Dongseo University

In digital radiography (DR) systems, the sensitivity index, also known as the exposure indicator, exhibits unique behavior, depending on the manufacturer. The exposure index (EI) was introduced by the International Electrotechnical Commission to standardize the sensitivity indices of DR systems produced by different manufacturers for general radiography. This EI value is directly proportional to the dose incident on the imaging detector, providing valuable insight into exposure dose levels and radiographic noise. Recently, technological advancements have enabled some DR systems to display the EI value on the console immediately after exposure. This study investigated the characteristics of the displayed EI value and radiographic noise for an indirect flat-panel detector across four X-ray beam qualities based on added aluminum filters: RQA3, 5, 7, and 9. The displayed EI values for all the X-ray beam qualities proportionally increased to the dose incident on the detector. However, the displayed EI values for RQA3 deviated from those for RQA5, 7, and 9 at the same doses incident on the imaging detector. Furthermore, the Wiener spectrum (WS) was used to evaluate radiographic noise. For similar EI values, the WS of RQA3, which had the highest dose incident on the imaging detector, was slightly lower than those of other X-ray beam qualities. Our findings suggest that the relationship between the displayed EI and radiographic noise varies with the X-ray beam quality.

Brain tumours (BTs) are among the most dangerous and life-threatening cancers in humans of all ages, and the early detection of BTs can make a huge difference to their treatment. However, grade recognition is a challenging issue for radiologists involved in automated diagnosis and healthcare monitoring. Recent research has been motivated by the search for deep learning-based mechanisms for segmentation and grading to assist radiologists in diagnostic analysis. Segmentation refers to the identification and delineation of tumour regions in medical images, while classification classifies based on tumour characteristics, such as the size, location and enhancement pattern. The main aim of this research is to design and develop an intelligent model that can detect and grade tumours more effectively. This research develops an aggregated architecture called LGCNet, which combines a local context attention network and a global context attention network. LGCNet makes use of information extracted through the task, dimension and scale. Specifically, a global context attention network is developed for capturing multiple-scale features, and a local context attention network is designed for specific tasks. Thereafter, both networks are aggregated, and the learning network is designed to balance all the tasks by combining the loss functions of the classification and segmentation. The main advantage of LGCNet is its dedicated network for a specific task. The proposed model is evaluated by considering the BraTS2019 dataset with different metrics, such as the Dice score, sensitivity, specificity and Hausdorff score. Comparative analysis with the existing model shows marginal improvement and provides scope for further research into BT segmentation and classification. The scope of this study focuses on the BraTS2019 dataset, with future work aiming to extend the applicability of the model to different clinical and imaging environments.

Background The chin tuck against resistance (CTAR) exercise is a rehabilitative technique for stroke patients with dysphagia. However, related clinical evidence remains unclear, and methodological improvements in this therapy are required. Objective This study aimed to investigate the effects of the modified CTAR exercise on swallowing‐related muscles and swallowing function in stroke patients with dysphagia. Methods Stroke patients with dysphagia ( n = 30) were randomly assigned to a multidirectional CTAR exercise group (md‐CTAR exercise group) and a vertical‐directional (vd‐CTAR exercise group) ( n = 15 per group). The md‐CTAR exercise group performed exercises in the left and right diagonal and vertical directions using a prototype device. The vd‐CTAR group performed only vertical exercises. Both groups performed the exercises for 5 days each week over a period of 6 weeks. Primary outcome measures included tongue strength, thickness and suprahyoid muscle activation. Secondary outcome measures included the Videofluoroscopic Dysphagia Scale (VDS) and Penetration–Aspiration Scale (PAS) based on videofluoroscopic study. Results The md‐CTAR group exhibited significantly higher maximal tongue strength, thickness and suprahyoid muscle activity than the vd‐CTAR group ( p < 0.05, all), as well as a significant decrease in the oral and pharyngeal phase of the VDS score ( p = 0.048 and 0.041) and PAS compared to the vd‐CTAR group ( p = 0.047). Conclusion md‐CTAR exercise is more effective than vd‐CTAR exercise in improving the oropharyngeal muscles and swallowing function in stroke patients with dysphagia. Therefore, the md‐CTAR exercise is recommended as a modified therapeutic exercise for dysphagia rehabilitation.

The smart manufacturing has revolutionised the intelligent predictive maintenance by integrating IoT technologies with big data analytics, artificial intelligence, cloud computing and other evolving technologies. An effective predictive maintenance demands not only measuring equipment, but the underlying ecosystem that starts with data acquisition from sensors and propagates all the way to visualisation on engineer friendly dashboards. For process monitoring and performance optimization in a smart factory, it is important to recognise time series events like equipment peaks, changeovers and failures. In this article, a model proposed is a deep convolutional LSTM autoencoder architecture using an autoencoder approach to classify real world machine and sensor data to condition based label. The proposed model outperformed baseline architectures. A window size of 45 was used to determine that the model produced a RMSE of 58.45, an MAE of 22.48, and a sMAPE of 0.869, most of which represents significant improvements of up to 37% over existing methods. Having a window size 90, it remained on top with an RMSE score of 72.16 and MAE of 29.64 and sMAPE of 0.847. These results show that it processed a real world manufacturing data and correctly estimated RUL and its complete predictive maintenance.

The global popularity of South Korean dramas (K-dramas), central to the “Korean Wave”, has significantly influenced international perceptions of Korea and its tourism appeal. This study examines the impact of K-drama consumption on American audiences’ intentions to visit South Korea, with a focus on the sustainability messaging embedded within the media. Integrating the theory of planned behavior (TPB) and uses and gratifications theory (UGT), this research explores how cultural perceptions and the engagement with Korean culture shape sustainable tourism attitudes and travel intentions. A survey of 554 U.S.-based participants reveals that positive cultural perceptions foster engagement, which mediates the relationship with sustainable attitudes and intentions to visit Korea. Furthermore, sustainability messaging in K-dramas enhances the connection between cultural engagement and eco-conscious travel behaviors. The findings highlight the influential role of the media in shaping sustainable tourism and offer strategic insights for leveraging K-dramas in tourism marketing. While K-dramas may not fulfill a direct diplomatic function, they contribute to Korea’s soft power by enhancing cultural exposure.

This research investigates the aesthetic evaluation of AI-generated neoplasticist artworks, exploring how well artificial intelligence systems, specifically Midjourney, replicate the core principles of neoplasticism, such as geometric forms, balance, and color harmony. The background of this study stems from ongoing debates about the legitimacy of AI-generated art and how these systems engage with established artistic movements. The purpose of the research is to assess whether AI can produce artworks that meet aesthetic standards comparable to human-created works. The research utilized Monroe C. Beardsley’s aesthetic emotion criteria and Noël Carroll’s aesthetic experience criteria as a framework for evaluating the artworks. A logistic regression analysis was conducted to identify key compositional elements in AI-generated neoplasticist works. The findings revealed that AI systems excelled in areas such as unity, color diversity, and overall artistic appeal but showed limitations in handling monochromatic elements. The implications of this research suggest that while AI can produce high-quality art, further refinement is needed for more subtle aspects of design. This study contributes to understanding the potential of AI as a tool in the creative process, offering insights for both artists and AI developers.

Background: This randomized and controlled pre- and post-test experimental study investigated the effects of a nonviolent communication education program on empathy, interpersonal relationships, stress, and resilience among Korean nursing students. Methods: We included 51 Korean nursing students from a university in Busan Metropolitan City, Republic of Korea, with 26 in the experimental group and 25 in the control group. Data were collected from May to August 2024, and the nonviolent communication education program was conducted for 8 hours daily. To confirm program effectiveness, the participants were asked to practice nonviolent communication during the 5 weeks of participation in the program, and a reflection journal was to be written daily. The collected data were analyzed using descriptive statistics, χ2-tests, independent t-tests, and a repeated-measures analysis of variance. Results: The over-time change in empathy and interpersonal relationship scores in the experimental group was significantly different from those in the control group (F=8.540, P<0.001 and F=3.654, P=0.029, respectively). However, the over-time change in stress and resilience scores in the experimental group was not significantly different from those in the control group (F=0.366, P=0.851 and F=0.256, P=0.775, respectively). Conclusion: The nonviolent communication education program was effective in promoting empathy and interpersonal relationships among nursing students. However, its effects on stress and resilience were not significant. Periodic implementation of the program coupled with stress-relief strategies may be effective. A long-term program must be implemented to verify changes in nursing students’ self-understanding. Moreover, further research is needed on the sustainability of the program’s effects.

Gross anatomy deals with the structure of the human body, and knowledge in this field is essential for dental staff so they can examine, diagnose, and treat patients accurately. In this study, clinical dentists were surveyed to obtain their opinions on the content and methods of gross anatomy education required for prospective dental staff and to identify differences in their perceptions in relation to their general characteristics. The study ultimately enrolled and analyzed 182 participants. The content of gross anatomy education was first divided into the whole body and the head and neck, each region being subdivided into local body parts (14 items) and detailed anatomical structures (22 items). The questions relating to gross anatomy education methods consisted of eight detailed items. Each item was quantified using a 5‐point Likert scale. When the clinical dentists' opinions on the degree to which anatomy knowledge by body parts is necessary for work performance were assessed, the head received the highest score among the 14 parts (4.73 ± 0.58). In the head and neck region, the temporomandibular joint received the highest score (4.83 ± 0.55). Among gross anatomy education methods, practice using medical images such as radiographs received the highest score (4.52 ± 0.62). These results can help trainers select the content and methods of gross anatomy education needed for future dental clinical workers, strengthening their capabilities by providing knowledge that will be practically helpful in clinical work.

Modular multilevel converters are widely used voltage source converters in a variety of applications, such as power transmission, renewable energy, and motor drives. Nevertheless, they introduce complexity to control methods and data processing, hence requiring additional sensors. Pulse width modulation and voltage balancing algorithms are essential for modular multilevel converters, and there are several approaches available to achieve voltage balance. This study presents a revised approach to multicarrier pulse width modulation for the modular multilevel converter, which incorporates a combination of DC carriers. This modulation approach employs the interleaving of the modulated waveform with a direct current (DC) level, while ensuring the preservation of quarter wave symmetry. This method has led to a reduction in the switching frequency of the Modular Multilevel Converter (MMC). Moreover, the paper covers the implementation of op-amp based capacitor measuring and sorting circuits for capacitor voltage balancing in a modular multilevel inverter with R load. Using a combination of direct current (DC) and triangular carriers in the PWM, the proposed method demonstrates a reduction in THD from 27.55% to 21.76% in phase voltage and switching frequency by 20%. The effectiveness of the method has been confirmed using Simulink/MATLAB. The simulation and experimental findings reveal a notable reduction as compared to conventional multicarrier PWM technique for modular multilevel converter (MMC) and find that the unique approach performs superiorly without compromising the integrity of the output voltage.

This study presents a novel approach to enhancing microparticle dispersion and ejection performance by utilizing a pendulum motion of a particle reservoir. Owing to their tendency to sediment in suspension, microparticles pose significant challenges in achieving consistent and repeatable ejections, often leading to nozzle clogging. To overcome these challenges, a three-axis automated particle dispensing system integrated with a rotational dispensing module was developed. The pendulum motions of the dispensing module were investigated to assess their impact on particle dispersion, including 90-degree, 180-degree, and 360-degree swings. The 360-degree pendulum motion sustained particle dispersion, leading to the consistent and reliable ejection of particles during continuous droplet ejection. Moreover, we evaluated a novel particle dispensing system, including the effects of particle suspension density and dispensing parameters on the ejection performance and dead volume of minute particle samples. Stable particle dispensing was achieved, with a CV below 7%, even at high concentrations (14% w/v). The number of ejected particles exhibited a linear relationship (R² = 99%) with suspension densities ranging from 1%–14% w/v. Furthermore, dispensing parameters such as the amplitude and duration of the applied pressure showed a linear correlation with both the number of ejected particles and the volume of ejected droplets (R² = 99%). The dead volume was 2 μl, representing 10% of the 20 μl small sample used. These results demonstrate the flexibility of the system in maintaining a high performance across a range of operational conditions. The findings highlight the potential of this rotational approach for enhancing the reliability and accuracy of particle dispensing in microfluidic applications.

Aim Digital health technology in swallowing rehabilitation offers personalized exercises, remote monitoring, and real‐time feedback, enhancing accessibility and effectiveness of therapy. This scoping review was conducted to summarize what types and features of digital technology‐based dysphagia rehabilitation interventions exist, how they are applied in patients with dysphagia, and what the effectiveness and facilitators and barriers to intervention application are. Methods We searched Medline Complete, Embase, CINAHL, Scopus, and gray literature for articles published between January 2000 and June 2023. We used subheadings and terms related to digital health, dysphagia, and rehabilitation to search for articles. The included studies were mapped according to the types and features, effectiveness, enablers, barriers, and future improvements of swallowing rehabilitation using digital technologies. Results Twenty‐five studies met the inclusion criteria. Three types of digital swallowing rehabilitation interventions were identified: home‐based rehabilitation using the mHealth app, synchronous telepractice and monitoring, as well as game‐based biofeedback and tracking. The included studies reported positive results regarding physiological changes in swallowing function, swallowing performance, and quality of life. Digital unfamiliarity, resources for digital access, and technical issues related to the failure of the mobile device operating system were identified as barriers to the use of digital swallowing rehabilitation technology and future improvements. Conclusions Digital technology has potential value in dysphagia rehabilitation. In the future, developing various interventions utilizing the advantages of digital technology and conducting additional research to validate their effectiveness is necessary. Additionally, improved digital familiarity, better accessibility, better technology, and management practices will be needed.

Background/Objectives: This study aimed to identify the factors influencing and predicting the frequency of depressive experiences among married working women in South Korea in the post-COVID-19 period (2022–2023). It examines how alterations in circumstances and the complex difficulties encountered by this demographic group may have shaped their depressive experiences. Through a comparative analysis of the group reporting depressive experiences and the group reporting no depressive experiences, the study delineates the factors influencing depressive experiences within the former group and the predictive factors within the latter group. The findings offer a comprehensive understanding of the factors that may contribute to mental health outcomes within this population. Methods: This study utilized data from the ninth wave (2022–2023) of the Korean Longitudinal Survey of Women and Families, conducted by the Korean Women’s Development Institute. The study included a total of 1735 participants. A zero-inflated negative binomial regression model was applied to analyze the frequency of depressive experiences and the influencing and predictive factors. Results: Among the participants, 38.9% reported no depressive experiences. The count model analysis revealed that subjective health status, physical activity, thoughts about husband, family decision-making, and work–family balance were significant factors associated with the frequency of depressive experiences. In the logistic model, key predictors for those without depression included the spouse’s education, physical activity, satisfaction with the spouse’s housework, and happiness with marital life. Conclusions: These findings provide essential empirical evidence for the development of targeted policies and interventions aimed at mitigating and preventing depression problem among married working women.

This review aims to explore the potential of hydrate technology, an emerging and innovative approach in the food and beverage sector for applications such as food preservation, carbonation, baking agents, and juice concentration. In particular, the latest advancements and limitations of hydrate technology are explored and discussed. Based on a collection of experimental and modeling data available elsewhere in literature, the dynamics of hydrate formation in the presence of food produce and beverages and their influence on growth kinetics, preservation, nutritional content, sensory qualities, and separation efficiency are discussed in detail. Furthermore, this review analyses thermodynamic and kinetic studies, modeling, and process engineering of hydrate technology. The goal is to bridge the gap between fundamental scientific insight and industrial‐scale adoption of gas hydrate utilization in food separation processes.

Mammalian injury responses are predominantly characterized by fibrosis and scarring rather than functional regeneration. This limited regenerative capacity in mammals could reflect a loss of pro-regeneration programs or active suppression by genes functioning akin to tumor suppressors. To uncover programs governing regeneration in mammals, we screened transcripts in human subjects following laser rejuvenation treatment and compared them to mice with enhanced Wound Induced Hair Neogenesis (WIHN), a rare example of mammalian organogenesis. We found that Rnasel-/- mice exhibit an increased regenerative capacity, with elevated WIHN through enhanced IL-36α. Consistent with RNase L's known role to stimulate caspase-1, we found that pharmacologic inhibition of caspases promoted regeneration in an IL-36 dependent manner in multiple epithelial tissues. We identified a negative feedback loop, where RNase L activated caspase-1 restrains the pro-regenerative dsRNA-TLR3 signaling cascade through the cleavage of toll-like adaptor protein TRIF. Through integrated single-cell RNA sequencing and spatial transcriptomic profiling, we confirmed Oas & Il36 genes to be highly expressed at the site of wounding and are elevated in Rnasel-/- mice wounds. This work suggests that RNase L functions as a regeneration repressor gene, in a functional tradeoff that tempers immune hyper-activation during viral infection at the cost of inhibiting regeneration.

Background/Objectives: This study explored factors influencing self-rated health (SRH) among community-dwelling older adults to better support its use in health screening and provide an alternative for older adults who may have difficulty working with lengthy assessment scales. Methods: The participants were 8379 individuals aged 65 years or older from the 2020 Elderly Survey. Data were collected in South Korea between September and November 2020. Descriptive statistics were calculated, and independent samples t-tests, a Kruskal–Wallis test, and weighted multiple regression analysis were conducted using IBM SPSS for Windows ver. 23.0. SRH factors, such as illness, daily living performance, nutritional status, depression, and cognitive impairment, were analyzed. Results: The greater the number of chronic conditions (β = −0.21, p < 0.001), the higher the dependence on activities of daily living and instrumental activities of daily living (β = −0.05, p = 0.002; β = −0.13, p < 0.001), the higher the depression score (β = −0.22, p < 0.001), the more severe the cognitive impairment (β = −0.04, p < 0.001), and the worse the SRH. Conclusions: Participants with high-risk medical conditions, such as cancer, stroke, and depression, thought their health was poor. However, they did not consider hypertension, malnutrition, and abnormal BMI as significantly affecting their health status. Therefore, these factors should be considered when measuring SRH in older adults.

Background Early identification of penetration/aspiration (P/A) risk in older adults with sarcopenia is crucial to prevent complications and maintain quality of life. Purpose To evaluate the diagnostic utility of orofacial muscle strength measurements for predicting the risk of P/A in older adults with sarcopenia. Methods In this observational and prospective study, we collated consecutive data from community‐dwelling older adults diagnosed with sarcopenia at a musculoskeletal disorder clinic. Altogether, 54 participants underwent orofacial muscle strength measurements (the index test) using the Iowa Oral Performance Instrument and a videofluoroscopic swallowing study (VFSS) (the reference test) to evaluate for the presence of P/A. Receiver operating characteristic (ROC) curve analysis was performed to determine orofacial muscle strength based on P/A. Results Overall, 34 patients showed penetration in the VFSS, although none of the patients showed signs of aspiration. The cut‐off for tongue strength to identify the risk of P/A was ≤ 20.5 kPa, with a sensitivity and specificity of 0.75 and 0.74, respectively; the area under the curve (AUC) was 0.88. The cut‐off for buccinator strength was ≤ 19.5 kPa, with a sensitivity and specificity of 0.65 and 0.68, respectively, with an AUC of 0.69. The cut‐off for lip muscle strength was ≤ 18.5 kPa, with a sensitivity and specificity of 0.65 and 0.71, respectively, with an AUC of 0.69. Conclusion The evaluation of buccinator and lip muscle strength did not demonstrate sufficient diagnostic utility for detecting the risk of P/A in older patients with sarcopenia; however, tongue strength showed reliable diagnostic utility for identifying the risk of P/A.

Choosing nutritious foods is essential for daily health, but finding recipes that match available ingredients and dietary preferences can be challenging. Traditional recommendation methods often lack personalization and accurate ingredient recognition. Personalized systems address this by integrating user preferences, dietary needs, and ingredient availability. This study presents Pic2Plate, a framework combining Vision-Language Models (VLMs) and Retrieval-Augmented Generation (RAG) to overcome these challenges. Pic2Plate uses advanced image recognition to extract ingredient lists from user images and RAG to retrieve and personalize recipe recommendations. Leveraging smartphone camera sensors ensures accessibility and portability. Pic2Plate’s performance was evaluated in two areas: ingredient detection accuracy and recipe relevance. The ingredient detection module, powered by GPT-4o, achieved strong results with precision (0.83), recall (0.91), accuracy (0.77), and F1-score (0.86), demonstrating effectiveness in recognizing diverse food items. A survey of 120 participants assessed recipe relevance, with model rankings calculated using the Bradley–Terry method. Pic2Plate’s VLM and RAG integration consistently outperformed other models. These results highlight Pic2Plate’s ability to deliver context-aware, reliable, and diverse recipe suggestions. The study underscores its potential to transform recipe recommendation systems with a scalable, user-centric approach to personalized cooking.