Search results for: ethanolic extract of propolis

Authors: Sweta Agrawal, Sachin Chaugule, Gargi Rane, Shashank More, Madhavi Indap

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Angiogenesis and metastasis are two of the most important hallmarks of cancer. Hence, most of the cancer therapies nowadays are multi-targeted so as to reduce resistance and have better efficacy. As synthetic molecules arise with a burden of their toxicities and side-effects, more and more research is being focussed on exploiting the vast natural resources of drugs, in the form of plants and animals. Although, the idea of using marine organisms as a source of pharmaceuticals is not new, the pace at which marine drugs are being discovered, has definitely up surged! In the present study, we have assessed the anti-angiogenic and in vitro anti-metastatic activity of aqueous fraction from the extract of marine gastropod Euchelus asper. The soft body of Euchelus Asper was extracted with methanol and named EAME. Partition chromatography of EAME gave three fractions EAME I, II and III. Biochemical analysis revealed the presence of proteins in EAME III. Preliminary analysis had revealed the anti-angiogenic activity was exhibited by EAME III out of the three fractions. Hereafter, EAME III (concentration 25µg/ml-400µg/ml) was tested on chick chorioallantoic membrane (CAM) model for the detailed analysis of its potential anti-angiogenic effect. In vitro testing of the fraction (concentration 0.25µg/ml - 1µg/ml), involved cytotoxicity by SRB assay, cell cycle analysis by flow cytometry and anti-proliferative effect by scratch wound healing assay on A549 lung carcinoma cells. Apart from this, a portion of treated CAM as well as conditioned medium from treated A549 were subjected to gelatin zymography for assessment of matrix metalloproteinases MMP-2 and MMP-9 levels. Our results revealed that EAME III exhibited significant anti-angiogenic activity on CAM which was also supported by histological observations. During histological studies of CAM, it was found that EAME III caused reduction in angiogenesis by altering the extracellular matrix of the CAM membrane. In vitro analysis disclosed that EAME III exhibited moderate cytotoxic effect on A549 cells and its effect was not dose-dependent. The results of flow cytometry confirmed that EAME III caused cell cycle arrest in A549 cell line as almost all of the treated cells were found in G1 phase. Further, the migration and proliferation of A549 was significantly reduced by EAME III as observed from the scratch wound assay. Moreover, Gelatin zymography analysis revealed that EAME III caused suppression of MMP-2 in CAM membrane and reduced MMP-9 and MMP-2 expression in A549 cells. This verified that the anti-angiogenic and anti-metastatic effects of EAME III were correlated with the suppression of MMP-2 and -9. To conclude, EAME III shows dual anti-tumour action by reducing angiogenesis and exerting anti-metastatic effect on lung cancer cells, thus it has the potential to be used as an anti-cancer agent against lung carcinoma.

Keywords: angiogenesis, anti-cancer, marine drugs, matrix metalloproteinases

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Authors: Tamou Nasser, Ahmed Essadki, Ali Boukhriss

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This paper presents the control of doubly fed induction generator (DFIG) used in the wind energy conversion systems. Maximum power point tracking (MPPT) strategy is used to extract the maximum of power during the conversion and taking care that the system does not exceed the operating limits. This is done by acting on the pitch angle to control the orientation of the turbine's blades. Having regard to its robustness and performance, active disturbance rejection control (ADRC) based on the extended state observer (ESO) is employed to achieve the control of both rotor and grid side converters. Simulations are carried out using MATLAB simulink.

Keywords: active disturbance rejection control, extended state observer, doubly fed induction generator, maximum power point tracking

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Authors: Chothmal, Basant Agarwal

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Sentiment analysis means to classify a given review document into positive or negative polar document. Sentiment analysis research has been increased tremendously in recent times due to its large number of applications in the industry and academia. Sentiment analysis models can be used to determine the opinion of the user towards any entity or product. E-commerce companies can use sentiment analysis model to improve their products on the basis of users’ opinion. In this paper, we propose a new One-class Support Vector Machine (One-class SVM) based sentiment analysis model for movie review documents. In the proposed approach, we initially extract features from one class of documents, and further test the given documents with the one-class SVM model if a given new test document lies in the model or it is an outlier. Experimental results show the effectiveness of the proposed sentiment analysis model.

Keywords: feature selection methods, machine learning, NB, one-class SVM, sentiment analysis, support vector machine

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Authors: Payal Abichandani, Rishi Prakash, Paras Nath Barwal, B. K. Murthy

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Information generated from various computerization processes is a potential rich source of knowledge for its designated community. To pass this information from generation to generation without modifying the meaning is a challenging activity. To preserve and archive the data for future generations it’s very essential to prove the authenticity of the data. It can be achieved by extracting the metadata from the data which can prove the authenticity and create trust on the archived data. Subsequent challenge is the technology obsolescence. Metadata extraction and standardization can be effectively used to resolve and tackle this problem. Metadata can be categorized at two levels i.e. Technical and Domain level broadly. Technical metadata will provide the information that can be used to understand and interpret the data record, but only this level of metadata isn’t sufficient to create trustworthiness. We have developed a tool which will extract and standardize the technical as well as domain level metadata. This paper is about the different features of the tool and how we have developed this.

Keywords: digital preservation, metadata, OAIS, PDI, XML

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Authors: Sadhana Agrawal, Tarkeshwari Verma, Shmbhavi Katyayan

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The magnificently accentuating phenomenon of luminescence has gathered a lot of attentions from last few decades. Probably defined as the one involving emission of light from certain kinds of substances on absorbing various energies in the form of external stimulus, the phenomenon claims a versatile pertinence. First observed and reported in an extract of Ligrium Nephriticum by Monards, the phenomenon involves turning of crystal clear water into colorful fluid when comes in contact with the special wood. In words of Sir G.G. Stokes, the phenomenon actually involves three different techniques – absorption, excitation and emission. With variance in external stimulus, the corresponding luminescence phenomenon is obtained. Here, this paper gives a concise discussion of thermoluminescence which is one of the types of luminescence obtained when the external stimulus is given in form of heat energy. A deep insight of thermoluminescence put forward a qualitative analysis of various parameters such as glow curves peaks, trap depth, frequency factors and order of kinetics.

Keywords: frequency factor, glow curve peaks, thermoluminescence, trap depth

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Authors: Salima Behdenna, Fatiha Barigou, Ghalem Belalem

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Due to the quick increase in the volume of Arabic opinions posted on various social media, Arabic sentiment analysis has become one of the most important areas of research. Compared to English, there is very little works on Arabic sentiment analysis, in particular aspect-based sentiment analysis (ABSA). In ABSA, aspect extraction is the most important task. In this paper, we propose a semantic aspect-based sentiment analysis approach for standard Arabic reviews to extract explicit aspect terms and identify the polarity of the extracted aspects. The proposed approach was evaluated using HAAD datasets. Experiments showed that the proposed approach achieved a good level of performance compared with baseline results. The F-measure was improved by 19% for the aspect term extraction tasks and 55% aspect term polarity task.

Keywords: sentiment analysis, opinion mining, Arabic, aspect level, opinion, polarity

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

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Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods.

Keywords: EEG, epilepsy, phase correlation, seizure

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Authors: Wided Bakari, Patrce Bellot, Mahmoud Neji

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With the development of electronic media and the heterogeneity of Arabic data on the Web, the idea of building a clean corpus for certain applications of natural language processing, including machine translation, information retrieval, question answer, become more and more pressing. In this manuscript, we seek to create and develop our own corpus of pair’s questions-texts. This constitution then will provide a better base for our experimentation step. Thus, we try to model this constitution by a method for Arabic insofar as it recovers texts from the web that could prove to be answers to our factual questions. To do this, we had to develop a java script that can extract from a given query a list of html pages. Then clean these pages to the extent of having a database of texts and a corpus of pair’s question-texts. In addition, we give preliminary results of our proposal method. Some investigations for the construction of Arabic corpus are also presented in this document.

Keywords: Arabic, web, corpus, search engine, URL, question, corpus building, script, Google, html, txt

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Authors: Adferotimi Banso

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A laboratory investigation was conducted to determine the effect of Justica flava and Aframonium melegueta on the growth of Aspergillus niger, Rhizopus stolonifer and Fusarium species in sweet orange and mango juices. Aqueous extract (3%v/v) of Justica flava and Aframonium melegueta reduced the growth of the fungi, a combination of 2% (v/v) each of Justica flava and Aframonium melegueta extracts reduced the growth better. Partial purification of aqueous extracts of Justica flava and Aframonium melegueta showed that ethyl acetate fraction of the extracts exhibited the highest level of inhibition of growth of the test fungi compared with diethyl ether and n-hexane fractions. The results suggest that extracts of Justica flava and Aframonium melegueta may be important substitutes for conventional chemical preservatives in the processing of fruit juices.

Keywords: aqueous, fraction, mango, orange, purification, sweet

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Authors: Avatharam Ganivada, Krishna Shah

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The Mask R-CNN is recently introduced by the team of Facebook AI Research (FAIR), which is mainly concerned with instance segmentation in images. Here, the Mask R-CNN is based on ResNet and feature pyramid network (FPN), where a single dropout method is employed. This paper provides a modified Mask R-CNN by adding multiple dropout methods into the Mask R-CNN. The proposed model has also utilized the concepts of Resnet and FPN to extract stage-wise network feature maps, wherein a top-down network path having lateral connections is used to obtain semantically strong features. The proposed model produces three outputs for each object in the image: class label, bounding box coordinates, and object mask. The performance of the proposed network is evaluated in the segmentation of every instance in images using COCO and cityscape datasets. The proposed model achieves better performance than the state-of-the-networks for the datasets.

Keywords: instance segmentation, object detection, convolutional neural networks, deep learning, computer vision

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Authors: Eman Alzahrani, Hala M. Abo-Dief, Ashraf T. Mohamed

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The present work is aimed at examining carbon steel oil pipelines corrosion using three natural extracts (Eruca Sativa, Rosell and Mango peels) that are used as inhibitors of different concentrations ranging from 0.05-0.1wt. %. Two sulphur compounds are used as corrosion mediums. Weight loss method was used for measuring the corrosion rate of the carbon steel specimens immersed in technical white oil at 100ºC at various time intervals in absence and presence of the two sulphur compounds. The corroded specimens are examined using the chemical wear test, scratch test and hardness test. The scratch test is carried out using scratch loads from 0.5 Kg to 2.0 Kg. The scratch width is obtained at various scratch load and test conditions. The Brinell hardness test is carried out and investigated for both corroded and inhibited specimens. The results showed that three natural extracts can be used as environmentally friendly corrosion inhibitors.

Keywords: inhibition, natural extract, oil pipelines corrosion, sulphur compounds

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Authors: J. Suneetha, Vijayalaxmi

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Sequential Pattern Mining involves applying data mining methods to large data repositories to extract usage patterns. Sequential pattern mining methodologies used to analyze the data and identify patterns. The patterns have been used to implement efficient systems can recommend on previously observed patterns, in making predictions, improve usability of systems, detecting events, and in general help in making strategic product decisions. In this paper, identified performance of approximate sequential pattern mining defines as identifying patterns approximately shared with many sequences. Approximate sequential patterns can effectively summarize and represent the databases by identifying the underlying trends in the data. Conducting an extensive and systematic performance over synthetic and real data. The results demonstrate that ApproxMAP effective and scalable in mining large sequences databases with long patterns.

Keywords: multiple data, performance analysis, sequential pattern, sequence database scalability

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Authors: Hee-Jeong Ahn, Kee-Won Kim, Seung-Hoon Kim

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The knowledge of the relationship between characters can help readers to understand the overall story or plot of the literary fiction. In this paper, we present a method for extracting the specific relationship between characters from a Korean literary fiction. Generally, methods for extracting relationships between characters in text are statistical or computational methods based on the sentence distance between characters without considering Korean linguistic features. Furthermore, it is difficult to extract the relationship with direction from text, such as one-sided love, because they consider only the weight of relationship, without considering the direction of the relationship. Therefore, in order to identify specific relationships between characters, we propose a statistical method considering linguistic features, such as syntactic patterns and speech verbs in Korean. The result of our method is represented by a weighted directed graph of the relationship between the characters. Furthermore, we expect that proposed method could be applied to the relationship analysis between characters of other content like movie or TV drama.

Keywords: data mining, Korean linguistic feature, literary fiction, relationship extraction

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Authors: Mihir Mouchum Hazarika, Maddali Ramgopal, Souvik Bhattacharyya

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The thermodynamic analysis shows that the performance of the R744 based transcritical refrigeration cycle drops drastically for higher ambient temperatures. This is due to the peculiar s-shape of the isotherm in the supercritical region. However, subcooling of the refrigerant at the gas cooler exit enhances the performance of the R744 based system. The present study is carried out to analyze the R744 based transcritical system with dedicated mechanical subcooling cycle. Based on this proposed cycle, the thermodynamic analysis is performed, and optimum operating parameters are determined. The amount of subcooling and the pressure ratio in the subcooling cycle are the parameters which are needed to be optimized to extract the maximum COP from this proposed cycle. It is expected that this study will be helpful in implementing the dedicated subcooling cycle with R744 based transcritical system to improve the performance.

Keywords: optimization, R744, subcooling, transcritical

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Authors: Sam Khozama, Ali M. Mayya

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Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

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Authors: Rauf R. Hussein, Devon M. Ramey

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Integrating Ground Penetrating Radar (GPR) with Geographic Information Systems (GIS) can provide valuable insights for various applications, such as archaeology, transportation, and utility locating. Although there has been progress toward automating the integration of GPR data with GIS, fully automatic integration has not been achieved yet. Additionally, manually integrating GPR data with GIS can be a time-consuming and error-prone process. In this study, actual, real-world GPR applications are presented, and a software named GPR-GIS 10 is created to interactively extract subsurface targets from GPR radargrams and automatically integrate them into GIS. With this software, it is possible to quickly and reliably integrate the two techniques to create informative subsurface maps. The results indicated that automatic integration of GPR with GIS can be an efficient tool to map and view any subsurface targets in their appropriate location in a 3D space with the needed precision. The findings of this study could help GPR-GIS integrators save time and reduce errors in many GPR-GIS applications.

Keywords: GPR, GIS, GPR-GIS 10, drone technology, automation

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Authors: Zhibo Xiao, Tharini Nayanika de Silva, Kezhi Mao

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In this paper, we present an evolving knowledge extraction system named AKEOS (Automatic Knowledge Extraction from Online Sources). AKEOS consists of two modules, including a one-time learning module and an evolving learning module. The one-time learning module takes in user input query, and automatically harvests knowledge from online unstructured resources in an unsupervised way. The output of the one-time learning is a structured vector representing the harvested knowledge. The evolving learning module automatically schedules and performs repeated one-time learning to extract the newest information and track the development of an event. In addition, the evolving learning module summarizes the knowledge learned at different time points to produce a final knowledge vector about the event. With the evolving learning, we are able to visualize the key information of the event, discover the trends, and track the development of an event.

Keywords: evolving learning, knowledge extraction, knowledge graph, text mining

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Authors: Anupalli Roja Rani, Pavithra Dasari

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Background: Among several, breast cancer has emerged as the most common female cancer in developing countries. It is the most common cause of cancer-related deaths worldwide among women. It is a molecularly and clinically heterogeneous disease. Moreover, it is a hormone–dependent tumor in which estrogens can regulate the growth of breast cells by binding with estrogen receptors (ERs). Moreover, the use of natural products in cancer therapeutics is due to their properties of biocompatibility and less toxicity. Plants are the vast reservoirs for various bioactive compounds. Coleus barbatus (Lamiaceae) contains anticancer properties against several cancer cell lines. Method: In the present study, an attempt is being made to enrich the knowledge of the anticancer activity of pure compounds extracted from Coleus barbatus (Andrew). On human breast cancer cell lines MCF-7. Here in, we are assessing the antiproliferative activity of Coleus barbatus (Andrew) plant extracts against MCF 7 and also evaluating their toxicity in normal human mammary cell lines such as Human Mammary Epithelial Cells (HMEC). The active fraction of plant extract was further purified with the help of Flash chromatography, Medium Pressure Liquid Chromatography (MPLC) and preparative High-Performance Liquid Chromatography (HPLC). The structure of pure compounds will be elucidated by using modern spectroscopic methods like Nuclear magnetic resonance (NMR), Electrospray Ionisation Mass Spectrometry (ESI-MS) methods. Later, the growth inhibition morphological assessment of cancer cells and cell cycle analysis of purified compounds were assessed using FACS. The growth and progression of signaling molecules HER2, GRP78 was studied by secretion assay using ELISA and expression analysis by flow cytometry. Result: Cytotoxic effect against MCF-7 with IC50 values were derived from dose response curves, using six concentrations of twofold serially diluted samples, by SOFTMax Pro software (Molecular device) and respectively Ellipticine and 0.5% DMSO were used as a positive and negative control. Conclusion: The present study shows the significance of various bioactive compounds extracted from Coleus barbatus (Andrew) root material. It acts as an anti-proliferative and shows cytotoxic effects on human breast cancer cell lines MCF7. The plant extracts play an important role pharmacologically. The whole plant has been used in traditional medicine for decades and the studies done have authenticated the practice. Earlier, as described, the plant has been used in the ayurveda and homeopathy medicine. However, more clinical and pathological studies must be conducted to investigate the unexploited potential of the plant. These studies will be very useful for drug designing in the future.

Keywords: coleus barbatus, HPLC, MPLC, NMR, MCF7, flash chromatograph, ESI-MS, FACS, ELISA.

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Authors: K. Harrat, M. Hidjeb, M. T’kint

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The aim of the present research work is to characterize a cement palm date fiber composite in order to be used in isolation and in the manufacture of new structural materials. This technique may possibly participate seriously in the preservation of the environment and develop a growing need for plant products. On one hand, It has been shown that the presence of natural fiber in the composite materials manufacture, based on hydraulic binder, has improved the mechanical behaviour of the material. On the Other hand, It has been proven that the durability of composite materials reinforced with untreated fibers was largely affected by the presence of organic matter. In order to extract the organic material, the fibers were treated with boiling water and then coated with different types of products. A considerable improvement in the sensitivity to water of the fibers, as well as in the mechanical strength and in the ductility of the composite material was observed. The fiber being sensitive to water, the study put the emphasis on its dimensional stability.

Keywords: cement composite, durability, heat treatment, mechanical behaviour, vegetal fiber

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Authors: Reenu Yadav, S. K. Yadav

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Herbal therapy has been used for daily oral health care to prevent, treat or cure oral conditions from halitosis to periodontal diseases. The importance of mouth and teeth cleanliness has been recognized from the earliest days of civilization to the 21st century. In the present study, leaves and seeds of Cordia obliqua and barks and twigs of Beautea monosperma, which is used traditionally for oral diseases was evaluated for its antimicrobial activity. The antimicrobial activity tests indicated that the methanolic extract exhibited stronger activities against the commonly encountered oral bacterial and fungal pathogens. The mouthwash formulation prepared and it is compared with marketed formulation HiOra. The results indicated that the herbal mouthwash could inhibit the growth of oral pathogens and may prevent plaque and other periodontal diseases caused by dental pathogens.

Keywords: herbal mouthwash, bio medicine, life sciences, herbal extracts

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Authors: Vahid Bairami Rad

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Extracting knowledge from spatial data like GIS data is important to reduce the data and extract information. Therefore, the development of new techniques and tools that support the human in transforming data into useful knowledge has been the focus of the relatively new and interdisciplinary research area ‘knowledge discovery in databases’. Thus, we introduce a set of database primitives or basic operations for spatial data mining which are sufficient to express most of the spatial data mining algorithms from the literature. This approach has several advantages. Similar to the relational standard language SQL, the use of standard primitives will speed-up the development of new data mining algorithms and will also make them more portable. We introduced a database-oriented framework for spatial data mining which is based on the concepts of neighborhood graphs and paths. A small set of basic operations on these graphs and paths were defined as database primitives for spatial data mining. Furthermore, techniques to efficiently support the database primitives by a commercial DBMS were presented.

Keywords: spatial data base, knowledge discovery database, data mining, spatial relationship, predictive data mining

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Authors: Fatimazahrae Elbakri, Azeddine Ibrahimi, Meryem Lemrani, Dris Belghyti

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Leishmaniasis represents a major public health problem because of the number of cases recorded each year and the wide distribution of the disease. It is a parasitic disease of flagellated protozoa transmitted by the bite of certain species of sandfly, causing a spectrum of clinical pathology in humans ranging from disfiguring skin lesions to fatal visceral leishmaniasis. Cutaneous leishmaniasis due to Leishmania major is a polymorphic disease; in fact, the infection can be asymptomatic, localized, or disseminated. The objective of this work is to determine the genomic diversity that contributes to clinical variability by trying to identify the variation in chromosome number and to extract SNPs and SNPs and InDels; it is based on four sequences (WGS) of Leishmania major available on NCBI in Fastq form, from three countries: Tunisia, Algeria, and Israel, the analysis is set up from a pipeline to facilitate the discovery of genetic diversity, in particular SNP and chromosomal somy.

Keywords: Leshmania major, cutaneous Leishmania, NGS, genomic, somy, variant calling

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Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

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The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

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Authors: Indira K., Valsamma Joseph, I. S. Bright

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Introduction :Biofuels could replace fossil fuels and reduce our carbon footprint on the planet by technological advancements needed for sustainable and economic fuel production. Micro algae have proven to be a promising source to meet the current energy demand because of high lipid content and production of high biomass rapidly. Marine diatoms, which are key contributors in the biofuel sector and also play a significant role in primary productivity and ecology with high biodiversity and genetic and chemical diversity, are less well understood than other microalgae for producing hydrocarbons. Method :The marine diatom samples selected for hydrocarbon analysis were a total of eleven, out of which 9 samples were from the culture collection of NCAAH, and the remaining two of them were isolated by serial dilution method to get a pure culture from a mixed culture of microalgae obtained from the various cruise stations (350&357) FORV Sagar Sampada along the west coast of India. These diatoms were mass cultured in F/2 media, and the biomass harvested. The crude extract was obtained from the biomass by homogenising with n-hexane, and the hydrocarbons was further obtained by passing the crude extract through 500mg Bonna Agela SPE column and the quantitative analysis was done by GCHRMS analysis using HP-5 column and Helium gas was used as a carrier gas(1ml/min). The injector port temperature was 2400C, the detector temperature was 2500C, and the oven was initially kept at 600C for 1 minute and increased to 2200C at the rate of 60C per minute, and the analysis of a mixture of long chain hydrocarbons was done .Results:In the qualitative analysis done, the most potent hydrocarbon was found to be Psammodictyon Panduriforme (NCAAH-9) with a hydrocarbon mass of 37.27mg/g of the biomass and 2.1% of the total biomass 0f 1.395g and the other potent producer is Biddulphia(NCAAH 6) with hydrocarbon mass of 25.4mg/g of biomass and percentage of hydrocarbon is 1.03%. In the quantitative analysis by GCHRMS, the long chain hydrocarbons found in most of the marine diatoms were undecane, hexadecane, octadecane 3ethyl 5,2 ethyl butyl, Eicosane7hexyl, hexacosane, heptacosane, heneicosane, octadecane 3 methyl, triacontane. The exact mass of the long chain hydrocarbons in all the marine diatom samples was found to be Nonadecane 12C191H40, Tritriacontane,13-decyl-13-heptyl 12C501H102, Octadecane,3ethyl-5-(2-ethylbutyl 12C261H54, tetratetracontane 12C441H89, Eicosane, 7-hexyl 12C261H54. Conclusion:All the marine diatoms screened produced long chain hydrocarbons which can be used as diesel fuel with good cetane value example, hexadecane, undecane. All the long chain hydrocarbons can further undergo catalytic cracking to produce short chain alkanes which can give good octane values and can be used as gasoline. Optimisation of hydrocarbon production with the most potent marine diatom yielded long chain hydrocarbons of good fuel quality.

Keywords: biofuel, hydrocarbons, marine diatoms, screening

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Authors: B. X. Tchomeni, A. A. Alugongo, T. B. Tengen

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In this paper, de Laval rotor system has been characterized by a hinge model and its transient response numerically treated for a dynamic solution. The effect of the ensuing non-linear disturbances namely rub and breathing crack is numerically simulated. Subsequently, three analysis methods: Orbit Analysis, Fast Fourier Transform (FFT) and Wavelet Transform (WT) are employed to extract features of the vibration signal of the faulty system. An analysis of the system response orbits clearly indicates the perturbations due to the rotor-to-stator contact. The sensitivities of WT to the variation in system speed have been investigated by Continuous Wavelet Transform (CWT). The analysis reveals that features of crack, rubs and unbalance in vibration response can be useful for condition monitoring. WT reveals its ability to detect non-linear signal, and obtained results provide a useful tool method for detecting machinery faults.

Keywords: Continuous wavelet, crack, discrete wavelet, high acceleration, low acceleration, nonlinear, rotor-stator, rub

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Authors: In-Geun Lim, Sung-Woong Ra

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As high resolution satellite images can be used, lots of studies are carried out for exploiting these images in various fields. This paper proposes the method based on mathematical morphology for extracting the ‘horse's hoof shaped object’. This proposed method can make an automatic object detection system to track the meaningful object in a large satellite image rapidly. Mathematical morphology process can apply in binary image, so this method is very simple. Therefore this method can easily extract the ‘horse's hoof shaped object’ from any images which have indistinct edges of the tracking object and have different image qualities depending on filming location, filming time, and filming environment. Using the proposed method by which ‘horse's hoof shaped object’ can be rapidly extracted, the performance of the automatic object detection system can be improved dramatically.

Keywords: facility detection, satellite image, object, mathematical morphology

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Authors: Nurcan Cetinkaya

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The aim of this paper is to bring about the utilization of Juncus acutus as an alternative roughage resource in ruminant nutrition. In Turkey, JA is prevailing plant of the natural grassland in Kizilirmak Delta, Samsun. Crude nutrient values such as crude protein (CP), ether extract (EE), organic matter (OM), neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin(ADL) including antioxidant activity, total phenolic and flavonoid compounds, total organic matter digestibility (OMD) and metabolisable energy (ME) values of Juncus acutus stem, seed, and also its mixture with maize silage were estimated. and published. Furthermore, the effects of JA over rumen cellulolitic bacteria were studied. The obtained results from different studies conducted on JA by our team show that Juncus acutus may be a new roughage source in ruminant nutrition.

Keywords: antioxidant activity, cellulolytic bacteria, Juncus acutus, organic matter digestibility

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Authors: Stepan Grebeniuk, Ersi Hodo, Henri Ruotsalainen, Paul Tavolato

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Substation security plays an important role in the power delivery system. During the past years, there has been an increase in number of attacks on automation networks of the substations. In spite of that, there hasn’t been enough focus dedicated to the protection of such networks. Aiming to design a specialized anomaly detection system based on machine learning, in this paper we will discuss the IEC 60870-5-104 protocol that is used for communication between substation and control station and focus on the simulation of the substation traffic. Firstly, we will simulate the communication between substation slave and server. Secondly, we will compare the system's normal behavior and its behavior under the attack, in order to extract the right features which will be needed for building an anomaly detection system. Lastly, based on the features we will suggest the anomaly detection system for the asynchronous protocol IEC 60870-5-104.

Keywords: Anomaly detection, IEC-60870-5-104, Machine learning, Man-in-the-Middle attacks, Substation security

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Authors: Christopher Mkirema Maghanga, Maurice Mghendi Mwamburi

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Parametric modeling provides a means to deeper understand the properties of materials. Drude, Brendel, Lorentz and OJL incorporated in SCOUT® software are some of the models used to study dielectric films. In our work, we utilized Brendel and Drude models to extract the optical constants from spectroscopic data of fabricated undoped and niobium doped titanium oxide thin films. The individual contributions by the two models were studied to establish how they influence the dielectric function. The effect of dopants on their influences was also analyzed. For the undoped films, results indicate minimal contribution from the Drude term due to the dielectric nature of the films. However as doping levels increase, the rise in the concentration of free electrons favors the use of Drude model. Brendel model was confirmed to work well with dielectric films - the undoped titanium Oxide films in our case.

Keywords: modeling, Brendel model, optical constants, titanium oxide, Drude Model

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Authors: Marc Toutain, Pascale Leconte, Antoine Gauthier

Abstract:

Introduction: Eating Disorders (ED), such as anorexia, bulimia, and binge eating, are psychiatric illnesses that mostly affect young people. The main symptoms concern eating (restriction, excessive food intake) and weight control behaviors (laxatives, vomiting). Psychological comorbidities (depression, executive function disorders, etc.) and problematic behaviors toward physical activity (PA) are commonly associated with ED. Acquaintances on ED risk factors are still lacking, and more community sample studies are needed to improve prevention and early detection. To our knowledge, studies are needed to specifically investigate the link between ED risk level, PA, and psychological risk factors in a community sample of adolescents. The aim of this study is to assess the relation between ED risk level, exercise (type, frequency, and motivations for engaging in exercise), and psychological factors based on the Jacobi risk factors model. We suppose that a high risk of ED will be associated with the practice of high caloric cost PA, motivations oriented to weight and shape control, and psychological disturbances. Method: An online survey destined for students has been sent to several middle schools and colleges in northwest France. This survey combined several questionnaires, the Eating Attitude Test-26 assessing ED risk; the Exercise Motivation Inventory–2 assessing motivations toward PA; the Hospital Anxiety and Depression Scale assessing anxiety and depression, the Contour Drawing Rating Scale; and the Body Esteem Scale assessing body dissatisfaction, Rosenberg Self-esteem Scale assessing self-esteem, the Exercise Dependence Scale-Revised assessing PA dependence, the Multidimensional Assessment of Interoceptive Awareness assessing interoceptive awareness and the Frost Multidimensional Perfectionism Scale assessing perfectionism. Machine learning analysis will be performed in order to constitute groups with a tree-based model clustering method, extract risk profile(s) with a bootstrap method comparison, and predict ED risk with a prediction method based on a decision tree-based model. Expected results: 1044 complete records have already been collected, and the survey will be closed at the end of May 2022. Records will be analyzed with a clustering method and a bootstrap method in order to reveal risk profile(s). Furthermore, a predictive tree decision method will be done to extract an accurate predictive model of ED risk. This analysis will confirm typical main risk factors and will give more data on presumed strong risk factors such as exercise motivations and interoceptive deficit. Furthermore, it will enlighten particular risk profiles with a strong level of proof and greatly contribute to improving the early detection of ED and contribute to a better understanding of ED risk factors.

Keywords: eating disorders, risk factors, physical activity, machine learning

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