Dynamic Content Research Articles

Study on the mechanical properties of Polyacrylic Acid/Chitosan double network hydrogels in dynamic water content

Abstract Hydrogels have demonstrated significant potential in wound dressings, owing to their adjustable water content, excellent biocompatibility, and other advantages. An ideal wound dressing requires absorbing exudate while maintaining mechanical properties to prevent secondary damage to the wound under external pressure. In this study, Polyacrylic Acid (PAA)/Chitosan (CS) double network hydrogels were prepared via radical polymerization, and the swelling ratio, compressive modulus, and other parameters were experimentally measured. A hyperelastic finite element model of the hydrogel was then established, and its rationality was validated by experiments. Based on the model, the impacts of CS content and dynamic water content on the compressive properties of PAA/CS hydrogels were analyzed. Tetracycline hydrochloride was loaded into PAA/CS hydrogels, the drug release performance and adhesion experiments were carried out. The results indicated that, compared with the hydrogels with 2.5% and 5% CS content, the hydrogel with 1% CS content had the highest swelling ratio, which was 1774%. Meanwhile, its equilibrium water content could attain 94.6%. When the water content varied within the range from 45% to 75%, the compressive strength of the hydrogel with 5% CS content ranged from 0.33 MPa to 1.22 MPa, similar to that of skin tissue. Additionally, the prepared drug-loaded PAA/CS hydrogels exhibited a drug release pattern approximately following the Korsmeyer-Peppas model within 7 days. They also exhibited remarkable adhesion to porcine skin and different substrate surfaces. This study provides a basis for the clinical application of PAA/CS double network hydrogels as wound dressings in the repair of infected wounds.

Integrating Multimodal Generative AI Technologies in Postgraduate Marketing Education

While industry practices evolve rapidly, marketing education in Australia and New Zealand faces challenges in keeping pace, particularly regarding the adoption of current marketing technologies (Harrigan et al., 2022). Generative AI, exemplified by systems like ChatGPT and DALL·E, has demonstrated benefits for learning (Baidoo-Anu & Ansah, 2023). However, despite its potential, there remains a dearth of practical guidance on effectively incorporating these technologies into marketing courses. This gap persists even as general frameworks for responsible and ethical AI use, such as the Australian Framework for Generative AI in Schools (2023), emerge. As the demand for graduates with generative AI skills grows in the job market, educators must explore innovative pedagogical approaches to bridge this gap. This academic poster presents an innovative application of generative artificial intelligence (GenAI) in the context of teaching digital marketing at the postgraduate level. Its purpose is to bridge the gap between academic theory and industry practice by encouraging educators to integrate AI tools into their curriculum through experiential learning pedagogy (Kolb, 2014), characterized by a learning process whereby knowledge is created through hands-on experiences. The poster exemplifies how various types of GenAI technologies — specifically text-based, image-based, and video-based — can enhance teaching content, tutorial exercises, and assessments within the digital marketing course. The poster showcases examples of how these GenAI tools are integrated in the course content, to guide students in generating innovative ideas for using AI in marketing to gain a competitive edge: Text-based GenAI: Tools like ChatGPT and Gemini can automatically generate search keywords for search engine marketing. By integrating text-based GenAI tools with established marketing technology (MarTech) tools such as Google Ads and Google Ads Keyword Planner, students engage in practical exercises that combine AI-generated initial ideas (e.g., search keywords) with further analysis (e.g., search volume, click-through rates, and bidding costs) using established MarTech tools. This hands-on approach enhances their learning experience and prepares them for real-world applications. Image-based GenAI: Platforms such as DALL·E, Midjourney, and Stable Diffusion enable the creation of custom images for display advertising, enhancing visual communication in marketing materials. Through experiential learning activities, students can explore ideas, seek unusual combinations, and inspire creativity faster with image-based GenAI tools, resulting in a greater variety of display ad materials. Video-based GenAI: Applications like Sora and Synthesia facilitate the production of short video clips suitable for social media marketing (e.g., YouTube Shorts, TikTok). By engaging in dynamic content creation exercises, students learn to streamline content creation, reduce manual work, and save both time and budget, thereby gaining practical skills in social media marketing. By incorporating these GenAI technologies through experiential learning pedagogy, educators can enrich the learning experience, foster critical thinking, and prepare students for the evolving landscape of digital marketing. Future research can study the use of GenAI in marketing education using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2016).

Contextual evaluation of the impact of dynamic urban window view content on view satisfaction

Movement is a preferred attribute in window views, but identifying the ideal amount of movement for different space types remains challenging. This study examined the impact of movement in urban window views on view satisfaction in four spaces: office, living room, restaurant and classroom. Fifty dynamic urban window views were recorded at 30 frames per second, and movement was quantified by examining the percentage of pixels that exhibited intensity changes between successive video frames. Window view compositional ratios were determined through semantic segmentation using a pretrained DeepLabv3+ model. Fifty participants evaluated the movement and view satisfaction in a virtual reality survey. Movement was sufficient when average change in the view after every 1/30 seconds ranged between 2% to 8% during the viewing period. Correlation analysis revealed that the strongest indicators of excessive, insufficient and sufficient movement are Ratio of Human-associated Dynamic Objects (RHDO), Building Ratio (BR), and Greenery Ratio (GR), respectively. It was established that occupants can tolerate higher movement and RHDO in restaurants and offices than in living rooms and classrooms. These insights can inform the allocation of space in mixed-use buildings. The movement quantification method can also be incorporated into other dynamic window view evaluation frameworks.

Investigation of the Impact of New Media on Movie Advertising

As the landscape of mass entertainment shifts towards new media—characterized by short-form videos, interactive content, and decentralized platforms—the traditional film industry is undergoing significant changes in its advertising strategies. This paper investigates the impact of new media on movie advertising by analyzing how digital channels, such as social media and streaming services, have transformed traditional promotional methods. Utilizing reliable secondary sources, this research explores various aspects of new media’s influence on film marketing, focusing on case studies like “Avengers: Endgame” and “Spider-Man: No Way Home” to highlight effective strategies. The findings reveal that new media offers unprecedented opportunities for audience engagement through dynamic and interactive content, surpassing the effectiveness of traditional methods. Social media campaigns, marked by high levels of engagement, viral content, and influencer collaborations, have proven particularly successful in driving film visibility and box office success. The research concludes that future studies should incorporate emerging platforms like TikTok, which present novel ways to connect with younger demographics and create shareable content. By understanding the evolving trends and integrating new media strategies, filmmakers can optimize their advertising approaches to enhance audience reach and maximize commercial success.

Unveiling the Power of AI-Driven Personalization: Transforming Consumer Behavior in the Age of Digital Marketing

Artificial intelligence (AI) is revolutionizing digital marketing, with AI-driven personalization emerging as a powerful tool for shaping consumer experiences and driving engagement. This study explores how AI-powered technologies, such as recommendation engines, dynamic content delivery, and targeted advertisements, are transforming consumer behavior in the digital landscape. By leveraging big data, machine learning, and predictive analytics, marketers are now able to deliver hyper-personalized experiences that cater to individual preferences, leading to enhanced customer satisfaction and increased conversion rates. This research examines the impact of AI-driven personalization on key aspects of consumer behavior, including decision-making processes, brand loyalty, and purchase intentions. Additionally, it addresses the ethical challenges that arise from the use of AI in marketing, particularly around data privacy, algorithmic bias, and consumer trust. Through case studies from various industries, this study provides a comprehensive analysis of how personalization influences consumer psychology and purchasing patterns. Ultimately, the research offers valuable insights into the benefits and risks of integrating AI into marketing strategies, helping businesses understand how to harness the power of AI while maintaining ethical standards and safeguarding customer relationships in an increasingly digital world. Moreover, the study delves into the nuances of AI-driven personalization by exploring how consumers respond differently based on demographic factors such as age, gender, and cultural background. By analyzing customer data in real-time, AI allows for more accurate segmentation, ensuring that marketing messages are highly relevant and resonate with individual consumers. This level of precision not only increases customer engagement but also fosters a sense of exclusivity, making consumers feel more valued and understood by the brand. The research also investigates the role of AI in predicting future consumer behavior, using historical data to forecast trends and preferences, thereby enabling brands to stay ahead of the competition. As AI algorithms continuously learn and adapt, they can refine their recommendations, becoming more effective over time. However, there are concerns about over-reliance on AI, as excessive personalization may lead to "filter bubbles," where consumers are only exposed to a limited range of products or content, potentially stifling creativity and innovation.

Using reflection and dialog feedback to promote the development of situated and dynamic pedagogical content knowledge

This study explores the reflection and dialog feedback of preservice teachers (PTs) in a one-semester chemistry teaching design and practice course with the aim of improving their situated and dynamic pedagogical content knowledge (PCK). Using a collective case study method, reflections and dialogs from 18 PTs across six teaching cycles were analyzed. The research results indicate that teaching reflection focuses on five themes: the diversity of student situations, the functionality of teaching contexts, the consistency of teaching content, the construction of teacher‒student dialog, and the progression of subject practice activities. The reflection of the PTs strengthened meaningful teaching content and corresponding specific skills, and effective dialog feedback provided external evaluation standards. The reflection prompted teachers to evaluate from multiple perspectives and levels and provided important insights to promote the development of situated and dynamic PCK. This study concludes with a discussion of the practical implications and potential for future research to develop a situated and dynamic PCK for PTs.

Decoding didactic discourse strategies in digital posters

The research paper explores the critical role of didactic discourse in educational settings, with a specific focus on digital environments. It elucidates the fundamental elements of digital didactic discourse, encompassing clear communication, structured content organization, engaging features, and accessibility considerations. The paper also delineates the advantages of utilizing digital posters over traditional formats, highlighting their interactivity, dynamic content, and ability to reach a broader audience. A comprehensive analysis of a digital poster on parts of speech serves as a case study to showcase the efficacy of structured content, practical examples, color coding, progression, contextualization, hyperlinks, accessibility features, clarity, scalability, and overall effectiveness in creating an engaging and informative educational resource. The findings underscore the significance of harnessing digital technologies in didactic discourse to elevate learning experiences, foster better comprehension among learners, and cater to diverse learning styles and needs. This research contributes to the ongoing discourse on integrating technology into education to optimize pedagogical approaches and enhance educational outcomes.

Revolutionizing Retail: An Empirical Study on the Impact of Generative AI in Omnichannel Strategies

This article examines the transformative impact of Generative AI (Gen AI) on omnichannel retail, exploring its role in enhancing customer experiences and streamlining operations across digital and physical platforms. Through a comprehensive analysis of current literature, industry case studies, and empirical data, the study investigates six key applications of Gen AI in retail: personalization and dynamic content creation, virtual shopping assistants, inventory and fulfillment optimization, marketing content generation, customer insights and sentiment analysis, and immersive AR/VR experiences. The findings reveal that Gen AI significantly improves personalization, operational efficiency, and cross-channel integration in retail. However, the research also identifies challenges in implementation, including data privacy concerns and the need for seamless technology integration. This article contributes to the growing body of knowledge on AI in retail and provides practical insights for retailers seeking to leverage Gen AI in their omnichannel strategies. The paper concludes by discussing future research directions and the potential long-term implications of Gen AI on consumer behavior and global retail practices.

The Impact of the Digital Transformation of Chinese Cultural Products on Marketing and Communication Strategies

In an era defined by globalization and rapid technological advancement, Chinas cultural industry is undergoing a significant transformation. This study investigates the profound effects of digital transformation on Chinas cultural industry, focusing on digital cultural innovation products such as digital art, virtual reality experiences, and online cultural creations. As globalization and technological advancements reshape cultural production, dissemination, and consumption, Chinas digital cultural sector has emerged as a significant global player. This research systematically reviews literature, industry reports, and case studies to analyze how Chinese digital cultural products are influencing marketing and communication strategies. The findings reveal that advancements in virtual reality, augmented reality, and artificial intelligence are leading to the creation of immersive and diverse cultural experiences, significantly enhancing consumer engagement and market reach. The study also highlights the transformative role of digital tools in marketing, including dynamic content creation and interactive experiences, while addressing challenges such as market saturation and the need for balanced engagement strategies. Future research should incorporate quantitative and qualitative data to further explore these dynamics.

Research on Real-time E-commerce Price Comparison System Using Python Web Scraping Technology

This paper presents a study on the development of a real-time price comparison system for e-commerce platforms using Python-based web scraping technologies. The objective is to analyze and compare product prices across major e-commerce platforms, including Taobao, JD.com, and Amazon, by leveraging different web scraping libraries such as BeautifulSoup, Scrapy, and Selenium. The study outlines the technical challenges associated with dynamic content extraction, anti-scraping mechanisms, and the effectiveness of various scraping tools. Through experimental evaluation, we compare the scraping efficiency, success rate, and data accuracy across the three platforms. The results indicate that JD.com, with its mostly static content, offers the highest scraping efficiency when using BeautifulSoup, while Amazon, despite slower page loading, achieves a high success rate using Scrapy. Taobao, on the other hand, presents significant challenges due to its dynamic loading and strict anti-scraping measures, making Selenium the most appropriate tool, albeit with slower processing times. This research provides valuable insights into the selection of optimal web scraping techniques for real-time price comparison systems and highlights the impact of platform-specific challenges on the accuracy and efficiency of data extraction.

Development and Evaluation of an AI-Based Chatbot for Preventing Social Media Addiction: A Waterfall Model Approach

The rapid growth of social media has transformed interaction and communication patterns, but it has also led to the rise of social media addiction, particularly among teenagers and young adults. This addiction, marked by compulsive usage and negative impacts on mental health and daily life, necessitates effective interventions. This research explores the development and evaluation of an AI-based chatbot designed to mitigate social media addiction by employing cognitive and behavioural strategies. The study utilizes the Waterfall model—a structured, sequential approach—in the chatbot’s development, encompassing stages from needs analysis to maintenance. The chatbot’s effectiveness was assessed through rigorous testing and user feedback. The methodology included problem analysis, system design, implementation, testing, and iterative improvements. A comprehensive needs analysis identified the psychological and behavioural factors contributing to social media addiction, leading to the design of a prototype chatbot integrated with AI for dynamic content adaptation and real-time feedback. The implementation phase focused on coding and system integration, followed by rigorous testing using Black Box Testing and the System Usability Scale (SUS) to ensure functionality and user-friendliness. Results indicate that the chatbot significantly reduced social media addiction scores, with a mean decrease from 55.21 to 50.17, supported by a highly significant p-value of <0.0001. User satisfaction was high, particularly regarding ease of use and information quality. However, user engagement declined over time, highlighting the need for ongoing content updates and feature enhancements. This study contributes to the field by providing insights into the application of the Waterfall model in AI chatbot development and offers a scalable solution for addressing social media addiction, with implications for future digital interventions in mental health.

Development and Implementation of a Web-Based Management Information System for Enhancing Computer Programming Training in Education

In the current era of information technology, web-based training systems are critical for enhancing the efficiency of training processes. This study focuses on the development of a Management Information System (MIS) tailored for computer programming training, utilizing a web-based training (WBT) platform. The primary objective is to introduce a framework for a web-based MIS that optimizes the management and delivery of computer programming courses. Computer programming education presents unique challenges, such as real-time participant tracking and dynamic content management, which are addressed by this MIS. The system was developed using the waterfall model, with data collected through interviews, observations, and requirements analysis. Key features include training schedule management, participant tracking, material organization, session management, evaluation tools, and reporting capabilities. Real-time feedback enhances participant engagement, while session management improves the overall learning experience. The web-based platform offers flexible access and simplifies the management of training processes, enabling users to track schedules, access materials, and monitor progress. Validation results show a high validity score (V = 0.883), exceeding the required threshold, and the materials received a validity score of 0.864, confirming their suitability for basic programming competency training at Medan State University. These results demonstrate the system’s effectiveness and potential for wider application, making it a valuable tool for improving the management and delivery of programming courses across various educational contexts.

Understanding of Water Transport through Membrane Electrode Assembly in PEFC

In a polymer electrolyte fuel cell (PEFC), water generation, electroosmosis drag, and back diffusion change the humidity, which affects transport properties such as proton conductivity, effective oxygen diffusion coefficient, and permeance of the feed gas through a membrane. Since the sorption and desorption of water in the electrolyte membrane is slower than other processes such as the oxygen reduction reaction, the proton transport, and the oxygen diffusion, dynamic water behavior should be considered to estimate the cell performance precisely. In this study, the water permeation flux through a membrane electrode assembly (MEA) was measured, and the effective water diffusion coefficients in a membrane and catalyst layer (CL) were formulated as a function of temperature and the moisture content respectively. Dynamic moisture content change in MEA was simulated with the measured effective diffusion coefficients. The experimental results showed that desorption was slower than sorption in the identical moisture content range particularly at the beginning, and this tendency was successfully reproduced using the moisture content distribution obtained by numerical simulation.

Artificial Intelligence (AI)-Powered Piracy

This research paper explores the impact of artificial intelligence (AI) on piracy in the digital environment. It looks at how piracy has increased in the digital era and how AI may help protect intellectual property and provide pirates new strategies. The use of AI in content piracy is covered in the study, including the creation of text and audio, deepfake films, and counterfeit goods. Additionally, it looks at AI-assisted piracy technologies including dynamic content matching, automated content distribution and scraping, and content monetization.

Effects of Kahoot! on K-12 Students’ Mathematics Achievement and Multi-Screen Addiction

Digital platforms are increasingly prevalent among young students in K-12 education, offering significant opportunities but also raising concerns about their effects on self-assessment and academic performance. This study investigates the effectiveness of Kahoot! compared to traditional instructional methods in enhancing mathematics achievement and its impact on multiple screen addiction (MSA) among Greek students aged 9 to 12 during a STEM summer camp. A quasi-experimental design was employed with a purposefully selected sample of one hundred and ten (n = 110) students, who were non-randomly divided into two groups: (a) an experimental group of fifty-five students (n = 55) who engaged with Kahoot! (using dynamic visual aids and interactive content) and (b) a control group of fifty-five students (n = 55) who received traditional instruction (using digital textbooks and PowerPoint slides with multimedia content) on laptops and tablets. The findings revealed a statistically significant difference in MSA scores, with the experimental group exhibiting lower MSA scores compared to their counterparts, indicating a positive impact on reducing screen addiction levels. While Kahoot! led to lower MSA levels, it significantly improved overall mathematical achievement, with a substantial effect size, suggesting a strong positive impact on learning outcomes. The current study highlights the importance of aligning educational tools with the intended outcomes and recommends further research to explore the broader impact of gamified learning on student engagement, screen addiction, and learning outcomes.

Advantages and Applicability of a Master Class Series as a Training Model for Mid-Career Medical Writers: Our Experience From the Perspectives of the Manager, Trainer, and Participants

Advancement of mid-career medical writers (MWs) to senior levels requires augmentation beyond the traditional training provided to MWs at all levels. In response to this need, online training and classes taught by more experienced writers in house, external lecturers, or a mixture of both delivered through didactic presentations should be supplemented with methods that allow for interaction between the trainer and trainees and for a tailored approach. We therefore designed an interactive, engaging, and dynamic training model that would draw on varied perspectives of colleagues. This Master Class Series was led by an external instructor and was based on a structured critique of regulatory document organization, content, and style. In this article, we share our experience from the perspectives of manager, trainer, and participant. The Master Class Series was successful in part because of its dynamic content. The participants felt that this training greatly improved their understanding of the broader contexts in which documents are used and their ability to develop coherent documents. Participants further felt that their ability to implement a top-down approach in strategy documents and in applying lean writing principles was enhanced. They all agreed that they greatly benefit from exposure to each other’s creativity in solving challenges in data and content presentation and concurred that it is very likely that they would sign up for a course with a similar model in the future. Our experience indicates that a Master Class Series is essential to the training of mid-career MWs.

Framework for content server placement using integrated learning in content delivery network

<p>Content placement is a significant concern in content delivery networks (CDN), irrespective of various evolving studies. Existing methodologies showcase various significant unaddressed issues concerning content placement approaches' complexities. Therefore, the proposed study presents a novel computational framework towards dynamic content placement strategy using a novel integrated machine learning approach. Simplified mathematical modelling is used to formulate and solve the content placement problem. At the same time, reinforcement learning and the sequential attentional neural network have been utilized to optimize the decision-making towards placement of content servers. Designed and assessed over a Python environment, the proposed scheme is witnessed to exhibit 35% reduced bandwidth utilization, 20% reduced delay, 23% reduced computational resource utilization, and 28% reduced algorithm processing time in contrast to existing predictive content placement schemes.</p>

The Research on Intelligent News Advertisement Recommendation Algorithm Based on Prompt Learning in End-to-End Large Language Model Architecture

With the explosive growth of information on the internet, users are increasingly facing the problem of information overload, making precise news and ad recommendations an important area of research. While traditional recommendation algorithms can meet user needs to some extent, they still have limitations in dealing with complex and changing user behaviors and dynamic content environments. This paper addresses the shortcomings of existing news and ad recommendation systems by proposing an intelligent recommendation algorithm based on an end-to-end large language model architecture. Firstly, we utilize the BERT model as the foundation, leveraging its powerful text representation capabilities to achieve deep semantic understanding of news and ad content, thereby capturing more detailed content features. Secondly, we apply prompt learning to fine-tune the BERT model, designing specific prompts for the model to better understand the implicit needs and preferences of users. Finally, we integrate these steps into an end-to-end architecture, enabling the model to achieve automated learning and optimization throughout the entire process from input to output, thus improving the precision and efficiency of recommendations. Experimental results demonstrate that the proposed method significantly outperforms traditional methods in the task of news and ad recommendation, not only enhancing the accuracy and relevance of recommendations but also effectively improving the model's interpretability and flexibility. This research explores new possibilities for the application of large language models in recommendation systems.

Comparative study on human responses in isolated and confined offices with an artificial window

People in isolated and confined offices are separated from both daylight and view, which negatively impacts their health. Research in aboveground space indicated that dynamic circadian lighting and views can enhance people's health. This study focused on the effects of artificial windows (AWs) on people's physiological and psychological responses and cognitive performance in these offices. Comparative evaluations were conducted for whether AWs perform differently from real windows (RWs) in similar conditions of dynamic view contents and dynamic lighting at eye level. Dynamic lighting and views' effects were also concerned. Four single-occupancy offices (1.95 m2) were built, each with a window frame measuring 850mm × 1300 mm. LED screen was utilized as AW. Twenty college students volunteered to participate in the experiment.The results indicated no significant differences between AWs and RWs on cognitive performance (with views), physiological parameters (without views), and mood and alertness. Although AWs without views were more favorable in lighting perceptions, RWs' significant advantages persisted in physiological parameters and lighting perceptions (with views), cognitive performance (without views), and restorative perception. Over lighting at eye level (>1500 lx) may have disadvantages in physiological parameters, cognitive performance, and mood, and AW's view did not show benefits in physiological parameters, restoration, or lighting perceptions.Introducing AW in windowless spaces could be beneficial. However, further research on AWs is necessary for better application, for example, balancing LED screens' luminance and resolution. This study contributes to understanding AWs' effects on human responses, providing valuable references for AWs’ application and optimization in isolated and confined offices.

Enhancing Clinical Practice: Creating Dynamic Medical Content in Electronic Medical Records.

The integration of Electronic Medical Records (EMRs) revolutionized healthcare but often retained limitations from paper-based structures. This study proposes a framework for developing dynamic medical content specifically adapted to the clinical context including medical specialty and diseases. Tailoring content to this dynamic context offers several benefits, including improved access to relevant information, streamlined workflows, and potentially better patient outcomes. We applied our framework to develop neurosurgical content, focusing on brain tumors. The method involves defining the medical specialty, outlining user journeys, and iteratively developing artifacts like assessment forms, dashboards, and order sets. Standardized terminologies ensure consistency and interoperability. Our results demonstrate a successful development of content meeting user needs and clinical relevance. While initial implementation focused on neurosurgery, exploring scalability and AI integration offers promising avenues for further advancement. Future studies could quantitatively evaluate the impact of this method on user satisfaction and patient outcomes.