Search results for: induction machine

Authors: Bachir Chemani, Rachid Halfaoui

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The aim of study was to evaluate pressure distribution characteristics of the elastic textile bandages using two instrumental techniques: a prototype Instrument and a load Transference. The prototype instrument which simulates shape of real leg has pressure sensors which measure bandage pressure. Using this instrument, the results show that elastic textile bandages presents different pressure distribution characteristics and none produces a uniform distribution around lower limb. The load transference test procedure is used to determine whether a relationship exists between elastic textile bandage structure and pressure distribution characteristics. The test procedure assesses degree of load, directly transferred through a textile when loads series are applied to bandaging surface. A range of weave fabrics was produced using needle weaving machine and a sewing technique. A textile bandage was developed with optimal characteristics far superior pressure distribution than other bandages. From results, we find that theoretical pressure is not consistent exactly with practical pressure. It is important in this study to make a practical application for specialized nurses in order to verify the results and draw useful conclusions for predicting the use of this type of elastic band.

Keywords: textile, cotton, pressure, venous ulcers, elastic

Procedia PDF Downloads 336

Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Yi-Ping Lo, Shi-Yao Wei, Chih-Chun Ma

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Since Google launched Google Glass in 2012, numbers of commercial wearable devices were released, such as smart belt, smart band, smart shoes, smart clothes ... etc. However, most of these devices perform as sensors to show the readings of measurements and few of them provide the interactive feedback to the user. Furthermore, these devices are single task devices which are not able to communicate with each other. In this paper a new health promotion system, Ubiquitous Life People Informatics Engine (U-Life PIE), will be presented. This engine consists of People Informatics Engine (PIE) and the interactive user interface. PIE collects all the data from the compatible devices, analyzes this data comprehensively and communicates between devices via various application programming interfaces. All the data and informations are stored on the PIE unit, therefore, the user is able to view the instant and historical data on their mobile devices any time. It also provides the real-time hands-free feedback and instructions through the user interface visually, acoustically and tactilely. These feedback and instructions suggest the user to adjust their posture or habits in order to avoid the physical injuries and prevent illness.

Keywords: machine learning, wearable devices, user interface, user experience, internet of things

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Authors: Ashish Ankus Yerunkar, Ravi Terkar

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Study aimed at evaluating the best process environment which could simultaneously satisfy requirements of both quality as well as productivity with special emphasis on reduction of cutting tool flank wear, because reduction in flank wear ensures increase in tool life. The predicted optimal setting ensured minimization of surface roughness. Purpose of this paper is focused on the analysis of optimum cutting conditions to get lowest surface roughness in turning SCM 440 alloy steel by Taguchi method. Design for the experiment was done using Taguchi method and 18 experiments were designed by this process and experiments conducted. The results are analyzed using ANOVA method. Taguchi method has depicted that the depth of cut has significant role to play in producing lower surface roughness followed by feed. The Cutting speed has lesser role on surface roughness from the tests. The vibrations of the machine tool, tool chattering are the other factors which may contribute poor surface roughness to the results and such factors ignored for analyses. The inferences by this method will be useful to other researches for similar type of study and may be vital for further research on tool vibrations, cutting forces etc.

Keywords: surface roughness (ra), machining, dry turning, taguchi method, turning process, anova method, mahr perthometer

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Authors: Fekkak Bouazza, Menaa Mohamed, Loukriz Abdelhamid, Krim Mohamed L.

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This paper presents modeling and control strategy for the grid connected wind turbine system based on Permanent Magnet Synchronous Generator (PMSG). The considered system is based on back-to-back converter topology. The Grid Side Converter (GSC) achieves the DC bus voltage control and unity power factor. The Machine Side Converter (MSC) assures the PMSG speed control. The PMSG is used as a variable speed generator and connected directly to the turbine without gearbox. The pitch angle control is not either considered in this study. Further, Optimal Tip Speed Ratio (OTSR) based MPPT control strategy is used to ensure the most energy efficiency whatever the wind speed variations. A filter (L) is put between the GSC and the grid to reduce current ripple and to improve the injected power quality. The proposed grid connected wind system is built under MATLAB/Simulink environment. The simulation results show the feasibility of the proposed topology and performance of its control strategies.

Keywords: wind, grid, PMSG, MPPT, OTSR

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Authors: Ageliki I. Katsarou, Andriana C. Kaliora, Antonia Chiou, Apostolos Papalois, Nick Kalogeropoulos, Nikolaos K. Andrikopoulos

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Oxidative stress is a major mechanism underlying CVDs while inflammation, an intertwined process with oxidative stress, is also linked to CVDs. Extra virgin olive oil (EVOO) is widely known to play a pivotal role in CVD prevention and CVD reduction. However, in most studies, olive oil constituents are evaluated individually and not as part of the native food, hence potential synergistic effects as drivers of EVOO beneficial properties may be underestimated. In this study, EVOO lipidic and polar phenolics fractions were evaluated for their effect on inflammatory (TNF-alpha) and oxidation (malondialdehyde/MDA) markers, in cholesterol-fed rats. Thereat, oils with discernible lipidic profile and polar phenolic content were used. Wistar rats were fed on either a high-cholesterol diet (HCD) or a HCD supplemented with oils, either commercially available, i.e. EVOO, sunflower oil (SO), or modified as to their polar phenol content, i.e. phenolics deprived-EVOO (EVOOd), SO enriched with the EVOO phenolics (SOe). After 9 weeks of dietary intervention, heart and blood samples were collected. HCD induced dylipidemia shown by increase in serum total cholesterol, low-density lipoprotein cholesterol (LDL-c) and triacylglycerols. Heart tissue has been affected by dyslipidemia; oxidation was indicated by increase in MDA in cholesterol-fed rats and inflammation by increase in TNF-alpha. In both cases, this augmentation was attenuated in EVOO and SOe diets. With respect to oxidation, SO enrichment with the EVOO phenolics brought its lipid peroxidation levels as low as in EVOO-fed rats. This suggests that phenolic compounds may act as antioxidant agents in rat heart. A possible mechanism underlying this activity may be the protective effect of phenolics in mitochondrial membrane against oxidative damage. This was further supported by EVOO/EVOOd comparison with the former presenting lower heart MDA content. As for heart inflammation, phenolics naturally present in EVOO as well as phenolics chemically added in SO, exhibited quenching abilities in heart TNF-alpha levels of cholesterol-fed rats. TNF-alpha may have played a causative role in oxidative stress induction while the opposite may have also happened, hence setting up a vicious cycle. Overall, diet supplementation with EVOO or SOe attenuated hypercholesterolemia-induced increase in MDA and TNF-alpha in Wistar rat hearts. This is attributed to phenolic compounds either naturally existing in olive oil or as fortificants in seed oil.

Keywords: extra virgin olive oil, hypercholesterolemic rats, MDA, polar phenolics, TNF-alpha

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Authors: Rim Chang Hyon, Song Hak Jin, Song Kwang Hyok, Jong Ki Hun

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The calibration of the articulated arm coordinate measuring machine (AACMM) is key to improving calibration accuracy and saving calibration time. To reduce the time consumed for calibration, we should choose the proper calibration gauges and develop a reasonable calibration method. In addition, we should get the exact optimal solution by accurately removing the rough errors within the experimental data. In this paper, we present a calibration method of the portable coordinate measuring arm (PCMA) using the 1.2m long bar guage with cone-holes. First, we determine the locations of the bar gauge and establish an optimal objective function for identifying the structural parameter errors. Next, we make a mathematical model of the calibration algorithm and present a new mathematical method to remove the rough errors within calibration data. Finally, we find the optimal solution to identify the kinematic parameter errors by using Levenberg-Marquardt algorithm. The experimental results show that our calibration method is very effective in saving the calibration time and improving the calibration accuracy.

Keywords: AACMM, kinematic model, parameter identify, measurement accuracy, calibration

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: MohammadSadegh SaeediFakher, Jafar Rouzegar, Hassan Assaee

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In this research, mechanical behavior and energy absorption capacity of empty and rubber-filled brazen circular tubes under quasi-static axial loading are investigated, experimentally. The brazen tubes were cut out of commercially available brazen circular tubes with the same length and diameter. Some of the specimens were filled with rubbers with three different shores and also, an empty tube was prepared. The specimens were axially compressed between two rigid plates in a quasi-static process using a Zwick testing machine. Load-displacement diagrams and energy absorption of the tested tubes were extracted from experimental data. The results show that filling the brazen tubes with rubber causes those to absorb more energy and the energy absorption of specimens are increased by increasing the shore of rubbers. In comparison to the empty tube, the first fold for the rubber-filled tubes occurs at lower load and it can be concluded that the rubber-filled tubes are better energy absorbers than the empty tubes. Also, in contrast with the empty tubes, the tubes that were filled with lower rubber shore deform asymmetrically.

Keywords: axial compression, quasi-static loading, folding, energy absorbers, rubber-filled tubes

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Authors: Sufia Maroof

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This empirical study investigated the web-translations of five Pakistani public offices (FPSC, FIA, HEC, USB, and Ministry of Finance) offering Urdu tab as an option to access information on their official websites. Triangulation of quantitative and qualitative research design informed the researcher of the semantic, lexical and syntactic caveats in these translations. The study hypothesized that majority of the Pakistani population is oblivious of the Supreme Court’s amendments in language policy concerning national and official language; hence, Urdu web-translations of the public departments have not been accessed effectively. Firstly, the researcher conducted an online survey, comprising of two sections, close ended and short answer based questions. Secondly, the researcher compiled corpus of the five selected websites in a tabular form to compare the data. Thirdly, the administrators of the departments had been contacted regarding the methods of translation and the expertise of the personnel involved. The corpus was assessed for TQA after examining the lexical, semantic, syntactical and technical alignment inaccuracies and imperfections. The study suggests the public offices to invest in their Urdu webs by either hiring expert translators or engaging expertise of a translation agency for this project to offer quality translation to public.

Keywords: machine translations, public offices, Urdu translations, websites

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Authors: Saim Iftikhar Awan, Farhan Ali

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Wind power has now become one of the most important resources of renewable energy. The machine which extracts kinetic energy from wind is wind turbine. This work is all about the electrical power analysis of horizontal axis wind turbine to check the efficiency of different configurations of wind turbines to get maximum output and comparison of experimental and Computational Fluid Dynamics (CFD) results. Different experiments have been performed to obtain that configuration with the help of which we can get the maximum electrical power output by changing the different parameters like the number of blades, blade shape, wind speed, etc. in first step experimentation is done, and then the similar configuration is designed in 3D CAD software. After a series of experiments, it has been found that the turbine with four blades at an angle of 75° gives maximum power output and increase in wind speed increases the power output. The models designed on CAD software are imported on ANSYS-FLUENT to predict mechanical power. This mechanical power is then converted into electrical power, and the results were approximately the same in both cases. In the end, a comparison has been done to compare the results of experiments and ANSYS-FLUENT.

Keywords: computational analysis, power efficiency, wind energy, wind turbine

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Authors: Yassamina Berkane, Leila Kloul, Yoann Demoli

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Quality of life, environmental impact, congestion of mobility means, and infrastructures remain significant challenges for urban mobility. Solutions like car sharing, spatial redesign, eCommerce, and autonomous vehicles will likely increase the unit veh-km and the density of cars in urban traffic, thus reducing congestion. However, the impact of such solutions is not clear for researchers. Congestion arises from growing populations that must travel greater distances to arrive at similar locations (e.g., workplaces, schools) during the same time frame (e.g., rush hours). This paper first reviews the research and application cases of urban congestion methods through recent years. Rethinking the question of time, it then investigates people’s willingness and flexibility to adapt their arrival and departure times from workplaces. We use neural networks and methods of supervised learning to apply a new methodology for predicting peoples' intentions from their responses in a questionnaire. We created and distributed a questionnaire to more than 50 companies in the Paris suburb. Obtained results illustrate that our methodology can predict peoples' intentions to reschedule their activities (work, study, commerce, etc.).

Keywords: urban mobility, decongestion, machine learning, neural network

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Authors: Nety Trisnawaty, Mirna Febriani

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The use of stainless steel wires in the field of dentistry is widely used, especially for orthodontic and prosthodontic treatment using stainless steel wire. The oral cavity is the ideal environment for corrosion, which can be caused by saliva. Prevention of corrosion on stainless steel wires can be done by using an organic or non-organic corrosion inhibitor. One of the organic inhibitors that can be used to prevent corrosion is black tea leaves extracts. To explain the comparison of chromium ions release for stainlees steel between artificial saliva and black tea leaves extracts. In this research we used artificial saliva, black tea leaves extracts, stainless steel wire and using Atomic Absorption Spectrophometric testing machine. The samples were soaked for 1, 3, 7 and 14 days in the artificial saliva and black tea leaves extracts. The results showed the difference of chromium ion release soaked in artificial saliva and black tea leaves extracts on days 1, 3, 7 and 14. Statistically, calculation with independent T-test with p < 0,05 showed a significant difference. The longer the duration of days, the more ion chromium were released. The conclusion of this study shows that black tea leaves extracts can inhibit the corrosion rate of stainless steel wires.

Keywords: chromium ion, stainless steel, artificial saliva, black tea leaves extracts

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Authors: H. K. Shivanand, Ranjith R. Hombal, Paraveej Shirahatti, Gujjalla Anil Babu, S. ShivaPrakash

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When it comes to the selection of materials the knowledge of materials science plays a vital role in selection and enhancements of materials properties. In the world of material science a composite material has the significant role based on its application. The composite materials are those in which two or more components having different physical and chemical properties are combined to create a new enhanced property substance. In this study three different materials (Kenaf, Kevlar and Graphene) been chosen based on their properties and a composite material is developed with help of vacuum bagging process. The fibers (Kenaf and Kevlar) and Resin(vinyl ester) ratio was maintained at 70:30 during the process and 0.5% 1% and 1.5% of Graphene was added during fabrication process. The material was machined to thedimension ofASTM standards(300×300mm and thickness 3mm)with help of water jet cutting machine. The composite materials were tested for Mechanical properties such as Interlaminar shear strength(ILSS) and Flexural strength. It is found that there is significant increase in material properties in the developed composite material.

Keywords: Kevlar, Kenaf, graphene, vacuum bagging process, Interlaminar shear strength test, flexural test

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Authors: Rawaa H. El-Bidweihy, Hisham M. Abdelsalam, Ihab A. El-Khodary

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In an era where machines run and shape our world, the need for a stable, non-ending source of energy emerges. In this study, the focus was on the solar energy in Egypt as a renewable source, the most important factors that could affect the solar energy’s market share throughout its life cycle production were analyzed and filtered, the relationships between them were derived before structuring a Bayesian network. Also, forecasted models were built for multiple factors to predict the states in Egypt by 2035, based on historical data and patterns, to be used as the nodes’ states in the network. 37 factors were found to might have an impact on the use of solar energy and then were deducted to 12 factors that were chosen to be the most effective to the solar energy’s life cycle in Egypt, based on surveying experts and data analysis, some of the factors were found to be recurring in multiple stages. The presented Bayesian network could be used later for scenario and decision analysis of using solar energy in Egypt, as a stable renewable source for generating any type of energy needed.

Keywords: ARIMA, auto correlation, Bayesian network, forecasting models, life cycle, partial correlation, renewable energy, SARIMA, solar energy

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

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Authors: David Pham, Yongfeng Zhang

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Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.

Keywords: explainable AI, graph attention network, graph neural network, node classification

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Authors: Nathenal Thomas Lambamo

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Based on the fact that more than 85% of the labor force and 90% of the export earnings are taken by agriculture in Ethiopia and it can be said that it is the backbone of the overall socio-economic activities in the country. Among the cereal crops that the agriculture sector provides for the country, wheat is the third-ranking one preceding teff and maize. In the present day, wheat is in higher demand related to the expansion of industries that use them as the main ingredient for their products. The local supply of wheat for these companies covers only 35 to 40% and the rest 60 to 65% percent is imported on behalf of potential customers that exhaust the country’s foreign currency reserves. The above facts show that the need for this crop in the country is too high and in reverse, the productivity of the crop is very less because of these reasons. Wheat disease is the most devastating disease that contributes a lot to this unbalance in the demand and supply status of the crop. It reduces both the yield and quality of the crop by 27% on average and up to 37% when it is severe. This study aims to detect the most frequent and degrading wheat diseases, Septoria and Leaf rust, using the most efficiently used subset of machine learning technology, deep learning. As a state of the art, a deep learning class classification technique called Convolutional Neural Network (CNN) has been used to detect diseases and has an accuracy of 99.01% is achieved.

Keywords: septoria, leaf rust, deep learning, CNN

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Authors: Chunying Fang, Haifeng Li, Lin Ma, Mancai Zhang

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Speech intelligibility assessment is an important measure to evaluate the functional outcomes of surgical and non-surgical treatment, speech therapy and rehabilitation. The assessment of pathological speech plays an important role in assisting the experts. Pathological speech usually is non-stationary and mutational, in this paper, we describe a multi-granularity combined feature schemes, and which is optimized by hierarchical visual method. First of all, the difference granularity level pathological features are extracted which are BAFS (Basic acoustics feature set), local spectral characteristics MSCC (Mel s-transform cepstrum coefficients) and nonlinear dynamic characteristics based on chaotic analysis. Latterly, radar chart and F-score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96-dimensions.The experimental results denote that new features by support vector machine (SVM) has the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation.

Keywords: pathological speech, multi-granularity feature, MSCC (Mel s-transform cepstrum coefficients), F-score, radar chart

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Authors: Ahmed Ashit

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Image fusion is a significant and efficient image processing method used for detecting different types of tumors. This method has been used as an effective combination technique for obtaining high quality images that combine anatomy and physiology of an organ. It is the main key in the huge biomedical machines for diagnosing cancer such as PET-CT machine. This thesis aims to develop an image analysis system for the detection of the eye tumor. Different image processing methods are used to extract the tumor and then mark it on the original image. The images are first smoothed using median filtering. The background of the image is subtracted, to be then added to the original, results in a brighter area of interest or tumor area. The images are adjusted in order to increase the intensity of their pixels which lead to clearer and brighter images. once the images are enhanced, the edges of the images are detected using canny operators results in a segmented image comprises only of the pupil and the tumor for the abnormal images, and the pupil only for the normal images that have no tumor. The images of normal and abnormal images are collected from two sources: “Miles Research” and “Eye Cancer”. The computerized experimental results show that the developed image fusion based eye tumor detection system is capable of detecting the eye tumor and segment it to be superimposed on the original image.

Keywords: image fusion, eye tumor, canny operators, superimposed

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Authors: Acacia A. Salomão, Willian L. Gomes da Silva, Gustavo G. Shimamoto, Matthieu Tubino

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Biodiesel physical and chemical properties depend on the raw material composition used in its synthesis. Saturated fatty acid esters confer high oxidative stability, while unsaturated fatty acid esters improve the cold flow properties. In this study, an alternative vegetal source - the macauba kernel oil - was used in the biodiesel synthesis instead of conventional sources. Macauba can be collected from native palm trees and is found in several regions in Brazil. Its oil is a promising source when compared to several other oils commonly obtained from food products, such as soybean, corn or canola oil, due to its specific characteristics. However, the usage of biodiesel made from macauba oil alone is not recommended due to the difficulty of producing macauba in large quantities. For this reason, this project proposes the usage of blends of the macauba oil with conventional oils. These blends were prepared by mixing the macauba biodiesel with biodiesels obtained from soybean, corn, and from residual frying oil, in the following proportions: 20:80, 50:50 e 80:20 (w/w). Three parameters were evaluated, using the standard methods, in order to check the quality of the produced biofuel and its blends: oxidative stability, cold filter plugging point (CFPP), and iodine value. The induction period (IP) expresses the oxidative stability of the biodiesel, the CFPP expresses the lowest temperature in which the biodiesel flows through a filter without plugging the system and the iodine value is a measure of the number of double bonds in a sample. The biodiesels obtained from soybean, residual frying oil and corn presented iodine values higher than 110 g/100 g, low oxidative stability and low CFPP. The IP values obtained from these biodiesels were lower than 8 h, which is below the recommended standard value. On the other hand, the CFPP value was found within the allowed limit (5 ºC is the maximum). Regarding the macauba biodiesel, a low iodine value was observed (31.6 g/100 g), which indicates the presence of high content of saturated fatty acid esters. The presence of saturated fatty acid esters should imply in a high oxidative stability (which was found accordingly, with IP = 64 h), and high CFPP, but curiously the latter was not observed (-3 ºC). This behavior can be explained by looking at the size of the carbon chains, as 65% of this biodiesel is composed by short chain saturated fatty acid esters (less than 14 carbons). The high oxidative stability and the low CFPP of macauba biodiesel are what make this biofuel a promising source. The soybean, corn and residual frying oil biodiesels also have low CFPP, but low oxidative stability. Therefore the blends proposed in this work, if compared to the common biodiesels, maintain the flow properties but present enhanced oxidative stability.

Keywords: biodiesel, blends, macauba kernel oil, stability oxidative

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Authors: Hongliang Zhang

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The use of chance procedures and randomizers in poetry-writing can be traced back to surrealist works, which, by appealing to Sigmund Freud's theories, were still logocentrism. In the 1960s, random permutation and combination were extensively used by the Oulipo, John Cage and Jackson Mac Low, which further deconstructed the metaphysical presence of writing. Today, the randomly-generated digital poetry has emerged as a genre of cybertext which should be co-authored by readers. At the same time, the classical theories have now been updated by cybernetics and media theories. N· Katherine Hayles put forward the concept of ‘the floating signifiers’ by Jacques Lacan to be the ‘the flickering signifiers’ , arguing that the technology per se has become a part of the textual production. This paper makes a historical review of the computer-generated poetry in the perspective of semiotics, emphasizing that the randomly-generated digital poetry which hands over the dual tasks of both interpretation and writing to the readers demonstrates the intervention of media technology in literature. With the participation of computerized algorithm and programming languages, poems randomly generated by computers have not only blurred the boundary between encoder and decoder, but also raises the issue of human-machine. It is also a significant feature of the cybertext that the productive process of the text is full of randomness.

Keywords: cybertext, digital poetry, poetry generator, semiotics

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Authors: A. Moualdia, D. J. Boudana, O. Bouchhida, A. Medjber

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Wind energy has many advantages, it does not pollute and it is an inexhaustible source. However, the cost of this energy is still too high to compete with traditional fossil fuels, especially on sites less windy. The performance of a wind turbine depends on three parameters: the power of wind, the power curve of the turbine and the generator's ability to respond to wind fluctuations. This paper presents a control chain conversion based on a double-fed asynchronous machine and flow-oriented. The supply system comprises of two identical converters, one connected to the rotor and the other one connected to the network via a filter. The architecture of the device is up by three commands are necessary for the operation of the turbine control extraction of maximum power of the wind to control itself (MPPT) control of the rotor side converter controlling the electromagnetic torque and stator reactive power and control of the grid side converter by controlling the DC bus voltage and active power and reactive power exchanged with the network. The proposed control has been validated in both modes of operation of the three-bladed wind 7.5 kW, using Matlab/Simulink. The results of simulation control technology study provide good dynamic performance and static.

Keywords: D.F.I.G, variable wind speed, hypersynchrone, energy quality, hyposynchrone

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Authors: Chia-Ju Peng, Shih-Jui Chen

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This paper proposes a method to discriminate electroencephalogram (EEG) signals between different concentration states using empirical mode decomposition (EMD). Brain-computer interface (BCI), also called brain-machine interface, is a direct communication pathway between the brain and an external device without the inherent pathway such as the peripheral nervous system or skeletal muscles. Attention level is a common index as a control signal of BCI systems. The EEG signals acquired from people paying attention or in relaxation, respectively, are decomposed into a set of intrinsic mode functions (IMF) by EMD. Fast Fourier transform (FFT) analysis is then applied to each IMF to obtain the frequency spectrums. By observing power spectrums of IMFs, the proposed method has the better identification of EEG attention level than the original EEG signals between different concentration states. The band power of IMF3 is the most obvious especially in β wave, which corresponds to fully awake and generally alert. The signal processing method and results of this experiment paves a new way for BCI robotic system using the attention-level control strategy. The integrated signal processing method reveals appropriate information for discrimination of the attention and relaxation, contributing to a more enhanced BCI performance.

Keywords: biomedical engineering, brain computer interface, electroencephalography, rehabilitation

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Authors: Gulnaz Erkomaishvili

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Introduction: The paper assesses the impact of the COVID-19 pandemic on the activities of small and medium-sized enterprises in Georgia, identifies their problems, and analyzes the state economic policy measures. During the pandemic, entrepreneurs named the imposition of restrictions, access to financial resources, shortage of qualified personnel, high tax rates, unhealthy competition in the market, etc. as the main challenges. The Georgian government has had to take special measures to mitigate the crisis impact caused by the pandemic. For example - in 2020, they mobilized more than 1,6 billion Gel for various eventsto support entrepreneurs. Small and medium-sized entrepreneurship development strategy is presented based on the research; Corresponding conclusions are made, and recommendations are developed. Objectives: The object of research is small and medium-sized enterprises and economic-political decisions aimed at their promotion.Methodology: This paper uses general and specific methods, in particular, analysis, synthesis, induction, deduction, scientific abstraction, comparative and statistical methods, as well as experts’ evaluation. In-depth interviews with experts were conducted to determine quantitative and qualitative indicators; Publications of the National Statistics Office of Georgia are used to determine the regularity between analytical and statistical estimations. Also, theoretical and applied research of international organizations and scientist-economists are used. Contributions: The COVID-19pandemic has had a significant impact on small and medium-sized enterprises. For them, Lockdown is a major challenge. Total sales volume decreased. At the same time, the innovative capabilities of enterprises and the volume of sales in remote channels have increased. As for the assessment of state support measures by small and medium-sizedentrepreneurs, despite the existence of support programs, a large number of entrepreneurs still do not evaluate the measures taken by the state positively. Among the desirable measures to be taken by the state, which would improve the activities of small and medium-sized entrepreneurs, who negatively or largely negatively assessed the activity of the state, named: tax incentives/exemption from certain taxes at the initial stage; Need for periodic trainings/organization of digital technologies, marketing training courses to improve the qualification of employees; Logic and adequacy of criteria when awarding grants and funding; Facilitating the finding of investors; Less bureaucracy, etc.

Keywords: small and medium enterprises, small and medium entrepreneurship, economic policy for small and medium entrepreneurship development, government regulations in Georgia, COVID-19 pandemic

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Authors: S. Sakhri

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Background: The use of Zoledronic acid (ZA) is an established place in the treatment of malignant tumors with a predilection for the skeleton of interest (in particular metastasis). Although the main target of Zoledronic acid was osteoclasts, there are preclinical data suggest that Zoledronic acid may have an antitumor effect on cells other than osteoclasts, including tumor cells. Antitumor activity, including the inhibition of tumor cell growth and the induction of apoptosis of tumor cells, inhibition of tumor cell adhesion and invasion, and anti-angiogenic effects have been demonstrated. Methods. From (2012 to 2014), 438 patients were included respondents the inclusion criteria, respectively. This is a prospective study over a 4 year period. Of all patients (N=438), 432 received neoadjuvant chemotherapy with Zoledronic acid. The primary end point was the pathologic complete response in advancer breast cancer stage. The secondary end point is to evaluate Clinical response according to RECIST criteria; estimate the bone density before and at the end of chemotherapy in women with locally advanced breast cancer, Toxicity Evaluation and Overall survival using Kaplan-Meier and log test. Result: The Objective response rate was 97% after (C4) with 3% stabilizations and 99, 3% of which 0.7% C8 after stabilization. The clinical complete response was 28% after C4 respectively, and 46.8% after C8, the pathologic complete response rate was 40.13% according to the classification Sataloff. We observed that the pathologic complete response rate was the most raised in the group including Her2 (luminal Her2 and Her2) the lowest in the triple negative group as classified by Sataloff. We found that the pCR is significantly higher in the age group (35-50 years) with 53.17%. Those who have more than 50 years in 2nd place with 27.7% and the lower in young woman 35 years pCR was 19%, not statistically significant, -The pCR was also in favor of the menopausal group in 51, 4%, and 48, 55% for non-menopausal women. The average duration of overall survival was also significantly in the subgroup (Luminal -Her2, Her2) compared with triple negative. It is 47.18 months in the luminal group vs. 38.95 in the triple negative group. -Was observed in our study a difference in quality of life between (C1) was the admission of the patient, and after (C8), we found an increase in general signs and a deterioration in the psychological state C1, in contrast to the C8 these general signs and mental status improves, up to 12, and 24 months. Conclusion The results of this study suggest that the addition of ZA to néoadjuvant CT has potential anti-cancer benefit in patients (Luminal -Her2, Her2) compared with triple negative with or without menopause status.

Keywords: HER2+, RH+, breast cancer, tyrosine kinase

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Authors: Juan Pablo Botero Torres, Alba Avila, Luis Felipe Giraldo

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Independent navigation and mobility through unknown spaces pose a challenge for the autonomy of visually impaired people (VIP), who have relied on the use of traditional assistive tools like the white cane and trained dogs. However, emerging visually assistive technologies (VAT) have proposed several human-machine interfaces (HMIs) that could improve VIP’s ability for self-guidance. Hereby, we introduce the design and implementation of a visually assistive device, Anajaa – Visual Substitution System (AVSS). This system integrates ultrasonic sensors with custom electronics, and computer vision models (convolutional neural networks), in order to achieve a robust system that acquires information of the surrounding space and transmits it to the user in an intuitive and efficient manner. AVSS consists of two modules: the sensing and the actuation module, which are fitted to a chest mount and belt that communicate via Bluetooth. The sensing module was designed for the acquisition and processing of proximity signals provided by an array of ultrasonic sensors. The distribution of these within the chest mount allows an accurate representation of the surrounding space, discretized in three different levels of proximity, ranging from 0 to 6 meters. Additionally, this module is fitted with an RGB-D camera used to detect potentially threatening obstacles, like staircases, using a convolutional neural network specifically trained for this purpose. Posteriorly, the depth data is used to estimate the distance between the stairs and the user. The information gathered from this module is then sent to the actuation module that creates an HMI, by the means of a 3x2 array of vibration motors that make up the tactile display and allow the system to deliver haptic feedback. The actuation module uses vibrational messages (tactones); changing both in amplitude and frequency to deliver different awareness levels according to the proximity of the obstacle. This enables the system to deliver an intuitive interface. Both modules were tested under lab conditions, and the HMI was additionally tested with a focal group of VIP. The lab testing was conducted in order to establish the processing speed of the computer vision algorithms. This experimentation determined that the model can process 0.59 frames per second (FPS); this is considered as an adequate processing speed taking into account that the walking speed of VIP is 1.439 m/s. In order to test the HMI, we conducted a focal group composed of two females and two males between the ages of 35-65 years. The subject selection was aided by the Colombian Cooperative of Work and Services for the Sightless (COOTRASIN). We analyzed the learning process of the haptic messages throughout five experimentation sessions using two metrics: message discrimination and localization success. These correspond to the ability of the subjects to recognize different tactones and locate them within the tactile display. Both were calculated as the mean across all subjects. Results show that the focal group achieved message discrimination of 70% and a localization success of 80%, demonstrating how the proposed HMI leads to the appropriation and understanding of the feedback messages, enabling the user’s awareness of its surrounding space.

Keywords: computer vision on embedded systems, electronic trave aids, human-machine interface, haptic feedback, visual assistive technologies, vision substitution systems

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Authors: Achirawatt Tungsombatsanti, Toansakul Santiboon, Kamon Ponkham

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Strategies of the STEM education was aimed to prepare of an interdisciplinary and applied approach for the instructional of science, technology, engineering, and mathematics in an integrated students for enhancing engagement of their science skills to the Problem-Based Learning (PBL) method in Borabu School with a sample consists of 80 students in 2 classes at the 12th grade level of their learning achievements on electromagnetic issue. Research administrations were to separate on two different instructional model groups, the 40-experimental group was designed with the STEM instructional experimenting preparation and induction in a 40-student class and the controlling group using the PBL was designed to students identify what they already know, what they need to know, and how and where to access new information that may lead to the resolution of the problem in other class. The learning environment perceptions were obtained using the 35-item Physics Laboratory Environment Inventory (PLEI). Students’ creating attitude skills’ sustainable development toward physics were assessed with the Test Of Physics-Related Attitude (TOPRA) The term scaling was applied to the attempts to measure the attitude objectively with the TOPRA was used to assess students’ perceptions of their science attitude toward physics. Comparisons between pretest and posttest techniques were assessed students’ learning achievements on each their outcomes from each instructional model, differently. The results of these findings revealed that the efficiency of the PLB and the STEM based on criteria indicate that are higher than the standard level of the 80/80. Statistically, significant of students’ learning achievements to their later outcomes on the controlling and experimental physics class groups with the PLB and the STEM instructional designs were differentiated between groups at the .05 level, evidently. Comparisons between the averages mean scores of students’ responses to their instructional activities in the STEM education method are higher than the average mean scores of the PLB model. Associations between students’ perceptions of their physics classes to their attitudes toward physics, the predictive efficiency R2 values indicate that 77%, and 83% of the variances in students’ attitudes for the PLEI and the TOPRA in physics environment classes were attributable to their perceptions of their physics PLB and the STEM instructional design classes, consequently. An important of these findings was contributed to student understanding of scientific concepts, attitudes, and skills as evidence with STEM instructional ought to higher responding than PBL educational teaching. Statistically significant between students’ learning achievements were differentiated of pre and post assessments which overall on two instructional models.

Keywords: learning instructional managements, problem-based learning, STEM education, method, enhancement, students learning achievements, science attitude, physics classes

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Authors: Siddhant Rao

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Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone. Despite the pervasiveness of this affliction, the current process to obtain an accurate breast cancer prognosis is tedious and time consuming. It usually requires a trained pathologist to manually examine histopathological images and identify the features that characterize various cancer severity levels. We propose MITOS-RCNN: a region based convolutional neural network (RCNN) geared for small object detection to accurately grade one of the three factors that characterize tumor belligerence described by the Nottingham Grading System: mitotic count. Other computational approaches to mitotic figure counting and detection do not demonstrate ample recall or precision to be clinically viable. Our models outperformed all previous participants in the ICPR 2012 challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14 challenge along with recently published works. Our model achieved an F- measure score of 0.955, a 6.11% improvement in accuracy from the most accurate of the previously proposed models.

Keywords: breast cancer, mitotic count, machine learning, convolutional neural networks

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Authors: Gaweł Magdalena, Lipkowska Anna, Olbert Magdalena, Frąckiewicz Ewelina, Librowski Tadeusz, Nowak Gabriel, Pilc Andrzej

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Heme oxygenase-1 (HO-1), also known as heat shock protein 32 (HSP32), has been shown to be implicated in cytoprotection in various organs. Its activation plays a significant role in acute and chronic inflammation, protecting cells from oxidative injury and apoptosis. This inducible isoform of HO catalyzes the first and rate-limiting step in heme degradation to produce equimolar quantities of biologically active products: carbon monoxide (CO), free iron and biliverdin. CO has been reported to possess anti-apoptotic properties. Moreover, it inhibits the production of proinflammatory cytokines and stimulates the synthesis of the anti-inflammatory interleukin-10 (IL-10), as well as promotes vasodilatation at sites of inflammation. The second product of catalytic HO-1 activity, free cytotoxic iron, is promptly sequestered into the iron storage protein ferritin, which lowers the pro-oxidant state of the cell. The third product, biliverdin, is subsequently converted by biliverdin reductase into the bile pigment bilirubin, the most potent endogenous antioxidant among the constituents of human serum, which modulates immune effector functions and suppresses inflammatory response. Furthermore, being one of the so-called stress proteins, HO-1 adaptively responds to different stressors, such as reactive oxygen species (ROS), inflammatory cytokines and heavy metals and thus protects cells against such conditions as ischemia, hemorrhagic shock, heat shock or hypoxia. It is suggested that pharmacologic modulation of HO-1 may represent an effective strategy for prevention of stress and drug-induced gastrointestinal toxicity. HO-1 is constitutively expressed in normal gastric, intestinal and colonic mucosa and up-regulated during inflammation. It has been proven that HO-1 up-regulated by hemin, heme and cobalt-protoporphyrin ameliorates experimental colitis. In addition, the up-regulation of HO-1 partially explains the mechanism of action of 5-aminosalicylic acid (5-ASA), which is used clinically as an anti-colitis agent. In 2009 Ueda et al. has reported for the first time that mucosal protection by Polaprezinc, a chelate compound of zinc and L-carnosine used as an anti-ulcer drug in Japan, is also attributed to induction of HO-1 in the stomach. Since then, inducers of HO-1 are desired subject of research, as they may constitute therapeutically effective anti-ulcer drugs.

Keywords: heme oxygenase-1, gastric lesions, gastroprotection, Polaprezinc

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