• The Journal of Asian Finance, Economics and Business
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• v.9 no.9
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• pp.271-281
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• 2022

The objectives of the research are to study the effects of emotional intelligence (EI), reward management (RM), and occupational health and safety (OHS), on employee performance (EP) within a Thai motor service and repair firm. Starting in January 2022 through the end of March 2022, the researchers used simple random sampling techniques to select 88 employees for the case study. The research instrument was a questionnaire with an IOC value between 0.67-1.00 and a reliability value α of 0.78. Survey participants were asked to contribute their opinions to a five-level opinion survey which was hosted on Google Forms. Descriptive statistics analysis (mean and standard deviation) and multiple linear regression analysis were done using SPSS for Windows version 21. The results showed that employee opinions concerning EI, RM, OHS, and EP were at a high level, with the three hypotheses testing showing statistical significance (p ≤ 0.01). The decision coefficients (R²) all revealed relationship strength with RM = 0.861, OHS = 0.853, and EI = 0.731.

• The Journal of Industrial Distribution & Business
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• v.12 no.6
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• pp.7-21
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• 2021

Purpose: Modern globalization and Western markets saturation has catalyzed the growth of culinary globalization into developing countries. The question was whether fast food consumers in frontier markets of Sub-Saharan Africa (Zimbabwe), either upholds national gastronomic tendencies in terms of consumer ethnocentrism and buy local or they adopt global fast food brands. Demographic consumer profiles were also analyzed as antecedents of consumer ethnocentrism. Research design, data and methodology: A sample size of 400 fast food-adult consumers was surveyed in the City of Harare. Data was captured on SPSS and Analysis of Moment Structure (AMOS). Hypothesis testing was done using sample t test (H₁), logistic regression (H₂) and multiple regression (H₃, ₄, ₅) analysis. Results: Consumer ethnocentrism in Zimbabwe was marginally above average and no statistically significant relationship between the levels of consumer ethnocentrism and adoption of foreign fast food brands was noted. Age had an inverse relationship; income had a positive association whilst gender had no statistical significance with consumer ethnocentrism. Conclusions: Despite the Zimbabwean consumers being marginally ethnocentric, international restaurateurs should invest in the Zimbabwean fast food market since their nature of being foreign has got an exotic appeal to the Zimbabwean consumers thereby enhancing their likelihood of success.

• Earthquakes and Structures
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• v.21 no.6
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• pp.641-652
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• 2021

Digging well foundation has been widely used in railway bridges due to its good economy and reliability. In other instances, bridges with digging well foundation still have damage risks during earthquakes. In this study, a new type of digging well foundation with prefabricated roots was proposed to reduce earthquake damage of these bridges. Quasi-static tests were conducted to investigate the failure mechanism of the root digging well foundation, and then to analyze seismic behaviors of the new type well foundation. The testing results indicated that these prefabricated roots could effectively limit the rotation and uplift of the digging well foundation and increase the lateral bearing capacity of the digging well foundation. The elastic critical load and ultimate load can be increased by 69% and 36% if prefabricated roots were added in the digging well foundation. The prefabricated roots drived more soil around the foundation to participate in working, the stiffness of the bridge pier with root digging well foundation was improved. Moreover, the root participation could improve the energy dissipation capacity of soil-foundation-pier interaction system. The conclusions obtained in this paper had important guiding significance for the popularization and application of the digging well foundation with prefabricated roots in earthquake-prone zones.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.107-121
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• 2022

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f₁-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.5
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• pp.28-33
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• 2022

A pipe is a hollow, long-form part that is primarily used to transport fluids, such as liquids or gases. Pipes are used in a range of applications in different fields from mechanical purposes to architecture and electrical uses. Despite the significance owing to various usability of pipes, few studies have been conducted using the physical property test method. The tensile test is widely used as a method to check the physical properties of the pipe. The existing pipe tension test contains the possibility to cause errors, which are fractures outside the gauge distance and cross-sectional deformation of the pipe. In this study, a novel pipe tension test method using a jig is presented and pipes with various materials are tested. It is expected that the proposed method can reduce errors that occur in conventional pipes and also obtain more accurate values to enable more efficient testing.

• Smart Structures and Systems
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• v.30 no.4
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• pp.421-432
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• 2022

Regarding the high significance of correct pan evaporation modeling, this study introduces two novel neuro-metaheuristic approaches to improve the accuracy of prediction for this parameter. Vortex search algorithms (VSA), sunflower optimization (SFO), and stochastic fractal search (SFS) are integrated with a multilayer perceptron neural network to create the VSA-MLPNN, SFO-MLPNN, and SFS-MLPNN hybrids. The climate data of Arcata-Eureka station (operated by the US environmental protection agency) belonging to the years 1986-1989 and the year 1990 are used for training and testing the models, respectively. Trying different configurations revealed that the best performance of the VSA, SFO, and SFS is obtained for the population size of 400, 300, and 100, respectively. The results were compared with a conventionally trained MLPNN to examine the effect of the metaheuristic algorithms. Overall, all four models presented a very reliable simulation. However, the SFS-MLPNN (mean absolute error, MAE = 0.0997 and Pearson correlation coefficient, R_P = 0.9957) was the most accurate model, followed by the VSA-MLPNN (MAE = 0.1058 and R_P = 0.9945), conventional MLPNN (MAE = 0.1062 and R_P = 0.9944), and SFO-MLPNN (MAE = 0.1305 and R_P = 0.9914). The findings indicated that employing the VSA and SFS results in improving the accuracy of the neural network in the prediction of pan evaporation. Hence, the suggested models are recommended for future practical applications.

• Steel and Composite Structures
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• v.44 no.6
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• pp.867-882
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• 2022

Predicting the compressive strength of concrete (CSoC) is of high significance in civil engineering. The CSoC is a highly dependent and non-linear parameter that requires powerful models for its simulation. In this work, two novel optimization techniques, namely evaporation rate-based water cycle algorithm (ER-WCA) and equilibrium optimizer (EO) are employed for optimally finding the parameters of a multi-layer perceptron (MLP) neural processor. The efficiency of these techniques is examined by comparing the results of the ensembles to a conventionally trained MLP. It was observed that the ER-WCA and EO optimizers can enhance the training accuracy of the MLP by 11.18 and 3.12% (in terms of reducing the root mean square error), respectively. Also, the correlation of the testing results climbed from 78.80% to 82.59 and 80.71%. From there, it can be deduced that both ER-WCA-MLP and EO-MLP can be promising alternatives to the traditional approaches. Moreover, although the ER-WCA enjoys a larger accuracy, the EO was more efficient in terms of complexity, and consequently, time-effectiveness.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.72.3-73
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• 2021

Baryon Acoustic Oscillations (BAO) are caused by acoustic density waves in the early universe and act as a standard ruler in the clustering pattern of galaxies in the late Universe. Measuring the BAO feature in the 2-point correlation function of a sample of galaxies allows us to estimate cosmological distances to the galaxies mean redshift, , which is important for testing and constraining the cosmology model. The BAO feature is also expected to appear in the higher order statistics. In this work we measure the generalized spatial N-point point correlation functions up to 4th order. We made measurements of the 2, 3, and 4-point correlation functions in the SDSS-III DR12 CMASS data, comprising of 777,202 galaxies. The errors and covariances matrices were estimated from 500 mock catalogues. We created a theoretical model for these statistics by measuring the N-point functions in halo catalogues produced by the approximate Lagrangian perturbation theory based simulation code, PINOCCHIO. We created simulations using initial conditions with and without the BAO feature. We find that the BAO is detected to high significance up to the 4-point correlation function.

• Computers and Concrete
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• v.33 no.1
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• pp.41-53
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• 2024

Under constant and cyclic axial compression, square composite short columns reinforced with Self Compacting Concrete (SCC) added with scrap rubber infilled inside steel tubes and with different types of concrete were cast and tested. The test is carried out to find the effectiveness of utilizing an aggregate manufactured from industrial waste and to address the problems associated with the need for alternative reinforcements along with waste management. The main testing parameters are the type of concrete, the effect of fiber inclusion, and the significance of rubber-infilled steel tubes. The failure modes of the columns and axial load-displacement curves of the steel tube-reinforced columns were all thoroughly investigated. According to the test results, all specimens failed due to compression failure with a longitudinal crack along the loading axis. The fiber-reinforced column specimens demonstrated improved ductility and energy absorption. In comparison to the normal-weight concrete columns, the lightweight concrete columns significantly improved the axial load-carrying capacity. The addition of basalt fiber to the columns significantly increased the yield stress and ultimate stress to 9.21%. The corresponding displacement at yield load and ultimate load was reduced to 10.36% and 28.79%, respectively. The precision of volumetric information regarding the obtained crack quantification, aggregates, and the fiber in concrete is studied in detail through image processing using MATLAB environment.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.253-262
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• 2024

Dynamic properties are pivotal in soil analysis, yet their experimental determination is hampered by complex methodologies and the need for costly equipment. This study aims to predict dynamic soil properties using static properties that are relatively easier to obtain, employing machine learning techniques. The static properties considered include soil cohesion, friction angle, water content, specific gravity, and compressional strength. In contrast, the dynamic properties of interest are the velocities of compressional and shear waves. Data for this study are sourced from 26 boreholes, as detailed in a geotechnical investigation report database, comprising a total of 130 data points. An importance analysis, grounded in the random forest algorithm, is conducted to evaluate the significance of each dynamic property. This analysis informs the prediction of dynamic properties, prioritizing those static properties identified as most influential. The efficacy of these predictions is quantified using the coefficient of determination, which indicated exceptionally high reliability, with values reaching 0.99 in both training and testing phases when all input properties are considered. The conventional method is used for predicting dynamic properties through Standard Penetration Test (SPT) and compared the outcomes with this technique. The error ratio has decreased by approximately 0.95, thereby validating its reliability. This research marks a significant advancement in the indirect estimation of the relationship between static and dynamic soil properties through the application of machine learning techniques.