The successful implementation of gamification requires a comprehensive understanding of game design elements, yet existing compilations often lack empirical rigor. Also, while the interplay of individual elements has been considered relevant, there is a lack of methods for combining them. One of the challenges in systematically extracting game design elements is the lack of access to computer game source code. Instead, game design elements are mostly compiled based on subjective experience or literature research. In order to overcome this obstacle, this article uses board game manuals as a new approach. It presents an artificial intelligence (AI)-based analysis of 8,300 board game manuals, identifying 97 detailed game design elements and their interactions as game design molecules. These findings form the EMPAMOS framework, offering precise descriptions of elements and combinations for gamification and game design. Its relevance is demonstrated through a survey and case studies in social work, museum exhibitions, and higher education, showcasing its innovative and practical applicability. The EMPAMOS framework serves as a systematic guideline for gamification and game design in research and practice.
Patients today seek a more advanced and personalized health-care system that keeps up with the pace of modern living. Cloud computing delivers resources over the Internet and enables the deployment of an infinite number of applications to provide services to many sectors. The primary limitation of these cloud frameworks right now is their limited scalability, which results in their inability to meet needs. An edge/fog computing environment, paired with current computing techniques, is the answer to fulfill the energy efficiency and latency requirements for the real-time collection and analysis of health data. Additionally, the Internet of Things (IoT) revolution has been essential in changing contemporary healthcare systems by integrating social, economic, and technological perspectives. This requires transitioning from unadventurous healthcare systems to more adapted healthcare systems that allow patients to be identified, managed, and evaluated more easily. These techniques allow data from many sources to be integrated to effectively assess patient health status and predict potential preventive actions. A subset of the Internet of Things, the Internet of Health Things (IoHT) enables the remote exchange of data for physical processes like patient monitoring, treatment progress, observation, and consultation. Previous surveys related to healthcare mainly focused on architecture and networking, which left untouched important aspects of smart systems like optimal computing techniques such as artificial intelligence, deep learning, advanced technologies, and services that includes 5G and unified communication as a service (UCaaS). This study aims to examine future and existing fog and edge computing architectures and methods that have been augmented with artificial intelligence (AI) for use in healthcare applications, as well as defining the demands and challenges of incorporating fog and edge computing technology in IoHT, thereby helping healthcare professionals and technicians identify the relevant technologies required based on their need for developing IoHT frameworks for remote healthcare. Among the crucial elements to take into account in an IoHT framework are efficient resource management, low latency, and strong security. This review addresses several machine learning techniques for efficient resource management in the IoT, where machine learning (ML) and AI are crucial. It has been noted how the use of modern technologies, such as narrow band-IoT (NB-IoT) for wider coverage and Blockchain technology for security, is transforming IoHT. The last part of the review focuses on the future challenges posed by advanced technologies and services. This study provides prospective research suggestions for enhancing edge and fog computing services for healthcare with modern technologies in order to give patients with an improved quality of life.
Background The healthcare sector is experiencing rapid digital advancements, with patients increasingly seeking quick and seamless interactions. Artificial intelligence (AI)-driven healthcare chatbots are becoming an integral part of elderly care, transforming provider-patient engagement and supporting health behavior goals tailored to individual preferences, needs, and limitations. Methods This study developed a comprehensive research framework incorporating various theoretical perspectives to explore the determinants of sustained use of AI-powered healthcare chatbots among older adults. The framework also examined the mediating influence of perceived humanness. The model was evaluated using partial least squares structural equation modeling (PLS-SEM) on cross-sectional data collected from 158 individuals aged 60 and above. Results The findings show that satisfaction with AI-powered chatbots is significantly influenced by facilitating conditions, perceived hedonic motivation, confirmation, performance expectancy, and effort expectancy. Perceived security also plays a critical role in shaping satisfaction and the intention to continue using these chatbots. Moreover, the analysis revealed that perceived humanness mediates the relationship between satisfaction and continuous use intentions among elderly users in Saudi Arabia. Discussion This research provides valuable insights into the factors influencing older adults’ acceptance of AI chatbots in Saudi Arabia, particularly in the post-COVID-19 era. These findings enrich academic discourse and offer actionable recommendations for healthcare organizations adapting to the evolving digital landscape.
Akihiko Teshigawara, Ai Iwauchi, Nei Fukasawa
et al.
Abstract Introduction Extracranial metastasis of a glioblastoma (GBM) is rare (0.4 to 2%). And its pathology and standard therapy has been not been established. We report a case of systemic metastasis detected 3 months after craniotomy for newly diagnosed GBM with autopsy findings. Case A 56-year-old woman presented with the progression of speech disorder. Brain MRI revealed tumor in the left temporal lobe and whole-body CT scan and blood tests showed no evidence of carcinoma. The tumor was subtotal removed. The pathological diagnosis was GBM, IDH-wild type, WHO grade 4 (MGMT methylated, GFAP (+), OLIG2 (+),). Stupp Regimen and bevacizumab were initiated, and the patient was discharged 2.5 months after surgery followed by chemotherapy as an outpatient. At 3months after surgery, her liver dysfunction rapidly progressed, and multiple nodules were found in the liver. A biopsy of the liver lesion showed that primitive tumor cells, both GFAP and OLIG2 negative, confirming that the lesion was a metastasis of GBM. She died of liver failure 4.5 months days after surgery. An autopsy revealed the primary tumor was disseminated to the central nerve system, and metastasized to the liver, the lungs, cardiovascular system, bone marrow, and lymph nodes. Discussion It is known that the most common sites of extracranial metastasis of GBM are to be bones, lymph nodes, lungs, and liver. The average duration from diagnosis of metastasis to death is 4 months, but for liver metastasis, it is shorter at 1.5 months compared to other organs. In the present case, the cause of death was rapid progression of liver failure. In recent years, there have been a number of reports of genetic analysis of GBM with extracranial metastasis. Since there have been few reports of autopsy in the previous literature, further analysis would be useful to elucidate pathogenesis and develop treatment option.
Abstract This paper proposes a novel framework for talent development in Indian National Oil Companies (NOCs), validated through Oil and Natural Gas Corporation's (ONGC) implementation. By integrating AI driven capability mapping, peer-led micro-communities, and location-agnostic role deployment, the model transforms structural constraints into strategic advantages. Key outcomes include 87.1% role-fitment gains, 15.7% consultant cost reduction, and 52.3% cross-functional participation (voluntary collaborations), based on initial results from the selected cohort. The replicable architecture demonstrates how NOCs can cultivate agile workforces amidst digital transformation.
This research investigates the impact of Artificial Intelligence (AI) on the teaching and learning of Computer Science (CS) in low-resourced schools. The study aims to understand how AI is currently being utilized in CS education, assess the benefits it offers, and identify the challenges faced by low-resourced schools in implementing these technologies. Two primary methods were employed: an interview with the founder of Rehan School, a low-resourced school utilizing AI tools like ChatGPT and virtual assistants, and a comparative study analyzing students' performance from low-resourced and well-resourced schools using different learning resources. Key findings reveal that AI technologies significantly enhance student engagement and learning outcomes in CS education by providing personalized learning experiences and access to advanced educational resources. However, limited infrastructure, funding, and staff training remain significant barriers to widespread adoption. The study concludes that while AI has the potential to bridge educational gaps in low-resourced settings, addressing these challenges is crucial for maximizing its benefits. The findings suggest a need for targeted policies and investments to support the integration of AI in education, particularly in underfunded schools.