Search results for: chemical classification

Authors: Meha Alouini, Wissem Mnif, Yasmine Souissi

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This work will be conducted within the framework of the environmental sustainable development. It involves waste recovering into biodiesel fuel. Low cost feedstocks such as waste of frying oil and animal fats have been utilized to replace refined vegetable oil for biodiesel production. Biodiesel which refers to fatty acid methyl esters (FAME) was carried out by both chemical and enzymatic reaction of transesterification. In order to compare the two studied reactions the obtained biodiesel was characterized by determining its esters content and its fuel properties according to the European standard EN 14214. It was noted that the chemical method gave the product with the best physical property. But the biological one was found more effective for obtaining important ester content. Thus it would be interesting to optimize the enzymatic pathway of production of biodiesel to obtain a better property of biodiesel.

Keywords: biodiesel, fatty acid methyl esters, transesterification, waste frying oil, waste beef fat

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Authors: K. A. Kemelov, Z. K. Maymekov, D. A. Sambaeva, W. Frenzel

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Benzo(a)pyrene is widespread carcinogenic and mutagenic environmental pollutant, which is formed in combustion processes of carbonaceous materials at high temperature and still health safety problem related benz(a)pyrene continues to remain actual. At the moment the mechanisms of formation of benzo(a)pyrene are not studied in detail, there is not concrete certain full scheme of synthesis of benzo(a)pyrene. Studies in this area are mainly dedicated to development of measuring tools and chemical reactions analyzes, or to obtain specific evidence of a large group of polycyclic aromatic hydrocarbons (PAHs). Consequently in this study we try to create physical and chemical model of oxidation and thermo destruction processes of benzo(a)pyrene, using critical thermodynamical parameters in order to estimate theoretical derivatives of benzo(a)pyrene and which conditions benzo(a)pyrene degraded into more harmful substances. According to this physical and chemical modeling of thermal destruction process of benzo(a)pyrene in wide ranges of change of temperature value were calculated. C20H12 - H2O-O2 system was taken for modeling of thermal destruction process of benzo(a)pyrene in order to establish distribution range of equilibrium structures and concentrations of molecules in a gas phase. Also technological ways of reduction of concentration of benzo(a)pyrene in a gas phase were supposed.

Keywords: benzo(a)pyrene, emission, PAH, thermodynamic parameters

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Authors: Ali Ulvi Uzer

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Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

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Authors: Tatiana Tunon, Gottfried Pestal

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Government agencies responsible for the management of fisheries resources publish many types of grey literature, and these materials are increasingly accessible to the public on agency websites. However, scope and quality vary considerably, and end-users need meta-data about the report series when deciding whether to use the information (e.g. apply the methods, include the results in a systematic review), or when prioritizing materials for archiving (e.g. library holdings, reference databases). A proposed taxonomy for these report series was developed based on a review of 41 report series from 6 government agencies in 4 countries (Canada, New Zealand, Scotland, and United States). Each report series was categorized according to multiple criteria describing peer-review process, content, and purpose. A robust classification tree was then fitted to these descriptions, and the resulting taxonomic groups were used to compare agency output from 4 countries using reports available in their online repositories.

Keywords: classification tree, fisheries, government, grey literature

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Authors: Ping Tan, Xiaomeng Su, Yi Shen

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The motion intention in the motor imagery braincomputer interface is identified by classifying the event-related desynchronization (ERD) and event-related synchronization ERS characteristics of sensorimotor rhythm (SMR) in EEG signals. When the subject imagines different limbs or different parts moving, the rhythm components and bandwidth will change, which varies from person to person. How to find the effective sensorimotor frequency band of subjects is directly related to the classification accuracy of brain-computer interface. To solve this problem, this paper proposes a Minimum Distance to Riemannian Mean Classification method based on Non-Uniform Filter Banks. During the training phase, the EEG signals are decomposed into multiple different bandwidt signals by using multiple band-pass filters firstly; Then the spatial covariance characteristics of each frequency band signal are computered to be as the feature vectors. these feature vectors will be classified by the MDRM (Minimum Distance to Riemannian Mean) method, and cross validation is employed to obtain the effective sensorimotor frequency bands. During the test phase, the test signals are filtered by the bandpass filter of the effective sensorimotor frequency bands, and the extracted spatial covariance feature vectors will be classified by using the MDRM. Experiments on the BCI competition IV 2a dataset show that the proposed method is superior to other classification methods.

Keywords: non-uniform filter banks, motor imagery, brain-computer interface, minimum distance to Riemannian mean

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Authors: Dercan Gencbas, Hatice Bebis, Sue Moorhead

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In the nursing process, many of the nursing classification systems were created to be used in international. From these, NANDA-I, Nursing Outcomes Classification (NOC) and Nursing Interventions Classification (NIC). In this direction, the main objective of this study is to establish a model for caregivers in hospitals and communities in Turkey and to ensure that nursing outputs are assessed by NOC-based measures. There are many scales to measure Urinary Incontinence (UI), which is very common in children, in old age, vaginal birth, NOC scales are ideal for use in the nursing process for comprehensive and holistic assessment, with surveys available. For this reason, the purpose of this study is to evaluate the validity of the NOC outputs and indicators used for UI NANDA-I. This research is a methodological study. In addition to the validity of scale indicators in the study, how much they will contribute to recovery after the nursing intervention was assessed by experts. Scope validations have been applied and calculated according to Fehring 1987 work model. According to this, nursing inclusion criteria and scores were determined. For example, if experts have at least four years of clinical experience, their score was 4 points or have at least one year of the nursing classification system, their score was 1 point. The experts were a publication experience about nursing classification, their score was 1 point, or have a doctoral degree in nursing, their score was 2 points. If the expert has a master degree, their score was 1 point. Total of 55 experts rated Fehring as a “senior degree” with a score of 90 according to the expert scoring. The nursing interventions to be applied were asked to what extent these indicators would contribute to recovery. For coverage validity tailored to Fehring's model, each NOC and NOC indicator from specialists was asked to score between 1-5. Score for the significance of indicators was from 1=no precaution to 5=very important. After the expert opinion, these weighted scores obtained for each NOC and NOC indicator were classified as 0.8 critical, 0.8 > 0.5 complements, > 0.5 are excluded. In the NANDA-I / NOC / NIC system (guideline), 5 NOCs proposed for nursing diagnoses for UI were proposed. These outputs are; Urinary Continence, Urinary Elimination, Tissue Integrity, Self CareToileting, Medication Response. After the scales are translated into Turkish, the weighted average of the scores obtained from specialists for the coverage of all 5 NOCs and the contribution of nursing initiatives exceeded 0.8. After the opinions of the experts, 79 of the 82 indicators were calculated as critical, 3 of the indicators were calculated as supplemental. Because of 0.5 > was not obtained, no substance was removed. All NOC outputs were identified as valid and usable scales in Turkey. In this study, five NOC outcomes were verified for the evaluation of the output of individuals who have received nursing knowledge of UI and variant types. Nurses in Turkey can benefit from the outputs of the NOC scale to perform the care of the elderly incontinence.

Keywords: nursing outcomes, content validity, nursing diagnosis, urinary incontinence

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Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: Igor Perechuda, Wojciech Cieśliński

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The paper identifies the features of Polish sports clubs in the particular organizational forms: profit and nonprofit. Identification and description of these features is carried out in terms of financial efficiency of the given organizational form. Under the terms of the efficiency the research allows you to specify the advantages of particular organizational sports club form and the following limitations. Paper considers features of sports clubs in range of Polish conditions as legal regulations. The sources of the functioning efficiency of sports clubs may lie in the organizational forms in which they operate. Each of the available forms can be considered either a for-profit or nonprofit enterprise. Depending on this classification there are different capabilities of increasing organizational and financial efficiency of a given sports club. Authors start with general classification and difference between for-profit and non-profit sport clubs. Next identifies specific financial and organizational conditions of both organizational form and then show examples of mixed activity forms and their efficiency effect.

Keywords: financial efficiency, for-profit, non-profit, sports club

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Authors: Sibel Kurt

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In this study, with the use of Goffee and Jones’s corporate culture classification and the scale of this classification, there aimed to analyze a company’s corporate culture which does not have a public relations or communication department. First of all, the type of corporate culture in the company had been determined. Then it questioned if there are subcultures which formed according to demographics or the department of work. In the survey questionnaire, there are 53 questions total. 6 of these questions are about demographics, and 47 of them are about corporate culture. 152 personnel of the company had answered the survey, and the data have been evaluated according to frequency, descriptive, and compare means tests. The type of corporate culture of the company was determined as the 'communal' from the typology of Goffee and Jones in the positive form. There are no subcultures in the company which bases on the demographics, but only one subculture has determined according to the department of work. As a result, the absence of public relations department, personnel’s low level of awareness about corporate culture, and the lack of information between management and employees has been revealed.

Keywords: corporate culture, subculture, public relations, organizational communication

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Authors: T. Garlan, I. Gabelotaud, S. Lucas, E. Marchès

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Production of a global sedimentological seabed map has been initiated in 1995 to provide the necessary tool for searches of aircraft and boats lost at sea, to give sedimentary information for nautical charts, and to provide input data for acoustic propagation modelling. This original approach had already been initiated one century ago when the French hydrographic service and the University of Nancy had produced maps of the distribution of marine sediments of the French coasts and then sediment maps of the continental shelves of Europe and North America. The current map of the sediment of oceans presented was initiated with a UNESCO's general map of the deep ocean floor. This map was adapted using a unique sediment classification to present all types of sediments: from beaches to the deep seabed and from glacial deposits to tropical sediments. In order to allow good visualization and to be adapted to the different applications, only the granularity of sediments is represented. The published seabed maps are studied, if they present an interest, the nature of the seabed is extracted from them, the sediment classification is transcribed and the resulted map is integrated in the world map. Data come also from interpretations of Multibeam Echo Sounder (MES) imagery of large hydrographic surveys of deep-ocean. These allow a very high-quality mapping of areas that until then were represented as homogeneous. The third and principal source of data comes from the integration of regional maps produced specifically for this project. These regional maps are carried out using all the bathymetric and sedimentary data of a region. This step makes it possible to produce a regional synthesis map, with the realization of generalizations in the case of over-precise data. 86 regional maps of the Atlantic Ocean, the Mediterranean Sea, and the Indian Ocean have been produced and integrated into the world sedimentary map. This work is permanent and permits a digital version every two years, with the integration of some new maps. This article describes the choices made in terms of sediment classification, the scale of source data and the zonation of the variability of the quality. This map is the final step in a system comprising the Shom Sedimentary Database, enriched by more than one million punctual and surface items of data, and four series of coastal seabed maps at 1:10,000, 1:50,000, 1:200,000 and 1:1,000,000. This step by step approach makes it possible to take into account the progresses in knowledge made in the field of seabed characterization during the last decades. Thus, the arrival of new classification systems for seafloor has improved the recent seabed maps, and the compilation of these new maps with those previously published allows a gradual enrichment of the world sedimentary map. But there is still a lot of work to enhance some regions, which are still based on data acquired more than half a century ago.

Keywords: marine sedimentology, seabed map, sediment classification, world ocean

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Authors: M. G. Dirodi, G. Gugliotta, C. Leonardi

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The member states shall establish zones and agglomerations throughout their territory to assess and manage air quality in order to comply with European directives. In Italy decree 155/2010, transposing Directive 2008/50/EC on ambient air quality and cleaner air for Europe, merged into a single act the previous provisions on ambient air quality assessment and management, including those resulting from the implementation of Directive 2004/107/EC relating to arsenic, cadmium, nickel, mercury, and polycyclic aromatic hydrocarbons in ambient air. Decree 155/2010 introduced stricter rules for identifying zones on the basis of the characteristics of the territory in spite of considering pollution levels, as it was in the past. The implementation of such new criteria has reduced the great variability of the previous zoning, leading to a significant reduction of the total number of zones and to a complete and uniform ambient air quality assessment and management throughout the Country. The present document is related to the new zones definition in Italy according to Decree 155/2010. In particular, the paper contains the description and the analysis of the outcome of zoning and classification.

Keywords: zones, agglomerations, air quality assessment, classification

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Authors: Zamouche Meriem, Mehcene Ismahan, Temmine Manel, Bencheikh Lehocine Mosaab, Meniai Abdeslam Hassen

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In this study, the adsorption of Malachite Green (MG) by chemical treated sewage sludge has been studied. The sewage sludge, collected from drying beds of the municipal wastewater treatment station of IBN ZIED, Constantine, Algeria, was treated by different acids such us HNO₃, H₂SO₄, H₃PO₄ for modifying its aptitude to removal the MG from aqueous solutions. The results obtained shows that the sewage sludge activated by sulfuric acid give the highest elimination amounts of MG (9.52 mg/L) compared by the other acids used. The effects of operation parameters have been investigated, the results obtained show that the adsorption capacity per unit of adsorbent mass decreases from 18.69 to 1.20 mg/g when the mass of the adsorbent increases from 0.25 to 4 g respectively, the optimum mass for which a maximum of elimination of the dye is equal to 0.5g. The increasing in the temperature of the solution results in a slight decrease in the adsorption capacity of the chemically treated sludge. The highest amount of dye adsorbed by CSSS (9.56 mg/g) was observed for the optimum temperature of 25°C. The chemical activated sewage sludge proved its effectiveness for the removal of the Red Congo (RC), but by comparison the adsorption of the two dyes studies, we noted that the sludge has more affinity to adsorb the (MG).

Keywords: adsorption, chemical activation, malachite green, sewage sludge

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Authors: Almahd Alshereef, Ahmed Alkilany, Hammad Said, Azuraliza Abu Bakar

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From time to time, electrical power grid is directed by the National Electricity Operator to conduct load shedding, which involves hours' power outages on the area of this study, Southern Electrical Grid of Libya (SEGL). Load shedding is conducted in order to alleviate pressure on the National Electricity Grid at times of peak demand. This approach has chosen a set of categories to study load-shedding problem considering the effect of the demand priorities on the operation of the power system during emergencies. Classification of category region for load shedding problem is solved by a new algorithm (the harmony algorithm) based on the "random generation list of category region", which is a possible solution with a proximity degree to the optimum. The obtained results prove additional enhancements compared to other heuristic approaches. The case studies are carried out on SEGL.

Keywords: optimization, harmony algorithm, load shedding, classification

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Authors: Rabah Haoui

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Hypersonic flows around spatial vehicles during their reentry phase in planetary atmospheres are characterized by intense aerothermodynamics phenomena. The aim of this work is to analyze high temperature flows around an axisymmetric blunt body taking into account chemical and vibrational non-equilibrium for air mixture species and the no slip condition at the wall. For this purpose, the Navier-Stokes equations system is resolved by the finite volume methodology to determine the flow parameters around the axisymmetric blunt body especially at the stagnation point and in the boundary layer along the wall of the blunt body. The code allows the capture of shock wave before a blunt body placed in hypersonic free stream. The numerical technique uses the Flux Vector Splitting method of Van Leer. CFL coefficient and mesh size level are selected to ensure the numerical convergence.

Keywords: hypersonic flow, viscous flow, chemical kinetic, dissociation, finite volumes, frozen and non-equilibrium flow

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder

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Authors: L. Belagraa, A. Belguendouz, Y. Rouabah, A. Bouzid, A. Noui, O. Kessal

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The use of cements CRS in aggressive environments showed a lot of benefits as like good mechanical responses and therefore better durability, however, their manufacturing consume a lot of clinker, which leads to the random hazardous deposits, the shortage of natural resources and the gas and the dust emissions mainly; (CO2) with its ecological negative impact on the environment. Technical, economic and environmental benefits by the use of blended cements have been reported and being considered as a research area of great interest. The purpose of this study is to evaluate the influence of the substitution of natural pozzolan on the physico-chemical properties of the new formulated binder and the mechanical behavior of mortar containing this binary cement. Hence, the pozzolan replacement is composed with different proportions (0%, 2.5%, 5%, 7.5% and 10%). The physico-chemical properties of cement resistant to sulfate (CRS) alternative composition were investigated. Further, the behavior of the mortars based on this binder is studied. These characteristics includes chemical composition, density and fineness, consistency, setting time, shrinkage, absorption and the mechanical response. The results obtained showed that the substitution of pozzolan at the optimal ratio of 5% has a positive effect on the resulting cement, greater specific surface area, reduced water demand, accelerating the process of hydration, a better mechanical responses and decreased absorption. Therefore, economic and ecological cement based on mineral addition like pozzolan could be possible as well as advantageous to the formulation of environmental mortars.

Keywords: Cement Resistant to Sulfate (CRS), environmental mortars mechanical response, physico-chemical properties, pozzolan

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Authors: Hamzeh Awad

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Disability is considered to be a worldwide complex phenomenon which rising at a phenomenal rate and caused by many different factors. Chronic diseases such as cardiovascular disease and diabetes can lead to mobility disability in particular and disability in general. The ICF is an integrative bio-psycho-social model of functioning and disability and considered by the World Health Organization (WHO) to be a reference for disability classification using its categories and core set to classify disorder’s functional limitations. Specialist programs at Sultan Bin Abdul Aziz Humanitarian City (SBAHC) are providing both inpatient and outpatient services have started to implement the ICF and use it as a problem solving tool in Rehab. Diabetes is leading contributing factor for disability and considered epidemic in several Gulf countries including the Kingdom of Saudi Arabia (KSA), where its prevalence continues to increase dramatically. Metabolic disorders, mainly diabetes are not well covered in Rehab field. The purpose of this study is present to research and clinical rehabilitation field of DREAM and ICF as a framework in clinical and research setting in Rehab service. Also, shed the light on using the ICF as problem solving tool at SBAHC. There are synergies between disability causes and wider public health priorities in relation to both chronic disease and disability prevention. Therefore, there is a need for strong advocacy and understanding of the role of ICF as a reference in Rehab settings in Middle East if we wish to seize the opportunity to reverse current trends of acquired disability in the region.

Keywords: international classification of functioning, disability and health (ICF), prototype, rehabilitation and diabetes

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Authors: R. Behrooz, D. Jahanfar, D. Reza

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Organic farming is a fundamental principle in sustainable agriculture. Preventing excessive contamination of water and soil with pesticides and chemical fertilizers is important in order to produce healthy food. For this purpose, two potato cultivars (Sante and Marfona) and seven levels of fertilizer (non-fertilizer, chemical fertilizer, granulated chicken manure, common manure, compost, vermicompost and tea compost) were evaluated by factorial experiment based on randomized complete block design (RCBD) with three replications. According to the results, the effect of different manure was significant on number of tubers per plant, tuber weight per plant and tuber yield. The highest value of these traits was obtained by using of chicken manure which was significantly superior to other treatments. However, there was no significant difference between the two varieties. According to the results, the use of chicken manure has produced the highest potato yield even in comparison with the use of chemical fertilizer.

Keywords: organic farming, organic manure, potato, tuber yield

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Authors: M. Nuhu, M. S. Amina, A. H. Aminu, A. J. Abbas, N. Salahudeen, A. Z. Yusuf

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Transesterification of jatropha methyl ester (JME) with the common polyol, trimethylolpropane (TMP) produced the TMP based ester which exhibits improved temperature properties. This paper discusses the physic-chemical properties of jatropha bio-lubricant base oil applicable for ISO VG32 and VG46 requirement. The catalyst employed for the JME was CaO synthesized in National Research Institute for Chemical Technology (NARICT) that gives 100% conversion. The molar ratio of JME to TMP was 3.5:1 and the catalyst (NaOCH3) loading were found to be 0.8% of the weight of the total reactants. The final fractionated jatropha bio-lubricant base was found to contain 11.95% monoesters, 43.89% diesters and 44.16% triesters (desired product). In addition, it was found that the bio-lubricant base oil produced is comparable to the ISO VG46 commercial standards for light and industrial gears applications and other plant based bio-lubricant.

Keywords: biodegradability, methyl ester, pour point, transesterification, viscosity index

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Authors: Tsega Asresa Mengistu, Getahun Tigistu

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Computer vision is an artificial intelligence subfield that allows computers and systems to retrieve meaning from digital images. It is applied in various fields of study self-driving cars, video surveillance, agriculture, Quality control, Health care, construction, military, and everyday life. Aromatic and medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, and other natural health products for therapeutic and Aromatic culinary purposes. Herbal industries depend on these special plants. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs, and going to export not only industrial raw materials but also valuable foreign exchange. There is a lack of technologies for the classification and identification of Aromatic and medicinal plants in Ethiopia. The manual identification system of plants is a tedious, time-consuming, labor, and lengthy process. For farmers, industry personnel, academics, and pharmacists, it is still difficult to identify parts and usage of plants before ingredient extraction. In order to solve this problem, the researcher uses a deep learning approach for the efficient identification of aromatic and medicinal plants by using a convolutional neural network. The objective of the proposed study is to identify the aromatic and medicinal plant Parts and usages using computer vision technology. Therefore, this research initiated a model for the automatic classification of aromatic and medicinal plants by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides the root, flower and fruit, latex, and barks. The study was conducted on aromatic and medicinal plants available in the Ethiopian Institute of Agricultural Research center. An experimental research design is proposed for this study. This is conducted in Convolutional neural networks and Transfer learning. The Researcher employs sigmoid Activation as the last layer and Rectifier liner unit in the hidden layers. Finally, the researcher got a classification accuracy of 66.4 in convolutional neural networks and 67.3 in mobile networks, and 64 in the Visual Geometry Group.

Keywords: aromatic and medicinal plants, computer vision, deep convolutional neural network

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Authors: Heichia Wang, Yalan Chao

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Social inequality is a persistent problem. One of the ways to solve this problem is through education. At present, vulnerable groups are often less geographically accessible to educational resources. However, compared with educational resources, communication equipment is easier for vulnerable groups. Now that information and communication technology (ICT) has entered the field of education, today we can accept the convenience that ICT provides in education, and the mobility that it brings makes learning independent of time and place. With mobile learning, teachers and students can start discussions in an online chat room without the limitations of time or place. However, because liquidity learning is quite convenient, people tend to solve problems in short online texts with lack of detailed information in a lack of convenient online environment to express ideas. Therefore, the ICT education environment may cause misunderstanding between teachers and students. Therefore, in order to better understand each other's views between teachers and students, this study aims to clarify the essays of the analysts and classify the students into several types of learning questions to clarify the views of teachers and students. In addition, this study attempts to extend the description of possible omissions in short texts by using external resources prior to classification. In short, by applying a short text classification, this study can point out each student's learning problems and inform the instructor where the main focus of the future course is, thus improving the ICT education environment. In order to achieve the goals, this research uses convolutional neural network (CNN) method to analyze short discussion content between teachers and students in an ICT education environment. Divide students into several main types of learning problem groups to facilitate answering student problems. In addition, this study will further cluster sub-categories of each major learning type to indicate specific problems for each student. Unlike most neural network programs, this study attempts to extend short texts with external resources before classifying them to improve classification performance. In short, by applying the classification of short texts, we can point out the learning problems of each student and inform the instructors where the main focus of future courses will improve the ICT education environment. The data of the empirical process will be used to pre-process the chat records between teachers and students and the course materials. An action system will be set up to compare the most similar parts of the teaching material with each student's chat history to improve future classification performance. Later, the function of short text classification uses CNN to classify rich chat records into several major learning problems based on theory-driven titles. By applying these modules, this research hopes to clarify the main learning problems of students and inform teachers that they should focus on future teaching.

Keywords: ICT education improvement, social equality, short text analysis, convolutional neural network

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Authors: Lavanya Annadi, Fahimeh Jafari

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Transportation brings immense benefits to society, but it also has its costs. Costs include such as the cost of infrastructure, personnel and equipment, but also the loss of life and property in traffic accidents on the road, delays in travel due to traffic congestion and various indirect costs in terms of air transport. More research has been done to identify the various factors that affect road accidents, such as road infrastructure, traffic, sociodemographic characteristics, land use, and the environment. The aim of this research is to predict the probabilistic crash prediction of vehicles using machine learning due to natural and structural reasons by excluding spontaneous reasons like overspeeding etc., in the United States. These factors range from weather factors, like weather conditions, precipitation, visibility, wind speed, wind direction, temperature, pressure, and humidity to human made structures like road structure factors like bump, roundabout, no exit, turning loop, give away, etc. Probabilities are dissected into ten different classes. All the predictions are based on multiclass classification techniques, which are supervised learning. This study considers all crashes that happened in all states collected by the US government. To calculate the probability, multinomial expected value was used and assigned a classification label as the crash probability. We applied three different classification models, including multiclass Logistic Regression, Random Forest and XGBoost. The numerical results show that XGBoost achieved a 75.2% accuracy rate which indicates the part that is being played by natural and structural reasons for the crash. The paper has provided in-deep insights through exploratory data analysis.

Keywords: road safety, crash prediction, exploratory analysis, machine learning

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Authors: Sasan Ghafaribijar, Javad Hakimi, Mohsen Arvin, Peyman Tahernezhad

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The Lar Igneous Suite (LIS), east of Iran, is part of post collisional alkaline magmatism related to Late Cretaceous- mid Eocene Sistan suture zone. The suite consists of a wide variety of igneous rocks, from volcanic to intrusive and hypabissal rocks such as tuffs, trachyte, monzonite, syenites and lamprophyres. Syenitic rocks which mainly occur in a giant ring dike and stocks, are shoshonitic to potassic-ultrapotassic (K₂O/Na₂O > 2 wt.%; MgO > 3 wt.%; K₂O > 3 wt.%) in composition and are also associated with Cu-Mo mineralization. In this study, chemical composition of biotites within the Lar syenites (LS) is determined by electron microprobe analysis. The results show that LS biotites are Ti-Mg-biotites (phlogopite) which contain relatively high Ti and Mg, and low Fe concentrations. The Mg/(Fe²⁺+ Mg) ratio in these biotites range between 0.56 and 0.73 that represent their transitionally chemical evolution. TiO₂ content in these biotites is high and in the range of 3.0-5.4 wt.%. These chemical characteristics indicate that the LS biotites are primary and have been crystallized directly from magma. The investigations also demonstrate that the LS biotites have crystallized from a magma of orogenic nature. Temperature and pressure are the most significant factors controlling Mg and Ti content in the LS biotites, respectively. The results show that the LS biotites crystallized at temperatures (T) between 800 to 842 °C and pressures (P) between 0.99 to 1.44 kbar. These conditions are indicative of a crystallization depth of 3.26-4.74 km.

Keywords: sistan suture zone, Lar Igneous Suite, zahedan, syenite, biotite

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Authors: Dina A. Selim, Eman Shawky, Rasha M. Abu El-Khair

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In the current study, UPLC-MS/MS combined to chemometrics were applied on seven Sri Lankan tea grades; Orange Pekoe, Flowery Pekoe, Broken Orange Pekoe Fannings, Broken Orange Pekoe black tea, green tea, silver tips and golden tips white tea grades for their comprehensive metabolic profiling. Certain metabolites, namely, Theasensinin C and E, theaflavin and theacitrin appeared to be the main chemical markers of black tea type, catechin, epicatechin, epigallocatechin, methyl epigallocatechin were the main discriminatory markers of green tea type, while theanine, oolongotheanine and quercetin glycosides were the main chemical markers of white tea type. Theogalloflavin, epigallocatechin and flavonoid glycosides were the main down-accumulated metabolites while theaflavin gallate, and N-ethyl pyrrolidinone epicatechin were the chief up- accumulated metabolites between whole and broken black tea leave grades while puerin A and C and gallic acid was the main down- accumulated metabolites and N-ethyl pyrrolidinone epicatechin gallate was the main up-accumulated one between broken and fanning black tea grades.

Keywords: tea grading, Sri Lankan tea, chemometrics, metabolomics, chemical markers

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Authors: Yasmine Abu Adla, Racha Soubra, Milana Kasab, Mohamad O. Diab, Aly Chkeir

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Over the past several years, researchers have shown a great interest in assessing the mobility of elderly people to measure their functional status. Usually, such an assessment is done by conducting tests that require the subject to walk a certain distance, turn around, and finally sit back down. Consequently, this study aims to provide an at home monitoring system to assess the patient’s status continuously. Thus, we proposed a technique to automatically detect when a subject sits down while walking at home. In this study, we utilized a Doppler radar system to capture the motion of the subjects. More than 20 features were extracted from the radar signals, out of which 11 were chosen based on their intraclass correlation coefficient (ICC > 0.75). Accordingly, the sequential floating forward selection wrapper was applied to further narrow down the final feature vector. Finally, 5 features were introduced to the linear discriminant analysis classifier, and an accuracy of 93.75% was achieved as well as a precision and recall of 95% and 90%, respectively.

Keywords: Doppler radar system, stand-to-sit phase, TUG test, machine learning, classification

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Authors: Aadityaa Singla

Abstract:

The big five personality traits are as follows: openness, conscientiousness, extraversion, agreeableness, and neuroticism. In order to get an insight into their personality, many flocks to these categories, which each have different meanings/characteristics. This information is important not only to individuals but also to career professionals and psychologists who can use this information for candidate assessment or job recruitment. The links between AI and psychology have been well studied in cognitive science, but it is still a rather novel development. It is possible for various AI classification models to accurately predict a personality question via ten input questions. This would contrast with the hundred questions that normal humans have to answer to gain a complete picture of their five personality traits. In order to approach this problem, various AI classification models were used on a dataset to predict what a user may answer. From there, the model's prediction was compared to its actual response. Normally, there are five answer choices (a 20% chance of correct guess), and the models exceed that value to different degrees, proving their significance. By utilizing an MLP classifier, decision tree, linear model, and K-nearest neighbors, they were able to obtain a test accuracy of 86.643, 54.625, 47.875, and 52.125, respectively. These approaches display that there is potential in the future for more nuanced predictions to be made regarding personality.

Keywords: machine learning, personally, big five personality traits, cognitive science

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Authors: Zeinab Farhat, Nicolas Errien, Romuald Wernert, Véronique Verriele, Frédéric Amiard, Philippe Daniel

Abstract:

Ovarian cancer, also known as the silent killer, is the fifth most common cancer among women worldwide, and its death rate is higher than that of other gynecological cancers. The low survival rate of women with high-grade serous ovarian carcinoma highlights the critical need for the development of new methods for early detection and diagnosis of the disease. The aim of this study was to evaluate if Raman spectroscopy combined with chemometric methods such as Principal Component Analysis (PCA) could differentiate between cancerous and normal tissues from different types of samples, such as paraffin embedding, chemical deparaffinized, formalin-fixed and fresh samples of the same normal and malignant ovarian tissue. The method was applied specifically to two critical spectral regions: the signature region (860-1000 〖cm〗^(-1)) and the high-frequency region (2800-3100 〖cm〗^(-1) ). The mean spectra of paraffin-embedded in normal and malignant tissues showed almost similar intensity. On the other hand, the mean spectra of normal and cancer tissues from chemical deparaffinized, formalin-fixed, and fresh samples show significant intensity differences. These spectral differences reflect variations in the molecular composition of the tissues, particularly lipids and proteins. PCA, which was applied to distinguish between cancer and normal tissues, was performed on whole spectra and on selected regions—the PCA score plot of paraffin-embedded shows considerable overlap between the two groups. However, the PCA score of chemicals deparaffinized, formalin-fixed, and fresh samples showed a good discrimination of tissue types. Our findings were validated by analyses of a set of samples whose status (normal and cancerous) was not previously known. The results of this study suggest that Raman Spectroscopy associated with PCA methods has the capacity to provide clinically significant differentiation between normal and cancerous ovarian tissues.

Keywords: Raman spectroscopy, ovarian cancer, signal processing, Principal Component Analysis, classification

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Authors: Hen Friman, Alex Schechter, Yeshayahu Nitzan, Rivka Cahan

Abstract:

The treatment of aromatic hydrocarbons in wastewater resulting from oil spills and chemical manufactories is becoming a key concern in many modern countries. Benzene, ethylbenzene, toluene and xylene (BETX) contaminate groundwater as well as soil. These compounds have an acute effect on human health and are known to be carcinogenic. Conventional removal of these toxic materials involves separation and burning of the wastes, however, the cost of chemical treatment is very high and energy consuming. Bioremediation methods for removal of toxic organic compounds constitute an attractive alternative to the conventional chemical or physical techniques. Bioremediation methods use microorganisms to reduce the concentration and toxicity of various chemical pollutants Toluene is biodegradable both aerobically and anaerobically, it can be growth inhibitory to microorganisms at elevated concentrations, even to those species that can use it as a substrate. In this research culture of Pseudomonas putida was grown in bath bio-reactor (BBR) with toluene 100 mg/l as a single carbon source under constant voltage of 125 mV, 250 mV and 500 mV. The culture grown in BBR reached to 0.8 OD660nm while the control culture that grown without external voltage reached only to 0.6 OD660nm. The residual toluene concentration after 147 h, in the BBR operated under external voltage (125 mV) was 22 % on average, while in the control BBR it was 81 % on average.

Keywords: bioremediation, aromatic hydrocarbons, BETX, toluene, pseudomonas putida

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Authors: Ali̇ Si̇nan Soğanci

Abstract:

The cement stabilization is one of the ground improvement method applied worldwide to increase the strength of clayey soils. The using of cement has got lots of advantages compared to other stabilization methods. Cement stabilization can be done quickly, the cost is low and creates a more durable structure with the soil. Cement can be used in the treatment of a wide variety of soils. The best results of the cement stabilization were seen on silts as well as coarse-grained soils. In this study, blocks of clay were taken from the Apa-Hotamış conveyance channel route which is 125km long will be built in Konya that take the water with 70m3/sec from Mavi tunnel to Hotamış storage. Firstly, the index properties of clay samples were determined according to the Unified Soil Classification System. The experimental program was carried out on compacted soil specimens with 0%, 7 %, 15% and 30 % cement additives and the results of unconfined compression strength were discussed. The results of unconfined compression tests indicated an increase in strength with increasing cement content.

Keywords: cement stabilization, unconfined compression test, clayey soils, unified soil classification system.

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Authors: Getu Dessalegn Asfaw, Yalew Dessalegn Asfaw

Abstract:

This research aims to examine the chemical composition of bamboo species in Ethiopia under the effect of age and culm height. The chemical composition of bamboo species in Ethiopia has not been investigated so far. The highest to the lowest cellulose and hemicellulose contents are Injibara (Y. alpina), Mekaneselam (Y. alpina), and Kombolcha (B. oldhamii), whereas lignin, extractives, and ash contents are Kombolcha, Mekanesealm, and Injibra, respectively. As a result of this research, the highest and lowest cellulose, hemicelluloses and lignin contents are at the age of 2 and 1 year old, respectively. Whereas extractives and ash contents are decreased at the age of the culm matured. The cellulose, hemicelluloses, lignin, and ash contents of the culm increase from the bottom to top along the height, however, extractive contents decrease from the bottom to top position. The cellulose content of Injibara, Kombolch, and Mekaneselam bamboo was recorded at 51±1.7–53±1.8%, 45±1.6%–48±1.5%, and 48±1.8–51±1.6%, and hemicelluloses content was measured at 20±1.2–23±1.1%, 17±1.0–19±0.9%, and 18±1.0–20±1.0%, lignin content was measured 19±1.0–21±1.1%, 27±1.2–29±1.1%, and 21±1.1–24±1.1%, extractive content was measured 3.9±0.2 –4.5±0.2%, 6.6±0.3–7.8±0.4%, and 4.7±0.2–5.2±0.1%, ash content was measured 1.6±0.1–2.1±0.1%, 2.8±0.1–3.5±0.2%, and 1.9±0.1–2.5±0.1% at the ages of 1–3 years old, respectively. This result demonstrated that bamboo species in Ethiopia can be a source of feedstock for lignocelluloses ethanol and bamboo composite production since they have higher cellulose content.

Keywords: age, bamboo species, culm height, chemical composition

Procedia PDF Downloads 103