Conventional Empirical Methods Research Articles

Differentiating Everyday Map Tasks: Unique Attention-Related Eye Movements and Electrophysiological Signatures of Map Use

Cartographic maps are ubiquitous in spatial activities such as localization, navigation, and travel exploration. Understanding how the map users interact with maps remains a challenge, however. Research to date has focused on the overt aspects of map users’ cognitive processes, using conventional empirical methods and eye tracking, but the covert aspects (e.g., brain activity) have been largely neglected. In this study, participants used Google Maps for four everyday tasks: global search, distance comparison, route following, and route planning. We recorded and analyzed users’ attention-related visual (eye movement measures by eye tracking) and electrophysiological (fixation-evoked P3-ERP and task-induced alpha/theta ERD/ERS by scalp electroencephalograms) responses. Results demonstrated efficient visual processing and expanded spatial exploration in global search and distance comparison tasks. Interestingly, more attentional resources were devoted to recognizing whether information matches or not in global search tasks, as shown by the larger P3 component for the map target. In contrast, route following and route planning tasks required intensified attention for information decoding and complicated cognitive processes, focusing predominantly on pertinent narrow map regions. Our findings highlight that the visual and electrophysiological signatures effectively capture the heterogeneity of user attention during different map tasks.

Multi-feature parallel topology optimization of fiber-reinforced coated structures based on a dual variable scale filtering method

Due to their outstanding mechanical properties, fiber-reinforced polymer composite (FRPC) structures have become a prominent research topic. Yet, to surpass existing performance limits, their design potential should be expanded beyond conventional empirical methods. Moreover, because of the high manufacturing costs, the protection of FRPC structures should be considered in the design phase to extend their service life. In this paper, we introduce an innovative configuration wherein metal coatings are strategically applied to the surfaces of FRPC structures. This technique aims to protect the internal matrix and fiber materials, and to inhibit the interlaminar delamination of composite laminates. Then, a corresponding full-scale topology optimization framework is built based on a density-based method. The coating, matrix, and fiber materials are considered collectively, avoiding issues such as excessive computation, non-convex optimization, and fiber discontinuity. Furthermore, a dual variable scale filtering (DVSF) method and its simplified scheme are presented to optimize coating thickness based on the physical information obtained from finite element analysis. Meanwhile, the co-evolution of solid topology and fiber morphology, driven by algorithms, facilitates the parallel optimization of multiple geometric features. Several typical examples, including wing ribs, are provided to illustrate the effectiveness and superiority of our approach, highlighting its engineering relevance.

Adaptive neural network controller for the rotating SMA actuator

In this research, we present a new data-driven Adaptive Neural Network (ANN) controller designed for a unique arc-shaped Shape Memory Alloy (SMA) actuator. The actuator generates rotational motion through two-dimensional shape restoration, which is significantly different from traditional linear SMA actuators. The SMA in an arc-shaped pattern tries to recover its original shape when heated, ultimately leading to rotational motion. Due to complexity in SMA motion, it is difficult to apply conventional empirical modeling methods in this research. Given this modelling difficulty, a model-based control approach may not be promising. Thus, a data-driven ANN is adopted here to control the SMA actuator without relying on precise modelling. This controller adeptly learns the actuator's dynamic behavior in real-time, fine-tuning its neural network weights to ensure optimal control. To validate its efficacy, we compare performance of the ANN controller with that of the traditional Proportional-Integral-Derivative (PID) and sliding mode controllers across two reference inputs (sinusoidal and square) under four different disturbance scenarios (input-output, output-only, input-only, and no disturbance). Our experimental results show the ANN controller can provide similar or slightly better performance in terms of tracking accuracy and disturbance rejection without rigorous parameter or gain tuning compared to traditional controllers. Our results verify advanced learning capabilities of the ANN controller and its potential in control of the SMA actuator.

Enhancing airflow dynamics in airjet spinning: A machine learning approach to optimize nozzle design

This research delves into using machine learning techniques to enhance the airflow dynamics in Airjet spinning. It focuses on understanding the factors that affect airflow by examining components of a spinning nozzle including the fiber inlet element, injector nozzle and spinning spindle. A prototype Airjet spinning nozzle was developed to evaluate the Intake Airflow and Airflow Rate, which serve as the basis for a simulation model. A total of 501 data points were empirically gathered, and machine learning methods were applied to uncover patterns and make predictions. The study combines conventional measurement techniques with data analysis tools, like linear regression, decision trees, random forest and support vector regression. After developing a applicable machine learning tool, the hyperparameters are optimized with the goal to improve the model’s reliability. Following this optimization process it was found that the CatBoost model outperformed ML models in terms of all performance metrics. Furthermore insights, into how nozzle features impact airflow dynamics were obtained through sHapley Additive exPlanations (SHAP) analysis. The results indicate that specific nozzle design parameters play a significant role in improving airflow dynamics. Integrating machine learning techniques into the design process marks a departure from conventional empirical methods. This scientific method allows for a more rapid and precise Airjet nozzle design process, fostering innovation in textile manufacturing technology.

Oil Formation Volume Factor Prediction Using Artificial Neural Network: A Case Study of Niger Delta Crudes

Artificial intelligence techniques provide an alternative to conventional empirical correlation methods when experimentally determined oil formation volume factors (OFVF) are lacking. A new mathematical model is proposed using an artificial neural network (ANN) for estimating the OFVF for the Niger Delta crude oils. The method consists of two stages: data decorrelation through principal component analysis (PCA) and OFVF estimation through ANN. Data decorrelation was used to reduce redundancy in the data which decreased the number of neurons in the hidden layer needed for an ANN to achieve high accuracy. In the development of the model, 316 data points were obtained from the Niger Delta region of Nigeria. Application of data cleaning, outliers’ elimination and PCA analysis reduced the data to 243 points. 213 data points were used to develop the model of which 75% was used for training, 15% for validation and 10% for testing. The remaining 30 data points were used to test the predictive capability of the proposed model. The results obtained were compared with widely accepted empirical correlations of Standing, Glaso, Vazquez, Ikiensikimama & Ajienka, and Al-Marhoun. The proposed new model performed better than all of them in terms of coefficient of correlation, AAPE and RMSE. Hence the ANN model will reduce cost, save time, and also predict the OFVF of Niger Delta crudes with higher precision.

Calculation Method for Traffic Load-Induced Permanent Deformation in Soils under Flexible Pavements

Rutting is one of the most common types of distress in flexible pavement structures. There are two fundamental methods of designing pavement structures: conventional empirical methods and analytical approaches. Many analytical and empirical design procedures assume that rutting is mostly of asphalt origin and can be reduced by limiting the vertical deformation or stress at the top of the subgrade, but they do not quantify the rutting depth itself. Mechanistic–empirical models to predict the permanent deformations of unbound pavement layers have been well investigated and are rather common in North America; however, they are not widely utilized in the rest of the world. To date, there is no generally accepted, widely recognized, and documented procedure for calculating permanent deformations and thus for determining the rutting depth in flexible pavement courses originating from the unbound granular layers. This paper presents a layered calculation method with which the deformation of soil layers (base, subbase, and subgrade courses) under flexible pavements due to repeated traffic load can be determined. In the first step, the cyclic strain amplitude is calculated using a nonlinear material model that is based on particle size distribution parameters (d50 and CU) and dependent on the mean normal stress, relative density, and actual strain level. In the second step, the HCA (High Cycle Accumulation) model is used to calculate the residual settlement of each sublayer as a function of the number of cycles. It is shown that the developed model is suitable for describing different types of subgrades and pavement cross-sections. It is also demonstrated with finite element calculations that the developed model describes both the elastic and plastic strains sufficiently accurately. The developed model can predict the settlement and rutting of pavement structures with sufficient accuracy based on easily available particle size distribution parameters without the need for complex laboratory and finite element tests.

Offshore towed-streamer positioning based on variance component estimation

In offshore towed-streamer seismic surveys, fusing observations from diverse navigation sensors to position streamers and hydrophones accurately is essential for high-quality seismic imaging and analysis. However, streamer positioning accuracy can be limited when nominal sensor precisions adopted in observation weighting become unrealistic in complex marine environments. This study used variance component estimation (VCE) to refine the weight determination of heteroscedastic observations involved in streamer positioning. First, a polynomial fitting model for streamer positioning was established. Subsequently, a simplified VCE method was presented in which the observations were classified based on both sensor type and distribution geometry. Finally, a six-streamer survey dataset acquired from the South China Sea was used to investigate the performance of the proposed method. A comparative analysis was performed between conventional empirical weighting methods and VCE methods implemented in various observation classification schemes. The experimental results verified the superiority of the stochastic model refinement in fully exploiting observations from multiple navigation sensors in offshore streamer positioning. Compared with the empirical weighting, VCE method reduced the mean standard deviation of the hydrophone positions by about 0.5 m.

The Age Effect in Second Language Acquisition and Its Study Design Method

The focus of this paper was the effect of age on second language acquisition. The effect of the critical stage of second language acquisition is introduced. A retrospective discussion on the influence of age on second language acquisition, this paper tackles the conventional empirical methods of age-effect studies and analyses and summarizes some of the key questions in related research. The different testing methods used in these studies are the main reason for the research's contradictory results on the effects of age in second languages, including subject selection, content study content and time for comparison. Because of these issues, reference principles for research design on the influence of second language acquisition age are proposed: appropriate criteria for selecting subjects and number of subjects. Extensively sampled subjects, tools for testing and uniform implementation of assessment criteria, and a dynamic and comprehensive examination of the effects of age. The implication of the study on the influence of age on second language teaching and learning is that different teaching methods for students of different ages and different language characteristics should be applied in second language teaching.

Reducing Circling Currents in a VHF Class Φ2 Inverter Based on a Fully Analytical Loss Model

This paper proposes a fully analytical loss model to reduce circling currents and improve the power efficiency of a class Φ2 inverter. Firstly, analytical expression of the switching node voltage is derived by analyzing its harmonic components. Based on the result, the power switch is modeled as a voltage source, where the circuit is simplified to a linear network and analytical expressions of branch currents are solved. Secondly, root mean square (RMS) values of branch currents and component losses are calculated to form the analytical loss model for a Φ2 inverter. The influence of circuit parameters on the circling current and power efficiency are thoroughly analyzed, which derives optimal design constraints to reduce circling currents of a class Φ2 inverter. Furthermore, detailed design guidance and equations are provided to calculate circuit parameters of a class Φ2 inverter, which reduces its circling currents and improves overall power efficiency. Finally, a class Φ2 inverter prototype is built, and experimental results demonstrate a 7% efficiency improvement compared to conventional empirical design methods.

A hybrid partial least squares regression-based real time pore pressure estimation method for complex geological drilling process

Accurate real-time estimation of pore pressure is essential for the geomechanical analysis of wellbore stability. Conventional empirical methods may find it difficult to capture pore pressure trends, especially in the complex geological environments. In this study, a data-driven pore pressure estimation method is developed on the basis of hybrid partial least squares regression. This method, which combines empirical methods, comprised three stages: data preprocessing, depth series segmentation, and model establishment and switching. First, concerning the existence of outliers and noises, an outlier detection and wavelet filtering algorithm are introduced to obtain reliable model parameters. Additionally, Pearson correlation-analysis is employed to determine strongly correlated attributes with pore pressure in the data preprocessing stage. Afterward, an online principal component analysis similarity method is proposed for depth series segmentation, considering the varying drilling depth. Finally, a real-time data-driven pore pressure estimation model that integrates conventional empirical methods is established on the basis of partial least squares regression, and a model switching strategy is further developed and will be activated when performance deteriorates. The proposed method can be applied to a wide range of formations, and a real case study is conducted using actual data from a drilling site in Utah. The mean absolute error and root mean square error of the proposed method achieve 0.5128 and 0.8056 in the online condition, and achieved 1.4592 and 2.0100 in the offline condition, which are at least 45% less than those of other nine well-known methods. The results indicate the superior performance of our method on this well.

Neural Network based Predictors for Evaporation Estimation at Jabalpur in Central India

Free water evaporation is an imperative parameter for estimation of crop water requirement, and irrigation scheduling. This study aims to evaluate different techniques to estimate evaporation with weather parameters inputs. Multilayer Perception (MLR), Radial Basis Function (RBF) based neural network, traditional statistical Linear Regression (LR) approach and conventional empirical methods of Linacre and Christianson were used to estimate the evaporation at Jabalpur station situated under Kymore Plateau and Satpura Hills Agro-climatic Zone of Madhya Pradesh in the Central India. The weather parameters considered for estimation of evaporation are temperature, humidity, sunshine hours and wind speed. Results indicate that MLP and RBF based models with input of all selected weather parameters is able to estimate evaporation much precisely than LR and empirical approaches. It was found that higher accuracy may be obtained with multiple weather data input and low accuracy with only temperature input. It was observed that with temperature used as input the performance accuracy reduces in estimating evaporation with the selected models. However, neural network approach seems to produce better results as compared to statistical and empirical approach. The neural network based model RBF found more efficient in estimation of evaporation as compared to MLP. This study suggests that evaporation can be estimated by RBF model of a station, where there is no standard instrument available for its observation.

Re-examining women leaders and military spending

Do women leaders enact more hawkish foreign policies? Some research argues women leaders are more likely to adopt aggressive and masculine characteristics to obtain national office. As a result, women leaders should exhibit more hawkish behavior than men. In this study we re-examine the relationship between the women leaders and foreign policy by focusing on military spending behavior. We argue that conventional empirical methods, such as linear regression, are ill-suited to examine data on women leaders and military spending. These methods are sensitive to outliers and small sample sizes: two characteristics of women leadership. To address these issues, we use the synthetic control method to estimate the military spending behavior of women leaders. By creating unique synthetic counterfactuals for three prominent women leaders – Thatcher, Gandhi, and Meir – we analyze what would have happened if a particular state had a male leader. Generalizing beyond these cases, we also conduct a multiple treatment test that examines the effect of women leadership jointly across multiple countries and time periods. We find that women leaders do not spend more on the military than men. We analyze plausible explanations of these null results and discuss their implications.

Theoretical studies of real-fluid oxidation of hydrogen under supercritical conditions by using the virial equation of state

The Virial equation of state (EoS) was employed to describe the real-fluid impact on H2 oxidation simulation. Different from conventional empirical EoS methods fitted by using critical pressures and temperatures, such as Redlich-Kwong (RK) EoS, the Virial method was constructed with including intermolecular interactions, potentials, and molecular polarizations coupled with real-fluid partition function theory in this work. The Virial method was for the first time incorporated into Cantera software to realize real-fluid simulations with deeper-level physical insights. A series adiabatic/isothermal flow reactor and ignition delay simulations in H2O, N2, and CO2 diluents were performed. The calculations of species compressibility factors and thermodynamic properties by using the Virial method showed a better agreement with experimental data in literature than using the RK method. The Virial method also possessed a better performance in predicting experimental H2 ignition delay times and H2 speciation profiles from literatures. The effects of real fluid through corrections of compressibility factors, thermodynamic properties, and chemical potentials on supercritical H2 oxidation simulations in H2O diluents have been rigorously analyzed, respectively. Moreover, the Virial method performed well at adiabatic conditions in the bath gas of polar molecules like H2O, due to its consideration of molecular polarizations and higher accuracy of thermodynamic property calculations. We believed the Virial method could not only provide accurate estimations of real-fluid behavior, but also provided physical insights in the intermolecular interactions under supercritical conditions.

Sequential minimal optimization for local scour around bridge piers

Accurate determination of scour depth (ds ) around bridge piers is a major concern and an essential criterion in the safe and economical design of bridge pier foundation. The estimation of ds by the conventional empirical methods is difficult due to the very complex mechanism of the 3D flow around the bridge piers. This paper proposes the Sequential Minimal Optimization Regression (SMOREG) approach for local pier scour depth estimation. Additionally, Gradient Boosted (GBM), K-Nearest Neighbors (K-NN), and Random Forest (RF) methods were developed to compare the statistical performance of SMOREG. The numerous reliable databases from the literature includes six input parameters such as pier width (b), pier length (l), skew of the pier to approach flow (θ), mean velocity (v), flow depth (y), the particle size for which 50 percent of the bed material (D 50), and an output parameter ds . It revealed that the SMOREG can build a relationship between ds and flow characteristics and provides an estimation with an R-value of 0.85 and a mean absolute error (MAE) of 0.35. The comparison between models developed in this study showed that SMOREG and RF gave higher prediction performance than GBM and K-NN with respect to synchronic evaluation between RMSE, R, and Standard Deviation. The sensitivity analysis were also performed to determine the efficiency of each input parameter in the estimation of ds . It is found that pier width and mean velocity of the flow are the most effective parameters than the other parameters to estimate ds . The SMOREG models for sensitivity yielded MAE values in the range of 0.34–0.39.

Method to Calculate Mining‐Induced Fracture Based on the Movement and Deformation of Overburden Strata

Mining‐induced fracture of overburden strata is intimately related to underground water disasters in coal mining. In this work, we develop an analytical model that uses the probability integral method to calculate the subsidence of the subsurface and the overburden strata. In the developed model, according to the failure characteristics of the mining‐induced strata, the horizontal deformation of the strata is expressed by the tensile rate of the elastic plate’s neutral plane to reflect the degree of fracture initiation and expansion. The distribution of the water‐flowing fractured zone (WFZ) in the overburden strata is calculated by substituting the probability integral function of overburden strata movement into the equation of layer tensile rate. The panel 31071 in Peigou coal mine is taken as a case study, and the height of the water‐flowing fractured zone (HWFZ) is determined by the proposed method. Conventional empirical methods and the proposed method are used to predict HWFZ in panels with mining schemes, and the results show that the model is particularly advantageous for inclined coal seam mining where the inclined mining size gradually increases and the coal seam burial depth gradually decreases. In such kind of situations, the overburden strata movement and deformation intensify and the mining fracture develops further with the progress of mining, a feature considered poorly by conventional empirical methods but well represented in the proposed method.

Stochastic Pattern of Health Care Spending: Evidence from Nonlinear Panel Data Analysis

This paper analyses the stochastic pattern of health care spending for 188 countries around the world which are categorized into four groups based on the World Bank income categories. The main motivation of this article is to evaluate whether the stochastic features of total health care expenditure varies if decomposed into private and public health care consumption. To achieve this goal, in addition to conventional empirical methods, latterly developed panel stationarity approaches are employed for both linear and nonlinear regression processes. Our results demonstrate that allowing for cross-section dependency between series and nonlinearity in the estimation process may lead to more often rejection of the null hypothesis of unit root for all series in panel data set.

DETERMINATION OF TOOL LIFE DEPENDING ON BASIC PROPERTIES OF MATERIAL AND PROCESSING CONDITIONS

As a result of experimental data obtained from previous articles, it was found that for a certain material of the cutter there is a relationship between the tool life and the measured medium temperature of the cutting edge, which can be applied to most materials. This article provides a method for determining this ratio when the number of experimental results reflect the service life of the cutter and when determining the corresponding average temperature of the cutter, not using measurements, but using a recently developed calculation method. Calculation theory is applied to determine temperature using life-to-temperature relationship and to calculate tool life under a much wider variety of cutting conditions and compare these results with experimental data. The main advantage of the method over conventional empirical methods for establishing tool life, such as the method based on the well-known Taylor equation, is that the calculation can be carried out taking into account changes in cutting conditions and material properties and that the determination can accordingly be carried out according to those limits at which it is possible to confidently predict the service life of the cutter of experimental studies.

Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

Difficulties are faced when formulating hydrological processes, including that of evapotranspiration (ET). Conventional empirical methods for formulating these possess some shortcomings. The artificial intelligence approach emerges as the best possible solution to map the relationships between climatic parameters and ET, even with limited knowledge of the interactions between variables. This review presents the state-of-the-art application of artificial intelligence models in ET estimation, along with different types and sources of data. This paper discovers the most significant climatic parameters for different climate patterns. The characteristics of the basic artificial intelligence models are also explored in this review. To overcome the pitfalls of the individual models, hybrid models which use techniques such as data fusion and ensemble modeling, data decomposition as well as remote sensing-based hybridization, are introduced. In particular, the principles and applications of the hybridization techniques, as well as their combinations with basic models, are explained. The review covers most of the related and excellent papers published from 2011 to 2019 to keep its relevancy in terms of time frame and field of study. Guidelines for the future prospects of ET estimation in research are advocated. It is anticipated that such work could contribute to the development of agriculture-based economy.

Immigrant locations and native residential preferences: Emerging ghettos or new communities?

Strong political movements voicing opposition to immigration are on the upswing. Does such potential antagonism translate into residential dynamics? We study whether natives fled from immigrant areas in reaction to the largest and fastest migration shock in the OECD. The inflow–causing the population of Spain to grow by 10% between 1998 and 2008–represented a largely new phenomenon, the size of which had not been factored into previous expectations, thereby providing quasi-experimental sources of variance. Our results show that immigrant inflows caused mild native displacement from denser, established neighborhoods, but also more real estate development in these areas. In parallel, both natives and immigrants were collocating in booming suburban communities, resulting in no changes to overall measures of ethnic segregation. In light of the results, we argue that whenever ethnic-minority arrivals spur the creation of new neighborhoods, conventional empirical methods overstate the degree of native flight.

Performance evaluation of a coupled method for the estimation of daily global solar radiation on a horizontal surface

The accurate estimation of the amount of global solar radiation potential on the earth’s surface is one of the most important parameters for solar energy investments and long-term climate studies. The most accurate solar radiation data are generally available in some ground stations where they are obtained by using direct measurement. However, the estimation of global solar radiation data can be conducted by some estimation methods in the areas that do not have this information. This study compares and discusses some approaches to identify the most accurate method for daily global solar radiation reaching on a horizontal surface. The comparisons are carried out between some selected conventional empirical methods taken from the related literature, the satellite-based HELIOSAT method and the coupled (hybrid) method which uses both ground data and satellite images. These selected methods have been tested previously and declared most accurate by many researchers for Turkey. The performance results were compared with ground-measured data and all methods were analyzed by using statistical errors in the selected site. According to the last six years of data results, the coupled method is better than the others (except the Angstrom method), with daily difference values (91%) and daily absolute difference values lower than 2.5 MJ m –2 day.