Shape evolution and shape coexistence in Pt isotopes: comparing interacting boson model configuration mixing and Gogny mean-field energy surfaces

The evolution of the total energy surface and the nuclear shape in the isotopic chain $^{172-194}$Pt are studied in the framework of the interacting boson model, including configuration mixing. The results are compared with a self-consistent Hartree-Fock-Bogoliubov calculation using the Gogny-D1S interaction and a good agreement between both approaches shows up. The evolution of the deformation parameters points towards the presence of two different coexisting configurations in the region 176 $\leq$ A $\leq$ 186.Comment: Submitted to PR

Comunicaciones aplicadas a la teleoperación

Presentación de la plataforma experimental de teleoperación basada en el sistema Grips de Kraft Telerobotics y presentación del hardware desarrollado para su adaptación a una plataforma abierta. Exposición de las diferentes formas de conexión obtenidas a través del hardware desarrollado para cerrar un bucle de control en un sistema bilateral de teleoperación. Estudio de diferentes protocolos de comunicación muy extendidos como USB y Ethernet, explicando sus fundamentos principales y el funcionamiento básico, y su aplicación en la robótica, en particular en sistemas Bilaterales de Teleoperación con exigencias de tiempo real. Presentación de resultados obtenidos y comparación entre protocolos en diversas situaciones planteadas

Two-Hand Virtual Object Manipulation Based on Networked Architecture

A setup for bimanual virtual object manipulation is described in this paper. Index and thumb fingers are inserted in the corresponding thimbles in order to perform virtual object manipulations. A gimble, with 3-rotational degrees of freedom, connects each thimble to the corresponding serial-parallel mechanical structure with 3 actuated DoF. As a result, each finger has 6 DoF, movements and forces can be reflected in any direction without any torque component. Scenarios for virtual manipulation are based on distributed architecture where each finger device has its own real-time controller. A computer receives the status of each finger and runs a simulation with the virtual object manipulation. The information of the Scenario is updated at a rate of 200 Hz. The information from the haptic controller is processed at 1 kHz; it provides a good realism for object manipulation

Dash Sylvereye:A Python Library for Dashboard-Driven Visualization of Large Street Networks

State-of-the-art open graph visualization tools like Gephi, KeyLines, and Cytoscape are not suitable for studying street networks with thousands of roads since they do not support simultaneously polylines for edges, navigable maps, GPU-accelerated rendering, interactivity, and the means for visualizing multivariate data. To fill this gap, we present Dash Sylvereye: a new Python library to produce interactive visualizations of primal street networks on top of tiled web maps. Thanks to its integration with the Dash framework, Dash Sylvereye can be used to develop web dashboards around temporal and multivariate street data. This is achieved by coordinating the various elements of a Dash Sylvereye visualization with other plotting and UI components provided by Dash. Additionally, Dash Sylvereye provides convenient functions to easily import OpenStreetMap street topologies obtained with the OSMnx library. Moreover, Dash Sylvereye uses WebGL for GPU-accelerated rendering when redrawing the road network. We conduct experiments to assess the performance of Dash Sylvereye on a commodity computer when exploiting software acceleration in terms of frames per second, CPU time, and frame duration. We show that Dash Sylvereye can offer fast panning speeds, close to 60 FPS, and CPU times below 20 ms, for street networks with thousands of edges, and above 24 FPS, and CPU times below 40 ms, for networks with dozens of thousands of edges. Additionally, we conduct a performance comparison against two state-of-the-art street visualization tools. We found Dash Sylvereye to be competitive when compared to the state-of-the-art visualization libraries Kepler.gl and city-roads. Finally, we describe a web dashboard application that exploits Dash Sylvereye for the analysis of a SUMO vehicle traffic simulation

INCARCERATION EFFECTS ON ATTAINING HIGHER EDUCATION FOR FORMERLY INCARCERATED YOUTH

Incarceration disrupts areas of a juvenile’s life on multiple levels, including personal, social, and educational. Incarceration can present many obstacles for youth who are in pursuit of furthering their education. This research project sought to assess if the five identified factors, including quality of precollege education, mentoring, reentry services, family supports, and socioeconomic status, played a role in adults, who were formerly incarcerated youth, pursuing higher levels of education. The study utilized an online survey to gather numerical data on the participant’s perception of how they believe these factors influenced them. A bivariate analysis was used to analyze if the identified factors had an influence on the pursuit of higher education for adults who were formerly incarcerated youth. A frequency analysis was completed to determine which of the five factors were perceived to be influential to participants. A bivariate analysis was completed to see if there were any relationships to key demographic variables and level of education. The factors deemed most influential were mentoring programs and family supports. The factors that were deemed least influential were reentry services and precollege education. The research findings have the potential to inform social work professionals of what specific programs and services formerly incarcerated populations can be referred to in order to support them on their educational journey

Dash Sylvereye:A WebGL-powered Library for Dashboard-driven Visualization of Large Street Networks

State-of-the-art open network visualization tools like Gephi, KeyLines, and Cytoscape are not suitable for studying street networks with thousands of roads since they do not support simultaneously polylines for edges, navigable maps, GPU-accelerated rendering, interactivity, and the means for visualizing multivariate data. To fill this gap, the present paper presents Dash Sylvereye: a new Python library to produce interactive visualizations of primal street networks on top of tiled web maps. Thanks to its integration with the Dash framework, Dash Sylvereye can be used to develop web dashboards around temporal and multivariate street data by coordinating the various elements of a Dash Sylvereye visualization with other plotting and UI components provided by the Dash framework. Additionally, Dash Sylvereye provides convenient functions to easily import OpenStreetMap street topologies obtained with the OSMnx library. Moreover, Dash Sylvereye uses WebGL for GPU-accelerated rendering when redrawing the road network. We conduct experiments to assess the performance of Dash Sylvereye on a commodity computer when exploiting software acceleration in terms of frames per second, CPU time, and frame duration. We show that Dash Sylvereye can offer fast panning speeds, close to 60 FPS, and CPU times below 20 ms, for street networks with thousands of edges, and above 24 FPS, and CPU times below 40 ms, for networks with dozens of thousands of edges. Additionally, we conduct a performance comparison against two state-of-the-art street visualization tools. We found Dash Sylvereye to be competitive when compared to the state-of-the-art visualization libraries Kepler.gl and city-roads. Finally, we describe a web dashboard application that exploits Dash Sylvereye for the analysis of a SUMO vehicle traffic simulation

Enhancing Epidemic Prediction Using Simulated Annealing for Parameter Optimization in Infection Network Inference

Understanding and predicting outbreaks of epidemics has become a major focus since COVID-19. Researchers have explored various methods, from basic curve fitting to complex machine learning techniques, to predict how the virus spreads. One promising method is the Network Inference-based Prediction Algorithm (NIPA), which uses the SIR-model and the least absolute shrinkage and selection operator to estimate how the infections spread over different regions. However, fine- tuning the regularization parameter of NIPA can be complicated because of the time-consuming process and sub-optimal result of k-fold Cross-Validation (CV). To overcome this, we suggest using Simulated Annealing (SA) to optimize NIPA’s regularization parameter and find an optimal value for the curing probability.Our study aims to combine SA with NIPA to make the process of choosing the optimal value for the parameters more effective. The results of the research show that the accuracy is improved and therefore indicate that SA is an acceptable alternative to CV, regardless of the computation time being higher

Exploring the gravitational model for ranking influential nodes in directed acyclic networks

In Social Network Analysis (SNA), the application of Directed Acyclic Graphs (DAGs) provides unique opportunities to explore structures where relationships have direction and do not form cycles, such as citation networks and organizational hierarchies. Recently, the gravitational model has gained recognition as an effective method for identifying influential spreaders within complex networks, a problem of relevance in SNA. While there have been numerous investigations into the gravitational model in undirected and cyclic graphs, the unique challenges and dynamics associated with DAGs have yet to be fully explored. In this study, we conduct a comprehensive analysis of the gravitational model for ranking nodes in DAGs. First, we introduce an efficient linear-time algorithm specifically designed to compute the gravitational index of nodes in large-scale DAGs. Next, using thousands of synthetic and empirical DAGs, we compare the impact of the gravitational index on the accuracy and resolution of node rankings across different mass indexes. We then examine how DAG structural properties influence the monotonicity of node rankings, with a particular focus on the k-shell index. We find that, in DAGs, the gravitational formula effectively enhances the monotonicity of k-shell centrality, though it is less effective for other types of centrality indexes. We also find that smaller, shorter, and highly centralized DAGs exhibit low ranking resolution across all centrality indexes examined in this study, including the gravity-based ones. Despite this challenge, our results demonstrate that the application of gravity-based models improves the ranking accuracy of several centrality measures across most of the studied DAG datasets