AI chatbots have emerged as powerful tools for providing text-based solutions to a wide range of everyday challenges. Selecting the appropriate chatbot is crucial for optimising outcomes. This paper presents a comprehensive comparative analysis of five leading chatbots: ChatGPT, Bard, Llama, Ernie, and Grok. The analysis is based on a systematic review of 28 scholarly articles. The review indicates that ChatGPT, developed by OpenAI, excels in educational, medical, humanities, and writing applications but struggles with real-time data accuracy and lacks open-source flexibility. Bard, powered by Google, leverages real-time internet data for problem solving and shows potential in competitive quiz environments, albeit with performance variability and inconsistencies in responses. Llama, an open-source model from Meta, demonstrates significant promise in medical contexts, natural language processing, and personalised educational tools, yet it requires substantial computational resources. Ernie, developed by Baidu, specialises in Chinese language tasks, thus providing localised advantages that may not extend globally due to restrictive policies. Grok, developed by Xai and still in its early stages, shows promise in providing engaging, real-time interactions, humour, and mathematical reasoning capabilities, but its full potential remains to be evaluated through further development and empirical testing. The findings underscore the context-dependent utility of each model and the absence of a singularly superior chatbot. Future research should expand to include a wider range of fields, explore practical applications, and address concerns related to data privacy, ethics, security, and the responsible deployment of these technologies.

Effective and efficient delivery of healthcare services requires comprehensive collaboration and coordination between healthcare entities and their complex inter-reliant activities. This inter-relation and coordination lead to different networks among diverse healthcare stakeholders. It is important to understand the varied dynamics of these networks to measure the efficiency of healthcare delivery services. To date, however, a work that systematically reviews these networks outlined in different studies is missing. This article provides a comprehensive summary of studies that have focused on networks and administrative health data. By summarizing different aspects including research objectives, key research questions, adopted methods, strengths and weaknesses, this research provides insights into the inherently complex and interlinked networks present in healthcare services. The outcome of this research is important to healthcare management and may guide further research in this area.

As a proportion of high grade cervical intraepithelial neoplasia (CIN2/3) are associated with equivocal cervical smears, which show borderline or mild dyskaryosis, follow up with repeat smears, colposcopy and biopsy is required. Since infection with oncogenic Human Papilloma Virus (HR HPV) has been found to be associated with the development of cervical cancer, HRHPV testing appears to be an alternative. The present study assesses if HRHPV testing can predict CIN2/3 in women referred for mild dyskaryosis and borderline cytological changes in an health authority with a referral policy to colposcopy after one single mild dyskaryotic Pap smear. The HPV DNA Hybrid Capture II (Digene/Abbott, Maidenhead) was evaluated on 110 consenting women with mild dyskaryosis and 23 women with persistent borderline changes, who were referred for colposcopy between May and November 2001. A cost comparison between two referral policies was performed. CIN2/3 was diagnosed histologically in 30 of 133 women (22%) with minor cytological abnormalities. As the Receiver Operator Characteristics plot suggested a cut-off of 3 pg/ml the HRHPV HCII was evaluated at 3 RLU (relative light units) and at the manufacturer's recommendation of 1 RLU. At both cut-offs sensitivity and negative predictive value were high at 97%. Specificity was low at 37% at a cut-off of 1 pg/ml and 46% at a cut-off of 3 RLU. To remain cost neutral in comparison to immediate colposcopy the costs for one HR HPV HC II must not exceed pound 34.37 per test at a cut off of 3 pg/ml. The negative likelihood ratio (NLR) was of good diagnostic value with 0.089 at 1 RLU and 0.072 at 3 RLU, which reduces the post-test probability for CIN2/3 to 2% in this population. Women with minor cytological disorders can be excluded from colposcopy on a negative HR HPV result.Specificity can be improved by restricting HR HPV testing to women with persistent borderline cytological changes or to women over 30 years.

This study explores the applications and implications of blockchain technology in the Australian higher education system, focusing on its integration with artificial intelligence (AI). By addressing critical challenges in credential verification, administrative efficiency, and academic integrity, this integration aims to enhance the global competitiveness of Australian higher education institutions. A comprehensive review of 25 recent research papers quantifies the benefits, challenges, and prospects of blockchain adoption in educational settings. Our findings reveal that 52% of the reviewed papers focus on systematic reviews, 28% focus on application-based studies, and 20% combine both approaches. The keyword analysis identified 287 total words, with “blockchain” and “education” as the most prominent themes. This study highlights blockchain’s potential to improve credential management, academic integrity, administrative efficiency, and funding mechanisms in education. However, challenges such as technical implementation (24%), regulatory compliance (32%), environmental concerns (28%), and data security risks (40%) must be addressed to achieve widespread adoption. This study also discusses critical prerequisites for successful blockchain integration, including infrastructure development, staff training, regulatory harmonisation, and the incorporation of AI for personalised learning. Our research concludes that blockchain, when strategically implemented and combined with AI, has the potential to transform the Australian higher education system, significantly enhancing its integrity, efficiency, and global competitiveness.

The Delta variant of COVID-19 has been found to be extremely difficult to contain worldwide. The complex dynamics of human mobility and the variable intensity of local outbreaks make measuring the factors of COVID-19 transmission a challenge. The inter-suburb road connection details provide a reliable proxy of the moving options for people between suburbs for a given region. By using such data from Greater Sydney, Australia, this study explored the impact of suburban road networks on two COVID-19-related outcomes measures. The first measure is COVID-19 vulnerability, which gives a low score to a more vulnerable suburb. A suburb is more vulnerable if it has the first COVID-19 case earlier and vice versa. The second measure is COVID-19 severity, which is proportionate to the number of COVID-19-positive cases for a suburb. To analyze the suburban road network, we considered four centrality measures (degree, closeness, betweenness and eigenvector) and core–periphery structure. We found that the degree centrality measure of the suburban road network was a strong and statistically significant predictor for both COVID-19 vulnerability and severity. Closeness centrality and eigenvector centrality were also statistically significant predictors for COVID-19 vulnerability and severity, respectively. The findings of this study could provide practical insights to stakeholders and policymakers to develop timely strategies and policies to prevent and contain any highly infectious pandemics, including the Delta variant of COVID-19.

With the worldwide appearance of Big Data and its prospective to citizens in all its endeavours, there has been a developing requirements for research on the business drivers influencing the adoption of Big Data services. This paper therefore focuses on Small to Midsized Enterprises-SMEs in Jordan; hoping to have a better delivery of Big Data services with increased transparency and availability of technology, and the improved interaction with SMEs. This paper reviewed the relationships between Big Data and SMEs in general in Jordan. In addition, it examines the barriers for adopting Big Data among SMEs in Jordan. The paper mainly has used secondary research data and methods to provide a broad investigation of the issues relevant to Big Data, the reasons of those issues for SMEs in Jordan. The research is subject to academic journal articles, project reports, media articles, government and corporation based documents and other appropriate information. The study found that currently Jordan has been capitalizing comprehensively in developing its ICT sector, aiming at improving the performance of its public and private sector organizations, effectiveness, accurateness, time and fulfillment. However, Jordan needs to overcome barriers for adopting Big Data among SMEs which will improve efficiency and organizational effectiveness. By this way, SMEs in Jordan will be able to deliver information, high-quality customer centric and performance-driven services, and procedures, using information and communication technologies (ICT) to change the way they engage with customers and other businesses.