Taiwan Association of Engineering and Technology Innovation: E-Journals
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Optimizing Lags and Hidden Layers in Hybrid Models for Forecasting Stock Return
Full text linkThis study aims to minimize the root mean square error for stock return by optimizing lags and hidden layers in a hybrid model. The model combines the autoregressive integrated moving average with the exogenous variables model as linear components. The residuals derived from linear components are used in artificial neural networks and Elman recurrent neural networks as non-linear components. A key feature of this approach is optimizing the selection of hidden layers and lags within the neural network, improving forecasting accuracy. The minimum mean square error forecast expression is derived, and the model is tested on stock price data during the COVID-19 period, marked by significant market shocks. The root mean square error results for the proposed model, traditional hybrid model, and traditional time series model range from 0.0004 to 0.01, 0.0006 to 0.01, and 0.006 to 0.03, respectively. The results show that the proposed model outperforms both traditional models
Efficient Model for Early Prediction of Heart Disease Using Ensemble Technique
Full text linkThe growing global burden of cardiovascular diseases has created an urgent need for advanced early-detection devices that revolutionize preventive cardiology. This research presents a novel two-stage ensemble (TSE) learning framework that outperforms traditional machine learning methods by integrating multiple complex algorithms, including random forest, adaptive boosting, gradient boosting machine, light gradient boosting machine, and extra trees classifier, into a highly accurate predictive system in stage 1. The approach incorporates a sophisticated preprocessing pipeline with feature scaling and the synthetic minority oversampling technique SMOTE to address the class imbalance and ensure robust input data quality. The model optimizes a meta-learner for enhanced predictions by leveraging meta-features derived from various classifiers. The developed TSE model, utilizing the CatBoost classifier in stage 2, achieved average accuracies of 92.5% and 90.19% on the Cleveland and Statlog datasets, respectively. This comprehensive ensemble framework significantly advances clinical decision support for early detection and intervention in cardiovascular disease
A Modified Exponential Model for Predicting the Fatigue Crack Growth Rate in a Pipeline Steel Under Pure Bending
Full text linkThe present work proposes a fatigue crack growth rate (FCGR) model for steel pipelines subjected to sinusoidal loading using a modified exponential function. The modification in the exponential function is made for the non-dimensional parameter using the stress intensity range (ΔK) as the crack driving force. The acceptable values of ΔK for FCGR in stage-I ranged between 17.45-20.46 MPa√m, between 20.46-21.41 MPa√m for stage-II, and between 21.41-21.98 MPa√m for stage-III. A new correlation is also developed between the specific growth rate and the non-dimensional number. The modified exponential function predicted the FCGR within the acceptable values for all three stages in the radial direction. It shows the best performance for stage-I of FCGR and the lowest for stage-III. The microstructure envisages shallowed microvoids, while the striations and secondary cracks are mostly perpendicular to the FCG direction
Modeling the Daily Average Temperature Data Using Stochastic Process and Neural Networks for Weather Derivatives
Full text linkWeather derivatives are financial instruments influenced by temperature fluctuations, impacting industries such as agriculture, tourism, and energy. Accurate temperature modeling is essential for improving risk assessment and hedging strategies. This study evaluates the effectiveness of two forecasting hybrid approaches: the Fourier Ornstein-Uhlenbeck (OU) process, a widely used stochastic model, and the Fourier-Elman Recurrent Neural Network (ERNN), a hybrid neural network-based model. Daily temperature data from Chiang Mai, Thailand, spanning January 2005 to December 2021, were analyzed. The predictive performance of each model was assessed using root mean square error (RMSE). The results indicate the Fourier ERNN model (RMSE = 0.106) significantly outperforms the Fourier OU process (RMSE = 2.299), demonstrating superior accuracy in capturing both seasonal and stochastic variations in temperature dynamics. Thus, deep learning-based hybrid models provide a more effective framework for temperature forecasting. The proposed approach has potential applications in climate risk management, weather derivative pricing, and decision-making in climate-sensitive sectors
A Review of the Stimulus–Organism–Response Paradigm and Environmental Education for Cruise Tourism: A Proposed Framework
Full text linkWith the rapid growth of the global cruise tourism industry and its increasing environmental impact, there is an urgent need to address sustainability challenges in line with the SDGs, especially in Taiwan. Despite the growing research on environmental education, there is a lack of a theoretical framework from the perspective of the stimulus-organism-response (S-O-R) paradigm that examines the relationships between external stimuli, internal organisms, and individual responses to environmental education in the context of cruise tourism. The proposed framework includes attention to environmental issues and awareness of consequences as external stimuli. These stimuli influence affective and cognitive processes, which are internal states of the organism. In turn, the affective and cognitive states drive pro-environmental behavioral responses. Additionally, the proposed framework incorporates two potential moderating factors: cultural differences and environmental education with emerging technologies. Implications for environmental education in cruise tourism are provided
Effective Recommendation Considering Customers’ Needs Using Review Texts with TF-IDF and Word2Vec: Case of Golf Course
Full text linkThis paper aims to recommend the most suitable golf course for each user by focusing on golf courses and analyzing customer reviews. Furthermore, by examining the recommendation results, the goal is to clarify the characteristics of each golf course from the user’s perspective and contribute to the promotion of each golf course. The procedure of this paper is first to extract user preferences using Word2vec and TF-IDF from reviews. Next, the extracted user preferences are matched with golf course features. Finally, recommendations are made based on the geographical relationship between the user and the golf course. As a result, a high accuracy rate is achieved. Additionally, some keywords that should be used in promotions for each golf course feature have been identified
Analyzing Boeing’s Supply Chain, Quality Control, and Certification Issues: Lessons from the 787 Dreamliner and 737 MAX
Full text linkThis study analyzes the impact of Boeing’s outsourcing strategy on aircraft safety and production efficiency, focusing on the 787 Dreamliner program. The intended benefits of cost reduction and accelerated production are examined against the realities of risk-sharing arrangements and documented issues like faulty materials from suppliers such as Kobe Steel. The study investigates how these outsourcing practices, coupled with Boeing’s self-certification license from the Federal Aviation Administration (FAA), contributed to lapses in regulatory oversight and quality control. Applying a risk analysis to Boeing’s supply chain, its risk treatment and monitoring processes are assessed. This study delves into the complexities and associated problems of Boeing’s risk-sharing supplier partnerships. Based on the findings, this study suggests enhancing supply chain resilience, ensuring regulatory adherence, and bolstering quality management systems to rebuild trust in Boeing’s manufacturing processes and support long-term sustainability
A Novel Diagnostic Approach for Smartphone-Induced Finger Disorders: An Exploratory Study
Full text linkSmartphone-related finger injuries are repetitive strain injuries caused by prolonged smartphone use. Despite the increasing prevalence of such conditions, few studies have focused on developing effective and accessible diagnostic methods. This study proposes the use of biomedical signals from the hand and fingers as diagnostic indices. Soft tissue stiffness and vibration frequency features under load are presented and tested as potential diagnostic indices. Testing revealed that the soft tissue stiffness parameter lacks reliability and suitable sensors, while the vibration frequency feature demonstrates excellent performance. After addressing several existing limitations, the vibration frequency under load emerges as the optimal diagnostic method for smartphone-related finger injuries
Flow Separation Characteristics of Tandem Minibus Model Configuration
Full text linkThis study aims to determine the characteristics of the pressure coefficient and fluid flow separation in a tandem minibus model using the Fluent 6.3.26 computational method and experimental testing in a wind tunnel. Pressure measurements are taken by installing 14 pressure taps connected to a manometer on a 1:40-scale minibus model. Tests were conducted at five different distances between minibuses in a series configuration at seven-speed levels. The results showed that at the highest speed tested, minimal flow separation occurred at a distance ratio of L/D = 0.455, with values of CP = -0.083 in the first minibus and CP = -0.250 in the second minibus. This configuration is identified as the optimal spacing to reduce aerodynamic disturbance in the tandem minibus system
Centralized Photovoltaic Heliostat Field Layout and Optical Perception Optimization Based on Improved Dung Beetle Optimization Algorithm
Full text linkThe gradual depletion of fossil fuels underscores the pressing need for technological advancements in renewable energy. These technologies are essential to address the inefficiencies in power generation from heliostat fields. This paper proposes an innovative heliostat field layout model aimed at significantly enhancing the efficiency of photovoltaic power generation. By carefully optimizing the positioning, height, and size of the heliostats, the model results in a substantial increase in annual heat output. Additionally, an improved Dung Beetle optimization algorithm (RCDBO) is introduced, which integrates random walk and cross strategy to enhance solving efficiency and accuracy while effectively avoiding premature convergence. Simulations demonstrate that the proposed algorithm achieves a 3% increase in efficiency compared to the traditional DBO algorithm, confirming the superiority of the RCDBO algorithm