Search results

This paper aims to investigate a novel impact time control guidance (ITCG) law based on the sliding mode control (SMC) for a nonmaneuvering target using the predicted interception point (PIP).

To intercept the target with the minimal miss distance and desired impact time, an estimation of time-to-go is introduced. This estimation results in a precise impact time for multimissiles salvo attack the target at the same time. Even for a large lead angle, the desired impact time is achieved by using the sliding mode and Lyapunov stability theory. The singularity issue of the proposed impact time guidance laws is also analyzed to achieve an arbitrary lead angle with the desired impact time.

Numerical scenarios with desired impact time are presented to illustrate the performance of the proposed ITCG law. Comparison with the state-of-art impact time guidance laws proves that the guidance law in this paper can enable the missile to intercept the target with minimal miss distance and final impact time error. This method enables multiple missiles to attack the target simultaneously with different distances and arbitrary lead angles.

An ITCG law based on sliding mode and Lyapunov stability theory is proposed, and the switching surface is designed based on a novel estimation time-to-go for the missile to intercept the target with minimal miss distance. To intercept the target with initial arbitrary lead angles and desired impact time, the authors analysis the singular issue in SMC to ensure that the missile can intercept the target with arbitrary lead angle. The proposed approach for a nonmaneuvering target using the PIP has simple forms, and therefore, they have the superiority of being implemented easily.

Online news websites provide huge amounts of timely news, bringing the challenge of recommending personalized news articles. Generative adversarial networks (GAN) based on collaborative filtering (CFGAN) can achieve effective recommendation quality. However, CFGAN ignores item contents, which contain more latent preference features than just user ratings. It is important to consider both ratings and item contents in making preference predictions. This study aims to improve news recommendation by proposing a GAN-based news recommendation model considering both ratings (implicit feedback) and the latent features of news content.

The collaborative topic modeling (CTM) can improve user preference prediction by combining matrix factorization (MF) with latent topics of item content derived from latent topic modeling. This study proposes a novel hybrid news recommendation model, Hybrid-CFGAN, which modifies the architecture of the CFGAN model with enhanced preference learning from the CTM. The proposed Hybrid-CFGAN model contains parallel neural networks – original rating-based preference learning and CTM-based preference learning, which consider both ratings and news content with user preferences derived from the CTM model. A tunable parameter is used to adjust the weights of the two preference learnings, while concatenating the preference outputs of the two parallel neural networks.

This study uses the dataset collected from an online news website, NiusNews, to conduct an experimental evaluation. The results show that the proposed Hybrid-CFGAN model can achieve better performance than the state-of-the-art GAN-based recommendation methods. The proposed novel Hybrid-CFGAN model can enhance existing GAN-based recommendation and increase the performance of preference predictions on textual content such as news articles.

As the existing CFGAN model does not consider content information and solely relies on history logs, it may not be effective in recommending news articles. Our proposed Hybrid-CFGAN model modified the architecture of the CFGAN generator by adding a parallel neural network to gain the relevant information from news content and user preferences derived from the CTM model. The novel idea of adjusting the preference learning from two parallel neural networks – original rating-based preference learning and CTM-based preference learning – contributes to improve the recommendation quality of the proposed model by considering both ratings and latent preferences derived from item contents. The proposed novel recommendation model can improve news recommendation, thereby increasing the commercial value of news media platforms.

Forecasting population movement trends is crucial for implementing effective policies to regulate labor force growth and understand demographic changes. Combining migration theory expertise and neural network technology can bring a fresh perspective to international migration forecasting research.

This study proposes a conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data problem, thereby enhancing the accuracy of migration forecasts.

The model was tested by forecasting migration flows between different countries and had good generalizability and validity. The results are robust as the proposed solutions can achieve lesser mean absolute error, mean squared error, root mean square error, mean absolute percentage error and R² values, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the traditional gravity model.

This study is significant because it demonstrates a highly effective technique for predicting international migration using conditional GANs. By incorporating migration knowledge into our models, we can achieve prediction accuracy, gaining valuable insights into the differences between various model characteristics. We used SHapley Additive exPlanations to enhance our understanding of these differences and provide clear and concise explanations for our model predictions. The results demonstrated the theoretical significance and practical value of the MK-CGAN model in predicting international migration.

This case study was built from secondary data such as news articles and videos. Several drafts of the case study with teaching note were tested in classroom settings and shared at a case writing conference. The case was revised based on feedback from students and roundtable discussions from the conference.

“What’s next: Ever Given after the Suez Canal incident (Evergreen Marine Corporation in, 2022)” explores the situation of the firm Evergreen Marine Corporation, a world-leading cargo shipping company headquartered in Taiwan, and its efforts to deal with challenges stemming from a pandemic and the global supply chain transition. The case provides background on the latest changes in global business environments, the Suez Canal Incident stemming from the grounding of Ever Given and firm-specific information, which would help students to understand the context affecting Evergreen Marine Corporation’s (EMC) strategic decisions. The case enables students to evaluate EMC’s overall position and to analyze the actions that they can take to deal with these challenges in a dynamic global environment.

This case would be appropriate for a course in strategy or international business, especially with the topic of international supply chain management.

This study aims to evaluate the current situation of education for sustainable development, climate change education and environmental education in a nationwide context. Methodologically, this study calls for more research to go beyond case studies and take a similar approach to examine university curricula and facilitate cross-country comparisons.

This paper examines the status of climate and sustainability curricula in Taiwan’s higher education system. Using the course catalog for the 2020–2021 academic year, the authors constructed a unique data set that includes 1,827 courses at 29 major universities in Taiwan. In each institution, the authors search for course titles that include “climate,” “sustainable/sustainability” and “environment/environmental” as keywords and code the courses according to their disciplines.

The finding highlights the variations across institutional types and subject matters. Public universities have an average of 4.94 related courses per 1,000 students, whereas private universities have only 3.13. In general, the relevant courses are more concentrated in the STEM and bioscience fields. The curricula, however, are seriously constrained by the disciplinary structure and foster few transdisciplinary perspectives.

The authors seek to go beyond case studies and offer one of the most comprehensive curricula samples at the national level. Taiwan adds an important data point, as the current literature focuses heavily on the USA and Europe.

This study aims to evaluate the efficacy of a digital medical library, including department-based electronic journal access, library training course participation and scholarly publications.

The data on full-text electronic journal access, participants of library training courses and scholarly publications were exported from a digital medical library database during 2017–2021. In addition, electronic journal access and library training courses were divided into high-level and low-level groups, while scholarly publications were divided into physician and non-physician groups.

The scholarly publications had a positive correlation to library training courses and electronic journal access. Furthermore, scholarly publications showed a significant difference between the high-level and low-level electronic journal access groups but not between the high-level and low-level library training course groups. Scholarly publications and electronic journal access had positive correlations for both the physician and non-physician groups. Scholarly publications and library training courses, and electronic journal access and library training courses had positive correlations only in the non-physician group.

The importance of electronic journal access for scholarly publications is suggested based on the findings of the present study. The training courses held by the medical library had a positive effect on scholarly publications for the non-physician group.

The current study provides insights relevant to the electronic journal access of library-supported scholarly publications among medical departments. These results can serve as a reference for medical library development planning and decision-making in the future.

The purpose of this study was to explore school principals’ job crafting profiles during the prolonged COVID-19 crisis in 2021, and investigate profile differences regarding principals’ own perceived servant leadership, stress and work meaningfulness.

Using latent profile analysis (LPA), two job crafting profiles were identified: (1) active crafters (55%) and (2) average crafters (45%). By auxiliary measurement-error-weighted-method (BCH), we examined whether and how job crafting profiles differed in terms of servant leadership, stress and work meaningfulness.

Active crafters reported higher than the overall mean level of approach-oriented job crafting (increasing job resources and demands), whereas average crafters reported an overall mean level of approach-oriented job crafting. Avoidance-oriented job crafting by decreasing hindering job demands did not differentiate the two profiles. Active crafters reported significantly higher servant leadership behavior, stress and work meaningfulness than average crafters.

Study findings provide new knowledge and reflect the implications that the unprecedented pandemic had for education. This study contributes to the existing literature within the scholarship of job crafting through empirical research during the prolonged COVID-19 pandemic. For practitioners, these study findings reflect contextual constraints, organizational processes and culture, and leadership in workplaces.

This paper aims to clarify the resistance degree of group road logistics to flood disaster resilience. The paper measures the resilience of group road logistics by establishing network structure model. The purpose of this study is to improve the resilience of road log.

This paper adopts Delphi method to collect data, interviews mainly flood management experts and supply chain risk management experts, and then analyzes the data through the network structure model combined with interpretative structure model (ISM) and analytical network process (ANP).

The results show that flood frequency and drainage systems are the main factors affecting the resilience of road transport logistics in urban areas. These research results provide useful guidance for the effective planning and design of urban road construction and infrastructure.

However, the main factors affecting the resilience of road transport logistics are likely to change with the development of factors such as climate, economy and environment. Therefore, in future work, the authors' research will focus on the further application of this evaluation method.

The results show that the impact of flooding on the four dimensions of road logistics resilience varies. This shows that in deciding what intervention measures are to be taken to improve the resilience of the road network to flooding, various measures need to be considered.

This paper provides a more scientific analysis of the risk management ability of the road network in the face of floods. In addition, it also provides a useful reference for urban road planners.

This paper addresses a clear need to study how to build models to improve the resilience of road logistics in flood risk.

This study aims to empirically analyse the influence mechanism of perceived interactivity in real estate APP which affects consumers' psychological well-being. With the growing application of human–machine interaction in real estate APP, it is crucial to utilize human–machine interaction to stimulate perceived interactivity between humans and machines to positively impact consumers' psychological well-being and sustainable development of real estate APP. However, it is unclear whether perceived interactivity improves consumers' psychological well-being.

This study proposes and examines a theoretical model grounded in the perceived interactivity theory, considers the relationship between perceived interactivity and consumers' psychological well-being and explores the mediating effect of perceived value and the moderating role of privacy concerns. It takes real estate APP as the research object, analyses the data of 568 consumer samples collected through questionnaires and then employs structural equation modelling to explore and examine the proposed theoretical model of this study.

The findings are that perceived interactivity (i.e. human–human interaction and human–information interaction) positively influences perceived value, which in turn affects psychological well-being, and that perceived value partially mediates the effect of perceived interaction on psychological well-being. More important findings are that privacy concerns not only negatively moderate human–information interaction on perceived value, but also negatively moderate the indirect effects of human–information interaction on users' psychological well-being through perceived value.

This study expands the context on perceived interaction and psychological well-being in the field of real estate APP, validating the mediating role and boundary conditions of perceived interactivity created by human–machine interaction on consumers' psychological well-being, and suggesting positive implications for practitioners exploring human–machine interaction technologies to improve the perceived interaction between humans and machines and thus enhance consumer psychological well-being and span sustainable development of real estate APP.

This study aims to learn the dynamic radar cross-section (RCS) of a deflection air brake.

The aircraft model with delta wing, V-shaped tail and blended wing body is designed, and high-precision unstructured grid technology is used to deal with the surface of air brake and fuselage. The calculation method based on multiple tracking and dynamic scattering is presented to calculate RCS.

The fuselage has a low scattering level, and the opening air brake will bring obvious dynamic RCS effects to itself and the whole machine. The average indicator of air brake RCS can be lower than –0.6 dBm² under the tail azimuth, while that of forward and lateral direction is lower. The mean RCS of fuselage is obviously higher than that of air brake, while the deflected air brake and its cabin can still provide strong scattering sources at some azimuths. When the air brake is opening, the change amplitude of the aircraft forward RCS can exceed 19.81 dBm².

This research has practical significance for the dynamic electromagnetic scattering analysis and stealth design of the air brake.

The calculation method for aircraft RCS considering air brake dynamic deflection has been established.