Search results for: Mboussi Serge Bertrand

Authors: Jixiang Zhang

Abstract:

A dynamic of Bertrand duopoly game is analyzed, where players use different production methods and choose their prices with bounded rationality. The equilibriums of the corresponding discrete dynamical systems are investigated. The stability conditions of Nash equilibrium under a local adjustment process are studied. The stability conditions of Nash equilibrium under a local adjustment process are studied. The stability of Nash equilibrium, as some parameters of the model are varied, gives rise to complex dynamics such as cycles of higher order and chaos. On this basis, we discover that an increase of adjustment speed of bounded rational player can make Bertrand market sink into the chaotic state. Finally, the complex dynamics, bifurcations and chaos are displayed by numerical simulation.

Keywords: Bertrand duopoly model, discrete dynamical system, heterogeneous expectations, nash equilibrium

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Authors: Kuate Tueguem William Norbert, Ngoh Dooh Jules Patrice, Kone Sangou Abdou Nourou, Mboussi Serge Bertrand, Chewachang Godwill Mih, Essome Sale Charles, Djuissi Tohoto Doriane, Ambang Zachee

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The objectives of this study were to evaluate the effects of foliar application of 24-epibrassinolide (EBR) on the development of rice helminthosporiosis caused by Bipolaris oryzae and its influence on the improvement of growth parameters and induction of the synthesis of defense substances in the rice plants. The experimental asset up involved a multifactorial split-plot with two varieties (NERICA 3 and local variety KAMKOU) and five treatments (T0: control, T1: EBR, T2: BANKO PLUS (fungicide), T3: NPK (chemical fertilizer), T4: mixture: NPK + BANKO PLUS + EBR) with three repetitions. Agro-morphological and epidemiological parameters, as well as substances for plant resistance, were evaluated over two growing seasons. The application of the EBR induced significant growth of the rice plants for the 2015 and 2016 growing seasons on the two varieties tested compared to the T0 treatment. At 74 days after sowing (DAS), NERICA 3 showed plant heights of 58.9 ± 5.4; 83.1 ± 10.4; 86.01 ± 9.4; 69.4 ± 11.1 and 87.12 ± 7.4 cm at T0; T1; T2; T3, and T4, respectively. Plant height for the variety KAMKOU varied from 87,12 ± 8,1; 88.1 ± 8.1 and 92.02 ± 6.3 cm in T1, T2, and T3 to 74.1 ± 8.6 and 74.21 ± 11.4 cm in T0 and T3. In accordance with the low rate of expansion of helminthosporiosis in experimental plots, EBR (T1) significantly reduced the development of the disease with severities of 0.0; 1.29, and 2.04%, respectively at 78; 92, and 111 DAS on the variety NERICA 3 compared with1; 3.15 and 3.79% in the control T0. The reduction of disease development/severity as a result of the application of EBR is due to the induction of acquired resistance of rice varieties through increased phenol (13.73 eqAG/mg/PMF) and total protein (117.89 eqBSA/mg/PMF) in the T1 treatment against 5.37 eqAG/mg/PMF and 104.97 eqBSA/mg/PMF in T0 for the NERICA 3 variety. Similarly, on the KAMKOU variety, 148.53 eqBSA/mg/PMF were protein and 6.10 eqAG/mg/PMF of phenol in T1. In summary, the results show the significant effect of EBR on plant growth, yield, synthesis of secondary metabolites and defense proteins, and disease resistance. The EBR significantly reduced losses of rice grains by causing an average gain of about 1.55 t/ha compared to the control and 1.00 t/ha compared to the NPK-based treatment for the two varieties studied. Further, the enzymatic activities of PPOs, POXs, and PR2s were higher in leaves from treated EBR-based plants. These results show that 24-epibrassinolide can be used in the control of helminthosporiosis of rice to reduce disease and increase yields.

Keywords: Oryza sativa, 24-epibrassinolide, helminthosporiosis, secondary metabolites, PR proteins, acquired resistance

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Authors: Jixiang Zhang, Yanhua Wang

Abstract:

A dynamic of Bertrand duopoly game is analyzed, where players use different production methods and choose their prices with bounded rationality. The equilibriums of the corresponding discrete dynamical systems are investigated. The stability conditions of Nash equilibrium under a local adjustment process are studied. The stability conditions of Nash equilibrium under a local adjustment process are studied. The stability of Nash equilibrium, as some parameters of the model are varied, gives rise to complex dynamics such as cycles of higher order and chaos. On this basis, we discover that an increase of adjustment speed of bounded rational player can make Bertrand market sink into the chaotic state. Finally, the complex dynamics, bifurcations and chaos are displayed by numerical simulation.

Keywords: Bertrand duopoly model, discrete dynamical system, heterogeneous expectations, nash equilibrium

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Authors: Hashem Sazegar

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In 1937, Vinograd of Russian Mathematician proved that each odd large number can be shown by three primes. In 1973, Chen Jingrun proved that each odd number can be shown by one prime plus a number that has maximum two primes. In this article, we state one proof for Goldbach’conjecture. Introduction: Bertrand’s postulate state for every positive integer n, there is always at least one prime p, such that n < p < 2n. This was first proved by Chebyshev in 1850, which is why postulate is also called the Bertrand-Chebyshev theorem. Legendre’s conjecture states that there is a prime between n2 and (n+1)2 for every positive integer n, which is one of the four Landau’s problems. The rest of these four basic problems are; (i) Twin prime conjecture: There are infinitely many primes p such that p+2 is a prime. (ii) Goldbach’s conjecture: Every even integer n > 2 can be written asthe sum of two primes. (iii) Are there infinitely many primes p such that p−1 is a perfect square? Problems (i), (ii), and (iii) are open till date.

Keywords: Bertrand-Chebyshev theorem, Landau’s problems, twin prime, Legendre’s conjecture, Oppermann’s conjecture

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Authors: Djoudi Ali

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In this article, we shall expose some of the more interesting interactions of philosophy and cybernetics, some philosophical issues arising in cybernetic systems, and some questions in philosophy of our daily life related to the artificial intelligence. Many of these are fruitfully explored in the article..This article will shed light also on the importance of science and technology in our life and what are the main problems of misusing the latest technologies known under artificial intelligence and cybernatics acoording to Bertrand Russell’s point of view; then to analyse his project of reforms inculding science progress risks , the article show also the whole aspect of the impact of technology on peace , nature and on individual daily behavior, we shall discuss all issues and defies imposing by this new era , The article will invest in showing what Russell will suggest to eliminate or to slow down the dangers of these changes and what are the main solutions to protect the indiviual’s rights and responsiblities In this article, We followed a different methodology, like analysis method and sometimes the historical or descriptive method, without forgetting criticizing some conclusions when it is logically needed In the end, we mentioned what is supposed to be solutions suggested by Bertrand Russell that should be taken into considerations during the next decades and how to protect our ennvironement and the human being of any risk of disappearing

Keywords: artificial intelligence, technology, cybernetics, sience

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Authors: Arūnas Burinskas

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Although Europe is on the threshold of a new industrial revolution called Industry 4.0, many believe that this will increase the flexibility of production, the mass adaptation of products to consumers and the speed of their service; it will also improve product quality and dramatically increase productivity. However, as expected, all the benefits of Industry 4.0 face many of the inevitable changes and challenges they pose. One of them is the inevitable transformation of current competition and business models. This article examines the possible results of competitive conversion from the classic Bertrand and Cournot models to qualitatively new competition based on innovation. Ability to deliver a new product quickly and the possibility to produce the individual design (through flexible and quickly configurable factories) by reducing equipment failures and increasing process automation and control is highly important. This study shows that the ongoing transformation of the competition model is changing the game. This, together with the creation of complex value networks, means huge investments that make it particularly difficult for small and medium-sized enterprises. In addition, the ongoing digitalization of data raises new concerns regarding legal obligations, intellectual property, and security.

Keywords: Bertrand and Cournot Competition, competition model, industry 4.0, industrial organisation, monopolistic competition

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Authors: Hashem Sazegar

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Legendre’s conjecture states that there is a prime number between n^2 and (n + 1)^2 for every positive integer n. In this paper we prove that every composite number between n2 and (n + 1)2 can be written u^2 − v^2 or u^2 − v^2 + u − v that u > 0 and v ≥ 0. Using these result as well as induction and residues (modq) we prove Legendre’s conjecture.

Keywords: bertrand-chebyshev theorem, landau’s problems, goldbach’s conjecture, twin prime, ramanujan proof

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Authors: Serge Provost, Abdous Saboor

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A q-analogue of the generalized extreme value distribution which includes the Gumbel distribution is introduced. The additional parameter q allows for increased modeling flexibility. The resulting distribution can have a finite, semi-infinite or infinite support. It can also produce several types of hazard rate functions. The model parameters are determined by making use of the method of maximum likelihood. It will be shown that it compares favourably to three related distributions in connection with the modeling of a certain hydrological data set.

Keywords: extreme value theory, generalized extreme value distribution, goodness-of-fit statistics, Gumbel distribution

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Authors: Serge Provost, Yishan Zang

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Following a review of various approaches that are utilized for classifying the tail behavior of a distribution, an easily implementable methodology that relies on an arctangent transformation is presented. The classification criterion is actually based on the difference between two specific quantiles of the transformed distribution. The resulting categories enable one to classify distributional tails as distinctly short, short, nearly medium, medium, extended medium and somewhat long, providing that at least two moments exist. Distributions possessing a single moment are said to be long tailed while those failing to have any finite moments are classified as having an extremely long tail. Several illustrative examples will be presented.

Keywords: arctangent transformation, tail classification, heavy-tailed distributions, distributional moments

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Authors: Serge B. Provost, Yishan Zhang

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A moment-based adjustment to the saddlepoint approximation is introduced in the context of density estimation. First applied to univariate distributions, this methodology is extended to the bivariate case. It then entails estimating the density function associated with each marginal distribution by means of the saddlepoint approximation and applying a bivariate adjustment to the product of the resulting density estimates. The connection to the distribution of empirical copulas will be pointed out. As well, a novel approach is proposed for estimating the support of distribution. As these results solely rely on sample moments and empirical cumulant-generating functions, they are particularly well suited for modeling massive data sets. Several illustrative applications will be presented.

Keywords: empirical cumulant-generating function, endpoints identification, saddlepoint approximation, sample moments, density estimation

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Authors: Antonin Jolly, Bertrand Aubry, Corentin Michel, Rebecca Freva

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Wind energy depends on wind strength and varies largely in time. When it is above the demand, it generates a loss while in the opposite case; energy needs are not fully satisfied. To overcome this problem specific to irregular energies, the process of pumped-storage hydroelectricity (PSH) is studied in present paper. A combination of wind turbine and pumped storage system is more predictable and is more compliant to provide electricity supply according to daily demand. PSH system is already used in several countries to accumulate electricity by pumping water during off-peak times into a storage reservoir, and to use it during peak times to produce energy. Present work discusses a feasibility study on size and financial productivity of PSH system actuated with wind turbines specific power.

Keywords: wind turbine, hydroelectricity, energy storage, pumped-storage hydroelectricity

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Authors: Richardson Ciguene, Bertrand Marron, Nicolas Habert

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Today, more and more consumers are purchasing their products and services online. At the same time, the penetration rate of very small and medium-sized businesses on marketplaces continues to increase, which has the direct impact of intensifying competition between sellers. Thus, only the best-optimized deals are ranked well by algorithms and are visible to consumers. However, it is almost impossible to know all the Brand Content rules and criteria established by marketplaces, which is essential to optimizing their product sheets, especially since these rules change constantly. In this paper, we propose to detail this question of Brand Content optimization by taking into account the case of Amazon in order to capture the scientific dimension behind such a subject. In a second step, we will present the genesis of our research project, DEEPERFECT, which aims to set up original methods and effective tools in order to help sellers present on marketplaces in the optimization of their branded content.

Keywords: e-commerce, scoring, marketplace, Amazon, brand content, product sheets

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Authors: Athmane Bakhta, Sebastien Leclaire, David Vidal, Francois Bertrand, Mohamed Cheriet

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This work presents a new three-dimensional variational model proposed for the simulation of ink seepage into paper sheets at the fiber level. The model, inspired by the Hising model, takes into account a finite volume of ink and describes the system state through gravity, cohesion, and adhesion force interactions. At the mesoscopic scale, the paper substrate is modeled using a discretized fiber structure generated using a numerical deposition procedure. A modified Monte Carlo method is introduced for the simulation of the ink dynamics. Besides, a multiphase lattice Boltzmann method is suggested to fine-tune the mesoscopic variational model parameters, and it is shown that the ink seepage behaviors predicted by the proposed model can resemble those predicted by a method relying on first principles.

Keywords: fibrous media, lattice Boltzmann, modelling and simulation, Monte Carlo, variational model

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Authors: Serge Lacroix

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The proposed study looks at the psychoeducational assessment of bilingual students, in English and French in this case. It will be the opportunity to look at language of assessment and specifically how certain tests can be administered in one language and others in another language. It is also a look into the questioning of the validity of the test scores that are obtained as well as the quality and generalizability of the conclusions that can be drawn. Bilingualism and multiculturalism, although in constant expansion, is not considered in norms development and remains a poorly understood factor when it is at play in the context of a psychoeducational assessment. Student placement, diagnoses, accurate measures of intelligence and achievement are all impacted by the quality of the assessment procedure. The same is true for questionnaires administered to parents and self-reports completed by bilingual students who, more often than not, are assessed in a language that is not their primary one or are compared to monolinguals not dealing with the same challenges or the same skills. Results show that students, when offered to work in a bilingual fashion, chooses to do so in a significant proportion. Recommendations will be offered to support educators aiming at expanding their skills when confronted with multilingual students in an assessment context.

Keywords: psychoeducational assessment, bilingualism, multiculturalism, intelligence, achievement

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Authors: Serge Mudinga Lemika, Samuel Olukayode Akinwamide, Aribo Sunday, Babatunde Abiodun Obadele, Peter Apata Olubambi

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Use of Duplex stainless steel has become predominant in applications where excellent corrosion resistance is of utmost importance. Corrosion behavior of duplex stainless steel induced with varying stress in a chloride media were studied. Characterization of as received 2205 duplex stainless steels were carried out to reveal its structure and properties tensile sample produced from duplex stainless steel was initially subjected to tensile test to obtain the yield strength. Stresses obtained by various percentages (20, 40, 60 and 80%) of the yield strength was induced in DSS samples. Corrosion tests were carried out in magnesium chloride solution at room temperature. Morphologies of cracks observed with optical and scanning electron microscope showed that samples induced with higher stress had its austenite and ferrite grains affected by pitting.

Keywords: duplex stainless steel, hardness, nanoceramics, spark plasma sintering

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Authors: Serge Provost, Yishan Zang

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Copulas are currently utilized in finance, reliability theory, machine learning, signal processing, geodesy, hydrology and biostatistics, among several other fields of scientific investigation. It follows from Sklar's theorem that the joint distribution function of a multidimensional random vector can be expressed in terms of its associated copula and marginals. Since marginal distributions can easily be determined by making use of a variety of techniques, we address the problem of securing the distribution of the copula. This will be done by using several approaches. For example, we will obtain bivariate least-squares approximations of the empirical copulas, modify the kernel density estimation technique and propose a criterion for selecting appropriate bandwidths, differentiate linearized empirical copulas, secure Bernstein polynomial approximations of suitable degrees, and apply a corollary to Sklar's result. Illustrative examples involving actual observations will be presented. The proposed methodologies will as well be applied to a sample generated from a known copula distribution in order to validate their effectiveness.

Keywords: copulas, Bernstein polynomial approximation, least-squares polynomial approximation, kernel density estimation, density approximation

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Authors: Justin Reverdi, Sixin Zhang, Serge Gratton, Said Aoues, Thomas Pellegrini

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In Vapor Cycle Systems, the flow sensor plays a key role in different monitoring and control purposes. However, physical sensors can be expensive, inaccurate, heavy, cumbersome, or highly sensitive to vibrations, which is especially problematic when embedded into an aircraft. The conception of a virtual sensor based on other standard sensors is a good alternative. In this paper, a data-driven model using a Convolutional Neural Network is proposed to estimate the flow of the compressor. To fit the model to our dataset, we tested different loss functions. We show in our application that a Dynamic Time Warping based loss function called DILATE leads to better dynamical performance than the vanilla mean squared error (MSE) loss function. DILATE allows choosing a trade-off between static and dynamic performance.

Keywords: deep learning, dynamic time warping, vapor cycle system, virtual sensor

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Authors: Lotem Sarid, Meirav Trebicz-Geffen, Serge Ankri

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Queuosine (Q) is a naturally occurring modified nucleoside that occurs in the first position of transfer RNA anticodons such as Asp, Asn, His, and Tyr. As eukaryotes lack pathways to synthesize queuine, the nucleobase of queuosine, they must obtain it from their diet or gut microbiota. Our previous work investigated the effects of queuine on the physiology of the eukaryotic parasite Entamoeba histolytica and defined the enzyme EhTGT responsible for its incorporation into tRNA. To our best knowledge, it is unknown how E. histolytica salvages Q from gut bacteria. We used N-acryloyl-3-aminophenylboronic acid (APB) PAGE analysis to demonstrate that E. histolytica trophozoites can salvage queuine from Q or E. coli K12 but not from the modified E. coli QueC strain, which cannot produce queuine. Next, we examined the role of EhDUF2419, a protein with homology to DNA glycosylase, as a queuine salvage enzyme in E. histolytica. When EhDUF2419 expression is silenced, it inhibits Q's conversion to queuine, resulting in a decrease in Q-tRNA levels. We also observed that Q protects control trophozoites from oxidative stress (OS), but not siEhDUF2419 trophozoites. Overall, our data reveal that EhDUF2419 is central for the salvaging of queuine from bacteria and for the resistance of the parasite to OS.

Keywords: entamoeba histolytica, epitranscriptomics, gut microbiota, queuine, queuosine, response to oxidative stress, tRNA modification.

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Authors: Justin Reverdi, Sixin Zhang, Saïd Aoues, Fabrice Gamboa, Serge Gratton, Thomas Pellegrini

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In vapor cycle systems, the mass flow sensor plays a key role for different monitoring and control purposes. However, physical sensors can be inaccurate, heavy, cumbersome, expensive, or highly sensitive to vibrations, which is especially problematic when embedded into an aircraft. The conception of a virtual sensor, based on other standard sensors, is a good alternative. This paper has two main objectives. Firstly, a data-driven model using a convolutional neural network is proposed to estimate the mass flow of the compressor. We show that it significantly outperforms the standard polynomial regression model (thermodynamic maps) in terms of the standard MSE metric and engineer performance metrics. Secondly, a semi-automatic segmentation method is proposed to compute the engineer performance metrics for real datasets, as the standard MSE metric may pose risks in analyzing the dynamic behavior of vapor cycle systems.

Keywords: deep learning, convolutional neural network, vapor cycle system, virtual sensor

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Authors: Serge B. Provost

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Numerous statistical inference and modeling methodologies are based on sample moments rather than the actual observations. A result justifying the validity of this approach is introduced. More specifically, it will be established that given the first n moments of a sample of size n, one can recover the original n sample points. This implies that a sample of size n and its first associated n moments contain precisely the same amount of information. However, it is efficient to make use of a limited number of initial moments as most of the relevant distributional information is included in them. Two types of density estimation techniques that rely on such moments will be discussed. The first one expresses a density estimate as the product of a suitable base density and a polynomial adjustment whose coefficients are determined by equating the moments of the density estimate to the sample moments. The second one assumes that the derivative of the logarithm of a density function can be represented as a rational function. This gives rise to a system of linear equations involving sample moments, the density estimate is then obtained by solving a differential equation. Unlike kernel density estimation, these methodologies are ideally suited to model ‘big data’ as they only require a limited number of moments, irrespective of the sample size. What is more, they produce simple closed form expressions that are amenable to algebraic manipulations. They also turn out to be more accurate as will be shown in several illustrative examples.

Keywords: density estimation, log-density, polynomial adjustments, sample moments

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Authors: A. Bachrata, N. Marie, F. Bertrand, J. B. Droin

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The in-depth understanding of severe accident propagation in Generation IV of nuclear reactors is important so that appropriate risk management can be undertaken early in their design process. This paper is focused on model improvements in the SIMMER code in order to perform studies of severe accident mitigation of Sodium Fast Reactor. During the design process of the mitigation devices dedicated to extraction of molten fuel from the core region, the molten fuel propagation from the core up to the core catcher has to be studied. In this aim, analytical as well as the complex thermo-hydraulic simulations with SIMMER-III code are performed. The studies presented in this paper focus on physical phenomena and associated physical models that influence the corium relocation. Firstly, the molten pool heat exchange with surrounding structures is analysed since it influences directly the instant of rupture of the dedicated tubes favouring the corium relocation for mitigation purpose. After the corium penetration into mitigation tubes, the fuel-coolant interactions result in formation of debris bed. Analyses of debris bed fluidization as well as sinking into a fluid are presented in this paper.

Keywords: corium, mitigation tubes, SIMMER-III, sodium fast reactor

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Authors: Genady Grabarnik, Serge Yaskolko

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Generative Artificial Intelligence (AI) has significantly expanded its applicability with the incorporation of Large Language Models (LLMs) and become a technology with promise to automate some areas that were very difficult to automate before. The paper describes the introduction of generative Artificial Intelligence into Introductory Computer and Data Science courses and analysis of effect of such introduction. The generative Artificial Intelligence is incorporated in the educational process two-fold: For the instructors, we create templates of prompts for generation of tasks, and grading of the students work, including feedback on the submitted assignments. For the students, we introduce them to basic prompt engineering, which in turn will be used for generation of test cases based on description of the problems, generating code snippets for the single block complexity programming, and partitioning into such blocks of an average size complexity programming. The above-mentioned classes are run using Large Language Models, and feedback from instructors and students and courses’ outcomes are collected. The analysis shows statistically significant positive effect and preference of both stakeholders.

Keywords: introductory computer and data science education, generative AI, large language models, application of LLMS to computer and data science education

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Authors: Sophonie Sarielle Jean Jacques Bertrand

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Background: Acute flaccid paralysis (PFA) is the sudden weakness or paralysis of muscles seen in children under 15 years of age. According to the WHO, PFA remains a real public health problem. For Haiti, the PFA represents a national priority. This study aims to describe the epidemiological profile of cases of Acute Flaccid Paralysis (PFA) in Haiti from 2018-2020. Methods: A descriptive cross-sectional study covering the period of 2018-2021 was carried out. epidemiological surveillance data PFA exported to Integrated Monitoring Evaluation Surveillance (MESI) were used. Sociodemographic variables were studied. Prevalence and clinical mortality rate were calculated. Epi Info 7.2 and Excel 2016 were used for data analysis. Results: 76 AFP cases were recorded for the period, or 13 (17%) in 2018, 23 (30%) in 2019, 8 (11%) in 2020 32 (42%) in 2021. Children aged 5-14 years accounted for 36% of cases (n= 26). The M/F sex ratio was 0.52, with a predominance of the female sex. The clinical mortality rate was 2.6%. The prevalence was 1.77/100,000 people. Conclusion: From 2018-2021, 76 cases of PFA cases were recorded in the 10 departments of the country, of which the West department was the most affected. Maintaining high vaccination coverage and a standard acute flaccid paralysis surveillance system are essential for the eradication of this condition. Strengthen epidemiological surveillance of PFA.

Keywords: epidemiology, PFA, Haïti, MESI

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Djamel Remache, Serge Dos Santos, Michael Cliez, Michel Gratton, Patrick Chabrand, Jean-Marie Rossi, Jean-Louis Milan

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Skin tissue is an inhomogeneous and anisotropic material. Uniaxial tensile testing is one of the primary testing techniques for the mechanical characterization of skin at large scales. In order to predict the mechanical behavior of materials, the direct or inverse analytical approaches are often used. However, in case of an inhomogeneous and anisotropic material as skin tissue, analytical approaches are not able to provide solutions. The numerical simulation is thus necessary. In this work, the uniaxial tensile test and the FEM (finite element method) based inverse method were used to identify the anisotropic mechanical properties of porcine skin tissue. The uniaxial tensile experiments were performed using Instron 8800 tensile machine®. The uniaxial tensile test was simulated with FEM, and then the inverse optimization approach (or the inverse calibration) was used for the identification of mechanical properties of the samples. Experimentally results were compared to finite element solutions. The results showed that the finite element model predictions of the mechanical behavior of the tested skin samples were well correlated with experimental results.

Keywords: mechanical skin tissue behavior, uniaxial tensile test, finite element analysis, inverse optimization approach

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Authors: Rafał Kamiński, Joel Rech, Philippe Bertrand, Christophe Desrayaud

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Titanium alloys are among the most important material in the aircraft industry, due to its low density, high strength, and corrosion resistance. However, these alloys are considered as difficult to machine because they have poor thermal properties and high reactivity with cutting tools. The Selective Laser Melting (SLM) process becomes even more popular through industry since it enables the design of new complex components, that cannot be manufactured by standard processes. However, the high temperature reached during the melting phase as well as the several rapid heating and cooling phases, due to the movement of the laser, induce complex microstructures. These microstructures differ from conventional equiaxed ones obtained by casting+forging. Parts obtained by SLM have to be machined in order calibrate the dimensions and the surface roughness of functional surfaces. The ball milling technique is widely applied to finish complex shapes. However, the machinability of titanium is strongly influenced by the microstructure. So the objective of this work is to investigate the influence of the SLM process, i.e. microstructure, on the machinability of titanium, compared to conventional forming processes. The machinability is analyzed by measuring surface roughness, cutting forces, cutting tool wear for a range of cutting conditions (depth of cut ap, feed per tooth fz, spindle speed N) in accordance with industrial practices.

Keywords: ball milling, microstructure, surface roughness, titanium

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Authors: Fadimatou Ngounouno Yamgouota, Isaac Bertrand Gbambié Mbowoub, Ismaila Ngounounob

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Barium and titanium bearing biotite from alkaline trachyte of Upper Benue valley, Northern Cameroon is studied. The iron enrichment index of mica (average I.E.=0.40) is intermediate between annite and phlogopite. The biotite phenocrysts contain up to 6.2 wt. % BaO and 9.8 wt. % TiO2. The BaO content of electron-microprobe mica is positively correlated with the Al2O3, TiO2, and FeO contents, and negatively correlated with the SiO2, K2O, and MgO contents. Ba and Ti rich micas are generally found in in SiO2 deficient rocks, whereas Ba and Ti bearing mica in this study occur in silica-saturated rocks. Most of the phenocrysts analysed have deficiencies in their octahedral and interlayer sites. Deficiencies in the octahedral sites may arise from the Ti vacancy and partly the Ti tschermakite substitution. On the other hand, deficiencies in the interlayer-site are due to the replacement of K by Ba. The substitution mechanism in the Upper Benue valley mica is characterized by Ba + 2Ti + 3Al =(K + Na + Ca) + 3(Mg + Fe + Mn) + 3Si, with an excellent correlation coefficient. Biotite compositions from the Upper Benue valley area fall between the quartz-fayalite-magnetite (QFM) and nickel-nickel-oxide (NNO) oxygen fugacity buffers. All these show that Upper Benue valley mica with high Ba and Ti contents may be formed from magmas rich in these elements.

Keywords: Benue valley, trachyte, biotite, mineral chemistry, enrichment

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Authors: Serge Tshibangu, Turgay Celik, Zenzo Ncube

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Road crashes kill nearly over a million people every year, and leave millions more injured or permanently disabled. Various annual reports reveal that the percentage of fatal crashes due to fatigue/driver falling asleep comes directly after the percentage of fatal crashes due to intoxicated drivers. This percentage is higher than the combined percentage of fatal crashes due to illegal/Un-Safe U-turn and illegal/Un-Safe reversing. Although a relatively small percentage of police reports on road accidents highlights drowsiness and fatigue, the importance of these factors is greater than we might think, hidden by the undercounting of their events. Some scenarios show that these factors are significant in accidents with killed and injured people. Thus the need for an automatic drivers fatigue detection system in order to considerably reduce the number of accidents owing to fatigue.This research approaches the drivers fatigue detection problem in an innovative way by combining cues collected from both temporal analysis of drivers’ faces and environment. Monotony in driving environment is inter-related with visual symptoms of fatigue on drivers’ faces to achieve fatigue detection. Optical and infrared (IR) sensors are used to analyse the monotony in driving environment and to detect the visual symptoms of fatigue on human face. Internal cues from drivers faces and external cues from environment are combined together using machine learning algorithms to automatically detect fatigue.

Keywords: intelligent transportation systems, bayesian networks, yawning computing, machine learning algorithms

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Authors: Kréhi Serge Agbli, Samuel Portebos, Michaël Salomon

Abstract:

Here a methodology is considered aiming at evaluating the economic benefit of the provision of a primary frequency control unit using a Battery Energy Storage System (BESS). In this methodology, two control types (basic and hysteresis) are implemented and the corresponding minimum energy storage system power allowing to maintain the frequency drop inside a given threshold under a given contingency is identified and compared using DigSilent’s PowerFactory software. Following this step, the corresponding energy storage capacity (in MWh) is calculated. As PowerFactory is dedicated to dynamic simulation for transient analysis, a first order model related to the IEEE 9 bus grid used for the analysis under PowerFactory is characterized and implemented on MATLAB-Simulink. Primary frequency control is simulated using the two control types over one-month grid's frequency deviation data on this Simulink model. This simulation results in the energy throughput both basic and hysteresis BESSs. It emerges that the 15 minutes operation band of the battery capacity allocated to frequency control is sufficient under the considered disturbances. A sensitivity analysis on the width of the control deadband is then performed for the two control types. The deadband width variation leads to an identical sizing with the hysteresis control showing a better frequency control at the cost of a higher delivered throughput compared to the basic control. An economic analysis comparing the cost of the sized BESS to the potential revenues is then performed.

Keywords: battery energy storage system, electrical network frequency stability, frequency control unit, PowerFactor

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Authors: Po-Jung Huang, Chi-Ching Lee, Bertrand Chin-Ming Tan, Yuan-Ming Yeh, Julie Lichieh Chu, Tin-Wen Chen, Cheng-Yang Lee, Ruei-Chi Gan, Hsuan Liu, Petrus Tang

Abstract:

Whole-exome sequencing focuses on the protein coding regions of disease/cancer associated genes based on a priori knowledge is the most cost-effective method to study the association between genetic alterations and disease. Recent advances in high throughput sequencing technologies and proteomic techniques has provided an opportunity to integrate genomics and proteomics, allowing readily detectable mutated peptides corresponding to mutated genes. Since sequence database search is the most widely used method for protein identification using Mass spectrometry (MS)-based proteomics technology, a mutant proteome database is required to better approximate the real protein pool to improve disease-associated mutated protein identification. Large-scale whole exome/genome sequencing studies were launched by National Cancer Institute (NCI), Broad Institute, and The Cancer Genome Atlas (TCGA), which provide not only a comprehensive report on the analysis of coding variants in diverse samples cell lines but a invaluable resource for extensive research community. No existing database is available for the collection of mutant protein sequences related to the identified variants in these studies. CMPD is designed to address this issue, serving as a bridge between genomic data and proteomic studies and focusing on protein sequence-altering variations originated from both germline and cancer-associated somatic variations.

Keywords: TCGA, cancer, mutant, proteome

Procedia PDF Downloads 554