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Abstract The development of quantum computers poses a risk to the cryptographic algorithms utilized by current public key infrastructure (PKI). High performance computing (HPC), inter- and intra-datacenter communications, and governmental communications are particularly vulnerable to security attacks by quantum computers. In response, there are on-going efforts on implementing post-quantum cryptography (PQC) despite the considerable overhead involved in processing PQC encrypted data packets in comparison with their classical counterparts. This work aims to help with the transition to quantum resistant cryptographic schemes by means of providing a thorough experimental performance comparison of the four algorithms selected by the National Institute of Standards and Technology (NIST) for standardization when implemented in a data processing unit (DPU). The experiments are conducted at line-rate, demonstrating for the first time operation at almost 100 Gbit/s and hence suitability for high-capacity data center environments.

There is extensive literature emerging in the field of dentistry with the aim to optimize clinical practice. Evidence-based guidelines (EBGs) are designed to collate diagnostic criteria and clinical treatment for a range of conditions based on high-quality evidence. Recently, advancements in Artificial Intelligence (AI) have instigated further queries into its applicability and integration into dentistry. Hence, the aim of this study was to develop a model that can be used to assess the accuracy of treatment recommendations for dental conditions generated by individual clinicians and the outcomes of AI outputs. For this pilot study, a Delphi panel of six experts led by CoTreat AI provided the definition and developed evidence-based recommendations for subgingival and supragingival calculus. For the rapid review—a pragmatic approach that aims to rapidly assess the evidence base using a systematic methodology—the Ovid Medline database was searched for subgingival and supragingival calculus. Studies were selected and reported based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), and this study complied with the minimum requirements for completing a restricted systematic review. Treatment recommendations were also searched for these same conditions in ChatGPT (version 3.5 and 4) and Bard (now Gemini). Adherence to the recommendations of the standard was assessed using qualitative content analysis and agreement scores for interrater reliability. Treatment recommendations by AI programs generally aligned with the current literature, with an agreement of up to 75%, although data sources were not provided by these tools, except for Bard. The clinician’s rapid review results suggested several procedures that may increase the likelihood of overtreatment, as did GPT4. In terms of overall accuracy, GPT4 outperformed all other tools, including rapid review (Cohen’s kappa 0.42 vs. 0.28). In summary, this study provides preliminary observations for the suitability of different evidence-generating methods to inform clinical dental practice.

In recent days, Indoor Air Quality (IAQ) has become a matter of interest for researchers from both scientific and industrial community. During pandemic situation public settle more time at indoor in place of outdoor environment. With rapid growth in science and technology people have progressive necessity for active facility parameters with respect to indoor temperature, humidity and wind movement etc. Asthma, lung cancer, hypertension, cardiovascular issues, and respiratory illnesses are just a few of the chronic health issues that have been linked to frequent exposure to hazardous material in enclosed spaces. However, real-time monitoring technologies are being adopted in smart city projects to identify and remedy such unfavourable conditions, ultimately improving the general quality of life. This paper surveyed the current proposed IAQ monitoring systems in terms of Internet of Things (IoT). In addition, the research work highlights composition of diverse monitoring systems, research summary, challenges together implying future directions for the researchers. The current level of knowledge on the close relationship between IoT and IAQ is presented in this research.