HighTech and Innovation Journal
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Performance Evaluation of Extended EWMA Chart for AR Model with Exogenous Variables
Full text linkThe extended exponentially weighted moving average (Extended EWMA) control chart is an effective statistical process control method for monitoring and identifying shifts in process mean, particularly when dealing with autocorrelated data. One key performance measure used to evaluate the capability of control charts in detecting changes is the average run length (ARL). The primary goal of this study is to present the explicit formulas for calculating the ARL of the extended EWMA control chart for autoregressive models with exogenous variables (ARX) and exponential white noise. Another purpose is to compare the performance of the extended EWMA and the classical EWMA control charts under various conditions. The explicit formulas are derived from the ARL integral equation, which is expressed by the Fredholm integral equation. The accuracy of the exact solutions has been verified using the numerical integral equation (NIE) methods that employ four different composite quadrature rules. The result shows that the ARL values obtained from both methods are similar, and the computation time for the proposed explicit formulas is less than 0.001 second. In comparing the two control charts, it is evident that the extended EWMA control chart outperforms the traditional control chart in detecting shifts in the process mean, as confirmed by various overall performance criteria. Additionally, two real datasets, namely SCB stock price and GDP percentage expansions, are applied to demonstrate the effectiveness of the relevant control charts. Doi: 10.28991/HIJ-2024-05-04-03 Full Text: PD
Application of Smart Model in the Analysis of Opera Heritage Archiving and Protection
Full text linkObjective: Opera Heritage in China is a rich and diverse cultural tradition passed down through generations. Today, Opera Heritage remains an essential part of Chinese culture and is celebrated through performances, festivals, and other cultural events. Efforts are being made to preserve and promote Opera Heritage through innovative technologies and approach such as digital archives, virtual reality, and community-based heritage management. Analysis: In this study, we suggested the SMART model that seeks to integrate these technologies and approaches into the preservation and promotion of Opera Heritage, ensuring that this rich cultural tradition can be enjoyed by future generations. We employed an ‘opera heritage' using the Smart model in this study. In this research, "Smart Heritage" was defined as heritage experiences that used the word "Smart Heritage" or "similar terms" to describe the smart model. Findings: Opera Heritage analysis paid close attention to cutting-edge tools to record developments that showcase technical independence. Due to language barriers, this article could only interpret a clear convergence between SMART and opera heritage protection. For China's Opera Heritage to be properly archived and protected, a holistic and cooperative strategy that incorporates cutting-edge technologies and community-based heritage management practises are essential. Conclusion:Innovating technologies such as virtual reality and digital archives have been employed in efforts to promote and preserve China's rich Opera Heritage. This study emphasises the necessity for an extensive strategy for the efficient preservation and protection of Opera Heritage and suggests the SMART model to incorporate these methods. Doi: 10.28991/HIJ-2024-05-02-09 Full Text: PD
Innovative Date Fruit Classifier Based on Scatter Wavelet and Stacking Ensemble
Full text linkDates are essential fruits loaded with vital nutrients that keep bones healthy and prevent bone-related disorders. Approximately 8.46 million tons of different types of dates are cultivated and produced annually around the globe. There are more than 400 types of dates that are time-consuming and expensive to produce. Classifying them using conventional methods is labor-intensive, and this is one of the biggest problems for the date industry. Dataset fruit classification plays a vital role in the food industry. Dates can be classified from a luxury class to a less quality class. Accordingly, the food industry needs an automotive date fruit classifier that can work in food factories. This study proposes a pioneering method to classify date fruit that relies on extracting features from the texture of dates using Scattering Wavelet Transformation (SWT). The SWT yields in numeric coefficients were found to be immune to the deformation of invariants. This feature set trains an ensemble classifier that combines a voting mechanism to eliminate overfitting. The ensemble classifier consists of a random forest, a support vector machine classifier, and a logistic regression hyper-learner. Our novel approach was tested on two benchmarked datasets. The first data set scored F1 between 0.95 and 1.0 at the same time. The second dataset registered F1 between 0.96 and 1.0 in each of the 20 date classes. Some dates are close to each other in texture, resulting in high false positives or recall, causing a lower F1 score accuracy degree. The novelty of this approach comes from the featured representative of each date class, relying on the texture of the fruit as a discriminative feature, not on the fruit shape or color, which may not be robust enough as distinguishable features, especially in date classes that are close to each other in shape. Doi: 10.28991/HIJ-2024-05-02-010 Full Text: PD
Combining Export- and Domestic Demand-Led Growth Hypotheses: Key Sustainable Development Amidst Global Dynamics
Full text linkExport-led growth has conventionally been regarded as a pivotal determinant of economic growth in developing countries. The article aims to affirm the vulnerability of Vietnam's export sector due to its dependence on foreign direct investment flows and external market demand and evaluate the validity of the export-led growth strategy being applied in Vietnam among evolving global dynamics. The review of relevant literature explored the theoretical foundations, theories, and concepts of export-led and domestic demand-led growth with regard to the causal link between exports and economic growth. Qualitative and secondary research methods were used to analyze statistical data sets on imports and exports and domestic demand components to highlight their impact on the country's GDP growth. The results showed that it is necessary to embrace both export-led growth and domestic demand-led growth as concurrent development paradigms, thereby ensuring the sustainability of Vietnam's economic growth. Doi: 10.28991/HIJ-2024-05-02-05 Full Text: PD
Research on Power Consumption Data Prediction of Distributed Photovoltaic Power Station
Full text linkAt present, the construction of distributed photovoltaic power stations in China lacks systematic and comprehensive preliminary planning; The construction cost exceeded the estimated estimate. After the completion of the project economic benefits cannot reach the expected income, project operating costs exceed expectations and other problems. In order to solve these problems, it is urgent to reasonably forecast the electricity consumption data of distributed photovoltaic power stations. Therefore, in order to solve these problems, a reliable model is established to predict the electricity consumption data of distributed photovoltaic power stations, and the indirect prediction method is used to forecast, that is, the irradiance of medium and long-term time scales is predicted by historical meteorological data, and then the system electricity consumption data is obtained. Among them, the model used is the Long short-term memory (LSTM) neural network model. Under the effect of this model, the electricity consumption data prediction of distributed photovoltaic power stations is carried out. The result shows that the MAPE of monthly prediction is 3.5%, and the annual prediction is 1.1%, which has ideal prediction accuracy and can achieve better prediction effect. This indirect forecasting method breaks the shackles of traditional forecasting methods, avoids the problems of data collection and other aspects, and is a new development trend and the performance of scientific and technological progress, which is conducive to the development of distributed photovoltaic power stations. Doi: 10.28991/HIJ-2024-05-04-05 Full Text: PD
Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure
Full text linkDue to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME's achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of pollution emissions are key driving forces for enhancing sustainability. Compared to other methodologies, Multi-Criteria Decision Making (MCDM) considers the interplay between conditions. This is why it is an excellent approach to assess the sustainability of any city. Our experimental findings highlight the impact of MCDM and sustainability towards achieving a city's sustainable development goals. Compared to other methods, the SC-MCDM system is more successful rate of 89.7%, a more sustainable rate of 92.1%, a more precise ratio 93%), more accurate (95%), and a less mean absolute error, and mean squared error rate of 8.3% while trying to achieve sustainable city development goals. Doi: 10.28991/HIJ-2024-05-04-018 Full Text: PD
Preventing Impaired Driving Using IoT on Steering Wheels Approach
Full text linkTo drive safely, one must be attentive, coordinated, have good judgment, and be able to respond quickly to changing conditions. In certain countries, improving safety may depend largely on reducing the number of impaired drivers on the road. Therefore, solutions are required to reduce the risk that is posed on the road by drivers who have been consuming alcohol while driving. Previous research has proposed the use of sensors for detecting driver impairment caused by alcohol intoxication. However, relying on a gas sensor alone may not be appropriate for detection. To reduce drunk driving, this study proposes an Internet of Things (IoT)-based tool that measures heart rate and analyzes the breath of a driver for traces of alcohol. The tool represents a vehicle that is made up of a DC motor. In the circumstance that the tool detects a higher than resting heart rate in the driver as well as an amount of alcohol in the driver's breath sample, the tool will immediately power down the DC motor and send an SMS to the registered emergency contact with the driver's precise position using the GPS module. The initial prototype demonstrates the tool as a potential aftermarket accessory for vehicles. The implication of this paper is that the designed tool might be of practical use to researchers in their attempts to determine and obtain information on alcohol intoxication. Doi: 10.28991/HIJ-2024-05-02-012 Full Text: PD
Analysis of Factors Influencing Online Learning Using the Decision Tree Method
Full text linkObjective: With the continuous development of online learning, the analysis of students' online learning has become increasingly important. Understanding which factors can influence students' engagement in online learning plays a crucial role in improving their learning performance. Methods: By utilizing web crawling techniques, students' online learning behavior data was collected from the Chinese University's massive open online courses (MOOC) platform. To address the imbalance in the dataset, a synthetic minority oversampling technique (SMOTE) was used. Course progress was used to reflect students' online learning status, which was categorized into interruptions and completions. Furthermore, to tackle the issue of low computational efficiency in the C4.5 decision tree algorithm, its calculation formula was improved to develop an improved version of C4.5. Findings: Of the several factors analyzed, the number of course chapters had the greatest impact on students' online learning, followed by the number of course evaluations and overall course scores. The classification of students' online learning situations based on an improved C4.5 algorithm revealed that the improved method achieved the highest accuracy rate of 0.942 and the shortest classification time of 0.165 s compared to methods such as the naive Bayesian and random forest algorithms. Novelty: This study designed an improved version of C4.5 to analyze the influencing factors in online learning, and its reliability was demonstrated through experiments, providing a new effective method for data analysis in online learning. Doi: 10.28991/HIJ-2024-05-02-018 Full Text: PD
Adoption of Blockchain Technology in Healthcare Supply Chain Management: A Review
Full text linkThe healthcare supply chain encounters difficulties with transparency, efficiency, and security, which have an impact on patient safety and the quality of treatment concerning the items involved. The use of blockchain technology, which has intrinsic characteristics such as confidentiality, transparency, and traceability, offers a possible resolution to tackle these problems. This paper aims to comprehensively review the adoption of blockchain technology in healthcare supply chain management, particularly in response to the challenges posed by the COVID-19 pandemic. It investigates the significance of efficient and transparent healthcare supply chains, focusing on blockchain's application in vaccine distribution, Personal Protective Equipment (PPE), drugs, medical devices and blood products. The analysis critically evaluates research papers proposing innovative blockchain-powered solutions, discussing their benefits, challenges, and the need for further research. Findings highlight blockchain's potential in enhancing vaccine traceability, preventing counterfeit vaccines, and ensuring equitable access to immunization. It also outlines blockchain's role in real-time tracking of PPE shipments, secure distribution of medical devices, managing blood products, and combating counterfeit drugs. The paper also emphasizes the prevalence of consortium-based and public blockchain implementations and the importance of smart contracts while advocating for addressing scalability and technological challenges. This review offers a critical assessment of blockchain's potential in fortifying healthcare supply chains during crises, underscoring the need for ongoing research and development to overcome implementation limitations. Doi: 10.28991/HIJ-2024-05-04-019 Full Text: PD
Evaluating Household Consumption Patterns: Comparative Analysis Using Ordinary Least Squares and Random Forest Regression Models
Full text linkThis research aims to decompose the contribution of socioeconomic factors towards household consumption expenditure using a regression approach, with log per capita expenditure as the dependent variable. Our study stands out as the first to utilise SHAP analysis and Machine Learning models to analyse household consumption expenditure. We select both OLS (linear) and Random Forest (nonlinear) models to compare how they estimate consumption expenditure differently. Both models explain about 85% of the variation in log per capita expenditure. The SHAP analysis reveals the nonlinear relationships inside the Random Forest model. Several insightful findings were suggested that can be integrated into current policy-making. The results are as follows: (1) Both models agree that income, household size, and educational level are major factors in the purchasing power of household heads. (2) The Random Forest model demonstrated a nonlinear contribution of age and household size towards log per capita expenditure, contrasting with previous studies that treated them as linear. (3) Household heads with a higher income and educational level tend to spend more. (4) Current policy should consider focusing on households with larger sizes and lower incomes, who tend to spend more despite earning less, primarily by assisting them with non-cash transfers and subsidies. Doi: 10.28991/HIJ-2024-05-02-019 Full Text: PD