal gravity model and the principle of space interaction, which is different from the traffic accessibility, market potential, and market access used in most of the literature. We argue that it is more appropriate to consider both the size impact and the time distance or trade cost impact of the two cities. The paper constructs a conceptual framework and theoretical model for the impact of highways on regional economic growth, measures the “minimum transit time” of highways between prefecture-level cities in China, and calculates the trade potential of prefecture-level cities. Through corresponding empirical model testing, we have obtained some meaningful conclusions.

Application of an Empirical Model to Improve Maximum Value Predictions in CFD-RANS: Insights from Four Scientific Domains

This study introduces an empirical model designed to predict the maximum values of time-dependent data across four turbulence-related fields: hydrogen combustion in renewable energy systems, urban microclimate effects on cultural heritage, shipping emissions, and road vehicle emissions. The model, which is based on the mean, standard deviation, and integral time scale, employs two parameters: a fixed exponent ‘ν’ (0.3) reflecting time scale sensitivity, and a variable parameter ‘b’ that accounts for application-specific uncertainties. Integrated into the Computational Fluid Dynamics (CFD) framework, specifically the Reynolds-Averaged Navier–Stokes (RANS) methodology, the model addresses the RANS approach’s limitation in predicting extreme values due to its inherent averaging process. By incorporating the empirical model, this study enhances RANS simulations’ ability to predict critical values, such as peak hydrogen concentrations and maximum urban wind speeds, which is essential for safety and reliability assessments. Validation against experimental and numerical data across the four fields demonstrates strong agreement, highlighting the model’s potential to improve CFD-RANS predictions of extreme events. This advancement offers significant implications for future CFD-RANS applications, particularly in scenarios demanding fast and reliable maximum value predictions.

An exploratory graphical analysis of the Montgomery-Åsberg Depression Rating Scale pre- and post-treatment using pooled antidepressant trial secondary data

BackgroundThe 10-item Montgomery-Åsberg Depression Rating Scale (MADRS) is a commonly used measure of depression in antidepressant clinical trials. Numerous studies have adopted classical test theory perspectives to assess the psychometric properties of this scale, finding generally positive results. However, its network configural structure and stability is unexplored across different time-points and treatment groups. AimsTo assess the network structure and stability of the MADRS in clinical settings pre- and post-treatment, and to determine a configurally invariant and stable model across time-points and treatment groups (placebo and intervention). MethodIndividual participant data for 6440 participants from 14 clinical trials of major depressive disorder was obtained from the data repository Vivli.org. Exploratory Graphical Analysis (EGA) was used to identify empirical models pre-treatment (baseline) and post-treatment (8-week outcome). Bootstrapping techniques were applied to obtain optimised configurally invariant models. ResultsEmpirical models presented with performance issues at baseline and for the placebo group at outcome. An abbreviated 8-item single-community model was found to be stable and configurally invariant across time-points and treatment groups. Symptoms such as low mood and lassitude showed most centrality across all models. LimitationsMetric invariance could not be explored due to research environment limitations. ConclusionsAn 8-item one-community variant of the MADRS may provide optimal performance when conducting network analyses of antidepressant clinical trial outcomes. Findings suggest that interventions targeting low mood and lassitude might be most efficacious in treating depression among clinical trial participants. Further considerations of the potential impact on trial design and analysis should be explored.

Integrating digitization into public administration: Impact on national security and the economy through spatial planning

The evolutionary development of the security sphere, on the one hand, and the rapid involvement of digital technologies (and, as a consequence, changes) on the other, formulate the first level of problematization of this study. The study seeks to provide evidence of how the dynamics of changes in the security sphere are formed by constructing an empirical model of changes and assessing the state security system within the framework of spatial planning under the influence of digital technologies. Particular attention in the study is paid to spatial planning and spatial development in smart cities the main threats to national security are systematized, located in closely intersecting planes of digital technologies, social processes, and economic interests. It is shown that areas covered by spatial planning, and particularly smart cities, pose a serious local threat to national and international security policy at the levels of political, social, technical, and economic governance.

Density of the Subcritical Adsorbate on Gold Surfaces: A Generic Empirical Model

Density of the Subcritical Adsorbate on Gold Surfaces: A Generic Empirical Model

Impact of Toolpath Pitch Distance on Cutting Tool Nose Radius Deviation and Surface Quality of AISI D3 Steel Using Precision Measurement Techniques.

The selection of the right tool path trajectory and the corresponding machining parameters for end milling is a challenge in mold and die industries. Subsequently, the selection of appropriate tool path parameters can reduce overall machining time, improve the surface finish of the workpiece, extend tool life, reduce overall cost, and improve productivity. This work aims to establish the performance of end milling process parameters and the impact of trochoidal toolpath parameters on the surface finish of AISI D3 steel. It especially focuses on the effect of the tool tip nose radius deviation on the surface quality using precision measurement techniques. The experimental design was carried out in a systematic manner using a face-centered central composite design (FCCD) within the framework of response surface methodology (RSM). Twenty different experiment trials were conducted by changing the independent variables, such as cutting speed, feed rate, and trochoidal pitch distance. The main effects and the interactions of these parameters were determined using analysis of variance (ANOVA). The optimal conditions were identified using a multiple objective optimization method based on desirability function analysis (DFA). The developed empirical models showed statistical significance with the best process parameters, which include a feed rate of 0.05 m/tooth, a trochoidal pitch distance of 1.8 mm, and a cutting speed of 78 m/min. Further, as the trochoidal pitch distance increased, variations in the tool tip cutting edge were observed on the machined surface due to peeling off of the coating layer. The flaws on the tool tip, which alter the edge micro-geometry after machining, resulted in up to 33.83% variation in the initial nose radius. Deviations of 4.25% and 5.31% were noted between actual and predicted values of surface roughness and the nose radius, respectively.

An empirical model for predicting saturation pressure of pure hydrocarbons in nanopores

Due to the confinement and strong adsorption to the pore wall in meso‑ and nano pores, fluid phase behavior in the confined media, such as the tight and shale reservoirs, can be significantly different from that in the bulk phase. A large amount of work has been done on the theoretical modeling of the phase behavior of hydrocarbons in the confined media. However, there are still inconsistencies in the theoretical models developed and validations of those models against experimental data are inadequate.In this study, we conducted a comprehensive review of experimental work on the phase behavior of hydrocarbons under confinement and analyzed various theoretical phase-behavior models. Emphasis was given to the modifications to the Peng-Robinson equation of state (PR EoS). Through the comparative analysis, we developed a modified alpha-function in PR EoS for accurate prediction of the saturation pressures of hydrocarbons in porous media. This modified alpha-function accounts for the pore size and was derived based on the regression results through minimizing the deviation between the experimentally measured and numerically calculated saturation pressure data. Meanwhile, the thermodynamic properties of propane were calculated in the bulk phase and in the nanopores. Finally, we validated the newly developed model using the experimental data in synthesized mesoporous materials and real reservoir rocks.By applying the modified PR EoS, a more accurate representation of the experimentally measured saturation pressure data in confined nanopores was achieved. This newly developed model not only enhanced the accuracy of the predictions but also provided valuable insights into the confinement effects on the phase behavior of hydrocarbons in nanopores. Notably, we observed significant changes in the properties of propane within confined nanopores, including suppressed saturation pressure and fugacity, indicating a greater tendency for the gas to remain in the liquid phase. Enthalpy of vaporization was found to increase highlighting increased difficulty in transitioning from liquid to gas phase under confinement. Additionally, the new model predicts an increased gas compressibility factor in the nanopores suggesting a close resemblance of ideal gas due to the counterbalance between the attractive and repulsive forces. To validate the model, new datasets containing saturation pressures of propane and ethane under a wide range of temperatures and pore sizes were employed. The newly developed model was further applied to the experimental data obtained in real rock samples (sandstones, limestones, and shales). Interestingly, it was observed that the phase change in these samples predominantly occurred in the smallest pores. This finding highlights the importance of considering the pore size distribution when studying the phase behavior of hydrocarbons in a capillary medium even if the rock has high permeability.This study provided a simple and easy-to-implement modification to the PR EoS for accurate prediction of the phase behavior of petroleum fluids under confinement. The newly developed model for calculating saturation pressures of hydrocarbons demonstrates improved accuracy in representing experimental data compared to the previous models, while offering a more straightforward and simplified approach.

PSVIII-21 Meta-analysis of nutritive factors influencing mean retention time of digesta in the gastrointestinal tract of broiler chickens

Abstract Broiler chicken meat is an important source of food protein for human consumption globally. Over the last several decades, dramatic changes have been made to broiler genetics, management, and nutrition to increase production efficiency. Different feeds with varying nutrient composition have been researched to target goals such as improving growth and feed conversion ratio. Part of understanding and further improving these feeding programs involves understanding the effects of the feed on nutrient digestibility and animal performance. As such, models are a key tool in the wholistic evaluation of diet, genetics, and management interactions. Towards developing a mechanistic digestion model for broilers, knowledge of mean retention time (MRT, min) of digesta in the gastrointestinal tract (GIT) across a variety of diets is required. To understand the relationship between MRT in broilers and their diet, a systematic literature search and meta-analysis was conducted where 34 articles were found to meet the inclusion criteria. MRT in these studies was measured in various parts of the GIT where 7 studies reported MRT in just one segment (e.g. jejunum), 12 studies reported MRT in multiple combined segments (e.g. jejunum + ileum) and 21 studies reported MRT of the entire GIT. Birds used in these studies included those in the starter, grower, and finisher phases as well as breeders. Nutritional composition of the diets was represented by data on metabolizable energy (ME, kcal/kg), crude fiber (% DM), crude fat (% DM), crude protein (% DM) and calcium (% DM) that was consistently reported across studies. A mixed model (PROC MIXED, in SAS), treating study as a random effect, and using a subset of the data collected where MRT was reported for the entire GIT, and limited to grower and finisher phases, was used to examine the relationship between the diet composition and MRT. Only ME (F1,28.1 = 4.25; P = 0.0486) was found to be significantly related to MRT, where an increase in one kcal/kg of ME resulted in a decrease of MRT by 0.091 minutes (within the range of 2,749 to 3,507 kcal/kg ME). Other nutritional components of the diet were found to have no significant relation to MRT (all P > 0.05), though next steps will examine how the source of ME (fat vs starch vs fiber) impacts this relationship. Although other factors (such as age and feed intake) are still to be considered, the initial results from this study provide valuable insights into how nutritional composition of poultry feeds relates to MRT. Further investigation will also quantify this relationship within segment of the GIT. This work not only provides valuable information to nutritionists, but also lays the groundwork for empirical model development to predict MRT of poultry feed, essential to poultry nutritional models.

Corporate Governance Attributes and Sustainability of Selected Commercial State Corporations in Kenya

The sustainability of an organization means a lot, for it guarantees social responsibility, financial savings, preservation of resources, reduction of waste, and environmental protection. Kenya's commercial State Corporations face instances of mismanagement, financial improprieties, and inadequate disclosure and have expressed concerns regarding the sustainability and efficacy of these institutions. Specifically, this investigation sought to address these issues by examining the link between corporate governance characteristics and the viability of commercial state corporations in Kenya. The objectives were to evaluate the effect of board members' occupational expertise, board committee meetings, board tenure and board size on the sustainability of commercial state corporations in Kenya. The research can be pegged on 5 major theories, which encompass the Triple Bottom Line Model, Agency Theory, Stakeholders Theory, and Resource Dependence Theory. The Explanatory research design and research design were used in the research. Panel regression analysis was the chosen empirical model, considering the panel data nature of the research. The audited financial reports of the five firms served as the source of secondary data using a data collection schedule guide. Census was utilized because the target population of five commercial state corporations was below the central limit theorem threshold of the normal population. Diagnostic tests for the study were multicollinearity, normality, stationarity, heteroscedasticity, and the Hausman specification test, which were conducted to ensure the sturdiness of the results. Data was analyzed using descriptive and inferential statistics. Descriptive analysis involves means and standard deviations, while inferential analysis utilizes panel regression analysis. Finally, the data analysis output was presented using tables. The study further concluded that the coefficient for board committee meetings was not significant, suggesting that meeting frequency does not significantly affect sustainability. Board tenure demonstrated a significant positive impact on sustainability, while board size also showed a significant positive effect. In an increasingly dynamic business environment, commercial state corporations should embrace innovation and adaptation to remain resilient and sustainable. This may involve leveraging emerging technologies, exploring new business models, and anticipating and responding to evolving market trends and regulatory requirements. Boards should encourage a culture of innovation and agility to drive long-term value creation and competitiveness.

Contact Hole Shrinkage: Simulation Study of Resist Flow Process and Its Application to Block Copolymers.

For vertical interconnect access (VIA) in three-dimensional (3D) structure chips, including those with high bandwidth memory (HBM), shrinking contact holes (C/Hs) using the resist flow process (RFP) represents the most promising technology for low-k1 (where CD=k1λ/NA,CD is the critical dimension, λ is wavelength, and NA is the numerical aperture). This method offers a way to reduce dimensions without additional complex process steps and is independent of optical technologies. However, most empirical models are heuristic methods and use linear regression to predict the critical dimension of the reflowed structure but do not account for intermediate shapes. In this research, the resist flow process (RFP) was modeled using the evolution method, the finite-element method, machine learning, and deep learning under various reflow conditions to imitate experimental results. Deep learning and machine learning have proven to be useful for physical optimization problems without analytical solutions, particularly for regression and classification tasks. In this application, the self-assembly of cylinder-forming block copolymers (BCPs), confined in prepatterns of the resist reflow process (RFP) to produce small contact hole (C/H) dimensions, was described using the self-consistent field theory (SCFT). This research paves the way for the shrink modeling of the enhanced resist reflow process (RFP) for random contact holes (C/Hs) and the production of smaller contact holes.

Sample size effects on landslide susceptibility models: A comparative study of heuristic, statistical, machine learning, deep learning and ensemble learning models with SHAP analysis

In landslide susceptibility assessment (LSA), inventory incompleteness impacts the accuracy of different models to varying degrees. However, this area remains under-researched. This study investigated six LSA models from heuristic, statistical, machine learning and ensemble learning models (analytical hierarchy process (AHP), frequency ratio (FR), logistic regression (LR), Keras based deep learning (KBDL), XGBoost, and LightGBM) across six different sample sizes (100%, 90%, 75%, 50%, 25%, and 10%). Results revealed that XGBoost and LightGBM consistently outperformed other models across all sample sizes. The LR and KBDL models followed, while FR model was the most affected by sample size variations. AHP, an empirical model, remained unaffected by sample size. Through SHapley Additive exPlanations (SHAP) analysis, elevation, NDVI, slope, land use, and distance to roads and rivers emerged as pivotal indicators for landslide occurrences in the study area, suggesting that human activities significantly influence these events. Five time-varying indicators regarding human activity and climate validated this inference, which provides a new method to identify landslide triggering factors, especially in areas of intense human activity. Based on the findings, a comprehensive framework for LSA is proposed to assist landslide managers in making informed decisions. Future research should focus on expanding model diversity to address the effects of sample size, enhancing the adaptability of the LSA framework, deepening the analysis of human activity impacts on landslides using explainable machine learning techniques, addressing temporal inventory incompleteness in LSA, and critically evaluating model sensitivity to sample size variations across multiple disciplines.

179 Charting the evolution of rumen microbial models from past to present

Abstract This review delves into the intricate realm of ruminal microbes, shedding light on their complexity beyond previously recognized dimensions. Existing publications have probed the essential facets of these microorganisms on a finer scale than previously acknowledged. Mathematical models have emerged to simulate the behavior of approximately 200 distinct bacterial species, 25 genera of ciliate protozoa, and five genera of anaerobic fungi, constituting about 10% of the total viable bacteria in the rumen. However, the expansive diversity and density of rumen microbes challenge the complete characterization of species or genera in relation to substrates or end products. Consequently, continuous revision and re-engineering of mathematical models become imperative. In the context of ruminants, prevalent models often categorize bacteria into fiber-fermenting and nonfiber-fermenting groups. An additional subgroup featuring hyper-ammonia-producing bacteria has been proposed, but its explicit integration into nutrition models remains unexplored. Furthermore, incorporating a more mechanistic evaluation of protozoa and fungal growth may be warranted to improve nutrition models. The time horizon and intervals at which models are constructed present another critical consideration, with feed efficiency variations among ruminants believed to be closely tied to differences in the ruminal microbiota. Addressing immediate concerns, this review underscores the need for a comprehensive exploration of factors influencing microbial growth in the rumen. Several indices have been suggested and adopted to predict microbial efficiencies, but they may provide simplification for practical application. Thus, a mechanistic approach should be considered to remove dependencies between estimates and improve predictive accuracy and precision. While artificial intelligence holds promise in elucidating gaps within empirical and mechanistic models, the generation of accurate, vetted datasets is paramount. Furthermore, this review posits that factors such as recycled nitrogen, endotoxins, and microbial population dynamics (i.e., cross-feeding and competition) may be limiting our understanding of microbial growth, suggesting the necessity of modeling these intricacies concurrently to achieve a more nuanced comprehension. This multifaceted approach aims to propel the understanding of ruminal microbial dynamics into new frontiers, paving the way for more informed and effective modeling in the future.

Hybrid XGB model for predicting unconfined compressive strength of solid waste-cement-stabilized cohesive soil

The utilization of cement has been found to have negative environmental impacts. In order to reduce the quantity of cement used and improve the mechanical properties of solid waste-cement-stabilized cohesive soil, the incorporation of solid waste as additives has been investigated. Unconfined compressive strength is a crucial parameter in geotechnical engineering. However, existing empirical formulas have limited accuracy and applicability when it comes to the unconfined compressive strength of solid waste-cement-stabilized cohesive soil. The machine learning model can be used to provide accurate and comprehensive predictions by considering the nonlinear relationships between independent and dependent variables. This study aims to propose a machine learning model tuned by optimization algorithms with high generalization performance in accurately predicting the unconfined compressive strength. Firstly, a database containing 474 specimens was developed. Secondly, eight machine learning models were established, composed five single models and three hybrid models, to train and test the database. Six performance indicators were employed to evaluate the generalization ability of these models. Finally, the optimal model was selected for analysis of the importance of the feature variables using shapley additive explanations, which were compared with those of the existing empirical model. The research findings indicated that, the extreme gradient boosting model tuned with tree-structured parzen estimators exhibited the highest predictive accuracy and generalization ability. The curing age, cement content, plastic limit, and water content were identified as the most critical factors influencing the unconfined compressive strength. Among the chemical components in solid waste, the aluminum oxide content and silicon dioxide content were found to significantly influence the unconfined compressive strength, while the impact of calcium oxide content was relatively minor. Furthermore, the optimal solid waste content was found to be around 10 %. This study made a significant contribution to the effective utilization of waste resources in the context of sustainable construction practices.

3 Hands -on 1: Applying system dynamics to develop “Flight Simulators” for sustainable animal production

Abstract Solving complex livestock production problems is a pressing issue for achieving long-term sustainability and profitability and often requires modeling techniques. The use of mathematical models is undoubtedly a daily practice in the livestock industry, especially nutrition, and has improved animal performance, productivity, and environmental sustainability while maintaining or reducing costs. Although many students and professionals use spreadsheets and existing empirical models for nutrition and management [NASEM (2016)], there is still a need to understand the complexity of livestock systems and the utility of flight simulator models. At the same time, more complex models (although robust) may fail to provide new insights for experienced nutritionists due to poor user-friendliness. A systems understanding goes beyond simply obtaining a desired output, such as optimizing a total mixed ration, but instead leads to identifying high-leverage solutions and gaining insight. Further, parameterizing and calibrating variables and equations and testing management scenarios is straightforward. However, developing causal feedback linkages (A to B and B to A) and identifying time delays is less intuitive and more challenging for novices. Model flight simulators grounded in fully documented, calibrated models provide a means to introduce practitioners to a methodology of insight generation because the user designs and runs the model scenarios for themselves, challenging their mental models. Such approaches are generally more impactful (compared with someone telling them) because they have gained insight into the system themselves, and, in the case of open source (white box models), they can explore equations and parameters. Therefore, understanding how to utilize dynamic models in scenario-based simulations is critical in training current and future modelers in animal science. This hands-on model training will cover the basics of System Dynamics modeling and allow participants to run real-time animal production simulations. Finally, participants will be “debriefed” to unpack “ah ha” moments that were unexpected. The debrief will include using models to develop accurate guidelines and recommendations for those who cannot use computer models and developing experimental designs to test hypotheses and “validate” model recommendations (proof in the pudding). Participants will gain knowledge of System Dynamics applications for animal production systems, experience using flight simulators, and their utility in teaching, informing, and guiding their livestock production or that of a client. The “flight simulator” will focus on production and nutrition with species-agnostic principles. Participants will also have a new tool to identify areas for improvement in model development (i.e., what is missing?), research questions, or industry needs. The need for modelers who can turn big data into insight, knowledge, and wisdom using a systems approach is becoming even more critical due to the increasing use of precision livestock farming. Thus, providing the livestock industry with trained systems modelers will help achieve current and future sustainability challenges.

Optimizing high energy density sulfur cathodes: A multivariate approach to electrode formulation and processing

Lithium-sulfur (Li-S) batteries involve complex solid-liquid-solid phase transformations during both discharging and charging processes, where cathode materials, formulation, and structure play a crucial role. Here, a design of experiments (DoE) methodology and an empirical model are developed to systematically explore the interactions and trade-offs among cathode factors and process variables, and to obtain generalizable effects estimates for the multivariate system. Compared to the conventional one-factor-at-a-time (OFAT) approach, this work demonstrates advantages in both efficiency and accuracy by allowing the data to guide future research and decisions. An optimized cathode formulation and processing parameters are predicted and validated experimentally, achieving over 1000 mAh g−1 in discharge capacity and improved cycling under practical lean electrolyte (4 µL mg−1 S) and high S-loading cathodes (>4 mg cm−2) conditions. The optimized cathode was scaled up and assembled into Li-S pouch cells, achieving 316 Wh kg−1 in cell-level energy, proving that the comprehensive and rigorous framework for optimizing complex systems with DoE leads to improved performance in a practical pouch cell system.

Simultaneous identification of propeller fluctuating forces and unknown parameters based on a Bayesian inversion approach

Accurate quantification of propeller excitation is essential for effective vibro-acoustic analysis and mitigation in marine vessels. This study introduces a Bayesian statistical framework that indirectly reconstruct uncertain forces and system parameters by fusing available prior information, forward modeling and measured vibration responses through Bayesian theorem. The Markov Chain Monte Carlo (MCMC) algorithm, enhanced with Polynomial Chaos Kriging (PC-Kriging) surrogates, is employed to facilitate efficient estimation of likelihood functions and robust uncertainty propagation, providing posterior probabilistic estimates and credible intervals for the identified forces. Additionally, the framework incorporates model class selection based on model evidence to determine the most plausible empirical spectrum model for the propeller broadband thrust spectrum. Specifically, this study proposes and evaluates two candidate empirical spectrum models, the Ornstein–Uhlenbeck (OU)-Gaussian and OU-Weibull models, which effectively capture the frequency–amplitude characteristics of unsteady thrust. Numerical results demonstrate substantial parameter uncertainty reduction, accurate thrust spectrum reconstruction, and effective quantification of statistical properties. Moreover, the OU-Gaussian model is revealed to be more plausible for characterizing the propeller thrust spectrum and response prediction. This framework may offer a practical solution for propeller excitation identification, enabling rapid vibro-acoustic performance assessments of marine vessels.

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.

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.

Has R&D contributed to productivity growth in China? The role of basic, applied and experimental R&D

Since the early 2000s, R&D expenditure in China has increased rapidly, with the country having the third highest R&D expenditure, next only to the OECD and USA, and having surpassed Japan since 2009. Furthermore, policies in China in recent years have emphasized the need to conduct more basic and applied R&D in order to overcome technological bottlenecks and risks in access to advanced technologies when faced with international geopolitical tension. However, in the meantime, the trend in measured total factor productivity (TFP) in China has been downward since 2010. A lack of TFP growth despite the significant investment in R&D raises the question of the impact of R&D expenditure on productivity growth in China. Therefore, this study aims to investigate this issue and estimate the effects of three types of R&D stocks (basic, applied and experimental R&D stock) on TFP in China, using newly constructed provincial panel data in China from 1998 to 2018. Various empirical models and control variables are adopted to take into account non-stationarity and spatial spill-over of the provincial R&D stock values over time. The analysis results are robust to various specifications and reveal a significant positive effect of overall R&D and experimental R&D on TFP in China, but basic R&D exerts no significant results, and the effects of applied R&D are mixed across specifications. Further analyses using the 1991–2018 national data demonstrated largely consistent results. These results suggest that experimental R&D has been crucial for enhancing TFP growth in China during the decades investigated, but evidence of basic and applied R&D driving TFP growth in China is lacking.