Radiative capture on 242Pu for MOX fuel reactors

Proposal: Radiative capture on 242Pu for MOX fuel reactorsThe use of MOX fuel (mixed-oxide fuel made of UO2 and PuO2) in nuclear reactors allows substituting a large fraction of the enriched Uranium by Plutonium reprocessed from spent fuel. Indeed around 66% of the plutonium from spent fuel is made of 239Pu and 241Pu, which are fissile in thermal reactors. A typical reactor of this type uses a fuel with 7% reprocessed Pu and 93% depleted U, thus profiting from both the spent fuel and the remaining 238U following the 235U enrichment. With the use of such new fuel compositions rich in Pu the better knowledge of the capture and fission cross sections of the Pu isotopes becomes very important. This is clearly stated in the recent OECD NEA’s “High Priority Request List” and in the WPEC-26 “Uncertainty and target accuracy assessment for innovative systems using recent covariance data evaluations” report. In particular, a new series of cross section evaluations have been recently carried out jointly by the European (JEFF) and United States (ENDF) nuclear data agencies. As the new evaluations on 240Pu and 241Am have been already completed, 242Pu is the next to be reevaluated, and the scarceness of capture data (only two TOF measurements from 1973 and 1976 are available and disagree with each other) calls for a new time-of flight capture cross section measurement. This will be the first measurement in 40 years and, with the use of more advanced techniques, shall provide a more reliable and accurate result. We propose to measure the capture cross section of 242Pu in the region from thermal up to at least 60 keV, aiming for a high energy limit of 500 keV. The experiment would make use of an array of 4 low neutron sensitivity C6D6 detectors and be carried out at the n_TOF EAR-1 (185 m flight path) measuring station. Compared to the current uncertainty of 35%, this measurement aims at an improved accuracy between 7% and 12% depending on the energy region.Preprin

Calibration of a multi-load cells weighing system based on neural networks

Multi-load cells weighting systems are based on a platform supported by four or more load cells, normally in parallel inputting lhe same signal conditioning unit. Because of mechanical and electrical paralleling tuning the gain of a load cell affects lhe behavior of the others, making the calibration difficult and tedious, specially withweightbridges for cars and trucb, requiring lhemotion of heavy weights around /arge p/atforms

Dissimilar Symmetric Word Pairs in the Human Genome

In this work we explore the dissimilarity between symmetric word pairs, by comparing the inter-word distance distribution of a word to that of its reversed complement. We propose a new measure of dissimilarity between such distributions. Since symmetric pairs with different patterns could point to evolutionary features, we search for the pairs with the most dissimilar behaviour. We focus our study on the complete human genome and its repeat-masked version.Comment: Submitted 13-Feb-2017; accepted, after a minor revision, 17-Mar-2017; 11th International Conference on Practical Applications of Computational Biology & Bioinformatics, PACBB 2017, Porto, Portugal, 21-23 June, 201

Forma rara de tumor carcinóide - caso clínico

ABSTRACTThe authors report a clinical case of carcinoid tumor, located at the antero-superior mediastjnum and of probable origin in thymus, whose local agressivity and metastatic pattern are in accordance with the references found in the medical literature about the clinical behaviour of the thymic carcinoid. They have not identified endocrine paraneoplasia, particularly the carcinoid syndrome.REV PORT PNEUMOL 2001; VII (4-5): 343-34

Detection of glaucoma using three-stage training with EfficientNet

[EN] This paper sets forth a methodology that is based on three-stage-training of a state-of-the-art network architecture previously trained on Imagenet, and iteratively finetuned in three steps; freezing first all layers, then re-training a specific number of them and finally training all the architecture from scratch, to achieve a system with high accuracy and reliability. To determine the performance of our technique a dataset consisting of 17.070 color cropped samples of fundus images, and that includes two classes, normal and abnormal, is used. Extensive evaluations using baselines models (VGG16, InceptionV3 and Resnet50) are carried out, in addition to thorough experimentation with the proposed pipeline using variants of EfficientNet and EfficientNetV2. The training procedure is described accurately, putting emphasis on the number of parameters trained, the confusion matrices (with analysis of false positives and false negatives), accuracy, and F1-score obtained at each stage of the proposed methodology. The results achieved show that the intelligent system presented for the task at hand is reliable, presents high precision, its predictions are consistent and the number of parameters needed to train are low compared to other alternatives.This work is supported by the HK Innovation and Technology Commission (InnoHK Project CIMDA), the HK Research Grants Council (Project CityU 11204821) and City University of Hong Kong (Project 9610034). We acknowledge the support of Universitat Politècnica de València; R&D project PID2021-122580NB-I00, funded by MCIN/AEI/ 10.13039/501100011033 and ERDF.De Zarzà, I.; De Curtò, J.; Tavares De Araujo Cesariny Calafate, CM. (2022). Detection of glaucoma using three-stage training with EfficientNet. Intelligent Systems with Applications. 16:1-10. https://doi.org/10.1016/j.iswa.2022.2001401101

A systems-level analysis of total-body PET data reveals complex skeletal metabolism networks in vivo

Bone is now regarded to be a key regulator of a number of metabolic processes, in addition to the regulation of mineral metabolism. However, our understanding of complex bone metabolic interactions at a systems level remains rudimentary. in vitro molecular biology and bioinformatics approaches have frequently been used to understand the mechanistic changes underlying disease at the cell level, however, these approaches lack the capability to interrogate dynamic multi-bone metabolic interactions in vivo. Here we present a novel and integrative approach to understand complex bone metabolic interactions in vivo using total-body positron emission tomography (PET) network analysis of murine 18F-FDG scans, as a biomarker of glucose metabolism in bones. In this report we show that different bones within the skeleton have a unique glucose metabolism and form a complex metabolic network, which could not be identified using single tissue simplistic PET standard uptake values analysis. The application of our approach could reveal new physiological and pathological tissue interactions beyond skeletal metabolism, due to PET radiotracers diversity and the advent of clinical total-body PET systems

Automatic Accident Detection: Assistance Through Communication Technologies and Vehicles

[EN] The symbiosis between communication technologies and vehicles offer a priceless opportunity to improve assistance to people injured in traffic accidents, providing information about the incident to reduce the response time of emergency services. Determining more accurately the required human and material resources for each particular accident could significantly reduce the number of victims. This paper presents our novel system prototype especially designed to detect and provide faster assistance for traffic accidents, thereby minimizing the consequences on the passengers¿ health. The proposed system requires each vehicle to be endowed with an On-Board Unit responsible for detecting and reporting accident situations to an external Control Unit that estimates its severity, allocating the necessary resources for the rescue operation. The development of our prototype based on off-the-shelf devices, and its validation at the Applus+ IDIADA Automotive Research Corporation facilities, shows that our system could notably reduce the time needed to alert and deploy the emergency services after an accident takes place.This work was partially supported by the Ministerio de Ciencia e Innovación, Spain, under Grant TIN2011-27543-C03-01, and by the Diputación General de Aragón, under Grant subvenciones destinadas a la formación y contratación de personal investigador.Fogue, M.; Garrido, P.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2012). Automatic Accident Detection: Assistance Through Communication Technologies and Vehicles. IEEE Vehicular Technology Magazine. 7(3):90-100. doi:10.1109/MVT.2012.2203877S901007

A system for automatic notification and severity estimation of automotive accidents

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.New communication technologies integrated into modern vehicles offer an opportunity for better assistance to people injured in traffic accidents. Recent studies show how communication capabilities should be supported by artificial intelligence systems capable of automating many of the decisions to be taken by emergency services, thereby adapting the rescue resources to the severity of the accident and reducing assistance time. To improve the overall rescue process, a fast and accurate estimation of the severity of the accident represent a key point to help emergency services better estimate the required resources. This paper proposes a novel intelligent system which is able to automatically detect road accidents, notify them through vehicular networks, and estimate their severity based on the concept of data mining and knowledge inference. Our system considers the most relevant variables that can characterize the severity of the accidents (variables such as the vehicle speed, the type of vehicles involved, the impact speed, and the status of the airbag). Results show that a complete Knowledge Discovery in Databases (KDD) process, with an adequate selection of relevant features, allows generating estimation models that can predict the severity of new accidents. We develop a prototype of our system based on off-the-shelf devices and validate it at the Applus+ IDIADA Automotive Research Corporation facilities, showing that our system can notably reduce the time needed to alert and deploy emergency services after an accident takes place.This work was partially supported by the Ministerio de Ciencia e Innovacion, Spain, under Grant TIN2011-27543-C03- 01, and by the Diputacion General de Aragon, under Grant "subvenciones destinadas a la formacion y contratacion de personal investigador."Fogue, M.; Garrido, P.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2014). A system for automatic notification and severity estimation of automotive accidents. IEEE Transactions on Mobile Computing. 13(5):948-963. https://doi.org/10.1109/TMC.2013.35S94896313

A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms

[EN] The development of communication technologies integrated in vehicles allows creating new protocols and applications to improve assistance in traffic accidents. Combining this technology with intelligent systems will permit to automate most of the decisions needed to generate the appropriate sanitary resource sets, thereby reducing the time from the occurrence of the accident to the stabilization and hospitalization of the injured passengers. However, generating the optimal allocation of sanitary resources is not an easy task, since there are several objectives that are mutually exclusive, such as assistance improvement, cost reduction, and balanced resource usage. In this paper, we propose a novel approach for the sanitary resources allocation in traffic accidents. Our approach is based on the use of multiobjective genetic algorithms, and it is able to generate a list of optimal solutions accounting for the most representative factors. The inputs to our model are: (i) the accident notification, which is obtained through vehicular communication systems, and (ii) the severity estimation for the accident, achieved through data mining. We evaluate our approach under a set of vehicular scenarios, and the results show that a memetic version of the NSGA-II algorithm was the most effective method at locating the optimal resource set, while maintaining enough variability in the solutions to allow applying different resource allocation policies. 2012 Elsevier Ltd. All rights reserved.This work was partially supported by the Ministerio de Ciencia e Innovacion, Spain, under Grant TIN2011-27543-C03-01, and by the Diputacion General de Aragon, under Grant "subvenciones destinadas a la formacion y contratacion de personal investigador".Fogue, M.; Garrido, P.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms. Expert Systems with Applications. 40(1):323-336. doi:10.1016/j.eswa.2012.07.056S32333640

Topology-based broadcast schemes for urban scenarios targeting adverse density conditions

© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works[EN] Research works regarding vehicular communications usually obviate assessing the proposals in scenarios including adverse vehicle densities, despite such scenarios are quite common in real urban environments. In this paper, we study the effect of these hostile conditions on the performance of different schemes providing warning message dissemination. We then propose the Junction Store and Forward (JSF) and the Nearest Junction Located (NJL) schemes, which were specially designed to be used in very low and very high density scenarios, respectively. Simulation results using real maps demonstrate how our proposed schemes are able to outperform existing warning message dissemination schemes in urban environments under adverse vehicle density conditions.This work was partially supported by the Ministerio de Ciencia e Innovacion´ , Spain, under Grant TIN2011-27543- C03-01, as well as the Government of Arag ´on and the European Social Fund (T91 Research Group).Sanguesa, JA.; Fogue, M.; Garrido, P.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM. (2014). Topology-based broadcast schemes for urban scenarios targeting adverse density conditions. En 2014 IEEE Wireless Communications and Networking Conference (WCNC). IEEE. 2564-2569. doi:10.1109/WCNC.2014.6952786S2564256