Search results for: vector median filtering

Authors: Piotr A. Kowalski, Kasper Sapala, Wiktor Warchalowski

Abstract:

PM10 is a suspended dust that primarily has a negative effect on the respiratory system. PM10 is responsible for attacks of coughing and wheezing, asthma or acute, violent bronchitis. Indirectly, PM10 also negatively affects the rest of the body, including increasing the risk of heart attack and stroke. Unfortunately, Poland is a country that cannot boast of good air quality, in particular, due to large PM concentration levels. Therefore, based on the dense network of Airly sensors, it was decided to deal with the problem of prediction of suspended particulate matter concentration. Due to the very complicated nature of this issue, the Machine Learning approach was used. For this purpose, Convolution Neural Network (CNN) neural networks have been adopted, these currently being the leading information processing methods in the field of computational intelligence. The aim of this research is to show the influence of particular CNN network parameters on the quality of the obtained forecast. The forecast itself is made on the basis of parameters measured by Airly sensors and is carried out for the subsequent day, hour after hour. The evaluation of learning process for the investigated models was mostly based upon the mean square error criterion; however, during the model validation, a number of other methods of quantitative evaluation were taken into account. The presented model of pollution prediction has been verified by way of real weather and air pollution data taken from the Airly sensor network. The dense and distributed network of Airly measurement devices enables access to current and archival data on air pollution, temperature, suspended particulate matter PM1.0, PM2.5, and PM10, CAQI levels, as well as atmospheric pressure and air humidity. In this investigation, PM2.5, and PM10, temperature and wind information, as well as external forecasts of temperature and wind for next 24h served as inputted data. Due to the specificity of the CNN type network, this data is transformed into tensors and then processed. This network consists of an input layer, an output layer, and many hidden layers. In the hidden layers, convolutional and pooling operations are performed. The output of this system is a vector containing 24 elements that contain prediction of PM10 concentration for the upcoming 24 hour period. Over 1000 models based on CNN methodology were tested during the study. During the research, several were selected out that give the best results, and then a comparison was made with the other models based on linear regression. The numerical tests carried out fully confirmed the positive properties of the presented method. These were carried out using real ‘big’ data. Models based on the CNN technique allow prediction of PM10 dust concentration with a much smaller mean square error than currently used methods based on linear regression. What's more, the use of neural networks increased Pearson's correlation coefficient (R²) by about 5 percent compared to the linear model. During the simulation, the R² coefficient was 0.92, 0.76, 0.75, 0.73, and 0.73 for 1st, 6th, 12th, 18th, and 24th hour of prediction respectively.

Keywords: air pollution prediction (forecasting), machine learning, regression task, convolution neural networks

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Authors: Kaomud Tyagi, Rajasree Chakraborty, Shantanu Kundu, Devkant Singha, Kailash Chandra, Vikas Kumar

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The melon thrips, Thrips palmi is a serious pest of a wide range of agriculture crops and also act as vectors for plant viruses (genus Tospovirus, family Bunyaviridae). More molecular data on this species is required to understand the cryptic speciation and evolutionary affiliations. Mitochondrial genomes have been widely used in phylogenetic and evolutionary studies in insect. So far, mitogenomes of five thrips species (Anaphothrips obscurus, Frankliniella intonsa, Frankliniella occidentalis, Scirtothrips dorsalis and Thrips imaginis) is available in the GenBank database. In this study, we sequenced the first complete mitogenome T. palmi and compared it with available thrips mitogenomes. We assembled the mitogenome from the whole genome sequencing data generated using Illumina Hiseq2500. Annotation was performed using MITOS web-server to estimate the location of protein coding genes (PCGs), transfer RNA (tRNAs), ribosomal RNAs (rRNAs) and their secondary structures. The boundaries of PCGs and rRNAs was confirmed manually in NCBI. Phylogenetic analyses were performed using the 13 PCGs data using maximum likelihood (ML) in PAUP, and Bayesian inference (BI) in MrBayes 3.2. The complete mitogenome of T. palmi was 15,333 base pairs (bp), which was greater than the genomes of A. obscurus (14,890bp), F. intonsa (15,215 bp), F. occidentalis (14,889 bp) and S. dorsalis South Asia strain (SA1) (14,283 bp), but smaller than the genomes of T. imaginis (15,407 bp) and S. dorsalis East Asia strain (EA1) (15,343bp). Like in other thrips species, the mitochondrial genome of T. palmi was represented by 37 genes, including 13 PCGs, large and small ribosomal RNA (rrnL and rrnS) genes, 22 transfer RNA (tRNAs) genes (with one extra gene for trn-Serine) and two A+T-rich control regions (CR1 and CR2). Thirty one genes were observed on heavy (H) strand and six genes on the light (L) strand. The six tRNA genes (trnG,trnK, trnY, trnW, trnF, and trnH) were found to be conserved in all thrips species mitogenomes in their locations relative to a protein-coding or rRNA gene upstream or downstream. The gene arrangements of T. palmi is very close to T. imaginis except the rearrangements in tRNAs genes: trnR (arginine), and trnE (glutamic acid) were found to be located between cox3 and CR2 in T. imaginis which were translocated between atp6 and CR1 in T. palmi; trnL1 (Leucine) and trnS1(Serine) were located between atp6 and CR1 in T. imaginis which were translocated between cox3 and CR2 in T. palmi. The location of CR1 upstream of nad5 gene was suggested to be ancestral condition of the thrips species in subfamily Thripinae, was also observed in T. palmi. Both the Maximum likelihood (ML) and Bayesian Inference (BI) phylogenetic trees generated resulted in similar topologies. The T. palmi was clustered with T. imaginis. We concluded that more molecular data on the diverse thrips species from different hierarchical level is needed, to understand the phylogenetic and evolutionary relationships among them.

Keywords: thrips, comparative mitogenomics, gene rearrangements, phylogenetic analysis

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Authors: Alberto Veses, Olga Sanhauja, María Soledad Callén, Tomás García

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The appropriate use of renewable sources emerges as a decisive point to minimize the environmental impact caused by fossil fuels use. Particularly, the use of lignocellulosic biomass becomes one of the best promising alternatives since it is the only carbon-containing renewable source that can produce bioproducts similar to fossil fuels and it does not compete with food market. Among all the processes that can valorize lignocellulosic biomass, pyrolysis is an attractive alternative because it is the only thermochemical process that can produce a liquid biofuel (bio-oil) in a simple way and solid and gas fractions that can be used as energy sources to support the process. However, in order to incorporate bio-oils in current infrastructures and further process in future biorefineries, their quality needs to be improved. Introducing different low-cost catalysts and/or incorporating different polymer residues to the process are some of the new, simple and low-cost strategies that allow the user to directly obtain advanced bio-oils to be used in future biorefineries in an economic way. In this manner, from previous thermogravimetric analyses, local agricultural wastes such as grape seeds (GS) were selected as lignocellulosic biomass while, waste tires (WT) were selected as polymer residue. On the other hand, CaO was selected as low-cost catalyst based on previous experiences by the group. To reach this aim, a specially-designed fixed bed reactor using N₂ as a carrier gas was used. This reactor has the peculiarity to incorporate a vertical mobile liner that allows the user to introduce the feedstock in the oven once the selected temperature (550 ºC) is reached, ensuring higher heating rates needed for the process. Obtaining a well-defined phase distribution in the resulting bio-oil is crucial to ensure the viability to the process. Thus, once experiments were carried out, not only a well-defined two layers was observed introducing several mixtures (reaching values up to 40 wt.% of WT) but also, an upgraded organic phase, which is the one considered to be processed in further biorefineries. Radical interactions between GS and WT released during the pyrolysis process and dehydration reactions enhanced by CaO can promote the formation of better-quality bio-oils. The latter was reflected in a reduction of water and oxygen content of bio-oil and hence, a substantial increase of its heating value and its stability. Moreover, not only sulphur content was reduced from solely WT pyrolysis but also potential and negative issues related to a strong acidic environment of conventional bio-oils were minimized due to its basic pH and lower total acid numbers. Therefore, acidic compounds obtained in the pyrolysis such as CO₂-like substances can react with the CaO and minimize acidic problems related to lignocellulosic bio-oils. Moreover, this CO₂ capture promotes H₂ production from water gas shift reaction favoring hydrogen-transfer reactions, improving the final quality of the bio-oil. These results show the great potential of grapes seeds to carry out the catalytic co-pyrolysis process with different plastic residues in order to produce a liquid bio-oil that can be considered as a high-quality renewable vector.

Keywords: advanced bio-oils, biorefinery, catalytic co-pyrolysis of biomass and waste tires, lignocellulosic biomass

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Authors: Duncan Wallace, M-Tahar Kechadi

Abstract:

In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: V. Mantey, N. Findlay, I. Maddox

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The quality and quantity of global satellite data have been increasing exponentially in recent years as spaceborne systems become more affordable and the sensors themselves become more sophisticated. This is a valuable resource for many applications, including disaster management and relief. However, while more information can be valuable, the volume of data available is impossible to manually examine. Therefore, the question becomes how to extract as much information as possible from the data with limited manpower. Buildings are a key feature of interest in satellite imagery with applications including telecommunications, population models, and disaster relief. Machine learning tools are fast becoming one of the key resources to solve this problem, and models have been developed to detect buildings in optical satellite imagery. However, by and large, most models focus on affluent regions where buildings are generally larger and constructed further apart. This work is focused on the more difficult problem of detection in populated regions. The primary challenge with detecting small buildings in densely populated regions is both the spatial and spectral resolution of the optical sensor. Densely packed buildings with similar construction materials will be difficult to separate due to a similarity in color and because the physical separation between structures is either non-existent or smaller than the spatial resolution. This study finds that training models until they are overfitting the input sample can perform better in these areas than a more robust, generalized model. An overfitted model takes less time to fine-tune from a generalized pre-trained model and requires fewer input data. The model developed for this study has also been fine-tuned using existing, open-source, building vector datasets. This is particularly valuable in the context of disaster relief, where information is required in a very short time span. Leveraging existing datasets means that little to no manpower or time is required to collect data in the region of interest. The training period itself is also shorter for smaller datasets. Requiring less data means that only a few quality areas are necessary, and so any weaknesses or underpopulated regions in the data can be skipped over in favor of areas with higher quality vectors. In this study, a landcover classification model was developed in conjunction with the building detection tool to provide a secondary source to quality check the detected buildings. This has greatly reduced the false positive rate. The proposed methodologies have been implemented and integrated into a configurable production environment and have been employed for a number of large-scale commercial projects, including continent-wide DEM production, where the extracted building footprints are being used to enhance digital elevation models. Overfitted machine learning models are often considered too specific to have any predictive capacity. However, this study demonstrates that, in cases where input data is scarce, overfitted models can be judiciously applied to solve time-sensitive problems.

Keywords: building detection, disaster relief, mask-RCNN, satellite mapping

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Authors: Anupama Singh, Papia Raj

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Though Municipal Solid Waste (MSW) is a worldwide problem, yet its implications are enormous in developing countries, as they are unable to provide proper Municipal Solid Waste Management (MSWM) for the large volume of MSW. As a result, the collected wastes are dumped in open dumping at landfilling sites while the uncollected wastes remain strewn on the roadside, many-a-time clogging drainage. Such unsafe and inadequate management of MSW causes various public health hazards. For example, MSW directly on contact or by leachate contaminate the soil, surface water, and ground water; open burning causes air pollution; anaerobic digestion between the piles of MSW enhance the greenhouse gases i.e., carbon dioxide and methane (CO2 and CH4) into the atmosphere. Moreover, open dumping can cause spread of vector borne disease like cholera, typhoid, dysentery, and so on. Patna, the capital city of Bihar, one of the most underdeveloped provinces in India, is a unique representation of this situation. Patna has been identified as the ‘garbage city’. Over the last decade there has been an exponential increase in the quantity of MSW generation in Patna. Though a large proportion of such MSW is recyclable in nature, only a negligible portion is recycled. Plastic constitutes the major chunk of the recyclable waste. The chemical composition of plastic is versatile consisting of toxic compounds, such as, plasticizers, like adipates and phthalates. Pigmented plastic is highly toxic and it contains harmful metals such as copper, lead, chromium, cobalt, selenium, and cadmium. Human population becomes vulnerable to an array of health problems as they are exposed to these toxic chemicals multiple times a day through air, water, dust, and food. Based on analysis of health data it can be emphasized that in Patna there has been an increase in the incidence of specific diseases, such as, diarrhoea, dysentry, acute respiratory infection (ARI), asthma, and other chronic respiratory diseases (CRD). This trend can be attributed to improper MSWM. The results were reiterated through a survey (N=127) conducted during 2014-15 in selected areas of Patna. Random sampling method of data collection was used to better understand the relationship between different variables affecting public health due to exposure to MSW and lack of MSWM. The results derived through bivariate and logistic regression analysis of the survey data indicate that segregation of wastes at source, segregation behavior, collection bins in the area, distance of collection bins from residential area, and transportation of MSW are the major determinants of public health issues. Sustainable recycling is a robust method for MSWM with its pioneer concerns being environment, society, and economy. It thus ensures minimal threat to environment and ecology consequently improving public health conditions. Hence, this paper concludes that sustainable recycling would be the most viable approach to manage MSW in Patna and would eventually reduce public health hazards.

Keywords: municipal solid waste, Patna, public health, sustainable recycling

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Authors: Arsyam Mawardi, Sony Suhandono, Azzania Fibriani, Fifi Fitriyah Masduki

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Malaria is an infectious disease caused by Plasmodium sp. This disease has a high prevalence in Indonesia, especially in Jayapura. The vaccine that is currently being developed has not been effective in overcoming malaria. This is due to the high polymorphism in the Plasmodium genome especially in areas that encode Plasmodium surface proteins. Merozoite Surface Protein 1 (MSP1) Plasmodium falciparum is a surface protein that plays a role in the invasion process in human erythrocytes through the interaction of Glycophorin A protein receptors and sialic acid in erythrocytes with Reticulocyte Binding Proteins (RBP) and Duffy Adhesion Protein (DAP) ligands in merozoites. MSP1 can be targeted to be a specific antigen and predicted epitope area which will be used for the development of diagnostic and malaria vaccine therapy. MSP1 consists of 17 blocks, each block is dimorphic, and has been marked as the K1 and MAD20 alleles. Exceptions only in block 2, because it has 3 alleles, among others K1, MAD20 and RO33. These polymorphisms cause allelic variations and implicate the severity of patients infected P. falciparum. In addition, polymorphism of MSP1 in Jayapura isolates has not been reported so it is interesting to be further identified and projected as a specific antigen. Therefore, in this study, we analyzed the allele polymorphism as well as detected the MSP1 epitope antigen candidate on block 2 P. falciparum. Clinical samples of selected malaria patients followed the consecutive sampling method, examining malaria parasites with blood preparations on glass objects observed through a microscope. Plasmodium DNA was isolated from the blood of malarial positive patients. The block 2 MSP1 gene was amplified using PCR method and cloned using the pGEM-T easy vector then transformed to TOP'10 E.coli. Positive colonies selection was performed with blue-white screening. The existence of target DNA was confirmed by PCR colonies and DNA sequencing methods. Furthermore, DNA sequence analysis was done through alignment and formation of a phylogenetic tree using MEGA 6 software and insilico analysis using IEDB software to predict epitope candidate for P. falciparum. A total of 15 patient samples have been isolated from Plasmodium DNA. PCR amplification results show the target gene size about ± 1049 bp. The results of MSP1 nucleotide alignment analysis reveal that block 2 MSP1 genes derived from the sample of malarial patients were distributed in four different allele family groups, K1 (7), MAD20 (1), RO33 (0) and MSP1_Jayapura (10) alleles. The most commonly appears of the detected allele is MSP1_Jayapura single allele. There was no significant association between sex variables, age, the density of parasitemia and alel variation (Mann Whitney, U > 0.05), while symptomatic signs have a significant difference as a trigger of detectable allele variation (U < 0.05). In this research, insilico study shows that there is a new epitope antigen candidate from the MSP1_Jayapura allele and it is predicted to be recognized by B cells with 17 amino acid lengths in the amino acid sequence 187 to 203.

Keywords: epitope candidate, insilico analysis, MSP1 P. falciparum, polymorphism

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Authors: Ayman Hijazi, Witold Kwapinski, J. J. Leahy

Abstract:

Finding a sustainable alternative energy to fossil fuel is an urgent need as various environmental challenges in the world arise. Therefore, formic acid (FA) decomposition has been an attractive field that lies at the center of biomass platform, comprising a potential pool of hydrogen energy that stands as a new energy vector. Liquid FA features considerable volumetric energy density of 6.4 MJ/L and a specific energy density of 5.3 MJ/Kg that qualifies it in the prime seat as an energy source for transportation infrastructure. Additionally, the increasing research interest in FA decomposition is driven by the need of in-situ H2 production, which plays a key role in the hydrogenation reactions of biomass into higher value components. It is reported elsewhere in literature that catalytic decomposition of FA is usually performed in poorly designed setup using simple glassware under magnetic stirring, thus demanding further energy investment to retain the used catalyst. it work suggests an approach that integrates designing a novel catalyst featuring magnetic property with a robust setup that minimizes experimental & measurement discrepancies. One of the most prominent active species for dehydrogenation/hydrogenation of biomass compounds is palladium. Accordingly, we investigate the potential of engrafting palladium metal onto functionalized magnetic nanoparticles as a heterogeneous catalyst to favor the production of CO-free H2 gas from FA. Using ordinary magnet to collect the spent catalyst renders core-shell magnetic nanoparticles as the backbone of the process. Catalytic experiments were performed in a jacketed batch reactor equipped with an overhead stirrer under inert medium. Through a novel approach, FA is charged into the reactor via high-pressure positive displacement pump at steady state conditions. The produced gas (H2+CO2) was measured by connecting the gas outlet to a measuring system based on the amount of the displaced water. The novelty of this work lies in designing a very responsive catalyst, pumping consistent amount of FA into a sealed reactor running at steady state mild temperatures, and continuous gas measurement, along with collecting the used catalyst without the need for centrifugation. Catalyst characterization using TEM, XRD, SEM, and CHN elemental analyzer provided us with details of catalyst preparation and facilitated new venues to alter the nanostructure of the catalyst framework. Consequently, the introduction of amine groups has led to appreciable improvements in terms of dispersion of the doped metals and eventually attaining nearly complete conversion (100%) of FA after 7 hours. The relative importance of the process parameters such as temperature (35-85°C), stirring speed (150-450rpm), catalyst loading (50-200mgr.), and Pd doping ratio (0.75-1.80wt.%) on gas yield was assessed by a Taguchi design-of-experiment based model. Experimental results showed that operating at lower temperature range (35-50°C) yielded more gas while the catalyst loading and Pd doping wt.% were found to be the most significant factors with a P-values 0.026 & 0.031, respectively.

Keywords: formic acid decomposition, green catalysis, hydrogen, mesoporous silica, process optimization, nanoparticles

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Authors: Ayman Hijazi, Witold Kwapinski, J. J. Leahy

Abstract:

Finding a sustainable alternative energy to fossil fuel is an urgent need as various environmental challenges in the world arise. Therefore, formic acid (FA) decomposition has been an attractive field that lies at the center of the biomass platform, comprising a potential pool of hydrogen energy that stands as a distinct energy vector. Liquid FA features considerable volumetric energy density of 6.4 MJ/L and a specific energy density of 5.3 MJ/Kg that qualifies it in the prime seat as an energy source for transportation infrastructure. Additionally, the increasing research interest in FA decomposition is driven by the need for in-situ H₂ production, which plays a key role in the hydrogenation reactions of biomass into higher-value components. It is reported elsewhere in the literature that catalytic decomposition of FA is usually performed in poorly designed setups using simple glassware under magnetic stirring, thus demanding further energy investment to retain the used catalyst. Our work suggests an approach that integrates designing a distinct catalyst featuring magnetic properties with a robust setup that minimizes experimental & measurement discrepancies. One of the most prominent active species for the dehydrogenation/hydrogenation of biomass compounds is palladium. Accordingly, we investigate the potential of engrafting palladium metal onto functionalized magnetic nanoparticles as a heterogeneous catalyst to favor the production of CO-free H₂ gas from FA. Using an ordinary magnet to collect the spent catalyst renders core-shell magnetic nanoparticles as the backbone of the process. Catalytic experiments were performed in a jacketed batch reactor equipped with an overhead stirrer under an inert medium. Through a distinct approach, FA is charged into the reactor via a high-pressure positive displacement pump at steady-state conditions. The produced gas (H₂+CO₂) was measured by connecting the gas outlet to a measuring system based on the amount of the displaced water. The uniqueness of this work lies in designing a very responsive catalyst, pumping a consistent amount of FA into a sealed reactor running at steady-state mild temperatures, and continuous gas measurement, along with collecting the used catalyst without the need for centrifugation. Catalyst characterization using TEM, XRD, SEM, and CHN elemental analyzer provided us with details of catalyst preparation and facilitated new venues to alter the nanostructure of the catalyst framework. Consequently, the introduction of amine groups has led to appreciable improvements in terms of dispersion of the doped metals and eventually attaining nearly complete conversion (100%) of FA after 7 hours. The relative importance of the process parameters such as temperature (35-85°C), stirring speed (150-450rpm), catalyst loading (50-200mgr.), and Pd doping ratio (0.75-1.80wt.%) on gas yield was assessed by a Taguchi design-of-experiment based model. Experimental results showed that operating at a lower temperature range (35-50°C) yielded more gas, while the catalyst loading and Pd doping wt.% were found to be the most significant factors with P-values 0.026 & 0.031, respectively.

Keywords: formic acid decomposition, green catalysis, hydrogen, mesoporous silica, process optimization, nanoparticles

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Authors: Patrick Cormier, Cesar Meylan, Matt Jensen, Dana Agar-Newman, Chloe Werle, Ming-Chang Tsai, Marc Klimstra

Abstract:

Sprint-derived horizontal force-velocity-power (FVP) profiles can be developed with adequate validity and reliability with satellite (GPS/GNSS) systems. However, FVP metrics are sensitive to small nuances in data processing procedures such that minor differences in defining the onset and end of the sprint could result in different FVP metric outcomes. Furthermore, in team-sports, there is a requirement for rapid analysis and feedback of results from multiple athletes, therefore developing standardized and automated methods to improve the speed, efficiency and reliability of this process are warranted. Thus, the purpose of this study was to compare different methods of sprint end detection on the development of FVP profiles from 10Hz GPS/GNSS data through goodness-of-fit and intertrial reliability statistics. Seventeen national team female soccer players participated in the FVP protocol which consisted of 2x40m maximal sprints performed towards the end of a soccer specific warm-up in a training session (1020 hPa, wind = 0, temperature = 30°C) on an open grass field. Each player wore a 10Hz Catapult system unit (Vector S7, Catapult Innovations) inserted in a vest in a pouch between the scapulae. All data were analyzed following common procedures. Variables computed and assessed were the model parameters, estimated maximal sprint speed (MSS) and the acceleration constant τ, in addition to horizontal relative force (F₀), velocity at zero (V₀), and relative mechanical power (Pmax). The onset of the sprints was standardized with an acceleration threshold of 0.1 m/s². The sprint end detection methods were: 1. Time when peak velocity (MSS) was achieved (zero acceleration), 2. Time after peak velocity drops by -0.4 m/s, 3. Time after peak velocity drops by -0.6 m/s, and 4. When the integrated distance from the GPS/GNSS signal achieves 40-m. Goodness-of-fit of each sprint end detection method was determined using the residual sum of squares (RSS) to demonstrate the error of the FVP modeling with the sprint data from the GPS/GNSS system. Inter-trial reliability (from 2 trials) was assessed utilizing intraclass correlation coefficients (ICC). For goodness-of-fit results, the end detection technique that used the time when peak velocity was achieved (zero acceleration) had the lowest RSS values, followed by -0.4 and -0.6 velocity decay, and 40-m end had the highest RSS values. For intertrial reliability, the end of sprint detection techniques that were defined as the time at (method 1) or shortly after (method 2 and 3) when MSS was achieved had very large to near perfect ICC and the time at the 40 m integrated distance (method 4) had large to very large ICCs. Peak velocity was reached at 29.52 ± 4.02-m. Therefore, sport scientists should implement end of sprint detection either when peak velocity is determined or shortly after to improve goodness of fit to achieve reliable between trial FVP profile metrics. Although, more robust processing and modeling procedures should be developed in future research to improve sprint model fitting. This protocol was seamlessly integrated into the usual training which shows promise for sprint monitoring in the field with this technology.

Keywords: automated, biomechanics, team-sports, sprint

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Authors: Antonio Boggia, Luisa Paolotti, Gianluca Massei, Lucia Rocchi, Elaine Pace, Maria Attard

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The aim of this paper is to present a methodology and a computer model for sustainability assessment based on the integration of Multi-criteria Decision Analysis (MCDA) with a Geographic Information System (GIS). It presents the result of a study for the implementation of a model for measuring sustainability to address the policy actions for the improvement of sustainability at territory level. The aim is to rank areas in order to understand the specific technical and/or financial support that is required to develop sustainable growth. Assessing sustainable development is a multidimensional problem: economic, social and environmental aspects have to be taken into account at the same time. The tool for a multidimensional representation is a proper set of indicators. The set of indicators must be integrated into a model, that is an assessment methodology, to be used for measuring sustainability. The model, developed by the Environmental Laboratory of the University of Perugia, is called GeoUmbriaSUIT. It is a calculation procedure developed as a plugin working in the open-source GIS software QuantumGIS. The multi-criteria method used within GeoUmbriaSUIT is the algorithm TOPSIS (Technique for Order Preference by Similarity to Ideal Design), which defines a ranking based on the distance from the worst point and the closeness to an ideal point, for each of the criteria used. For the sustainability assessment procedure, GeoUmbriaSUIT uses a geographic vector file where the graphic data represent the study area and the single evaluation units within it (the alternatives, e.g. the regions of a country, or the municipalities of a region), while the alphanumeric data (attribute table), describe the environmental, economic and social aspects related to the evaluation units by means of a set of indicators (criteria). The use of the algorithm available in the plugin allows to treat individually the indicators representing the three dimensions of sustainability, and to compute three different indices: environmental index, economic index and social index. The graphic output of the model allows for an integrated assessment of the three dimensions, avoiding aggregation. The presence of separate indices and graphic output make GeoUmbriaSUIT a readable and transparent tool, since it doesn’t produce an aggregate index of sustainability as final result of the calculations, which is often cryptic and difficult to interpret. In addition, it is possible to develop a “back analysis”, able to explain the positions obtained by the alternatives in the ranking, based on the criteria used. The case study presented is an assessment of the level of sustainability in the six regions of Malta, an island state in the middle of the Mediterranean Sea and the southernmost member of the European Union. The results show that the integration of MCDA-GIS is an adequate approach for sustainability assessment. In particular, the implemented model is able to provide easy to understand results. This is a very important condition for a sound decision support tool, since most of the time decision makers are not experts and need understandable output. In addition, the evaluation path is traceable and transparent.

Keywords: GIS, multi-criteria analysis, sustainability assessment, sustainable development

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Authors: Mayank Gupta, Siba Prasad Samal, Vasu Kakkirala

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The internet world and its priorities have changed considerably in the last decade. Browsing on smart phones has increased manifold and is set to explode much more. Users spent considerable time browsing different websites, that gives a great deal of insight into user’s preferences. Instead of plain information classifying different aspects of browsing like Bookmarks, History, and Download Manager into useful categories would improve and enhance the user’s experience. Most of the classification solutions are server side that involves maintaining server and other heavy resources. It has security constraints and maybe misses on contextual data during classification. On device, classification solves many such problems, but the challenge is to achieve accuracy on classification with resource constraints. This on device classification can be much more useful in personalization, reducing dependency on cloud connectivity and better privacy/security. This approach provides more relevant results as compared to current standalone solutions because it uses content rendered by browser which is customized by the content provider based on user’s profile. This paper proposes a Naive Bayes based lightweight classification engine targeted for a resource constraint devices. Our solution integrates with Web Browser that in turn triggers classification algorithm. Whenever a user browses a webpage, this solution extracts DOM Tree data from the browser’s rendering engine. This DOM data is a dynamic, contextual and secure data that can’t be replicated. This proposal extracts different features of the webpage that runs on an algorithm to classify into multiple categories. Naive Bayes based engine is chosen in this solution for its inherent advantages in using limited resources compared to other classification algorithms like Support Vector Machine, Neural Networks, etc. Naive Bayes classification requires small memory footprint and less computation suitable for smartphone environment. This solution has a feature to partition the model into multiple chunks that in turn will facilitate less usage of memory instead of loading a complete model. Classification of the webpages done through integrated engine is faster, more relevant and energy efficient than other standalone on device solution. This classification engine has been tested on Samsung Z3 Tizen hardware. The Engine is integrated into Tizen Browser that uses Chromium Rendering Engine. For this solution, extensive dataset is sourced from dmoztools.net and cleaned. This cleaned dataset has 227.5K webpages which are divided into 8 generic categories ('education', 'games', 'health', 'entertainment', 'news', 'shopping', 'sports', 'travel'). Our browser integrated solution has resulted in 15% less memory usage (due to partition method) and 24% less power consumption in comparison with standalone solution. This solution considered 70% of the dataset for training the data model and the rest 30% dataset for testing. An average accuracy of ~96.3% is achieved across the above mentioned 8 categories. This engine can be further extended for suggesting Dynamic tags and using the classification for differential uses cases to enhance browsing experience.

Keywords: chromium, lightweight engine, mobile computing, Naive Bayes, Tizen, web browser, webpage classification

Procedia PDF Downloads 136

Authors: Sofia N. Gonçalves, Duarte M. S. Albuquerque, José C. F. Pereira

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The energy transition is spurred by structural changes in energy demand, supply, and prices. Microwave technology was first proposed as a faster alternative for cooking food. It was found that food heated instantly when interacting with high-frequency electromagnetic waves. The dielectric properties account for a material’s ability to absorb electromagnetic energy and dissipate this energy in the form of heat. Many energy-intense industries could benefit from electromagnetic heating since many of the raw materials are dielectric at high temperatures. Limestone sedimentary rock is a dielectric material intensively used in the cement industry to produce unslaked lime. A numerical 3D model was implemented in COMSOL Multiphysics to study the limestone continuous processing under microwave heating. The model solves the two-way coupling between the Energy equation and Maxwell’s equations as well as the coupling between heat transfer and chemical interfaces. Complementary, a controller was implemented to optimize the overall heating efficiency and control the numerical model stability. This was done by continuously matching the cavity impedance and predicting the required energy for the system, avoiding energy inefficiencies. This controller was developed in MATLAB and successfully fulfilled all these goals. The limestone load influence on thermal decomposition and overall process efficiency was the main object of this study. The procedure considered the Verification and Validation of the chemical kinetics model separately from the coupled model. The chemical model was found to correctly describe the chosen kinetic equation, and the coupled model successfully solved the equations describing the numerical model. The interaction between flow of material and electric field Poynting vector revealed to influence limestone decomposition, as a result from the low dielectric properties of limestone. The numerical model considered this effect and took advantage from this interaction. The model was demonstrated to be highly unstable when solving non-linear temperature distributions. Limestone has a dielectric loss response that increases with temperature and has low thermal conductivity. For this reason, limestone is prone to produce thermal runaway under electromagnetic heating, as well as numerical model instabilities. Five different scenarios were tested by considering a material fill ratio of 30%, 50%, 65%, 80%, and 100%. Simulating the tube rotation for mixing enhancement was proven to be beneficial and crucial for all loads considered. When uniform temperature distribution is accomplished, the electromagnetic field and material interaction is facilitated. The results pointed out the inefficient development of the electric field within the bed for 30% fill ratio. The thermal efficiency showed the propensity to stabilize around 90%for loads higher than 50%. The process accomplished a maximum microwave efficiency of 75% for the 80% fill ratio, sustaining that the tube has an optimal fill of material. Electric field peak detachment was observed for the case with 100% fill ratio, justifying the lower efficiencies compared to 80%. Microwave technology has been demonstrated to be an important ally for the decarbonization of the cement industry.

Keywords: CFD numerical simulations, efficiency optimization, electromagnetic heating, impedance matching, limestone continuous processing

Procedia PDF Downloads 145

Authors: Z. Gharaee

Abstract:

Recognizing actions performed by others is important in our daily lives since it is necessary for communicating with others in a proper way. We perceive an action by observing the kinematics of motions involved in the performance. We use our experience and concepts to make a correct recognition of the actions. Although building the action concepts is a life-long process, which is repeated throughout life, we are very efficient in applying our learned concepts in analyzing motions and recognizing actions. Experiments on the subjects observing the actions performed by an actor show that an action is recognized after only about two hundred milliseconds of observation. In this study, hierarchical action recognition architecture is proposed by using growing grid layers. The first-layer growing grid receives the pre-processed data of consecutive 3D postures of joint positions and applies some heuristics during the growth phase to allocate areas of the map by inserting new neurons. As a result of training the first-layer growing grid, action pattern vectors are generated by connecting the elicited activations of the learned map. The ordered vector representation layer receives action pattern vectors to create time-invariant vectors of key elicited activations. Time-invariant vectors are sent to second-layer growing grid for categorization. This grid creates the clusters representing the actions. Finally, one-layer neural network developed by a delta rule labels the action categories in the last layer. System performance has been evaluated in an experiment with the publicly available MSR-Action3D dataset. There are actions performed by using different parts of human body: Hand Clap, Two Hands Wave, Side Boxing, Bend, Forward Kick, Side Kick, Jogging, Tennis Serve, Golf Swing, Pick Up and Throw. The growing grid architecture was trained by applying several random selections of generalization test data fed to the system during on average 100 epochs for each training of the first-layer growing grid and around 75 epochs for each training of the second-layer growing grid. The average generalization test accuracy is 92.6%. A comparison analysis between the performance of growing grid architecture and self-organizing map (SOM) architecture in terms of accuracy and learning speed show that the growing grid architecture is superior to the SOM architecture in action recognition task. The SOM architecture completes learning the same dataset of actions in around 150 epochs for each training of the first-layer SOM while it takes 1200 epochs for each training of the second-layer SOM and it achieves the average recognition accuracy of 90% for generalization test data. In summary, using the growing grid network preserves the fundamental features of SOMs, such as topographic organization of neurons, lateral interactions, the abilities of unsupervised learning and representing high dimensional input space in the lower dimensional maps. The architecture also benefits from an automatic size setting mechanism resulting in higher flexibility and robustness. Moreover, by utilizing growing grids the system automatically obtains a prior knowledge of input space during the growth phase and applies this information to expand the map by inserting new neurons wherever there is high representational demand.

Keywords: action recognition, growing grid, hierarchical architecture, neural networks, system performance

Procedia PDF Downloads 133

Authors: Rahbel Rahman, Rogério Meireles Pinto, Margareth Santos Zanchetta

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Background: Existing shortcomings of current health-service delivery include poor teamwork, competencies that do not address consumer needs, and episodic rather than continuous care. Brazil’s Sistema Único de Saúde (Unified Health System, UHS) is acknowledged worldwide as a model for delivering community-based care through Estratégia Saúde da Família (FHS; Family Health Strategy) interdisciplinary teams, comprised of Community Health Agents (in Portuguese, Agentes Comunitário de Saude, ACS), nurses, and physicians. FHS teams are mandated to collectively offer clinical care, disease prevention services, vector control, health surveillance and social services. Our study compares medical providers (nurses and physicians) and community-based providers (ACS) on their perceptions of work environment, professional skills, cognitive capacities and job context. Global health administrators and policy makers can leverage on similarities and differences across care providers to develop interprofessional training for community-based primary care. Methods: Cross-sectional data were collected from 168 ACS, 62 nurses and 32 physicians in Brazil. We compared providers’ demographic characteristics (age, race, and gender) and job context variables (caseload, work experience, work proximity to community, the length of commute, and familiarity with the community). Providers perceptions were compared to their work environment (work conditions and work resources), professional skills (consumer-input, interdisciplinary collaboration, efficacy of FHS teams, work-methods and decision-making autonomy), and cognitive capacities (knowledge and skills, skill variety, confidence and perseverance). Descriptive and bi-variate analysis, such as Pearson Chi-square and Analysis of Variance (ANOVA) F-tests, were performed to draw comparisons across providers. Results: Majority of participants were ACS (64%); 24% nurses; and 12% physicians. Majority of nurses and ACS identified as mixed races (ACS, n=85; nurses, n=27); most physicians identified as males (n=16; 52%), and white (n=18; 58%). Physicians were less likely to incorporate consumer-input and demonstrated greater decision-making autonomy than nurses and ACS. ACS reported the highest levels of knowledge and skills but the least confidence compared to nurses and physicians. ACS, nurses, and physicians were efficacious that FHS teams improved the quality of health in their catchment areas, though nurses tend to disagree that interdisciplinary collaboration facilitated their work. Conclusion: To our knowledge, there has been no study comparing key demographic and cognitive variables across ACS, nurses and physicians in the context of their work environment and professional training. We suggest that global health systems can leverage upon the diverse perspectives of providers to implement a community-based primary care model grounded in interprofessional training. Our study underscores the need for in-service trainings to instill reflective skills of providers, improve communication skills of medical providers and curative skills of ACS. Greater autonomy needs to be extended to community based providers to offer care integral to addressing consumer and community needs.

Keywords: global health systems, interdisciplinary health teams, community health agents, community-based care

Procedia PDF Downloads 208

Authors: V. V. Fomin, A. P. Mikhailovich, E. M. Agapitov, V. E. Rogachev, E. A. Kostousova, E. S. Perekhodova

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Ground-level landscape photos can be used as a source of objective data on woody vegetation and vegetation dynamics. We proposed a method for processing, analyzing, and presenting ground photographs, which has the following advantages: 1) researcher has to form holistic representation of the study area in form of a set of interlapping ground-level landscape photographs; 2) it is necessary to define or obtain characteristics of the landscape, objects, and phenomena present on the photographs; 3) it is necessary to create new or supplement existing textual descriptions and annotations for the ground-level landscape photographs; 4) single or multiple ground-level landscape photographs can be used to develop specialized geoinformation layers, schematic maps or thematic maps; 5) it is necessary to determine quantitative data that describes both images as a whole, and displayed objects and phenomena, using algorithms for automated image analysis. It is suggested to match each photo with a polygonal geoinformation layer, which is a sector consisting of areas corresponding with parts of the landscape visible in the photos. Calculation of visibility areas is performed in a geoinformation system within a sector using a digital model of a study area relief and visibility analysis functions. Superposition of the visibility sectors corresponding with various camera viewpoints allows matching landscape photos with each other to create a complete and wholesome representation of the space in question. It is suggested to user-defined data or phenomenons on the images with the following superposition over the visibility sector in the form of map symbols. The technology of geoinformation layers’ spatial superposition over the visibility sector creates opportunities for image geotagging using quantitative data obtained from raster or vector layers within the sector with the ability to generate annotations in natural language. The proposed method has proven itself well for relatively open and clearly visible areas with well-defined relief, for example, in mountainous areas in the treeline ecotone. When the polygonal layers of visibility sectors for a large number of different points of photography are topologically superimposed, a layer of visibility of sections of the entire study area is formed, which is displayed in the photographs. Also, as a result of this overlapping of sectors, areas that did not appear in the photo will be assessed as gaps. According to the results of this procedure, it becomes possible to obtain information about the photos that display a specific area and from which points of photography it is visible. This information may be obtained either as a query on the map or as a query for the attribute table of the layer. The method was tested using repeated photos taken from forty camera viewpoints located on Ray-Iz mountain massif (Polar Urals, Russia) from 1960 until 2023. It has been successfully used in combination with other ground-based and remote sensing methods of studying the climate-driven dynamics of woody vegetation in the Polar Urals. Acknowledgment: This research was collaboratively funded by the Russian Ministry for Science and Education project No. FEUG-2023-0002 (image representation) and Russian Science Foundation project No. 24-24-00235 (automated textual description).

Keywords: woody, vegetation, repeated, photographs

Procedia PDF Downloads 25

Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales

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In this presentation, preliminary results are given for the modeling and calibration of two different industrial winding MIMO (Multiple Input Multiple Output) processes using machine learning techniques. In contrast to previous approaches which have typically used ‘black-box’ linear statistical methods together with a definition of the mechanical behavior of the process, we use non-linear machine learning algorithms together with a ‘white-box’ rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process in order to control de-tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case. Case 1, Tension Control of a Winding Process. A plastic web is unwound from a first reel, goes over a traction reel and is rewound on a third reel. The objectives are: (i) to train a model to predict the web tension and (ii) calibration to find the input values which result in a given tension. Case 2, Friction Force Control of a Micro-Pullwinding Process. A core+resin passes through a first die, then two winding units wind an outer layer around the core, and a final pass through a second die. The objectives are: (i) to train a model to predict the friction on die2; (ii) calibration to find the input values which result in a given friction on die2. Different machine learning approaches are tested to build models, Kernel Ridge Regression, Support Vector Regression (with a Radial Basis Function Kernel) and MPART (Rule Induction with continuous value as output). As a previous step, the MPART rule induction algorithm was used to build an explicative model of the error (the difference between expected and real friction on die2). The modeling of the error behavior using explicative rules is used to help improve the overall process model. Once the models are built, the inputs are calibrated by generating Gaussian random numbers for each input (taking into account its mean and standard deviation) and comparing the output to a target (desired) output until a closest fit is found. The results of empirical testing show that a high precision is obtained for the trained models and for the calibration process. The learning step is the slowest part of the process (max. 5 minutes for this data), but this can be done offline just once. The calibration step is much faster and in under one minute obtained a precision error of less than 1x10-3 for both outputs. To summarize, in the present work two processes have been modeled and calibrated. A fast processing time and high precision has been achieved, which can be further improved by using heuristics to guide the Gaussian calibration. Error behavior has been modeled to help improve the overall process understanding. This has relevance for the quick optimal set up of many different industrial processes which use a pull-winding type process to manufacture fibre reinforced plastic parts. Acknowledgements to the Openmind project which is funded by Horizon 2020 European Union funding for Research & Innovation, Grant Agreement number 680820

Keywords: data model, machine learning, industrial winding, calibration

Procedia PDF Downloads 213

Authors: Xingyu Gao, Qiang Wu

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Patents play a crucial role in protecting innovation and intellectual property. Early prediction of the impact of artificial intelligence (AI) patents helps researchers and companies allocate resources and make better decisions. Understanding the key factors that influence patent impact can assist researchers in gaining a better understanding of the evolution of AI technology and innovation trends. Therefore, identifying highly impactful patents early and providing support for them holds immeasurable value in accelerating technological progress, reducing research and development costs, and mitigating market positioning risks. Despite the extensive research on AI patents, accurately predicting their early impact remains a challenge. Traditional methods often consider only single factors or simple combinations, failing to comprehensively and accurately reflect the actual impact of patents. This paper utilized the artificial intelligence patent database from the United States Patent and Trademark Office and the Len.org patent retrieval platform to obtain specific information on 35,708 AI patents. Using six machine learning models, namely Multiple Linear Regression, Random Forest Regression, XGBoost Regression, LightGBM Regression, Support Vector Machine Regression, and K-Nearest Neighbors Regression, and using early indicators of patents as features, the paper comprehensively predicted the impact of patents from three aspects: technical, social, and economic. These aspects include the technical leadership of patents, the number of citations they receive, and their shared value. The SHAP (Shapley Additive exPlanations) metric was used to explain the predictions of the best model, quantifying the contribution of each feature to the model's predictions. The experimental results on the AI patent dataset indicate that, for all three target variables, LightGBM regression shows the best predictive performance. Specifically, patent novelty has the greatest impact on predicting the technical impact of patents and has a positive effect. Additionally, the number of owners, the number of backward citations, and the number of independent claims are all crucial and have a positive influence on predicting technical impact. In predicting the social impact of patents, the number of applicants is considered the most critical input variable, but it has a negative impact on social impact. At the same time, the number of independent claims, the number of owners, and the number of backward citations are also important predictive factors, and they have a positive effect on social impact. For predicting the economic impact of patents, the number of independent claims is considered the most important factor and has a positive impact on economic impact. The number of owners, the number of sibling countries or regions, and the size of the extended patent family also have a positive influence on economic impact. The study primarily relies on data from the United States Patent and Trademark Office for artificial intelligence patents. Future research could consider more comprehensive data sources, including artificial intelligence patent data, from a global perspective. While the study takes into account various factors, there may still be other important features not considered. In the future, factors such as patent implementation and market applications may be considered as they could have an impact on the influence of patents.

Keywords: patent influence, interpretable machine learning, predictive models, SHAP

Procedia PDF Downloads 18

Authors: Babak Forouraghi

Abstract:

A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations of previous approaches, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with attention mechanism. In a previous work on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: transformers, generative ai, gene expression design, classification

Procedia PDF Downloads 29

Authors: Mingqian Zhang

Abstract:

Tip-enhanced Raman spectroscopy (TERS) is a scanning probe technique for individual objects and structured surfaces investigation that provides a wealth of enhanced spectral information with nanoscale spatial resolution and high detection sensitivity. It has become a powerful and promising chemical and physical information detection method in the nanometer scale. The TERS technique uses a sharp metallic tip regulated in the near-field of a sample surface, which is illuminated with a certain incident beam meeting the excitation conditions of the wave-vector matching. The local electric field, and, consequently, the Raman scattering, from the sample in the vicinity of the tip apex are both greatly tip-enhanced owning to the excitation of localized surface plasmons and the lightning-rod effect. Typically, a TERS setup is composed of a scanning probe microscope, excitation and collection optical configurations, and a Raman spectroscope. In the illumination configuration, an objective lens or a parabolic mirror is always used as the most important component, in order to focus the incident beam on the tip apex for excitation. In this research, a novel TERS setup was built up by introducing a plasmonic lens to the excitation optics as a focusing device. A plasmonic lens with symmetry breaking semi-annular slits corrugated on gold film was designed for the purpose of generating concentrated sub-wavelength light spots with strong longitudinal electric field. Compared to conventional far-field optical components, the designed plasmonic lens not only focuses an incident beam to a sub-wavelength light spot, but also realizes a strong z-component that dominants the electric field illumination, which is ideal for the excitation of tip-enhancement. Therefore, using a PL in the illumination configuration of TERS contributes to improve the detection sensitivity by both reducing the far-field background and effectively exciting the localized electric field enhancement. The FDTD method was employed to investigate the optical near-field distribution resulting from the light-nanostructure interaction. And the optical field distribution was characterized using an scattering-type scanning near-field optical microscope to demonstrate the focusing performance of the lens. The experimental result is in agreement with the theoretically calculated one. It verifies the focusing performance of the plasmonic lens. The optical field distribution shows a bright elliptic spot in the lens center and several arc-like side-lobes on both sides. After the focusing performance was experimentally verified, the designed plasmonic lens was used as a focusing component in the excitation configuration of TERS setup to concentrate incident energy and generate a longitudinal optical field. A collimated linearly polarized laser beam, with along x-axis polarization, was incident from the bottom glass side on the plasmonic lens. The incident light focused by the plasmonic lens interacted with the silver-coated tip apex and enhanced the Raman signal of the sample locally. The scattered Raman signal was gathered by a parabolic mirror and detected with a Raman spectroscopy. Then, the plasmonic lens based setup was employed to investigate carbon nanotubes and TERS experiment was performed. Experimental results indicate that the Raman signal is considerably enhanced which proves that the novel TERS configuration is feasible and promising.

Keywords: longitudinal electric field, plasmonics, raman spectroscopy, tip-enhancement

Procedia PDF Downloads 346

Authors: Babak Forouraghi

Abstract:

A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

Procedia PDF Downloads 33

Authors: Zuhal Kurt, Meral Kurt, Bilge T. Bal, Kemal Ozkan

Abstract:

Shade matching and reproduction is the most important element of success in prosthetic dentistry. Until recently, shade matching procedure was implemented by dentists visual perception with the help of shade guides. Since many factors influence visual perception; tooth shade matching using visual devices (shade guides) is highly subjective and inconsistent. Subjective nature of this process has lead to the development of instrumental devices. Nowadays, colorimeters, spectrophotometers, spectroradiometers and digital image analysing systems are used for instrumental shade selection. Instrumental devices have advantages that readings are quantifiable, can obtain more rapidly and simply, objectively and precisely. However, these devices have noticeable drawbacks. For example, translucent structure and irregular surfaces of teeth lead to defects on measurement with these devices. Also between the results acquired by devices with different measurement principles may make inconsistencies. So, its obligatory to search for new methods for dental shade matching process. A computer-aided system device; digital camera has developed rapidly upon today. Currently, advances in image processing and computing have resulted in the extensive use of digital cameras for color imaging. This procedure has a much cheaper process than the use of traditional contact-type color measurement devices. Digital cameras can be taken by the place of contact-type instruments for shade selection and overcome their disadvantages. Images taken from teeth show morphology and color texture of teeth. In last decades, a new method was recommended to compare the color of shade tabs taken by a digital camera using color features. This method showed that visual and computer-aided shade matching systems should be used as concatenated. Recently using methods of feature extraction techniques are based on shape description and not used color information. However, color is mostly experienced as an essential property in depicting and extracting features from objects in the world around us. When local feature descriptors with color information are extended by concatenating color descriptor with the shape descriptor, that descriptor will be effective on visual object recognition and classification task. Therefore, the color descriptor is to be used in combination with a shape descriptor it does not need to contain any spatial information, which leads us to use local histograms. This local color histogram method is remain reliable under variation of photometric changes, geometrical changes and variation of image quality. So, coloring local feature extraction methods are used to extract features, and also the Scale Invariant Feature Transform (SIFT) descriptor used to for shape description in the proposed method. After the combination of these descriptors, the state-of-art descriptor named by Color-SIFT will be used in this study. Finally, the image feature vectors obtained from quantization algorithm are fed to classifiers such as Nearest Neighbor (KNN), Naive Bayes or Support Vector Machines (SVM) to determine label(s) of the visual object category or matching. In this study, SVM are used as classifiers for color determination and shade matching. Finally, experimental results of this method will be compared with other recent studies. It is concluded from the study that the proposed method is remarkable development on computer aided tooth shade determination system.

Keywords: classifiers, color determination, computer-aided system, tooth shade matching, feature extraction

Procedia PDF Downloads 404

Authors: Evgenia Iakovleva, Juha Koivisto, Pasi Karppinen, J. Inkinen, Mikko Alava

Abstract:

Preventing the spread of infectious diseases throughout the worldwide is one of the most important tasks of modern health care. Infectious diseases not only account for one fifth of the deaths in the world, but also cause many pathological complications for the human health. Touch surfaces pose an important vector for the spread of infections by varying microorganisms, including antimicrobial resistant organisms. Further, antimicrobial resistance is reply of bacteria to the overused or inappropriate used of antibiotics everywhere. The biggest challenges in bacterial detection by existing methods are non-direct determination, long time of analysis, the sample preparation, use of chemicals and expensive equipment, and availability of qualified specialists. Therefore, a high-performance, rapid, real-time detection is demanded in rapid practical bacterial detection and to control the epidemiological hazard. Among the known methods for determining bacteria on the surfaces, Hyperspectral methods can be used as direct and rapid methods for microorganism detection on different kind of surfaces based on fluorescence without sampling, sample preparation and chemicals. The aim of this study was to assess the relevance of such systems to remote sensing of surfaces for microorganisms detection to prevent a global spread of infectious diseases. Bacillus subtilis and Escherichia coli with different concentrations (from 0 to 10x8 cell/100µL) were detected with hyperspectral camera using different filters as visible visualization of bacteria and background spots on the steel plate. A method of internal standards was applied for monitoring the correctness of the analysis results. Distances from sample to hyperspectral camera and light source are 25 cm and 40 cm, respectively. Each sample is optically imaged from the surface by hyperspectral imaging system, utilizing a JAI CM-140GE-UV camera. Light source is BeamZ FLATPAR DMX Tri-light, 3W tri-colour LEDs (red, blue and green). Light colors are changed through DMX USB Pro interface. The developed system was calibrated following a standard procedure of setting exposure and focused for light with λ=525 nm. The filter is ThorLabs KuriousTM hyperspectral filter controller with wavelengths from 420 to 720 nm. All data collection, pro-processing and multivariate analysis was performed using LabVIEW and Python software. The studied human eye visible and invisible bacterial stains clustered apart from a reference steel material by clustering analysis using different light sources and filter wavelengths. The calculation of random and systematic errors of the analysis results proved the applicability of the method in real conditions. Validation experiments have been carried out with photometry and ATP swab-test. The lower detection limit of developed method is several orders of magnitude lower than for both validation methods. All parameters of the experiments were the same, except for the light. Hyperspectral imaging method allows to separate not only bacteria and surfaces, but also different types of bacteria, such as Gram-negative Escherichia coli and Gram-positive Bacillus subtilis. Developed method allows skipping the sample preparation and the use of chemicals, unlike all other microbiological methods. The time of analysis with novel hyperspectral system is a few seconds, which is innovative in the field of microbiological tests.

Keywords: Escherichia coli, Bacillus subtilis, hyperspectral imaging, microorganisms detection

Procedia PDF Downloads 179

Authors: Bensaid A., Mostephaoui T., Nedjai R.

Abstract:

Nowadays, wetlands are the subject of contradictory debates opposing scientific, political and administrative meanings. Indeed, given their multiple services (drinking water, irrigation, hydrological regulation, mineral, plant and animal resources...), wetlands concentrate many socio-economic and biodiversity issues. In some regions, they can cover vast areas (>100 thousand ha) of the landscape, such as the Camargue area in the south of France, inside the Rhone delta. The high biological productivity of wetlands, the strong natural selection pressures and the diversity of aquatic environments have produced many species of plants and animals that are found nowhere else. These environments are tremendous carbon sinks and biodiversity reserves depending on their age, composition and surrounding environmental conditions, wetlands play an important role in global climate projections. Covering more than 3% of the earth's surface, wetlands have experienced since the beginning of the 1990s a tremendous revival of interest, which has resulted in the multiplication of inventories, scientific studies and management experiments. The geographical and physical characteristics of the wetlands of the central region conceal a large number of natural habitats that harbour a great biological diversity. These wetlands, one of the natural habitats, are still influenced by human activities, especially agriculture, which affects its layout and functioning. In this perspective, decision-makers need to delimit spatial objects (natural habitats) in a certain way to be able to take action. Thus, wetlands are no exception to this rule even if it seems to be a difficult exercise to delimit a type of environment as whose main characteristic is often to occupy the transition between aquatic and terrestrial environment. However, it is possible to map wetlands with databases, derived from the interpretation of photos and satellite images, such as the European database Corine Land cover, which allows quantifying and characterizing for each place the characteristic wetland types. Scientific studies have shown limitations when using high spatial resolution images (SPOT, Landsat, ASTER) for the identification and characterization of small wetlands (1 hectare). To address this limitation, it is important to note that these wetlands generally represent spatially complex features. Indeed, the use of very high spatial resolution images (>3m) is necessary to map small and large areas. However, with the recent evolution of artificial intelligence (AI) and deep learning methods for satellite image processing have shown a much better performance compared to traditional processing based only on pixel structures. Our research work is also based on spectral and textural analysis on THR images (Spot and IRC orthoimage) using two object-oriented approaches, the nearest neighbour approach (k-NN) and the Super Vector Machine approach (SVM). The k-NN approach gave good results for the delineation of wetlands (wet marshes and moors, ponds, artificial wetlands water body edges, ponds, mountain wetlands, river edges and brackish marshes) with a kappa index higher than 85%.

Keywords: land development, GIS, sand dunes, segmentation, remote sensing

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Authors: Jessica P. Fiorotti, Maria C. Freitas, Caio J. B. Coutinho-Rodrigues, Mariana G. Camargo, Emily S. Mesquita, Amanda R. C. Corval, Ricardo O. B. Bitencourt, Allan F. Marciano, Diva D. Spadacci-Morena, Patricia S. Golo, Isabele C. Angelo, Vania R. E. P. Bittencourt

Abstract:

The bovine tick, Rhipicephalus microplus, is an arthropod of great importance in veterinary medicine leading to anemia, weight loss, animals' leather depreciation and also acting as a vector of many pathogens. In this way, the parasitism causes a loss of 3.24 billion dollars per year in Brazil. Knowingly, entomopathogenic fungi act as natural controller of some arthropods, acting mainly by active penetration through the cuticle. However, it can also act on the hemolymph and through the production of mycotoxins. Hemocytes are responsible for the cellular immune response and participate in the processes of phagocytosis, nodulation and encapsulation and may undergo changes when challenged by pathogens. The aim of the present study was to evaluate changes in R. microplus hemocytes after inoculation of Metarhizium robertsii using transmission electron microscopy. The isolate ARSEF 2575 and 200 engorged R. microplus females were used. The groups were divided into control, in which the females were inoculated with 5 μL of sterile distilled water solution and 0.1% Tween 80, and a group inoculated with 5 μL of fungal suspension at the concentration of 10⁷ conidia mL⁻¹. The experiment was performed in duplicate and each group contained 50 females. Twenty-four hours after fungal inoculation, hemolymph was collected through the cuticle dorsal surface perforation of the tick females. After collection, the hemolymph samples were centrifuged at 500 x g for 3 minutes at 4 °C, the plasma was discarded and the hemocyte pellet was resuspended in 50 μl PBS. The suspension material was fixed in 2% glutaraldehyde in Millonig buffer for three hours. After fixation, the material was centrifuged at 500 x g for 3 minutes, the supernatant was discarded and the cells were resuspended in a wash solution. Subsequently, the cells were post-fixed with 1% osmium tetroxide in phosphate buffer for one hour at room temperature and dehydrated in increasing concentrations of ethanol, and then embedded in Epon resin. The ultrathin sections were examined under the LEO EM 906E transmission electron microscopy at 80kV. The ultrastructural results revealed that.in control group, the cells were considered intact, in which the granulocytes were observed with granules of different electrodensities, intact mitochondria and cytoplasm without vacuolization. In addition, granulocytes showed plasma membrane projections similar to pseudopodia. Plasmatocytes presented as irregularly shaped cells, with the eccentric nucleus, agranular cytoplasm and some cells presented pseudopodia. Nevertheless, in the group exposed to the fungus, most of the cells presented in degeneration. The granulocytes found had fewer granules in the cytoplasm and more vacuoles. Plasmatocytes, after treatment, presented many vacuoles also in the cytoplasm and the lysosomes presented great amount of electrodense material in their interior. Thus, the results suggest that the fungus has a depressant action in the immune system of the tick, not only by the cell degranulation, but also suggesting that this leads to morphological changes in the hemocytes and may even trigger processes such as phagocytosis.

Keywords: bovine tick, cellular defense, entomopathogenic fungi, immune response

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Zachary Blanks, Solomon Sonya

Abstract:

Poaching presents a serious threat to endangered animal species, environment conservations, and human life. Additionally, some poaching activity has even been linked to supplying funds to support terrorist networks elsewhere around the world. Consequently, agencies dedicated to protecting wildlife habitats have a near intractable task of adequately patrolling an entire area (spanning several thousand kilometers) given limited resources, funds, and personnel at their disposal. Thus, agencies need predictive tools that are both high-performing and easily implementable by the user to help in learning how the significant features (e.g. animal population densities, topography, behavior patterns of the criminals within the area, etc) interact with each other in hopes of abating poaching. This research develops a classification model using machine learning algorithms to aid in forecasting future attacks that is both easy to train and performs well when compared to other models. In this research, we demonstrate how data imputation methods (specifically predictive mean matching, gradient boosting, and random forest multiple imputation) can be applied to analyze data and create significant predictions across a varied data set. Specifically, we apply these methods to improve the accuracy of adopted prediction models (Logistic Regression, Support Vector Machine, etc). Finally, we assess the performance of the model and the accuracy of our data imputation methods by learning on a real-world data set constituting four years of imputed data and testing on one year of non-imputed data. This paper provides three main contributions. First, we extend work done by the Teamcore and CREATE (Center for Risk and Economic Analysis of Terrorism Events) research group at the University of Southern California (USC) working in conjunction with the Department of Homeland Security to apply game theory and machine learning algorithms to develop more efficient ways of reducing poaching. This research introduces ensemble methods (Random Forests and Stochastic Gradient Boosting) and applies it to real-world poaching data gathered from the Ugandan rain forest park rangers. Next, we consider the effect of data imputation on both the performance of various algorithms and the general accuracy of the method itself when applied to a dependent variable where a large number of observations are missing. Third, we provide an alternate approach to predict the probability of observing poaching both by season and by month. The results from this research are very promising. We conclude that by using Stochastic Gradient Boosting to predict observations for non-commercial poaching by season, we are able to produce statistically equivalent results while being orders of magnitude faster in computation time and complexity. Additionally, when predicting potential poaching incidents by individual month vice entire seasons, boosting techniques produce a mean area under the curve increase of approximately 3% relative to previous prediction schedules by entire seasons.

Keywords: ensemble methods, imputation, machine learning, random forests, statistical analysis, stochastic gradient boosting, wildlife protection

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Authors: Robert Miller, Fred Soltero, Sandra Allan, Denise Bonilla

Abstract:

The Tropical Bont Tick (TBT), Amblyomma variegatum, was introduced to the Caribbean in the mid-1700s. Since it has spread throughout the Caribbean dispersed by cattle egrets (Bubulcus ibis). Tropical Bont Ticks vector many pathogens to livestock and humans. However, only the livestock diseases heartwater, Ehrlichia (Cowdria) ruminantium, and dermatophilosis, Dermatophilus congolensis, are associated with TBT in the Caribbean. African tick bite fever (Rickettsia africae) is widespread in Caribbean TBT but human cases are rare. The Caribbean Amblyomma Programme (CAP) was an effort led by the Food and Agricultural Organization to eradicate TBTs from participating islands. This 10-year effort successfully eradicated TBT from many islands. However, most are reinfested since its termination. Pheromone technology has been developed to aid in TBT control. Although not part of the CAP treatment scheme, this research established that pheromones in combination with pesticide greatly improves treatment efficiencies. Additionally, pheromone combined with CO₂ traps greatly improves active surveillance success. St. Croix has a history of TBT outbreaks. Passive surveillance detected outbreaks in 2016 and in May of 2021. Surveillance efforts are underway to determine the extent of TBT on St Croix. Puerto Rico is the next island in the archipelago and is at a greater risk of re-infestation due to active outbreaks in St Croix. Tropical Bont Ticks were last detected in Puerto Rico in the 1980s. The infestation started on the small Puerto Rican island of Vieques, the closest landmass to St Croix, and spread to the main island through cattle movements. This infestation was eradicated with the help of the Tropical Cattle Tick (TCT), Rhipicephalus (Boophilus) microplus, eradication program. At the time, large percentages of Puerto Rican cattle were treated for ticks along with the necessary material and manpower mobilized for the effort. Therefore, a shift of focus from the TCT to TBT prevented its establishment in Puerto Rico. Currently, no large-scale treatment of TCTs occurs in Puerto Rico. Therefore, the risk of TBT establishment is now greater than it was in the 1980s. From Puerto Rico, the risk of TBT movement to the American continent increases significantly. The establishment of TBTs in the Americas would cause $1.2 billion USD in losses to the livestock industry per year. The USDA Agricultural Research Service recently worked with the USDA Animal Health Inspection Service and the Puerto Rican Department of Agriculture to modernize the management of the TCT. This modernized program uses safer pesticides and has successfully been used to eradicate pesticide-susceptible and -resistant ticks throughout the island. The objective of this work is to prevent the infestation of Puerto Rico by TBTs by combining the current TCT management efforts with TBT surveillance in Vieques. The combined effort is designed to eradicate TCT from Vieques while using the treated cattle as trap animals for TBT using pheromone impregnated tail tags attached to treated animals. Additionally, active surveillance using CO₂-baited traps combined with pheromone will be used to actively survey the environment for free-living TBT. Knowledge gained will inform TBT control efforts in St. Croix.

Keywords: Amblyomma variegatum, caribbean, eradication, Rhipicephalus (boophilus) microplus, pheromone

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Authors: M. Mueller, M. Kuehn, M. Voelker

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In small and medium-sized enterprises (SMEs), the challenge is to create a well-grounded and reliable basis for process analysis, optimization and planning due to a lack of data. SMEs have limited access to methods with which they can effectively and efficiently analyse processes and identify cause-and-effect relationships in order to generate the necessary database and derive optimization potential from it. The implementation of digitalization within the framework of Industry 4.0 thus becomes a particular necessity for SMEs. For these reasons, the abstract presents an analysis methodology that is subject to the objective of developing an SME-appropriate methodology for efficient, temporarily feasible data collection and evaluation in flexible production and logistics systems as a basis for process analysis and optimization. The overall methodology focuses on retrospective, event-based tracing and analysis of material flow objects. The technological basis consists of Bluetooth low energy (BLE)-based transmitters, so-called beacons, and smart mobile devices (SMD), e.g. smartphones as receivers, between which distance data can be measured and derived motion profiles. The distance is determined using the Received Signal Strength Indicator (RSSI), which is a measure of signal field strength between transmitter and receiver. The focus is the development of a software-based methodology for interpretation of relative movements of transmitters and receivers based on distance data. The main research is on selection and implementation of pattern recognition methods for automatic process recognition as well as methods for the visualization of relative distance data. Due to an existing categorization of the database regarding process types, classification methods (e.g. Support Vector Machine) from the field of supervised learning are used. The necessary data quality requires selection of suitable methods as well as filters for smoothing occurring signal variations of the RSSI, the integration of methods for determination of correction factors depending on possible signal interference sources (columns, pallets) as well as the configuration of the used technology. The parameter settings on which respective algorithms are based have a further significant influence on result quality of the classification methods, correction models and methods for visualizing the position profiles used. The accuracy of classification algorithms can be improved up to 30% by selected parameter variation; this has already been proven in studies. Similar potentials can be observed with parameter variation of methods and filters for signal smoothing. Thus, there is increased interest in obtaining detailed results on the influence of parameter and factor combinations on data quality in this area. The overall methodology is realized with a modular software architecture consisting of independently modules for data acquisition, data preparation and data storage. The demonstrator for initialization and data acquisition is available as mobile Java-based application. The data preparation, including methods for signal smoothing, are Python-based with the possibility to vary parameter settings and to store them in the database (SQLite). The evaluation is divided into two separate software modules with database connection: the achievement of an automated assignment of defined process classes to distance data using selected classification algorithms and the visualization as well as reporting in terms of a graphical user interface (GUI).

Keywords: event-based tracing, machine learning, process classification, parameter settings, RSSI, signal smoothing

Procedia PDF Downloads 100

Authors: Margarita Dufresne

Abstract:

This study devoted to development TPS for small space re-usable launchers. We have used SIRIUS design for S1 prototype. Multiphysics coupling for hypersonic reactive flow and thermos-structural analysis with and without ablation is provided by -CCM+ and COMSOL Multiphysics and FASTRAN and ACE+. Flow around hypersonic flight vehicles is the interaction of multiple shocks and the interaction of shocks with boundary layers. These interactions can have a very strong impact on the aeroheating experienced by the flight vehicle. A real gas implies the existence of a gas in equilibrium, non-equilibrium. Mach number ranged from 5 to 10 for first stage flight.The goals of this effort are to provide validation of the iterative coupling of hypersonic physics models in STAR-CCM+ and FASTRAN with COMSOL Multiphysics and ACE+. COMSOL Multiphysics and ACE+ are used for thermal structure analysis to simulate Conjugate Heat Transfer, with Conduction, Free Convection and Radiation to simulate Heat Flux from hypersonic flow. The reactive simulations involve an air chemical model of five species: N, N2, NO, O and O2. Seventeen chemical reactions, involving dissociation and recombination probabilities calculation include in the Dunn/Kang mechanism. Forward reaction rate coefficients based on a modified Arrhenius equation are computed for each reaction. The algorithms employed to solve the reactive equations used the second-order numerical scheme is obtained by a “MUSCL” (Monotone Upstream-cantered Schemes for Conservation Laws) extrapolation process in the structured case. Coupled inviscid flux: AUSM+ flux-vector splitting The MUSCL third-order scheme in STAR-CCM+ provides third-order spatial accuracy, except in the vicinity of strong shocks, where, due to limiting, the spatial accuracy is reduced to second-order and provides improved (i.e., reduced) dissipation compared to the second-order discretization scheme. initial unstructured mesh is refined made using this initial pressure gradient technique for the shock/shock interaction test case. The suggested by NASA turbulence models are the K-Omega SST with a1 = 0.355 and QCR (quadratic) as the constitutive option. Specified k and omega explicitly in initial conditions and in regions – k = 1E-6 *Uinf^2 and omega = 5*Uinf/ (mean aerodynamic chord or characteristic length). We put into practice modelling tips for hypersonic flow as automatic coupled solver, adaptative mesh refinement to capture and refine shock front, using advancing Layer Mesher and larger prism layer thickness to capture shock front on blunt surfaces. The temperature range from 300K to 30 000 K and pressure between 1e-4 and 100 atm. FASTRAN and ACE+ are coupled to provide high-fidelity solution for hot hypersonic reactive flow and Conjugate Heat Transfer. The results of both approaches meet the CIRCA wind tunnel results.

Keywords: hypersonic, first stage, high speed compressible flow, shock wave, aerodynamic heating, conugate heat transfer, conduction, free convection, radiation, fastran, ace+, comsol multiphysics, star-ccm+, thermal protection system (tps), space launcher, wind tunnel

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