Search results for: independent work

Authors: Lucas Sátiro Do Carmo, Betzabel Noemi Silva Carrera, Shigueo Watanabe, J. F. D. Chubaci

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Beryl is a silicate with chemical formula Be₃Al₂(SiO₃)₆ commonly found in Brazil. It has a few colored variations used as jewelry, like Aquamarine (blueish), Emerald (green) and Heliodor (yellow). The color of each variation depends on the dopant that is naturally present in the crystal lattice. In this work, Heliodor pellets of 5 mm diameter and 1 mm thickness have been produced and investigated using thermoluminescence (TL) to evaluate its potential for use as gamma ray’s dosimeter. The results show that the pellets exhibited a prominent TL peak at 205 °C that grows linearly with dose when irradiated from 1 Gy to 1000 Gy. A comparison has been made between powdered and sintered dosimeters. The results show that sintered pellets have higher sensitivity than powder dosimeter. The TL response of this mineral is satisfactory for radiation dosimetry applications in the studied dose range.

Keywords: dosimetry, beryl, gamma rays, sintered pellets, new material

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Authors: Alessandro Evangelista, Vito M. Manghisi, Michele Gattullo, Enricoandrea Laviola

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In this work, we propose a custom visualization technique for Out-Of-View-Objects in Virtual and Augmented Reality applications using Head Mounted Displays. In the last two decades, Augmented Reality (AR) and Virtual Reality (VR) technologies experienced a remarkable growth of applications for navigation, interaction, and collaboration in different types of environments, real or virtual. Both environments can be potentially very complex, as they can include many virtual objects located in different places. Given the natural limitation of the human Field of View (about 210° horizontal and 150° vertical), humans cannot perceive objects outside this angular range. Moreover, despite recent technological advances in AR e VR Head-Mounted Displays (HMDs), these devices still suffer from a limited Field of View, especially regarding Optical See-Through displays, thus greatly amplifying the challenge of visualizing out-of-view objects. This problem is not negligible when the user needs to be aware of the number and the position of the out-of-view objects in the environment. For instance, during a maintenance operation on a construction site where virtual objects serve to improve the dangers' awareness. Providing such information can enhance the comprehension of the scene, enable fast navigation and focused search, and improve users' safety. In our research, we investigated how to represent out-of-view-objects in HMD User Interfaces (UI). Inspired by commercial video games such as Call of Duty Modern Warfare, we designed a customized Compass. By exploiting the Unity 3D graphics engine, we implemented our custom solution that can be used both in AR and VR environments. The Compass Bar consists of a graduated bar (in degrees) at the top center of the UI. The values of the bar range from -180 (far left) to +180 (far right), the zero is placed in front of the user. Two vertical lines on the bar show the amplitude of the user's field of view. Every virtual object within the scene is represented onto the compass bar as a specific color-coded proxy icon (a circular ring with a colored dot at its center). To provide the user with information about the distance, we implemented a specific algorithm that increases the size of the inner dot as the user approaches the virtual object (i.e., when the user reaches the object, the dot fills the ring). This visualization technique for out-of-view objects has some advantages. It allows users to be quickly aware of the number and the position of the virtual objects in the environment. For instance, if the compass bar displays the proxy icon at about +90, users will immediately know that the virtual object is to their right and so on. Furthermore, by having qualitative information about the distance, users can optimize their speed, thus gaining effectiveness in their work. Given the small size and position of the Compass Bar, our solution also helps lessening the occlusion problem thus increasing user acceptance and engagement. As soon as the lockdown measures will allow, we will carry out user-tests comparing this solution with other state-of-the-art existing ones such as 3D Radar, SidebARs and EyeSee360.

Keywords: augmented reality, situation awareness, virtual reality, visualization design

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Authors: Abdulmajeed Bashir Mlitan, Ethan Hack

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Soil samples were collected from and around Alkomos cement factory, Alkomos town, Libya. Soil physiochemical properties were determined. In addition, olive leaves were scanned for their fungal content. This work can conclude that the results obtained for the examined physiochemical characteristics of soil in the area studied prove that cement dust from the Alkomos cement factory in Libya has had a significant impact on the soil. The affected soil properties are pH and total calcium content. These characteristics were found to be higher than those in similar soils from the same area. The increment of soil pH in the same area may be a result of precipitation of cement dust over the years. Different responses were found in each season and each site. For instance, the dominance of fungi of soil and leaves was lowest at 100 m from the factory and the evenness and diversity increased at this site compared to the control area and 250 m from the factory.

Keywords: pollution, soil microbial, alkomos, Libya

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Authors: Rashi Jain

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The Wireless Body Area Networks (WBANs) is an emerging interdisciplinary area where small sensors are placed on/within the human body. These sensors monitor the physiological activities and vital statistics of the body. The data from these sensors is aggregated and communicated to a remote doctor for immediate attention or to a database for records. On 6 Feb 2012, the IEEE 802.15.6 task group approved the standard for Body Area Network (BAN) technologies. The standard proposes the physical and MAC layer for the WBANs. The work provides an introduction to WBANs and overview of the physical and MAC layers of the standard. The physical layer specifications have been covered. A comparison of different protocols used at MAC layer is drawn. An introduction to the network layer and security aspects of the WBANs is made. The WBANs suffer certain limitations such as regulation of frequency bands, minimizing the effect of transmission and reception of electromagnetic signals on the human body, maintaining the energy efficiency among others. This has slowed down their implementation.

Keywords: vehicular networks, sensors, MicroController 8085, LTE

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Authors: Bader Alruwaili, Surajit Ray

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Finite mixtures are a flexible and powerful tool that can be used for univariate and multivariate distributions, and a wide range of research analysis has been conducted based on the multivariate normal mixture and multivariate of a t-mixture. Determining the number of modes is an important activity that, in turn, allows one to determine the number of homogeneous groups in a population. Our work currently being carried out relates to the study of the modality of the skew normal distribution in the univariate and multivariate cases. For the skew normal distribution, the aims are associated with studying the modality of the skew normal distribution and providing the ridgeline, the ridgeline elevation function, the $\Pi$ function, and the curvature function, and this will be conducive to an exploration of the number and location of mode when mixing the two components of skew normal distribution. The subsequent objective is to apply these results to the application of real world data sets, such as flow cytometry data.

Keywords: mode, modality, multivariate skew normal, finite mixture, number of mode

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Authors: Matthew Yeager, Christopher Willy, John Bischoff

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The conceptualization and design phases of a system lifecycle consume a significant amount of the lifecycle budget in the form of direct tasking and capital, as well as the implicit costs associated with unforeseeable design errors that are only realized during downstream phases. Ad hoc or iterative approaches to generating system requirements oftentimes fail to consider the full array of feasible systems or product designs for a variety of reasons, including, but not limited to: initial conceptualization that oftentimes incorporates a priori or legacy features; the inability to capture, communicate and accommodate stakeholder preferences; inadequate technical designs and/or feasibility studies; and locally-, but not globally-, optimized subsystems and components. These design pitfalls can beget unanticipated developmental or system alterations with added costs, risks and support activities, heightening the risk for suboptimal system performance, premature obsolescence or forgone development. Supported by rapid advances in learning algorithms and hardware technology, sensors and sensor systems have become commonplace in both commercial and industrial products. The evolving array of hardware components (i.e. sensors, CPUs, modular / auxiliary access, etc…) as well as recognition, data fusion and communication protocols have all become increasingly complex and critical for design engineers during both concpetualization and implementation. This work seeks to develop and utilize a non-deterministic approach for sensor system design within the multi-attribute tradespace exploration (MATE) paradigm, a technique that incorporates decision theory into model-based techniques in order to explore complex design environments and discover better system designs. Developed to address the inherent design constraints in complex aerospace systems, MATE techniques enable project engineers to examine all viable system designs, assess attribute utility and system performance, and better align with stakeholder requirements. Whereas such previous work has been focused on aerospace systems and conducted in a deterministic fashion, this study addresses a wider array of system design elements by incorporating both traditional tradespace elements (e.g. hardware components) as well as popular multi-sensor data fusion models and techniques. Furthermore, statistical performance features to this model-based MATE approach will enable non-deterministic techniques for various commercial systems that range in application, complexity and system behavior, demonstrating a significant utility within the realm of formal systems decision-making.

Keywords: multi-attribute tradespace exploration, data fusion, sensors, systems engineering, system design

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Authors: S. Wiak, L. Szymanski, Z. Kolacinski, G. Raniszewski, L. Pietrzak, Z. Staniszewska

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The term ‘cancer’ is given to a collection of related diseases that may affect any part of the human body. It is a pathological behaviour of cells with the potential to undergo abnormal breakdown in the processes that control cell proliferation, differentiation, and death of particular cells. Although cancer is commonly considered as modern disease, there are beliefs that drastically growing number of new cases can be linked to the extensively prolonged life expectancy and enhanced techniques for cancer diagnosis. Magnetic hyperthermia therapy is a novel approach to cancer treatment, which may greatly contribute to higher efficiency of the therapy. Employing carbon nanotubes as nanocarriers for magnetic particles, it is possible to decrease toxicity and invasiveness of the treatment by surface functionalisation. Despite appearing in recent years, magnetic particle hyperthermia has already become of the highest interest in the scientific and medical environment. The reason why hyperthermia therapy brings so much hope for future treatment of cancer lays in the effect that it produces in malignant cells. Subjecting them to thermal shock results in activation of numerous degradation processes inside and outside the cell. The heating process initiates mechanisms of DNA destruction, protein denaturation and induction of cell apoptosis, which may lead to tumour shrinkage, and in some cases, it may even cause complete disappearance of cancer. The factors which have the major impact on the final efficiency of the treatment include temperatures generated inside the tissues, time of exposure to the heating process, and the character of an individual cancer cell type. The vast majority of cancer cells is characterised by lower pH, persistent hypoxia and lack of nutrients, which can be associated to abnormal microvasculature. Since in healthy tissues we cannot observe presence of these conditions, they should not be seriously affected by elevation of the temperature. The aim of this work is to investigate the influence of iron content in iron filled Carbon Nanotubes on the desired nanoparticles for cancer therapy. In the article, the development and demonstration of the method and the model device for hyperthermic selective destruction of cancer cells are presented. This method was based on the synthesis and functionalization of carbon nanotubes serving as ferromagnetic material nanocontainers. The methodology of the production carbon- ferromagnetic nanocontainers (FNCs) includes the synthesis of carbon nanotubes, chemical, and physical characterization, increasing the content of a ferromagnetic material and biochemical functionalization involving the attachment of the key addresses. The ferromagnetic nanocontainers were synthesised in CVD and microwave plasma system. The research work has been financed from the budget of science as a research project No. PBS2/A5/31/2013.

Keywords: hyperthermia, carbon nanotubes, cancer colon cells, radio frequency field

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Authors: A. Benatiallah, A. Hadjadj, D. Benatiallah, F. Abaidi, A. Harrouz

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Photovoltaic system is established as a reliable and economical source of electricity in rural and Sahara areas, especially in developing countries where the population is dispersed, has low consumption of energy and the grid power is not extended to these areas due to viability and financial problems. The production of energy by the photovoltaic system is very fluctuates and depend of meteorological conditions. Wind is a very important and often neglected parameter in the behavior of the solar module. The electric performances of a solar module to the silicon are very appreciable to the blows; in the present work we have studies the behavior of multi-crystal solar module according to the density of dust, and the principals electric feature of the solar module. An evaluation permits to affirm that a solar module under the effect of sand will collect a lower flux to the normal conditions.

Keywords: photovoltaic, multi-crystal module, experimental, effect of dust, performances

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Authors: Blaise Fyama, Elie Museng, Grace Mukoma

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In this article we present a java implementation of video telephony using the SIP protocol (Session Initiation Protocol). After a functional analysis of the SIP protocol, we relied on the work of Italian researchers of University of Parma-Italy to acquire adequate libraries for the development of our own communication tool. In order to optimize the code and improve the prototype, we used, in an incremental approach, test techniques based on a static analysis based on the evaluation of the complexity of the software with the application of metrics and the number cyclomatic of Mccabe. The objective is to promote the emergence of local start-ups producing IP video in a well understood local context. We have arrived at the creation of a video telephony tool whose code is optimized.

Keywords: static analysis, coding complexity metric mccabe, Sip, uml

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Authors: Rattama Rattanawongsa

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As technologies continue to develop and evolve, online learning has become one of the most popular ways of gaining access to learning. Worldwide, many students are engaging in both online and blended courses in growing numbers through e-learning. However, online learning is a form of teaching that has many benefits for learners but still has some limitations. The high attrition rates of students tend to be due to lack of motivation to succeed. Gamification is the use of game design techniques, game thinking and game mechanics in non-game context, such as learning. The gamifying method can motivate students to learn with fun and inspire them to continue learning. This paper aims to describe how the gamification work in the context of learning. The first part of this paper present the concept of gamification. The second part is described the psychological perspectives of gamification, especially motivation and flow theory for gamifying design. The result from this study will be described into the guidelines for effective learning design using a gamification concept.

Keywords: gamification, e-learning, motivation, flow theory

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Authors: João Rato, Nuno Costa

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The human verbal communication is a two-way process which requires a mutual understanding that will result in some considerations. This kind of communication, also called dialogue, besides the supposed human agents it can also be performed between human agents and machines. The interaction between Men and Machines, by means of a natural language, has an important role concerning the improvement of the communication between each other. Aiming at knowing the performance of some speech recognition systems, this document shows the results of the accomplished tests according to the Word Error Rate evaluation method. Besides that, it is also given a set of information linked to the systems of Man-Machine communication. After this work has been made, conclusions were drawn regarding the Speech Recognition Systems, among which it can be mentioned their poor performance concerning the voice interpretation in noisy environments.

Keywords: automatic speech recognition, man-machine conversation, speech recognition, spoken dialogue systems, word error rate

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Authors: Bourfaa Fouzia, Meryem Lamri Zeggar, Adjimi Amel, Mohammed Salah Aida, Nadir Attaf

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Semiconductor photocatalysts such as ZnO has attracted much attention in recent years due to their various applications for the degradation of organic pollutants in water, air and in dye sensitized photovoltaic solar cell. In the present work, ZnO thin films were prepared by ultrasonic spray pyrolysis by using different precursors namely: Acetate, chloride and zinc nitrate in order to investigate their influence on ZnO photocatalytic activity. The films crystalline structure was studied by mean of X-ray diffraction measurements (XRD) and the films surface morphology by Scanning Electron Microscopy (SEM). The films optical properties were studied by mean of UV–visible spectroscopy. The prepared films were tested for the degradation of the red reactive dye largely used in textile industry. As a result, we found that the zinc nitrate is the best precursor to prepare ZnO thin films suitable for a good photocatalytic activity.

Keywords: precursor, thins films, spray pyrolysis, zinc oxide

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Authors: Samir Zeghlache, Abderrahmen Bouguerra, Kamel Kara, Djamel Saigaa

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The control of the trirotor helicopter includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. This paper presents a control strategy for an underactuated six degrees of freedom (6 DOF) trirotor helicopter, based on the coupling of the fuzzy logic control and sliding mode control (SMC). The main purpose of this work is to eliminate the chattering phenomenon. To achieve our purpose we have used a fuzzy logic control to generate the hitting control signal, also the non linear observer is then synthesized in order to estimate the unmeasured states. Finally simulation results are included to indicate the trirotor UAV with the proposed controller can greatly alleviate the chattering effect and remain robust to the external disturbances.

Keywords: fuzzy sliding mode control, trirotor helicopter, dynamic modelling, underactuated systems

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Authors: Khadija Bouzaachane, El Mahdi El Guarmah

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The classification of low-resolution satellite images has been a worthwhile and fertile field that attracts plenty of researchers due to its importance in monitoring geographical areas. It could be used for several purposes such as disaster management, military surveillance, agricultural monitoring. The main objective of this work is to classify efficiently and accurately low-resolution satellite images by using novel technics of deep learning and reinforcement learning. The images include roads, residential areas, industrial areas, rivers, sea lakes, and vegetation. To achieve that goal, we carried out experiments on the sentinel-2 images considering both high accuracy and efficiency classification. Our proposed model achieved a 91% accuracy on the testing dataset besides a good classification for land cover. Focus on the parameter precision; we have obtained 93% for the river, 92% for residential, 97% for residential, 96% for the forest, 87% for annual crop, 84% for herbaceous vegetation, 85% for pasture, 78% highway and 100% for Sea Lake.

Keywords: classification, deep learning, reinforcement learning, satellite imagery

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Authors: I. Slim, H. Akkari, A. Ben Abdallah, I. Bhouri, M. Hedi Bedoui

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Osteoporosis is a common disease characterized by low bone mass and deterioration of micro-architectural bone tissue, which provokes an increased risk of fracture. This work treats the texture characterization of trabecular bone radiographs. The aim was to analyze according to clinical research a group of 174 subjects: 87 osteoporotic patients (OP) with various bone fracture types and 87 control cases (CC). To characterize osteoporosis, Fractal and MultiFractal (MF) methods were applied to images for features (attributes) extraction. In order to improve the results, a new method of MF spectrum based on the q-stucture function calculation was proposed and a combination of Fractal and MF attributes was used. The Support Vector Machines (SVM) was applied as a classifier to distinguish between OP patients and CC subjects. The features fusion (fractal and MF) allowed a good discrimination between the two groups with an accuracy rate of 96.22%.

Keywords: fractal, micro-architecture analysis, multifractal, osteoporosis, SVM

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Authors: Asli Faiza, Khamouli Saida

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With the development of computer tools over the past 20 years. Molecular modeling and, more precisely, molecular docking has very quickly entered field of pharmaceutical research. EGFR enzyme involved in cancer disease.Our work consists of studying the inhibition of EGFR (1M17) with deferent inhibitors derived from quinazoline and quinoline by molecular docking. The values of ligands L148 and L177 are the best ligands for inhibit the activity of 1M17 since it forms a stable complex with this enzyme by better binding to the active site. The results obtained show that the ligands L148 and L177 give weak interactions with the active site residues EGFR (1M17), which stabilize the complexes formed of this ligands, which gives a better binding at the level of the active site, and an RMSD of L148 [1,9563 Å] and of L177 [ 1,2483 Å]. [1, 9563, 1.2483] Å

Keywords: docking, EGFR, quinazoline, quinoliène, MOE

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Authors: Joseph Govha, Sharon Mudutu

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The research details the treatment of distillery waste water by making use of activated carbon prepared from orange peels as an adsorbent. Adsorption was carried out at different conditions to determine the optimum conditions that work best for the removal of color in distillery waste water using orange peel activated carbon. Adsorption was carried out at different conditions by varying contact time, adsorbent dosage, pH, testing for color intensity and Biological Oxygen Demand. A maximum percentage color removal of 88% was obtained at pH 7 at an adsorbent dosage of 1g/20ml. Maximum adsorption capacity was obtained from the Langmuir isotherm at R2=0.98.

Keywords: distillery, waste water, orange peel, activated carbon, adsorption

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Authors: Nir Nissim, Erez Shalom, Tomer Lancewiki, Yuval Elovici, Yuval Shahar

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Background: A Medical Device (MD) is an instrument, machine, implant, or similar device that includes a component intended for the purpose of the diagnosis, cure, treatment, or prevention of disease in humans or animals. Medical devices play increasingly important roles in health services eco-systems, including: (1) Patient Diagnostics and Monitoring; Medical Treatment and Surgery; and Patient Life Support Devices and Stabilizers. MDs are part of the medical device eco-system and are connected to the network, sending vital information to the internal medical information systems of medical centers that manage this data. Wireless components (e.g. Wi-Fi) are often embedded within medical devices, enabling doctors and technicians to control and configure them remotely. All these functionalities, roles, and uses of MDs make them attractive targets of cyber-attacks launched for many malicious goals; this trend is likely to significantly increase over the next several years, with increased awareness regarding MD vulnerabilities, the enhancement of potential attackers’ skills, and expanded use of medical devices. Significance: We propose to develop and implement Cyber-Med, a unique collaborative project of Ben-Gurion University of the Negev and the Clalit Health Services Health Maintenance Organization. Cyber-Med focuses on the development of a comprehensive detection framework that relies on a critical attack repository that we aim to create. Cyber-Med will allow researchers and companies to better understand the vulnerabilities and attacks associated with medical devices as well as providing a comprehensive platform for developing detection solutions. Methodology: The Cyber-Med detection framework will consist of two independent, but complementary detection approaches: one for known attacks, and the other for unknown attacks. These modules incorporate novel ideas and algorithms inspired by our team's domains of expertise, including cyber security, biomedical informatics, and advanced machine learning, and temporal data mining techniques. The establishment and maintenance of Cyber-Med’s up-to-date attack repository will strengthen the capabilities of Cyber-Med’s detection framework. Major Findings: Based on our initial survey, we have already found more than 15 types of vulnerabilities and possible attacks aimed at MDs and their eco-system. Many of these attacks target individual patients who use devices such pacemakers and insulin pumps. In addition, such attacks are also aimed at MDs that are widely used by medical centers such as MRIs, CTs, and dialysis engines; the information systems that store patient information; protocols such as DICOM; standards such as HL7; and medical information systems such as PACS. However, current detection tools, techniques, and solutions generally fail to detect both the known and unknown attacks launched against MDs. Very little research has been conducted in order to protect these devices from cyber-attacks, since most of the development and engineering efforts are aimed at the devices’ core medical functionality, the contribution to patients’ healthcare, and the business aspects associated with the medical device.

Keywords: medical device, cyber security, attack, detection, machine learning

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Authors: Pratap Chandra Pati, Prabina Rajib, Parama Barai

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The traditional finance theory neglects the role of sentiment factor in asset pricing. However, the behavioral approach to asset-pricing based on noise trader model and limit to arbitrage includes investor sentiment as a priced risk factor in the assist pricing model. Investor sentiment affects stock more that are vulnerable to speculation, hard to value and risky to arbitrage. It includes small stocks, high volatility stocks, growth stocks, distressed stocks, young stocks and non-dividend-paying stocks. Since the introduction of Chicago Board Options Exchange (CBOE) volatility index (VIX) in 1993, it is used as a measure of future volatility in the stock market and also as a measure of investor sentiment. CBOE VIX index, in particular, is often referred to as the ‘investors’ fear gauge’ by public media and prior literature. The upward spikes in the volatility index are associated with bouts of market turmoil and uncertainty. High levels of the volatility index indicate fear, anxiety and pessimistic expectations of investors about the stock market. On the contrary, low levels of the volatility index reflect confident and optimistic attitude of investors. Based on the above discussions, we investigate whether market-wide fear levels measured volatility index is priced factor in the standard asset pricing model for the Indian stock market. First, we investigate the performance and validity of Fama and French three-factor model and Carhart four-factor model in the Indian stock market. Second, we explore whether India volatility index as a proxy for fearful market-based sentiment indicators affect the cross section of stock returns after controlling for well-established risk factors such as market excess return, size, book-to-market, and momentum. Asset pricing tests are performed using monthly data on CNX 500 index constituent stocks listed on the National stock exchange of India Limited (NSE) over the sample period that extends from January 2008 to March 2017. To examine whether India volatility index, as an indicator of fear sentiment, is a priced risk factor, changes in India VIX is included as an explanatory variable in the Fama-French three-factor model as well as Carhart four-factor model. For the empirical testing, we use three different sets of test portfolios used as the dependent variable in the in asset pricing regressions. The first portfolio set is the 4x4 sorts on the size and B/M ratio. The second portfolio set is the 4x4 sort on the size and sensitivity beta of change in IVIX. The third portfolio set is the 2x3x2 independent triple-sorting on size, B/M and sensitivity beta of change in IVIX. We find evidence that size, value and momentum factors continue to exist in Indian stock market. However, VIX index does not constitute a priced risk factor in the cross-section of returns. The inseparability of volatility and jump risk in the VIX is a possible explanation of the current findings in the study.

Keywords: India VIX, Fama-French model, Carhart four-factor model, asset pricing

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Authors: Selvakumar Ulaganathan, Ivo Couckuyt, Francesco Ferranti, Tom Dhaene, Eric Laermans

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Variable-fidelity surrogate modelling offers an efficient way to approximate function data available in multiple degrees of accuracy each with varying computational cost. In this paper, a Kriging-based variable-fidelity surrogate modelling approach is introduced to approximate such deterministic data. Initially, individual Kriging surrogate models, which are enhanced with gradient data of different degrees of accuracy, are constructed. Then these Gradient enhanced Kriging surrogate models are strategically coupled using a recursive CoKriging formulation to provide an accurate surrogate model for the highest fidelity data. While, intuitively, gradient data is useful to enhance the accuracy of surrogate models, the primary motivation behind this work is to investigate if it is also worthwhile incorporating gradient data of varying degrees of accuracy.

Keywords: Kriging, CoKriging, Surrogate modelling, Variable- fidelity modelling, Gradients

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Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: S. Benbia, W. Bouafia, D. Khellaf, A. Chennaf, M. Yahia

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Our study was designed to highlight changes in certain biochemical parameters (CH, TG, HDL, GOT, GPT, LDL, and CRP), obese women infertile fertile witnesses and research potential pathophysiological link between obesity and infertility in this population of women. This practical work was focused on a population of 24 obese women infertile, compared to controls, subjects without any pathology causing disruption of parameters to be studied to determine the contribution of obesity in the etiology of infertility. The assay results revealed a highly significant difference between the two groups in serum CH, TG, HDL, TGO and TGP (P < 0.0001) and in the rate of LDL (p = 0.0017) and CRP (p = 0.02). The hormonal balance also shows a significant difference between the two groups (P < 0.0001).The present study indicates that obesity is associated with infertility, but there is no direct pathophysiological link between obesity and infertility has not been determined. Further in-depth studies are needed to determine the exact mechanism by which overweight leads to female infertility.

Keywords: obesity, fertility, infertility, biochemical, women

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Authors: Shivani Singh, Shantanu Nakhare, Kalyani Nair, Rohan Shetty

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As social networking is growing at a rapid pace today it is vital that we work on improving its management. Research has shown that the content present in online social networks may have significant influence on impressionable minds. If such platforms are misused, it will lead to negative consequences. Detecting insults or inappropriate messages continues to be one of the most challenging aspects of Online Social Networks (OSNs) today. We address this problem through a Machine Learning Based Soft Text Classifier approach using Support Vector Machine algorithm. The proposed system acts as a screening mechanism the alerts the user about such messages. The messages are classified according to their subject matter and each comment is labeled for the presence of profanity and insults.

Keywords: machine learning, online social networks, soft text classifier, support vector machine

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Authors: Ahmed Hashim, Aseel Abdullah

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In this work, the fracture toughness of new green composite based on bio-PMMA resin reinforced with randomly short corn natural fiber of constant weight fraction by 10% wt was investigated. The corn fiber surface was modified by mercerization treatment with two different concentrations of sodium hydroxide (3, and 5% NaOH) for 1.5 and 3 hours respectively. The effect of mercerization treatment on the fracture behavior of the green composites was analyzed by FTIR spectra. NaOH concentration of 3% for 1.5 hrs. That was used for corn fiber green composite should the highest improvement in terms of plane strain fracture toughness KIC which increased by 62 % compared to untreated fiber composite material. On the other hand, increased both concentrations of alkali solution to 5% NaOH and time of soaking to 3 hrs. reduced the values of KIC lower than the value of the unfilled material.

Keywords: green composites, fracture toughness, corn natural fiber, bio-PMMA

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Authors: Julia Ive, Lucia Specia

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Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

Procedia PDF Downloads 155

Authors: S. O. Ologe, U. P. Anaidhuno, Duru C. A.

Abstract:

This research investigated the design and analysis of a rear bumper of an automobile with a hybrid polymer composite of OPEBF/Banana fibre. OPEBF/Banana fibre hybrid polymers composite is of low cost, lightweight, as well as possesses satisfactory mechanical properties. In this research work, hybrid composites have been developed using the hand layup technique based on the percentage combination of OPEBF/Banana fibre at 10:90, 20:80, 30:70, 40:60, 50:50. 60:40, 70:30. 20:80, 90:10, 95:5. The mechanical properties in the context of compressive strength of 65MPa, a flexural strength of 20MPa, and impact strength of 3.25Joule were observed, and the simulation analysis on the induction of 500N load at the factor of safety of 3 was observed to have displayed a good strength suitable for automobile bumper with the advantages of weight reduction.

Keywords: OPEBF, Banana, fibre, hybrid

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Authors: Aboozar Aghaei, Kamran Dehghani

Abstract:

In the present work, the dissimilar Monel400 and SS316 were joined by friction stir welding (FSW). The applied rotating speed was 400 rpm, whereas the traverse speed varied between 50 and 150 mm/min. At a constant rotating speed, the sound welds were obtained at the welding speeds of 50 and 100 mm/min. However, a groove-like defect was formed when the welding speed exceeded 100 mm/min. The mechanical properties of the joints were evaluated using tensile and fatigue tests. The fatigue strength of dissimilar FSWed specimens was higher than that of both Monel400 and SS316. To study the failure behavior of FSWed specimens, the fracture surfaces were analyzed using a scanning electron microscope (SEM). The failure analysis indicates that different mechanisms may contribute to the fracture of welds. This was attributed to the dissimilar characteristics of dissimilar materials exhibiting different failure behaviors.

Keywords: frictions stir welding, stainless steel, Monel400, mechanical properties

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Authors: Edlira Kalemi, Sule Yildirim-Yayilgan

Abstract:

Online Social Networks (OSNs) are nowadays being used widely and intensively for crime investigation and prevention activities. As they provide a lot of information they are used by the law enforcement and intelligence. An extensive review on existing solutions and models for collecting intelligence from this source of information and making use of it for solving crimes has been presented in this article. The main focus is on smart solutions and models where ontologies have been used as the main approach for representing criminal domain knowledge. A framework for a prototype ontology named SC-Ont will be described. This defines terms of the criminal domain ontology and the relations between them. The terms and the relations are extracted during both this review and the discussions carried out with domain experts. The development of SC-Ont is still ongoing work, where in this paper, we report mainly on the motivation for using smart ontology models and the possible benefits of using them for solving crimes.

Keywords: criminal digital evidence, social media, ontologies, reasoning

Procedia PDF Downloads 377

Authors: Ahmed A. Elshami, Stephanie Bonnet, Abdelhafid Khelidj

Abstract:

Corrosion inhibitors are widely used in concrete industry to reduce the corrosion rate of steel rebar which is present in contact with aggressive environments. The present work aims to using Zamzam water from well located within the Masjid al-Haram in Mecca, Saudi Arabia 20 m (66 ft) east of the Kaaba, the holiest place in Islam as corrosion inhibitor for steel in rain water and simulated acid rain. The effect of Zamzam water was investigated by electrochemical impedance spectroscopy (EIS) and Potentiodynamic polarization techniques in Department of Civil Engineering - IUT Saint-Nazaire, Nantes University, France. Zamzam water is considered to be one of the most important steel corrosion inhibitor which is frequently used in different industrial applications. Results showed that zamzam water gave a very good inhibition for steel corrosion in rain water and simulated acid rain.

Keywords: Zamzam water, corrosion inhibitor, rain water, simulated acid rain

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Authors: D. Beziakina, E. Bulgakova

Abstract:

This study continues the research on Tuvinian intonation and presents a general cross-dialect analysis of intonation of Tuvinian declarative utterances, specifically the character of the tone movement in order to test the hypothesis about the prevalence of level tone in some Tuvinian dialects. The results of the analysis of basic pitch characteristics of Tuvinian speech (in general and in comparison with two other Turkic languages - Uzbek and Azerbaijani) are also given in this paper. The goal of our work was to obtain the ranges of pitch parameter values typical for Tuvinian speech. Such language-specific values can be used in speaker identification systems in order to get more accurate results of ethnic speech analysis. We also present the results of a cross-dialect analysis of declarative intonation in the poorly studied Tuvinian language.

Keywords: speech analysis, statistical analysis, speaker recognition, identification of person

Procedia PDF Downloads 453