Search Results (1,791)

This study presents a deep learning-based model for predicting annual mean sea level (MSL) in the East Sea, with a focus on the Ulleungdo Island region, which maintains an independent vertical datum. To account for long-term tidal variability, the model enables continuous estimation of hourly and annual MSL values. Two recurrent neural network (RNN) architectures—Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)—were constructed and compared. Observational tide gauge data from 1 January 2000 to 3 August 2018 (covering 18.6 years and a full tidal nodal cycle) were preprocessed through missing-value and outlier treatment, followed by min–max normalization, and then structured for sequential learning. Comparative analysis demonstrated that the GRU model slightly outperformed the LSTM model in predictive accuracy and training stability. As a result, the GRU model was selected to produce annual MSL forecasts for the period 2018–2021. The GRU achieved a mean RMSE of approximately 0.44 cm during this prediction period, indicating robust performance in forecasting hourly sea level variations. The findings highlight the potential of deep learning methods to support vertical datum determination in island regions and to provide reliable sea level estimates for integration into coastal and oceanographic modeling. The proposed approach offers a scalable framework for long-term sea level prediction under evolving geodetic conditions. Full article

With the gradual decline in COVID-19 cases, there is a need to re-visit the mental health of adolescents and emerging adults in the post-pandemic period. Several observations can be highlighted from the scientific literature. First, while some studies suggest that mental health of young people has worsened in the post-pandemic period, there are inconsistent and conflicting findings. Second, there are more studies on psychological morbidity than on positive psychological attributes. Third, compared with the West, there are relatively fewer Chinese studies. Fourth, compared with adolescents, there are relatively fewer studies on emerging adults. Based on these observations of the existing literature, I have detailed several reflections on the mental health of young people, including enhancing positive psychological attributes in young people through positive youth development (PYD) programs, building up the individual resilience of young people, strengthening family resilience, adopting multidisciplinary, interdisciplinary and transdisciplinary approaches in understanding the mental health of young people, building more well-articulated theoretical models, charting future research directions, and developing intervention strategies in the post-pandemic period. Full article

Background/Objectives: This study evaluates students’ awareness and perceptions of interdisciplinary education. It focuses specifically on digital dentistry among students in the Department of Dental Technology. The findings aim to support the development of interdisciplinary courses and programs to enhance students’ skills in response to the growing digitalization of dental healthcare. Methods: A cross-sectional survey was conducted using a 23-item online questionnaire administered to a total of 203 students to collect data on general characteristics, perceptions of interdisciplinary education, the perceived necessity of such education, and the demand for interdisciplinary training, including topics related to CAD/CAM and 3D printing technologies. A t-test was performed to analyze grade-level differences in perceptions. Correlation analysis was conducted between perceptions and digital dental laboratory technology skills. Results: Despite the relatively low level of awareness regarding interdisciplinary education, students expressed a strong perceived need for it. A total of 76.6% of respondents preferred to collaborate with the Department of Dental Hygiene. No clear link exists between students’ perceptions of interdisciplinary education and their digital dental competencies. Practical training is more important than awareness. A significant difference in competencies was seen between lower- and higher-year students, indicating that advanced programs for higher-year students may be effective. Conclusions: Clear guidance on interdisciplinary education can enhance student understanding and acceptance. Interdisciplinary education with the dental hygiene department may increase engagement. Full article

In this study, we present a professional development programme for teachers launched to introduce Computer Science and Informatics (CSI) in primary education in Slovenia. The study aims to examine which CSI core concepts teachers most frequently choose to integrate into their lessons when given the freedom to select the topics within the framework, and to explore how students engage with and respond to these activities, as reported in teachers’ reflections. This study is based on reflective feedback from forty-seven teachers from seven primary schools who implemented interdisciplinary lessons that integrate CSI content into existing primary school curricula. Qualitative data from 152 reflections were used to support our research findings. The results show that teachers most frequently introduced the concepts from the content area of algorithms and programming. In contrast, content areas such as computing systems, networks and the internet, data and analysis, and impacts of computing received less attention. Teachers reported that students were motivated and engaged, although some challenges emerged, including difficulties in solving tasks or following instructions. As this pilot study reports on the first year of a two-year initiative, the findings provide preliminary insights into how a structured professional development programme for teachers can support interdisciplinary approaches in CSI education. Full article

This study evaluated the performance of a hybrid heat pump system integrating photovoltaic–thermal (PVT) panels with a standing column well (SCW) geothermal system in a strawberry greenhouse. The PVT panels, installed over 10% of the area of a 175 m³ greenhouse, stored excess solar heat in an aquifer to offset the reduced efficiency of the geothermal source during extended operation. The results showed that the hybrid system can supply 11,253 kWh of heat energy during the winter, maintaining the night time indoor temperature at 10 °C even when outdoor conditions dropped to −10.5 °C. The PVT system captured 11,125 kWh of solar heat during heating the off season, increasing the heat supply up to 22,378 kWh annually. Additionally, the system generated 3839 kWh of electricity, which significantly offset the 36.72% of the annual pump system electricity requirements, enhancing the system coefficient of performance (COP) of 3.38. Strawberry production increased by 4% with 78% heating cost saving compared to a kerosene boiler system. The results show that the PVT system effectively supports the geothermal system, improving heating performance and demonstrating the feasibility of hybrid renewable energy in smart farms to enhance efficiency, reduce fossil fuel use, and advance carbon neutrality. Full article

Foodborne pathogens remain a significant public health concern, necessitating the development of rapid, sensitive, and reliable detection methods for various food matrices. Traditional biosensors, while effective in many contexts, often face limitations related to complex sample environments, signal interpretation, and on-site usability. The integration of artificial intelligence (AI) into biosensing platforms offers a transformative approach to address these challenges. This review critically examines recent advancements in AI-assisted biosensors for detecting foodborne pathogens in various food samples, including meat, dairy products, fresh produce, and ready-to-eat foods. Emphasis is placed on the application of machine learning and deep learning to improve biosensor accuracy, reduce detection time, and automate data interpretation. AI models have demonstrated capabilities in enhancing sensitivity, minimizing false results, and enabling real-time, on-site analysis through innovative interfaces. Additionally, the review highlights the types of biosensing mechanisms employed, such as electrochemical, optical, and piezoelectric, and how AI optimizes their performance. While these developments show promising outcomes, challenges remain in terms of data quality, algorithm transparency, and regulatory acceptance. The future integration of standardized datasets, explainable AI models, and robust validation protocols will be essential to fully harness the potential of AI-enhanced biosensors for next-generation food safety monitoring. Full article

In this study, a driving test was conducted using a small-tracked forestry tractor with a scale of 1/11 in the shape of an actual tractor to assess safety under dynamic conditions. The driving conditions resulting in lateral overturning were derived. Additionally, an angular velocity sensor was used to analyze the variation in roll angular velocity with driving conditions. Driving condition variables comprised obstacle height, ground slope angle, and driving speed. Obstacle height had five levels between 0 and 40 mm in 10 mm intervals, and ground slope angle had 11 levels at 5° intervals from 0° to 50°. Driving speed had three levels: 0.07, 0.11, and 0.13 m/s. The ground slope angle resulting in lateral overturning in the driving scenario was lower than that in non-driving under all conditions. Roll angular velocity increased as obstacle height and tractor driving speed increased. However, ground slope angle did not significantly affect angular velocity. Roll angular velocity at the moment of lateral overturning was about 90 deg/s regardless of driving conditions. A certain critical angular velocity was found to induce lateral overturning, and adjusting the driving method such as reducing driving speed and making turns when the roll angular velocity of the tractor approached the critical value improved safety. However, the quantitative results from the small tractor cannot be directly applied to full-size tractors. Although numerical values may differ, this study focused on capturing the overall trends in lateral overturning considering various driving conditions. Future studies can improve the practical applicability of these findings by determining the critical angular velocity of various full-size tractors. Full article

Background/Objectives: Accurate blood glucose forecasting is critical for closed-loop insulin delivery systems to support effective disease management in people with type 1 diabetes (T1D). While long short-term memory (LSTM) neural networks have shown strong performance in glucose prediction tasks, the relative performance of individualized versus aggregated training remains underexplored. Methods: In this study, we compared LSTM models trained on individual-specific data to those trained on aggregated data from 25 T1D subjects using the HUPA UCM dataset. Results: Despite having access to substantially less training data, individualized models achieved comparable prediction accuracy to aggregated models, with mean root mean squared error across 25 subjects of 22.52 ± 6.38 mg/dL for the individualized models, 20.50 ± 5.66 mg/dL for the aggregated models, and Clarke error grid Zone A accuracy of 84.07 ± 6.66% vs. 85.09 ± 5.34%, respectively. Subject-level analyses revealed only modest differences between the two approaches, with some individuals benefiting more from personalized training. Conclusions: These findings suggest that accurate and clinically reliable glucose prediction is achievable using personalized models trained on limited individual data, with important implications for adaptive, on-device training, and privacy-preserving applications. Full article

Objectives: Chronic pain corresponds to hypersensitivity to painful stimuli; however, its relation to acute pain sensitivity in children is poorly understood. We explored this relationship by comparing acute and chronic pain measures, along with related factors, in children with chronic pain syndromes versus controls, before and after therapeutic intervention. Methods: This prospective controlled cohort study involved 57 children with chronic pain undergoing intensive interdisciplinary pain treatment in a hospital-based pain rehabilitation program and 50 controls. Participants, aged 7–18, were tested using a cold pressor task (CPT) at admission, discharge, and first follow-up visit. Data on sleep, anxiety, psychological distress, functional impairment, and pain were collected. Results: Significant differences were found between control and treatment groups in average pain threshold (p < 0.001), pain tolerance (p = 0.035), sleep visual analog scale (VAS) (p < 0.001), functional disability inventory (p < 0.001), patient reported outcomes information system anxiety assessment tool (p < 0.001), general anxiety disorder 7-item scale (p < 0.001), pain VAS (p < 0.001) and total brief symptom inventory (BSI) (p < 0.001) scores at admission with children with chronic pain scoring worse on all measures save the pain VAS during the CPT. After treatment and at follow-up, function and mental health measures improved but not acute pain threshold. Conclusions: At treatment completion, function and mental health significantly improved but acute pain threshold and sleep quality were unchanged. These findings suggest that while chronic pain treatment improves overall function and mental health, acute pain thresholds may not be a suitable indicator for evaluating the efficacy of interventions. Full article

Hepatic cancer is a world health concern due to its high lethality. The main risk factor worldwide is having hepatic cirrhosis. The etiology of hepatic cirrhosis has changed in recent years, with metabolic-associated steatotic liver disease (MASLD) becoming the leading cause, displacing hepatitis C and B viruses and alcoholic liver disease. It is of the utmost importance to develop screening programs in at-risk populations for early detection. The survival rate of HCC, as determined by a group of specialists or an interdisciplinary committee, is a challenge we have taken on in a public health hospital in Ecuador. This retrospective study identified 71 patients diagnosed with hepatocellular carcinoma, mostly middle-aged men with a history of liver cirrhosis. No significant association was found between the presence of cirrhosis, laboratory abnormalities, and survival. However, the identification by imaging vascular invasion and extrahepatic extension were associated. This study highlights that patients with liver lesions identified through HCC screening have a higher survival rate over a one-year follow-up period. Full article

Microplastic pollution is an escalating environmental and public health issue. Defined as plastic particles less than 5 mm in size, microplastics have been found in oceans, rivers, food, drinking water, air, and even human tissues. While scientific research on microplastics has expanded significantly, public understanding and behavioral change remain limited. This literature scan synthesizes global findings on public awareness, perceptions, and responses to microplastics, drawing from surveys, focus groups, and online behavioral data collected by existing studies. It explores the following: (1) general knowledge and perceived environmental and health risks; (2) trust in scientific and governmental sources; (3) willingness to adopt behavioral changes; (4) attitudes toward policy and corporate responsibility. Public concern is high, especially regarding marine life and food safety, but varies across populations based on education, socioeconomic status, and media exposure. Despite growing concern, psychological distance and persistent knowledge gaps hinder meaningful action. Communication strategies such as school programs, media campaigns, and eco-labels show mixed success, while regulatory interventions like plastic bags or microbead bans are more effective when supported by clear public messaging. This literature scan highlights the need for interdisciplinary collaboration to close the knowledge–behavior–policy gap and strengthen public engagement, particularly in urban settings where consumption and waste generation are concentrated. Full article

The transition toward sustainable production requires engineering and science education to adopt active, interdisciplinary, and practice-oriented teaching strategies. This article presents a comparative analysis of two educational initiatives implemented in Panama aimed at fostering sustainability competencies at the university and secondary school levels. The first initiative, developed at the Technological University of Panama, integrates project-based learning and circular economy principles into an extracurricular module focused on production planning, sustainable design, and quality management. Students created prototypes using recycled HDPE and additive manufacturing technologies within a simulated startup environment. The second initiative, carried out in two public secondary schools, applied project- and challenge-based learning through the Design Thinking framework, supporting teachers and students in addressing real-world sustainability challenges. Both programs emphasize hands-on learning, creativity, and iterative development, embedding environmental awareness and innovation in both formal and informal educational settings. The article identifies key opportunities and challenges in implementing active methodologies for sustainability education. Challenges such as limited infrastructure and rigid schedules were identified, along with lessons learned for future implementation. Students connected local issues to global goals like the SDGs and saw themselves as agents of change. These initiatives offer practical models for advancing sustainability education through innovation and interdisciplinary collaboration. Full article

Background/Objectives: Psychosocial care for siblings and caregivers of youth with cancer (SCYC) is a critical yet under-implemented component of comprehensive pediatric oncology care, as outlined by the Standards for Psychosocial Care for Children with Cancer and Their Families. Despite evidence supporting psychosocial interventions, such as integrative care interventions, as effective for stress mitigation and coping, barriers to implementation include revenue-generating funding models and siloed psychosocial disciplines, which hinder accessibility for adult caregivers within pediatric institutions and geographically dispersed families. This manuscript describes the relevant extant literature as well as a model for leveraging short-term funding opportunities and interdisciplinary collaboration to develop integrative care programs for these underserved groups. Methods: Philanthropic funding supported part-time child life specialist and creative arts therapist deployment to develop and implement integrative virtual group programs, as well as interdisciplinary integrative programs, to serve SCYC. Attendance, engagement, and qualitative feedback were used for program iteration and supported the transition to institutional funding. Results: Integrative programs provided 331 caregiver and sibling encounters during the two-year pilot. Qualitative feedback from caregivers highlighted the value of virtual services in reaching geographically dispersed families and addressing feelings of isolation among SCYC at the universal and targeted levels of care. Communication about these key outcomes led to operational funding and sustained integrated care programs. Conclusions: This manuscript illustrates a successful model of leveraging philanthropic funding to support the development of integrative care programs to serve SCYC. Future research should focus on refining the clinical and financial feasibility of such models and assessing their impact on family well-being. Full article

Advances in sensor technology have significantly improved the efficiency and precision of agricultural spraying. Unmanned aerial vehicles (UAVs) are widely utilized for applying plant protection products (PPPs) and fertilizers, offering enhanced spatial control and operational flexibility. This study evaluated the performance of an autonomous UAV-based precision spraying system that applies variable rates based on zone levels defined in a prescription map. The system integrates real-time kinematic global navigation satellite system positioning with a proximity-triggered spray algorithm. Field experiments on a rice field were conducted to assess spray accuracy and fertilization efficacy with liquid fertilizer. Spray deposition patterns on water-sensitive paper showed that the graded strategy distinguished among zone levels, with the highest deposition in high-spray zones, moderate in medium zones, and minimal in no-spray zones. However, entry and exit deviations—used to measure system response delays—averaged 0.878 m and 0.955 m, respectively, indicating slight lags in spray activation and deactivation. Fertilization results showed that higher application levels significantly increased the grain-filling rate and thousand-grain weight (both p < 0.001), but had no significant effect on panicle number or grain count per panicle (p > 0.05). This suggests that increased fertilization primarily enhances grain development rather than overall plant structure. Overall, the system shows strong potential to optimize inputs and yields, though UAV path tracking errors and system response delays require further refinement to enhance spray uniformity and accuracy under real-world applications. Full article

The predictive models performance on embedded devices represents a significant technical challenge for applications for real-time Predicting of Photovoltaic Solar Energy Generation (PPSEG). This study evaluated the computational feasibility of the Hybrid Prediction Method (HPM), focusing on the extraction of nine visual features extracted from 180° hemispheric all-sky images, processed on the Raspberry Pi 4 Model B microcomputer. The experiment, conducted with 100 images at different resolutions, demonstrated that the proposed pipeline is operationally feasible in all tested configurations. Processing times were significantly reduced with decreasing resolution, remaining compatible with embedded applications. However, an increase in normalized absolute error of up to 8% was observed at 25% resolution, especially in the measurement of cloud motion, which is sensitive to the loss of spatial detail. The other measurements remained stable and had low error levels. The main contribution of this work lies in the validation of a pipeline and measurement of embedded computer vision performance for HPM, enabling its actual implementation and promoting advances in the development of short-term PPSEG solutions. Full article