The disconnect between technology and traditional practices poses a significant challenge. Many healthcare professionals and individuals struggle to navigate the influx of emerging technologies, hindering the full realization of their potential in revolutionizing health literacy and medical practice. The lack of cohesive understanding and integration of technologies like mobile applications, wearable devices, artificial intelligence, and telemedicine impedes the seamless delivery of healthcare services and obstructs individuals from actively managing their health. Transformative Approaches to Patient Literacy and Healthcare Innovation offers a comprehensive solution to bridge the gap between healthcare and technology. Delving into the dynamic fusion of these domains, it unravels the transformative power of technology applications, showcasing how they enhance health literacy and empower individuals to make informed decisions about their well-being. By providing insights into the integration of mobile health apps, electronic health records, extended reality, artificial intelligence, and more, the book equips readers with the knowledge needed to navigate the evolving healthcare landscape with confidence. For academic scholars seeking a roadmap to understand and harness the potential of technology in healthcare, this book is an indispensable resource. Embrace the present and future of healthcare excellence by joining this exploration of innovation. Overcome the challenges, democratize access to knowledge, and usher in a new era where technology becomes the catalyst for improved health outcomes and patient-centered care.

Assessing the preparedness of Ghana's health sector is a crucial task that requires a comprehensive and multi-faceted approach. Ghana's health sector faces many challenges, including limited resources, inadequate infrastructure, and workforce shortages, which can impede the delivery of quality healthcare services to the population. Thus, building a strong health resilience system is essential to cope with catastrophic events, and the capacity to prepare and effectively respond to pandemics. The COVID-19 pandemic has highlighted the critical role of digital technologies in managing public health emergencies. In the context of long-term pandemic management, digital transformation can provide numerous benefits, such as improving the speed and efficiency of response, enhancing communication and collaboration, and enabling remote access to essential services. Empirically, our study found that individual and systemic resilience are significant predictors of long-term pandemic management. Conversely, community resilience in times of crisis is not a significant predictor.

In an age where technology seamlessly integrates into healthcare, identifying effective strategies for this integration becomes paramount. The Transformative Approaches to Patient Literacy and Healthcare Innovation book seeks to establish itself as a pivotal resource by investigating the deep impact of innovative technologies on the healthcare sector. This book brings together a cadre of respected professionals and academics across various fields to dissect the role of novel technologies in enhancing patient literacy and advancing healthcare delivery. It provides a comprehensive examination of how cutting-edge advancements like artificial intelligence, telemedicine, serious games, and extended realities are revolutionizing patient care and healthcare delivery. Aimed at a broad audience of healthcare providers, researchers, policy makers, educators, and patients, this compilation aims to foster a nuanced comprehension of both the benefits and complexities introduced by these technological innovations. Through a blend of contemporary research findings, illustrative case studies, and discussions on ethical implications, the book encourages readers to critically assess and embrace these innovations in healthcare. It offers insight into the present technological landscape and casts a visionary look towards the future, enabling readers to grasp the evolving dynamics of healthcare in the digital era.

Carbonation depth involves complex physical process and interactions of multiple variables and is thus extremely complicated to predict in concrete structures. It is imperative to quantify this depth due to its vital role in the corrosion of rebars in recycled aggregate concrete (RAC). This paper developed a novel carbonation depth prediction model from a large database of 445 experimental results using artificial neural network (ANN). The relative importance and effect of the independent parameters in the carbonation depth are identified using Garson index and parametric analysis, respectively. Among all the architectures considered, the N 8-10-1 having 10 nodes in the hidden layer provided the best prediction in good agreement with experimental results. The model demonstrated superior performance relative to existing carbonation depth equations in the literature. Despite the presence of fuzziness in the data, the effect of each variable in the development of carbonation is explored in great detail. The model proposed here can provide a robust prediction of carbonation depth that can be used as a basis for assessing the structural health of recycled aggregate concrete structures.

In the past few decades, the transmission of data over an unsecure channel has resulted in an increased rate of hacking. The requirement to make multimedia data more secure is increasing day by day. Numerous algorithms have been developed to improve efficiency and robustness in the encryption process. In this article, a novel and secure image encryption algorithm is presented. It is based on a modified chaotic logistic map (CLM) that provides the advantage of taking less computational time to encrypt an input image. The encryption algorithm is based on Shannon’s idea of using a substitution–permutation and one-time pad network to achieve ideal secrecy. The CLM is used for substitution and permutation to improve randomness and increase dependency on the encryption key. Various statistical tests are conducted, such as keyspace analysis, complexity analysis, sensitivity analysis, strict avalanche criteria (SAC), histogram analysis, entropy analysis, mean of absolute deviation (MAD) analysis, correlation analysis, contrast analysis and homogeneity, to give a comparative analysis of the proposed algorithm and verify its security. As a result of various statistical tests, it is evident that the proposed algorithm is more efficient and robust as compared to previous ones.

This letter highlights a pressing issue regarding the absence of established editorial policies for the utilization of AI tools (e.g., ChatGPT) in the peer review process. The increasing adoption of AI tools in academic publishing necessitates the formulation of standardized guidelines to ensure fairness, transparency, and accountability. Without clear editorial policies, there is a threat of compromising the integrity of the peer review process and undermining the credibility of academic publications. Urgent attention is needed to address this gap and establish robust protocols that govern the use of AI tools in peer review.

Location information is the most crucial information used in context-aware applications, e-commerce and IoT-based consumer applications. Traditional methods doesn’t focus on network coverage, accuracy, hardware cost, and noise in dense environment. To defeat these issues, this paper presents a novel localization algorithm for UWB nodes adopting self-calibration and ToA measurement for context-aware applications. The Link quality induction values are used instead of RSSI for distance estimation by costing technique. A calibration factor (CF) is further introduce to automatically update the location information in mobility. As the signal strength can be distorted heavily due to shadowing and multi-path fading, the localization is estimated in noisy condition and extended Kalman filtering (EKF) is applied to refine the node coordinates. Simulation results shows that the positioning error is decreased with an overall accuracy of 0.23m and standard-deviation of 0.76m.

As technology rapidly advances, the complexity of societal challenges grows, necessitating intelligent solutions that can adapt and evolve. However, developing such solutions requires a deep understanding of computational intelligence (CI) and its application in addressing real-world problems. Moreover, ethical considerations surrounding AI, such as bias and accountability, are crucial to ensure responsible development and deployment of intelligent systems. Open AI and Computational Intelligence for Society 5.0 offers a comprehensive exploration of CI, providing insights into intelligent systems' theory, design, and application. This book is a practical guide for scientists, engineers, and researchers seeking to develop thoughtful solutions for complex societal issues. Integrating disruptive technologies and frameworks illuminates the path toward creating intelligent machines collaborating with humans to enhance problem-solving and improve quality of life.

The adoption of innovative technologies is critical for improving teaching practices and student learning outcomes. Among these, artificial intelligence (AI) is emerging as a transformative tool capable of reshaping traditional educational paradigms. ChatGPT, a sophisticated language model developed by OpenAI, offers numerous opportunities for educators to enhance pedagogical effectiveness and streamline lesson preparation processes. This study explores the efficacy of ChatGPT in lesson preparation by surveying and interviewing teachers at Dr. Josefa Jara Martinez High School in the Philippines. It aims to understand their attitudes towards and experiences with integrating ChatGPT into their teaching practices. Despite the promising potential of AI in education, the adoption of such technologies in the Philippines faces significant barriers, including limited awareness, access issues, and concerns about technology integration. The findings reveal that while teachers recognize the benefits of using ChatGPT, such as improved efficiency and personalized instruction, challenges like lack of training and ethical concerns remain prevalent. The study underscores the need for comprehensive professional development programs and robust ethical guidelines to support the effective and responsible use of AI tools in education. The results show that teachers have a wide range of opinions, but many of them agree that ChatGPT has the potential to make lesson planning easier, offer individualized learning resources, and keep students interested in class. On the other hand, issues with consistency with curriculum requirements, dependability, and general efficacy were also apparent. The study sheds light on the challenges associated with integrating AI into education and makes recommendations for professional development, focused assistance, and ethical considerations to help high schools adopt AI technologies responsibly. Teachers can optimize learning experiences, improve teaching effectiveness, and give students the tools they need to succeed in the digital age by tackling these issues and utilizing AI's transformative potential.

Wave energy derived from oceanic kinetic forces is a highly promising renewable energy source. As global efforts to incorporate renewable energy into the grid increase, accurate wave energy forecasting becomes essential for optimizing energy harvesting and grid integration. This paper examines the latest developments in machine learning (ML) approaches, focusing on deep learning (DL), ensemble methods, and hybrid models used for forecasting ocean wave energy. It highlights the strengths and weaknesses of various approaches in capturing the complex nonlinear dynamics of ocean waves, including predicting energy flux, significant wave height (SWH), and wave period. Additionally, the paper explores how hybrid models, combining physical models with ML, have emerged as powerful tools for improving forecast accuracy over traditional methods. This review concludes with insights into future directions, emphasizing the potential of advanced techniques like transformers, generative adversarial networks (GANs), and real-time data assimilation for enhancing prediction reliability and computational efficiency.