Profiling public perception of emerging technologies: Gene editing, brain chips and exoskeletons. A data-analytics framework.

Three AI developments, classified as forms of human enhancements, center around progress at the intersection of AI, nanotechnology and biotechnology. Our research advances the understanding of AI and human enhancement by data-driven analytics and offers practical tools for future research and societal applications. It is based on a survey that was launched by PRC in February 2021 to more than 5,000 respondents from the U.

A Recommendation System for Prosumers Based on Large Language Models.

As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min or even 5-min intervals, including weather forecasts, outputs from renewable energy source (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the local energy market (LEM). The goal for these prosumers is to reduce costs while ensuring their home's comfort levels are maintained.

A value sharing method for heterogeneous energy communities archetypes.

A novel value sharing (VS) method is proposed that distributes the energy communities (ECs) value based on the individual contribution to the total surplus/deficit. It considers the of each EC member and allocates a higher share to members who contribute to the EC revenue. The lowest share is received by the members with the highest demand that has to be supplied from the shared generation or from the grid, contributing to the EC cost.

How Fast to Avoid Carbon Emissions: A Holistic View on the RES, Storage and Non-RES Replacement in Romania.

The European Union targets aim to replace the non-renewable energy sources (non-RES) of coal, oil and gas (COG) generation with RES and storage (RES-S). The replacement of COG-generating units will lead to a decrease in CO emissions and a better living environment. Starting from this desideratum, in this paper, we create several scenarios to replace COG in Romania with RES-S, reconsider future energy mixes and engage with a more creative planning in order to meet the clean energy transition path.

Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning.

Detecting fraud related to electricity consumption is usually a difficult challenge as the input datasets are sometimes unreliable due to missing and inconsistent records, faults, misinterpretation of meter reading remarks, status, etc. In this paper, we obtain meaningful insights from fraud detection using real datasets of Tunisian electricity consumption metered by conventional meters. We propose an extensive feature engineering approach using the structured query language (SQL) analytic functions.

Evaluation of Dispersion Methods and Mechanical Behaviour of Glass Fibre Composites with Embedded Self-Healing Systems.

The present paper is focused on evaluating the most suitable dispersion method in the epoxy matrix of two self-healing systems containing dicyclopentadiene (DCPD) and 5-ethylidene-2-norbornene (ENB) monomers encapsulated in a urea-formaldehyde (UF) shell, prior to integration, fabrication and impact testing of specimens. Both microstructural analysis and three-point bending tests were performed to evaluate and assess the optimum dispersion method. It was found that ultrasonication damages the microcapsules of both healing systems, thus magnetic stirring was used for the dispersion of both healing systems in the epoxy matrix.

Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data.

In this paper, we report a study having as a main goal the obtaining of a method that can provide an accurate forecast of the residential electricity consumption, refining it up to the appliance level, using sensor recorded data, for residential smart homes complexes that use renewable energy sources as a part of their consumed electricity, overcoming the limitations of not having available historical meteorological data and the unwillingness of the contractor to acquire such data periodically in the future accurate short-term forecasts from a specialized institute due to the implied costs. In this purpose, we have developed a mixed artificial neural network (ANN) approach using both non-linear autoregressive with exogenous input (NARX) ANNs and function fitting neural networks (FITNETs). We have used a large dataset containing detailed electricity consumption data recorded by sensors, monitoring a series of individual appliances, while in the NARX case we have also used timestamps datasets as exogenous variables.