Anti-vibration gloves and the forearm efforts during tools operations

The objective of this paper is to analyze the forearm muscular contraction levels associated to the use of anti-vibration gloves, by comparing the contraction levels with gloves and without gloves. Two different vibration tools were used in a simulated work environment: (1) A compact Duty Multi-Cutter Bosch and (2) and a Percussion Drill with a drill bit Ø20 mm. Standard operations were performed by each subject in the following materials: (1) Performing cross- sectional cuts in 80x40 mm pine section and (2) performing 20 mm diameter holes in a concrete slab 2 x 2 m, 70 mm thick. The forearm contraction level were measured by surface electromyography in four different muscles: Flexor Digitorum Superficialis (FDS), Flexor Carpi Ulnaris (FCU), Extensor Carpi Radialis Longus (ECRL) e Extensor Carpi Ulnaris (ECU). For the flexor muscles (FDS, FCU), an increase tendency in muscular contraction was observed when the operations are performed without gloves (2-5% MVE increase in the FDS and 3-9% MVE increase in the FCU). For the extensor muscles ECU a decrease tendency in muscular contraction was observed when the operations are performed without gloves (1-10% MVE decrease). Any tendency was found in the ECRL muscle. ECU was the muscle with the highest contraction level for 79% and 71% of the operators, during the operations respectively with the multi-cutter (P50= 27-30%MVE) and with the percussion drill (P50=46-55%MVE). As a final conclusion from this study, anti- vibration gloves may increase the forearm fatigue in the posterior region of the forearm (ECU muscle) during operations with the mentioned tools

Downstream processing development of enveloped viruses for clinical applications: innovative tools for rational process optimization

Dissertation presented to obtain a Ph.D. degree in Engineering and Technology Sciences, Biotechnology at the Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

Adapting Meeting Tools to Agent Decision

Electricity markets are complex environments comprising several negotiation mechanisms. MASCEM (Multi- Agent System for Competitive Electricity Markets) is a simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. ALBidS (Adaptive Learning Strategic Bidding System) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This paper aims to complement ALBidS strategies usage by MASCEM players, providing, through the Six Thinking Hats group decision technique, a means to combine them and take advantages from their different perspectives. The combination of the different proposals resulting from ALBidS’ strategies is performed through the application of a Genetic Algorithm, resulting in an evolutionary learning approach.