Enhancing museum experience through deep learning and multimedia technology.

Amidst the swift progression of artificial intelligence (AI) technology, the museum sector has witnessed a notable inclination towards its adoption. This manuscript endeavours to amplify the interactive milieu of contemporary museum patrons by amalgamating a deep learning algorithm with multimedia technology. The crux of our investigation is the exploration of an adaptive convolutional neural network (CNN) to enrich the interactive engagement of museum visitors. Initially, we leverage the adaptive CNN for the image recognition chore pertaining to museum artifacts and exhibits, thereby facilitating automatic recognition and categorization. Furthermore, to surmount the constraints of conventional pooling algorithms in image feature extraction, we suggest an adaptive pooling algorithm, grounded in the maximum pooling algorithm paradigm. Subsequently, multimedia algorithms are amalgamated into the interactive apparatus, enabling visitors to immerse in exhibits and avail more profound information and experiences. Through juxtaposition with traditional image processing algorithms, the efficacy of our proposed algorithm within a museum ambiance is assessed. Experimental outcomes evince that our algorithm attains superior accuracy and robustness in artifact identification and classification endeavours. In comparison to alternative algorithms, our methodology furnishes more precise and comprehensive displays and interpretations, accurately discerning and categorizing a myriad of exhibit types. This research unveils innovative notions for the digital metamorphosis and advancement of modern museums. Through the incorporation of avant-garde deep learning algorithms and multimedia technologies, the museum visitor experience is elevated, proffering more enthralling and interactive displays. The elucidations of this manuscript hold substantial merit for the continual evolution and innovation within the museum industry.

Effects of cue and instructor demonstration on the learning of Chinese characters for Chinese as a second language beginners.

This study purports to investigate the effects of cue and instructor demonstration on Chinese as a second language (CSL) beginners' Chinese character learning performance, cognitive load, learning motivation and attitude. In the current research, 100 CSL beginners were randomly assigned to four experimental groups, i.e., instructor demonstration cued character, instructor demonstration non-cued character, non-instructor demonstration cued character and non-instructor demonstration non-cued character. Participants were instructed to watch an instructional video and subsequently complete a post-test and a questionnaire. The results show that (1) in the presence of instructor demonstration, the cued characters can noticeably reduce CSL beginners' cognitive load and enhance their learning attitudes towards character learning, enabling them to achieve better performance in character stroke but not in radical and structure, and (2) in the presence of cued characters, the instructor demonstration can noticeably reduce CSL beginners' cognitive load and increase their learning motivation and attitudes towards character learning but can not improve their character learning performance. The findings have significant implications for educators and instructional designers of Chinese and other non-alphabetic languages, such as Kanji and Hangul.

Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model.

Introduction: To enhance the precision of evaluating the impact of urban environments on resident health, this study introduces a novel fuzzy intelligent computing model designed to address health risk concerns using multi-media environmental monitoring data. Methods: Three cities were selected for the study: Beijing (B City), Kunming (K City), and Wuxi (W City), representing high, low, and moderate pollution levels, respectively. The study employs a Fuzzy Inference System (FIS) as the chosen fuzzy intelligent computing model, synthesizing multi-media environmental monitoring data for the purpose of urban health risk assessment. Results: (1) The model reliably estimates health risks across diverse cities and environmental conditions. (2) There is a positive correlation between PM2.5 concentrations and health risks, though the impact of noise levels varies by city. In cities B, K, and W, the respective correlation coefficients are 0.65, 0.55, and 0.7. (3) The Root Mean Square Error (RMSE) values for cities B, K, and W, are 0.0132, 0.0125, and 0.0118, respectively, indicating that the model has high accuracy. The R2 values for the three cities are 0.8963, 0.9127, and 0.9254, respectively, demonstrating the model's high explanatory power. The residual values for the three cities are 0.0087, 0.0075, and 0.0069, respectively, indicating small residuals and demonstrating robustness and adaptability. (4) The model's p-values for the Indoor Air Quality Index (IAQI), Thermal Comfort Index (TCI), and Noise Pollution Index (NPI) all satisfy p < 0.05 for the three cities, affirming the model's credibility in estimating health risks under varied urban environments. Discussion: These results showcase the model's ability to adapt to diverse geographical conditions and aid in the accurate assessment of existing risks in urban settings. This study significantly advances environmental health risk assessment by integrating multidimensional data, enhancing the formulation of comprehensive environmental protection and health management strategies, and providing scientific support for sustainable urban planning.

Facilitating active learning of sectional anatomy with technology-enhanced small-group tasks: Assessment of knowledge gains, technology usability, and students' perceptions.

Learning 2D sectional anatomy facilitates the comprehension of 3D anatomical structures, anatomical relationships, and radiological anatomy. However, the efficacy of technology-enhanced collaborative instructional activities in sectional anatomy remains unclear, especially if theoretical frameworks, namely the Cognitive Theory of Multimedia Learning (CTML), are applied in instructional design. Thus, this study compared the educational impact of distinct 45-min-long technology-enhanced collaborative learning tasks in sectional anatomy. A sample of 115 first-year medical students was randomly divided into three experimental groups that used different supporting technologies to learn the sectional anatomy of the chest: IMAIOS e-learning platform and Microsoft Surface Hub (n = 37); anatomage table (n = 38); anatomage table with CTML-based presets (n = 40). Prelearning and postlearning tests revealed that significant knowledge gains in sectional anatomy were obtained by all groups even though no inter-group differences were found. Moreover, a five-point Likert scale questionnaire showed that the learning session was highly valued by all participants and that users of the anatomage with CTML-based presets reported higher enjoyment than users of the IMAIOS system (mean difference = 0.400; p = 0.037). In addition, students using the IMAIOS system and the anatomage with CTML-based presets provided System Usability Scale (SUS) scores of 67.64 and 67.69, respectively, reaching the benchmark of usability. By contrast, students using the anatomage table without presets awarded a SUS score of 64.14. These results suggest that the integration of multimedia technologies in anatomy teaching and learning should be grounded on CTML principles of instructional design. Otherwise, students' perceptions of ed-tech usability are potentially hindered.

Multimedia distribution and release characteristics of emerging PFAS in wastewater treatment plants in Tianjin, China.

Legacy and emerging PFAS in the air, wastewater, and sludge from two wastewater treatment plants (WWTPs) in Tianjin were investigated in this study. The semi-quantified nontarget PFAS accounted for up to 99 % of Æ©PFAS in the gas phase, and aqueous film-forming foam (AFFF)-related PFAS were predominant in wastewater (up to 2250 ng/L, 79 % of Æ©PFAS) and sludge (up to 4690 ng/g, 95 % of Æ©PFAS). Furthermore, field-derived air particle-gas, air-wastewater, and wastewater particle-wastewater distribution coefficients of emerging PFAS are characterized, which have rarely been reported. The emerging substitute p-perfluorous nonenoxybenzenesulfonate (OBS) and AFFF-related cationic and zwitterionic PFAS show a stronger tendency to partition into particle phase in air and wastewater than perfluorooctane sulfonic acid (PFOS). The estimated total PFAS emissions from the effluent and sludge of WWTP A were 202 kg/y and 351 kg/y, respectively. While the target PFAS only accounted for 20-33 % of the total emissions, suggesting a significant underestimation of environmental releases of the nontarget PFAS and unknown perfluoroalkyl acid precursors through the wastewater and sludge disposal. Overall, this study highlights the importance of comprehensive monitoring and understanding the behavior of legacy and emerging PFAS in wastewater systems, and fills a critical gap in our understanding of PFAS exposure.

Study of the impact of introducing a multimedia learning tool in podiatric medical courses.

BACKGROUND: Medical students face the challenge of learning vast amounts of complex information. Existing research suggests improved learning outcomes using multimedia resources but reports on their impact on podiatric education are scarce. To explore the potential of multimedia-based learning tools in enriching medical education, this study examined the impact of Osmosis, a platform featuring interactive videos, flashcards, and self-assessment quizzes on podiatric medical student outcomes. METHODS: This quasi-experimental study examined the impact of Osmosis, a multimedia learning platform with videos, flashcards, and quizzes, on podiatric medical students' learning outcomes. Two cohorts (T = Osmosis access, N = 86; C = no access, N = 87) took Pharmacology and Podiatric Medicine courses consecutively. Final exam scores, final course grades, platform usage metrics (median weekly videos watched, flashcards, and quizzes), and student experience surveys were analyzed. RESULTS: Analyses revealed no statistically significant differences in final exam scores between the groups in Pharmacology and Podiatric Medicine. While the treatment group exhibited a slight upward trend, further research is required for conclusive evidence. Student perceptions of Osmosis were overwhelmingly positive, with 90.2% of students agreeing that it facilitated concept learning and understanding compared to 54.9% for the textbook. Similarly, 80.4% of the treatment group felt that Osmosis enhanced their test performance, exceeding the 54.9% recorded for the textbook. Correlation analysis indicates a plausible connection between platform usage and academic success, as reflected by moderate positive correlations (r = [0.14, 0.28]) with final grades. Logistic regression analysis revealed that students with Osmosis access were 2.88 times more likely to score 90% or higher on the Pharmacology final exam (p < 0.05) and exhibited increased odds of achieving high (90%+) final course grades in Podiatric Medicine (OR = 2.71). CONCLUSIONS: These findings suggest that Osmosis holds promise as a tool to support podiatric medical student learning. While the lack of statistically significant differences in final exam scores warrants further investigation, the positive student perceptions, high engagement rates, and increased odds of high scores in specific areas indicate the potential for Osmosis to positively impact academic outcomes. Therefore, a multimedia-based resource like Osmosis appears to show promise as a tool to support podiatric medical education. The limitations inherent in the quasi-experimental design necessitate further studies to confirm its effectiveness and long-term impact on podiatric medical education.

Is there an impact of a video-based patient informed consent in elective hand surgery?

BACKGROUND: Patient informed consent is a crucial subject in preoperative care of patients before elective hand surgery, ensuring that patients have the necessary information and a comprehensive understanding to make autonomous decisions. The use of video-based informed consent systems is an innovative concept to enhance the consent process with multimedia tools. In addition to the conventional process, mostly relying on verbal communication and written documents, the video-based approach aims to present information in a standardized and visually appealing format. METHODS: In this study, 33 patients were asked to watch a video on a tablet about the planned elective hand surgery after a conventional pre-treatment consultation including informed consent throughout verbal explanations and paper forms by an attending physician or resident. All patients were asked to complete a questionnaire after watching the video. RESULTS: An overwhelming majority of participants, specifically 97.0%, stated that the video improved their understanding of the upcoming surgery. 90.9% of the participant would refer the video to other patients undergoing elective hand surgery, while 72.7% of participants indicated that they would have appreciated the opportunity to view an informational video before undergoing different types of surgeries in the past. CONCLUSION: The use of a video-based patient information system in elective hand surgery had a positive impact on patient education and satisfaction with the informed consent process. Therefore, it is a powerful tool in preoperative management to guarantee a standardized and educative informed consent.

Cross-media transfer of polycyclic aromatic hydrocarbons in the Naples metropolitan area, southern Italy.

At present, an in-depth knowledge of polycyclic aromatic hydrocarbons (PAHs) in the multimedia system of the urban environment remains limited. Taking the Naples metropolitan area (NMA) for instance, we simulated the cross-media transfer of PAHs using a multimedia urban model, involving air, water, soil, sediment, vegetation, and impervious film. The results indicated that the predicted PAH values in 2015 match well with their corresponding in-situ monitoring data. The PAH emission inventory and the simulated mass in various media all showed a downward trend from 2015 to 2020 due to national energy conservation policies and Corona Virus Disease 2019. The simulated mass of PAHs in the soil and sediment phases was 896.8 and 232.7 kg in 2020, respectively, contributing together to 96.7% of PAHs in the NMA. And they were identified as the greatest sinks for PAHs, and exhibited the longest retention duration, with values of PAH persistence reaching approximately 548.8 - 2,0642.3 hours. The results of transfer fluxes indicated that local emissions and atmospheric advection were the primary routes affecting the distribution of PAHs. The sensitivity analysis indicated that atmospheric advection rate was the most critical parameter for air, soil, vegetation, and film, whereas water concentration and sediment degradation rate were vital for water and sediment, respectively. This study offered valuable insights into how human activity contributes to the status and fate of PAHs in the urban environment.

Knowledge revision processes during reading: How pictures influence the activation of outdated information.

Outdated information (i.e., information that is not or no longer accurate) continues to be automatically activated during reading and can hinder learning processes. Thus, it is important to understand which factors influence the activation of outdated information and, therefore, knowledge revision processes. In three online experiments, we investigated how illustrating updated or outdated information via pictures influences the activation of outdated information. In Experiments 1 (N = 421) and 2 (N = 422), we varied whether participants read texts containing outdated information that was later updated (outdated text) or texts containing only updated information (consistent text). In addition, the updated information was or was not illustrated by a picture. In Experiment 3 (N = 441), participants read outdated texts, and we varied whether the outdated, the updated, or no information was illustrated. In all experiments, we measured reading times for a target sentence referring to the updated information and the sentence following the target sentence. Results showed that target sentences' reading times were faster for illustrated than for non-illustrated texts (Experiments 1 and 2). Moreover, reading times were longer when the outdated information was illustrated than when the updated information was illustrated (Experiment 3). These results suggest that pictures overall facilitate cognitive processes during reading, but their content matters: Pictures showing the updated information had a greater impact on reading times than pictures showing the outdated information. The results extend existing theories on knowledge revision but also reading comprehension, by demonstrating how pictures might influence cognitive processes during reading.

Effect of digital multimedia on the adoption of agricultural green production technology among farmers in Liaoning Province, China.

Agricultural green production technology (AGPT) is essential for the sustainable development of agriculture, yet adoption rates among farmers are often low due to limited access to education and information. Based on the extended technology acceptance model, this study takes Liaoning, a major agricultural province in China, as a representative region to explore how digital multimedia influences the adoption of AGPT by farmers. The findings show that the perceived ease of use of these technologies significantly enhances farmers' intentions to adopt, while perceived risks detract from these intentions. Although digital multimedia effectively promotes AGPT by improving its perceived ease of use and usefulness, its role in mitigating perceived risks is minimal. Based on these results, we recommend that the government improve the quality of information on digital platforms by involving experts in the field and offer specific digital marketing training to potential users to increase the adoption of sustainable agricultural technologies.

Assessing the quality of experience in wireless networks for multimedia applications: A comprehensive analysis utilizing deep learning-based techniques.

In the context of the burgeoning progression of wireless network technology and the corresponding escalation in the demand for mobile Internet-based multimedia transmission services, the task of preserving and augmenting user satisfaction has emerged as an imperative concern. This necessitates a sophisticated and accurate evaluation of multimedia service quality within the sphere of wireless networks. To systematically address the nuanced issue of user experience quality, the present study introduces a novel method for evaluating multimedia Quality of Experience (QoE) in wireless networks, employing an advanced deep learning model as the underlying analytical framework. Initially, the research undertakes the task of modeling the video session process, giving due consideration to the status of each temporal interval within the session's architecture. Subsequently, the challenge of QoE prediction is dissected and investigated through the lens of recurrent neural networks (RNNs), culminating in the proposition of an all-encompassing QoE prediction model that harmoniously integrates video information, Quality of Service (QoS) data, user behavior analytics, and facial expression analysis. The empirical segment of this research serves to validate the efficacy of the suggested video QoE evaluation method, engaging both quantitative and qualitative comparison metrics with contemporaneous state-of-the-art QoE models, employing the RTVCQoE dataset as the empirical foundation. The experimental findings illuminate that the QoE model elucidated in this study transcends competing models in performance metrics such as PLCC, SRCC, and KRCC. Consequently, this investigation stands as a seminal contribution to academic literature, furnishing an exacting and dependable QoE evaluation methodology. Such a contribution augments the user experience landscape in multimedia services within wireless networks, and instigates further scholarly exploration and technological innovation in the mobile Internet domain.

Batch equilibrium experiments and modeling reveal weak temperature dependence of cyclic volatile methylsiloxane sorption in soil/sediment organic carbon-water systems.

The organic carbon normalized partition coefficient, KOC, describes the equilibrium distribution of a chemical between water and organic carbon in soil or sediment. It is a key parameter in evaluating chemical persistence, mass distribution, and transport using multimedia fate and transport models. Considerable uncertainty remains about the KOC values of cyclic volatile methylsiloxane (cVMS) compounds, and in particular the dependence of KOC on temperature. In this study, we used a batch equilibrium (BE) method to measure KOC values and their temperature dependence between ∼5 and 25 °C for octamethylcyclotetrasiloxane (D4) and decamethylcyclopentasiloxane (D5) with soil and sediments. Approximate log KOC values at 25 °C were 4.5-5.0 for D4 and 5.5-6.1 for D5 with different sorbents, and decreased by 0.3 log units or less at 4-5 °C. Enthalpies of sorption, ΔHOC, obtained for the different sorbents ranged from +7.2 to +16 kJ mol-1, with average values of +7.9 and +13 kJ mol-1 for D4 and D5, respectively. These values differ in magnitude and direction from those reported elsewhere based on KOC values determined by a novel dynamic purge-and-trap (PnT) method, but are consistent with predictions based on their solvation properties. A new fugacity-based multimedia model incorporating sorption/desorption kinetics was developed and used to predict concentrations in the phases of BE and PnT systems during desorption of cVMS under different experimental and ideal conditions. Model simulations suggested that KOC values for cVMS compounds derived from the PnT systems could be influenced by sorption disequilibrium between water and solids controlled by desorption rates from the particle phase to water, and subsequent losses due to volatilization and degradation. This has the potential to result in overestimation of KOC values when fitting the experimental data of cVMS mass remaining in a PnT system over time, which could explain the observed differences between the methods.

A game-based multimedia application to improve the ability of clinical nurses to perform geriatric care: A randomized controlled trial.

BACKGROUND: Given the realities of global aging, maintaining Comprehensive Geriatric Assessment (CGA) abilities among clinical nurses is very important. Newer methods of continuing education are needed to engage nurses in CGA education. Using multimedia and game-based applications in CGA education (CGA APP) may be an effective method for continuing education. OBJECTIVES: To test the effectiveness of CGA APP in improving nurses' confidence in their abilities to perform geriatric care. DESIGN: A randomized, controlled trial were adopted. SETTING: An 1343-bed tertiary-care medical center in southern Taiwan. PARTICIPANTS: A total of 1250 nurses met inclusion criteria in 35 adult wards. We employed stratified sampling to recruit a total of 132 nurses proportional to the number of nurses in each ward, from January to March 2019. METHODS: Based on the Octalysis gamification framework, the CGA APP was developed. Participants were randomly assigned to either an CGA APP or a control group, which received traditional classroom learning of the same content. The main outcome was improvement in confidence in geriatric care ability were measured before and end of the training session, and six months later. RESULTS: There were no differences in baseline characteristics (except years of experience as a registered nurse), knowledge, attitudes or confidence of geriatric care between the two groups. Clinical nurses in the CGA APP group demonstrated significantly higher confidence in their geriatric care abilities than control group immediately after the intervention (75.85 ± 10.71 vs. 65.93 ± 8.49, p < 0.001) and six-months later (71.13 ± 9.69 vs. 63.57 ± 8.78, p < 0.001). After using GEE to control the confounding variable, the CGA APP group remained significantly higher confidence in their geriatric care abilities than control group. CONCLUSIONS: Use of multimedia game-based applications my better engage and teach practicing clinical than traditional learning methods. Our findings suggest that such interventions be further developed and tested for a larger variety of continuing education needs.

Applying augmented reality multimedia technology to construct a platform for translation and teaching system.

This paper investigates the integration of augmented reality (AR) technology into English translation teaching for college students, emphasizing the pivotal role of innovative teaching methods in enhancing students' translation skills and learning experiences. To address the issue of insufficient interest in English translation teaching, the paper initially assesses the purpose and significance of learning English translation through a questionnaire survey, elucidating challenges encountered in English language acquisition. Subsequently, adhering to AR principles, a teaching demonstration platform rooted in AR technology is conceived and developed, intricately aligned with English translation instruction. The platform serves as a solution to issues in English learning, such as inadequate course comprehension, low utilization of teaching resources, and instructors' lack of experience. The research culminates in the analysis of survey results, wherein the quantitative disparities in translation ability between students utilizing the research platform and those subjected to traditional teaching methods are examined. The findings underscore the positive impact of the AR-based research platform on improving students' translation proficiency. The AR platform heightens learners' engagement in the learning process, contributes to constructing a robust knowledge framework, and enhances overall learning outcomes. The platform offers educators opportunities to optimize experimental courses and elevate teaching standards. The paper's outcomes present novel pedagogical scenarios for learners, propose technical solutions for other technical disciplines and furnish a theoretical foundation and application model for a new generation of experimental demonstration platforms.

Effectiveness of preoperative multimedia educational sessions on the levels of anxiety and satisfaction among women undergoing cesarean: a systematic review and meta-analysis.

This systematic review and meta-analysis aimed to explore the effectiveness of preoperative multimedia educational sessions on the levels of anxiety and satisfaction among women undergoing cesarean section (CS). The Scopus, PubMed, and Cochrane databases were searched without language limitations for eligible randomized controlled trials (RCTs) published from their inception up to October 15, 2023. A random-effect meta-analysis was conducted, and the quality of this meta-analysis was evaluated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. Seven RCTs (n = 1006 women) met the inclusion criteria. Preoperative multimedia sessions were found to significantly reduce anxiety levels (n = 6 RCTs, weighted mean difference: -3.10; 95 percent confidence intervals (CI): -4.48, -1.73; I2 = 44.24 percent, moderate certainty of evidence) compared to the control group. However, there was no significant difference between the two groups regarding women's satisfaction after surgery (n = 5 RCTs, risk ratio: 1.37; 95 percent CI: 0.76, 2.50; I2 = 98.26 percent, moderate certainty of evidence). Leave-one-out sensitivity analysis showed robustness of the outcomes. The findings of this meta-analysis suggest that providing multimedia educational programs before surgery could potentially have a beneficial effect on the levels of anxiety experienced by women undergoing CS. However, it is imperative to validate these results through larger samples and multi-centered RCTs.

Research on the prediction of English topic richness in the context of multimedia data.

With the evolution of the Internet and multimedia technologies, delving deep into multimedia data for predicting topic richness holds significant practical implications in public opinion monitoring and data discourse power competition. This study introduces an algorithm for predicting English topic richness based on the Transformer model, applied specifically to the Twitter platform. Initially, relevant data is organized and extracted following an analysis of Twitter's characteristics. Subsequently, a feature fusion approach is employed to mine, extract, and construct features from Twitter blogs and users, encompassing blog features, topic features, and user features, which are amalgamated into multimodal features. Lastly, the combined features undergo training and learning using the Transformer model. Through experimentation on the Twitter topic richness dataset, our algorithm achieves an accuracy of 82.3%, affirming the efficacy and superior performance of the proposed approach.

Comparative study of AR infographic posters vs. offline videos for micro-video delivery in cardiology education.

Augmented reality has promised a new paradigm in medical education. Multimedia videos are the most preferred assent for augmentation. So, this study aimed to assess the effect of using an augmented reality infographic poster for delivering micro-videos on the knowledge and satisfaction of medical students in cardiology rotation. Sixty students participated in this quasi-experimental study and were allocated to three study groups; namely routine method, routine method plus offline micro-video delivery, and routine method plus micro-video delivery in an augmented reality infographic poster. The students' knowledge and satisfaction were evaluated through a multiple-choice question pre and post-test and a satisfaction questionnaire respectively. Within-group comparison of pre and post-test scores showed a significant increase in each study group (all p-values = 0.000). The highest post-test score was for the offline micro-video delivery group and pairwise comparisons of post-test scores showed a significant difference between this group and the control one (p-value = 0.013). Additionally, the augmented reality infographic poster group had the highest satisfaction score (p-value = 0.000). This experience showed the positive effect of micro-videos in clinical education. Although students were satisfied with accessing these videos through an augmented reality infographic poster, their knowledge acquisition was better when they received them offline.

Occurrence and spatiotemporal distribution of natural and synthetic steroid hormones in soil, water, and sediment systems in suburban agricultural area of Guangzhou City, China.

Steroid hormones are highly potent compounds that can disrupt the endocrine systems of aquatic organisms. This study explored the spatiotemporal distribution of 49 steroid hormones in agricultural soils, ditch water, and sediment from suburban areas of Guangzhou City, China. The average concentrations of Σsteroid hormones in the water, soils, and sediment were 97.7 ng/L, 4460 ng/kg, and 9140 ng/kg, respectively. Elevated hormone concentrations were notable in water during the flood season compared to the dry season, whereas an inverse trend was observed in soils and sediment. These observations were attributed to illegal wastewater discharge during the flood season, and sediment partitioning of hormones and manure fertilization during the dry season. Correlation analysis further showed that population, precipitation, and number of slaughtered animals significantly influenced the spatial distribution of steroid hormones across various districts. Moreover, there was substantial mass transfer among the three media, with steroid hormones predominantly distributed in the sediment (60.8 %) and soils (34.4 %). Risk quotients, calculated as the measured concentration and predicted no-effect concentration, exceeded 1 at certain sites for some hormones, indicating high risks. This study reveals that the risk assessment of steroid hormones requires consideration of their spatiotemporal variability and inter-media mass transfer dynamics in agroecosystems.

Development and validation of a point-of-care nursing mobile tool to guide the diagnosis of malnutrition in hospitalized adult patients: a multicenter, prospective cohort study.

Malnutrition is a prevalent and severe issue in hospitalized patients with chronic diseases. However, malnutrition screening is often overlooked or inaccurate due to lack of awareness and experience among health care providers. This study aimed to develop and validate a novel digital smartphone-based self-administered tool that uses facial features, especially the ocular area, as indicators of malnutrition in inpatient patients with chronic diseases. Facial photographs and malnutrition screening scales were collected from 619 patients in four different hospitals. A machine learning model based on back propagation neural network was trained, validated, and tested using these data. The model showed a significant correlation (p < 0.05) and a high accuracy (area under the curve 0.834-0.927) in different patient groups. The point-of-care mobile tool can be used to screen malnutrition with good accuracy and accessibility, showing its potential for screening malnutrition in patients with chronic diseases.

Effects and prediction of cognitive load on encoding model of brain response to auditory and linguistic stimuli in educational multimedia.

Multimedia is extensively used for educational purposes. However, certain types of multimedia lack proper design, which could impose a cognitive load on the user. Therefore, it is essential to predict cognitive load and understand how it impairs brain functioning. Participants watched a version of educational multimedia that applied Mayer's principles, followed by a version that did not. Meanwhile, their electroencephalography (EEG) was recorded. Subsequently, they participated in a post-test and completed a self-reported cognitive load questionnaire. The audio envelope and word frequency were extracted from the multimedia, and the temporal response functions (TRFs) were obtained using a linear encoding model. We observed that the behavioral data are different between the two groups and the TRFs of the two multimedia versions were different. We saw changes in the amplitude and latencies of both early and late components. In addition, correlations were found between behavioral data and the amplitude and latencies of TRF components. Cognitive load decreased participants' attention to the multimedia, and semantic processing of words also occurred with a delay and smaller amplitude. Hence, encoding models provide insights into the temporal and spatial mapping of the cognitive load activity, which could help us detect and reduce cognitive load in potential environments such as educational multimedia or simulators for different purposes.