The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. The research problems were whether computer vision techniques, geospatial data mining, simulation-based digital twins, and real-time monitoring technology optimize remote sensing robots. Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were leveraged by a Shiny app to obtain the flow diagram comprising evidence-based collected and managed data (the search results and screening procedures). Throughout January and July 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms comprising “Internet of Robotic Things” + “big data management algorithms”, “deep learning-based object detection technologies”, and “geospatial simulation and sensor fusion tools”. As the analyzed research was published between 2017 and 2022, only 379 sources fulfilled the eligibility standards. A total of 105, chiefly empirical, sources have been selected after removing full-text papers that were out of scope, did not have sufficient details, or had limited rigor For screening and quality evaluation so as to attain sound outcomes and correlations, we deployed AMSTAR (Assessing the Methodological Quality of Systematic Reviews), AXIS (Appraisal tool for Cross-Sectional Studies), MMAT (Mixed Methods Appraisal Tool), and ROBIS (to assess bias risk in systematic reviews). Dimensions was leveraged as regards initial bibliometric mapping (data visualization) and VOSviewer was harnessed in terms of layout algorithms.

The purpose of our systematic review was to inspect the recently published research on Internet of Robotic Things (IoRT) and harmonize the assimilations it articulates on remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms. The research problems were whether robotic manufacturing processes and industrial wireless sensor networks shape IoRT and lead to improved product quality by use of remote big data management tools, whether IoRT devices communicate autonomously regarding event modeling and forecasting by leveraging machine learning and clustering algorithms, sensing and computing technologies, and image processing tools, and whether smart connected objects, situational awareness algorithms, and edge computing technologies configure IoRT systems and cloud robotics in relation to distributed task coordination through visual perception and environment mapping algorithms. A Shiny app was harnessed for Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to configure the flow diagram integrating evidence-based gathered and processed data (the search outcomes and screening procedures). A quantitative literature review of ProQuest, Scopus, and the Web of Science databases was carried out throughout June and October 2022, with search terms including “Internet of Robotic Things” + “remote big data management tools”, “sensing and computing technologies”, and “visual perception and environment mapping algorithms”. Artificial intelligence and intelligent workflows by use of AMSTAR (Assessing the Methodological Quality of Systematic Reviews), Dedoose, DistillerSR, and SRDR (Systematic Review Data Repository) have been deployed as data extraction tools for literature collection, screening, and evaluation, for document flow monitoring, for inspecting qualitative and mixed methods research, and for establishing robust outcomes and correlations. For bibliometric mapping by use of data visualization, Dimensions AI was leveraged and with regards to layout algorithms, VOSviewer was harnessed.

Research background: With growing evidence of biometric identification techniques as authentication, there is a pivotal need for comprehending contactless payments by use of facial recognition algorithms in retail, restaurant, and hotel business models. Purpose of the article: In this research, previous findings were cumulated showing that harnessing facial recognition payment applications as software-based contactless biometric algorithms results in remarkably qualitative enhancement in purchasing experience. Methods: Throughout March and November 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was carried out, with search terms including ?facial recognition payment technology?, ?facial recognition payment system?, ?facial recognition payment application?, ?face recognition-based payment service?, ?facial authentication for mobile payment transactions?, and ?contactless payment through facial recognition algorithms.? As the analyzed research was published between 2017 and 2021, only 187 articles satisfied the eligibility criteria. By removing questionable or unclear findings (limited/nonessential data), results unsubstantiated by replication, too general content, or having quite similar titles, 38, mainly empirical, sources were selected. The Systematic Review Data Repository was harnessed, a software program for the gathering, processing, and analysis of data for our systematic review. The quality of the selected scholarly sources was assessed by employing the Mixed Method Appraisal Tool. Findings & value added: Harnessing facial recognition payment applications as software-based contactless biometric algorithms results in remarkably qualitative enhancement in purchasing experience. Subsequent attention should be directed to whether perceived value and trust shape customers? adoption of biometric recognition payment devices.

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Drug-induced liver injury (DILI) is uncommon but potentially lethal. Over 6 years, 2533 children with acute liver disease were identified in our center, 48 of which suffered from toxic hepatitis, and 40 exhibited DILI (22 paracetamol-related, 14 albendazole-related). The most affected children were in the 13–17-year-old age group. The mean time between drug ingestion and disease diagnosis was 25.4 h for paracetamol-related DILI and 21.6 days for the albendazole-related group. Clinical features were mostly gastrointestinal, jaundice being reported in 30% of the cases. Regarding the type of liver injury, for 70% of the patients it was hepatocellular (mostly paracetamol toxicity), for 11% cholestatic, and for 19% mixed (albendazole-related). The mean initial ALT value was 1020 U/L for all DILIs. Coagulopathy was only identified for the acetaminophen-related group. The median number of hospitalization days was 6.9 for DILI related to acetaminophen ingestion, compared with 7 for the idiosyncratic pattern. When applying the DILI severity index, 81% of the patients were categorized as having a mild hepatic ailment, while 19% had a moderate–severe or severe disease. No deaths were reported in the study group. The diagnosis of DILI involves the exclusion of other causes of liver injury; therefore, it is considered one of the most challenging diagnoses in hepatology.