Iterative lead compensation control of nonlinear marine vessels manoeuvring models

This paper addresses the problem of control design and implementation for a nonlinear marine vessel manoeuvring model. The authors consider a highly nonlinear vessel 4 DOF model as the basis of this work. The control algorithm here proposed consists of a combination of two methodologies: (i) an iteration technique that approximates the original nonlinear model by a sequence of linear time varying equations whose solution converge to the solution of the original nonlinear problem and (ii) a lead compensation design in which for each of the iterated linear time varying system generated, the controller is optimized at each time on the interval for better tracking performance. The control designed for the last iteration is then applied to the original nonlinear problem. Simulations and results here presented show a good performance of the approximation methodology and also an accurate tracking for certain manoeuvring cases under the control of the designed lead controller. The main characteristic of the nonlinear system's response is the reduction of the settling time and the elimination of the steady state error and overshoot

Iterated Nonlinear Control of Ship's Manoeuvring Models

This paper addresses the control design for a nonlinear vessel manoeuvring model. The authors consider a highly nonlinear vessel 4 DOF model. The proposed control algorithm consists of a combination of an iteration technique that approximates the original nonlinear model by a sequence of linear time varying (LTV) equations whose solution converge to the solution of the original nonlinear problem and, a lead compensation design in which for each of the iterated linear time varying systems, the controller is optimized at each time on the interval. The control designed for the last iteration is then applied to the original nonlinear problem. Simulations results show good performance of this approximation methodology and accurate tracking for certain manoeuvring cases under the control of the designed lead controller. The main characteristic of the nonlinear system's response are the reduction of the settling time and the elimination of the steady state error and overshoot

The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)

Glioblastoma, a highly aggressive primary brain tumor, is associated with poor patient outcomes. Although magnetic resonance imaging (MRI) plays a critical role in diagnosing, characterizing, and forecasting glioblastoma progression, public MRI repositories present significant drawbacks, including insufficient postoperative and follow-up studies as well as expert tumor segmentations. To address these issues, we present the "R\'io Hortega University Hospital Glioblastoma Dataset (RHUH-GBM)," a collection of multiparametric MRI images, volumetric assessments, molecular data, and survival details for glioblastoma patients who underwent total or near-total enhancing tumor resection. The dataset features expert-corrected segmentations of tumor subregions, offering valuable ground truth data for developing algorithms for postoperative and follow-up MRI scans. The public release of the RHUH-GBM dataset significantly contributes to glioblastoma research, enabling the scientific community to study recurrence patterns and develop new diagnostic and prognostic models. This may result in more personalized, effective treatments and ultimately improved patient outcomes

BN/Graphene/BN Transistors for RF Applications

In this letter, we demonstrate the first BN/Graphene/BN field effect transistor for RF applications. The BN/Graphene/BN structure can preserve the high mobility of graphene, even when it is sandwiched between a substrate and a gate dielectric. Field effect transistors (FETs) using a bilayer graphene channel have been fabricated with a gate length LG=450 nm. A current density in excess of 1 A/mm and DC transconductance close to 250 mS/mm are achieved for both electron and hole conductions. RF characterization is performed for the first time on this device structure, giving a current-gain cut-off frequency fT=33 GHz and an fT.LG product of 15 GHz.um. The improved performance obtained by the BN/Graphene/BN structure is very promising to enable the next generation of high frequency graphene RF electronics.Comment: 3 pages, 5 figures, accepted for publication in IEEE Electron Device Letter

Use of the gyrotheodolite in underground networks of long high-speed railway tunnels

The quality of geodetic networks for guiding Tunnel Boring Machines (TBMs) inside long tunnels depends largely on the correct use of a gyroscope. These networks are based on a series of control points at the tunnel entrance, and link each station by means of survey observations as they advance along the tunnel. Once, the networks are used to guide the TBM, they are no longer checked again. It is necessary to perform high accuracy astronomical observations to stars in order to determine the gyrotheodolite constant. Since astronomical observations cannot be made inside tunnels, geodetic azimuths have to be used for the computations. However, these azimuths cannot theoretically be compared with the astronomical azimuths obtained by the gyrotheodolite. An alternative is to compute the instrument constant using the values of the deviation of the vertical derived from a geoid model. That is the approach used in this work where a methodology for the design of underground networks in long tunnels is also presented. This procedure has been implemented during the construction of the Guadarrama and Pajares high-speed railway tunnels (Spain)

Iterative Self-Tuning Minimum Variance Control of a Nonlinear Autonomous Underwater Vehicle Maneuvering Model

This paper addresses the problem of control design for a nonlinear maneuvering model of an autonomous underwater vehicle. The control algorithm is based on an iteration technique that approximates the original nonlinear model by a sequence of linear time-varying equations equivalent to the original nonlinear problem and a self-tuning control method so that the controller is designed at each time point on the interval for trajectory tracking and heading angle control. This work makes use of self-tuning minimum variance principles. The benefit of this approach is that the nonlinearities and couplings of the system are preserved, unlike in the cases of control design based on linearized systems, reducing in this manner the uncertainty in the model and increasing the robustness of the controller. The simulations here presented use a torpedo-shaped underwater vehicle model and show the good performance of the controller and accurate tracking for certain maneuvering cases

Algunos datos para la monitorización del acuífero Alto Guadalentín

En el presente artículo se presentan un análisis de series temporales de datos relativos a la evolución niveles piezométricos de la zona del Alto del Guadalentín (Murcia) y un análisis paralelo de series temporales de datos GNSS referentes a las altitudes de la zona. El objetivo de este análisis pretende establecer posibles correlaciones entre la subsidencia recogida por la red de estaciones GPS nacionales y el aumento de la profundidad piezométrica a la que se encuentran los acuíferos. Este objetivo se desarrollará mediante el análisis de los niveles piezométricos en comparación con las diferencias de altitud registradas en estaciones GNSS nacionales

Metodología de diseño, observación y cálculo de redes geodésicas interiores en túneles de ferrocarril de alta velocidad

El guiado de las Tuneladoras durante su avance debe apoyarse en los puntos determinados inmediatamente detrás de ellas. Conseguir las precisiones requeridas presentaría algunas dificultades en condiciones normales con respecto al exterior, pero en el interior de un túnel se plantean ciertos factores que ensombrecen el panorama. El más importante y difícil de tratar es el de la refracción lateral. El diseño de redes interiores es uno de los principales problemas, desde el punto de vista geodésico y topográfico, el cual tiene unas características tales que todos los textos de topografía de precisión aconsejan evitar. Con estas redes se va guiando la tuneladora y en ningún momento dicha red vuelve a tener comprobación sobre otros puntos de control. A medida que la red va avanzando, las precisiones obtenidas de sus coordenadas van empeorando de forma exponencial. Este trabajo establece una metodología para el diseño de redes planimétricas interiores. ABSTRACT The guiding of TBMs (Tunnel Boring Machines) along their advance must be based on points placed immediately behind them. Achieving good accuracies results would present some difficulties for normal exterior conditions, but inside a tunnel certain factors clouding the outlook arise. The most important and difficult factor to deal with, has to do with lateral refraction. Internal geodetic networks design is one of the more challenging problems from the geodetic and topographic point of view, as the bibliographical review dealing with this subject, advises to avoid this practice. This network allows only guiding the TBM, so that it is not used for further checking or quality control. As the network progresses, the reached coordinate accuracies deteriorate exponentially. This work establishes a methodology for the design of internal horizontal networks

Efficacy, safety and patient reported outcomes (PROS) in adult patients with atopic dermatitis treated with dupilumab at week-52 in usual clinical practice

P15 Background: Dupilumab, an anti-interleikin-4-receptor-a monoclonal antibody, is a new treatment for atopic dermatitis in adults. Objective: To evaluate – at week 52 – patient reported outcomes, satisfaction, efficacy and safety, with dupilumab in adult patients with moderate-to-severe atopic dermatitis refractory to the usual treatments previously performed under conditions of usual clinical practice. Methods: Twelve patients were enrolled. Patients from our hospital, under routine clinical practice, were treated with subcutaneous dupilumab 300 mg every 2 weeks. The outcomes were evaluated at baseline, week 4, 8, 12, 16, 28 , 40 and week 52. The variables evaluated were: itch, difficulty to sleep, previous stressful life events, severity (SCORAD), anxiety and depression symptoms (HADS), quality of life (DLQI, EQ5D3L), satisfaction, adherence to the treatment, efficacy and safety. Results: At week 52 significant improvement was observed in severity, itch, difficulty to sleep, anxiety and depression symptoms, and quality of life. Satisfaction with dupilumab compared to previous treatments was significantly higher in all aspects assessed. No significant dupilumab-induced laboratory abnormalities were noted, and adverse events were mild and transient. Conclusions: Dupilumab used under routine clinical practice for 52 weeks improved atopic dermatitis signs and symptoms, with a good safety profile and patient satisfaction

Predicting short-term survival after gross total or near total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

Producción CientíficaRadiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (<6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics