Blockchain technology has emerged as a transformative solution for secure, immutable, and decentralized data management across diverse domains, including economics, healthcare, and supply chain management. Given its soaring adoption, it is crucial to assess the suitability of various blockchain platforms for specific applications. This study evaluates the performance of Hyperledger Fabric (HF) and private Ethereum (Geth) to analyze their scalability (node count), throughput (transactions per second (TPS)), and latency (measured in milliseconds). A benchmarking tool was developed in-house to assess the execution of key smart contract functions—QueryUser, CreateUser, TransferMoney, and IssueMoney—under varying transaction loads (10–1000 transactions) and network sizes (2–16 node count). The results indicate that HF performs significantly better than private Ethereum in terms of invoke functions, achieving up to 5× throughput and up to 26× lower latency. However, private Ethereum excels in query operations because of its account-based ledger model. While Hyperledger Fabric scales efficiently within moderate transaction volumes, it experiences concurrency limitations beyond 1000 transactions, whereas private Ethereum processes up to 10,000 transactions, albeit with performance fluctuations due to gas fees. The findings offer valuable insights into the strengths and tradeoffs of both platforms, informing optimal blockchain selection for enterprise applications that require high transaction efficiency.

This study aimed to investigate the use of social robots as an interactive learning approach for treating children diagnosed with autism spectrum disorder (ASD). A review was conducted using the meta-analysis technique to compile pertinent research. An analysis was performed on the results of the online search process, which gathered information on pertinent research published until 31 January 2025, from three publication databases: IEEE Xplore, SCOPUS, and Google Scholar. One hundred and seven papers out of the 591 publications that were retrieved satisfied the previously established inclusion and exclusion criteria. Despite the differences in methodology and heterogeneity, the data were synthesized narratively. This review focuses on the various types of social robots used to treat ASD, as well as their communication mechanisms, development areas, target behaviors, challenges, and future directions. Both practitioners and seasoned researchers looking for a fresh approach to their next project will find this review a useful resource that offers broad summaries of state-of-the-art research in this field.