Search results for: measuring accuracy

Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

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Authors: Max Butler

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Melanoma accounts for 80% of skin cancer-related deaths globally. The poor prognosis and increasing incidence of melanoma impose a significant burden on global healthcare systems. Early detection, precise diagnosis, and preventative strategies are critical to improving patient outcomes. Dermoscopy is the gold standard for specialist assessments of pigmented skin lesions, as it can differentiate between benign and malignant growths with greater accuracy than visual inspection. In the United Kingdom, guidelines from the National Institute of Clinical Excellence (NICE) state dermoscopy should be used in all specialist assessments of pigmented skin lesions. Compliance with this guideline is low, resulting in missed and delayed melanoma diagnoses. To address this problem, a quality improvement project was initiated at Buckinghamshire Healthcare Trust (BHT) within the plastic surgery department. The target group was a trainee and consultant plastic surgeons conducting outpatient skin cancer clinics. Analysis of clinic documentation over a one-month period found that only 62% (38/61) of patients referred with pigmented skin lesions were examined using dermoscopy. To increase dermoscopy rates, teaching was delivered to the department highlighting national guidelines and the evidence base for dermoscopic examination. In addition, clinic paperwork was redesigned to include a text box for dermoscopic examination. Reauditing after the intervention found a significant increase in dermoscopy rates (52/61, p = 0.014). In conclusion, implementing a quality improvement project with targeted teaching and documentation template templates successfully increased dermoscopy rates. This is a promising step toward improving early melanoma detection and patient outcomes.

Keywords: melanoma, dermoscopy, plastic surgery, quality improvement

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Authors: Pengfei Meng, Shuangcheng Jia, Qian Li

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We proposed a real-time high-precision semantic segmentation network based on a multi-resolution feature fusion module, the auxiliary feature extracting module, upsampling module, and atrous spatial pyramid pooling (ASPP) module. We designed a feature fusion structure, which is integrated with sufficient features of different resolutions. We also studied the effect of side-branch structure on the network and made discoveries. Based on the discoveries about the side-branch of the network structure, we used a side-branch auxiliary feature extraction layer in the network to improve the effectiveness of the network. We also designed upsampling module, which has better results than the original upsampling module. In addition, we also re-considered the locations and number of atrous spatial pyramid pooling (ASPP) modules and modified the network structure according to the experimental results to further improve the effectiveness of the network. The network presented in this paper takes the backbone network of Bisenetv2 as a basic network, based on which we constructed a network structure on which we made improvements. We named this network deep multiple-resolution bilateral networks for real-time, referred to as DMBR-Net. After experimental testing, our proposed DMBR-Net network achieved 81.2% mIoU at 119FPS on the Cityscapes validation dataset, 80.7% mIoU at 109FPS on the CamVid test dataset, 29.9% mIoU at 78FPS on the COCOStuff test dataset. Compared with all lightweight real-time semantic segmentation networks, our network achieves the highest accuracy at an appropriate speed.

Keywords: multi-resolution feature fusion, atrous convolutional, bilateral networks, pyramid pooling

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Authors: Mohamed Boullouz, Mohamed Louay Metougui

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Accurate modeling of greenhouse gas (GHG) emissions, crop growth, soil productivity, and biochemical processes is crucial considering escalating global concerns about climate change and the urgent need to improve agricultural sustainability. The application of the denitrification-disintegration (DNDC) model in the context of Morocco's unique agro-climate is thoroughly investigated in this study. Our main research hypothesis is that the DNDC model offers an effective and powerful tool for precisely simulating a wide range of significant parameters, including greenhouse gas emissions, crop growth, yield potential, and complex soil biogeochemical processes, all consistent with the intricate features of environmental Moroccan agriculture. In order to verify these hypotheses, a vast amount of field data covering Morocco's various agricultural regions and encompassing a range of soil types, climatic factors, and crop varieties had to be gathered. These experimental data sets will serve as the foundation for careful model calibration and subsequent validation, ensuring the accuracy of simulation results. In conclusion, the prospective research findings add to the global conversation on climate-resilient agricultural practices while encouraging the promotion of sustainable agricultural models in Morocco. A policy architect's and an agricultural actor's ability to make informed decisions that not only advance food security but also environmental stability may be strengthened by the impending recognition of the DNDC model as a potent simulation tool tailored to Moroccan conditions.

Keywords: greenhouse gas emissions, DNDC model, sustainable agriculture, Moroccan cropping systems

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Authors: M. Bilal Ishfaq, Adnan N. Qureshi

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COVID-19 is the most infectious disease these days, it was first reported in Wuhan, the capital city of Hubei in China then it spread rapidly throughout the whole world. Later on 11 March 2020, the World Health Organisation (WHO) declared it a pandemic. Since COVID-19 is highly contagious, it has affected approximately 219M people worldwide and caused 4.55M deaths. It has brought the importance of accurate diagnosis of respiratory diseases such as pneumonia and COVID-19 to the forefront. In this paper, we propose a hybrid approach for the automated detection of COVID-19 using medical imaging. We have presented the hybridization of manually extracted and convolutional features. Our approach combines Haralick texture features and convolutional features extracted from chest X-rays and CT scans. We also employ a minimum redundancy maximum relevance (MRMR) feature selection algorithm to reduce computational complexity and enhance classification performance. The proposed model is evaluated on four publicly available datasets, including Chest X-ray Pneumonia, COVID-19 Pneumonia, COVID-19 CTMaster, and VinBig data. The results demonstrate high accuracy and effectiveness, with 0.9925 on the Chest X-ray pneumonia dataset, 0.9895 on the COVID-19, Pneumonia and Normal Chest X-ray dataset, 0.9806 on the Covid CTMaster dataset, and 0.9398 on the VinBig dataset. We further evaluate the effectiveness of the proposed model using ROC curves, where the AUC for the best-performing model reaches 0.96. Our proposed model provides a promising tool for the early detection and accurate diagnosis of COVID-19, which can assist healthcare professionals in making informed treatment decisions and improving patient outcomes. The results of the proposed model are quite plausible and the system can be deployed in a clinical or research setting to assist in the diagnosis of COVID-19.

Keywords: COVID-19, feature engineering, artificial neural networks, radiology images

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Authors: Muhammad Suhail Ibrahim, Ahmad Mujtaba

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Background: The accuracy of the most frequently used tests for diagnosing Helicobacter pylori is always under consideration in clinical settings. A reliable diagnosis is crucial to confirm the success of therapy. Objective: The aim of this research was to study the isolation frequency of H. pylori from patients compatible with gastritis or gastric ulcer and to compare some feasible non-invasive and invasive methods for the diagnosis of infection. Materials and Methods: Ninety-six gastric biopsy and blood samples were obtained with various gastroduodenal symptoms after obtaining informed consent. The biopsies were analyzed and compared using the culture, microscopic examination, histopathology, Rapid urease RUT), serology, biochemical, antibiotic susceptibility test and molecular method. Results: A number of 40 (41.67%) were considered H. pylori positive in both histopathology and RUT. On the other hand, 46 patients were positive against anti IgA and IgG by ELISA. Eighteen biopsies were positive according to the culture test. This was further confirmed by endoscopic examination, urease, catalase and oxidase tests. A high percentage of resistance to polymyxin B, amoxicillin, and kanamycin was observed (100, 88.89, and 77.78%, respectively). A gene (Cag A) was also detected by using molecular technique which appeared positive in 16 patients. The sensitivity/specificity (%) of diagnostic method was 95/77 for histology, 100/83.5 for rapid urease, 85.7/90 for gram staining, 100/66.6 for IgG serology, 100/79.5 for IgA serology, 100/75.0 for PCR, 100/79.04 for combination of RUT and IgG serology and 100/92.4 for combination of RUT, gram staining and IgG serology. Conclusion: In view of the result obtained, PCR appeared to be the most reliable test. However, higher sensitivity and specificity were also recorded for other tests. So, for more accurate results, it is advisable not to rely solely on a single method for detection.

Keywords: helicobacter pylori, isolation, detection, culture, urease, polymerase chain reaction, antibiotic susceptibility test, dyspeptic patients

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Authors: Jasmeet Kaur, Anup Singh, Shashikant Iyengar, Arun Kumar, Ira Sahay

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Latent autoimmune diabetes in adults (LADA) is an irreversible autoimmune disease that affects insulin production. LADA is characterized by the production of Glutamic acid decarboxylase (GAD) antibodies, which is similar to type 1 diabetes. Individuals with LADA may eventually develop overt diabetes and require insulin. In this condition, the pancreas produces little or no insulin, which is a hormone used by the body to allow glucose to enter cells and produce energy. While type 1 diabetes was traditionally associated with children and teenagers, its prevalence has increased in adults as well. LADA is frequently misdiagnosed as type 2 diabetes, especially in adulthood when type 2 diabetes is more common. LADA develops in adulthood, usually after age 30. Managing LADA involves metabolic control with exogenous insulin and prolonging the life of surviving beta cells, thereby slowing the disease's progression. This case study examines the impact of approximately 3 months of low-carbohydrate dietary intervention in a 42-year-old woman with LADA who was initially misdiagnosed as having type 2 diabetes. Her c-peptide was 0.13 and her HbA1c was 9.3% when this trial began. Low-carbohydrate interventions have been shown to improve blood sugar levels, including fasting, post-meal, and random blood sugar levels, as well as haemoglobin levels, blood pressure, energy levels, sleep quality, and satiety levels. The use of low-carbohydrate dietary intervention significantly reduces both hypo- and hyperglycaemia events. During the 3 months of the study, there were 2 to 3 hyperglycaemic events owing to physical stress and a single hypoglycaemic event. Low-carbohydrate dietary therapies lessen insulin dose inaccuracy, which explains why there were fewer hyperglycaemic and hypoglycaemic events. In three months, the glycated haemoglobin (HbA1c) level was reduced from 9.3% to 6.3%. These improvements occur without the need for caloric restriction or physical activity. Stress management was crucial aspect of the treatment plan as stress-induced neuroendocrine hormones can cause immunological dysregulation. Additionally, supplements that support immune system and reduce inflammation were used as part of the treatment during the trial. Long-term studies are needed to track disease development and corroborate the claim that such dietary treatments can prolong the honeymoon phase in LADA. Various factors can contribute to additional autoimmune attacks, so measuring c-peptide is crucial on a regular basis to determine whether insulin levels need to be adjusted.

Keywords: autoimmune, diabetes, LADA, low_carb, nutrition

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Authors: Charles Sikwanda

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The use of geosynthetic materials in geotechnical engineering projects has rapidly increased over the past several years. These materials have resulted in improved performance and cost reduction of geotechnical structures as compared to the use of conventional materials. However, working with geosynthetics requires knowledge of interface parameters for design. These parameters are typically determined by the large direct shear device in accordance with ASTM-D5321 and ASTM-D6243 standards. Although these laboratory tests are standardized, the quality of the results can be largely affected by several factors that include; the shearing rate, applied normal stress, gripping mechanism, and type of the geosynthetic specimens tested. Amongst these factors, poor surface gripping of a specimen is the major source of the discrepancy. If the specimen is inadequately secured to the shearing blocks, it experiences progressive failure and shear strength that deviates from the true field performance of the tested material. This leads to inaccurate, unsafe, and cost ineffective designs of projects. Currently, the ASTM-D5321 and ASTM-D6243 standards do not provide a standardized gripping system for geosynthetic shear strength testing. Over the years, researchers have come up with different gripping systems that can be used such as; glue, metal textured surface, sandblasting, and sandpaper. However, these gripping systems are regularly not adequate to sufficiently secure the tested specimens to the shearing device. This has led to large variability in test results and difficulties in results interpretation. Therefore, this study was aimed at determining the effects of gripping systems in geosynthetic interface shear strength testing using a 300 x 300 mm direct shear box. The results of the research will contribute to easy data interpretation and increase result accuracy and reproducibility.

Keywords: geosynthetics, shear strength parameters, gripping systems, gripping

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Authors: Jaypalsinh Parmar, Pintu Nakrani, Bhaumik Shah

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Digital Elevation Model (DEM) is elevation data of the virtual grid on the ground. DEM can be used in application in GIS such as hydrological modelling, flood forecasting, morphometrical analysis and surveying etc.. For morphometrical analysis the stream flow network plays a very important role. DEM lacks accuracy and cannot match field data as it should for accurate results of morphometrical analysis. The present study focuses on comparing the Agree method and the conventional Shortest path method for finding out morphometric parameters in the flat region of the Lower Tapi Basin which is located in the western India. For the present study, open source SRTM (Shuttle Radar Topography Mission with 1 arc resolution) and toposheets issued by Survey of India (SOI) were used to determine the morphometric linear aspect such as stream order, number of stream, stream length, bifurcation ratio, mean stream length, mean bifurcation ratio, stream length ratio, length of overland flow, constant of channel maintenance and aerial aspect such as drainage density, stream frequency, drainage texture, form factor, circularity ratio, elongation ratio, shape factor and relief aspect such as relief ratio, gradient ratio and basin relief for 53 catchments of Lower Tapi Basin. Stream network was digitized from the available toposheets. Agree DEM was created by using the SRTM and stream network from the toposheets. The results obtained were used to demonstrate a comparison between the two methods in the flat areas.

Keywords: agree method, morphometric analysis, lower Tapi basin, shortest path method

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Authors: Mahdiyeh Zafaranchi

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With rapid development of urbanization and improvement of living standards in the world, energy consumption and carbon emissions of the building sector are expected to increase in the near future; because of that, energy-saving issues have become more important among the engineers. Besides, the building sector is a major contributor to energy consumption and carbon emissions. The concept of efficient building appeared as a response to the need for reducing energy demand in this sector which has the main purpose of shifting from standard buildings to low-energy buildings. Although energy-saving should happen in all steps of a building during the life cycle (material production, construction, demolition), the main concept of efficient energy building is saving energy during the life expectancy of a building by using passive and active systems, and should not sacrifice comfort and quality to reach these goals. The main aim of this study is to investigate passive strategies (do not need energy consumption or use renewable energy) to achieve energy-efficient buildings. Energy retrofit measures were explored by eQuest software using a case study as a base model. The study investigates predictive accuracy for the major factors like thermal transmittance (U-value) of the material, windows, shading devices, thermal insulation, rate of the exposed envelope, window/wall ration, lighting system in the energy consumption of the building. The base model was located in Istanbul, Turkey. The impact of eight passive parameters on energy consumption had been indicated. After analyzing the base model by eQuest, a final scenario was suggested which had a good energy performance. The results showed a decrease in the U-values of materials, the rate of exposing buildings, and windows had a significant effect on energy consumption. Finally, savings in electric consumption of about 10.5%, and gas consumption by about 8.37% in the suggested model were achieved annually.

Keywords: efficient building, electric and gas consumption, eQuest, Passive parameters

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Authors: Walter Vera-Ortega, Idoia Busnadiego, Sam J. Wilson

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Introduction: Human Immunodeficiency Virus type 1 (HIV-1) is responsible for the acquired immunodeficiency syndrome (AIDS), a leading cause of death worldwide infecting millions of people each year. Despite intensive research in vaccine development, therapies against HIV-1 infection are not curative, and the huge genetic variability of HIV-1 challenges to drug development. Current animal models for HIV-1 research present important limitations, impairing the progress of in vivo approaches. Macaques require a CD8+ depletion to progress to AIDS, and the maintenance cost is high. Mice are a cheaper alternative but need to be 'humanized,' and breeding is not possible. The development of an HIV-1 clone able to replicate in mice is a challenging proposal. The lack of human co-factors in mice impedes the function of the HIV-1 accessory proteins, Tat and Rev, hampering HIV-1 replication. However, Tat and Rev function can be replaced by constitutive/chimeric promoters, codon-optimized proteins and the constitutive transport element (CTE), generating a novel HIV-1 clone able to replicate in mice without disrupting the amino acid sequence of the virus. By minimally manipulating the genomic 'identity' of the virus, we propose the generation of an HIV-1 clone able to replicate in mice to assist in antiviral drug development. Methods: i) Plasmid construction: The chimeric promoters and CTE copies were cloned by PCR using lentiviral vectors as templates (pCGSW and pSIV-MPCG). Tat mutants were generated from replication competent HIV-1 plasmids (NHG and NL4-3). ii) Infectivity assays: Retroviral vectors were generated by transfection of human 293T cells and murine NIH 3T3 cells. Virus titre was determined by flow cytometry measuring GFP expression. Human B-cells (AA-2) and Hela cells (TZMbl) were used for infectivity assays. iii) Protein analysis: Tat protein expression was determined by TZMbl assay and HIV-1 capsid by western blot. Results: We have determined that NIH 3T3 cells are able to generate HIV-1 particles. However, they are not infectious, and further analysis needs to be performed. Codon-optimized HIV-1 constructs are efficiently made in 293T cells in a Tat and Rev independent manner and capable of packaging a competent genome in trans. CSGW is capable of generating infectious particles in the absence of Tat and Rev in human cells when 4 copies of the CTE are placed preceding the 3’LTR. HIV-1 Tat mutant clones encoding different promoters are functional during the first cycle of replication when Tat is added in trans. Conclusion: Our findings suggest that the development of an HIV-1 Tat-Rev independent clone is challenging but achievable aim. However, further investigations need to be developed prior presenting our HIV-1 clone as a candidate model for research.

Keywords: codon-optimized, constitutive transport element, HIV-1, long terminal repeats, research model

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Authors: Sweta Sinha

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Rapid evolution of technology has changed language pedagogy as well as perspectives on language use, leading to strategic changes in discourse studies. We are now firmly embedded in a time when digital technologies have become an integral part of our daily lives. This has led to generalized approaches to English Language Teaching (ELT) which has raised two-pronged concerns in linguistically diverse settings: a) the diverse linguistic background of the learner might interfere/ intervene with the learning process and b) the differential level of already acquired knowledge of target language might make the classroom practices too easy or too difficult for the target group of learners. ELT needs a more systematic and differential pedagogical approach for greater efficiency and accuracy. The present research analyses the need of identifying learner groups based on different levels of target language proficiency based on a longitudinal study done on 150 undergraduate students. The learners were divided into five groups based on their performance on a twenty point scale in Listening Speaking Reading and Writing (LSRW). The groups were then subjected to varying durations of technology aided language learning sessions and their performance was recorded again on the same scale. Identifying groups and introducing differential teaching and learning strategies led to better results compared to generalized teaching strategies. Language teaching includes different aspects: the organizational, the technological, the sociological, the psychological, the pedagogical and the linguistic. And a facilitator must account for all these aspects in a carefully devised differential approach meeting the challenge of learner diversity. Apart from the justification of the formation of differential groups the paper attempts to devise framework to account for all these aspects in order to make ELT in multilingual setting much more effective.

Keywords: differential groups, English language teaching, language pedagogy, multilingualism, technology aided language learning

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Authors: Marzia Bilkiss, Muhammad J. A. Shiddiky, Mostafa K. Masud, Prabhakaran Sambasivam, Ido Bar, Jeremy Brownlie, Rebecca Ford

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A greater achievement in the Integrated Disease Management (IDM) to prevent the loss would result from early diagnosis and quantitation of the causal pathogen species for accurate and timely disease control. This could significantly reduce costs to the growers and reduce any flow on impacts to the environment from excessive chemical spraying. Necrotrophic fungal disease botrytis grey mould, caused by Botrytis cinerea and Botrytis fabae, significantly reduce temperate legume yield and grain quality during favourable environmental condition in Australia and worldwide. Several immunogenic and molecular probe-type protocols have been developed for their diagnosis, but these have varying levels of species-specificity, sensitivity, and consequent usefulness within the paddock. To substantially improve speed, accuracy, and sensitivity, advanced nanoparticle-based biosensor approaches have been developed. For this, two sets of primers were designed for both Botrytis cinerea and Botrytis fabae which have shown the species specificity with initial sensitivity of two genomic copies/µl in pure fungal backgrounds using multiplexed quantitative PCR. During further validation, quantitative PCR detected 100 spores on artificially infected legume leaves. Simultaneously an electro-catalytic assay was developed for both target fungal DNA using functionalised magnetic nanoparticles. This was extremely sensitive, able to detect a single spore within a raw total plant nucleic acid extract background. We believe that the translation of this technology to the field will enable quantitative assessment of pathogen load for future accurate decision support of informed botrytis grey mould management.

Keywords: biosensor, botrytis grey mould, sensitive, species specific

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Małgorzata Golonka, Jadwiga Laska

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Self-healing polymers are one of the most investigated groups of smart materials. As materials engineering has recently focused on the design, production and research of modern materials and future technologies, researchers are looking for innovations in structural, construction and coating materials. Based on available scientific articles, it can be concluded that most of the research focuses on the self-healing of cement, concrete, asphalt and anticorrosion resin coatings. In our study, a method of obtaining and testing the properties of several types of microcapsules for use in self-healing polymer materials was developed. A method to obtain microcapsules exhibiting various mechanical properties, especially compressive strength was developed. The effect was achieved by using various polymer materials to build the shell: urea-formaldehyde resin (UFR), melamine-formaldehyde resin (MFR), melamine-urea-formaldehyde resin (MUFR). Dicyclopentadiene (DCPD) was used as the core material due to the possibility of its polymerization according to the ring-opening olefin metathesis (ROMP) mechanism in the presence of a solid Grubbs catalyst showing relatively high chemical and thermal stability. The ROMP of dicyclopentadiene leads to a polymer with high impact strength, high thermal resistance, good adhesion to other materials and good chemical and environmental resistance, so it is potentially a very promising candidate for the self-healing of materials. The capsules were obtained by condensation polymerization of formaldehyde with urea, melamine or copolymerization with urea and melamine in situ in water dispersion, with different molar ratios of formaldehyde, urea and melamine. The fineness of the organic phase dispersed in water, and consequently the size of the microcapsules, was regulated by the stirring speed. In all cases, to establish such synthesis conditions as to obtain capsules with appropriate mechanical strength. The microcapsules were characterized by determining the diameters and their distribution and measuring the shell thickness using digital optical microscopy and scanning electron microscopy, as well as confirming the presence of the active substance in the core by FTIR and SEM. Compression tests were performed to determine mechanical strength of the microcapsules. The highest repeatability of microcapsule properties was obtained for UFR resin, while the MFR resin had the best mechanical properties. The encapsulation efficiency of MFR was much lower compared to UFR, though. Therefore, capsules with a MUFR shell may be the optimal solution. The chemical reaction between the active substance present in the capsule core and the catalyst placed outside the capsules was confirmed by FTIR spectroscopy. The obtained autonomous repair systems (microcapsules + catalyst) were introduced into polyethylene in the extrusion process and tested for the self-repair of the material.

Keywords: autonomic self-healing system, dicyclopentadiene, melamine-urea-formaldehyde resin, microcapsules, thermoplastic materials

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Authors: Kirsi Snellman, Johannes Gartner, , Katja Upadaya

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A transition towards a future where the economy serves society so that it evolves within the safe operating space of the planet calls for fundamental changes in the way managers think, feel and act, and make decisions that relate to social and environmental issues. Sustainable decision-making in organizations are often challenging tasks characterized by trade-offs between environmental, social and financial aspects, thus often bringing forth ethical concerns. Although there have been significant developments in incorporating uncertainty into environmental decision-making and measuring constructs and dimensions in ethical behavior in organizations, the majority of sustainable decision-making models are rationalist-based. Moreover, research in psychology indicates that one’s readiness to make a decision depends on the individual’s state of mind, the feasibility of the implied change, and the compatibility of strategies and tactics of implementation. Although very informative, most of this extant research is limited in the sense that it often directs attention towards the rational instead of the emotional. Hence, little is known about the role of emotions in sustainable decision making, especially in situations where decision-makers evaluate a variety of options and use their feelings as a source of information in tackling the uncertainty. To fill this lacuna, and to embrace the uncertainty and perceived risk involved in decisions that touch upon social and environmental aspects, it is important to add emotion to the evaluation when aiming to reach the one right and good ethical decision outcome. This analysis builds on recent findings in moral psychology that associate feelings and intuitions with ethical decisions and suggests that emotions can sensitize the manager to evaluate the rightness or wrongness of alternatives if ethical concerns are present in sustainable decision making. Capturing such sensitive evaluation as triggered by intuitions, we suggest that rational justification can be complemented by using emotions as a tool to tune in to what feels right in making sustainable decisions. This analysis integrates ethical decision-making theories with recent advancements in emotion theories. It determines the conditions under which emotions play a role in sustainability decisions by contributing to a personal equilibrium in which intuition and rationality are both activated and in accord. It complements the rationalist ethics view according to which nothing fogs the mind in decision making so thoroughly as emotion, and the concept of cheater’s high that links unethical behavior with positive affect. This analysis contributes to theory with a novel theoretical model that specifies when and why managers, who are more emotional, are, in fact, more likely to make ethical decisions than those managers who are more rational. It also proposes practical advice on how emotions can convert the manager’s preferences into choices that benefit both common good and one’s own good throughout the transition towards a more sustainable future.

Keywords: emotion, ethical decision making, intuition, sustainability

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Authors: Joshna Rani S., Ahmadi Banu

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Artificial Intelligence (AI) has a significant task to carry out in the medical care contributions of things to come. As AI, it is the essential capacity behind the advancement of accuracy medication, generally consented to be a painfully required development in care. Albeit early endeavors at giving analysis and treatment proposals have demonstrated testing, we anticipate that AI will at last dominate that area too. Given the quick propels in AI for imaging examination, it appears to be likely that most radiology, what's more, pathology pictures will be inspected eventually by a machine. Discourse and text acknowledgment are now utilized for assignments like patient correspondence and catch of clinical notes, and their utilization will increment. The best test to AI in these medical services areas isn't regardless of whether the innovations will be sufficiently skilled to be valuable, but instead guaranteeing their appropriation in day by day clinical practice. For far reaching selection to happen, AI frameworks should be affirmed by controllers, coordinated with EHR frameworks, normalized to an adequate degree that comparative items work likewise, instructed to clinicians, paid for by open or private payer associations, and refreshed over the long haul in the field. These difficulties will, at last, be survived, yet they will take any longer to do as such than it will take for the actual innovations to develop. Therefore, we hope to see restricted utilization of AI in clinical practice inside 5 years and more broad use inside 10 years. It likewise appears to be progressively evident that AI frameworks won't supplant human clinicians for a huge scope, yet rather will increase their endeavors to really focus on patients. Over the long haul, human clinicians may advance toward errands and work plans that draw on remarkably human abilities like sympathy, influence, and higher perspective mix. Maybe the lone medical services suppliers who will chance their professions over the long run might be the individuals who will not work close by AI

Keywords: artificial intellogence, health care, breast cancer, AI applications

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Authors: Dongguk Im, Gunfeel Moon, Younwoo Nam, Kangwoo Chun

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Recognition of the needs about protecting environment throughout worldwide is widespread. In the shipping industry, International Maritime Organization (IMO) has been regulating pollutants emitted from ships by MARPOL 73/78. Recently, the Marine Environment Protection Committee (MEPC) of IMO, at its 68th session, approved the definition of Black Carbon (BC) specified by the following physical properties (light absorption, refractory, insolubility and morphology). The committee also agreed to the need for a protocol for any voluntary measurement studies to identify the most appropriate measurement methods. Filter Smoke Number (FSN) based on light absorption is categorized as one of the IMO relevant BC measurement methods. EUROMOT provided a FSN measurement data (measured by smoke meter) of 31 different engines (low, medium and high speed marine engines) of member companies at the 3rd International Council on Clean Transportation (ICCT) workshop on marine BC. From the comparison of FSN, the results indicated that BC emission from low speed marine diesel engines was ranged from 0.009 to 0.179 FSN and it from medium and high speed marine diesel engine was ranged 0.012 to 3.2 FSN. In consideration of measured the low FSN from low speed engine, an experimental study was conducted using both a low speed marine diesel engine (2 stroke, power of 7,400 kW at 129 rpm) and a high speed marine diesel engine (4 stroke, power of 403 kW at 1,800 rpm) under E3 test cycle. The results revealed that FSN was ranged from 0.01 to 0.16 and 1.09 to 1.35 for low and high speed engines, respectively. The measurement equipment (smoke meter) ranges from 0 to 10 FSN. Considering measurement range of it, FSN values from low speed engines are near the detection limit (0.002 FSN or ~0.02 mg/m3). From these results, it seems to be modulated the measurement range of the measurement equipment (smoke meter) for enhancing measurement accuracy of marine BC and evaluation on performance of BC abatement technologies.

Keywords: black carbon, filter smoke number, international maritime organization, marine diesel engine (two and four stroke), particulate matter

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Authors: Xianping Du, Runlu Miao, Guanjun Zhang, Libo Cao, Feng Zhu

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Current vehicle safety standards and human body injury criteria were established based on the biomechanical response of Euro-American human body, without considering the difference in the body anthropometry and injury characteristics among different races, particularly the East Asian people with smaller body size. Absence of such race specific design considerations will negatively influence the protective performance of safety products for these populations, and weaken the accuracy of injury thresholds derived. To resolve these issues, in this study, we aim to develop a race specific finite element model to simulate the impact response of the lower extremity of a 50th percentile East Asian (Chinese) male. The model was built based on medical images for the leg of an average size Chinese male and slightly adjusted based on the statistical data. The model includes detailed anatomic features and is able to simulate the muscle active force. Thirteen biomechanical tests available in the literature were used to validate its biofidelity. Using the validated model, a pedestrian-car impact accident taking place in China was re-constructed computationally. The results show that the newly developed lower leg model has a good performance in predicting dynamic response and tibia fracture pattern. An additional comparison on the fracture tolerance of the East Asian and Euro-American lower limb suggests that the current injury criterion underestimates the degree of injury of East Asian human body.

Keywords: lower limb, East Asian body characteristics, traffic accident reconstruction, finite element analysis, injury tolerance

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Xingyu Peng, Qingyuan Hu, Xuebin Zhu, Xi Yuan

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Neutron spectrometry has contributed much to the development of nuclear physics since 1932 and has also become an importance tool in several other fields, notably nuclear technology, fusion plasma diagnostics and radiation protection. Compared with neutron fluxes, neutron spectra can provide more detailed information on the internal physical process of neutron sources, such as fast neutron reactors, fusion plasma, fission-fusion hybrid reactors, and so on. However, high performance neutron spectrometer is not so commonly available as it requires the use of large and complex instrumentation. This work describes the development and characterization of a compact magnetic proton recoil (MPR) spectrometer for high-resolution measurements of fast neutron spectra. The compact MPR spectrometer is featured by its large recoil angle, small size permanent analysis magnet, short beam transport line and dual-purpose detector array for both steady state and pulsed neutron spectra measurement. A 3-dimensional electromagnetic particle transport code is developed to simulate the response function of the spectrometer. Simulation results illustrate that the performance of the spectrometer is mainly determined by n-p recoil foil and proton apertures, and an overall energy resolution of 3% is achieved for 14 MeV neutrons. Dedicated experiments using alpha source and mono-energetic neutron beam are employed to verify the simulated response function of the compact MPR spectrometer. These experimental results show a good agreement with the simulated ones, which indicates that the simulation code possesses good accuracy and reliability. The compact MPR spectrometer described in this work is a valuable tool for fast neutron spectra measurements for the fission or fusion devices.

Keywords: neutron spectrometry, magnetic proton recoil spectrometer, neutron spectra, fast neutron

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Authors: Aymen Laadhari

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During the last decade, an increasing number of contributions have been made in the fields of scientific computing and numerical methodologies applied to the study of the hemodynamics in the heart. In contrast, the numerical aspects concerning the interaction of pulsatile blood flow with highly deformable thin leaflets have been much less explored. This coupled problem remains extremely challenging and numerical difficulties include e.g. the resolution of full Fluid-Structure Interaction problem with large deformations of extremely thin leaflets, substantial mesh deformations, high transvalvular pressure discontinuities, contact between leaflets. Although the Lagrangian description of the structural motion and strain measures is naturally used, many numerical complexities can arise when studying large deformations of thin structures. Eulerian approaches represent a promising alternative to readily model large deformations and handle contact issues. We present a fully Eulerian finite element methodology tailored for the simulation of pulsatile blood flow in the aorta and sinus of Valsalva interacting with highly deformable thin leaflets. Our method enables to use a fluid solver on a fixed mesh, whilst being able to easily model the mechanical properties of the valve. We introduce a semi-implicit time integration scheme based on a consistent NewtonRaphson linearization. A variant of the classical Newton method is introduced and guarantees a third-order convergence. High-fidelity computational geometries are built and simulations are performed under physiological conditions. We address in detail the main features of the proposed method, and we report several experiments with the aim of illustrating its accuracy and efficiency.

Keywords: eulerian, level set, newton, valve

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Authors: Adam Majczak, Michał Bialy, Zbigniew Czyż, Zdzislaw Kaminski

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Increasingly stringent emission standards for passenger cars require us to find alternative drives. The share of electric vehicles in the sale of new cars increases every year. However, their performance and, above all, range cannot be today successfully compared to those of cars with a traditional internal combustion engine. Battery recharging lasts hours, which can be hardly accepted due to the time needed to refill a fuel tank. Therefore, the ways to reduce the adverse features of cars equipped with electric motors only are searched for. One of the methods is a combination of an electric engine as a main source of power and a small internal combustion engine as an electricity generator. This type of drive enables an electric vehicle to achieve a radically increased range and low emissions of toxic substances. For several years, the leading automotive manufacturers like the Mazda and the Audi together with the best companies in the automotive industry, e.g., AVL have developed some electric drive systems capable of recharging themselves while driving, known as a range extender. An electricity generator is powered by a Wankel engine that has seemed to pass into history. This low weight and small engine with a rotating piston and a very low vibration level turned out to be an excellent source in such applications. Its operation as an energy source for a generator almost entirely eliminates its disadvantages like high fuel consumption, high emission of toxic substances, or short lifetime typical of its traditional application. The operation of the engine at a constant rotational speed enables a significant increase in its lifetime, and its small external dimensions enable us to make compact modules to drive even small urban cars like the Audi A1 or the Mazda 2. The algorithm to maintain a constant speed was investigated on the engine dynamometer with an eddy current brake and the necessary measuring apparatus. The research object was the Aixro XR50 rotary engine with the electronic power supply developed at the Lublin University of Technology. The load torque of the engine was altered during the research by means of the eddy current brake capable of giving any number of load cycles. The parameters recorded included speed and torque as well as a position of a throttle in an inlet system. Increasing and decreasing load did not significantly change engine speed, which means that control algorithm parameters are correctly selected. This work has been financed by the Polish Ministry of Science and Higher Education.

Keywords: electric vehicle, power generator, range extender, Wankel engine

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Authors: Ana Neves, John Leander, Ignacio Gonzalez, Raid Karoumi

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Systematic monitoring and inspection are needed to assess the present state of a structure and predict its future condition. If an irregularity is noticed, repair actions may take place and the adequate intervention will most probably reduce the future costs with maintenance, minimize downtime and increase safety by avoiding the failure of the structure as a whole or of one of its structural parts. For this to be possible decisions must be made at the right time, which implies using systems that can detect abnormalities in their early stage. In this sense, Structural Health Monitoring (SHM) is seen as an effective tool for improving the safety and reliability of infrastructures. This paper explores the decision-making problem in SHM regarding the maintenance of civil engineering structures. The aim is to assess the present condition of a bridge based exclusively on measurements using the suggested method in this paper, such that action is taken coherently with the information made available by the monitoring system. Artificial Neural Networks are trained and their ability to predict structural behavior is evaluated in the light of a case study where acceleration measurements are acquired from a bridge located in Stockholm, Sweden. This relatively old bridge is presently still in operation despite experiencing obvious problems already reported in previous inspections. The prediction errors provide a measure of the accuracy of the algorithm and are subjected to further investigation, which comprises concepts like clustering analysis and statistical hypothesis testing. These enable to interpret the obtained prediction errors, draw conclusions about the state of the structure and thus support decision making regarding its maintenance.

Keywords: artificial neural networks, clustering analysis, model-free damage detection, statistical hypothesis testing, structural health monitoring

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Authors: Peng Hao Wang, Garam Kim, Ronald Sterkenburg

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The applications of composite materials within the aviation industry has been increasing at a rapid pace.  However, the growing applications of composite materials have also led to growing demand for more tooling to support its manufacturing processes. Tooling and tooling maintenance represents a large portion of the composite manufacturing process and cost. Therefore, the industry’s adaptability to new techniques for fabricating high quality tools quickly and inexpensively will play a crucial role in composite material’s growing popularity in the aviation industry. One popular tool fabrication technique currently being developed involves additive manufacturing such as 3D printing. Although additive manufacturing and 3D printing are not entirely new concepts, the technique has been gaining popularity due to its ability to quickly fabricate components, maintain low material waste, and low cost. In this study, a team of Purdue University School of Aviation and Transportation Technology (SATT) faculty and students investigated the effectiveness of a 3D printed composite compression mold. A 3D printed composite compression mold was fabricated by 3D scanning a steel valve cover of an aircraft reciprocating engine. The 3D printed composite compression mold was used to fabricate carbon fiber versions of the aircraft reciprocating engine valve cover. The 3D printed composite compression mold was evaluated for its performance, durability, and dimensional stability while the fabricated carbon fiber valve covers were evaluated for its accuracy and quality. The results and data gathered from this study will determine the effectiveness of the 3D printed composite compression mold in a mass production environment and provide valuable information for future understanding, improvements, and design considerations of 3D printed composite molds.

Keywords: additive manufacturing, carbon fiber, composite tooling, molds

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Authors: Raja Lahiani

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This study is concerned with scrutinising translations into English and French of references to locations in the desert of pre-Islamic Arabia. These references are used in the Source Text (ST) within a poetic image. Reference is made to the names of three different mountains in Arabia, namely Qatan, Sitar, and Yadhbul. As these mountains are referred to in the context of the poet’s description of the density and expansion of the clouds, it is crucial to know that while Sitar and Yadhbul are close to each other, Qatan is far away from them. This distance was functional for the poet to describe the expansion of the clouds. This reflects the spacious place (desert) he handled, and the fact that it was possible for him to physically see what he described. The purpose of this image is for the poet to communicate the vastness of the space he managed to see as he was in a moment of contemplation. Thus, knowledge of this characteristic about the setting is capital for the receiver to understand the communicative function of the verse. A corpus of eighteen translations is gathered. These vary between verse and prose renderings. The methodology adopted in this research work is comparative. Comparison is conducted at both the synchronic and diachronic levels; every translation shall be compared to the ST and then to previous translations. The comparative work will prove at the end that the translators who target historical facts do not necessarily succeed in preserving the image of the ST. It also proves that the more recent the translation is, the deeper the translator’s awareness is the link between imagery, setting, and point of view. Since the late eighteenth century and until nowadays, pre-Islamic poetry has been translated into Western languages. Translators differ as to motives, sources, priorities and intellectual backgrounds. A translator's skopoi undoubtedly affect the way s/he handles aspects of the ST. When it comes to culture-specific aspects and details related to setting, the problem is even more complex. Setting is a very important factor that reveals a great deal of the culture of pre-Islamic Arabia as this is remote in place, historical framework and literary tradition from its translators. History is present in pre-Islamic poetry, which justifies the important literature that has been written to extract information and data from it. These are imbedded not only by signalling given facts, events, and meditations but also by means of references to specific locations and landmarks that used to exist at the time. Spatial setting is an integral part of a literary text as it places it within its historical context. The importance of the translator’s awareness of spatial anthropological data before indulging in the process of translation is tested. This is also crucial in measuring the effect of setting loss and setting gain in translation. The findings of this research would ultimately evaluate the extent to which a comparative methodology is reliable in investigating the role of spatial setting awareness in translation.

Keywords: historical context, translation, comparative literature, spatial setting

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Authors: Haley Gershon

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The transition from using fossil fuel-based building material to formulating eco-friendly and biobased building materials plays a key role in sustainable building. The growing demand on a global level for biobased materials in the building and construction industries heightens the importance of carbon-14 testing, an analytical method used to determine the percentage of biobased content that comprises a material’s ingredients. This presentation will focus on the use of carbon-14 analysis within the building materials sector. Carbon-14, also known as radiocarbon, is a weakly radioactive isotope present in all living organisms. Any fossil material older than 50,000 years will not contain any carbon-14 content. The radiocarbon method is thus used to determine the amount of carbon-14 content present in a given sample. Carbon-14 testing is performed according to ASTM D6866, a standard test method developed specifically for biobased content determination of material in solid, liquid, or gaseous form, which requires radiocarbon dating. Samples are combusted and converted into a solid graphite form and then pressed onto a metal disc and mounted onto a wheel of an accelerator mass spectrometer (AMS) machine for the analysis. The AMS instrument is used in order to count the amount of carbon-14 present. By submitting samples for carbon-14 analysis, manufacturers of building materials can confirm the biobased content of ingredients used. Biobased testing through carbon-14 analysis reports results as percent biobased content, indicating the percentage of ingredients coming from biomass sourced carbon versus fossil carbon. The analysis is performed according to standardized methods such as ASTM D6866, ISO 16620, and EN 16640. Products 100% sourced from plants, animals, or microbiological material are therefore 100% biobased, while products sourced only from fossil fuel material are 0% biobased. Any result in between 0% and 100% biobased indicates that there is a mixture of both biomass-derived and fossil fuel-derived sources. Furthermore, biobased testing for building materials allows manufacturers to submit eligible material for certification and eco-label programs such as the United States Department of Agriculture (USDA) BioPreferred Program. This program includes a voluntary labeling initiative for biobased products, in which companies may apply to receive and display the USDA Certified Biobased Product label, stating third-party verification and displaying a product’s percentage of biobased content. The USDA program includes a specific category for Building Materials. In order to qualify for the biobased certification under this product category, examples of product criteria that must be met include minimum 62% biobased content for wall coverings, minimum 25% biobased content for lumber, and a minimum 91% biobased content for floor coverings (non-carpet). As a result, consumers can easily identify plant-based products in the marketplace.

Keywords: carbon-14 testing, biobased, biobased content, radiocarbon dating, accelerator mass spectrometry, AMS, materials

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Authors: Phanindra Prasad Thummala, Umran Tezcan Un, Ahmet Ozan Celik

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Removal of CO₂ by MEA (monoethanolamine) in structured packing columns depends highly on the gas-liquid interfacial area and film thickness (liquid load). CFD (computational fluid dynamics) is used to find the interfacial area, film thickness and their impact on mass transfer in gas-liquid flow effectively in any column geometry. In general modeling approaches used in CFD derive mass transfer parameters from standard correlations based on penetration or surface renewal theories. In order to avoid the effect of assumptions involved in deriving the correlations and model the mass transfer based solely on fluid properties, state of art approaches like one fluid formulation is useful. In this work, the one fluid formulation was implemented and evaluated for modeling the physical mass transfer of CO₂ by N₂O analogy in OpenFOAM CFD software. N₂O analogy avoids the effect of chemical reactions on absorption and allows studying the amount of CO₂ physical mass transfer possible in a given geometry. The computational domain in the current study was a flat plate with gas and liquid flowing in the countercurrent direction. The effect of operating parameters such as flow rate, the concentration of MEA and angle of inclination on the physical mass transfer is studied in detail. Liquid side mass transfer coefficients obtained by simulations are compared to the correlations available in the literature and it was found that the one fluid formulation was effectively capturing the effects of interface surface instabilities on mass transfer coefficient with higher accuracy. The high mesh refinement near the interface region was found as a limiting reason for utilizing this approach on large-scale simulations. Overall, the one fluid formulation is found more promising for CFD studies involving the CO₂ mass transfer.

Keywords: one fluid formulation, CO₂ absorption, liquid mass transfer coefficient, OpenFOAM, N₂O analogy

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Authors: S. D. El Wakil, M. Pladsen

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Fiber reinforced polymeric (FRP) composites are finding wide-spread industrial applications because of their exceptionally high specific strength and specific modulus of elasticity. Nevertheless, it is very seldom to get ready-for-use components or products made of FRP composites. Secondary processing by machining, particularly drilling, is almost always required to make holes for fastening components together to produce assemblies. That creates problems since the FRP composites are neither homogeneous nor isotropic. Some of the problems that are encountered include the subsequent damage in the region around the drilled hole and the drilling – induced delamination of the layer of ply, that occurs both at the entrance and the exit planes of the work piece. Evidently, the functionality of the work piece would be detrimentally affected. The current work was carried out with the aim of eliminating or at least minimizing the work piece damage associated with drilling of FPR composites. Each test specimen involves a woven reinforced graphite fiber/epoxy composite having a thickness of 12.5 mm (0.5 inch). A large number of test specimens were subjected to drilling operations with different combinations of feed rates and cutting speeds. The drilling induced damage was taken as the absolute value of the difference between the drilled hole diameter and the nominal one taken as a percentage of the nominal diameter. The later was determined for each combination of feed rate and cutting speed, and a matrix comprising those values was established, where the columns indicate varying feed rate while and rows indicate varying cutting speeds. Next, the analysis of variance (ANOVA) approach was employed using Minitab software, in order to obtain the combination that would improve the drilling induced damage. Experimental results show that low feed rates coupled with low cutting speeds yielded the best results.

Keywords: drilling of composites, dimensional accuracy of holes drilled in composites, delamination and charring, graphite-epoxy composites

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Authors: Patrícia T. Souza, Marina Ansolin, Eduardo A. C. Batista, Antonio J. A. Meirelles, Matthieu Tubino

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Lipid oxidation is a major cause of the deterioration of the quality of the biodiesel, because the waste generated damages the engines. Among the main undesirable effects are the increase of viscosity and acidity, leading to the formation of insoluble gums and sediments which cause the blockage of fuel filters. The auto-oxidation is defined as the spontaneous reaction of atmospheric oxygen with lipids. Unsaturated fatty acids are usually the components affected by such reactions. They are present as free fatty acids, fatty esters and glycerides. To determine the oxidative stability of biodiesels, through the induction period, IP, the Rancimat method is used, which allows continuous monitoring of the induced oxidation process of the samples. During the oxidation of the lipids, volatile organic acids are produced as byproducts, in addition, other byproducts, including alcohols and carbonyl compounds, may be further oxidized to carboxylic acids. By the methodology developed in this work using ion chromatography, IC, analyzing the water contained in the conductimetric vessel, were quantified organic anions of carboxylic acids in samples subjected to oxidation induced by Rancimat. The optimized chromatographic conditions were: eluent water:acetone (80:20 v/v) with 0.5 mM sulfuric acid; flow rate 0.4 mL min-1; injection volume 20 µL; eluent suppressor 20 mM LiCl; analytical curve from 1 to 400 ppm. The samples studied were methyl biodiesel from soybean oil and unsaturated fatty acids standards: oleic, linoleic and linolenic. The induced oxidation kinetics curves were constructed by analyzing the water contained in the conductimetric vessels which were removed, each one, from the Rancimat apparatus at prefixed intervals of time. About 3 g of sample were used under the conditions of 110 °C and air flow rate of 10 L h-1. The water of each conductimetric Rancimat measuring vessel, where the volatile compounds were collected, was filtered through a 0.45 µm filter and analyzed by IC. Through the kinetic data of the formation of the organic anions of carboxylic acids, the formation rates of the same were calculated. The observed order of the rates of formation of the anions was: formate >>> acetate > hexanoate > valerate for the oleic acid; formate > hexanoate > acetate > valerate for the linoleic acid; formate >>> valerate > acetate > propionate > butyrate for the linolenic acid. It is possible to suppose that propionate and butyrate are obtained mainly from linolenic acid and that hexanoate is originated from oleic and linoleic acid. For the methyl biodiesel the order of formation of anions was: formate >>> acetate > valerate > hexanoate > propionate. According to the total rate of formation these anions produced during the induced degradation of the fatty acids can be assigned the order of reactivity: linolenic acid > linoleic acid >>> oleic acid.

Keywords: anions of carboxylic acids, biodiesel, ion chromatography, oxidation

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