Energy Viewer vs BioStatBot

Energy Viewer allows solar power plant owners to remotely monitor system's output and home's consumption*, with or without an energy storage system installed, for all FIMER inverter(s). Keep your energy usage and savings in check from anywhere you want: - Dynamically adaptable to any plant configuration; - Synoptic card for monitoring energy flows and performance of your PV plants;* - Energy monitoring card(s) for home's consumption tracking, with dedicated dashboards and KPIs;* - Compatible with FIMER FLEXA AC Wallbox: configure and manage your e-Car, remotely monitor charging process and trends; - Plant status in-App notifications, for monitoring active and closed events, and remote firmware updates; - Compatible with VSN 700-05 data-logger. Enhance your autonomy thanks to a smart and easy-to-use app, free for life, leveraging the power of the Aurora Vision® cloud-based platform. *Available only with a supported bi-directional meter installed (For more info please visit: https://www.fimer.com/sites/default/files/FIMER__Meters%20supported%20by%20FIMER_EN_Rev.B.pdf)

The BioStatBot app is a virtual assistant that provides support and statistical education for healthcare professionals. It boasts a high accuracy rate across various statistical scenarios involving both categorical and numerical variables, as well as different study groups. BioStatBot correctly recommends the appropriate statistical test in 96.9% of evaluations. In terms of statistical assumptions and the level of detail in its responses, BioStatBot achieves accuracy 99% of the time. For post-hoc test recommendations when comparing three or more groups, the app's accuracy is 86.7%. Overall, the BioStatBot app demonstrates strong performance in recommending statistical tests, making it a highly valuable tool for the healthcare field. All users will have access to all content within the app. BioStatBot app was developed in accordance with - Wang L et al. Applications and Concerns of ChatGPT and Other Conversational Large Language Models in Health Care: Systematic Review. J Med Internet Res. 2024. https://www.jmir.org/2024/1/e22769. - Kolbinger FR, et al. Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis. Commun Med (Lond). 2024;4:71. doi: 10.1038/s43856-024-00492-0. - de Araujo BMM, et al. PAINe: An Artificial Intelligence-based Virtual Assistant to Aid in the Differentiation of Pain of Odontogenic versus Temporomandibular Origin. J Endod. 2024:S0099-2399(24)00524-7. doi: 10.1016/j.joen.2024.09.008