Earticle

VR based learning has emerged within the aviation industry as a sustainable digital alternative while traditional methods are moving away from sustainability due to heavy resources. This research investigates how VR based aviation service education impacts learning outcomes within the SDGs framework. This research has utilized 289 samples from cabin service management students , the result of this research presented that VR features positively influence learning outcomes through presence, control and active engagement, and enjoyment. Specifically, the mediating variable of control and active engagement was identified as the most influential driver. This research reached findings that students perceive VR as a realistic environment facilitating autonomous interaction, leading to professional outcomes as potential cabin crew. Overall, this research demonstrates that VR-based education provides a sustainable infrastructure by reducing physical resources while enhancing human capacity.

As artificial intelligence (AI) increasingly drives decision-making in the financial sector, ensuring fairness in machine-learning models has become critical. Bias in AI models can lead to discriminatory practices, undermining public trust and restricting access to essential financial services. While existing financial services leverage AI to enhance efficiency and accuracy, these systems can inadvertently produce unfair outcomes for specific groups defined by sensitive attributes, such as gender and race. This study addresses the challenge of mitigating bias in loan-approval models by applying fairness-aware machine-learning techniques. We investigate two distinct constraint-based strategies for bias mitigation: fairness- and accuracy-constrained models. These strategies are evaluated using logistic regression (LR) and a large-scale, contemporary financial dataset from the Korea Credit Information Services. The results demonstrate that fairness-constrained models achieve a superior balance between fairness and accuracy compared to a conventional LR model. Furthermore, we highlight the importance of tailored data preprocessing and carefully selecting relevant sensitive attributes (e.g., gender, age, nationality) in enhancing fairness outcomes. The findings underscore the necessity of integrating fairness considerations into every stage of the AI model development lifecycle within finance, ensuring equitable outcomes without compromising predictive performance.

Objective: This study was conducted to investigate the effects of a ZEP metaverse-supported, problem-based learning (PBL) approach on student engagement and applied learning in Orthotics & Prosthetics courses in physical therapy education. Design: We conducted a descriptive case study. Methods: Undergraduate physical therapy students participated in a 12-week course structured into five weeks of theoretical instruction and seven weeks of project-based learning. Utilizing the ZEP metaverse platform, students formed teams to create companies marketing virtual assistive devices, and collaboratively organized a simulated orthotic and prosthetic exposition. Through designing virtual exhibition booths, interactive product presentations, and company narratives, the students engaged in roleplay and active knowledge construction. Learning activities were conducted in a hybrid format combining face-to-face lectures and metaverse-based projects. After the course, a structured Likert scale survey and open-ended questions were used to evaluate student engagement, satisfaction, and perception of learning experience value. Results: Over 90% of participants reported high levels of engagement and satisfaction. Students highlighted improvements in communication, creativity, and confidence in applying clinical knowledge. Such immersive contextual learning experiences contribute to the development of problem-solving and collaboration skills, aligning with the core principles of situated learning theory. Conclusions: Metaverse-based PBL can serve as an effective pedagogical strategy to enhance student participation, creativity, and application of theoretical knowledge to physical therapy education. This approach highlights the potential of integrating digital platforms into competency-based professional health curricula.

This study advances empathetic dialogue generation in Korean by integrating emotional learning with artificial intelligence (AI). It addresses the limited understanding of how AI-generated expressions preserve human emotional meaning. A hybrid system was developed using a fine-tuned XLM-RoBERTa model for 24-category emotion classification and ChatGPT-5 for dialogue generation, operating in context-only and emotion-aware modes. Using 18 Korean empathetic dialogue scenarios (108 utterances) from AI Hub, evaluations included exploratory data analysis, BLEU, GLEU, BERTScore, and large language model (LLM)-as-a-judge assessments with five external models (ClovaX, Gemini, Perplexity, Claude, and Copilot). Emotion-aware responses were longer (133.7 ± 24.8 characters), more lexically diverse (53.7 ± 8.1 tokens), and preferred by LLM judges in 72.2% of cases, despite comparable semantic similarity (BERTScore > 0.85). The findings highlight the promise of emotion-aware AI for empathetic applications in mental health, education, and customer service, while emphasizing ethical challenges in human-AI interaction.

Machine learning has emerged as a transformative technology with the potential to revolutionize library services by enhancing precision and efficiency in various operational aspects. This study explores into the significance of machine learning in libraries, exploring its applications, challenges, and opportunities for optimization. The integration of machine learning algorithms enables libraries to streamline resource management, personalize user experiences, and automate tasks to meet evolving user demands. However, implementing machine learning in library operations poses challenges related to data collection, pre-processing, and ethical considerations. Strategies for enhancing precision through data labelling, annotation, and improving recommendation systems using machine learning are essential for maximizing the impact of these technologies. Evaluating the performance of machine learning models in library settings is crucial for assessing their effectiveness and ensuring reliable outcomes. Furthermore, ethical considerations must be prioritized to safeguard user privacy and mitigate algorithmic biases. Looking ahead, future trends and opportunities for machine learning in libraries hold promise for advancing service delivery, promoting innovation, and creating more user-centric library experiences.

The purpose of this study is to examine how evaluation metrics influence the perception and performance of question answering (QA) systems, particularly focusing on their effectiveness in QA tasks. We compare four different models: BERT, BioBERT, BioClinicalBERT, and RoBERTa, utilizing ten EPIC-QA questions to assess each model's answer extraction performance. The analysis employs both semantic and lexical metrics. The outcomes reveal clear model-specific behaviors: Bio-ClinicalBERT initially identified irrelevant phrases before focusing on relevant information, whereas BERT and BioBERT continually converge on similar answers, exhibiting a high degree of similarity. RoBERTa, on the other hand, demonstrates effective use of long-range dependencies in text. Semantic metrics outperform lexical metrics, with BERTScore attaining the maximum accuracy (0.97), highlighting the significance of semantic evaluation. Our findings indicate that the choice of evaluation metrics significantly influences the perceived efficacy of models, suggesting that semantic metrics offer more nuanced and insightful assessments of QA system performance. This study contributes to the field of natural language processing and machine learning by providing guidelines for selecting evaluation metrics that align with the strengths and weaknesses of various QA approaches.

Academic libraries are at the forefront of supporting their parent institutions in teaching and learning, research activities, and community services for the students and faculty members, but, the researchers observed that some of the research emanating from faculty members in academic institutions particularly universities remains largely unknown, unrecognized and invisible on the global scene. This present paper is therefore a modest attempt towards addressing the issue of enhancing the faculty research visibility in the institutions of higher learning by the academic libraries. It also examines the extent academic libraries in public universities in Nigeria use research visibility channels to increase the global visibility of their faculty members. Difficulties encountered by librarians and ways of tackling the visibility of the faculty were also examined. A descriptive survey research design was adopted and the population consisted of all the 162 librarians in public universities in South-East (S.E), Nigeria. Telephone calls and Online Questionnaire were used for data collection. The number of librarians was obtained through phone calls from the Heads of each of the Libraries. The Online Questionnaire was submitted to the WhatsApp platforms of librarians in Nigeria- Academic and Research Libraries (ARL) and Chartered Librarians in Nigeria Connect (CLN-Connect). The questionnaire was structured in such a way that only the Librarians in Public universities in the S.E. Nigeria will respond to it. At the end of the day only 120 librarians responded, at a response rate of 74%. The study was analysed using tables, percentages and charts. The study recommended that librarians who are unaware of RVCs and its utilization should go for training to acquire the knowledge that will enable them enhance the global visibility of faculty staff, Management of Public universities in S.E, Nigeria should in addition to addressing copyright issues by the use of disclaimer notices and creative common licensing and provision of infrastructural facilities e.g. steady power supply, High power brand Internet connectivity, establishment of an Institutional Repository, etc, also should mandate the faculty staff to release their productive work to the library for onward submission to the RVCs platforms for enhancement of their global visibility.

Recipe generation is an important task in both research and real life. In this study, we explore several pretrained language models that generate recipes from a list of text-based ingredients. Our recipe-generation models use a standard self-attention mechanism in Transformer and integrate a re-attention mechanism in Vision Transformer. The models were trained using a common paradigm based on cross-entropy loss and the BRIO paradigm combining contrastive and cross-entropy losses to achieve the best performance faster and eliminate exposure bias. Specifically, we utilize a generation model to produce N recipe candidates from ingredients. These initial candidates are used to train a BRIO-based recipe-generation model to produce N new candidates, which are used for iteratively fine-tuning the model to enhance the recipe quality. We experimentally evaluated our models using the RecipeNLG and CookingVN-recipe datasets in English and Vietnamese, respectively. Our best model, which leverages BART with re-attention and is trained using BRIO, outperforms the existing models.

Most advanced countries that are members of the World Physiotherapy have established a 4-year education system or specialized graduate school system for physical therapists based on national standards. They have also expanded their laws and systems to provide physical therapists with the autonomy and independence to offer services in their clinics. However, compared with developed countries in North America and Europe, there are issues with the autonomy and independence of physical therapists in Korea related to national regulations. Social status and recognition of the profession are also lagging. Korea is expected to become a super-aged society by 2025. To reduce the financial burden of healthcare and welfare on the government, it is necessary to extend the time spent by older adults on independent activities and minimize their time spent using medical services. To achieve this goal and maximize the active life of older adults, a plan to efficiently use licensed physical therapists in the country should be prepared. Korea should increase the license utilization rate of physical therapists to reduce waste at the national level and increase the professional hope of the younger generations of physical therapists. To create a healthcare policy focusing on the use of physical therapy personnel, similar to that in advanced countries, it is necessary to unify educational systems and produce excellent physical therapists. Providing professional autonomy can help physical therapists develop a sense of job satisfaction. Outstanding talent will choose physical therapy as a profession if they can see hope for their future careers, and if physical therapy services in Korea are similar to those delivered in advanced countries, physical therapy in Korea can develop into a healthcare service that people desire.

Autophagy is a process of eliminating damaged or unnecessary proteins and organelles, thereby maintaining intracellular homeostasis. Deregulation of autophagy is associated with several diseases including cancer. Contradictory dual roles of autophagy have been well established in cancer. Cytoprotective mechanism of autophagy has been extensively investigated for overcoming resistance to cancer therapies including radiotherapy, targeted therapy, immunotherapy, and chemotherapy. Selective autophagy inhibitors that directly target autophagic process have been developed for cancer treatment. Efficacies of autophagy inhibitors have been tested in various pre-clinical cancer animal models. Combination therapies of autophagy inhibitors with chemotherapeutics are being evaluated in clinal trials. In this review, we will focus on genetical and pharmacological perturbations of autophagy-related proteins in different steps of autophagic process and their therapeutic benefits. We will also summarize combination therapies of autophagy inhibitors with chemotherapies and their outcomes in pre-clinical and clinical studies. Understanding of current knowledge of development, progress, and application of cytoprotective autophagy inhibitors in combination therapies will open new possibilities for overcoming drug resistance and improving clinical outcomes.

Background: The external rotation (ER) exercise in performed at a 90° abduction of the shoulder joint is an effective to strengthen the infraspinatus. However, failure of the humeral head to control axial rotation during exercise can be increased the posterior deltoid over activity. Biofeedback training is an effective method of promoting motor learning and control it could look forward to activate the infraspinatus selectively by controlling the humeral head during exercise. Objects: The aim of this study was investigated that whether biofeedback for axial rotation was effective to activate selectively the infraspinatus during ER exercise. Methods: The 15 healthy males participated, and all subjects performed both ER exercise in a sitting position with shoulder abducted 90° under conditions with and without axial rotation biofeedback. Exercise was performed in a range of 90° ER, divided into three phases: concentric, isometric, and eccentric. The infraspinatus and posterior deltoid muscle activity were observed using surface electromyography. Results: Both infraspinatus activity (p < 0.01) and infraspinatus to posterior deltoid activity ratio (p = 0.01) were significantly higher with biofeedback however, posterior deltoid activity was significantly lower with biofeedback (p = 0.01). The infraspinatus muscle activity and muscle activity ratio were the highest in the isometric contraction type, and there were significant differences for all contraction types (p < 0.05). Whereas, the posterior deltoid activity was the lowest in the isometric contraction type, and showed a significant difference between isometric and other two contraction types (p < 0.05), but no significant different between concentric and eccentric contraction. Conclusion: Our results indicate that the axial rotation biofeedback during sitting ER exercise might be effective method to activating selective infraspinatus muscle and recommended to enhance the dynamic stability of the shoulder joint.

The primary function of an electronic records management system (ERMS) is to support organisations in providing effective records management services by enabling efficient remote access to the organisations' records. This helps the organisation to continue running during emergency events, such as the COVID-19 pandemic. The need to study ERMS for accessing records remotely has increased dramatically, due to the increase in daily use. The situation arising from the COVID-19 pandemic has increased the need for implementing proper digital systems, such as ERMS, to enable efficient work processes and enhance business continuity. An ERMS has the potential to allow organisations to create records and workflows off-site. During a pandemic, the ability to structure processes digitally helps in maintaining operations remotely. This study aims to provide a narrative review of the ERMS literature with an emphasis on explaining the primary components of ERMS that act as enablers for the implementation of the system in the oil and gas sector of developing countries. The current study proposes ERMS roles and responsibilities that could enhance business continuity. The authors use a qualitative narrative review and analyse the literature related to this study and its findings. The results show that, in cases of risk or crises, staff members need to have easy access to their records and documents to remain productive. An ERMS allows professionals to remain active and work off-site. Thus, ERMS play a significant role in protecting an organisation's content through the monitoring and control over who has authorisation to access its records.

Wireless networks have become integral to society as they provide mobility and scalability advantages. However, their disadvantage is that they cannot control the media, which makes them vulnerable to various types of attacks. One example of such attacks is the evil twin access point (AP) attack, in which an authorized AP is impersonated by mimicking its service set identifier (SSID) and media access control (MAC) address. Evil twin APs are a major source of deception in wireless networks, facilitating message forgery and eavesdropping. Hence, it is necessary to detect them rapidly. To this end, numerous methods using clock skew have been proposed for evil twin AP detection. However, clock skew is difficult to calculate precisely because wireless networks are vulnerable to noise. This paper proposes an evil twin AP detection method that uses a multiple-feature-based machine learning classification algorithm. The features used in the proposed method are clock skew, channel, received signal strength, and duration. The results of experiments conducted indicate that the proposed method has an evil twin AP detection accuracy of 100% using the random forest algorithm.

Currently, microarray gene expression data take advantage of the sufficient classification of cancers, which addresses the problems relating to cancer causes and treatment regimens. However, the sample size of gene expression data is often restricted, because the price of microarray technology on studies in humans is high. We propose enhancing the gene expression classification of support vector machines with generative adversarial networks (GAN-SVMs). A GAN that generates new data from original training datasets was implemented. The GAN was used in conjunction with nonlinear SVMs that efficiently classify gene expression data. Numerical test results on 20 low-sample-size and very high-dimensional microarray gene expression datasets from the Kent Ridge Biomedical and Array Expression repositories indicate that the model is more accurate than state-of-the-art classifying models.

The narrow-down approach, separately composed of search and classification stages, is an effective way of dealing with large-scale hierarchical text classification. Recent approaches introduce methods of incorporating global, local, and path information extracted from web taxonomies in the classification stage. Meanwhile, in the case of utilizing path information, there have been few efforts to address existing limitations and develop more sophisticated methods. In this paper, we propose an expansion method to effectively exploit category path information based on the observation that the existing method is exposed to a term mismatch problem and low discrimination power due to insufficient path information. The key idea of our method is to utilize relevant information not presented on category paths by adding more useful words. We evaluate the effectiveness of our method on state-of-the art narrow-down methods and report the results with in-depth analysis.

Black jujube was made by aging dried jujube and its physiochemical characteristics, antioxidant activities and ${\alpha}$-glucosidase inhibitory activities were evaluated. The moisture and sugar contents were increased depending on the period of aging times and the pH was reduced thereby increasing acidity. The color of black jujube extract was changed from red to black resulting in decreases of Hunter color values L, a and b. As the aging progressed, sucrose was decomposed by increasing glucose and fructose, indicating higher contents of the total reducing sugars. Among the six different types of organic acids extracted from dried jujube, the levels of oxalic acid and citric acid were increased as the aging progressed. The total polyphenol contents in ethanol and water extracts of dried jujube were 7.74 and 8.12 mg/g, respectively. The water extract of black jujube aged for 48 hr contained the highest polyphenol contents at 16.82 mg/g. The 5'-hydroxymethylfurfural (5'-HMF) contents of black jujube extract significantly increased by longer aging times, and contained higher contents in the ethanol extract than water extract. The ethanol extract of black jujube showed the highest 5'-HMF content with 338.89 mg% after aging for 3 days. Also, $IC_{50}$ values of black jujube aged for 72 hr evaluated by DPPH and ABTS radical assays were 0.54 and 0.59 mg/mL, respectively. ${\alpha}$-Glucosidase inhibitory activities of black jujube at the concentration of 3.33 mg/mL (ethanol extract) increased from 65 to 80 % after aging for 72 hr.

A classic document clustering technique may incorrectly classify documents into different clusters when documents that should belong to the same cluster do not have any shared terms. Recently, to overcome this problem, internal and external knowledge-based approaches have been used for text document clustering. However, the clustering results of these approaches are influenced by the inherent structure and the topical composition of the documents. Further, the organization of knowledge into an ontology is expensive. In this paper, we propose a new enhanced text document clustering method using non-negative matrix factorization (NMF) and WordNet. The semantic terms extracted as cluster labels by NMF can represent the inherent structure of a document cluster well. The proposed method can also improve the quality of document clustering that uses cluster labels and term weights based on term mutual information of WordNet. The experimental results demonstrate that the proposed method achieves better performance than the other text clustering methods.

서지관계모형을 위한 개념적 모델인 IFLA의 FRBR (Functional Requirements for Bibliographic Records)에 대한 대안적인 접근이 발표되어 왔지만, FRBR 모델의 내부적인 일관성에 대해서는 많은 연구가 진행되어 있지 않은 실정이다. 본 연구는 FRBR 객체-관계 (entity-relationship) 모델에서 객체들 (entities)간의 관계(relationships) 에서 발생하는 상속 (inheritance) 속성과 관련된 모델의 내부적 모순점을 지적하며, 이러한 문제를 해결하기 위해 FRBR 모델의 표현성(expressiveness)을 강화하기 위한 여러 대안적인 방안을 논의한다.

The Functional Requirements for Bibliographic Records (FRBR) is a 'conceptual model of the bibliographic universe' developed by the International Federation of Library Associations and Institutions (IFLA). Although some studies have suggested improvements in FRBR, and others explore alternative approaches, less attention has been paid to analyzing the internal coherence and consistency of the FRBR view as presented not only in the FRBR entity-relationship model and text of the FRBR document, but also in the related explanations and presentations of FRBR expositors. Our investigations have noted some interesting discrepancies between the general FRBR approach as presented in various expository documents and the specific account presented in the FRBR ER model and the FRBR document. We see that in one case these discrepancies can be easily remedied by adding additional modeling constructs and assertions, but in another case (the supposed 'inheritance' of attributes across the Group 1 entities), there is a substantial difficulty in maintaining a consistent model. We discuss several alternative approaches to enhancing the expressiveness of FRBR in order remedy this problem. We note that none is entirely satisfactory.

This paper shows the use of wavelet transformation combined with inverse acoustics to reconstruct the surface velocity of a noise source. This approach uses the boundary element analysis based on the measured sound pressure at a set of field points, the Helmholtz integral equations and wavelet transformation for reconstructing the normal surface velocity field. The reconstructed field can be diverged due to the small measurement errors in the case of nearfield acoustic holography (NAH) using an inverse boundary element method. In order to avoid this instability in the inverse problem, the reconstruction process should include some form of regularization for enhancing the resolution of source images. The usual method of regularization has been the truncation of wave vectors associated with small singular values, although the order of an optimal truncation is difficult to determine. In this paper, a wavelet transformation is applied to reduce the computation time for inverse acoustics and to enhance the reconstructed vibration field. The computational speed-up is achieved, with solution time being reduced to $14.5\%$.

Indocyanine green (ICG) lymphography has rapidly emerged as a key imaging modality for improving the diagnosis and management of lymphedema. ICG lymphography offers superior sensitivity and detailed assessment of dermal backflow patterns, functional lymphatic channels, and collateral drainage routes. Evidence also supports its role in guiding targeted rehabilitation, including ICG-guided lymphedema treatment. Clinicians can enhance diagnostic precision and potentially improve therapeutic effectiveness by integrating ICG lymphography into clinical practice. This review summarizes current evidence on its clinical utility, highlighting how real-time visualization of superficial lymphatic pathways and lymph nodes allows for detection of dysfunction, and improved treatment planning. Future work should focus on standardizing protocols and developing quantitative metrics to expand its values in comprehensive lymphedema care.