A {\mu}-TPC detector for the characterization of low energy neutron fields

The AMANDE facility produces monoenergetic neutron fields from 2 keV to 20 MeV for metrological purposes. To be considered as a reference facility, fluence and energy distributions of neutron fields have to be determined by primary measurement standards. For this purpose, a micro Time Projection Chamber is being developed to be dedicated to measure neutron fields with energy ranging from 8 keV up to 1 MeV. In this work we present simulations showing that such a detector, which allows the measurement of the ionization energy and the 3D reconstruction of the recoil nucleus, provides the determination of neutron energy and fluence of these neutron fields

Novel recoil nuclei detectors to qualify the AMANDE facility as a Standard for mono-energetic neutron fields

The AMANDE facility at IRSN-Cadarache produces mono-energetic neutron fields from 2 keV to 20 MeV with metrological quality. To be considered as a standard facility, characteristics of neutron field i.e fluence distribution must be well known by a device using absolute measurements. The development of new detector systems allowing a direct measurement of neutron energy and fluence has started in 2006. Using the proton recoil telescope principle with the goal of increase the efficiency, two systems with full localization are studied. A proton recoil telescope using CMOS sensor (CMOS-RPT) is studied for measurements at high energies and the helium 4 gaseous micro-time projection chamber (microTPC He4) will be dedicated to the lowest energies. Simulations of the two systems were performed with the transport Monte Carlo code MCNPX, to choose the components and the geometry, to optimize the efficiency and detection limits of both devices or to estimate performances expected. First preliminary measurements realised in 2008 demonstrated the proof of principle of these novel detectors for neutron metrology.Comment: to appear in Radiation Measurements, Proc. of 24th International Conference on Nuclear Tracks in Solids (Bologna, 1-5 September 2008

Optimal Power Flow Solution Using Ant Manners for Electrical Network

This paper presents ant manners and the collective intelligence for electrical network. Solutions for Optimal Power Flow (OPF) problem of a power system deliberate via an ant colony optimization metaheuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines. Simulation results on the IEEE 30-bus electrical network show that the ant colony optimization method converges quickly to the global optimum

Accurate Detection of Alzheimer’s Disease Using Lightweight Deep Learning Model on MRI Data

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairment and aberrant protein deposition in the brain. Therefore, the early detection of AD is crucial for the development of effective treatments and interventions, as the disease is more responsive to treatment in its early stages. It is worth mentioning that deep learning techniques have been successfully applied in recent years to a wide range of medical imaging tasks, including the detection of AD. These techniques have the ability to automatically learn and extract features from large datasets, making them well suited for the analysis of complex medical images. In this paper, we propose an improved lightweight deep learning model for the accurate detection of AD from magnetic resonance imaging (MRI) images. Our proposed model achieves high detection performance without the need for deeper layers and eliminates the use of traditional methods such as feature extraction and classification by combining them all into one stage. Furthermore, our proposed method consists of only seven layers, making the system less complex than other previous deep models and less time-consuming to process. We evaluate our proposed model using a publicly available Kaggle dataset, which contains a large number of records in a small dataset size of only 36 Megabytes. Our model achieved an overall accuracy of 99.22% for binary classification and 95.93% for multi-classification tasks, which outperformed other previous models. Our study is the first to combine all methods used in the publicly available Kaggle dataset for AD detection, enabling researchers to work on a dataset with new challenges. Our findings show the effectiveness of our lightweight deep learning framework to achieve high accuracy in the classification of AD