Search Results (324)

Amidst the escalating demand for high-precision structural health monitoring in large-scale engineering applications, carbon fiber-reinforced polymer fiber Bragg grating (CFRP-FBG) sensors have emerged as a pivotal technology for fatigue life evaluation, owing to their exceptional sensitivity and intrinsic immunity to electromagnetic interference. A time-series predictive architecture based on long short-term memory (LSTM) networks is developed in this work to facilitate intelligent fatigue life assessment of structures subjected to complex cyclic loading by capturing and modeling critical spectral characteristics of CFRP-FBG sensors, specifically the side-mode suppression ratio and main-lobe peak-to-valley ratio. To enhance model robustness and generalization, Principal Component Analysis (PCA) was employed to isolate the most salient spectral features, followed by data preprocessing via normalization and model optimization through the integration of the Adam optimizer and Dropout regularization strategy. Relative to conventional Backpropagation (BP) neural networks, the LSTM model demonstrated a substantial improvement in predicting the side-mode suppression ratio, achieving a 61.62% reduction in mean squared error (MSE) and a 34.99% decrease in root mean squared error (RMSE), thereby markedly enhancing robustness to outliers and ensuring greater overall prediction stability. In predicting the peak-to-valley ratio, the model attained a notable 24.9% decrease in mean absolute error (MAE) and a 21.2% reduction in root mean squared error (RMSE), thereby substantially curtailing localized inaccuracies. The forecasted confidence intervals were correspondingly narrower and exhibited diminished fluctuation, highlighting the LSTM architecture’s enhanced proficiency in capturing nonlinear dynamics and modeling temporal dependencies. The proposed method manifests considerable practical engineering relevance and delivers resilient intelligent assistance for the seamless implementation of CFRP-FBG sensor technology in structural health monitoring and fatigue life prognostics. Full article

Background: Artificial intelligence (AI) is being increasingly promoted as a means to enhance diagnostic accuracy, to streamline workflows, and to improve overall care quality in primary care. However, empirical evidence on how primary care physicians (PCPs) perceive, engage with, and emotionally respond to AI technologies in everyday clinical settings remains limited. Concerns persist regarding AI’s usability, transparency, and potential impact on professional identity, workload, and the physician–patient relationship. Methods: This qualitative study investigated the lived experiences and perceptions of 28 PCPs practicing in diverse outpatient settings across Germany. Participants were purposively sampled to ensure variation in age, practice characteristics, and digital proficiency. Data were collected through in-depth, semi-structured interviews, which were audio-recorded, transcribed verbatim, and subjected to rigorous thematic analysis employing Mayring’s qualitative content analysis framework. Results: Participants demonstrated a fundamentally ambivalent stance toward AI integration in primary care. Perceived advantages included enhanced diagnostic support, relief from administrative burdens, and facilitation of preventive care. Conversely, physicians reported concerns about workflow disruption due to excessive system prompts, lack of algorithmic transparency, increased cognitive and emotional strain, and perceived threats to clinical autonomy and accountability. The implications for the physician–patient relationship were seen as double-edged: while some believed AI could foster trust through transparent use, others feared depersonalization of care. Crucial prerequisites for successful implementation included transparent and explainable systems, structured training opportunities, clinician involvement in design processes, and seamless integration into clinical routines. Conclusions: Primary care physicians’ engagement with AI is marked by cautious optimism, shaped by both perceived utility and significant concerns. Effective and ethically sound implementation requires co-design approaches that embed clinical expertise, ensure algorithmic transparency, and align AI applications with the realities of primary care workflows. Moreover, foundational AI literacy should be incorporated into undergraduate health professional curricula to equip future clinicians with the competencies necessary for responsible and confident use. These strategies are essential to safeguard professional integrity, support clinician well-being, and maintain the humanistic core of primary care. Full article

Virtual reality interaction design and story co-creation design for multiple users is an interdisciplinary research field that merges human–computer interaction, creative design, and virtual reality technologies. Story co-creation design enables multiple users to collectively generate and share narratives, allowing them to contribute to the storyline, modify plot trajectories, and craft characters, thereby facilitating a dynamic storytelling experience. Through advanced virtual reality interaction design, collaboration and social engagement can be further enriched to encourage active participation. This study investigates the facilitation of narrative creation and enhancement of storytelling skills in virtual reality by leveraging existing research on story co-creation design and virtual reality technology. Subsequently, we developed and evaluated the virtual reality card-based collaborative storytelling platform Co-Relay. By analyzing interaction data and user feedback obtained from user testing and experimental trials, we observed substantial enhancements in user engagement, immersion, creativity, and fulfillment of emotional and social needs compared to a conventional web-based storytelling platform. The primary contribution of this study lies in demonstrating how the incorporation of story co-creation can elevate storytelling proficiency, plot development, and social interaction within the virtual reality environment. Our novel methodology offers a fresh outlook on the design of collaborative narrative creation in virtual reality, particularly by integrating participatory multi-user storytelling platforms that blur the traditional boundaries between creators and audiences, as well as between fiction and reality. Full article

The rapid development of science and technology enables technological innovations to change the way of English oral learning. Based on the use of a large language model (LLM), we developed a novel dynamic evaluation system for oral English, LLM-DAELSL, which combines daily oral habits and a textbook outline. The model integrates commonly used vocabulary from everyday social speech and authoritative prior knowledge, such as oral language textbooks. It also combines traditional large-scale semantic models with probabilistic algorithms to serve as an oral assessment tool for undergraduate students majoring in English-related fields in universities. The model provides corrective feedback to effectively enhance the proficiency of English learners through guided training at any time and place. The technological principle of the model involves inputting prior template knowledge into the language model for reverse guidance and utilizing the textbooks provided by China’s Ministry of Education. The model facilitates the practice and evaluation of pronunciation, grammar, vocabulary, and fluency. The six-month tracking results showed that the oral proficiency of the system learners was significantly improved in the four aspects, which provides a reference for other language learning method developments. Full article

Digital competences (DC) are vital for improving employability, especially in tourism, where adapting to technology and communicating effectively are key. Proficiency in digital tools and a second language (SL) significantly enhances organizational performance and competitiveness, supporting sustainable development and innovation in dynamic business environments. This study explores the causal link between digital competences and employability dimensions, including second-language skills, in SMEs within the tourism sector in Quindío and Valle del Cauca, Colombia. Using a quantitative approach, data from 114 employees were collected through a semi-structured survey and analyzed via partial least squares structural equation modelling (PLS-SEM) to determine significant relationships. The results reveal that digital competences significantly enhance technological management, occupational experience (OE), anticipation and optimization (AO), and personal flexibility (PF). These skills contribute to sustainable tourism by promoting adaptability, innovation, and inclusive employability. Additionally, second-language proficiency demonstrates strong explanatory power in communication-related aspects. The findings highlight the need for tourism enterprises to prioritize digital upskilling, integrate research and innovation into job functions, strengthen adaptability to organizational changes, and view second-language development as a strategic resource. This study offers valuable insights for designing targeted training strategies aligned with the sector’s dynamic demands and advances the broader discourse on digital literacy in workforce development. Full article

In response to challenges around student engagement and teacher technological proficiency, this paper looks at the impact of gamification on students’ mathematical resilience whilst monitoring their mathematical anxiety plus investigating teachers’ experiences, willingness, and professional development ambitions to utilise gamified instructional tools in the mathematics classroom. Drawing on strategies to motivate students, the aim of this paper is to unbundle gamification in enacting curriculum change and the role of teacher professional development in using the pedagogical approach in mathematics in Ireland. Ireland is currently experiencing second-level curriculum reforms that are placing particular emphasis on digital competence and technological fluency from both teachers and students. With teachers highlighting the gap in educators’ pedagogical skills for the smooth roll out of recent curriculum reform due to the lack of knowledge and competency in technological teaching strategies, this study is both relevant and timely. Games have been used in multiple industries aiming to motivate participants and increase engagement on a particular matter. However, the term “gamification” has been coined by Pelling as the use of games in a non-gaming context. Current students are very technologically savvy due to the exposure of software applications from a young age and the integration of technological appliances in all walks of life. Traditional teaching and learning strategies are potentially seen as monotonous and somewhat boring to today’s students. Utilising game-based design such as leaderboards, points, and badges encourages motivation and enhances engagement of students. With this in mind, and the rate of change in mathematics curricula globally in recent years, there is a significant emphasis on the necessity of professional development initiatives to adapt at the same rate. Full article

The rapid integration of artificial intelligence (AI) in education has transformed traditional teaching methodologies, particularly within Outcome-Based Education (OBE), in higher education. Based on the Technological Pedagogical Content Knowledge (TPACK) model and the OBE system, this present study investigates how teachers perceive AI applications, specifically ChatGPT, in enhancing instructional design and student performance. The research develops a new AI-based instructional planning model, incorporating AI ChatGPT capabilities, teacher competencies, and their direct and indirect effects on student outcomes. This study employs quantitative research design using Structural Equation Modeling (SEM) to validate the proposed model. Data were collected from 320 university teachers in Pakistan using a structured survey distributed through WhatsApp and email. Findings from the direct path analysis indicate that AI ChatGPT capabilities significantly enhance instructional planning (β = 0.33, p < 0.001) and directly impact student performance (β = 0.20, p < 0.001). Teacher competencies also play an important role in instructional planning (β = 0.37, p < 0.001) and student performance (β = 0.16, p = 0.020). The indirect path analysis reveals that instructional planning mediates the relationship between AI ChatGPT capabilities and student performance (β = 0.160, p < 0.001), as well as between teacher competencies and student performance (β = 0.180, p < 0.001). The R-square values indicate that instructional planning explains 41% of its variance, while student performance accounts for 56%. These findings provide theoretical contributions by extending AI adoption models in education and offer practical implications for integrating AI tools in teaching. This study emphasizes the need for professional development programs to enhance educators’ AI proficiency and suggests policy recommendations for AI-driven curriculum development. Full article

Background/Objectives: The goal of educators is to leave no learner behind. Ultrasounds require dexterity and 3D image interpretation. They are technologically complex, and current medical residency programs lack a reliable means of assessing this ability among their trainees. This prompts consideration as to whether background characteristics or certain pre-existing skills can serve as indicators of learning aptitude for ultrasounds. The objective of this study was to determine whether these characteristics and skills are indicative of learning aptitude for ultrasounds. Methods: This prospective study was conducted with third-year medical students rotating in emergency medicine at the New York Presbyterian Brooklyn Methodist Hospital, Brooklyn, NY, USA. First, students were given a pre-test survey to assess their background characteristics. Subsequently, a psychomotor task (Purdue Pegboard) and visual–spatial task (Revised Purdue Spatial Visualization Tests) were administered to the students. Lastly, an ultrasound task was given to identify the subxiphoid cardiac view. A rubric assessed ability, and proficiency was determined as a 75% or higher score in the ultrasound task. Results: In total, 97 students were tested. An analysis of variance (ANOVA) was used to ascertain if any background characteristics from the pre-test survey was associated with the ultrasound task score. The student’s use of cadavers to learn anatomy had the most correlation (p-value of 0.02). Assessing the psychomotor and visual–spatial tasks, linear regressions were used against the ultrasound task scores. Correspondingly, the p-values were 0.007 and 0.008. Conclusions: Ultrasound ability is based on hand–eye coordination and spatial relationships. Increased aptitude in these abilities may forecast future success in this skill. Those who may need more assistance can have their training tailored to them and further support offered. Full article

The swift evolution of information technology and growing connectivity in critical applications have elevated cybersecurity, protecting and certifying software and designs against rising cyber threats. For example, software and hardware have become highly susceptible to various threats, like reverse engineering, cloning, tampering, and IP piracy. While various techniques exist to enhance software and hardware security, including encryption, native code, and secure server-side execution, obfuscation emerges as a preeminent and cost-efficient solution to address these challenges. Obfuscation purposely converts software and hardware to improve complexity for probable adversaries, targeting obscure realization operations while preserving safety and functionality. Former research has commonly engaged features of obfuscation, deobfuscation, and obfuscation detection approaches. A novel departure from conventional research methodologies, this revolutionary comprehensive article reviews these approaches in depth. It explicates the correlations and dynamics among them. Furthermore, it conducts a meticulous comparative analysis, evaluating obfuscation techniques across parameters such as the methodology, testing procedures, efficacy, associated drawbacks, market applicability, and prospects for future enhancement. This review aims to assist organizations in wisely electing obfuscation techniques for firm protection against threats and enhances the strategic choice of deobfuscation and obfuscation detection techniques to recognize vulnerabilities in software and hardware products. This empowerment permits organizations to proficiently treat security risks, guaranteeing secure software and hardware solutions, and improving user satisfaction for maximized profitability. Full article

Construction safety is critical, and unmanned aerial vehicles (UAVs) have emerged as a transformative tool to enhance safety management in the sector. While UAVs are widely recognized for their efficacy, limited research has specifically addressed the barriers to their integration into construction safety management systems. This study aims to identify, prioritize, and analyze the interrelationships among these barriers to aid in their effective resolution. Using a mixed-methods approach, this research combines a systematic literature review (SLR) to identify barriers and a questionnaire survey to prioritize and examine their interconnections. The findings reveal significant barriers, including restricted airspace, inadequate safety regulations, limited flight durations, collision risks, insufficient piloting skills, lack of UAV awareness, resistance to new technologies, human errors, training needs, and legal constraints. Restricted airspace emerged as the most critical barrier, strongly linked to flight duration limitations and piloting proficiency. This study also highlights regional disparities: respondents from developed nations emphasized collision risks, legal restrictions, and resistance to new technologies, while those from developing countries focused on restricted areas, limited flight time, and piloting expertise. These findings emphasize the importance of addressing region-specific challenges and tailoring strategies to facilitate UAV integration, paving the way for safer and more efficient construction practices. Full article

Background: The rapid integration of artificial intelligence (AI) into medical education in developing nations necessitates that educators develop comprehensive AI competencies and readiness. This study explores AI competence and readiness among medical educators in higher education, focusing on the five key dimensions of the ADELE technique: (A) AI Awareness, (D) Development of AI Skills, (E) AI Efficacy, (L) Leanings Towards AI, and (E) AI Enforcement. Structured surveys were used to assess AI competencies and readiness among medical educators for the sustainable integration of AI in medical education. Methods: A cross-sectional study was conducted using a 40-item survey distributed to 253 educators from the Middle East (Saudi Arabia, Egypt, Jordan) and South Asia (India, Pakistan, Philippines). Statistical analyses examined variations in AI competency and readiness by gender and nationality and assessed their predictive impact on the adoption of sustainable AI in medical education. Results: The findings revealed that AI competency and readiness are the primary drivers of sustainable AI adoption, highlighting the need to bridge the gap between theoretical knowledge and practical application. No significant differences were observed based on gender or discipline, suggesting a balanced approach to AI education. However, ethical perspectives on AI integration varied between Middle East and South Asian educators, likely reflecting cultural influences. Conclusions: This study underscores the importance of advancing from foundational AI knowledge to hands-on applications while promoting responsible AI use. The ADELE technique provides a strategic approach to enhancing AI competency in medical education within developing nations, fostering both technological proficiency and ethical awareness among educators. Full article

Transseptal puncture (TSP) is an essential step for left heart procedures that allows access to the left atrium (LA) through the fossa ovalis (FO) of the interatrial septum (IS). Initially developed for diagnostic purposes, today, it is performed for procedures that require large-bore device delivery systems and complex three-dimensional navigation in the left atrium. TSP supports various interventions, including atrial fibrillation ablation, left atrial appendage closure, and transcatheter mitral valve repair and replacement. While traditionally performed with Brockenbrough needles under fluoroscopic guidance, the integration of transesophageal and intracardiac echocardiography (TEE/ICE) has significantly improved its safety and precision. Despite its generally high success rate, TSP poses challenges in complex anatomies or for less experienced operators, with complications such as cardiac tamponade, aortic root puncture, and embolic events. Anatomical variations, such as thickened or floppy septa, further complicate the procedure. Technological advancements, including radiofrequency-based systems and specialized guidewires, have enhanced safety in difficult cases. Effective training, including echocardiography and complication management, is vital for operator proficiency. This review outlines the procedural steps for safe TSP, emphasizing proper equipment selection, anatomical considerations, and vascular access techniques. Common complications are discussed alongside management strategies. Advanced tools and techniques for addressing challenging scenarios are highlighted. Full article

While AI integration in higher education has transformative potential, existing studies may not fully capture the unique socio-cultural and institutional contexts of top-tier universities in China. This study investigates students’ perceptions of AI utilization at a leading Chinese university, drawing on the Technology Acceptance Model (TAM). Quantitative data were collected via a 5-point Likert scale questionnaire (n = 253), complemented by open-ended qualitative responses. Results revealed that while they viewed AI as useful for enhancing efficiency and easy to use, concerns about content accuracy, over-reliance, and ethical issues persisted. Their high interest in AI contrasted with lower self-assessed proficiency, highlighting a gap between enthusiasm and competence. Institutional support significantly motivated adoption, whereas social influence played a lesser role. Students valued AI’s support in language learning, writing, research, and programming but noted its limitations in complex problem-solving. They also called for human-centric AI tools offering emotional support and personalized guidance. These findings may offer educators, policymakers, and AI developers valuable insights to address students’ concerns and optimize learning experiences in competitive academic environments. Full article

The elevated precision of data regarding the Earth’s surface, facilitated by the enhanced interoperability among various GNSSs (Global Navigation Satellite Systems), enables the classification of land use and land cover (LULC) via satellites equipped with optical sensors, such as Sentinel-2 of the Copernicus program, which is crucial for land use management and environmental planning. Likewise, data from SAR satellites, such Copernicus’ Sentinel-1 and Jaxa’s ALOS PALSAR, provide diverse environmental investigations, allowing different types of spatial information to be analysed thanks to the particular features of analysis based on radar. Nonetheless, in optical satellites, the relatively low resolution of Sentinel-2 satellites may impede the precision of supervised AI classifiers, crucial for ongoing land use monitoring, especially during the training phase, which can be expensive due to the requirement for advanced technology and extensive training datasets. This project aims to develop an AI classifier utilising high-resolution training data and the resilient architecture of ResNet, in conjunction with the Remote Sensing Image Classification Benchmark (RSI-CB128). ResNet, noted for its deep residual learning capabilities, significantly enhances the classifier’s proficiency in identifying intricate patterns and features from high-resolution images. A test dataset derived from Sentinel-2 raster images is utilised to evaluate the effectiveness of the neural network (NN). Our goals are to thoroughly assess and confirm the efficacy of an AI classifier utilised on high-resolution Sentinel-2 photos. The findings indicate substantial enhancements compared to current classification methods, such as U-Net, Vision Transformer (ViT), and OBIA, underscoring ResNet’s transformative capacity to elevate the precision of land use classification. Full article

Based on acoustic feature analysis, this study systematically examines the differences in vowel pronunciation characteristics among Mandarin learners at various proficiency levels. A speech corpus containing samples from advanced, intermediate, and elementary learners (N = 50) and standard speakers (N = 10) was constructed, with a total of 5880 samples. Support Vector Machine (SVM) and ID3 decision tree algorithms were employed to classify vowel formant parameters (F1-F2) patterns. The results demonstrate that SVM significantly outperforms the ID3 algorithm in vowel classification, with an average accuracy of 92.09% for the three learner groups (92.38% for advanced, 92.25% for intermediate, and 91.63% for elementary), an improvement of 2.05 percentage points compared to ID3 (p < 0.05). Learners’ vowel production exhibits systematic deviations, particularly pronounced in complex vowels for the elementary group. For instance, the apical vowel “ẓ” has a deviation of 2.61 Bark (standard group: F1 = 3.39/F2 = 8.13; elementary group: F1 = 3.42/F2 = 10.74), while the advanced group’s deviations are generally less than 0.5 Bark (e.g., vowel “a” deviation is only 0.09 Bark). The difficulty of tongue position control strongly correlates with the deviation magnitude (r = 0.87, p < 0.001). This study confirms the effectiveness of objective assessment methods based on formant analysis in speech acquisition research, provides a theoretical basis for algorithm optimization in speech evaluation systems, and holds significant application value for the development of Computer-Assisted Language Learning (CALL) systems and the improvement of multi-ethnic Mandarin speech recognition technology. Full article