Search Results (628)

The present study analyzes individual differences in the facilitative processing of grammatical gender by heritage speakers of Spanish, asking whether these differences correlate with lexical proficiency. Results from an eye-tracking study in the Visual World Paradigm replicate prior findings that, as a group, heritage speakers of Spanish show facilitative processing of gender. Importantly, in a follow-up within-group analysis, we test whether three measures of lexical proficiency—oral picture-naming, verbal fluency, and LexTALE—predict individual performance. We find that lexical proficiency, as measured by LexTALE, predicts overall word recognition; however, we observe no effects of the other measures and no evidence that lexical proficiency modulates the strength of the facilitative effect. Our results highlight the importance of carefully selecting tools for proficiency assessment in experimental studies involving heritage speakers, underscoring that the absence of evidence for an effect of proficiency based on a single measure should not be taken as evidence of absence. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

Academic English support is crucial for English as an Additional Language (EAL) nursing students in English-medium nursing education programmes. However, empirical research on content and language integrated learning (CLIL) within this specific context remains limited. This study, informed by recent advancements in translanguaging and trans-semiotising (TL-TS) theory, investigates the patterns of teacher collaboration in nursing CLIL and its impact when employing a TL-TS pedagogical approach. Analysis of students’ pre- and post-tests and multimodal classroom interactions reveals that effective collaboration between nursing specialists and language experts in CLIL can be fostered by (1) aligning with language education principles through the incorporation of internally persuasive discourse (IPD) about language learning and TL-TS practices; (2) simulating potential professional contingencies and co-developing coping strategies using TL-TS; and (3) elucidating nursing language norms through TL-TS and IPD. We advocate for re-imagination of CLIL in English-medium nursing education through an organistic–procedural TL perspective and highlight its potential to enhance EAL nursing students’ development of language proficiency and professional competencies. 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

Background and Objectives: Three-dimensional (3D) scanning is increasingly utilized in medical practice, from orthotics to surgical planning. However, traditional hand measurement techniques remain inconsistent and prone to human error and are often time-consuming. This research evaluates the practicality of a commercial 3D scanning method by comparing the accuracy of scans conducted by two user groups. Materials and Methods: This study evaluated the following two groups: an experimental group (n = 45) and a control group (n = 42). A total of 261 hand scans were captured using the Structure Sensor Pro 3D scanner for iPad (Structure, Boulder, CO, USA). The scans were then evaluated using Meshmixer software (version 3.5.474), analyzing key parameters, such as surface area, volume, number of vertices, and triangles, etc. Furthermore, a digital literacy test and a user experience survey were conducted to support a more comprehensive evaluation of participant performance within the study. Results: The experimental group outperformed the control group on all measured parameters, including surface area, volume, vertices, triangle, and gap count, with large effect sizes observed. User experience data revealed that participants in the experimental group rated the 3D scanner significantly higher across all dimensions, particularly in ease of use, excitement, supportiveness, and practicality. Conclusions: A short 15 min training session can promote scan reliability, demonstrating that even minimal instruction improves users’ proficiency in 3D scanning, fundamental for supporting clinical accuracy in diagnosis, surgical planning, and personalized device manufacturing Full article

Effective language processing relies on pattern detection. Spanish monolinguals predict verb tense through stress–suffix associations: a stressed first syllable signals present tense, while an unstressed first syllable signals past tense. Low-proficiency second language (L2) Spanish learners struggle to detect these associations, and we investigated whether they benefit from game-based training. We examined the effects of four variables on their ability to detect stress–suffix associations: three linguistic variables—verbs’ lexical stress (oxytones/paroxytones), first-syllable structure (consonant–vowel, CV/consonant–vowel–consonant, CVC), and phonotactic probability—and one learner variable—working memory (WM) span. Beginner English learners of Spanish played a digital game focused on stress–suffix associations for 10 days and completed a Spanish proficiency test (Lextale-Esp), a Spanish background and use questionnaire, and a Corsi WM task. The results revealed moderate gains in the acquisition of stress–suffix associations. Accuracy gains were observed for CV verbs and oxytones, and overall reaction times (RTs) decreased with gameplay. Higher-WM learners were more accurate and slower than lower-WM learners in all verb-type conditions. Our findings suggest that prosody influences word activation and that digital gaming can help learners attend to L2 inflectional morphology. Full article

The Every Student Succeeds Act requires that U.S. states provide a public evaluation of the performance of each public school while providing broad discretion in how states devise performance frameworks. One common method consists of states assigning each school an A–F letter grade based on English and math proficiency rates and other measures of academic performance. Proponents of the summary letter-grade system cite its simplicity as a virtue, while detractors contend that the system is simplistic to a fault. To bring greater clarity to these ongoing debates, we solicited opinions from parents regarding state letter-grade systems. We conducted semi-structured focus groups with parents in Arizona, North Carolina, and Texas (three focus groups per state). These conversations revealed that most parents were not aware that the state grades schools. Once the performance framework was explained, most parents expressed a belief that it is overly simplistic and insufficiently deferential to what they perceive as the subjective nature of school quality. Parents also revealed substantial tension between their conception of school quality and the way it is operationalized in the report card, with the latter ascribing much greater importance to state test scores. Full article

This study creates and tests a machine learning model that can predict fuel use and emissions (NO_x, CO₂, CO, HC, PN) from a marine internal combustion engine when it is running normally. The model learned from data collected from conventional diesel fuel experiments. Subsequently, we evaluated its ability to transfer by employing the parameters associated with waste cooking oil (WCO) biodiesel and its 60/40 diesel mixture. The machine learning model demonstrated exceptional proficiency in forecasting diesel mode (R² > 0.95), effectively encapsulating both long-term trends and short-term fluctuations in fuel consumption and emissions across various load regimes. Upon the incorporation of WCO data, the model maintained its capacity to identify trends; however, it persistently overestimated emissions of CO, HC, and PN. This discrepancy arose primarily from the differing chemical composition of the fuel, particularly in terms of oxygen content and density. A significant correlation existed between indicators of incomplete combustion and the utilization of fuel. Nonetheless, NO_x exhibited an inverse relationship with indicators of combustion efficiency. The findings indicate that the model possesses the capability to estimate emissions in real time, requiring only a modest amount of additional training to operate effectively with alternative fuels. This approach significantly diminishes the necessity for prolonged experimental endeavors, rendering it an invaluable asset for the formulation of fuel strategies and initiatives aimed at mitigating carbon emissions in maritime operations. Full article

This paper presents a Multimodal Hidden Markov Model (MHMM) framework specifically designed for real-time human proficiency assessment, integrating physiological (Heart Rate Variability (HRV)), behavioral (Task Completion Time (TCT)), and subjective (NASA Task Load Index (NASA-TLX)) data streams to infer latent human proficiency states in industrial settings. Using published empirical data from the surgical training literature, a comprehensive simulation study was conducted, with the MHMM (Trained) achieving 92.5% classification accuracy, significantly outperforming unimodal Hidden Markov Model (HMM) variants 61–63.9% and demonstrating competitive performance with advanced models such as Long Short-Term Memory (LSTM) networks 90%, and Conditional Random Field (CRF) 88.5%. The framework exhibited robustness across stress-test scenarios, including sensor noise, missing data, and imbalanced class distributions. A key advantage of the MHMM over black-box approaches is its interpretability by providing quantifiable transition probabilities that reveal learning rates, forgetting patterns, and contextual influences on proficiency dynamics. The model successfully captures context-dependent effects, including task complexity and cumulative fatigue, through dynamic transition matrices. When demonstrated through simulation, this framework establishes a foundation for developing adaptive operator-AI collaboration systems in Industry 5.0 environments. The MHMM’s combination of high accuracy, robustness, and interpretability makes it a promising candidate for future empirical validation in real-world industrial, healthcare, and training applications in which it is critical to understand and support human proficiency development. Full article

Background: The effective use of electronic health records (EHRs) is an essential clinical skill, but medical schools have traditionally provided limited systematic teaching on the topic. Inefficient use of EHRs results in delays in diagnosis, fragmented care, and clinician burnout. This study investigates the impact on medical students’ confidence, efficiency, and proficiency in extracting clinically pertinent information from patient records following an organised EHR teaching programme. Methods: This observational cohort involved 60 final-year medical students from three London medical schools. Participants received a structured three-phase intervention involving an introductory workshop, case-based hands-on practice, and guided reflection on EHR navigation habits. Pre- and post-intervention testing involved mixed-method surveys, simulated case tasks, and faculty-assessed data retrieval exercises to measure changes in students’ confidence, efficiency, and ability to synthesise patient information. Quantitative data were analysed using paired t-tests, while qualitative reflections were theme-analysed to identify shifts in clinical reasoning. Results: All 60 students successfully finished the intervention and assessments. Pre-intervention, only 28% students reported feeling confident in using EHRs effectively, with a confidence rating of 3.0. Post-intervention, 87% reported confidence with a rating of 4.5 (p < 0.01). Efficiency in the recovery of critical patient information improved from 3.2 to 4.6 (p < 0.01). Students also demonstrated enhanced awareness regarding system-related issues, such as information overload and fragmented documentation, and provided recommendations on enhancing data synthesis for clinical decision making. Conclusions: This study emphasises the value of structured EHR instruction in enhancing the confidence and proficiency of medical students in using electronic records. The integration of structured EHR education to medical curricula can better prepare future physicians in managing information overload, improve diagnostic accuracy, and enhance the quality of patient care. Future research should explore the long-term impact of structured EHR training on clinical performance, diagnostic accuracy, and patient outcomes during real-world clinical placements and postgraduate training. Full article

This study proposes a new benchmark to evaluate the cultural understanding and natural language processing capabilities of large language models based on Sino-Korean words and four-character idioms. Those are essential linguistic and cultural assets in Korea. Reflecting the official question types of the Korean Hanja Proficiency Test, we constructed four question categories—four-character idioms, synonyms, antonyms, and homophones—and systematically compared the performance of GPT-based and non-GPT LLMs. GPT-4o showed the highest accuracy and explanation quality. However, challenges remain in distinguishing the subtle nuances of individual characters and in adapting to uniquely Korean meanings as opposed to standard Chinese character interpretations. Our findings reveal a gap in LLMs’ understanding of Korea-specific Hanja culture and underscore the need for evaluation tools reflecting these cultural distinctions. Full article

Accurate prediction of ship fuel consumption is essential for improving energy efficiency, optimizing mission planning, and ensuring operational integrity at sea. However, during complex tasks such as high-speed maneuvers, fuel consumption exhibits complex dynamics characterized by the coexistence of baseline drift and transient peaks that conventional models often fail to capture accurately, particularly the abrupt peaks. In this study, a hybrid prediction model, DGM-SVR, is presented, combining a rolling dynamic grey model (DGM (1,1)) with support vector regression (SVR). The DGM (1,1) adapts to the dynamic fuel consumption baseline and trends via a rolling window mechanism, while the SVR learns and predicts the residual sequence generated by the DGM, specifically addressing the high-amplitude fuel spikes triggered by maneuvers. Validated on a simulated dataset reflecting typical fuel spike characteristics during high-speed maneuvers, the DGM-SVR model demonstrated superior overall prediction accuracy (MAPE and RMSE) compared to standalone DGM (1,1), moving average (MA), and SVR models. Notably, DGM-SVR reduced the test set’s MAPE and RMSE by approximately 21% and 34%, respectively, relative to the next-best DGM model, and significantly improved the predictive accuracy, magnitude, and responsiveness in predicting fuel consumption spikes. The findings indicate that the DGM-SVR hybrid strategy effectively fuses DGM’s trend-fitting strength with SVR’s proficiency in capturing spikes from the residual sequence, offering a more reliable and precise method for dynamic ship fuel consumption forecasting, with considerable potential for ship energy efficiency management and intelligent operational support. This study lays a foundation for future validation on real-world operational data. Full article

We explored the use of artificial intelligence (AI) in enhancing the English proficiency of students in the English as a Foreign Language (EFL) course through a startup product development curriculum. In the course, real-world business scenarios of startup companies were offered for students to analyze English communication skills on crowdfunding platforms and in product promotional videos. The EFL students used entrepreneurial skills to create and present their product videos in a team to the class who acted as potential investors. Pre- and post-test analyses were conducted to assess the impact of AI-assisted learning on enhancing English listening and reading ability. Significant improvements were observed, suggesting AI-enhanced entrepreneurial experiences and the listening and reading ability of the EFL students. Full article

This study examined the temporal relationship between fundamental movement skills (FMSs) and sport-specific skills (SSSs) in children aged 7 to 10. Based on the premise that FMSs are the basis for sport skills, we implemented a 10-week intervention program targeting two FMSs (running and stationary dribbling) and one SSS (speed dribbling), followed by immediate and long-term assessments. Using a path-modeling approach, we tested two models: one examining whether FMSs were associated with sport skill performance at the same time point and another exploring whether this influence emerged over time. Results revealed significant FMS and SSS improvements immediately after the intervention program. However, significant associations between the FMSs and SSS emerged only at later time points (8 to 20 months post-intervention), suggesting the delayed influence of the FMSs on the SSS. These findings support that while FMSs are essential for developing more complex skills, their effect may not be immediately observable, emphasizing the importance of long-term follow-up. The results also align with theoretical models contending that proficiency in FMS and sustained practice opportunities are key to integrating fundamental and sport-specific motor skills and may represent an important foundation for public health initiatives advocating early FMS interventions as a strategy to promote lifelong physical activity and sustained engagement in sports. 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