During hands-on cooperative surgery, the use of a redundant robot allows to address encumbrance issues in the Operating Room (OR), which can occur due to the presence of large medical instrumentation, such as the surgical microscope. This work presents a new Null Space Optimization (NSO) strategy to constraint the position of the manipulator's elbow within predefined range of motions, according to the spatial requirements of the specific procedure, also taking into account the physical joint limits of the robotic assistant. The proposed strategy was applied to the 7 degrees of freedom (dof) lightweight robot LWR4+. The performance of the NSO was compared to two state-of-the-art null space optimization strategies, i.e. damped posture and fixed optimal posture, over a pool of three non-expert users in both strict (20deg) and negligible (100deg) angular encumbrance limitations. The NSO strategy was proved versatile in providing wide elbow mobility together with safe distance from relevant continuity null space boundaries, guaranteeing smooth guidance trajectories. Future works would be performed in order to evaluate the potential feasibility of NSO in a real surgical scenario.
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http://dx.doi.org/10.1109/EMBC.2015.7319481 | DOI Listing |
Sci Rep
December 2024
Laboratorio de Fluidodinámica, Facultad de Ingeniería, Universidad de Buenos Aires/CONICET, Paseo Colón 850 CABA, Buenos Aires, Argentina.
The oil and gas industry faces two significant challenges, including rising global temperatures and depletion of reserves. Enhanced recovery techniques such as polymer flooding have positioned themselves as an alternative that attracts international attention thanks to increased recovery factors with low emissions. However, existing physical models need further refinement to improve predictive accuracy and prevent design failures in polymer flooding projects.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Computer Science, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia.
The traditional optimization approaches suffer from certain problems like getting stuck in local optima, low speed, susceptibility to local optima, and searching unknown search spaces, thus requiring reliance on single-based solutions. Herein, an Improved Aquila Optimizer (IAO) is proposed, which is a unique meta-heuristic optimization method motivated by the hunting behavior of Aquila. An improved version of Aquila optimizer seeks to increase effectiveness and productivity.
View Article and Find Full Text PDFEnviron Int
December 2024
ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Spain; Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre, Rotterdam, the Netherlands; ICREA, Barcelona, Spain. Electronic address:
Background: More than 80% of the Canadian population lives in urban settings. Urban areas usually bring exposure to poorer air quality, less access to green spaces, and higher building density. These environmental factors may endanger child development.
View Article and Find Full Text PDFJ Transl Med
December 2024
Department of Biological Science, University of Tulsa, Tulsa, Oklahoma, USA.
Background: The mechanisms enabling sperm to locate unfertilized eggs within the fallopian tubes remain a subject of debate in reproductive biology. Previous studies using polytocous mammals observed a 1:1 sperm-egg ratio within the ampulla at the time of fertilization. From these observations, it is hypothesized that this mechanism could be linked to sperm-egg fusion, such that unfertilized eggs may attract sperm until fusion occurs, whereupon the attraction ceases.
View Article and Find Full Text PDFEJNMMI Phys
December 2024
Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Purpose: This study aimed to implement high-end positron emission tomography (PET) equipment to assist conventional PET equipment in improving image quality via a distribution learning-based diffusion model.
Methods: A diffusion model was first trained on a dataset of high-quality (HQ) images acquired by a high-end PET device (uEXPLORER scanner), and the quality of the conventional PET images was later improved on the basis of this trained model built on null-space constraints. Data from 180 patients were used in this study.
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