Empirical Model Research Articles

TaskFinder: A Semantics-Based Methodology for Visualization Task Recommendation

Data visualization has entered the mainstream, and numerous visualization recommender systems have been proposed to assist visualization novices, as well as busy professionals, in selecting the most appropriate type of chart for their data. Given a dataset and a set of user-defined analytical tasks, these systems can make recommendations based on expert coded visualization design principles or empirical models. However, the need to identify the pertinent analytical tasks beforehand still exists and often requires domain expertise. In this work, we aim to automate this step with TaskFinder, a prototype system that leverages the information available in textual documents to understand domain-specific relations between attributes and tasks. TaskFinder employs word vectors as well as a custom dependency parser along with an expert-defined list of task keywords to extract and rank associations between tasks and attributes. It pairs these associations with a statistical analysis of the dataset to filter out tasks irrelevant given the data. TaskFinder ultimately produces a ranked list of attribute–task pairs. We show that the number of domain articles needed to converge to a recommendation consensus is bounded for our approach. We demonstrate our TaskFinder over multiple domains with varying article types and quantities.

TaskFinder: A Semantics-Based Methodology for Visualization Task Recommendation

Data visualization has entered the mainstream, and numerous visualization recommender systems have been proposed to assist visualization novices, as well as busy professionals, in selecting the most appropriate type of chart for their data. Given a dataset and a set of user-defined analytical tasks, these systems can make recommendations based on expert coded visualization design principles or empirical models. However, the need to identify the pertinent analytical tasks beforehand still exists and often requires domain expertise. In this work, we aim to automate this step with TaskFinder, a prototype system that leverages the information available in textual documents to understand domain-specific relations between attributes and tasks. TaskFinder employs word vectors as well as a custom dependency parser along with an expert-defined list of task keywords to extract and rank associations between tasks and attributes. It pairs these associations with a statistical analysis of the dataset to filter out tasks irrelevant given the data. TaskFinder ultimately produces a ranked list of attribute–task pairs. We show that the number of domain articles needed to converge to a recommendation consensus is bounded for our approach. We demonstrate our TaskFinder over multiple domains with varying article types and quantities.

Empirical Modelling of Power Requirements in Olive Pruning Residue Shredding: Effects of Varying Moisture Content and Rotary Speeds

Empirical Modelling of Power Requirements in Olive Pruning Residue Shredding: Effects of Varying Moisture Content and Rotary Speeds

Fabrication of tips for scanning probe magnetometry by diamond growth

The use of quantum sensors is promising detailed insights into physical phenomena such as magnetism or superconductivity. One example of such quantum sensors is a microscopic diamond tip containing nitrogen vacancy (NV) centers, which is capable of producing correlated measurements of vectorial magnetic fields and the sample topography on the nanoscale. In this study, we present a chemical vapor deposition (CVD) process to produce diamond tips with NV centers by overgrowing microstructured diamond substrates. The resulting diamond tips exhibit a radius of curvature of approximately 10 nm, suitable for use as a probe in an atomic force microscope. The magnetic sensitivity of the CVD-grown diamond tips is characterized with pulsed measurements of the optically detected magnetic resonance, which yield a minimum magnetic sensitivity of 60 µT Hz−1 . The growth of the diamond microstructures is observed to differ from the commonly used geometric model predicting CVD growth of bulk diamond crystals. We identify an empirical model for the growth behavior of the microstructures by taking into account processes described in the step flow growth model for crystals. Additionally, we demonstrate the applicability of the developed CVD growth process to membrane substrates required for the preparation of magnetometry-capable diamond tips.

Taming Two-Dimensional Polymerization by a Machine-Learning Discovered Crystallization Model.

Rapidly synthesizing high-quality two-dimensional covalent organic frameworks (2D COFs) is crucial to their practical applications. Here, we use a machine-learning approach that overcomes the challenges associated with bottom-up model derivation for the non-classical 2D COF crystallization processes. The resulting model, referred to as NEgen1, establishes correlations among the induction time, nucleation rate, growth rate, bond-forming rate constants, and common solution synthesis conditions for 2D COFs that grow by a nucleation-elongation mechanism. The results elucidate the detailed competition between the nucleation and growth dynamics in solution, which has been inappropriately described previously by classical, empirical models with assumptions invalid for 2D COF polymerization. By understanding the dynamic processes at play, the NEgen1 model reveals a simple strategy of gradually increasing monomer addition speed for growing large 2D COF crystals. This insight enables us to rapidly synthesize large COF-5 colloids, which could only be achieved previously by prolonged reaction times or by introducing chemical modulators. These results highlight the potential for systematically improving the crystal quality of 2D COFs, which has wide-reaching relevance for many of the applications for which 2D COFs are speculated to be valuable.

Taming Two‐Dimensional Polymerization by a Machine‐Learning Discovered Crystallization Model

Rapidly synthesizing high‐quality two‐dimensional covalent organic frameworks (2D COFs) is crucial to their practical applications. Here, we use a machine‐learning approach that overcomes the challenges associated with bottom‐up model derivation for the non‐classical 2D COF crystallization processes. The resulting model, referred to as NEgen1, establishes correlations among the induction time, nucleation rate, growth rate, bond‐forming rate constants, and common solution synthesis conditions for 2D COFs that grow by a nucleation‐elongation mechanism. The results elucidate the detailed competition between the nucleation and growth dynamics in solution, which has been inappropriately described previously by classical, empirical models with assumptions invalid for 2D COF polymerization. By understanding the dynamic processes at play, the NEgen1 model reveals a simple strategy of gradually increasing monomer addition speed for growing large 2D COF crystals. This insight enables us to rapidly synthesize large COF‐5 colloids, which could only be achieved previously by prolonged reaction times or by introducing chemical modulators. These results highlight the potential for systematically improving the crystal quality of 2D COFs, which has wide‐reaching relevance for many of the applications for which 2D COFs are speculated to be valuable.

Análisis de mecanismos bioquímico-fisiológicos involucrados en las respuestas a sequía en soja

Water deficit limits soybean crop productivity and reduces yield stability. Many parameters have been associated with tolerance to water deficit, where the control of leaf water loss is by far the most important. Since most of the water loss occurs through the stomata, the objective of this thesis was to deepen the understanding of the response to water deficit in soybeans through the control of stomatal closure and to determine how this can affect the water consumption of soybeans and its tolerance to water deficit. To achieve this, we delved into the study of stomatal closure control. We focused on the role of nitric oxide (•NO) as a key molecule in the control of stomatal opening/closing. •NO kinetic accumulation was analyzed from the establishment of the hydric deficit, and its association with stomatal closure was assessed. Our work showed an endogenous •NO accumulation at the guard cell level before stomatal closure, which allowed us to postulate that in the early perception of stress there is an accumulation of •NO that inhibits stomatal closure, potentially through blocking responses to ABA. This initial fine control of the stomatal closure by •NO would be decisive to define the final responses of water loss/consumption during the establishment of the water deficit. Furthermore, we developed an empirical mathematical model in soybean that describes the kinetics of water consumption under water deficit conditions of a controlled plant-pot-substrate growth system. The model defined two parameters that determine the water consumption curve, t0.5 (time at which the system lost half of the maximum amount of potentially evapotranspirable water) and Gw(t0.5) (stomatal conductance at t0.5). A correspondence analysis between t0.5 and Gw(t0.5), and the genetic structure of two soybean populations, indicated the existence of genetic associations with consumption phenotypes based on these parameters. On the other hand, using data from the drought susceptibility index (DSI) in the field, generated by previous work on one of the populations of this thesis, and through a correspondence analysis, a grouping between the model parameter t0.5 and DSI is observed. This would indicate that the developed phenotyping strategy could also be useful in the characterization of genotypes at the field level.

Evaluation of the Condition of the Bottom of the Tanks for Petroleum Products-Forecast of the Remaining Operating Life

Corrosion rate measurements in the tank enable us to develop a new model for predicting the remaining operating period of the tank. The empirical model for the study of bottom plate thicknesses and corrosion pits is more conservative than the standard linear model, as it considers the autocatalytic nature of the corrosion process. We used the double exponential distribution of maximum values (Gumbel's) to evaluate the maximum depth of pits, and the double exponential distribution of minimum values to evaluate the minimum values of the plate thickness. A comparison of the values of the parameters obtained using linear extrapolation and exponential models indicates the unreliability of linear extrapolation, since disregarding dynamic processes underestimates the actual rate of corrosion.

Efficient high‐fidelity deep convolutional generative adversarial network model for received signal strength reconstruction in indoor environments

AbstractWith the rapid development of wireless communication systems, particularly in the era of 5G and the Internet of Things, deploying wireless communication networks in indoor environments has become crucial. Indoor infrastructure deployment necessitates innovative approaches for efficiently and accurately obtaining received signal strength (RSS) maps. However, traditional methods for acquiring RSS maps, such as empirical and deterministic models, are limited by significant inaccuracies and high computational demands. Empirical models often fail to capture the complex dynamics of indoor environments, resulting in deviations from actual signal behaviours. On the other hand, deterministic models, while more accurate, are computationally intensive due to their reliance on detailed physical modelling of wave propagation. This study introduces a machine learning approach based on deep convolutional generative adversarial networks (DCGAN) aimed at reconstructing indoor RSS maps with minimal RSS measurements. By leveraging DCGAN's generative and adversarial training capabilities, the method not only surpasses traditional interpolation methods in efficiency and precision but also offers new possibilities for the rapid deployment and optimization of wireless communication systems in indoor environments.

Reliability analysis of normal strength CFST rectangular columns through multiple approaches

Concrete-filled steel tube (CFST) rectangular columns have several benefits for structural engineering and buildings. The advantages of both steel and concrete are combined in these columns, creating a composite structural element with improved strength, ductility, aesthetics, seismic performance, construction efficiency, and cost efficiency. The current study aims to evaluate the mechanical performance of CFST rectangular specimens using an analytical approach that combines 3D-finite element analysis (3D-FEA) and artificial neural network (ANN) modeling. This methodology relies on a comprehensive database comprising 1045 sampling points from experimental studies. To simulate the response of CFST rectangular columns, an improved plasticity model for confined concrete was utilized in the 3D-FEA. This modeling considered various geometrical parameters and material features to accurately simulate the behavior of confined concrete and steel tubes. Comparison statistics were used to validate the 3D-FEA models against the experimental dataset of CFST specimens, confirming their accuracy. Furthermore, a parametric analysis was conducted using the 3D-FEA model to observe the potential impact of varying geometric and material-related variables on CFST specimen behavior. In addition to the 3D-FEA, a novel ANN model was developed to predict the axial strength of CFST specimens based on the experimental database. The comparison of R2 values among the empirical model, ANN model, and 3D-FEA model indicated that the ANN estimates were 18% more accurate than the empirical model and 9% more accurate than the 3D-FEA model. When considering the sampling points from the established database, both the empirical technique and the 3D-FEA model showed a strong association, with an R2 value of 0.842. However, the empirical and ANN-derived estimates exhibited an even higher correlation, with an R2 value of 0.916, while the predictions of the 3D-FEA and ANN models showed a slightly lower but still significant correlation, with an R2 value of 0.868.

Interpretable machine learning models for predicting the bond strength between UHPC and normal-strength concrete

Ultra-high-performance concrete (UHPC) boasts superior mechanical properties and durability, rendering it a viable material to repair and reinforce pre-existing concrete structures. The complexity of the bond behavior between UHPC and normal-strength concrete (NC) presents a challenge to make precise predictions of the interfacial bond strength. This study employed four machine learning algorithms, namely artificial neural network, random forest, adaptive boosting, and categorical gradient boosting, to predict the interfacial bond strength of UHPC-NC. A database of 95 samples of slant shear tests was collected from existing literature. The input variables for modeling include joint angle, compressive strength of NC and UHPC, surface treatment, interfacial moisture, curing age, and curing method. The impact of each feature on the slant shear bond strength was assessed using SHapley Additive exPlanations and Partial Dependence Plots. Results show that the categorical gradient boosting model performed the best on the testing set, achieving an R2 of 0.948, RMSE of 1.408, and MAE of 1.028. The accuracy of all four machine learning models surpassed that of two empirical models (AASHTO LRFD 2014 and Eurocode 2). The top three features influencing the interfacial bond strength were identified as the surface treatment, joint angle, and compressive strength of NC, while the impact of the curing method and the interfacial moisture were comparatively limited. The proposed machine learning model exhibits potential as a valuable tool for assisting structural retrofitting and rehabilitation with UHPC.

Analysis of Ionospheric Delay Correction Model Performance During Geomagnetic Storms

AbstractIonospheric delay, as one of the largest error sources in radio propagation, can only be corrected for this error using the ionospheric delay correction model for Global Navigation Satellite System (GNSS) single‐frequency users. In this paper, the 2021 geomagnetic storm event is selected, and based on the measured ionospheric data from the GNSS observatory, the perturbation of the ionosphere by the geomagnetic storm event is analyzed, and it is found that the response of the ionosphere to the geomagnetic storm has obvious differences in the response characteristics and response time in different latitude regions. The performance of the global ionospheric map (GIM), the empirical model, and the broadcast ionospheric model during the geomagnetic storm‐induced ionospheric perturbation is analyzed and the change in the accuracy of each ionospheric model during the geomagnetic storm‐induced ionospheric perturbation is investigated, using the measured electron content of the GNSS as a benchmark. The results show that there is good agreement between the GIM products and the measured electron content during the period of ionospheric calm and the period of ionospheric perturbation. It is worth noting that geomagnetic storms do not necessarily lead to a decrease in the accuracy of ionospheric delay‐correction models, and in some cases, the models that were originally under‐accurate show a tendency to improve their accuracy during the period of perturbation instead. Neither the broadcast ionospheric model nor the electron content of the empirical model output responds to geomagnetic storm‐induced ionospheric perturbations.

Empirical Model Variability: Developing a new global optimisation approach to populate compression and compaction mixture rules

This study systematically evaluated the predictive accuracy of common empirical models for pharmaceutical powder compaction. A dataset of nine placebo and twelve active pharmaceutical ingredient (API) loaded blend formulations (four APIs at three drug loadings) was fitted to the widely used empirical tablet compression (Gurnham, Heckel, and Kawakita) and compaction (Ryshkewitch-Duckworth and Leuenberger) models. At low API loadings (<20w/w%), all models achieved R2 above 90 % and RRMSE (relative root mean squared error) below 0.1. However, as API loads increased, overall model performance decreased, notably in the Heckel model. A parameter variability analysis identified multiple parameter pairs achieving acceptable fits. Consequently, a novel global optimization approach was developed populating arithmetic, geometric, and harmonic mixture rules for empirical tuning parameters. This method outperformed the traditional line of best fit approach. A cross validation study revealed that this method is capable of predicting tuning parameters which achieve an acceptable Goodness of Fit for new blends. Finally, with the restriction of maintaining consistent parameters for the placebo blend, the proposed method could substantially reduce the experimental requirements and API consumption for the exploration of new blends.

Investigation of the bubble deflection using graphics processing unit accelerated multiple relaxation time lattice Boltzmann method: The effect of bubble size and liquid viscosity

Bubble deflection is important for bubble motion and bubble mass transfer in multiphase reactors. However, the mechanism triggering the instability is controversial. In this study, we first perform a graphics processing unit-accelerated multiple relaxation time lattice Boltzmann method (MRT-LBM) to simulate the motion process of single bubbles (1.8–8.0 mm) freely rising in stationary liquids with different viscosities. Then, we further analyze the influence of the bubble size and liquid viscosity on the characteristics of the time-dependent bubble motion, including the bubble shape, velocity, and deflection, based on the high-precision numerical simulation results. The results show that the formation and shedding of bubble wake vortices are responsible for the oscillation of the bubble shape. The interaction of bubble wake vortices and bubble shape is the primary cause of bubble deflection. Finally, an empirical model, taking into account the bubble acceleration and deflection angle, is developed to characterize the effect of bubble size and liquid viscosity on the drag coefficient of a rising bubble. This study provides insight into the bubble deflection and the bubble dynamics.

Numerical study on nanofluidic transport mechanism in Ellis flow within curved channel comprising compliant walls subjected to peristaltic activity

Peristaltic movement of fluid flows has significant applications in biomedical engineering, medicine, human physiology, etc. Specifically, it is very useful to understand and cure the very common intestinal diseases in human beings. A number of theoretical and empirical models are used to analyze peristaltic movement. In this work, the peristaltic movement of nanofluid is modeled with a non-Newtonian Ellis fluid model in a curved channel with compliant wall properties. The effects of Brownian motion, thermophoresis, and nonlinear radiations are considered in the heat transfer for better thermal analysis. The mathematical modeling of the physical problem yields the nonlinear partial differential equations with boundary conditions. First, the governing partial differential equations are non-dimensionalized, and then the resultant system is simplified by using the assumptions of a small Reynolds number and long wavelength. Then the obtained boundary value problem of differential equations is solved with the built-in Mathematica command NDSolve. The accuracy and reliability of the adopted procedure are verified by comparing the computed results with the reported literature. The impacts of the pertinent parameters (Brownian motion, thermal radiation, mixed convection, and thermophoresis phenomenon) on thermal energy, velocity, concentration, heat transfer rate, and stress at the lower wall are analyzed both in qualitative and quantitative manners. This study revealed some interesting facts, such as the peristaltic-driven motion of nanoliquid is strongly influenced by wall properties (i.e., wall elasticity, mass density, and wall damping). In addition, the flow experienced more resistance in the case of larger wall damping, but larger wall elasticity and mass density provide favorable movement for fluid motion. In addition, mixed convection plays a vital role in heat transfer and nanoparticle concentration in the curved domain. In addition, the curved channel walls have a higher stress factor than straight-plane channels. The results of the current study are very useful to understand many biological phenomena, such as the peristaltic movement of liquid during dialysis, food movement through the intestine, etc.

Numerical investigation on sample population requirement for mean particle diameter analysis

The determination of the minimum particle number needed to meet error specifications and confidence levels poses a fundamental challenge in particle size analysis. Conventional models, primarily designed for log-normal distributions, may yield inaccuracies when applied to other distribution functions. This study introduces a numerical approach to explore the necessary sample size for measuring different types of mean diameters. The methodology involves distribution conversion, sample generation, repeated sampling, and error estimation. Specifically, Gates–Gaudin–Schuhmann (GGS) and Rosin–Rammler (RR) distributions serve as representative models in this investigation. The impact of sample size, span ratio, and boundary sizes on relative errors is examined. Additionally, an empirical model is formulated, simplifying the calculation of the requisite sample size when the relative error and mass-based span ratio are provided. The proposed method is also verified using experimental particle size data that follow the RR distribution.

Effect of complex void structures on flow channeling and heterogeneous heat transfer in fractured rocks

Natural or hydraulic fractures in enhanced geothermal systems (EGS) typically have complex void structures due to the presence of surface contact. In this study, fluid flow and heat transfer processes in three-dimensional rough fractures with different contact ratios are investigated. Stronger flow channeling and heterogeneous heat transfer are observed in fracture voids with increasing surface contacts, and the fluid flow exhibits nonlinear characteristics due to the complex fracture void structures. Parts of fracture voids with large apertures and void areas contribute negligibly to the overall flow due to their weak connectivity to the main flow channels. Removing the low-flow regions does not significantly impact the flow channeling and heterogeneous heat transfer in the fracture voids. However the heat exchange in the low-flow regions results in differences in the overall heat transfer coefficient and heat extraction rate between the reconstructed and original fractures. An empirical model is proposed to describe the overall heat transfer coefficient in fractures with complex void structures. Furthermore, the flow and heat transfer processes in the highly contacted fractures are not significantly affected by the change in flow direction, indicating that the anisotropic degree of the fracture void structures decreases as the contact areas increases. This study may further deepen the understanding of fluid flow and heat transfer in realistic fractured rocks at high temperatures, which is critical for geothermal energy extraction.

Failure analysis of a separator under various thermal loading: A numerical study

In the present study, Computational Fluid Dynamics (CFD) simulations were carried out on a separator subjected to three different fire scenarios: pool fire, jet fire and fireballs. The empirical models applicable to such plants are not developed and conventional radiation models leads to an overestimation of the heat flux and safety distances. Therefore an alternative tool like CFD is used to model the potential equally probable fire scenarios mentioned above in a model oil and gas separator plant. Validations are performed by determining the flame length,maximum flame temperature and its location above pan surface in case of pool fire and comparing them with literature data. It is found that in case of flame length the deviation from literature data is about 10% while this deviation is less than 5% for the maximum flame temperature. For jet fire the average surface temperature is 561 K while this value is 423 K in case of pool fire and 317 K for fireballs. When it comes to the radiative flux, an average value of 29 kW/m2 is found for jet fires while this value is only 14 kW/m2 for pool fire and 23 kW/m2 for fireballs. The thermal loads on the separator vessel due to above fire scenarios are also calculated from which it can be concluded that from safety point of view jet fires are the most critical one. The results obtained form simulations are also compared with that of data from empirical models.

Empirical Model of Equatorial ElectroJet (EEJ) Using Long‐Term Observations From the Indian Sector

AbstractThe Equatorial Electrojet (EEJ) is one of the important near‐earth space weather phenomena which exhibits significant diurnal, seasonal and solar activity variations. This paper investigates the EEJ variations at diurnal, seasonal and solar cycle time scales from the Indian sector and portrays a new empirical EEJ field model developed using the observations spanning over nearly two solar cycles. The Method of Naturally Orthogonal Components (MNOC), also known as Principal Component Analysis (PCA), was employed to extract the dominant patterns of principal diurnal, semi‐diurnal, and ter‐diurnal components contributing to the EEJ variation. The amplitudes of these diurnal, semi‐diurnal, and ter‐diurnal components in EEJ are found to vary significantly with the season and solar activity. The seasonal and solar activity dependencies of these principal components are modeled using suitable bimodal distribution functions. Finally, the empirical model for EEJ field was built by combining the principal components with their corresponding modeled amplitudes. This model accurately reproduces the diurnal, seasonal and solar activity variations of EEJ. The modeled monthly mean variations of EEJ field at ground exhibit excellent correlation of 0.96 with the observations with the root mean square error &lt;5 nT. It also successfully captures the seasonal and solar activity variations of Counter Electrojet (CEJ). Finally, this model named “Indian Equatorial Electrojet (IEEJ) Model” is made publicly available for interested scientific users (https://iigm.res.in/system/files/IEEJ_model.html).

Synergistic impact of corrosion pitting on the rotating bending fatigue of additively manufactured 316L stainless steel: Integrated experimental and modeling analyses

In this study, the ex-situ rotating bending corrosion fatigue behavior of laser powder bed fused stainless steel (SS) 316L has been studied incorporating experimental analyses and modeling. The findings revealed significant variations (almost 20 %) in fatigue lifetime in pre-corroded specimens relative to non-corroded (virgin) specimens because of corrosion-derived pitting phenomena on the surface. To this end, the fatigue strength of virgin (non-corroded) and pre-corroded SS 316L were recorded at 242 MPa and 203 MPa, respectively. Upon conducting the fatigue tests and extracting stress-life trends, advanced microstructural characterization and postmortem analyses were used to quantify fatigue crack behavior and fracture surface. An empirical model is also developed employing depth-sensing indentation testing, linear elastic fracture mechanics, and computer tomography scans to propose an optimum predictive trend. The analysis comparing existing models with our proposed model demonstrates reasonable agreement between experimental findings and the predicted life for the studied laser powder bed fused SS 316L material exposed to corrosion rotating bending fatigue test. The findings of this research carry important contributions to the structural integrity and longevity of marine engineering, ship construction, naval operations, and naval aviation where materials and components are exposed to cyclic loading and corrosion.