Simple modeling of the thermal history of d.c. plasma sprayed agglomerated nanosized zirconia particles

International audienceIn this work, are presented the results of a model coupling both dynamic and thermal histories of a single zirconia particle injected into a d.c plasma jet. The model developed calculates the heat transfer and phase changes within the particle along its trajectory. It is based on the Stefan problem with an explicit determination of the position of the interface solid/liquid. The evaporation is described according to the approach “Back pressure” The model is adapted to the calculation of thermal and dynamic behaviors of agglomerated particles

Robust Application of New Deep Learning Tools: An Experimental Study in Medical Imaging

El trabajo forma parte de la tesis doctoral del primer autor, Dr. Laith Alzubaidi, siendo José Santamaría investigador invitado por el autor del artículo en la co-supervision de dicha tesis doctoral, correspondiendo este con uno de los varios artículos científicos que fueron desarrollados y publicados durante y después de la tesis doctoral del Dr. Alzubaidi.Nowadays medical imaging plays a vital role in diagnosing the various types of diseases among patients across the healthcare system. Robust and accurate analysis of medical data is crucial to achieving a successful diagnosis from physicians. Traditional diagnostic methods are highly time-consuming and prone to handmade errors. Cost is reduced and performance is improved by adopting computer-aided diagnosis methods. Usually, the performance of traditional machine learning (ML) classification methods much depends on both feature extraction and selection methods that are sensitive to colors, shapes, and sizes, which conveys a complex solution when facing classification tasks in medical imaging. Currently, deep learning (DL) tools have become an alternative solution to overcome the drawbacks of traditional methods that make use of handmade features. In this paper, a new DL approach based on a hybrid deep convolutional neural network model is proposed for the automatic classification of several different types of medical images. Specifically, gradient vanishing and over-fitting issues have been properly addressed in the proposed model in order to improve its robustness by means of different tested techniques involving residual links, global average pooling layers, dropout layers, and data augmentation. Additionally, we employed the idea of parallel convolutional layers with the aim of achieving better feature representation by adopting different filter sizes on the same input and then concatenated as a result. The proposed model is trained and tested on the ICIAR 2018 dataset to classify hematoxylin and eosin-stained breast biopsy images into four categories: invasive carcinoma, in situ carcinoma, benign tumors, and normal tissue. As the experimental results show, our proposed method outperforms several of the state-of-the-art methods by achieving rate values of 93.2% and 89.8% for both image- and patch-wise image classification tasks, respectively. Moreover, we fine- tuned our model to classify foot images into two classes in order to test its robustness by considering normal and abnormal diabetic foot ulcer (DFU) image datasets. In this case the model achieved an F1 score value of 94.80% on the public DFU dataset and 97.3% on the private DFU dataset. Lastly, transfer learning (TL) has been adopted to validate the proposed model with multiple classes with the aim of classifying six different wound types. This approach significantly improves the accuracy rate from a rate of 76.92% when trained from scratch to 87.94% when TL was considered. Our proposed model has proven its suitability and robustness by addressing several medical imaging tasks dealing with complex and challenging scenarios

Les stenoses tracheales acquises: Experiencede l’hopitalhabibthameur

Introduction: Acquired tracheal stenoses represent rare but serious disease. They are often secondary to inappropriate management of patients under artificial ventilation. The goal of this study is to evaluate our results in the management of these stenoses and to assess the benefits and the limits different therapeutic means.Materials and methods : We carry a retrospective study about 18 cases of acquired tracheal stenoses treated and followed in our department between 1999 and 2006. Initial endoscopic and radiological explorations have been performed in all cases. Treatment of the stenoses was medical, endoscopic and/or surgical. Follow-up was clinical and endoscopicwith a mean period of 22 months.Results : All patients were victims of pathology needing intubation. Tracheotomy was performed after intubation in 50% of cases after a mean period of 12 days (5-20 days). Dyspnea and dysphonia were the major functional symptoms. Initial endoscopy showed a double tracheal stenosis in one case. Stenoses were initialy fibrous in 72.2% of cases and evolutivein 27.8% of cases. CT scan performed in 12 cases and MRI in 2 others allowed to better study stenosis characteristics. RFE was performed in 6 cases and showed an obstructive syndrome in all of them. All patients received medical treatment. Before a definitive treatment, dilatation was performed in 11 cases (61%) and stenting in one other (5.6%). Laser diode therapy was used in 11 patients including 2 cases having postoperative recurrence. Tracheal resection and reconstruction wasperformed in 11 cases (61%) having extensive and severe stenoses with involvement of tracheal cartilage.Conclusion : Acquired tracheal stenoses represent a serious complication with high morbidity. If tracheal resection and reconstruction remains the gold standard treatment, endoscopy represents now a major alternative in their management. Nevertheless, prevention should be considered, given that most stenoses are iatrogenic due to traumatic or prolongedintubations

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

El trabajo forma parte de la tesis doctoral del primer autor, Dr. Laith Alzubaidi, siendo José Santamaría investigador invitado por el autor del artículo en la co-supervision de dicha tesis doctoral, siendo este uno de los varios artículos científicos que fueron desarrollados y publicados durante y después de la tesis doctoral del Dr. Laith Alzubaidi.In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achiev‑ ing outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well‑known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contrib‑ uted several works reviewing the State‑of‑the‑Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most impor‑ tant aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL tech‑ niques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High‑Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion

Design and implement WSN/IoT smart parking management system using microcontroller

With the dramatic expansion of new networks such as Wireless Sensor Network (WSN) and Internet-of-Things (IoT), tremendous opportunities have been emerged to incorporate such technologies for valuable tasks. One of these tasks is the smart car parking where there is an imperative demand to manage the parkings in various facilities which may help drivers to save their time. Several research studies have addressed this task using wide range of approaches. However, the energy consumption is still a serious concern. This paper proposes a smart car parking based on cloud-based approach along with variety of sensors. Passive Infrared Sensors (PIRs) have been used to sense the object motion. While Light Dependent Resistor (LDR) sensors have been utilized to sense the light of the parking alarm and display inmformation regarding the occupied and non-occupied parking lots. Finally, multi-micro controller of Arduino have been exploited in order to transmit the information collected to the server. Finally, a prototype Android application has been developed in order to recieve the infromation from the server. Results of simulation showed the efficacy of the proposed method

Characteristics of Different Systems for the Solar Drying of Crops

Solar dryers are used to enable the preservation of agricultural crops, food processing industries for dehydration of fruits and vegetables, fish and meat drying, dairy industries for production of milk powder, seasoning of wood and timber, textile industries for drying of textile materials. The fundamental concepts and contexts of their use to dry crops is discussed in the chapter. It is shown that solar drying is the outcome of complex interactions particular between the intensity and duration of solar energy, the prevailing ambient relative humidity and temperature, the characteristics of the particular crop and its pre-preparation and the design and operation of the solar dryer

7th Drug hypersensitivity meeting: part two

No abstract availabl

Deployment of AI-based RBF network for photovoltaics fault detection procedure

In this paper, a fault detection algorithm for photovoltaic systems based on artificial neural networks (ANN) is proposed. Although, a rich amount of research is available in the field of PV fault detection using ANN, this paper presents a novel methodology based on only two inputs for the training, validating and testing of the Radial Basis Function (RBF) network achieving unprecedented detection accuracy of 98.1%. The proposed methodology goes beyond data normalisation and implements a ‘mapping of inputs’ approach to the data set before exposing it to the network for training. The accuracy of the proposed network is further endorsed through testing of the network in partial shading and overcast conditions