A finite element analysis was used to compare the effect of different designs of implant-retained overdentures and fixed full-arch implant-supported prosthesis on stress distribution in edentulous mandible. Four models of an human mandible were constructed. In the OR (O'ring) group, the mandible was restored with an overdenture retained by four unsplinted implants with O'ring attachment; in the BC (bar-clip) -C and BC groups, the mandibles were restored with overdentures retained by four splinted implants with bar-clip anchor associated or not with two distally placed cantilevers, respectively; in the FD (fixed denture) group, the mandible was restored with a fixed full-arch four-implant-supported prosthesis. Models were supported by the masticatory muscles and temporomandibular joints. A 100-N oblique load was applied on the left first molar. Von Mises (σvM), maximum (σmax) and minimum (σmin) principal stresses (in MPa) analyses were obtained. BC-C group exhibited the highest stress values (σvM=398.8, σmax=580.5 and σmin=-455.2) while FD group showed the lowest one (σvM=128.9, σmax=185.9 and σmin=-172.1). Within overdenture groups, the use of unsplinted implants reduced the stress level in the implant/prosthetic components (59.4% for σvM, 66.2% for σmax and 57.7% for σmin versus BC-C group) and supporting tissues (maximum stress reduction of 72% and 79.5% for σmax, and 15.7% and 85.7% for σmin on the cortical and trabecular bones, respectively). Cortical bone exhibited greater stress concentration than the trabecular bone for all groups. The use of fixed implant dentures and removable dentures retained by unsplinted implants to rehabilitate edentulous mandible reduced the stresses in the periimplant bone tissue, mucosa and implant/prosthetic components.

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This work investigates the efficiency of the process of load disaggregation, considering only the values of active power. To perform the task, we use data collected from the NILM (Non-Intrusive Load Monitoring) measurement method, presented in the Rainforest Automation Energy Dataset (RAE) and Reference Energy Disagreggation Dataset (REDD) database. A strategy of assigning labels using combinations of equipment in use, by status ON/OFF, and also by choosing an appropriate temporal data window is discussed. Also, the performance of very well-known machine learning algorithms such as k-Nearest Neighbor (kNN), Decision Tree, and Random Forest are evaluated. The results show a very efficient and low computer complexity strategy presenting values of F1-Score above 95%, for RAE and REDD database. As presented in table 1, the proposed approach presents the highest F1-Score, compared to other methods in the literature, considering all appliances in the REDD database. The greatest benefit of the approach consists in the possibility of applying the disaggregation process in a household without smart outlets, under the restriction that the training and test houses hold identical or similar appliances.

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