Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Selection Effects on Gradient Descent Logistic Regression for Medical Data Classification

: In recent years, a number of researchers have concentrated on medical data analytics because machine intelligence in medical diagnosis is a new trend for enormous medical applications. Generally, medical datasets are massive in size, so traditional classifiers suffered from overfitting and under-fitting problem of training set. In this paper, Gradient Descent Logistic Regression (GDLR) classification method is proposed for medical data classification. The Pearson Correlation Coefficient (PCC) is used to calculate the correlation between the features. After that, Random Forest (RF) algorithm ranks the features and selects the most relevant features to improve performance of the medical data classification. The regression technique processes the features effective and analyse the feature importance based on the weight values. The Random Forest (RF) assigns the features importance in the tree structure. The random forest is used to select the features and features are applied for the GDLR to classify effectively. The GDLR method further analysis the features for effectively analysis the feature importance based on the weight values and more relevant features are identified than the RF. The experimental analysis demonstrated that the performance of GDLR algorithm achieved better than traditional methods Neural Network for Threshold Selection (NNTS) and Mean Selection (MS). The accuracy of the proposed GDLR method achieved as 97.5% in the Hepatitis dataset, while existing mean selection method has the accuracy of 82.58%.
Mask-Pix2Pix Network for Overexposure Region Recovery of Solar Image

Overexposure may happen for imaging of solar observation as extremely violet solar bursts occur, which means that signal intensity goes beyond the dynamic range of imaging system of a telescope, resulting in loss of signal. For example, during solar flare, Atmospheric Imaging Assembly (AIA) of Solar Dynamics Observatory (SDO) often records overexposed images/videos, resulting loss of fine structures of solar flare. This paper makes effort to retrieve/recover missing information of overexposure by exploiting deep learning for its powerful nonlinear representation which makes it widely used in image reconstruction/restoration. First, a new model, namely, mask-Pix2Pix network, is proposed for overexposure recovery. It is built on a well-known Pix2Pix network of conditional generative adversarial network (cGAN). In addition, a hybrid loss function, including an adversarial loss, a masked L1 loss and a edge mass loss/smoothness, are integrated together for addressing challenges of overexposure relative to conventional image restoration. Moreover, a new database of overexposure is established for training the proposed model. Extensive experimental results demonstrate that the proposed mask-Pix2Pix network can well recover missing information of overexposure and outperforms the state of the arts originally designed for image reconstruction tasks.
Evaluating C-CAP: reflecting on the dichotomy of curriculum design and approval

A recent posting on the PiP blog outlined the use of heuristic evaluation techniques to optimise PiP’s Class and Course Approval Pilot (C-CAP) system prior to further evaluation. Heuristic compliance was considered imperative for two reasons: minimising users' extraneous cognitive load during user acceptance testing, and; optimising the user acceptance testing data. A report summarising the principal findings of the user acceptance testing has recently been published on the PiP website; but I promised last time to make some further comments about the user acceptance testing.
Processing and characterization of pine epoxy based composites

Now- a- days, natural fiber attracted the attention of researchers because of their eco-friendly nature and very demanding in industries. The natural fibers are eco-friendly, biodegradable, abundantly available, non-toxic, non-abrasive, renewable, and cheap, and have low density. Among various fibers, pine fiber is widely used because of its excellent mechanical properties. For preparing a composite material, its mechanical behavior depends on many factors such as fiber content, orientation, length etc. The objective of this research is to investigate the effect of fiber loading and on the mechanical behavior of pine fiber reinforced epoxy-based composites. The present work mainly focuses on the evaluation of tensile strength, flexural strength and impact strength of the pine-epoxy composite. It also concludes the effect of fiber loading on tensile strength, flexural strength and impact strength of the pine-epoxy composite.Now- a- days, natural fiber attracted the attention of researchers because of their eco-friendly nature and very demanding in industries. The natural fibers are eco-friendly, biodegradable, abundantly available, non-toxic, non-abrasive, renewable, and cheap, and have low density. Among various fibers, pine fiber is widely used because of its excellent mechanical properties. For preparing a composite material, its mechanical behavior depends on many factors such as fiber content, orientation, length etc. The objective of this research is to investigate the effect of fiber loading and on the mechanical behavior of pine fiber reinforced epoxy-based composites. The present work mainly focuses on the evaluation of tensile strength, flexural strength and impact strength of the pine-epoxy composite. It also concludes the effect of fiber loading on tensile strength, flexural strength and impact strength of the pine-epoxy composite.
Demonette2 - Une base de données dérivationnelles du français à grande échelle : premiers résultats

Cet article presente la conception et le developpement de Demonette2, une base de donnees derivationnelle a grande echelle du francais, developpee dans le cadre du projet ANR Demonext (ANR-17-CE23-0005). L'article decrit les objectifs du projet, la structure de la base et expose les premiers resultats du projet, en mettant l'accent sur un enjeu crucial : la question du codage semantique des entrees et des relations. ABSTRACT Demonette2-A large scale derivational database for French: first results This paper presents the design and development of Demonette2, a large-scale derivational database of French, developed as part of the ANR Demonext project (ANR-17-CE23-0005). It describes the objectives of the project, the structure of the database and presents the first results of the project, focusing on the question of the semantic encoding of lexical units and their relationships. MOTS-CLES : base de donnees derivationnelles, codage semantique, paradigmes derivationnels, lexique francais.
Tibetan Multi-dialect Speech and Dialect Identity Recognition

Tibetan language has very limited resource for conventional automatic speech recognition so far. It lacks of enough data, sub-word unit, lexicons and word inventories for some dialects. And speech content recognition and dialect classification have been treated as two independent tasks and modeled respectively in most prior works. But the two tasks are highly correlated. In this paper, we present a multi-task WaveNet model to perform simultaneous Tibetan multi-dialect speech recognition and dialect identification. It avoids processing the pronunciation dictionary and word segmentation for new dialects, while, in the meantime, allows training speech recognition and dialect identification in a single model. The experimental results show our method can simultaneously recognize speech content for different Tibetan dialects and identify the dialect with high accuracy using a unified model. The dialect information used in output for training can improve multi-dialect speech recognition accuracy, and the low-resource dialects got higher speech content recognition rate and dialect classification accuracy by multi-dialect and multi-task recognition model than task-specific models.
Managed Annihilation: An Unnatural History of the Newfoundland Cod Collapse

Citer cet article Hubbard, J. (2012). Managed Annihilation: An Unnatural History of the Newfoundland Cod Collapse. By Dean Bavington. (Vancouver: UBC Press, 2010. xxxii + 186 p., ill., maps. ISBN 978-0-7748-17486 $32.95). Scientia Canadensis, 35(1-2), 183–187. https:// doi.org/10.7202/1014009ar Ce document est protégé par la loi sur le droit d'auteur. L'utilisation des services d'Érudit (y compris la reproduction) est assujettie à sa politique d'utilisation que vous pouvez consulter en ligne. [https://apropos.erudit. org/fr/usagers/politique-dutilisation/]
Association between smoking and dental caries among people of Dhanbad district, Jharkhand, India

Introduction: Smoking as a public health problem harms one’s general and oral well-being, leading to increased morbidity and mortality. Aim: The aim of this study was to find out the association between dental caries and smokers (current and past) with nonsmokers among people of Dhanbad District, Jharkhand, India. Materials and Methods: The present cross-sectional study was conducted among 1364 (448 smokers, 440 past smokers, and 446 nonsmokers), and the patients were divided among three age groups (35–44, 45–60, and >60 years). Results: It was revealed that the highest mean decayed missing and filled teeth was seen among smokers (3.65 ± 5.78) followed by past smokers (3.01 ± 2.66) and nonsmokers (3.01 ± 2.66). Conclusion: Efforts are required by clinicians to educate people about the ill-effects of tobacco and provide tobacco cessation counseling to reduce the burden of morbidity and mortality caused by tobacco.
Effective optimal dismantling strategy for interdependent networks based on residual theory

Because of interdependence between different network layers, interdependent networks are more fragile than single-layer networks, and large-scale iterative paralysis occurs easily. How to seek nodes whose removal can effectively dismantle networks has attracted great research attention. In this paper, a novel optimal dismantling strategy Maximum Entropy Centrality (EC) and overlapping betweenness (OB) based on residual theory (ECOB) is proposed. In the ECOB, the residual theory is used to detect the highest influence nodes according to the quality of the residual networks. In addition, to make sorting more accurate, EC and OB parameters are both considered in the node selection mechanism. Simulation shows that the ECOB strategy performs much better than existing methods both in artificial interdependent networks and real-world interdependent networks. This is thanks to the introduced ECOB node selection algorithm with proper parameter criterions.
Wafer Defect Pattern Recognition and Analysis Based on Convolutional Neural Network

Wafer map defect pattern contains the critical information about semiconductor manufacturing, effective defect analysis technology can improve the yield of products. This paper develops a wafer defect pattern recognition and analysis method based on convolutional neural network (CNN). We build an 8-layer CNN model to inspect wafer map defect and a 13-layer model to classify defect patterns. To analyze the defects, we first extract features based on the classification network, then perform PCA dimensionality reduction and similarity ranking, we infer the root causes of tested samples according to the retrieved wafer maps. In particular, we analyze the impacts of different network layers and feature dimensions on image retrieval performance, it turns out that the appropriate dimensionality reduction can increase the accuracy and speed of wafer map retrieval. The models are trained and tested on the WM-811K wafer map dataset, which are collected from real wafer maps. The experimental results show that the proposed method is able to identify common wafer defect patterns and analyze the root causes accurately and effectively.
Hydrodynamic coupling distance between a falling sphere and downstream wall

In solid-liquid two phase flow, the knowledge of how descending solid particles affected by the presence of downstream wall is important. This work studies at what interstitial distance the velocity of a vertically descending sphere is affected by a downstream wall as a consequence of wall-modified hydrodynamic forces through a validated dynamic model. This interstitial distance-the hydrodynamic coupling distance Sc-is found to decay monotonically with the approach Stokes number St which compares the particle inertia to viscous drag characterized by the quasi-steady Stokes\' drag. The scaling relation Sc-St-1 decays monotonically as literature below the value of St equal to 10. However, the faster diminishing rate is found above the threshold value from St=10-40. Furthermore, an empirical relation of Sc-St shows dependence on the drop height which clearly indicates the non-negligible effect of unsteady hydrodynamic force components, namely the added mass force and the history force. Finally, we attempt a fitting relation which embedded the particle acceleration effect in the dependence of fitting constants on the diameter-scaled drop height.
Low Dose Sarin Leads To Murine Cardiac Dysfunction

Abstract : It has been reported that low dose sarin is associated with long-term pathology in the brain and heart; however, the effects of sarin on the heart have yet to be determined. In addition, sarin has been implicated as an etiological agent in Gulf War Illness. Thus, the role of sarin in producing illness has important military implications. This study used echocardiography, electrocardiography, and histology to determine sarin's effect on the murine cardiovascular system. C57BL/6J mice were injected with sarin at 0.4 LD50, 0.5 LD50, or saline on two consecutive days and studied for 10 weeks post exposure. The sarin animals had marked increases in heart weight to body weight ratios (p = 0.026) and the left ventricular lumen size was significantly decreased (p = 0.0014). In addition, cardiomyocytes were significantly larger in the sarin mice (p = 0.025), and atrial/brain natriuretic peptide levels were increased (p = 0.028 and 0.010, respectively). Results of the electrocardiograms show significant ST/T-wave changes in the sarin groups (p = 0.0015 and 0.032, respectively). Similarly, echocardiograms showed significantly decreased performance of the left ventricle in the sarin animals. This study indicates that sarin plays a role in cardiac remodeling and reducing cardiac performance.
Helmholtzspulen zur Kalibrierung von Magnetfeldsensoren

Helmholtz coils for the calibration of magnetic field sensors Generation, distribution and use of electrical energy is necessarily combined with low frequency electromagnetical radiation. At workplaces and in domestic environments electromagnetic fields are regarded as "important environmental polutants". Research is focusing an dose-response relationships, epidemiological investigations and potential therapeutical use of low frequency electromagnetic fields. In all of these investigations the knowledge of the magnetic field strength is necessary, in many cases derived from measurements. When developing suitable field sensors and /or measurement instrumentation well known magnetic test and calibration fields are necessary. In many cases Helmholtz coils can provide these magnetic fields with sufficient homogenity. Based an the Biot-Savart-law three dimensional magnetic fields of Helmholtz coils have been analytically described and coil parameters as inductivity and resonant frequency have been calculated. As a calibration device für low frequency magnetic sensors a three dimensional set of Helmholtz coils has been set up and tested.
Prediction of Sulfur Content in Electric Furnace Matte Using Machine Learning

Sulfur content in Electric Furnace Matte is one of the key parameters in nickel matte process control at PT Vale Indonesia Tbk. (PTVI), a nickel matte smelter in Indonesia. Currently the compliance to standard specification is relatively low due to process variability and control limitation. The sulfur content in Electric Furnace Matte depend on sulfur addition and other operating conditions. In this research, a machine learning approach is used to predict sulfur content in Electric Furnace Matte based on selected predictors. Linear and support vector regression models were built on the training data and used to predict sulfur content on testing data. The performance of each models were evaluated and compared. The linear model shows a 0.5843 coefficient correlation between test data and prediction, with a mean square error (MSE) 0.4207. The support vector regression (SVR), a non-linear model, is built with the same predictors. SVR model improve the correlation to 0.9408 and reduce the MSE to 0.0762. The research has shown the practicality of applying machine learning in nickel matte processing and open opportunity for further research.
Nurses' Knowledge and Attitude Related to Organ Donation

NURSES' KNOWLEDGE AND ATTITUDE RELATED TO ORGAN DONATION By Maureen E. Bishop Nurses today are caring for a wide array of patients that may include potential organ donors or organ transplant recipients. The purpose of this descriptive correlational study was to explore the knowledge nurses possess and the attitude they hold regarding organ donation, using Jean Watson's theory of human caring as the conceptual framework. A convenience sample of approximately 200 registered nurses in a 300-bed medical center, that does not have an organ transplant program was surveyed. The knowledge level had a mean score of 6.69 with a possible high of 11. This suggests that the knowledge level of the registered nurses regarding organ donation is insufficient. The registered nurses were found to have a positive attitude regarding the organ donation process and that their attitude would be influenced by increasing their knowledge of the organ donation process. This study suggests the need for further research into the effectiveness of current education programs for healthcare professionals regarding the organ donation process. Future research should also focus on alternative
Micro-drilled optical fiber for enhanced laser strain sensors

In this work, we present an experimental measurement of temperature and strain sensitivities of a micro-drilled optical fiber (MDOF). The MDOF consisted of a quasi-randomly distributed reflector along a single mode fiber (SMF). A fiber cavity laser based on MDOF was experimentally studied, attaining a single-wavelength laser emission centered at 1568.6nm. The output power level obtained from this single-laser oscillation when pumped at 140mW was around - 9.6dBm, and an optical signal to noise ratio (OSNR) of around 45dB was measured. Although temperature sensitivities of fiber Bragg gratings used as sensors are similar to our MDOF, strain sensitivity is enhanced around one order of magnitude when the MDOF was used.
Silicon nitride for structural parts of space applications

Silicon nitride is a ceramic material that has very interesting properties for space applications, especially for optical instruments and telescopes. Indeed, the objective for such structures is chiefly stability to ensure performance, associated with low mass and high stiffness to sustain launch loads. With its high strength and stiffness and low CTE, Silicon Nitride is therefore extremely well suited for stable Space structures. Thales Alenia Space has been using Silicon Nitride for more than a decade, and in that framework has evaluated the ability of the material to address those needs and requirements for complex designs such as tubes, brackets, brazed supports, beams and light-weighted plates. All these structures have been tested, qualified and are now flight proven. In order to improve knowledge and mastering of strength properties, a characterization campaign of the material is under way in a GSTP funded by CNES and driven by ESA. This paper describes the design of this test campaign, the choices for the sample types and dimensions, and prediction of the expected results. In addition to the mechanical strength testing, X-ray tomography has been implemented in order to detect flaws beforehand and to investigate the ability to predict failure from the extracted information. This will be especially useful since verification (in particular proof tests that are commonly used on ceramics for Space applications) is based on the relationship between strength and flaws. It is expected that by improving this knowledge a more straightforward verification process can be derived.
Management Training Strategies for Developing Countries

This book is to be a resource to North Americans and Europeans working overseas as trainers and consultants 3rd World nationals working in other developing countries and indigenous trainers working within their own countries. Purposes are 1) to assess current state of the art public management training in 3rd World countries; 2) to outline the variety of training activities utilized in 3rd World countries to develop managerial competencies; 3) to identify the more effective approaches to providing management training relevant to 3rd World contexts; 4) to examine strengths/weaknesses of each of the management training approaches; 5) to provide a scholarly analysis with practical recommendations that can guide effective management training efforts. The book discusses the need for management training in less developed countries describes 4 approaches to training and discusses the implications of management training for the future.
Lagrangian Trajectory Simulation of Floating Objects in the State of São Paulo Coastal Region

In this work a computational model is presented in order to simulate the trajectory of objects near the Ilhabela island region, in São Paulo coastline, Brazil. The MOHID platform (MOdelagem HIDrodinâmica - Hydrodynamics Modelling) was used with the downscalling technique used to obtain local hydrodynamic currents at local scale. Two different applications are tested, the first is the hypothetical trajectory of a dead cetacean specimen drifting that could have happened in fact if it was not arrested to a more adequate spot near Ilhabela island in November, 2017, and the second is the simulation for the drift of floating objects that resulted from an accidental release of containers at the Port of Santos in August, 2017. The use of these technologies has great potential for researchers interested to simulate different drift occurrences near the Brazilian costal region.
Polyvinylidene fluoride (PVDF) as a feedstock for material extrusion additive manufacturing

This study aims to investigate the material extrusion additive manufacturing (MEAM) deposition parameters for creating viable 3-D printed polyvinylidene fluoride (PVDF) structures with a balanced mix of mechanical and electrical properties. ,Different combinations of deposition conditions are tested, and the influence of these parameters on the final dimensional accuracy, semi-crystalline phase microstructure and effective mechanical strength of MEAM homopolymer PVDF printed parts is experimentally assessed. Considering printed part integrity, appearance, print time and dimensional accuracy, MEAM parameters for PVDF are suggested. ,A range of viable printing parameters for MEAM fabricated PVDF Kynar 740 objects of different heights and in-plane length dimensions was determined. For PVDF structures printed under the suggested conditions, the mechanical response and the microstructure development related to Piezoelectric response are reported. ,This research first reports on a range of parameters that have been confirmed to facilitate effective MEAM printing of 3-D PVDF objects, presents effects of the individual parameters and gives the mechanical and microstructure properties of PVDF structures fabricated under the suggested deposition conditions.