Search results for: medical image processing

Authors: Jindong Gu, Matthias Schubert, Volker Tresp

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In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.

Keywords: one-class classification, outlier detection, generative adversary networks, semi-supervised learning

Procedia PDF Downloads 136

Authors: Ploysai Ohama, Nuttawadee Hanchengchai, Thiva Saksri

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Natural dye extracted from Pterocarpus indicus was applied to a cotton fabric and silk yarn by dyeing processing different eco-friendly mordants. Analytical studies such as UV–VIS spectrophotometry and gravimetric analysis were performed on the extracts. The color of each dyed material was investigated in terms of the CIELAB (L*, a* and b*) and K/S values. Cotton fabric dyed without mordants had a shade of greenish-brown, while those post-mordanted with selected eco-friendly mordants such as alum, lemon juice and limewater result in a variety of brown and darker color shade of fabric.

Keywords: natural dyes, plant materials, dyeing, mordant

Procedia PDF Downloads 394

Authors: Moumita Chanda, Md. Fazlul Karim Patwary

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Smoking is one of the most popular recreational drug use behaviors, and it contributes to birth defects, COPD, heart attacks, and erectile dysfunction. To completely eradicate this disease, it is imperative that it be identified and treated. Numerous smoking cessation programs have been created, and they demonstrate how beneficial it may be to help someone stop smoking at the ideal time. A tomography meter is an effective smoking detector. Other wearables, such as RF-based proximity sensors worn on the collar and wrist to detect when the hand is close to the mouth, have been proposed in the past, but they are not impervious to deceptive variables. In this study, we create a machine that can discriminate between smokers and non-smokers in real-time with high sensitivity and specificity by watching and collecting the human lung and analyzing the X-ray data using machine learning. If it has the highest accuracy, this machine could be utilized in a hospital, in the selection of candidates for the army or police, or in university entrance.

Keywords: CNN, smoker detection, non-smoker detection, OpenCV, artificial Intelligence, X-ray Image detection

Procedia PDF Downloads 66

Authors: Fabiano Araujo Soares, Sauro Emerick Salomoni, Joao Paulo Lima da Silva, Igor Luiz Moura, Adson Ferreira da Rocha

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This paper presents a tool developed in the Matlab platform. It was developed to simplify the analysis of surface electromyography signals (S-EMG) in a way accessible to users that are not familiarized with signal processing procedures. The tool receives data by commands in window fields and generates results as graphics and excel tables. The underlying math of each S-EMG estimator is presented. Setup window and result graphics are presented. The tool was presented to four non-engineer users and all of them managed to appropriately use it after a 5 minutes instruction period.

Keywords: S-EMG estimators, electromyography, surface electromyography, ARV, RMS, MDF, MNF, CV

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Authors: Khadija Al-Hosni, Moon Fai Chan, Mohammed Al-Azri

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Background: Several studies suggest that most school-age adolescents are poorly informed on cancer warning signs and risk factors. Providing adolescents with sufficient knowledge would increase their awareness in adulthood and improve seeking behaviors later. Significant: The results will provide a clear vision in assisting key decision-makers in formulating policies on the students' awareness programs towards cancer. So, the likelihood of avoiding cancer in the future will be increased or even promote early diagnosis. Objectives: to evaluate the effectiveness of an education program designed to increase awareness of cancer signs and symptoms risk factors, improve the behavior of seeking help among school students in Oman, and address the barriers to obtaining medical help. Methods: A randomized controlled trial with two groups was conducted in Oman. A total of 1716 students (n=886/control, n= 830/education), aged 15-17 years, at 10th and 11th grade from 12 governmental schools 3 in governorates from 20-February-2022 to 12-May-2022. Basic demographic data were collected, and the Cancer Awareness Measure (CAM) was used as the primary outcome. Data were collected at baseline (T0) and 4 weeks after (T1). The intervention group received an education program about cancer's cause and its signs and symptoms. In contrast, the control group did not receive any education related to this issue during the study period. Non-parametric tests were used to compare the outcomes between groups. Results: At T0, the lamp was the most recognized cancer warning sign in control (55.0%) and intervention (55.2%) groups. However, there were no significant changes at T1 for all signs in the control group. In contrast, all sign outcomes were improved significantly (p<0.001) in the intervention group, the highest response was unexplained pain (93.3%). Smoking was the most recognized risk factor in both groups: (82.8% for control; 84.1% for intervention) at T0. However, there was no significant change in T1 for the control group, but there was for the intervention group (p<0.001), the highest identification was smoking cigarettes (96.5%). Too scared was the largest barrier to seeking medical help by students in the control group at T0 (63.0%) and T1 (62.8%). However, there were no significant changes in all barriers in this group. Otherwise, being too embarrassed (60.2%) was the largest barrier to seeking medical help for students in the intervention group at T0 and too scared (58.6%) at T1. Although there were reductions in all barriers, significant differences were found in six of ten only (p<0.001). Conclusion: The intervention was effective in improving students' awareness of cancer symptoms, warning signs (p<0.001), and risk factors (p<0.001 reduced the most addressed barriers to seeking medical help (p<0.001) in comparison to the control group. The Ministry of Education in Oman could integrate awareness of cancer within the curriculum, and more interventions are needed on the sociological part to overcome the barriers that interfere with seeking medical help.

Keywords: adolescents, awareness, cancer, education, intervention, student

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Authors: Daniel Nelson, Valeria La Saponara

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Material extrusion is an additive manufacturing modality that continues to show great promise in the ability to create low-cost, highly intricate, and exceedingly useful structural elements. As more capable and versatile filament materials are devised, and the resolution of manufacturing systems continues to increase, the need to understand and predict manufacturing-induced warping will gain ever greater importance. The following study presents an in situ remote sensing and data analysis construct that allows for the in situ mapping and quantification of surface displacements induced by residual stresses on a specified test structure. This proof-of-concept experimental process shows that it is possible to provide designers and manufacturers with insight into the manufacturing parameters that lead to the manifestation of these deformations and a greater understanding of the behavior of these warping events over the course of the manufacturing process.

Keywords: additive manufacturing, deformation, digital image correlation, fused filament fabrication, residual stress, warping

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Authors: Nikolaos Vassilas, Theocharis Tsenoglou, Djamchid Ghazanfarpour

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In this work we explore the capability of the mean shift algorithm as a powerful preprocessing tool for improving the quality of spatial data, acquired from airborne scanners, from densely built urban areas. On one hand, high resolution image data corrupted by noise caused by lossy compression techniques are appropriately smoothed while at the same time preserving the optical edges and, on the other, low resolution LiDAR data in the form of normalized Digital Surface Map (nDSM) is upsampled through the joint mean shift algorithm. Experiments on both the edge-preserving smoothing and upsampling capabilities using synthetic RGB-z data show that the mean shift algorithm is superior to bilateral filtering as well as to other classical smoothing and upsampling algorithms. Application of the proposed methodology for 3D reconstruction of buildings of a pilot region of Athens, Greece results in a significant visual improvement of the 3D building block model.

Keywords: 3D buildings reconstruction, data fusion, data upsampling, mean shift

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Authors: Keiko Shimazu, Yasuhiro Maida, Tetsuya Sugata, Daisuke Tamakoshi, Kenji Makabe, Haruki Suzuki

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In this paper, we report how to acquire serious victims’ locations in the Acute Stage of Large-scale Disasters, in an Emergency Information Network System designed by us. The background of our concept is based on the Great East Japan Earthquake occurred on March 11^th, 2011. Through many experiences of national crises caused by earthquakes and tsunamis, we have established advanced communication systems and advanced disaster medical response systems. However, Japan was devastated by huge tsunamis swept a vast area of Tohoku causing a complete breakdown of all the infrastructures including telecommunications. Therefore, we noticed that we need interdisciplinary collaboration between science of disaster medicine, regional administrative sociology, satellite communication technology and systems engineering experts. Communication of emergency information was limited causing a serious delay in the initial rescue and medical operation. For the emergency rescue and medical operations, the most important thing is to identify the number of casualties, their locations and status and to dispatch doctors and rescue workers from multiple organizations. In the case of the Tohoku earthquake, the dispatching mechanism and/or decision support system did not exist to allocate the appropriate number of doctors and locate disaster victims. Even though the doctors and rescue workers from multiple government organizations have their own dedicated communication system, the systems are not interoperable.

Keywords: crisis management, disaster mitigation, messing, MGRS, military grid reference system, satellite communication system

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Authors: Hung Lin-Zin, Lai Mei-Yen

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Prostate cancer is the most commonly diagnosed male cancer in the U.S. The prevalence is around 1 in 8. The etiology of prostate cancer is still unknown, but some predisposing factors, such as age, black race, family history, and obesity, may increase the risk of the disease. In 2020, a total of 7,178 Taiwanese people were nearly diagnosed with prostate cancer, accounting for 5.88% of all cancer cases, and the incidence rate ranked fifth among men. In that year, the total number of deaths from prostate cancer was 1,730, accounting for 3.45% of all cancer deaths, and the death rate ranked 6th among men, accounting for 94.34% of the cases of male reproductive organs. Looking for domestic and foreign literature on the use of OMOP (Observational Medical Outcomes Partnership, hereinafter referred to as OMOP) database analysis, there are currently nearly a hundred literature published related to nursing-related health problems and nursing measures built in the OMOP general data model database of medical institutions are extremely rare. The OMOP common data model construction analysis platform is a system developed by the FDA in 2007, using a common data model (common data model, CDM) to analyze and monitor healthcare data. It is important to build up relevant nursing information from the OMOP- CDM database to assist our daily practice. Therefore, we choose prostate cancer patients who are our popular care objects and use the OMOP- CDM database to explore the common associated health problems. With the assistance of OMOP-CDM database analysis, we can expect early diagnosis and prevention of prostate cancer patients' comorbidities to improve patient care.

Keywords: OMOP, nursing diagnosis, health problem, prostate cancer

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Authors: Phitsanu Phunphetchphan

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The purposes of this research was to investigate opinions of Rajabhat University staff towards performance of the university council committee by focusing on (1) personal characteristics of the committees; (2) duties designated by the university council; and (3) relationship between university council and staff. The population of this study included all high level of management from Suan Sunandha Rajabhat University which made a total of 200 respondents. Data analysis included frequency, percentage, mean and standard deviation. The findings revealed that the majority of staff rated the performance of university council at a high level. The 'overall appropriate qualification of the university council' was rated as the highest score while 'good governance' was rated as the lowest mean score. Moreover, the findings also revealed that the relationship between university council’s members and the staff was rated at a high level while 'the integrity of policy implementation' was rated as the lowest score.

Keywords: investigation, performance, university council, management

Procedia PDF Downloads 232

Authors: Mihriban Korukluoglu, Goksen Arik, Cagla Erdogan, Selen Kocakoglu

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Kefir is a traditional fermented refreshing beverage which is known for its valuable and beneficial properties for human health. Mainly yeast species, lactic acid bacteria (LAB) strains and fewer acetic acid bacteria strains live together in a natural matrix named “kefir grain”, which is formed from various proteins and polysaccharides. Different microbial species live together in slimy kefir grain and it has been thought that synergetic effect could take place between microorganisms, which belong to different genera and species. In this research, yeast and LAB were isolated from kefir samples obtained from Uludag University Food Engineering Department. The cell morphology of isolates was screened by microscopic examination. Gram reactions of bacteria isolates were determined by Gram staining method, and as well catalase activity was examined. After observing the microscopic/morphological and physical, enzymatic properties of all isolates, they were divided into the groups as LAB and/or yeast according to their physicochemical responses to the applied examinations. As part of this research, the antagonistic/synergistic efficacy of the identified five LAB and five yeast strains to each other were determined individually by disk diffusion method. The antagonistic or synergistic effect is one of the most important properties in a co-culture system that different microorganisms are living together. The synergistic effect should be promoted, whereas the antagonistic effect is prevented to provide effective culture for fermentation of kefir. The aim of this study was to determine microbial interactions between identified yeast and LAB strains, and whether their effect is antagonistic or synergistic. Thus, if there is a strain which inhibits or retards the growth of other strains found in Kefir microflora, this circumstance shows the presence of antagonistic effect in the medium. Such negative influence should be prevented, whereas the microorganisms which have synergistic effect on each other should be promoted by combining them in kefir grain. Standardisation is the most desired property for industrial production. Each microorganism found in the microbial flora of a kefir grain should be identified individually. The members of the microbial community found in the glue-like kefir grain may be redesigned as a starter culture regarding efficacy of each microorganism to another in kefir processing. The main aim of this research was to shed light on more effective production of kefir grain and to contribute a standardisation of kefir processing in the food industry.

Keywords: antagonistic effect, kefir, lactic acid bacteria (LAB), synergistic, yeast

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Authors: Marek Dosbaba

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Within the mining sector, SEM-based Automated Mineralogy (AM) has been the standard application for quickly and efficiently handling mineral processing tasks. Over the last decade, the trend has been to analyze larger numbers of samples, often with a higher level of detail. This has necessitated a shift from interactive sample analysis performed by an operator using a SEM, to an increased reliance on offline processing to analyze and report the data. In response to this trend, TESCAN TIMA Mineral Analyzer is designed to quickly create a virtual copy of the studied samples, thereby preserving all the necessary information. Depending on the selected data acquisition mode, TESCAN TIMA can perform hyperspectral mapping and save an X-ray spectrum for each pixel or segment, respectively. This approach allows the user to browse through elemental distribution maps of all elements detectable by means of energy dispersive spectroscopy. Re-evaluation of the existing data for the presence of previously unconsidered elements is possible without the need to repeat the analysis. Additional tiers of data such as a secondary electron or cathodoluminescence images can also be recorded. To take full advantage of these information-rich datasets, TIMA utilizes a new archiving tool introduced by TESCAN. The dataset size can be reduced for long-term storage and all information can be recovered on-demand in case of renewed interest. TESCAN TIMA is optimized for network storage of its datasets because of the larger data storage capacity of servers compared to local drives, which also allows multiple users to access the data remotely. This goes hand in hand with the support of remote control for the entire data acquisition process. TESCAN also brings a newly extended open-source data format that allows other applications to extract, process and report AM data. This offers the ability to link TIMA data to large databases feeding plant performance dashboards or geometallurgical models. The traditional tabular particle-by-particle or grain-by-grain export process is preserved and can be customized with scripts to include user-defined particle/grain properties.

Keywords: Tescan, electron microscopy, mineralogy, SEM, automated mineralogy, database, TESCAN TIMA, open format, archiving, big data

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Authors: Touila Ahmed, Elie Louis, Hamza Gharbi

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State of the art question answering models have recently shown impressive performance especially in a zero-shot setting. This approach is particularly useful when confronted with a highly diverse domain such as the legal field, in which it is increasingly difficult to have a dataset covering every notion and concept. In this work, we propose a flexible generative question answering approach to contract analysis as well as a weakly supervised procedure to leverage unannotated data and boost our models’ performance in general, and their zero-shot performance in particular.

Keywords: question answering, contract analysis, zero-shot, natural language processing, generative models, self-supervision

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Authors: Anyia Nduka, Aslan Bin Amad Senin, Ayu Azrin Binti Abdul Aziz

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Telemedicine is a rapidly developing discipline with enormous promise for better healthcare results for patients. To meet the demands of patients and the healthcare sector, medical providers must be proficient in telemedicine and also need government funding for infrastructure and core competencies. In this study, we surveyed general hospitals in Kuala Lumpur and Selangor to investigate patient’s impressions of both the positive and negative aspects of government funding for telemedicine and its level of acceptance. This survey was conducted in accordance with the Diffusion of Innovations (DOI) hypothesis; the survey instruments were designed through a Google Form and distributed to patients and every member of the medical team. The findings suggested a framework for categorizing patients' levels of technology use and acceptability, which provided practical consequences for healthcare. We therefore recommend the increase in technical assistance and government-backed funding of telemedicine by bolstering the entire system.

Keywords: technology acceptance, quality assurance, digital transformation, cost management.

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Authors: Patricia Jiménez, Rafael Corchuelo, Hassan A. Sleiman

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Web mining is gaining importance at an increasing pace. Currently, there are many complementary research topics under this umbrella. Their common theme is that they all focus on applying knowledge discovery techniques to data that is gathered from the Web. Sometimes, these data are relatively easy to gather, chiefly when it comes from server logs. Unfortunately, there are cases in which the data to be mined is the data that is displayed on a web document. In such cases, it is necessary to apply a pre-processing step to first extract the information of interest from the web documents. Such pre-processing steps are performed using so-called information extractors, which are software components that are typically configured by means of rules that are tailored to extracting the information of interest from a web page and structuring it according to a pre-defined schema. Paramount to getting good mining results is that the technique used to extract the source information is exact, which requires to evaluate and compare the different proposals in the literature from an empirical point of view. According to Google Scholar, about 4 200 papers on information extraction have been published during the last decade. Unfortunately, they were not evaluated within a homogeneous framework, which leads to difficulties to compare them empirically. In this paper, we report on an original information extraction evaluation method. Our contribution is three-fold: a) this is the first attempt to provide an evaluation method for proposals that work on semi-structured documents; the little existing work on this topic focuses on proposals that work on free text, which has little to do with extracting information from semi-structured documents. b) It provides a method that relies on statistically sound tests to support the conclusions drawn; the previous work does not provide clear guidelines or recommend statistically sound tests, but rather a survey that collects many features to take into account as well as related work; c) We provide a novel method to compute the performance measures regarding unsupervised proposals; otherwise they would require the intervention of a user to compute them by using the annotations on the evaluation sets and the information extracted. Our contributions will definitely help researchers in this area make sure that they have advanced the state of the art not only conceptually, but from an empirical point of view; it will also help practitioners make informed decisions on which proposal is the most adequate for a particular problem. This conference is a good forum to discuss on our ideas so that we can spread them to help improve the evaluation of information extraction proposals and gather valuable feedback from other researchers.

Keywords: web information extractors, information extraction evaluation method, Google scholar, web

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Authors: Steven Latré, Frederik Desplentere, Ilya Straumit, Stepan V. Lomov

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A method is proposed in order to create a three-dimensional finite element model representing fibre reinforced insulation materials for the simulation software Siemens NX. VoxTex software, a tool for quantification of µCT images of fibrous materials, is used for the transformation of microtomography images of random fibre reinforced composites into finite element models. An automatic tool was developed to execute the import of the models to the thermal solver module of Siemens NX. The paper describes the numerical tools used for the image quantification and the transformation and illustrates them on several thermal simulations of fibre reinforced insulation blankets filled with low thermal conductive fillers. The calculation of thermal conductivity is validated by comparison with the experimental data.

Keywords: analysis, modelling, thermal, voxel

Procedia PDF Downloads 275

Authors: Mariam Matiashvili

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Argumentation is an integral part of our daily communications - formal or informal. Argumentative reasoning, techniques, and language tools are used both in personal conversations and in the business environment. Verbalization of the opinions requires the use of extraordinary syntactic-pragmatic structural quantities - arguments that add credibility to the statement. The study of argumentative structures allows us to identify the linguistic features that make the text argumentative. Knowing what elements make up an argumentative text in a particular language helps the users of that language improve their skills. Also, natural language processing (NLP) has become especially relevant recently. In this context, one of the main emphases is on the computational processing of argumentative texts, which will enable the automatic recognition and analysis of large volumes of textual data. The research deals with the linguistic analysis of the argumentative structures of Georgian political speeches - particularly the linguistic structure, characteristics, and functions of the parts of the argumentative text - claims, support, and attack statements. The research aims to describe the linguistic cues that give the sentence a judgmental/controversial character and helps to identify reasoning parts of the argumentative text. The empirical data comes from the Georgian Political Corpus, particularly TV debates. Consequently, the texts are of a dialogical nature, representing a discussion between two or more people (most often between a journalist and a politician). The research uses the following approaches to identify and analyze the argumentative structures Lexical Classification & Analysis - Identify lexical items that are relevant in argumentative texts creating process - Creating the lexicon of argumentation (presents groups of words gathered from a semantic point of view); Grammatical Analysis and Classification - means grammatical analysis of the words and phrases identified based on the arguing lexicon. Argumentation Schemas - Describe and identify the Argumentation Schemes that are most likely used in Georgian Political Speeches. As a final step, we analyzed the relations between the above mentioned components. For example, If an identified argument scheme is “Argument from Analogy”, identified lexical items semantically express analogy too, and they are most likely adverbs in Georgian. As a result, we created the lexicon with the words that play a significant role in creating Georgian argumentative structures. Linguistic analysis has shown that verbs play a crucial role in creating argumentative structures.

Keywords: georgian, argumentation schemas, argumentation structures, argumentation lexicon

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Authors: Sukanya Devi Ramachandran, Vaishnavi Muralidharan, Kavya Chandrasekaran

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Polycaprolactone is polymer belonging to the polyester family that has noticeable characteristics of biodegradability and biocompatibility which is essential for medical applications. Polycaprolactone is produced by the ring opening polymerization of the monomer epsilon-Caprolactone (ε-CL) which is a closed ester, comprising of seven-membered ring. This process is normally catalysed by metallic components such as stannous octoate. It is difficult to remove the catalysts after the reaction, and they are also toxic to the human body. An alternate route of using enzymes as catalysts is being employed to reduce the toxicity. Lipase enzyme is a subclass of esterase that can easily attack the ester bonds of ε-CL. This research paper throws light on the extraction of lipase from germinating sunflower seeds and the activity of the biocatalyst in the polymerization of ε-CL. Germinating Sunflower seeds were crushed with fine sand in phosphate buffer of pH 6.5 into a fine paste which was centrifuged at 5000rpm for 10 minutes. The clear solution of the enzyme was tested for activity at various pH ranging from 5 to 7 and temperature ranging from 40oC to 70oC. The enzyme was active at pH6.0 and at 600C temperature. Polymerization of ε-CL was done using toluene as solvent with the catalysis of lipase enzyme, after which chloroform was added to terminate the reaction and was washed in cold methanol to obtain the polymer. The polymerization was done by varying the time from 72 hours to 6 days and tested for the molecular weight and the conversion of the monomer. The molecular weight obtained at 6 days is comparably higher. This method will be very effective, economical and eco-friendly to produce as the enzyme used can be regenerated as such at the end of the reaction and can be reused. The obtained polymers can be used for drug delivery and other medical applications.

Keywords: lipase, monomer, polycaprolactone, polymerization

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Authors: Gehad S. Kaseb, Mona F. Ahmed

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Arabic Sentiment Analysis (SA) is one of the most common research fields with many open areas. Few studies apply SA to Arabic dialects. This paper proposes different pre-processing steps and a modified methodology to improve the accuracy using normal Support Vector Machine (SVM) classification. The paper works on two datasets, Arabic Sentiment Tweets Dataset (ASTD) and Extended Arabic Tweets Sentiment Dataset (Extended-AATSD), which are publicly available for academic use. The results show that the classification accuracy approaches 86%.

Keywords: Arabic, classification, sentiment analysis, tweets

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Authors: Nikoloz Jalabadze, Roin Chedia, Lili Nadaraia, Levan Khundadze

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Nanocrystalline materials in powder condition can be manufactured by a number of different methods, however manufacture of composite materials product in the same nanocrystalline state is still a problem because the processes of compaction and synthesis of nanocrystalline powders go with intensive growth of particles – the process which promotes formation of pieces in an ordinary crystalline state instead of being crystallized in the desirable nanocrystalline state. To date spark plasma sintering (SPS) has been considered as the most promising and energy efficient method for producing dense bodies of composite materials. An advantage of the SPS method in comparison with other methods is mainly low temperature and short time of the sintering procedure. That finally gives an opportunity to obtain dense material with nanocrystalline structure. Graphene has recently garnered significant interest as a reinforcing phase in composite materials because of its excellent electrical, thermal and mechanical properties. Graphene nanoplatelets (GNPs) in particular have attracted much interest as reinforcements for ceramic matrix composites (mostly in Al2O3, Si3N4, TiO2, ZrB2 a. c.). SPS has been shown to fully densify a variety of ceramic systems effectively including Al2O3 and often with improvements in mechanical and functional behavior. Alumina consolidated by SPS has been shown to have superior hardness, fracture toughness, plasticity and optical translucency compared to conventionally processed alumina. Knowledge of how GNPs influence sintering behavior is important to effectively process and manufacture process. In this study, the effects of GNPs on the SPS processing of Al2O3 are investigated by systematically varying sintering temperature, holding time and pressure. Our experiments showed that SPS process is also appropriate for the synthesis of nanocrystalline powders of alumina-graphene composites. Depending on the size of the molds, it is possible to obtain different amount of nanopowders. Investigation of the structure, physical-chemical, mechanical and performance properties of the elaborated composite materials was performed. The results of this study provide a fundamental understanding of the effects of GNP on sintering behavior, thereby providing a foundation for future optimization of the processing of these promising nanocomposite systems.

Keywords: alumina oxide, ceramic matrix composites, graphene nanoplatelets, spark-plasma sintering

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Authors: Marie-Eve Joret, Marijn Van Vliet, Flavio Camarrone, Marc M. Van Hulle

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Event-related potentials (ERPs) provide evidence that the human brain can process and understand music at a pre-attentive level. Music-specific ERPs include the Early Right Anterior Negativity (ERAN) and a late Negativity (N5). This study aims to further investigate this issue using two types of syntactic manipulations in music: mild violations, containing no out-of-key tones and strong violations, containing out-of-key tones. We will examine whether both manipulations will elicit the same ERPs.

Keywords: ERAN ERPs, Music, N5, P3, ERPs, Music, N5 component, P3 component

Procedia PDF Downloads 257

Authors: Luís R. Silva, Fábio Jesus, Catarina Bento, Ana C. Gonçalves

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Over the last few years, the traditional medicine has gained ground at nutritional and pharmacological level. The natural products and their derivatives have great importance in several drugs used in modern therapeutics. Plant-based systems continue to play an essential role in primary healthcare. Additionally, the utilization of their plant parts, such as leaves, stems and flowers as nutraceutical and pharmaceutical products, can add a high value in the natural products market, not just by the nutritional value due to the significant levels of phytochemicals, but also by to the high benefit for the producers and manufacturers business. Stems of Prunus avium L. are a byproduct resulting from the processing of cherry, and have been consumed over the years as infusions and decoctions due to its bioactive properties, being used as sedative, diuretic and draining, to relief of renal stones, edema and hypertension. In this work, we prepared a hydroethanolic and infusion extracts from stems of P. avium collected in Fundão Region (Portugal), and evaluate the phenolic profile by LC/DAD, antioxidant capacity, α-glucosidase inhibitory activity and protection of human erythrocytes against oxidative damage. The LC-DAD analysis allowed to the identification of 19 phenolic compounds, catechin and 3-O-caffolquinic acid were the main ones. In a general way, hydroethanolic extract proved to be more active than infusion. This extract had the best antioxidant activity against DPPH• (IC50=22.37 ± 0.28 µg/mL) and superoxide radical (IC50=13.93 ± 0.30 µg/mL). Furthermore, it was the most active concerning inhibition of hemoglobin oxidation (IC50=13.73 ± 0.67 µg/mL), hemolysis (IC50=1.49 ± 0.18 µg/mL) and lipid peroxidation (IC50=26.20 ± 0.38 µg/mL) on human erythrocytes. On the other hand, infusion revealed to be more efficient towards α-glucosidase inhibitory activity (IC50=3.18 ± 0.23 µg/mL) and against nitric oxide radical (IC50=99.99 ± 1.89 µg/mL). The Sweet cherry sector is very important in Fundão Region (Portugal), and taking profit from the great wastes produced during processing of the cherry to produce added-value products, such as food supplements cannot be ignored. Our results demonstrate that P. avium stems possesses remarkable antioxidant and free radical scavenging properties. It is therefore, suggest, that P. avium stems can be used as a natural antioxidant with high potential to prevent or slow the progress of human diseases mediated by oxidative stress.

Keywords: stems, Prunus avium, phenolic compounds, biological potential

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Authors: Hernández Eva Selene, Reyna Mary Carmen, Rivera Héctor, Barragán Irving

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We propose an approach that jointly addresses the layout of a facility and the scheduling of a sequence of jobs. In real production, these two problems are interrelated. However, they are treated separately in the literature. Our approach is an extension of the job shop problem with transportation delay, where the location of the machines is selected among possible sites. The model minimizes the makespan, using the short processing times rule with two algorithms; the first one considers all the permutations for the location of machines, and the second only a heuristic to select some specific permutations that reduces computational time. Some instances are proved and compared with literature.

Keywords: layout problem, job shop scheduling problem, concurrent scheduling and layout problem, metaheuristic

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Authors: Sandabad Sara, Sayd Tahri Yassine, Hammouch Ahmed

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The development of science has allowed computer scientists to touch the medicine and bring aid to radiologists as we are presenting it in our article. Our work focuses on the detection and localization of tumors areas in the human brain; this will be a completely automatic without any human intervention. In front of the huge volume of MRI to be treated per day, the radiologist can spend hours and hours providing a tremendous effort. This burden has become less heavy with the automation of this step. In this article we present an automatic and effective tumor detection, this work consists of two steps: the first is the image filtering using the filter Nl-means, then applying the expectation maximization algorithm (EM) for retrieving the tumor mask from the brain MRI and extracting the tumor area using the mask obtained from the second step. To prove the effectiveness of this method multiple evaluation criteria will be used, so that we can compare our method to frequently extraction methods used in the literature.

Keywords: MRI, Em algorithm, brain, tumor, Nl-means

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Authors: Francielle A. Vargas, Thiago A. S. Pardo

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According to the standard philosophical definition, lying is saying something that you believe to be false with the intent to deceive. For deception detection, the FBI trains its agents in a technique named statement analysis, which attempts to detect deception based on parts of speech (i.e., linguistics style). This method is employed in interrogations, where the suspects are first asked to make a written statement. In this poster, we model false statements using linguistics style. In order to achieve this, we methodically analyze linguistic features in a corpus of fake news in the Portuguese language. The results show that they present substantial lexical, syntactic and semantic variations, as well as punctuation and emotion distinctions.

Keywords: deception detection, linguistics style, computational linguistics, natural language processing

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Authors: Nadia Miranda Lorick, Neela Badrie, Marsha Singh

Abstract:

Cassava (Manihot esculenta, Crantz) which is also known as ‘yucca’ or ‘manioc’ has been acknowledged as a millennium crop which has been utilized for food security purposes. The crop provides considerable amount of energy. The aim of the study was to assess consumer groups of both ‘fresh’ and ‘frozen’ in terms of their perceptions toward the quality and safety attributes of frozen cassava. The questionnaire included four sections: consumer demographics, consumer perceptions on quality attributes of ‘frozen’ cassava, consumer knowledge, awareness and attitudes toward food safety of ‘frozen’ cassava and consumer suggestions toward the improvement of frozen cassava. A face-to-face questionnaire was administered to 200 consumers of cassava between April and May 2016. The criteria for inclusion in the survey were that they must be 15 years and over and consumer of cassava. The sections of the questionnaire included demographics of respondents, consumer perception on quality and safety attributes of cassava and suggestions for the improvement of the value-added product. The data was analysed by descriptive and chi-square using SPSS as well as qualitative information was captured. Only 17% of respondents purchased frozen cassava and this was significantly (P<0.05) associated to income. Some (15%) of fresh cassava purchasers had never heard of frozen cassava products and 7.5% o perceived that these products were unhealthy for consumption. More than half (51.3%) of the consumers (all from the ‘fresh’ cassava group) believed that there were ‘no toxins’ within cassava. The ‘frozen’ cassava products were valued for convenience but purchasers were least satisfied with ‘value for money’ (50%), ‘product safety’ (50%) and ‘colour’ (52.9%). Cassava purchasers demonstrated highest dissatisfaction levels with the quality attribute: value for money (6.6%, 11.8%) respectively. The most predominant area outlined by respondents for frozen cassava improvement was promotion /advertising/education (23%). The ‘frozen’ cassava purchasers were ‘least satisfied’ thus most concern that clean knives and clean surface would not be used agro- processing. Fresh cassava purchasers were comparatively more knowledgeable on the potential existence of naturally occurring toxins in cassava, however with 1% respondents being able to specifically identify the toxin as ‘cyanide’. Dangerous preservatives (31%), poor hygiene (30%) and chemicals from the packaging (11%) were identified as some sources of contamination of ‘frozen’ cassava. Purchasers of frozen cassava indicated that the information on packaging label was unclear (P<0.01) when compared to ‘fresh’ cassava consumers.

Keywords: consumer satisfaction, convenience, cyanide toxin, product safety, price, label

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Authors: Leyla Esfandiari

Abstract:

Rapid, sensitive detection of nucleic acid (NA) molecules of specific sequence is of interest for a range of diverse health-related applications such as screening for genetic diseases, detecting pathogenic microbes in food and water, and identifying biological warfare agents in homeland security. Sequence-specific nucleic acid detection platforms rely on base pairing interaction between two complementary single stranded NAs, which can be detected by the optical, mechanical, or electrochemical readout. However, many of the existing platforms require amplification by polymerase chain reaction (PCR), fluorescent or enzymatic labels, and expensive or bulky instrumentation. In an effort to address these shortcomings, our research is focused on utilizing the cutting edge nanotechnology and microfluidics along with resistive pulse electrical measurements to design and develop a cost-effective, handheld and highly-sensitive nanopore-based sensor for point-of-care medical diagnostics.

Keywords: diagnostics, nanopore, nucleic acids, sensor

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Authors: Kumar Sambhav Pandey, Anamika Singh

Abstract:

The continuous researches in the field of computer architecture basically aims at accelerating the computational speed and to gain enhanced performance. In this era, the superscalar, sub-scalar concept has not gained enough attention for improving the computation performance. In this paper, we have presented a sub-scalar approach to utilize the parallelism present with in the data while processing. The main idea is to split the data into individual smaller entities and these entities are processed with a defined known set of instructions. This sub-scalar approach to the MIPS architecture can bring out significant improvement in the computational speedup. MIPS-I is the basic design taken in consideration for the development of sub-scalar MIPS64 for increasing the instruction level parallelism (ILP) and resource utilization.

Keywords: dataword, MIPS, processor, sub-scalar

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Authors: Adesola C. Odole, Michael O. Ogunlana, Nse A. Odunaiya, Olufemi O. Oyewole, Chidozie E. Mbada, Ogochukwu K. Onyeso, Ayomikun F. Ayodeji, Opeyemi M. Adegoke, Iyanuoluwa Odole, Comfort T. Sanuade, Moyosooreoluwa E. Odole, Oluwagbohunmi A. Awosoga

Abstract:

Purpose: The Nigerian healthcare industry is bedeviled with infrastructural decay, inadequate funding and staffing, and a dysfunctional healthcare system. This study investigated the influence of health professionals’ well-being and quality of work-life (QoWL) on the quality of care (QoC) of patients in Nigeria. Methods: The study was a multicentre cross-sectional survey conducted at four tertiary health institutions in southwest Nigeria. Participants’ demographic information, well-being, quality of work-life, and quality of care were obtained using four standardized questionnaires. Data were summarized using descriptive statistics of frequency (percentage) and mean (standard deviation). Inferential statistics included Chi-square, Pearson’s correlation, and independent samples t-test analyses. Results: Medical practitioners (n=609) and nurses (n=570) constituted 74.6% of all the health professionals, with physiotherapists, pharmacists, and medical laboratory scientists constituting 25.4%. The mean (SD) participants’ well-being = 71.65% (14.65), quality of life = 61.8% (21.31), quality of work-life = 65.73% (10.52) and quality of care = 70.14% (12.77). Participants’ quality of life had a significant negative correlation with the quality of care, while well-being and quality of work-life had a significant positive correlation with the quality of care. Conclusion: We concluded that health professionals’ well-being and quality of work-life are important factors that influence their productivity and, ultimately, the quality of care rendered to patients. The hospital management and policymakers should ensure improved work-related factors to improve the well-being of health professionals. This will enhance the quality of care given to patients and ultimately reduce brain drain and medical tourism.

Keywords: health professionals, quality of care, quality of life, quality of work-life, well-being

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Authors: Surita Maini, Sanjay Dhanka

Abstract:

Machine learning (ML) involves developing algorithms and statistical models that enable computers to learn and make predictions or decisions based on data without being explicitly programmed. Because of its unlimited abilities ML is gaining popularity in medical sectors; Medical Imaging, Electronic Health Records, Genomic Data Analysis, Wearable Devices, Disease Outbreak Prediction, Disease Diagnosis, etc. In the last few decades, many researchers have tried to diagnose Breast Cancer (BC) using ML, because early detection of any disease can save millions of lives. Working in this direction, the authors have proposed a hybrid ML technique RBF SVM, to predict the BC in earlier the stage. The proposed method is implemented on the Breast Cancer UCI ML dataset with 569 instances and 32 attributes. The authors recorded performance metrics of the proposed model i.e., Accuracy 98.24%, Sensitivity 98.67%, Specificity 97.43%, F1 Score 98.67%, Precision 98.67%, and run time 0.044769 seconds. The proposed method is validated by K-Fold cross-validation.

Keywords: breast cancer, support vector classifier, machine learning, hyper parameter tunning

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