Flexible Control in Nanometrology

The unceasing development of new small products has increased constantly by introducing multiple facilities in line production, reduced life cycles of new innovative products, and high-precision techniques that require automation and robotization of the nanotechnology production processes. Classic size products are made in normal series and deal little change over the years, while in the field of nanotechnology, product life cycles were shortened significantly, and series production must adapt to the market challenges. Considering the fast changes and multiple innovations in production, we propose equipment that offers a high degree of flexibility and performance for quality products. To compensate efficiently, the fluctuations may appear in production series; a flexible control system is designed to adjust production for large number of items or for various models of processing. The control equipment dedicated to nanotechnologies developed by INCDMTM Bucharest offers solutions for automation processes adapted to various operations and for quick response occurring in nano-production. A modular special design offers flexibility during the process, handling and interoperable ones, along with the possibility of changes facilitated by software that controls the entire verification process and parameter selection for each checked item’s admissibility

Valorisation of miscanthus giganteus biomass and agricultural residues for sustainable suplly of thermal energy in rural areas

New trends regarding thermal conversion of different type of solid biomass to provide renewable bioenergy for rural communities include the use of energy crops and agricultural residues in an efficient way. Romanian policy for bioenergy asks for considering alternative options to demonstrate sustainability of biomass supply chain (logistics by available quantities, transport, processing equipments, customers’ needs) and facilitating the development and competitiveness of market availability for biomass including pressed products (pellets, briquettes) to create optimal conversion of biomass in local heating systems. In this perspective the actual study shows the potential of C4 perennial grass Miscanthus giganteus and some indigenous resources as cereal straw, wood biomass (orchard trees pruning, saw dust, wood chips) or other agricultural residues (home grown biomass) to ensure the local requirements for heating by promoting low-carbon technologies and to achieve European and national target for renewable energy by 2020. Miscanthus giganteus biomass harvest from the scientific farm Moara Domneasca was tested, by varying mass percentage (20%, 60% or 80%), for pressing capacity in briquetting installations, in combination with different kind of vegetable biomass, in order to identify optimal blends by ensuring improved energy efficiency and cost-effective production. Lower calorific power (net calorific value - NCV) of different Miscanthus blends with agricultural vegetable residues have presented values varying from 15.8 MJ/kg up to 18.1 MJ/kg depending on elemental contents (C, H, N, S) and lignin concentration, respectively the type of biomass burned. The study pointed out a great potential of using solid biofuels, available in rural areas and therefore the opportunity of developing energy plants for local heating systems using sustainable biomass resources

Food Recognition and Food Waste Estimation Using Convolutional Neural Network

In this study, an evaluation of food waste generation was conducted, using images taken before and after the daily meals of people aged between 20 and 30 years in Serbia, for the period between 1 January and 31 April in 2022. A convolutional neural network (CNN) was employed for the tasks of recognizing food images before the meal and estimating the percentage of food waste according to the photographs taken. Keeping in mind the vast variates and types of food available, the image recognition and validation of food items present a generally very challenging task. Nevertheless, deep learning has recently been shown to be a very potent image recognition procedure, while CNN presents a state-of-the-art method of deep learning. The CNN technique was implemented to the food detection and food waste estimation tasks throughout the parameter optimization procedure. The images of the most frequently encountered food items were collected from the internet to create an image dataset, covering 157 food categories, which was used to evaluate recognition performance. Each category included between 50 and 200 images, while the total number of images in the database reached 23,552. The CNN model presented good prediction capabilities, showing an accuracy of 0.988 and a loss of 0.102, after the network training cycle. The average food waste per meal, in the frame of the analysis in Serbia, was 21.3%, according to the images collected for food waste evaluation