HighTech and Innovation Journal
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Study on International Settlement of Enterprises' Export Trade Business using Risk Management
Full text linkObjectives: This paper aims to analyze the international settlement risks of enterprise export trade businesses using the analytic hierarchy process (AHP) method. Methods: Firstly, the international settlement risks were divided into three levels, including 13 evaluation indicators. Then an evaluation matrix was established. After the consistency test, the indicator and hierarchical weights were calculated for analysis. Findings: The country risk was 0.2081, the foreign exchange risk was 0.2104, the contract risk was 0.4608, the transportation risk was 0.4422, and the credit risk was 0.4852. Among these risks in international settlement, credit risk posed the greatest risk, followed by contract and transportation risks, while foreign exchange and country risks were relatively lower. Novelty:When assessing international settlement risks, the AHP was used, and a judgment matrix was employed to calculate the weights for each level. Doi: 10.28991/HIJ-2023-04-03-08 Full Text: PD
Evaluating the Determinants of Young Runners' Continuance Intentions toward Wearable Devices
Full text linkRunning has gained popularity as a fitness activity in China, with a growing number of young runners utilizing wearable devices to monitor their running routines and engage in quantified self-practices. The continuous evolution of wearable devices in terms of products and services has expanded the choices available to young runners. Therefore, there is a need to analyze the factors influencing the continuance intention of young runners, providing insights into how to promote the sustained growth of these products or services in the market. This study is grounded in the Technology Acceptance Model and the Theory of Planned Behavior, with an extension incorporating the quantified self to explore the impact of users' continuance intentions to use wearable devices. A survey was conducted among 468 young runners who already used wearable devices, and the data collected were analyzed using PLS-SEM. The results indicate that perceived usefulness and attitudes from the Technology Acceptance Model positively influence intentions for continued use. Additionally, subjective norms according to the Theory of Planned Behavior positively influence continuance use intentions. However, perceived behavioral control does not have a significant effect on continuance use intentions. Conversely, the Quantified-Self positively influences continuance use intentions and partially mediates the relationship between perceived usefulness and continuance use intentions. This research has several theoretical implications for the Theory of Planned Behavior, the Technology Acceptance Model, and the Quantified-Self research construct. Moreover, this study has practical implications for practitioners concerning the adoption and acceptance of wearable devices by young people. This approach enables practitioners to target and implement precise strategies to meet the current demands of the young runner market. Doi: 10.28991/HIJ-2023-04-04-02 Full Text: PD
Consumer's Personality Traits and Knowledge-sharing Behavior on Shoppertainment Platforms: The Mediating Role of Subjective Well-being and Trust
Full text linkObjectives: This research analyzed the direct and indirect influences of consumer personality on knowledge-sharing behavior through shoppertainment platforms using subjective well-being and trust as mediators. Methods/Analysis: A questionnaire survey was developed and distributed to 320 consumers with familiarity and experience in purchasing products from the TikTok shop and sharing knowledge, information, news, and purchasing experiences with the Thai TikTok community. This study adopted non-probability and purposive sampling techniques, with measurements and structural model assessments performed before hypothesis testing using the partial least squares structural equation model (PLS-SEM) with SmartPLS statistical software. Findings: Extraversion and openness to experience had a direct positive influence on trust, while neuroticism showed a direct negative influence on trust. Extraversion had a direct positive influence on subjective well-being, while neuroticism showed a direct negative influence on subjective well-being. Both trust and subjective well-being directly influenced knowledge-sharing behavior on the shoppertainment platform. Extraversion and openness to experience positively influenced knowledge-sharing behavior on the shoppertainment platform via trust, while neuroticism negatively influenced knowledge-sharing behavior on the shoppertainment platform through trust. Importantly, extraversion, openness to experience, and agreeableness positively influenced knowledge-sharing behavior on the shoppertainment platform via subjective well-being, with neuroticism negatively influencing knowledge-sharing behavior on the platform through subjective well-being in the same manner. Novelty/Improvement:Results contribute to an improved understanding of the mechanisms of a robust and competitive online retail business model in the digital era that can best deliver business sustainability by elevating consumers' knowledge-sharing behaviors to facilitate purchasing decisions on goods or services via shoppertainment platforms. Doi: 10.28991/HIJ-2023-04-01-014 Full Text: PD
Comprehensive Evaluation of Deep Neural Network Architectures for Parawood Pith Estimation
Full text linkAccurate pith estimation is crucial for maintaining the quality of wood products. This study delves into deep learning techniques for precise Parawood pith estimation, employing popular convolutional neural networks (ResNet50, MobileNet, and Xception) with adapted regression heads. Through variations in regression functions, optimizers, and training epochs, the most effective models were pinpointed. Xception, coupled with Huber Loss regression, Nadam optimizer, and 200 epochs, showcased superior performance, achieving a 4.48 mm mean error (with a standard deviation of 3.69 mm) in Parawood. Notably, benchmarking on the Douglas Fir dataset yielded similar results (2.81 mm mean error, standard deviation: 1.57 mm). These findings underscore deep learning's potential for Parawood and Douglas Fir pith estimation, offering substantial benefits to wood industry quality control and production efficiency. By harnessing advanced machine learning techniques, this study advances wood industry processes, promoting the adoption of state-of-the-art technology in forestry and wood science. Doi: 10.28991/HIJ-2023-04-03-06 Full Text: PD
Study of Optimization of Tourists' Travel Paths by Several Algorithms
Full text linkThe purpose of this paper is to optimize the tourism path to make the distance shorter. The article first constructed a model for tourism route planning and then used particle swarm optimization (PSO), genetic algorithm (GA), and ant colony algorithms to solve the model separately. Finally, a simulation experiment was conducted on tourist attractions in the suburbs of Taiyuan City to compare the path optimization performance of the three algorithms. The three path optimization algorithms all converged during the process of finding the optimal path. Among them, the ant colony algorithm exhibited the fastest and most stable convergence, resulting in the smallest model fitness value. The travel route obtained through the ant colony algorithm had the shortest distance, and this algorithm required minimal time for optimization. The novelty of this article lies in the enumeration and description of various algorithms used for optimizing travel paths, as well as the comparison of three different travel route optimization algorithms through simulation experiments. Doi: 10.28991/HIJ-2023-04-02-012 Full Text: PD
Development, Service-Oriented Architecture, and Security of Blockchain Technology for Industry 4.0 IoT Application
Full text linkThe Internet of Things (IoT) paradigm is laying the groundwork for a world in which many of our everyday devices will be connected and will interact with their surroundings to gather data and automate some operations. Among other things, such a concept necessitates seamless authentication, data privacy, security, attack resilience, simplicity of deployment, and self-maintenance. Blockchain, a technology created with the Bitcoin cryptocurrency, can provide such advantages. To create blockchain-based IoT (BIoT) applications, a full discussion of how to modify blockchain to meet the unique requirements of IoT is offered in this paper. The most important BIoT applications are detailed after a brief introduction to blockchain, with the goal of highlighting how blockchain can affect conventional cloud-based IoT applications. Then, several factors that have an impact on the design, development, and deployment of a BIoT application are covered, along with present obstacles and potential improvements. Lastly, a list of recommendations is provided to help future BIoT researchers and developers understand some of the problems that need to be solved before deploying the upcoming generation of BIoT applications. Doi: 10.28991/HIJ-2023-04-01-010 Full Text: PD
European Real Estate Properties Valuation: Ten Years After Adopting IFRS 13
Full text linkIFRS 13 had its mandatory implementation on January 1st, 2013. The new accounting standard, which represents one step closer to harmonization between U.S. GAAP and IFRS, aims to eliminate inconsistencies in fair value measurement and its related disclosures through the introduction of new reporting requirements, specifically for assets and liabilities with no active markets. Although these demands also encompass information concerning financial instruments, our focus was on the disclosure changes related to the fair value of investment properties, previously regulated solely by IAS 40. As investment properties comprise the majority of assets in the real estate industry, this sector was further examined. Through a statistical analysis of the sample companies' annual reports for the periods immediately before and after the implementation of IFRS 13, the purpose of our descriptor-explanatory study was to investigate the level of compliance with IFRS 13 fair value disclosure requirements for investment properties in real estate companies in Europe. In order to answer this question, we first scrutinized the level of compliance with the new disclosure requirements brought up by the standard and then, intermediated by an adaptation of the model developed by Beretta & Bozzolan (2008), measured the disclosure quality for both periods considered. After data collection and analysis, our findings reveal that IFRS 13 does affect the disclosure quality of investment properties in real estate companies in Europe. Overall compliance is very high, while disclosure quality has increased since the implementation of IFRS 13. As a way to further broaden the research related to the more extensive disclosure requirements under IFRS 13, we suggest additional studies be undertaken where the point of view of the real estate companies could be explored. Moreover, it would be interesting to investigate whether the increased number of disclosures, both in relation to quantity and quality, is relevant from an analyst's standpoint. Doi: 10.28991/HIJ-2023-04-03-04 Full Text: PD
Design of 360° Dead-Angle-Free Smart Desk Lamp based on Visual Tracking
Full text linkObjectives: This study aims to design a dead-angle-free smart desk lamp. Methods: The convolutional neural network (CNN) algorithm was used to realize the identification and positioning of objects. Then, the desk lamp arm was driven according to positioning to realize dead-angle-free illumination. In the subsequent testing, the designed desk lamp was compared with others driven by the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms. Findings: The CNN algorithm implemented in the smart desk lamp demonstrated superior target recognition performance and positioning accuracy when compared to the other two algorithms. Moreover, with this algorithm, the smart desk lamp efficiently generated tracking responses for targets and displayed minimal positioning errors once tracking became stable. Novelty:The novelty of this article lies in the utilization of the CNN algorithm to achieve visual tracking for a smart desk lamp, which serves as the basis for its automatic adjustment. Doi: 10.28991/HIJ-2023-04-04-05 Full Text: PD
Interdisciplinary Studies of Jet Systems using Euler Methodology and Computational Fluid Dynamics Technologies
Full text linkThis study aims to conduct interdisciplinary research using computerized solutions to inventive problems in fluidics. The chosen direction of work relates to the scientific search for new opportunities for extremal control of the thrust vector within a complete geometric sphere (with the range of rotation angle change for the thrust vector being ±180° in any direction). This study assesses the prospects for the emergence of patentable innovative solutions for maneuverable unmanned vehicles. One of the most urgent tasks is to increase the process efficiency in forming fluid medium flow, expanding opportunities for controlling this flow parameter. The research uses an interdisciplinary approach with simulation modeling. The authors of the paper reveal new possibilities for using an ejector with two curved mixing chambers to create special jet units. Calculations (CFD) have confirmed the performance of the simulator ejector when controlling the thrust vector with 90° and 180° rotation. Manufacturing physical micromodels used additive technologies to allow simulation modeling under laboratory conditions. Using "data mining” methods, it was shown for the first time that, based on Euler's ideas and methodology, it is possible to create a new methodology for teaching and solving inventive problems. The research results apply to power engineering and unmanned vehicles. Some results of scientific studies can be used to create special computer programs working together with artificial intelligence to create advanced techniques and technologies. Doi: 10.28991/HIJ-2023-04-04-01 Full Text: PD
A Framework to Estimate the Key Point Within an Object Based on a Deep Learning Object Detection
Full text linkAutomatic identification of key points within objects is crucial in various application domains. This paper presents a novel framework for accurately estimating the key point within an object by leveraging deep neural network-based object detection. The proposed framework is built upon a training dataset annotated with four non-overlapping bounding boxes, one of which shares a coordinate with the key point. These bounding boxes collectively cover the entire object, enabling automatic annotation if region annotations around the key point exist. The trained object detector is then utilized to generate detection results, which are subsequently post-processed to estimate the key point. To validate the effectiveness of the framework, experiments were conducted using two distinct datasets: cross-sectional images of a parawood log and pupil images. The experimental results demonstrate that our proposed framework surpasses previously proposed approaches in terms of precision, recall, F1-score, and other domain-specific metrics. The improvement in performance can be attributed to the unique annotation strategy and the fusion of object detection and key point estimation within a unified deep learning framework. The contribution of this study lies in introducing a novel framework for closely estimating key points within objects based on deep neural network-based object detection. By leveraging annotated training data and post-processing techniques, our approach achieves superior performance compared to existing methods. This work fills a critical gap in the field by integrating object detection and key point estimation, which has received limited attention in previous research. Our framework provides valuable insights and advancements in key point estimation techniques, offering potential applications in precise object analysis and understanding. Doi: 10.28991/HIJ-2023-04-01-08 Full Text: PD