Search results for: space vector pulse wide modulation

Authors: Soukaina Motia, Nadia El Alami El Hassani, Alassane Diouf, Benachir Bouchikhi, Nezha El Bari

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Parabens are the antimicrobial molecules largely used in cosmetic products as a preservative agent. Among them, the methylparaben (MP) is the most frequently used ingredient in cosmetic preparations. Nevertheless, their potential dangers led to the development of sensible and reliable methods for their determination in environmental samples. Firstly, a sensitive and selective molecular imprinted polymer (MIP) based on screen-printed gold electrode (Au-SPE), assembled on a polymeric layer of carboxylated poly(vinyl-chloride) (PVC-COOH), was developed. After the template removal, the obtained material was able to rebind MP and discriminate it among other interfering species such as glucose, sucrose, and citric acid. The behavior of molecular imprinted sensor was characterized by Cyclic Voltammetry (CV), Differential Pulse Voltammetry (DPV) and Electrochemical Impedance Spectroscopy (EIS) techniques. Then, the biosensor was found to have a linear detection range from 0.1 pg.mL^-1 to 1 ng.mL^-1 and a low limit of detection of 0.12 fg.mL^-1 and 5.18 pg.mL^-1 by DPV and EIS, respectively. For applications, this biosensor was employed to determine MP content in four wastewaters in Meknes city and two cosmetic products (shower gel and shampoo). The operational reproducibility and stability of this biosensor were also studied. Secondly, another MIP biosensor based on tungsten trioxide (WO₃) functionalized by gold nanoparticles (Au-NPs) assembled on a polymeric layer of PVC-COOH was developed. The main goal was to increase the sensitivity of the biosensor. The developed MIP biosensor was successfully applied for the MP determination in wastewater samples and cosmetic products.

Keywords: cosmetic products, methylparaben, molecularly imprinted polymer, wastewater

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Authors: B. Nageshwar Rao

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Urinary tract infection (UTI) represents the most commonly acquired bacterial infection worldwide, with substantial morbidity, mortality, and economic burden. The objective of the study is to characterize the microbial profiles of uropathogenic in the obstetric population by RTPCR. Study design: An observational cross-sectional study was performed at a single tertiary health care hospital among 50 pregnant women with UTIs, including asymptomatic and symptomatic patients attending the outpatient department and inpatient department of Obstetrics and Gynaecology.Methods: Serotyping and genes detection of various uropathogens were studied using RTPCR. Pulse filed gel electrophoresis methods were used to determine the various genetic profiles. Results: The present study shows that CsgD protein, involved in biofilm formation in Escherichia coli, VIM1, IMP1 genes for Klebsiella were identified by using the RTPCR method. Our results showed that the prevalence of VIM1 and IMP1 genes and CsgD protein in E.coli showed a significant relationship between strong biofilm formation, and this may be due to the prevalence of specific genes. Finally, the genetic identification of RTPCR results for both bacteria was correlated with each other and concluded that the above uropathogens were common isolates in producing Biofilm in the pregnant woman suffering from urinary tract infection in our hospital observational study.

Keywords: biofilms, Klebsiella, E.coli, urinary tract infection

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Authors: Priya Arora, Ashutosh Mishra

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Gene identification, towards the pursuit of mutated genes, leading to Parkinson’s disease, puts forward a challenge towards proactive cure of the disorder itself. Computational analysis is an effective technique for exploring genes in the form of protein sequences, as the theoretical and manual analysis is infeasible. The limitations and effectiveness of a particular computational method are entirely dependent on the previous data that is available for disease identification. The article presents a sequence-based classification method for the identification of genes responsible for Parkinson’s disease. During the initiation phase, the physicochemical properties of amino acids transform protein sequences into a feature vector. The second phase of the method employs Jaccard distances to select negative genes from the candidate population. The third phase involves artificial neural networks for making final predictions. The proposed approach is compared with the state of art methods on the basis of F-measure. The results confirm and estimate the efficiency of the method.

Keywords: disease gene identification, Parkinson’s disease, physicochemical properties of amino acid, protein sequences

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Beata Zima, Octavio A. Márquez Reyes, Masoud Mohammadgholiha, Jochen Moll, Luca de Marchi

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This document presents the approach of using compressed sensing in signal encoding and information transferring within a guided wave sensor network, comprised of specially designed frequency steerable acoustic transducers (FSATs). Wave propagation in a damaged plate was simulated using commercial FEM-based software COMSOL. Guided waves were excited by means of FSATs, characterized by the special shape of its electrodes, and modeled using PIC255 piezoelectric material. The special shape of the FSAT, allows for focusing wave energy in a certain direction, accordingly to the frequency components of its actuation signal, which makes available a larger monitored area. The process begins when a FSAT detects and records reflection from damage in the structure, this signal is then encoded and prepared for transmission, using a combined approach, based on Compressed Sensing Matching Pursuit and Quadrature Amplitude Modulation (QAM). After codification of the signal is in binary chars the information is transmitted between the nodes in the network. The message reaches the last node, where it is finally decoded and processed, to be used for damage detection and localization purposes. The main aim of the investigation is to determine the location of detected damage using reconstructed signals. The study demonstrates that the special steerable capabilities of FSATs, not only facilitate the detection of damage but also permit transmitting the damage information to a chosen area in a specific direction of the investigated structure.

Keywords: data compression, ultrasonic communication, guided waves, FEM analysis

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Authors: Yiling Shao, Megan Dai

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This paper attempts to explore whether the cultural infrastructure supported by the Chinese government is still subject to a logic of “strict regulation”.Previous studies have pointed out that the "paternalism" tendency of China's cultural policy always leads to excessive government intervention in cultural development, while Chinese cultural practitioners can only seek cultural autonomy in the cracks of supervision. This can also explain why Chinese cultural policies sometimes have different effects than the official expectations.But this only reflects one aspect of China's cultural policy. In fact, the welfare cultural infrastructure funded by the government seems to highlight the principles of "safeguarding citizens' cultural rights" and "citizens' voluntary" rather than "indoctrination" and "enlightenment", What new features of China's cultural policy are reflected behind this policy orientation that is completely different from the logic of "regulation", which has also become an important issue in this paper. Based on the field survey of a cultural infrastructure (Gao ming District Cultural Center) in Gao ming District, Fo shan City, Guangdong Province, China, for nearly one year, the authors have obtained many text and picture materials.The paper discusses the dual role of cultural centers in China's cultural policy -both as a formal commitment by the state to protect citizens' basic cultural rights and as a social space for citizens to use preferential policies to obtain cultural capital. All in all, the author have conclued three operational logics of the cultural infrastructure currently supported by the Chinese government (at least in developed areas): first, the cultural center has become a versatile cultural space; second, grass-roots cultural cadres can be described as "policy entrepreneurs"; third, ordinary citizens will use the officially supported cultural infrastructure to increase cultural capital. This paper argues that, in comparison to the common “regulatory hand” in the field of cultural industries, in cultural infrastructure supported by state, the authorities and citizens are not in conflict. On the contrary, authorities must adopt a de-regulatory "pleasing" strategy to gain the support of citizens.

Keywords: cultural infrastructure, cultural capital, deregulation, policy entrepreneur

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Authors: K. D. Mroczek

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In the study presented institutional context is discussed in terms of companies’ entry mode choice. In contrary to many previous analyses, instead of using one or two aggregated variables, a set of eleven determinants is used to establish equity and non-equity internationalization friendly conditions. Based on secondary data, 140 countries are analysed and grouped into clusters revealing similar framework. The range of the economies explored is wide as it covers all regions distinguished by The World Bank. The results can prove a useful alternative for operationalization of institutional variables in further research concerning entry modes or strategic management in international markets.

Keywords: clustering, entry mode choice, institutional environment, transaction costs

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Authors: Bryony Pound

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This study investigated how views of green or urban space affect learning performance. It provides evidence of the value of visual access to greenspace in work and learning environments, and builds on the extensive research into the cognitive and learning-related benefits of access to green and natural spaces, particularly in learning environments. It demonstrates that benefits of visual access to natural spaces whilst learning can produce statistically significant faster responses than those facing urban views after only 5 minutes. The primary hypothesis of this research was that a greenspace view would improve short-term learning. Participants were randomly assigned to either a view of parkland or of urban buildings from the same room. They completed a psychological test of two stages. The first stage consisted of a presentation of words from eight different categories (four manmade and four natural). Following this a 2.5 minute break was given; participants were not prompted to look out of the window, but all were observed doing so. The second stage of the test involved a word recognition/false memory test of three types. Type 1 was presented words from each category; Type 2 was non-presented words from those same categories; and Type 3 was non-presented words from different categories. Participants were asked to respond with whether they thought they had seen the words before or not. Accuracy of responses and reaction times were recorded. The key finding was that reaction times for Type 2 words (highest difficulty) were significantly different between urban and green view conditions. Those with an urban view had slower reaction times for these words, so a view of greenspace resulted in better information retrieval for word and false memory recognition. Importantly, this difference was found after only 5 minutes of exposure to either view, during winter, and with a sample size of only 26. Greenspace views improve performance in a learning task. This provides a case for better visual access to greenspace in work and learning environments.

Keywords: benefits, greenspace, learning, restoration

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Authors: Simone Dell'Agnello

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There are laser retroreflectors on the Moon and no laser retroreflectors on Mars. Here we describe the design, construction, qualification and imminent deployment of next-generation, optimized laser retroreflectors on the Moon and on Mars (where they will be the first ones). These instruments are positioned by time-of-flight measurements of short laser pulses, the so-called 'laser ranging' technique. Data analysis is carried out with PEP, the Planetary Ephemeris Program of CfA (Center for Astrophysics). Since 1969 Lunar Laser Ranging (LLR) to Apollo/Lunokhod laser retro-reflector (CCR) arrays supplied accurate tests of General Relativity (GR) and new gravitational physics: possible changes of the gravitational constant Gdot/G, weak and strong equivalence principle, gravitational self-energy (Parametrized Post Newtonian parameter beta), geodetic precession, inverse-square force-law; it can also constraint gravitomagnetism. Some of these measurements also allowed for testing extensions of GR, including spacetime torsion, non-minimally coupled gravity. LLR has also provides significant information on the composition of the deep interior of the Moon. In fact, LLR first provided evidence of the existence of a fluid component of the deep lunar interior. In 1969 CCR arrays contributed a negligible fraction of the LLR error budget. Since laser station range accuracy improved by more than a factor 100, now, because of lunar librations, current array dominate the error due to their multi-CCR geometry. We developed a next-generation, single, large CCR, MoonLIGHT (Moon Laser Instrumentation for General relativity high-accuracy test) unaffected by librations that supports an improvement of the space segment of the LLR accuracy up to a factor 100. INFN also developed INRRI (INstrument for landing-Roving laser Retro-reflector Investigations), a microreflector to be laser-ranged by orbiters. Their performance is characterized at the SCF_Lab (Satellite/lunar laser ranging Characterization Facilities Lab, INFN-LNF, Frascati, Italy) for their deployment on the lunar surface or the cislunar space. They will be used to accurately position landers, rovers, hoppers, orbiters of Google Lunar X Prize and space agency missions, thanks to LLR observations from station of the International Laser Ranging Service in the USA, in France and in Italy. INRRI was launched in 2016 with the ESA mission ExoMars (Exobiology on Mars) EDM (Entry, descent and landing Demonstration Module), deployed on the Schiaparelli lander and is proposed for the ExoMars 2020 Rover. Based on an agreement between NASA and ASI (Agenzia Spaziale Italiana), another microreflector, LaRRI (Laser Retro-Reflector for InSight), was delivered to JPL (Jet Propulsion Laboratory) and integrated on NASA’s InSight Mars Lander in August 2017 (launch scheduled in May 2018). Another microreflector, LaRA (Laser Retro-reflector Array) will be delivered to JPL for deployment on the NASA Mars 2020 Rover. The first lunar landing opportunities will be from early 2018 (with TeamIndus) to late 2018 with commercial missions, followed by opportunities with space agency missions, including the proposed deployment of MoonLIGHT and INRRI on NASA’s Resource Prospectors and its evolutions. In conclusion, we will extend significantly the CCR Lunar Geophysical Network and populate the Mars Geophysical Network. These networks will enable very significantly improved tests of GR.

Keywords: general relativity, laser retroreflectors, lunar laser ranging, Mars geodesy

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Authors: Sabrina Ladjama, Aicha Rizi, Azzedine Abbaci

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In this work, we use the crossover model to formulate a comprehensive fundamental equation of state for the thermodynamic properties for several n-alkanes in the critical region that extends to the classical region. This equation of state is constructed on the basis of comparison of selected measurements of pressure-density-temperature data, isochoric and isobaric heat capacity. The model can be applied in a wide range of temperatures and densities around the critical point for n-heptane. It is found that the developed model represents most of the reliable experimental data accurately.

Keywords: crossover model, critical region, fundamental equation, n-heptane

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Authors: Lavanya Madhuri Bollipo, K. V. Kadambari

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Objective: To discuss key issues in the diagnosis of Parkinson disease (PD), To discuss features influencing PD progression, To discuss importance of brain SPECT data in PD diagnosis, and To discuss the essentiality of machine learning techniques in early diagnosis of PD. An accurate and early diagnosis of PD is nowadays a challenge as clinical symptoms in PD arise only when there is more than 60% loss of dopaminergic neurons. So far there are no laboratory tests for the diagnosis of PD, causing a high rate of misdiagnosis especially when the disease is in the early stages. Recent neuroimaging studies with brain SPECT using 123I-Ioflupane (DaTSCAN) as radiotracer shown to be widely used to assist the diagnosis of PD even in its early stages. Machine learning techniques can be used in combination with image analysis procedures to develop computer-aided diagnosis (CAD) systems for PD. This paper addressed recent studies involving diagnosis of PD in its early stages using brain SPECT data with Machine Learning Techniques.

Keywords: Parkinson disease (PD), dopamine transporter, single-photon emission computed tomography (SPECT), support vector machine (SVM)

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Authors: Ja’afar Umar, Adamu Aliyu Aliero

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Plant growth, biochemical parameters, zinc metal concentrations were determined for Capsicum frutescens L. in response to varied concentration of zinc metal. The plant exhibited a decline in the vegetative parameters measured. Free proline and glycine betaine content increases with increasing concentration of zinc metal and differ significantly (P<0.05). It can be concluded that the osmolyte (pro and GB) accumulations, and high length of stem and wide leaf expansion are possible indicator of tolerance to heavy metals (Zinc) in Capsicum frutescens.

Keywords: zinc metal, osmolyte, Capsicum frutescens, stress

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Authors: Temidayo Otunniyi

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This paper presents a low computational channelization algorithm for the multi-standards platform using poly phase implementation of a critically sampled hybrid Trigonometry generalized Discrete Fourier Transform, (HGDFT). An HGDFT channelization algorithm exploits the orthogonality of two trigonometry Fourier functions, together with the properties of Quadrature Mirror Filter Bank (QMFB) and Exponential Modulated filter Bank (EMFB), respectively. HGDFT shows improvement in its implementation in terms of high reconfigurability, lower filter length, parallelism, and medium computational activities. Type 1 and type 111 poly phase structures are derived for real-valued HGDFT modulation. The design specifications are decimated critically and over-sampled for both single and multi standards receiver platforms. Evaluating the performance of oversampled single standard receiver channels, the HGDFT algorithm achieved 40% complexity reduction, compared to 34% and 38% reduction in the Discrete Fourier Transform (DFT) and tree quadrature mirror filter (TQMF) algorithm. The parallel generalized discrete Fourier transform (PGDFT) and recombined generalized discrete Fourier transform (RGDFT) had 41% complexity reduction and HGDFT had a 46% reduction in oversampling multi-standards mode. While in the critically sampled multi-standard receiver channels, HGDFT had complexity reduction of 70% while both PGDFT and RGDFT had a 34% reduction.

Keywords: software defined radio, channelization, critical sample rate, over-sample rate

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Authors: Elisabetta Baldassini

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Responses to atrocities are always unique and context-dependent. They cannot be foretold nor easily prompted. However, the events of the twentieth century had set the scene for the international community to explore new and more robust systems in response to war atrocities, with the ultimate goal being the restoration and maintenance of peace and security. The outlawry of war and the attribution of individual liability for international crimes were two major landmarks that set the roots for the development of international criminal law. From the London Conference (1945) for the establishment of the first international military tribunal in Nuremberg to Rome at the inauguration of the first permanent international criminal court, the development of international criminal law has shaped in itself a fluctuating degree of tensions between justice and maintenance of international peace and security, the cardinal dichotomy of this article. The adoption of judicial measures to achieve peace indeed set justice as an essential feature at the heart of the new international system. Blackhole of this dichotomy is the crime of aggression. Aggression was at first the key component of a wide body of peace projects prosecuted under the charges of crimes against peace. However, the wide array of controversies around aggression mostly related to its definition, determination and the involvement of the Security Council silenced, partly, a degree of efforts and agreements. Notwithstanding the establishment of the International Criminal Court (ICC), jurisdiction over the crime of aggression was suspended until an agreement over the definition and the conditions for the Court’s exercise of jurisdiction was reached. Compromised over the crime was achieved in Kampala in 2010 and the Court’s jurisdiction over the crime of aggression was eventually activated on 17 July 2018. China has steadily supported the advancement of international criminal justice together with the establishment of a permanent international judicial body to prosecute grave crimes and has proactively participated at the various stages of the codification and development of the crime of aggression. However, China has also expressed systematic reservations and setbacks. With the use of primary and secondary sources, including semi-structured interviews, this research aims at analyzing the role that China has played throughout the substantive historical development of the crime of aggression, demonstrating a sharp inclination in the maintenance of international peace and security. Such state behavior seems to reflect national and international political mechanisms that gravitate around a distinct rationale that involves a share of culture and tradition.

Keywords: maintenance of peace and security, cultural expression of justice, crime of aggression, China

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Authors: Jihène Khalifa

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This study analyzed the relationships between Tunisia’s economic growth, food security, agricultural productivity, and climate change using the ARDL model for the period from 1990 to 2022. The ARDL model reveals a positive correlation between economic growth and lagged agricultural productivity. Additionally, the vector autoregressive (VAR) model highlights the beneficial impact of lagged agricultural productivity on economic growth and the negative effect of rainfall on economic growth. Granger causality analysis identifies unidirectional relationships from economic growth to agricultural productivity, crop production, food security, and temperature variations, as well as from temperature variations to crop production. Furthermore, a bidirectional causality is established between crop production and food security. The study underscores the impact of climate change on crop production and suggests the need for adaptive strategies to mitigate these climate effects.

Keywords: economic growth, agriculture, food security, climate change, ARDl, VAR

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Authors: Nikat Parveen, M. Ananthi

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Big data is one of the emerging technology, which collects the data from various sensors and those data will be used in many fields. Data retrieval is one of the major issue where there is a need to extract the exact data as per the need. In this paper, large amount of data set is processed by using the feature selection. Feature selection helps to choose the data which are actually needed to process and execute the task. The key value is the one which helps to point out exact data available in the storage space. Here the available data is streamed and R-Center is proposed to achieve this task.

Keywords: big data, key value, feature selection, retrieval, performance

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Authors: Yan Ping Chen, Hau Yin Chung

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The Ideal Profile Method (IPM) is a newly developed descriptive sensory analysis conducted by consumers without previous training. To perform this test, both the perceived and the ideal intensities from the judgements of consumers on products’ attributes, as well as their hedonic ratings were collected for formulating an ideal product (the most liked one). In addition, Ideal Profile Analysis (IPA) was conducted to check the consistency of the ideal data at both the panel and consumer levels. In this test, 12 commercial plain sufus bought from Hong Kong local market were tested by 113 consumers according to the IPM, and rated on 22 attributes. Principal component analysis was used to profile the perceived and the ideal spaces of tested products. The consistency of ideal data was then checked by IPA. The result showed that most consumers shared a common ideal. It was observed that the sensory product space and the ideal product space were structurally similar. Their first dimensions all opposed products with intense fermented related aroma to products with less fermented related aroma. And the predicted ideal profile (the estimated liking score around 7.0 in a 9.0-point scale) got higher hedonic score than the tested products (the average liking score around 6.0 in a 9.0-point scale). For the majority of consumers (95.2%), the stated ideal product considered as a potential ideal through checking the R2 coefficient value. Among all the tested products, sample-6 was the most popular one with consumer liking percentage around 30%. This product with less fermented and moldy flavour but easier to melt in mouth texture possessed close sensory profile according to the ideal product. This experiment validated that data from untrained consumers could be guided as useful information. Appreciated sensory characteristics could be served as reference in the optimization of the commercial plain sufu.

Keywords: ideal profile method, product development, sensory evaluation, sufu (fermented soybean curd)

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Authors: N. Gharbi

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Constraint Satisfaction problems are mathematical problems that are often used to model many real-world problems for which we look if there exists a solution satisfying all its constraints. Table constraints are important for modeling parts of many problems since they list all combinations of allowed or forbidden values. However, they admit practical limitations because they are sometimes too large to be represented in a direct way. In this paper, we present a survey of the different categories of the proposed approaches to compress table constraints in order to reduce both space and time complexities.

Keywords: constraint programming, compression, data mining, table constraints

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Authors: Vesile Evrim, Aliyu Awwal

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By the evolvement in technology, the way of expressing opinions switched the direction to the digital world. The domain of politics as one of the hottest topics of opinion mining research merged together with the behavior analysis for affiliation determination in text which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 are constituted by Linguistic Inquiry and Word Count (LIWC) features are tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that Decision Tree, Rule Induction and M5 Rule classifiers when used with SVM and IGR feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “function” as an aggregate feature of the linguistic category, is obtained as the most differentiating feature among the 68 features with 81% accuracy by itself in classifying articles either as Republican or Democrat.

Keywords: feature selection, LIWC, machine learning, politics

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Authors: Abolfazl Zaraki, Yoshikatsu Hayashi, Harry Thorpe, Vincent Strong, Gisle-Andre Larsen, William Holderbaum

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Thanks to the high maneuverability of the cable-driven hyper-redundant manipulators (HRMs), this class of robots has shown a superior capability in highly confined and unstructured space applications. Although the large number of degrees of freedom (DOF) of HRMs enhances the motion flexibility and the robot’s reachability range, it highly increases the complexity of the kinematic configuration which makes the kinematic control problem very challenging or even impossible to solve. This paper presents our current progress achieved on the development of a kinematic-based leader-follower control system which is designed to control not only the robot’s body posture but also to control the trajectory of the robot’s movement in a semi-autonomous manner (the human operator is retained in the robot’s control loop). To obtain the forward kinematic model, the coordinate frames are established by the classical Denavit–Hartenburg (D-H) convention for a hyper-redundant serial manipulator which has a controlled cables-driven mechanism. To solve the inverse kinematics of the robot, unlike the conventional methods, a leader-follower mechanism, based on the sequential inverse kinematic, is followed. Using this mechanism, the inverse kinematic problem is solved for all sequential joints starting from the head joint to the base joint of the robot. To verify the kinematic design and simulate the robot motion, the MATLAB robotic toolbox is used. The simulation result demonstrated the promising capability of the proposed leader-follower control system in controlling the robot motion and trajectory in our confined space application.

Keywords: hyper-redundant robots, kinematic analysis, semi-autonomous control, serial manipulators

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Authors: Janice Haworth, Karamoko Camara, Marie-Therèse Dramou, Kokoly Haba, Daniel Léno, Augustin Mara, Adama Noël Oulari, Silafa Tolno, Noël Zoumanigui

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The traditional rhythms of the West African country of Guinea have played a centuries-long role in defining the different people groups that make up the country. Throughout their history, before and since colonization by the French, the different ethnicities have used their traditional music as a distinct part of their historical identities. That is starting to change. Guinea is an impoverished nation created in the early twentieth-century with little regard for the history and cultures of the people who were included. The traditional rhythms of the different people groups and their heritages have remained. Fifteen individual traditional Guinean rhythms were chosen to represent popular rhythms from the four geographical regions of Guinea. Each rhythm was traced back to its native village and video recorded on-site by as many different local performing groups as could be located. The cyclical patterns rhythms were transcribed via a circular, spatial design and then copied into a box notation system where sounds happening at the same time could be studied. These rhythms were analyzed for their consistency-over-performance in a Fundamental Rhythm Pattern analysis so rhythms could be compared for how they are changing through different performances. The analysis showed that the traditional rhythm performances of the Middle and Forest Guinea regions were the most cohesive and showed the least evidence of change between performances. The role of music in each of these regions is both limited and focused. The Coastal and High Guinea regions have much in common historically through their ethnic history and modern-day trade connections, but the rhythm performances seem to be less consistent and demonstrate more changes in how they are performed today. In each of these regions the role and usage of music is much freer and wide-spread. In spite of advances being made as a country, different ethnic groups still frequently only respond and participate (dance and sing) to the music of their native ethnicity. There is some evidence that this self-imposed musical barrier is beginning to change and evolve, partially through the development of better roads, more access to electricity and technology, the nation-wide Ebola health crisis, and a growing self-identification as a unified nation.

Keywords: cultural identity, Guinea, traditional rhythms, west Africa

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Authors: Abdelsabour G. A. Khaled, Ahmed W. A. Abdalla And Sabry Y. M. Mahmoud

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Banana plants showing typical mosaic and yellow stripes on leaves as symptoms were collected from Assiut Governorate in Egypt. The causal agent was identified as Cucumber mosaic virus (CMV) on the basis of symptoms, transmission, serology, transmission electron microscopy and reverse transcription polymerase chain reaction (RT-PCR). Coat protein (CP) gene was amplified using gene specific primers for coat protein (CP), followed by cloning into desired cloning vector for sequencing. In this study the CMV was transmitted into propagation host either by aphid or mechanically. The transmission was confirmed through Direct Antigen Coating Enzyme Linked Immuno Sorbent Assay (DAC-ELISA). Analysis of the 120 deduced amino acid sequence of the coat protein gene revealed that the EG-A strain of CMV shared from 97.50 to 98.33% with those strains belonging to subgroup IA. The cluster analysis grouped the Egyptian isolate with strains Fny and Ri8 belonging sub-group IA. It appears that there occurs a high incidence of CMV infecting banana belonging to IA subgroup in most parts of Egypt.

Keywords: banana, CMV, transmission, CP gene, RT-PCR

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Authors: Dejene Gizaw Kidane

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This study examines the impact of foreign direct investment on economic growth of Ethiopia using yearly time-series data for 1974 through 2013. Economic growth is proxies by real per capita gross domestic product and foreign direct investment proxies by the inflow of foreign direct investment. Other control variables such as gross domestic saving, trade, government consumption and inflation has been incorporated. In order to fully account for feedbacks, a vector autoregressive model is utilized. The results show that there is a stable, long-run relationship between foreign direct investment and economic growth. The variance decomposition results show that the main sources of Ethiopia economic growth variations are due largely own shocks. The pairwise Granger causality results show that there is a unidirectional causality that runs from FDI to economic growth of Ethiopia. Hence, the researcher therefore recommends that, FDI facilitate economic growth, so the government has to exert much effort in order to attract more FDI into the country.

Keywords: real per capita GDP, FDI, co-integration, VECM, Granger causality

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Authors: Yonggu Jang, Jisong Ryu, Woosik Lee

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The study aims to address the limitations of existing underground facility exploration equipment in terms of exploration depth range, relative depth measurement, data processing time, and human-centered ground penetrating radar image interpretation. The study proposed the use of 3D absolute positioning technology to develop a precision underground facility exploration system. The aim of this study is to establish a precise exploration system for underground facilities based on 3D absolute positioning technology, which can accurately survey up to a depth of 5m and measure the 3D absolute location of precise underground facilities. The study developed software and hardware technologies to build the precision exploration system. The software technologies developed include absolute positioning technology, ground surface location synchronization technology of GPR exploration equipment, GPR exploration image AI interpretation technology, and integrated underground space map-based composite data processing technology. The hardware systems developed include a vehicle-type exploration system and a cart-type exploration system. The data was collected using the developed exploration system, which employs 3D absolute positioning technology. The GPR exploration images were analyzed using AI technology, and the three-dimensional location information of the explored precise underground facilities was compared to the integrated underground space map. The study successfully developed a precision underground facility exploration system based on 3D absolute positioning technology. The developed exploration system can accurately survey up to a depth of 5m and measure the 3D absolute location of precise underground facilities. The system comprises software technologies that build a 3D precise DEM, synchronize the GPR sensor's ground surface 3D location coordinates, automatically analyze and detect underground facility information in GPR exploration images and improve accuracy through comparative analysis of the three-dimensional location information, and hardware systems, including a vehicle-type exploration system and a cart-type exploration system. The study's findings and technological advancements are essential for underground safety management in Korea. The proposed precision exploration system significantly contributes to establishing precise location information of underground facility information, which is crucial for underground safety management and improves the accuracy and efficiency of exploration. The study addressed the limitations of existing equipment in exploring underground facilities, proposed 3D absolute positioning technology-based precision exploration system, developed software and hardware systems for the exploration system, and contributed to underground safety management by providing precise location information. The developed precision underground facility exploration system based on 3D absolute positioning technology has the potential to provide accurate and efficient exploration of underground facilities up to a depth of 5m. The system's technological advancements contribute to the establishment of precise location information of underground facility information, which is essential for underground safety management in Korea.

Keywords: 3D absolute positioning, AI interpretation of GPR exploration images, complex data processing, integrated underground space maps, precision exploration system for underground facilities

Procedia PDF Downloads 59

Authors: Robert Caulk

Abstract:

A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

Procedia PDF Downloads 82

Authors: Nassibeh Hosseini-Vasoukolaei, Amir Ahmad Akhavan, Mahmood Jeddi-Tehrani, Ali Khamesipour, Mohammad Reza Yaghoobi Ershadi, Kamhawi Shaden, Valenzuela Jesus, Hossein Mirhendi, Mohammad Hossein Arandian

Abstract:

Zoonotic cutaneous leishmaniasis (ZCL) is an endemic disease in many rural areas of Iran. Sand flies were collected from rural areas of Esfahan province and were identified using valid identification keys. DNA was extracted from sand flies and Nested PCRs were done using specific primers. In this study, 44 out of 152 (28.9 %) sand flies were infected with L. majoralone. Eight sand flies showed mixed infection: four sand flies (2.6 %) were infected with L. major, L. turanicaand L. gerbili, one sand fly (0.7 %) was infected with L. major and L. turanica and three sand flies (2 %) were infected with L. turanicaand L. gerbili. Our results demonstrate the natural infection of P. papatasi sand fly with three species of L. major, L. turanica and L. gerbili which are circulating among R. opimusreservoir host and P. papatasi sand fly vector in central Iran.

Keywords: Phlebotomus papatasi, Leishmania major, Leishmania turanica, Leishmania gerbili, mixed infection, Iran

Procedia PDF Downloads 468

Authors: Alka Jakhar, Harmanpreet Kaur Sandhu, Samaresh Das

Abstract:

The Terahertz (THz) technology-based devices are heightening at an alarming rate on account of the wide range of applications in imaging, security, communication, and spectroscopic field. The various available room operational THz detectors, including Golay cell, pyroelectric detector, field-effect transistors, and photoconductive antennas, have some limitations such as narrow-band response, slow response speed, transit time limits, and complex fabrication process. There is an urgent demand to explore new materials and device structures to accomplish efficient THz detection systems. Recently, TMDs including topological semimetals and topological insulators such as PtSe₂, MoTe₂, WSe₂, and PtTe₂ provide novel feasibility for photonic and optical devices. The peculiar properties of these materials, such as Dirac cone, fermions presence, nonlinear optical response, high conductivity, and ambient stability, make them worthy for the development of the THz devices. Here, the platinum telluride (PtTe₂) based devices have been demonstrated for THz detection in the frequency range of 0.1-1 THz. The PtTe₂ is synthesized by direct selenization of the sputtered platinum film on the high-resistivity silicon substrate by using the chemical vapor deposition (CVD) method. The Raman spectra, XRD, and XPS spectra confirm the formation of the thin PtTe₂ film. The PtTe₂ channel length is 5µm and it is connected with a bow-tie antenna for strong THz electric field confinement in the channel. The characterization of the devices has been carried out in a wide frequency range from 0.1-1 THz. The induced THz photocurrent is measured by using lock-in-amplifier after preamplifier. The maximum responsivity is achieved up to 1 A/W under self-biased mode. Further, this responsivity has been increased by applying biasing voltage. This photo response corresponds to low energy THz photons is mainly due to the photo galvanic effect in PtTe₂. The DC current is induced along the PtTe₂ channel, which is directly proportional to the amplitude of the incident THz electric field. Thus, these new topological semimetal materials provide new pathways for sensitive detection and sensing applications in the THz domain.

Keywords: terahertz, detector, responsivity, topological-semimetals

Procedia PDF Downloads 157

Authors: Jai Myung Yang, Na Young Kim, Sung Ho Shin

Abstract:

The adverse reactions of current live smallpox vaccines and potential use of smallpox as a bioterror weapon have heightened the development of new effective vaccine for this infectious disease. In the present study, DNA vaccine vector was produced which was optimized for expression of the vaccinia virus L1 antigen in the mouse model. A plasmid IgM-tL1R, which contains codon-optimized L1R gene, was constructed and fused with an IgM signal sequence under the regulation of a SV40 enhancer. The expression and secretion of recombinant L1 protein was confirmed in vitro 293 T cell. Mice were administered the DNA vaccine by electroporation and challenged with vaccinia virus. We observed that immunization with IgM-tL1R induced potent neutralizing antibody responses and provided complete protection against lethal vaccinia virus challenge. Isotyping studies reveal that immunoglobulin G2 (IgG2) antibody predominated after the immunization, indicative of a T helper type 1 response. Our results suggest that an optimized DNA vaccine, IgM-tL1R, can be effective in stimulating anti-vaccinia virus immune response and provide protection against lethal orthopoxvirus challenge.

Keywords: DNA vaccine, electroporation, L1R, vaccinia virus

Procedia PDF Downloads 261

Authors: Noor Abdelhamid

Abstract:

Almost half of Cairo’s growing population is housed in self-built, self-governed informal settlements serving as an alternative in the absence of government-provided public housing. These settlements emerged as the spatial expression of informal practices or activities operating outside regulated, formal frameworks. A comprehensive narrative of political events, administrative decisions, and urban policies set the stage for the growth of informal expression in Egypt. The purpose of this qualitative inquiry is to portray informal self-governance practiced by residents in the Cairo region. This research argues that informal spatial practices offer an alternative urban framework for bottom-up development in the absence of government provisions. In the context of this study, informal self-governance is defined as the residents’ autonomous control and use of public urban space in informal settlements. The case study for this research is Ard al-Liwa, a semi-formal settlement representing the majority of informal settlement typologies in Egypt, which consist of the formal occupation of land through an uncontrolled land subdivision, zoning, and construction. An inductive methodological approach is adopted to first study informal practices as singular activities and then as components of a larger environment. The collected set of empirical data consists of audiovisual material and observations obtained during regular site visits and interviews with residents native to the settlement. Methods of analysis are synthesized to identify themes in the data: the static and dynamic use of sidewalks, the urban traces of informal building allocation and construction, the de facto right to urban space, and the resultant spatial patterns. The paper concludes by positioning the research in the context of the current architectural practice, questioning the role, and responsibility, of designers in these self-governed urban regions.

Keywords: Egypt, informal settlements, self-governance, urban framework

Procedia PDF Downloads 154

Authors: F. Q. Shao, W. Q. Wang, Y. Yin, T. P. Yu, D. B. Zou, J. M. Ouyang

Abstract:

The Radiation Pressure Acceleration (RPA) mechanism is very promising in laser-driven ion acceleration because of high laser-ion energy conversion efficiency. Although some experiments have shown the characteristics of RPA, the energy of ions is quite limited. The ion energy obtained in experiments is only several MeV/u, which is much lower than theoretical prediction. One possible limiting factor is the transverse instability incited in the RPA process. The transverse instability is basically considered as the Rayleigh-Taylor (RT) instability, which is a kind of interfacial instability and occurs when a light fluid pushes against a heavy fluid. Multi-dimensional particle-in-cell (PIC) simulations show that the onset of transverse instability will destroy the acceleration process and broaden the energy spectrum of fast ions during the RPA dominant ion acceleration processes. The evidence of the RT instability driven by radiation pressure has been observed in a laser-foil interaction experiment in a typical RPA regime, and the dominant scale of RT instability is close to the laser wavelength. The development of transverse instability in the radiation-pressure-acceleration dominant laser-foil interaction is numerically examined by two-dimensional particle-in-cell simulations. When a laser interacts with a foil with modulated surface, the internal instability is quickly incited and it develops. The linear growth and saturation of the transverse instability are observed, and the growth rate is numerically diagnosed. In order to optimize interaction parameters, a method of information entropy is put forward to describe the chaotic degree of the transverse instability. With moderate modulation, the transverse instability shows a low chaotic degree and a quasi-monoenergetic proton beam is produced.

Keywords: information entropy, radiation pressure acceleration, Rayleigh-Taylor instability, transverse instability

Procedia PDF Downloads 340