ELECTROCHEMICAL CHARACTERIZATION OF NEW 1,5-BENZODIAZEPINE DERIVATIVES

Indexación: Web of Science; Scielo.Cyclic voltammetry was used to study the electrochemical behavior of new 1,5-benzodiazepine derivatives on a glassy carbon electrode. Well-defined oxidation peaks were observed in DMSO. The Electrochemical response of the glassy carbon electrode was evaluated as function of the scan rate, showing that the electron transfer process for compound (5) and (6) resulted to be controlled by diffusion.http://ref.scielo.org/3nfd9

Sistemas de Transporte de Datos. Práctica 1: Encaminamiento dinámico con IPv4

Encaminamiento dinámico con IPv4

Modelling and simulation of a multi-fingered robotic hand for grasping tasks

This paper develops the kinematic, dynamic and contact models of a three-fingered robotic hand (BarrettHand) in order to obtain a complete description of the system which is required for manipulation tasks. These models do not only take into account the mechanical coupling and the breakaway mechanism of the under-actuated robotic hand but they also obtain the force transmission from the hand to objects, which are represented as triangle meshes. The developed models have been implemented on a software simulator based on the Easy Java Simulations platform. Several experiments have been performed in order to verify the accuracy of the proposed models with regard to the real physic system.This work is supported by the Spanish Ministries of Education, Science and Innovation through the research project DPI2008-02647 ('Intelligent Manipulation through Haptic Perception and Visual Servoing by Using an Articulated Structure situated over a Robotic Manipulator') and the grant AP2005-1458

RoboToy Demoulding: Robotic Demoulding System for Toy Manufacturing Industry

Industrial environments and product manufacturing processes are currently being automated and robotized. Nowadays, it is common to have robots integrated in the automotive industry, robots palletizing in the food industry and robots performing welding tasks in the metal industry. However, there are many traditional and manual sectors out of date with technology, such as the toy manufacturing industry. This work describes a new robotic system able to perform the demoulding task in a toy manufacturing process, which is a tedious labor-intensive and potentially hazardous task for human operators. The system is composed of specialised machinery about the rotational moulding manufacturing process, cameras, actuators, and a collaborative robot. A vision-based algorithm makes this system capable of handling soft plastic pieces which are deformable and flexible during demoulding. The system reduces the stress and potential injuries to human operators, allowing them to perform other tasks wit h higher dexterity requirements or relocate to other sub-tasks of the process where the physical effort is minor

A new automatic method for demoulding plastic parts using an intelligent robotic system

Nowadays, there are many different industrial processes in which people spend several hours performing tedious and repetitive tasks. Furthermore, most of these processes involve the manipulation of dangerous materials or machinery, such as the toy manufacturing, where people handle ovens with high temperatures and make weary physical effort for a long period of time during the process. In this work, it is presented an automatic and innovative collaborative robotic system that is able to deal with the demoulding task during the manufacturing process of toy dolls. The intelligent robotic system is composed by an UR10e robot with a RealSense RGB-D camera integrated which detects the pieces in the mould using a developed vision-based algorithm and extracts them by means of a custom gripper located and the end of the robot. We introduce a pipeline to perform the demoulding task of different plastic pieces relying in the use of this intelligent robotic system. Finally, to validate this approach, the automatic method has been successfully implemented in a real toy factory providing a novel approach in this traditional manufacturing process. The paper describes the robotic system performance using different forces and velocities, obtaining a success rate of more than 90% in the experimental results.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has been carried out within the scope of an Industrial PhD at AIJU in the context of the SOFTMANBOT Project, with European funding from the Horizon 2022 research programme (G.A 869855). In addition, it has been supported by the UAIND21-06B grant of the University of Alicante

Control of Redundant Joint Structures Using Image Information During the Tracking of Non-Smooth Trajectories

Visual information is increasingly being used in a great number of applications in order to perform the guidance of joint structures. This paper proposes an image-based controller which allows the joint structure guidance when its number of degrees of freedom is greater than the required for the developed task. In this case, the controller solves the redundancy combining two different tasks: the primary task allows the correct guidance using image information, and the secondary task determines the most adequate joint structure posture solving the possible joint redundancy regarding the performed task in the image space. The method proposed to guide the joint structure also employs a smoothing Kalman filter not only to determine the moment when abrupt changes occur in the tracked trajectory, but also to estimate and compensate these changes using the proposed filter. Furthermore, a direct visual control approach is proposed which integrates the visual information provided by this smoothing Kalman filter. This last aspect permits the correct tracking when noisy measurements are obtained. All the contributions are integrated in an application which requires the tracking of the faces of Asperger children

Direct Visual Servoing Framework based on Optimal Control for Redundant Joint Structures

This paper presents a new framework based on optimal control to define new dynamic visual controllers to carry out the guidance of any serial link structure. The proposed general method employs optimal control to obtain the desired behaviour in the joint space based on an indicated cost function which determines how the control effort is distributed over the joints. The proposed approach allows the development of new direct visual controllers for any mechanical joint system with redundancy. Finally, authors show experimental results and verifications on a real robotic system for some derived controllers obtained from the control framework.This work was funded by the Spanish Ministry of Economy, the European FEDER funds and the Valencia Regional Government, through the research projects DPI2012-32390 and PROMETEO/2013/085

Biosensors in Rehabilitation and Assistance Robotics

Robotic developments in the field of rehabilitation and assistance have seen a significant increase in the last few years [...

Human Pose Detection for Robotic-Assisted and Rehabilitation Environments

Assistance and rehabilitation robotic platforms must have precise sensory systems for human–robot interaction. Therefore, human pose estimation is a current topic of research, especially for the safety of human–robot collaboration and the evaluation of human biomarkers. Within this field of research, the evaluation of the low-cost marker-less human pose estimators of OpenPose and Detectron 2 has received much attention for their diversity of applications, such as surveillance, sports, videogames, and assessment in human motor rehabilitation. This work aimed to evaluate and compare the angles in the elbow and shoulder joints estimated by OpenPose and Detectron 2 during four typical upper-limb rehabilitation exercises: elbow side flexion, elbow flexion, shoulder extension, and shoulder abduction. A setup of two Kinect 2 RGBD cameras was used to obtain the ground truth of the joint and skeleton estimations during the different exercises. Finally, we provided a numerical comparison (RMSE and MAE) among the angle measurements obtained with OpenPose, Detectron 2, and the ground truth. The results showed how OpenPose outperforms Detectron 2 in these types of applications.Óscar G. Hernández holds a grant from the Spanish Fundación Carolina, the University of Alicante, and the National Autonomous University of Honduras

Porcine Models of Pancreatic Cancer: Current Status and Future

The KRASG12D and TP53R172H mutations are the leading causes of pancreatic cancer, with the third deadliest cancer mortality rate. Majority of the pancreatic cancer research utilizes mouse models. Our lab is currently studying porcine as a suitable model to study pancreatic cancer due to the comparative anatomy and genomic similarities between humans and porcine. Our first approach to develop the porcine pancreatic cancer model was to orthotopically implant the transformed and mutated primary pig pancreatic epithelial cells into the pig pancreas. The second approach was using the Oncopig model with KRASG12D and TP53R172H genetic mutations. The mutated genes were expressed by injecting adenovirus with Cre recombinase to develop tumors. Hematoxylin and Eosin staining, immunohistochemistry, plasmid, and RNA isolation were used to study the porcine pancreas before and after the tumor induction. Results showed that humans and porcine have similar histological characteristics showing comprising islets, ducts, and acinar cells. For the orthotopic model, after two weeks of orthotopic implantation, there were signs of pancreatitis and some evidence of tumor-like cells, but not tumors. Some signs were the formation of acinar to ductal metaplasia, massive immune cell responses, and vimentin expression in the regions of desmoplasia. Our results showed that the Oncopigs did generate pancreatic tumors successfully and are currently being characterized for future use, and the orthotopic model showed promising results. However, implanted tumor cells will be given additional help to allow them to survive from host immune response by over-expressing PD-L1 on the implanted tumor cells surface.https://digitalcommons.unmc.edu/surp2022/1041/thumbnail.jp