Search results for: quantum genetic algorithm

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: Daniele Calvi, Loris Franchi, Sabrina Corpino

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Thanks to the latest Mars mission, the planetary exploration has made enormous strides over the past ten years increasing the interest of the scientific community and beyond. These missions aim to fulfill many complex operations which are of paramount importance to mission success. Among these, a special mention goes to the Entry, Descent and Landing (EDL) functions which require a dedicated system to overcome all the obstacles of these critical phases. The general objective of the system is to safely bring the spacecraft from orbital conditions to rest on the planet surface, following the designed mission profile. For this reason, this work aims to develop a simulation tool integrating the re-entry trajectory algorithm in order to support the EDL design during the preliminary phase of the mission. This tool was used on a reference unmanned mission, whose objective is finding bio-evidence and bio-hazards on Martian (sub)surface in order to support the future manned mission. Regarding the concept of operations (CONOPS) of the mission, it concerns the use of Space Penetrator Systems (SPS) that will descend on Mars surface following a ballistic fall and will penetrate the ground after the impact with the surface (around 50 and 300 cm of depth). Each SPS shall contain all the instrumentation required to sample and make the required analyses. Respecting the low-cost and low-mass requirements, as result of the tool, an Entry Descent and Impact (EDI) system based on inflatable structure has been designed. Hence, a solution could be the one chosen by Finnish Meteorological Institute in the Mars Met-Net mission, using an inflatable Thermal Protection System (TPS) called Inflatable Braking Unit (IBU) and an additional inflatable decelerator. Consequently, there are three configurations during the EDI: at altitude of 125 km the IBU is inflated at speed 5.5 km/s; at altitude of 16 km the IBU is jettisoned and an Additional Inflatable Braking Unit (AIBU) is inflated; Lastly at about 13 km, the SPS is ejected from AIBU and it impacts on the Martian surface. Since all parameters are evaluated, it is possible to confirm that the chosen EDI system and strategy verify the requirements of the mission.

Keywords: EDL, Mars, mission, SPS, TPS

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Authors: Anthony F. Peressini

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The concept of race has recently come prominently back into discussion in the context of medicine and medical science, along with renewed effort to biologize racial concepts. This paper argues that this renewed effort to biologize race by way of medicine and population genetics fail on their own terms, and more importantly, that the philosophical project of biologizing race ought to be recognized for what it is—a retrograde racial project—and abandoned. There is clear agreement that standard racial categories and concepts cannot be grounded in the old way of racial naturalism, which understand race as a real, interest-independent biological/metaphysical category in which its members share “physical, moral, intellectual, and cultural characteristics.” But equally clear is the very real and pervasive presence of racial concepts in individual and collective consciousness and behavior, and so it remains a pressing area in which to seek deeper understanding. Recent philosophical work has endeavored to reconcile these two observations by developing a “thin” conception of race, grounded in scientific concepts but without the moral and metaphysical content. Such “thin,” science-based analyses take the “commonsense” or “folk” sense of race as it functions in contemporary society as the starting point for their philosophic-scientific projects to biologize racial concepts. A “philosophic-scientific analysis” is a special case of the cornerstone of analytic philosophy: a conceptual analysis. That is, a rendering of a concept into the more perspicuous concepts that constitute it. Thus a philosophic-scientific account of a concept is an attempt to work out an analysis of a concept that makes use of empirical science's insights to ground, legitimate and explicate the target concept in terms of clearer concepts informed by empirical results. The focus in this paper is on three recent philosophic-scientific cases for retaining “race” that all share this general analytic schema, but that make use of “medical necessity,” population genetics, and human genetic clustering, respectively. After arguing that each of these three approaches suffers from internal difficulties, the paper considers the general analytic schema employed by such biologizations of race. While such endeavors are inevitably prefaced with the disclaimer that the theory to follow is non-essentialist and non-racialist, the case will be made that such efforts are not neutral scientific or philosophical projects but rather are what sociologists call a racial project, that is, one of many competing efforts that conjoin a representation of what race means to specific efforts to determine social and institutional arrangements of power, resources, authority, etc. Accordingly, philosophic-scientific biologizations of race, since they begin from and condition their analyses on “folk” conceptions, cannot pretend to be “prior to” other disciplinary insights, nor to transcend the social-political dynamics involved in formulating theories of race. As a result, such traditional philosophical efforts can be seen to be disciplinarily parochial and to address only a caricature of a large and important human problem—and thereby further contributing to the unfortunate isolation of philosophical thinking about race from other disciplines.

Keywords: population genetics, ontology of race, race-based medicine, racial formation theory, racial projects, racism, social construction

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Authors: Samir Chawdhury, Guido Morgenthal

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The paper presents a novel extension of Vortex Particle Methods (VPM) where the study aims to reproduce a template simulation of complex flow field that is generated from impulsively started flow past an upstream bluff body at certain Reynolds number Re-Vibration of a structural system under upstream wake flow is often considered its governing design criteria. Therefore, the attention is given in this study especially for the reproduction of wake flow simulation. The basic methodology for the implementation of the flow reproduction requires the downstream velocity sampling from the template flow simulation; therefore, at particular distances from the upstream section the instantaneous velocity components are sampled using a series of square sampling-cells arranged vertically where each of the cell contains four velocity sampling points at its corner. Since the grid free Lagrangian VPM algorithm discretises vorticity on particle elements, the method requires transformation of the velocity components into vortex circulation, and finally the simulation of the reproduction of the template flow field by seeding these vortex circulations or particles into a free stream flow. It is noteworthy that the vortex particles have to be released into the free stream exactly at same rate of velocity sampling. Studies have been done, specifically, in terms of different sampling rates and velocity sampling positions to find their effects on flow reproduction quality. The quality assessments are mainly done, using a downstream flow monitoring profile, by comparing the characteristic wind flow profiles using several statistical turbulence measures. Additionally, the comparisons are performed using velocity time histories, snapshots of the flow fields, and the vibration of a downstream bluff section by performing wake buffeting analyses of the section under the original and reproduced wake flows. Convergence study is performed for the validation of the method. The study also describes the possibilities how to achieve flow reproductions with less computational effort.

Keywords: vortex particle method, wake flow, flow reproduction, wake buffeting analysis

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Authors: Fathi Kallel, Abdulelah Alabd Uljabbar, Abdulrahman Aldukhail, Abdulaziz Alomran

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The brain is an important organ in our body since it is responsible about the majority actions such as vision, memory, etc. However, different diseases such as Alzheimer and tumors could affect the brain and conduct to a partial or full disorder. Regular diagnosis are necessary as a preventive measure and could help doctors to early detect a possible trouble and therefore taking the appropriate treatment, especially in the case of brain tumors. Different imaging modalities are proposed for diagnosis of brain tumor. The powerful and most used modality is the Magnetic Resonance Imaging (MRI). MRI images are analyzed by doctor in order to locate eventual tumor in the brain and describe the appropriate and needed treatment. Diverse image processing methods are also proposed for helping doctors in identifying and analyzing the tumor. In fact, a large Computer Aided Diagnostic (CAD) tools including developed image processing algorithms are proposed and exploited by doctors as a second opinion to analyze and identify the brain tumors. In this paper, we proposed a new advanced CAD for brain tumor identification, classification and feature extraction. Our proposed CAD includes three main parts. Firstly, we load the brain MRI. Secondly, a robust technique for brain tumor extraction is proposed. This technique is based on both Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). DWT is characterized by its multiresolution analytic property, that’s why it was applied on MRI images with different decomposition levels for feature extraction. Nevertheless, this technique suffers from a main drawback since it necessitates a huge storage and is computationally expensive. To decrease the dimensions of the feature vector and the computing time, PCA technique is considered. In the last stage, according to different extracted features, the brain tumor is classified into either benign or malignant tumor using Support Vector Machine (SVM) algorithm. A CAD tool for brain tumor detection and classification, including all above-mentioned stages, is designed and developed using MATLAB guide user interface.

Keywords: MRI, brain tumor, CAD, feature extraction, DWT, PCA, classification, SVM

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Authors: Christopher B. Obida, George A. Blackburn, James D. Whyatt, Kirk T. Semple

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In many regions of the world, especially in developing countries, river network data are outdated or completely absent, yet such information is critical for supporting important functions such as flood mitigation efforts, land use and transportation planning, and the management of water resources. In this study, a method was developed for delineating river networks using Sentinel 1 imagery. Unsupervised classification was applied to multi-temporal Sentinel 1 data to discriminate water bodies from other land covers then the outputs were combined to generate a single persistent water bodies product. A thinning algorithm was then used to delineate river centre lines, which were converted into vector features and built into a topologically structured geometric network. The complex river system of the Niger Delta was used to compare the performance of the Sentinel-based method against alternative freely available water body products from United States Geological Survey, European Space Agency and OpenStreetMap and a river network derived from a Shuttle Rader Topography Mission Digital Elevation Model. From both raster-based and vector-based accuracy assessments, it was found that the Sentinel-based river network products were superior to the comparator data sets by a substantial margin. The geometric river network that was constructed permitted a flow routing analysis which is important for a variety of environmental management and planning applications. The extracted network will potentially be applied for modelling dispersion of hydrocarbon pollutants in Ogoniland, a part of the Niger Delta. The approach developed in this study holds considerable potential for generating up to date, detailed river network data for the many countries where such data are deficient.

Keywords: Sentinel 1, image processing, river delineation, large scale mapping, data comparison, geometric network

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Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

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Authors: Zhuanfang Fred Zhang, Tim C. Johnson, Yilin Fang, Chris E. Strickland

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A variety of geophysical techniques are available to image the opaque subsurface with little or no contact with the soil. It is common to conduct time-lapse surveys of different types for a given site for improved results of subsurface imaging. Regardless of the chosen survey methods, it is often a challenge to process the massive amount of survey data. The currently available software applications are generally based on the one-dimensional assumption for a desktop personal computer. Hence, they are usually incapable of imaging the three-dimensional (3D) processes/variables in the subsurface of reasonable spatial scales; the maximum amount of data that can be inverted simultaneously is often very small due to the capability limitation of personal computers. Presently, high-performance or integrating software that enables real-time integration of multi-process geophysical methods is needed. E4D-MP enables the integration and inversion of time-lapsed large-scale data surveys from geophysical methods. Using the supercomputing capability and parallel computation algorithm, E4D-MP is capable of processing data across vast spatiotemporal scales and in near real time. The main code and the modules of E4D-MP for inverting individual or combined data sets of time-lapse 3D electrical resistivity, spectral induced polarization, and gravity surveys have been developed and demonstrated for sub-surface imaging. E4D-MP provides capability of imaging the processes (e.g., liquid or gas flow, solute transport, cavity development) and subsurface properties (e.g., rock/soil density, conductivity) critical for successful control of environmental engineering related efforts such as environmental remediation, carbon sequestration, geothermal exploration, and mine land reclamation, among others.

Keywords: gravity survey, high-performance computing, sub-surface monitoring, electrical resistivity tomography

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Authors: Srinaath Anbu Durai, Wang Zhaoxia

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Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

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Authors: Shaikah Alsubayae, Gianluigi Casse, Carlos Chavez, Jon Taylor, Alan Taylor, Mohammad Alsulimane

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Ensuring accurate radiotherapy with carbon therapy requires precise monitoring of radiation dose distribution within the patient's body. This monitoring is essential for targeted tumor treatment, minimizing harm to healthy tissues, and improving treatment effectiveness while lowering side effects. In our investigation, we employed a methodological approach to monitor secondary proton doses in carbon therapy using Monte Carlo simulations. Initially, Geant4 simulations were utilized to extract the initial positions of secondary particles formed during interactions between carbon ions and water. These particles included protons, gamma rays, alpha particles, neutrons, and tritons. Subsequently, we studied the relationship between the carbon ion beam and these secondary particles. Interaction Vertex Imaging (IVI) is valuable for monitoring dose distribution in carbon therapy. It provides details about the positions and amounts of secondary particles, particularly protons. The IVI method depends on charged particles produced during ion fragmentation to gather information about the range by reconstructing particle trajectories back to their point of origin, referred to as the vertex. In our simulations regarding carbon ion therapy, we observed a strong correlation between some secondary particles and the range of carbon ions. However, challenges arose due to the target's unique elongated geometry, which hindered the straightforward transmission of forward-generated protons. Consequently, the limited protons that emerged mostly originated from points close to the target entrance. The trajectories of fragments (protons) were approximated as straight lines, and a beam back-projection algorithm, using recorded interaction positions in Si detectors, was developed to reconstruct vertices. The analysis revealed a correlation between the reconstructed and actual positions.

Keywords: radiotherapy, carbon therapy, monitoring of radiation dose, interaction vertex imaging

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Authors: Naum Simanovsky, Vladimir Goldman, Michael Zaidman

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Background: The optimal algorithm for the management of symptomatic tarsal coalition is still under discussion in pediatric literature. It's debatable what surgical steps are essential to achieve the best outcome. Method: The investigators retrospectively reviewed the records of twelve patients with symptomatic tarsal coalition that were treated operatively between 2017 and 2019. Only painful flat feet were operated. Two patients were excluded from the study due to lack of sufficient follow-up. Ten of eleven feet were treated with the combination of calcaneal lengthening osteotomy (CLO) and resection of coalition (RC). Only one foot was operated with CLO alone. In half of our patients, Achilles lengthening was performed. For two children, medial plication was added. Short leg cast was applied to all children for 6-8 weeks, and soft shoe insoles for medial arch support were prescribed after. Demographic, clinical, and radiographic records were reviewed. The outcome was evaluated using American Orthopedic Foot and Ankle Society (AOFAS) Ankle Hindfoot Score. Results: There were seven boys and three girls. The mean age at the time of surgery was 13.9 (range 12 to 17) years, and the mean follow-up was 18 (range 8 to 34) months. The early complications included one superficial wound infection and dehiscence. Late complication includes two children with residual forefoot supination. None of our patients required additional operations during the follow-up period. All feet achieved complete deformity correction or dramatic improvement. In the last follow-up, seven feet were painless, and four children had some mild pain after intensive activities. All feet achieved excellent and good scoring on AOFAS. Conclusions: Many patients with talocalcaneal coalition also have rigid or stiff, painful, flat feet. For these patients, the resection of coalition with concomitant CLO can be safely recommended.

Keywords: Tarsal coalition, calcaneal lengthening osteotomy., flat foot, coalition resection

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Authors: Jacques M. Berner, Lehlogonolo Maloma

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Context: Glyphosate, a widely used herbicide, has raised concerns about its impact on various crops. Amaranthus cruentus, an important grain crop species, is particularly susceptible to glyphosate. Understanding the specific disruptions caused by glyphosate on the photosynthetic process in Amaranthus cruentus is crucial for assessing its effects on crop productivity and ecological sustainability. Research Aim: This study aimed to investigate the dose-dependent impact of glyphosate on the photochemical efficiency of Amaranthus cruentus using the OJIP transient analysis. The goal was to assess the specific disruptions caused by glyphosate on key parameters of photosystem II. Methodology: The experiment was conducted in a controlled greenhouse environment. Amaranthus cruentus plants were exposed to different concentrations of glyphosate, including half, recommended, and double the recommended application rates. The photochemical efficiency of the plants was evaluated using non-invasive chlorophyll a fluorescence measurements and subsequent analysis of OJIP transients. Measurements were taken on 1-hour dark-adapted leaves using a Hansatech Handy PEA+ chlorophyll fluorimeter. Findings: The study's results demonstrated a significant reduction in the photochemical efficiency of Amaranthus cruentus following glyphosate treatment. The OJIP transients showed distinct alterations in the glyphosate-treated plants compared to the control group. These changes included a decrease in maximal fluorescence (FP) and a delay in the rise of the fluorescence signal, indicating impairment in the energy conversion process within the photosystem II. Glyphosate exposure also led to a substantial decrease in the maximum quantum yield efficiency of photosystem II (FV/FM) and the total performance index (PItotal), which reflects the overall photochemical efficiency of photosystem II. These reductions in photochemical efficiency were observed even at half the recommended dose of glyphosate. Theoretical Importance: The study provides valuable insights into the specific disruptions caused by glyphosate on the photochemical efficiency of Amaranthus cruentus. Data Collection and Analysis Procedures: Data collection involved non-invasive chlorophyll a fluorescence measurements using a chlorophyll fluorimeter on dark-adapted leaves. The OJIP transients were then analyzed to assess specific disruptions in key parameters of photosystem II. Statistical analysis was conducted to determine the significance of the differences observed between glyphosate-treated plants and the control group. Question Addressed: The study aimed to address the question of how glyphosate exposure affects the photochemical efficiency of Amaranthus cruentus, specifically examining disruptions in the photosynthetic electron transport chain and overall photochemical efficiency. Conclusion: The study demonstrates that glyphosate severely impairs the photochemical efficiency of Amaranthus cruentus, as indicated by the alterations in OJIP transients. Even at half the recommended dose, glyphosate caused significant reductions in photochemical efficiency. These findings highlight the detrimental effects of glyphosate on crop productivity and emphasize the need for further research to evaluate its long-term consequences and ecological implications in agriculture. The authors gratefully acknowledge the support from North-West University for making this research possible.

Keywords: glyphosate, amaranthus cruentus, ojip transient analysis, pitotal, photochemical efficiency, chlorophyll fluorescence, weeds

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Authors: Banafsheh Nikmehr, Mohsen Bahrami, Yueqiang Song, Anuradha Koduru, Ayse K. Vuruskan, Hongkun Lu, Mallory Pitts, Tolga B. Mesen, Tamer M. Yalcinkaya

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The number of people who receive in vitro fertilization (IVF) treatment has increased on a startling trajectory over the past two decades. Despite advances in this field, particularly the introduction of intracytoplasmic sperm injection (ICSI) and the preimplantation genetic screening (PGS), the IVF success remains low. A major factor contributing to IVF failure is embryonic aneuploidy (abnormal chromosome content), which often results in miscarriage and birth defects. Although PGS is often used as the standard diagnostic tool to identify aneuploid embryos, it is an invasive approach that could affect the embryo development, and yet inaccessible to many patients due its high costs. As such, there is a clear need for a non-invasive cost-effective approach to identify euploid embryos for single embryo transfer (SET). The reported differences between morphokinetic behaviors of aneuploid and euploid embryos has shown promise to address this need. However, current literature is inconclusive and further research is urgently needed to translate current findings into clinical diagnostics. In this ongoing study, we found significant differences between morphokinetic behaviors of euploid and aneuploid embryos that provides important insights and reaffirms the promise of such behaviors for developing non-invasive methodologies. Methodology—A total of 242 embryos (euploid: 149, aneuploid: 93) from 74 patients who underwent IVF treatment in Carolinas Fertility Clinics in Winston-Salem, NC, were analyzed. All embryos were incubated in an EmbryoScope incubator. The patients were randomly selected from January 2019 to June 2021 with most patients having both euploid and aneuploid embryos. All embryos reached the blastocyst stage and had known PGS outcomes. The ploidy assessment was done by a third-party testing laboratory on day 5-7 embryo biopsies. The morphokinetic variables of each embryo were measured by the EmbryoViewer software (Uniesense FertiliTech) on time-lapse images using 7 focal depths. We compared the time to: pronuclei fading (tPNf), division to 2,3,…,9 cells (t2, t3,…,t9), start of embryo compaction (tSC), Morula formation (tM), start of blastocyst formation (tSC), blastocyst formation (tB), and blastocyst expansion (tEB), as well as intervals between them (e.g., c23 = t3 – t2). We used a mixed regression method for our statistical analyses to account for the correlation between multiple embryos per patient. Major Findings— The average age of the patients was 35.04 yrs. The average patient age associated with euploid and aneuploid embryos was not different (P = 0.6454). We found a significant difference in c45 = t5-t4 (P = 0.0298). Our results indicated this interval on average lasts significantly longer for aneuploid embryos - c45(aneuploid) = 11.93hr vs c45(euploid) = 7.97hr. In a separate analysis limited to embryos from the same patients (patients = 47, total embryos=200, euploid=112, aneuploid=88), we obtained the same results (P = 0.0316). The statistical power for this analysis exceeded 87%. No other variable was different between the two groups. Conclusion— Our results demonstrate the importance of morphokinetic variables as potential biomarkers that could aid in non-invasively characterizing euploid and aneuploid embryos. We seek to study a larger population of embryos and incorporate the embryo quality in future studies.

Keywords: IVF, embryo, euploidy, aneuploidy, morphokinteic

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Authors: Matthias Steffan, Franz Haas

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The production process grinding is one of the latest steps in a value-added manufacturing chain. Within this step, workpiece geometry and surface roughness are determined. Up to this process stage, considerable costs and energy have already been spent on components. According to the current state of the art, therefore, large safety reserves are calculated in order to guarantee a process capability. Especially for non-circular grinding, this fact leads to considerable losses of process efficiency. With present technology, various non-circular geometries on a workpiece must be grinded subsequently in an oscillating process where X- and Q-axis of the machine are coupled. With the approach of RPM-Synchronous Noncircular Grinding, such workpieces can be machined in an ordinary plung grinding process. Therefore, the workpieces and the grinding wheels revolutionary rate are in a fixed ratio. A non-circular grinding wheel is used to transfer its geometry onto the workpiece. The authors use a worldwide unique machine tool that was especially designed for this technology. Highest revolution rates on the workpiece spindle (up to 4500 rpm) are mandatory for the success of this grinding process. This grinding approach is performed in a two-step process. For roughing, a highly porous vitrified bonded grinding wheel with medium grain size is used. It ensures high specific material removal rates for efficiently producing the non-circular geometry on the workpiece. This process step is adapted by a force control algorithm, which uses acquired data from a three-component force sensor located in the dead centre of the tailstock. For finishing, a grinding wheel with a fine grain size is used. Roughing and finishing are performed consecutively among the same clamping of the workpiece with two locally separated grinding spindles. The approach of RPM-Synchronous Noncircular Grinding shows great efficiency enhancement in non-circular grinding. For the first time, three-dimensional non-circular shapes can be grinded that opens up various fields of application. Especially automotive industries show big interest in the emerging trend in finishing machining.

Keywords: efficiency enhancement, finishing machining, non-circular grinding, rpm-synchronous grinding

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Authors: Gianpaolo Ghiani, Emanuela Guerriero, Emanuele Manni, Alessandro Romano

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In this article, we deal with a variant of the classical course timetabling problem that has a practical application in many areas of education. In particular, in this paper we are interested in high schools remedial courses. The purpose of such courses is to provide under-prepared students with the skills necessary to succeed in their studies. In particular, a student might be under prepared in an entire course, or only in a part of it. The limited availability of funds, as well as the limited amount of time and teachers at disposal, often requires schools to choose which courses and/or which teaching units to activate. Thus, schools need to model the training offer and the related timetabling, with the goal of ensuring the highest possible teaching quality, by meeting the above-mentioned financial, time and resources constraints. Moreover, there are some prerequisites between the teaching units that must be satisfied. We first present a Mixed-Integer Programming (MIP) model to solve this problem to optimality. However, the presence of many peculiar constraints contributes inevitably in increasing the complexity of the mathematical model. Thus, solving it through a general purpose solver may be performed for small instances only, while solving real-life-sized instances of such model requires specific techniques or heuristic approaches. For this purpose, we also propose a heuristic approach, in which we make use of a fast constructive procedure to obtain a feasible solution. To assess our exact and heuristic approaches we perform extensive computational results on both real-life instances (obtained from a high school in Lecce, Italy) and randomly generated instances. Our tests show that the MIP model is never solved to optimality, with an average optimality gap of 57%. On the other hand, the heuristic algorithm is much faster (in about the 50% of the considered instances it converges in approximately half of the time limit) and in many cases allows achieving an improvement on the objective function value obtained by the MIP model. Such an improvement ranges between 18% and 66%.

Keywords: heuristic, MIP model, remedial course, school, timetabling

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Authors: Julia Kapr, Andrea Rossi, Haribaskar Ramachandran, Marius Pollet, Ilka Egger, Selina Dangeleit, Katharina Koch, Jean Krutmann, Ellen Fritsche

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It is widely acknowledged that animal models do not always represent human disease. Especially human brain development is difficult to model in animals due to a variety of structural and functional species-specificities. This causes significant discrepancies between predicted and apparent drug efficacies in clinical trials and their subsequent failure. Emerging alternatives based on 3D in vitro approaches, such as human brain spheres or organoids, may in the future reduce and ultimately replace animal models. Here, we present a human induced pluripotent stem cell (hiPSC)-based 3D neural in a vitro disease model for the Cockayne Syndrome B (CSB). CSB is a rare hereditary disease and is accompanied by severe neurologic defects, such as microcephaly, ataxia and intellectual disability, with currently no treatment options. Therefore, the aim of this study is to investigate the molecular and cellular defects found in neural hiPSC-derived CSB models. Understanding the underlying pathology of CSB enables the development of treatment options. The two CSB models used in this study comprise a patient-derived hiPSC line and its isogenic control as well as a CSB-deficient cell line based on a healthy hiPSC line (IMR90-4) background thereby excluding genetic background-related effects. Neurally induced and differentiated brain sphere cultures were characterized via RNA Sequencing, western blot (WB), immunocytochemistry (ICC) and multielectrode arrays (MEAs). CSB-deficiency leads to an altered gene expression of markers for autophagy, focal adhesion and neural network formation. Cell migration was significantly reduced and electrical activity was significantly increased in the disease cell lines. These data hint that the cellular pathologies is possibly underlying CSB. By induction of autophagy, the migration phenotype could be partially rescued, suggesting a crucial role of disturbed autophagy in defective neural migration of the disease lines. Altered autophagy may also lead to inefficient mitophagy. Accordingly, disease cell lines were shown to have a lower mitochondrial base activity and a higher susceptibility to mitochondrial stress induced by rotenone. Since mitochondria play an important role in neurotransmitter cycling, we suggest that defective mitochondria may lead to altered electrical activity in the disease cell lines. Failure to clear the defective mitochondria by mitophagy and thus missing initiation cues for new mitochondrial production could potentiate this problem. With our data, we aim at establishing a disease adverse outcome pathway (AOP), thereby adding to the in-depth understanding of this multi-faced disorder and subsequently contributing to alternative drug development.

Keywords: autophagy, disease modeling, in vitro, pluripotent stem cells

Procedia PDF Downloads 100

Authors: Simone Raimondi De Souza, Glaucia Maria Moraes De Oliveira, Ronir Raggio Luiz, Glorimar Rosa

Abstract:

Introduction: Obesity is among the main risk factors for cardiovascular disease (CVD). Genesis is multifactorial, including genetic, hormonal and environmental factors disorders, among which inadequate feeding pattern, for which nutritional counseling strategies have proven effective. The consumption of beta-glucans (soluble fibers that reportedly promote satiety) present in oat bran can be an effective strategy for preventing and treating obesity. Other benefits have been observed with oat bran consumption, such as reduction of hypercholesterolemia and hyperglycemia, two other risk factors for CVD. Objectives: To analyze the effect of oat bran consumption associated with nutritional counseling in reducing body mass index (BMI), blood cholesterol, glucose profile, waist and neck circumference in obese individuals, and to evaluate the change in eating pattern. Methods: clinical trial, randomized, double-blind, placebo-controlled, lasting 90 days with adults of both genders, with BMI ≥30kg/m2. The study was approved by the Ethics in Research involving human beings in a public institute of cardiology, in Rio de Janeiro, Brazil. Individuals were invited to participate and accepted formally by signing the Terms of Consent. Participants were randomized into oat bran group (gOB) or placebo group (gPCB) and received, respectively: morning prepared consisting of 40g oat bran, 30g of skimmed milk powder and 1g sweetener sucralose; refined flour 40g rice, 30g of milk powder and 1g sweetener sucralose. The Ten Steps to Healthy Eating, of Brazilian Ministry of Health were used to support the nutritional counseling. Variables analyzed: gender; age; BMI, waist circumference (WC) neck circumference (NC); systolic blood pressure (SBP); diastolic blood pressure (DBP); food consumption, total cholesterol (TC), LDL-cholesterol (LDL-c), HDL-cholesterol (HDL-c), non-HDL cholesterol (nHDLc), triglycerides (TG), fasting glucose (FG), fasting insulin (FI) and HOMA-IR. Dietary intake was assessed by 24-hour dietary recall. The Diet Quality Index revised for the Brazilian population (IQD-R) assessed quality of feeding pattern. Statistical analyzes were performed using SPSS version 21, considering statistically significant p-value less than 0.05. Results: A total of 38 participants were included, age = 50 ± 7,6years, 63% women. 19 subjects were placed in gOB and 19 in gPCB. After intervention, statistically significant reductions were observed in the following parameters: in gOB: IQD-R, TC, LDL-c, nHDL-c, FI, SBP, DBP, BMI, WC, NC; in gPCB: IQD-R, LDL-c, SBP, DBP, BMI, WC, NC. No statistically significant differences were observed in the results between groups. Conclusion: Our results reinforce nutritional counseling as important strategy for prevention and treatment of obesity and suggest that inclusion of oat bran in daily diet can bring additional benefits controlling risk factors for CVD. More studies are needed to establish all benefits of oat bran to human health as well as the ideal daily dose for consumption.

Keywords: oat bran, cardiovascular disease, nutritional counseling, obesity

Procedia PDF Downloads 204

Authors: Nisha Sharma, Mili Kaur, Ashutosh Halder, Seema Kaushal, Manoj Kumar, Manish Jain

Abstract:

Purpose: To investigate microRNAs (miRNA) as an epigenomic etiological factor in idiopathic non-obstructive azoospermia (NOA). In order to achieve the same, an association was seen between occupational exposure to radiation, thermal, and chemical factors and idiopathic cases of non-obstructive azoospermia, and later, testicular differential miRNA expression profiling was done in exposure group NOA cases. Method: It is a prospective study in which 200 apparent idiopathic male factor infertility cases, who have been advised to undergo testicular fine needle aspiration (FNA) evaluation, are recruited. A detailed occupational history was taken to understand the possible type of exposure due to the nature and duration of work. A total of 26 patients were excluded upon XY-FISH and Yq microdeletion tests due to the presence of genetic causes of infertility, 6 hypospermatogeneis (HS), six Sertoli cell-only syndrome (SCOS), and six normospermatogeneis patients testicular FNA samples were used for RNA isolation followed by small RNA sequencing and nCounter miRNA expression analysis. Differential miRNA expression profile of HS and SCOS patients was done. A web-based tool, miRNet, was used to predict the interacting compounds or chemicals using the shortlisted miRNAs with high fold change. The major limitation encountered in this study was the insufficient quantity of testicular FNA sample used for total RNA isolation, which resulted in a low yield and RNA integrity number (RIN) value. Therefore, the number of RNA samples admissible for differential miRNA expression analysis was very small in comparison to the total number of patients recruited. Results: Differential expression analysis revealed 69 down-regulated and 40 up-regulated miRNAs in HS and 66 down-regulated and 33 up-regulated miRNAs in SCOS in comparison to normospermatogenesis controls. The miRNA interaction analysis using the miRNet tool showed that the differential expression profiles of HS and SCOS patients were associated with arsenic trioxide, bisphenol-A, calcium sulphate, lithium, and cadmium. These compounds are reproductive toxins and might be responsible for miRNA-mediated epigenetic deregulation leading to NOA. The association between occupational risk factor exposure and the non-exposure group of NOA patients was not statistically significant, with ꭓ2 (3, N= 178) = 6.70, p= 0.082. The association between individual exposure groups (radiation, thermal, and chemical) and various sub-types of NOA is also not significant, with ꭓ2 (9, N= 178) = 15.06, p= 0.089. Functional analysis of HS and SCOS patients' miRNA profiles revealed some important miR-family members in terms of male fertility. The miR-181 family plays a role in the differentiation of spermatogonia and spermatocytes, as well as the transcriptional regulation of haploid germ cells. The miR-34 family is expressed in spermatocytes and round spermatids and is involved in the regulation of SSCs differentiation. Conclusion: The reproductive toxins might adopt the miRNA-mediated mechanism of disease development in idiopathic cases of NOA. Chemical compound induced; miRNA-mediated epigenetic deregulation can give a future perspective on the etiopathogenesis of the disease.

Keywords: microRNA, non-obstructive azoospermia (NOA), occupational exposure, hypospermatogenesis (HS), Sertoli cell only syndrome (SCOS)

Procedia PDF Downloads 54

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

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Authors: Monish Sengupta, Benjamin Heydecker, Daniel Woodland

Abstract:

New train control systems rely mainly on Automatic Train Protection (ATP) and Automatic Train Operation (ATO) dynamically to control the speed and hence performance. The ATP and the ATO form the vital element within the CBTC (Communication Based Train Control) and within the ERTMS (European Rail Traffic Management System) system architectures. Reliable and accurate measurement of train location, speed and acceleration are vital to the operation of train control systems. In the past, all CBTC and ERTMS system have deployed a balise or equivalent to correct the uncertainty element of the train location. Typically a CBTC train is allowed to miss only one balise on the track, after which the Automatic Train Protection (ATP) system applies emergency brake to halt the service. This is because the location uncertainty, which grows within the train control system, cannot tolerate missing more than one balise. Balises contribute a significant amount towards wayside maintenance and studies have shown that balises on the track also forms a constraint for future track layout change and change in speed profile.This paper investigates the causes of the location uncertainty that is currently experienced and considers whether it is possible to identify an effective filter to ascertain, in conjunction with appropriate sensors, more accurate speed, distance and location for a CBTC driven train without the need of any external balises. An appropriate sensor fusion algorithm and intelligent sensor selection methodology will be deployed to ascertain the railway location and speed measurement at its highest precision. Similar techniques are already in use in aviation, satellite, submarine and other navigation systems. Developing a model for the speed control and the use of Kalman filter is a key element in this research. This paper will summarize the research undertaken and its significant findings, highlighting the potential for introducing alternative approaches to train positioning that would enable removal of all trackside location correction balises, leading to huge reduction in maintenances and more flexibility in future track design.

Keywords: ERTMS, CBTC, ATP, ATO

Procedia PDF Downloads 389

Authors: Luis Amador Jimenez, Md. Shohel Amin

Abstract:

Pavement performance models are based on projections of observed traffic loads, which makes uncertain to study funding strategies in the long run if history does not repeat. Neural networks can be used to estimate deterioration rates but the learning rate and momentum have not been properly investigated, in addition, economic evolvement could change traffic flows. This study addresses both issues through a case study for roads of Montreal that simulates traffic for a period of 50 years and deals with the measurement error of the pavement deterioration model. Travel demand models are applied to simulate annual average daily traffic (AADT) every 5 years. Accumulated equivalent single axle loads (ESALs) are calculated from the predicted AADT and locally observed truck distributions combined with truck factors. A back propagation Neural Network (BPN) method with a Generalized Delta Rule (GDR) learning algorithm is applied to estimate pavement deterioration models capable of overcoming measurement errors. Linear programming of lifecycle optimization is applied to identify M&R strategies that ensure good pavement condition while minimizing the budget. It was found that CAD 150 million is the minimum annual budget to good condition for arterial and local roads in Montreal. Montreal drivers prefer the use of public transportation for work and education purposes. Vehicle traffic is expected to double within 50 years, ESALS are expected to double the number of ESALs every 15 years. Roads in the island of Montreal need to undergo a stabilization period for about 25 years, a steady state seems to be reached after.

Keywords: pavement management system, traffic simulation, backpropagation neural network, performance modeling, measurement errors, linear programming, lifecycle optimization

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Authors: Daria Zabolotnaya, Svetlana Degtyareva, Veronika Kanestri, Danila Konnov

Abstract:

An adequate choice of the initial antiretroviral treatment determines the treatment efficacy. In the clinical guidelines in Russia non-nucleoside reverse transcriptase inhibitors (NNRTIs) are still considered to be an option for first-line treatment while pretreatment drug resistance (PDR) testing is not routinely performed. We conducted a cohort retrospective study in HIV-positive treatment naïve patients of the H-clinic (Moscow, Russia) who performed PDR testing from July 2017 to November 2021. All the information was obtained from the medical records anonymously. We analyzed the mutations in reverse transcriptase and protease genes. RT-sequences were obtained by AmpliSens HIV-Resist-Seq kit. Drug resistance was defined using the HIVdb Program v. 8.9-1. PDR was estimated using the Stanford algorithm. Descriptive statistics were performed in Excel (Microsoft Office, 2019). A total of 261 HIV-1 infected patients were enrolled in the study including 197 (75.5%) male and 64 (24.5%) female. The mean age was 34.6±8.3 years. The median CD4 count – 521 cells/µl (IQR 367-687 cells/µl). Data on risk factors of HIV-infection were scarce. The total quantity of strains containing mutations in the reverse transcriptase gene was 75 (28.7%). From these 5 (1.9%) mutations were associated with PDR to nucleoside reverse transcriptase inhibitors (NRTIs) and 30 (11.5%) – with PDR to NNRTIs. The number of strains with mutations in protease gene was 43 (16.5%), from these only 3 (1.1%) mutations were associated with resistance to protease inhibitors. For NNRTIs the most prevalent PDR mutations were E138A, V106I. Most of the HIV variants exhibited a single PDR mutation, 2 were found in 3 samples. Most of HIV variants with PDR mutation displayed a single drug class resistance mutation. 2/37 (5.4%) strains had both NRTIs and NNRTIs mutations. There were no strains identified with PDR mutations to all three drug classes. Though earlier data demonstrated a lower level of PDR in HIV treatment naïve population in Russia and our cohort can be not fully representative as it is taken from the private clinic, it reflects the trend of increasing PDR especially to NNRTIs. Therefore, we consider either pretreatment testing or giving the priority to other drugs as first-line treatment necessary.

Keywords: HIV, resistance, mutations, treatment

Procedia PDF Downloads 70

Authors: Andreas Kerstan, Darren Robey, Wesam Alvan, David Troiani

Abstract:

Over the last 3.5 years, Quantum Cascade Lasers (QCL) technology has become increasingly important in infrared (IR) microscopy. The advantages over fourier transform infrared (FTIR) are that large areas of a few square centimeters can be measured in minutes and that the light intensive QCL makes it possible to obtain spectra with excellent S/N, even with just one scan. A firmly established solution of the laser direct infrared imaging (LDIR) 8700 is the analysis of microplastics. The presence of microplastics in the environment, drinking water, and food chains is gaining significant public interest. To study their presence, rapid and reliable characterization of microplastic particles is essential. Significant technical hurdles in microplastic analysis stem from the sheer number of particles to be analyzed in each sample. Total particle counts of several thousand are common in environmental samples, while well-treated bottled drinking water may contain relatively few. While visual microscopy has been used extensively, it is prone to operator error and bias and is limited to particles larger than 300 µm. As a result, vibrational spectroscopic techniques such as Raman and FTIR microscopy have become more popular, however, they are time-consuming. There is a demand for rapid and highly automated techniques to measure particle count size and provide high-quality polymer identification. Analysis directly on the filter that often forms the last stage in sample preparation is highly desirable as, by removing a sample preparation step it can both improve laboratory efficiency and decrease opportunities for error. Recent advances in infrared micro-spectroscopy combining a QCL with scanning optics have created a new paradigm, LDIR. It offers improved speed of analysis as well as high levels of automation. Its mode of operation, however, requires an IR reflective background, and this has, to date, limited the ability to perform direct “on-filter” analysis. This study explores the potential to combine the filter with an infrared reflective surface filter. By combining an IR reflective material or coating on a filter membrane with advanced image analysis and detection algorithms, it is demonstrated that such filters can indeed be used in this way. Vibrational spectroscopic techniques play a vital role in the investigation and understanding of microplastics in the environment and food chain. While vibrational spectroscopy is widely deployed, improvements and novel innovations in these techniques that can increase the speed of analysis and ease of use can provide pathways to higher testing rates and, hence, improved understanding of the impacts of microplastics in the environment. Due to its capability to measure large areas in minutes, its speed, degree of automation and excellent S/N, the LDIR could also implemented for various other samples like food adulteration, coatings, laminates, fabrics, textiles and tissues. This presentation will highlight a few of them and focus on the benefits of the LDIR vs classical techniques.

Keywords: QCL, automation, microplastics, tissues, infrared, speed

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Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

Abstract:

Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

Procedia PDF Downloads 299

Authors: Cristy Stagnar, Nina Hubig, Diana Ivankovic

Abstract:

The epigenome is a compelling candidate for mediating long-term responses to environmental effects modifying disease risk. The main goal of this research is to develop a machine learning-based DNA methylation score, which will be valuable in delineating the unique contribution of paternal epigenetic modifications to the germline impacting childhood health outcomes. It will also be a useful tool in validating self-reports of nonsmoking and in adjusting epigenome-wide DNA methylation association studies for this early-life exposure. Using secondary data from two population-based methylation profiling studies, our DNA methylation score is based on CpG DNA methylation measurements from cord blood gathered from children whose fathers smoked pre- and peri-conceptually. Each child’s mother and father fell into one of three class labels in the accompanying questionnaires -never smoker, former smoker, or current smoker. By applying different machine learning algorithms to the accessible resource for integrated epigenomic studies (ARIES) sub-study of the Avon longitudinal study of parents and children (ALSPAC) data set, which we used for training and testing of our model, the best-performing algorithm for classifying the father smoker and mother never smoker was selected based on Cohen’s κ. Error in the model was identified and optimized. The final DNA methylation score was further tested and validated in an independent data set. This resulted in a linear combination of methylation values of selected probes via a logistic link function that accurately classified each group and contributed the most towards classification. The result is a unique, robust DNA methylation score which combines information on DNA methylation and early life exposure of offspring to paternal smoking during pregnancy and which may be used to examine the paternal contribution to offspring health outcomes.

Keywords: epigenome, health outcomes, paternal preconception environmental exposures, supervised machine learning

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Authors: Young-Tae Lee, Hee-Chang Lim

Abstract:

This paper presents the performance characteristics of Darrieus-type vertical axis wind turbine (VAWT) with NACA airfoil blades. The performance of Darrieus-type VAWT can be characterized by torque and power. There are various parameters affecting the performance such as chord length, helical angle, pitch angle and rotor diameter. To estimate the optimum shape of Darrieustype wind turbine in accordance with various design parameters, we examined aerodynamic characteristics and separated flow occurring in the vicinity of blade, interaction between flow and blade, and torque and power characteristics derived from it. For flow analysis, flow variations were investigated based on the unsteady RANS (Reynolds-averaged Navier-Stokes) equation. Sliding mesh algorithm was employed in order to consider rotational effect of blade. To obtain more realistic results we conducted experiment and numerical analysis at the same time for three-dimensional shape. In addition, several parameters (chord length, rotor diameter, pitch angle, and helical angle) were considered to find out optimum shape design and characteristics of interaction with ambient flow. Since the NACA airfoil used in this study showed significant changes in magnitude of lift and drag depending on an angle of attack, the rotor with low drag, long cord length and short diameter shows high power coefficient in low tip speed ratio (TSR) range. On the contrary, in high TSR range, drag becomes high. Hence, the short-chord and long-diameter rotor produces high power coefficient. When a pitch angle at which airfoil directs toward inside equals to -2° and helical angle equals to 0°, Darrieus-type VAWT generates maximum power.

Keywords: darrieus wind turbine, VAWT, NACA airfoil, performance

Procedia PDF Downloads 347

Authors: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

Abstract:

Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

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Authors: Ana Londral, Burcu Demiray, Marcus Cheetham

Abstract:

Speech recording is a methodology used in many different studies related to cognitive and behaviour research. Modern advances in digital equipment brought the possibility of continuously recording hours of speech in naturalistic environments and building rich sets of sound files. Speech analysis can then extract from these files multiple features for different scopes of research in Language and Communication. However, tools for analysing a large set of sound files and automatically extract relevant features from these files are often inaccessible to researchers that are not familiar with programming languages. Manual analysis is a common alternative, with a high time and efficiency cost. In the analysis of long sound files, the first step is the voice segmentation, i.e. to detect and label segments containing speech. We present a comprehensive methodology aiming to support researchers on voice segmentation, as the first step for data analysis of a big set of sound files. Praat, an open source software, is suggested as a tool to run a voice detection algorithm, label segments and files and extract other quantitative features on a structure of folders containing a large number of sound files. We present the validation of our methodology with a set of 5000 sound files that were collected in the daily life of a group of voluntary participants with age over 65. A smartphone device was used to collect sound using the Electronically Activated Recorder (EAR): an app programmed to record 30-second sound samples that were randomly distributed throughout the day. Results demonstrated that automatic segmentation and labelling of files containing speech segments was 74% faster when compared to a manual analysis performed with two independent coders. Furthermore, the methodology presented allows manual adjustments of voiced segments with visualisation of the sound signal and the automatic extraction of quantitative information on speech. In conclusion, we propose a comprehensive methodology for voice segmentation, to be used by researchers that have to work with large sets of sound files and are not familiar with programming tools.

Keywords: automatic speech analysis, behavior analysis, naturalistic environments, voice segmentation

Procedia PDF Downloads 260

Authors: Ling Liyun

Abstract:

In the field of contemporary architecture, complex forms of architectural works continue to emerge in the world, along with some new terminology emerged: digital architecture, parametric design, algorithm generation, building information modeling, CNC construction and so on. Architects gradually mastered the new skills of mathematical logic in the form of exploration, virtual simulation, and the entire design and coordination in the construction process. Digital construction technology has a greater degree in controlling construction, and ensure its accuracy, creating a series of new construction techniques. As a result, the use of digital technology is an improvement and expansion of the practice of digital architecture design revolution. We worked by reading and analyzing information about the digital architecture development process, a large number of cases, as well as architectural design and construction as a whole process. Thus current developments were introduced and discussed in our paper, such as architectural discourse, design theory, digital design models and techniques, material selecting, as well as artificial intelligence space design. Our paper also pays attention to the representative three cases of digital design and construction experiment at great length in detail to expound high-informatization, high-reliability intelligence, and high-technique in constructing a humane space to cope with the rapid development of urbanization. We concluded that the opportunities and challenges of the shift existed in architectural paradigms, such as the cooperation methods, theories, models, technologies and techniques which were currently employed in digital design research and digital praxis. We also find out that the innovative use of space can gradually change the way people learn, talk, and control information. The past two decades, digital technology radically breaks the technology constraints of industrial technical products, digests the publicity on a particular architectural style (era doctrine). People should not adapt to the machine, but in turn, it’s better to make the machine work for users.

Keywords: artificial intelligence, collaboration, digital architecture, digital design theory, material selection, space construction

Procedia PDF Downloads 112

Authors: J. Ritonja, R. Brezovnik, M. Petrun, B. Polajžer

Abstract:

Many modern synchronous generators in power systems are extremely weakly damped. The reasons are cost optimization of the machine building and introduction of the additional control equipment into power systems. Oscillations of the synchronous generators and related stability problems of the power systems are harmful and can lead to failures in operation and to damages. The only useful solution to increase damping of the unwanted oscillations represents the implementation of the power system stabilizers. Power system stabilizers generate the additional control signal which changes synchronous generator field excitation voltage. Modern power system stabilizers are integrated into static excitation systems of the synchronous generators. Available commercial power system stabilizers are based on linear control theory. Due to the nonlinear dynamics of the synchronous generator, current stabilizers do not assure optimal damping of the synchronous generator’s oscillations in the entire operating range. For that reason the use of the robust power system stabilizers which are convenient for the entire operating range is reasonable. There are numerous robust techniques applicable for the power system stabilizers. In this paper the use of sliding mode control for synchronous generator stability improvement is studied. On the basis of the sliding mode theory, the robust power system stabilizer was developed. The main advantages of the sliding mode controller are simple realization of the control algorithm, robustness to parameter variations and elimination of disturbances. The advantage of the proposed sliding mode controller against conventional linear controller was tested for damping of the synchronous generator oscillations in the entire operating range. Obtained results show the improved damping in the entire operating range of the synchronous generator and the increase of the power system stability. The proposed study contributes to the progress in the development of the advanced stabilizer, which will replace conventional linear stabilizers and improve damping of the synchronous generators.

Keywords: control theory, power system stabilizer, robust control, sliding mode control, stability, synchronous generator

Procedia PDF Downloads 202