Search results for: software-based validation
449 Nucleotide Based Validation of the Endangered Plant Diospyros mespiliformis (Ebenaceae) by Evaluating Short Sequence Region of Plastid rbcL Gene
Authors: Abdullah Alaklabi, Ibrahim A. Arif, Sameera O. Bafeel, Ahmad H. Alfarhan, Anis Ahamed, Jacob Thomas, Mohammad A. Bakir
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Diospyros mespiliformis (Hochst. ex A.DC.; Ebenaceae) is a large deciduous medicinal plant. This plant species is currently listed as endangered in Saudi Arabia. Molecular identification of this plant species based on short sequence regions (571 and 664 bp) of plastid rbcL (ribulose-1, 5-biphosphate carboxylase) gene was investigated in this study. The endangered plant specimens were collected from Al-Baha, Saudi Arabia (GPS coordinate: 19.8543987, 41.3059349). Phylogenetic tree inferred from the rbcL gene sequences showed that this species is very closely related with D. brandisiana. The close relationship was also observed among D. bejaudii, D. Philippinensis and D. releyi (≥99.7% sequence homology). The partial rbcL gene sequence region (571 bp) that was amplified by rbcL primer-pair rbcLaF-rbcLaR failed to discriminate D. mespiliformis from the closely related plant species, D. brandisiana. In contrast, primer-pair rbcL1F-rbcL724R yielded longer amplicon, discriminated the species from D. brandisiana and demonstrated nucleotide variations in 3 different sites (645G>T; 663A>C; 710C>G). Although D. mespiliformis (EU980712) and D. brandisiana (EU980656) are very closely related species (99.4%); however, studied specimen showed 100% sequence homology with D. mespiliformis and 99.6% with D. brandisiana. The present findings showed that rbcL short sequence region (664 bp) of plastid rbcL gene, amplified by primer-pair rbcL1F-rbcL724R, can be used for authenticating samples of D. mespiliforformis and may provide help in authentic identification and management process of this medicinally valuable endangered plant species.Keywords: Diospyros mespiliformis, endangered plant, identification partial rbcL
Procedia PDF Downloads 422448 Simulation-Based Validation of Safe Human-Robot-Collaboration
Authors: Titanilla Komenda
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Human-machine-collaboration defines a direct interaction between humans and machines to fulfil specific tasks. Those so-called collaborative machines are used without fencing and interact with humans in predefined workspaces. Even though, human-machine-collaboration enables a flexible adaption to variable degrees of freedom, industrial applications are rarely found. The reasons for this are not technical progress but rather limitations in planning processes ensuring safety for operators. Until now, humans and machines were mainly considered separately in the planning process, focusing on ergonomics and system performance respectively. Within human-machine-collaboration, those aspects must not be seen in isolation from each other but rather need to be analysed in interaction. Furthermore, a simulation model is needed that can validate the system performance and ensure the safety for the operator at any given time. Following on from this, a holistic simulation model is presented, enabling a simulative representation of collaborative tasks – including both, humans and machines. The presented model does not only include a geometry and a motion model of interacting humans and machines but also a numerical behaviour model of humans as well as a Boole’s probabilistic sensor model. With this, error scenarios can be simulated by validating system behaviour in unplanned situations. As these models can be defined on the basis of Failure Mode and Effects Analysis as well as probabilities of errors, the implementation in a collaborative model is discussed and evaluated regarding limitations and simulation times. The functionality of the model is shown on industrial applications by comparing simulation results with video data. The analysis shows the impact of considering human factors in the planning process in contrast to only meeting system performance. In this sense, an optimisation function is presented that meets the trade-off between human and machine factors and aids in a successful and safe realisation of collaborative scenarios.Keywords: human-machine-system, human-robot-collaboration, safety, simulation
Procedia PDF Downloads 359447 Benefits of a Topical Emollient Product in the Management of Canine Nasal Hyperkeratosis
Authors: Christelle Navarro, Sébastien Viaud, Carole Gard, Bruno Jahier
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Background: Idiopathic or familial nasal hyperkeratosis (NHK) may be considered a cosmetic issue in its uncomplicated form. Nevertheless, prevention of secondary lesions such as fissures or infections could be advised by proper management. The objective of this open-field study is to evaluate the benefits of a moisturizing balm in privately owned dogs with NHK, using an original validation grid for both investigator and owner assessments. Methods: Dogs with idiopathic or familial NHK received a vegetable-based ointment (Sensiderm® Balm, MP Labo, France) BID for 60 days. A global dermatological score (GDS) was defined using the sum of 4 criteria (“dryness,” “lichenification”, “crusts,” and “affected area”) on a 0 (no) to 3 (severe or > 2/3 extension) scale. Evaluation of this GDS (0-12) on D0, D30, and D60, by owners and investigators was the main outcome. The score’s percentage decrease versus D0, the evolution of each individual score, the correlation between observers, and the evaluation of clinical improvement and animal discomfort on VAS (0-10) during follow-up were analysed. Results: The global dermatological score significantly decreased over time (p<0.0001) for all observers. The decrease reached 44.9% and 54.3% at D30 and 54.5% and 62.3% at D60, for investigators and owners, respectively. “Dryness”, “Lichenification,” and “Affected area scores” decreased significantly and steadily over time compared to Day 0 for both investigators and owners (p < 0.001 and p = 0.001 for investigator assessment of dryness). All but one score (lichenification) were correlated at all times between observers (only at D60 for crusts). Whoever the observer, clinical improvement was always above 7. At D30 and until D60, “animal discomfort” was more than halved. Owner satisfaction was high as soon as D30 (8.1/10). No adverse effects were reported. Conclusion and clinical importance: The positive results confirm the benefits and safety of a moisturizing balm when used in dogs with uncomplicated NHK.Keywords: hyperkeratosis, nose, dog, moisturizer
Procedia PDF Downloads 121446 Investigation of Failure Mechanisms of Composite Laminates with Delamination and Repaired with Bolts
Authors: Shuxin Li, Peihao Song, Haixiao Hu, Dongfeng Cao
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The interactive deformation and failure mechanisms, including local bucking/delamination propagation and global bucking, are investigated in this paper with numerical simulation and validation with experimental results. Three dimensional numerical models using ABAQUS brick elements combined with cohesive elements and contact elements are developed to simulate the deformation and failure characteristics of composite laminates with and without delamination under compressive loading. The zero-thickness cohesive elements are inserted on the possible path of delamination propagation, and the inter-laminate behavior is characterized by the mixed-mode traction-separation law. The numerical simulations identified the complex feature of interaction among local buckling and/or delamination propagation and final global bucking for composite laminates with delamination under compressive loading. Firstly there is an interaction between the local buckling and delamination propagation, i.e., local buckling induces delamination propagation, and then delamination growth further enhances the local buckling. Secondly, the interaction between the out-plan deformation caused by local buckling and the global bucking deformation results in final failure of the composite laminates. The simulation results are validated by the good agreement with the experimental results published in the literature. The numerical simulation validated with experimental results revealed that the degradation of the load capacity, in particular of the compressive strength of composite structures with delamination, is mainly attributed to the combined local buckling/delamination propagation effects. Consequently, a simple field-bolt repair approach that can hinder the local buckling and prevent delamination growth is explored. The analysis and simulation results demonstrated field-bolt repair could effectively restore compressive strength of composite laminates with delamination.Keywords: cohesive elements, composite laminates, delamination, local and global bucking, field-bolt repair
Procedia PDF Downloads 115445 Experimental and Modal Determination of the State-Space Model Parameters of a Uni-Axial Shaker System for Virtual Vibration Testing
Authors: Jonathan Martino, Kristof Harri
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In some cases, the increase in computing resources makes simulation methods more affordable. The increase in processing speed also allows real time analysis or even more rapid tests analysis offering a real tool for test prediction and design process optimization. Vibration tests are no exception to this trend. The so called ‘Virtual Vibration Testing’ offers solution among others to study the influence of specific loads, to better anticipate the boundary conditions between the exciter and the structure under test, to study the influence of small changes in the structure under test, etc. This article will first present a virtual vibration test modeling with a main focus on the shaker model and will afterwards present the experimental parameters determination. The classical way of modeling a shaker is to consider the shaker as a simple mechanical structure augmented by an electrical circuit that makes the shaker move. The shaker is modeled as a two or three degrees of freedom lumped parameters model while the electrical circuit takes the coil impedance and the dynamic back-electromagnetic force into account. The establishment of the equations of this model, describing the dynamics of the shaker, is presented in this article and is strongly related to the internal physical quantities of the shaker. Those quantities will be reduced into global parameters which will be estimated through experiments. Different experiments will be carried out in order to design an easy and practical method for the identification of the shaker parameters leading to a fully functional shaker model. An experimental modal analysis will also be carried out to extract the modal parameters of the shaker and to combine them with the electrical measurements. Finally, this article will conclude with an experimental validation of the model.Keywords: lumped parameters model, shaker modeling, shaker parameters, state-space, virtual vibration
Procedia PDF Downloads 263444 QSAR Modeling of Germination Activity of a Series of 5-(4-Substituent-Phenoxy)-3-Methylfuran-2(5H)-One Derivatives with Potential of Strigolactone Mimics toward Striga hermonthica
Authors: Strahinja Kovačević, Sanja Podunavac-Kuzmanović, Lidija Jevrić, Cristina Prandi, Piermichele Kobauri
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The present study is based on molecular modeling of a series of twelve 5-(4-substituent-phenoxy)-3-methylfuran-2(5H)-one derivatives which have potential of strigolactones mimics toward Striga hermonthica. The first step of the analysis included the calculation of molecular descriptors which numerically describe the structures of the analyzed compounds. The descriptors ALOGP (lipophilicity), AClogS (water solubility) and BBB (blood-brain barrier penetration), served as the input variables in multiple linear regression (MLR) modeling of germination activity toward S. hermonthica. Two MLR models were obtained. The first MLR model contains ALOGP and AClogS descriptors, while the second one is based on these two descriptors plus BBB descriptor. Despite the braking Topliss-Costello rule in the second MLR model, it has much better statistical and cross-validation characteristics than the first one. The ALOGP and AClogS descriptors are often very suitable predictors of the biological activity of many compounds. They are very important descriptors of the biological behavior and availability of a compound in any biological system (i.e. the ability to pass through the cell membranes). BBB descriptor defines the ability of a molecule to pass through the blood-brain barrier. Besides the lipophilicity of a compound, this descriptor carries the information of the molecular bulkiness (its value strongly depends on molecular bulkiness). According to the obtained results of MLR modeling, these three descriptors are considered as very good predictors of germination activity of the analyzed compounds toward S. hermonthica seeds. This article is based upon work from COST Action (FA1206), supported by COST (European Cooperation in Science and Technology).Keywords: chemometrics, germination activity, molecular modeling, QSAR analysis, strigolactones
Procedia PDF Downloads 279443 Hyper Parameter Optimization of Deep Convolutional Neural Networks for Pavement Distress Classification
Authors: Oumaima Khlifati, Khadija Baba
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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.Keywords: distress pavement, hyperparameters, automatic classification, deep learning
Procedia PDF Downloads 82442 Alternating Expectation-Maximization Algorithm for a Bilinear Model in Isoform Quantification from RNA-Seq Data
Authors: Wenjiang Deng, Tian Mou, Yudi Pawitan, Trung Nghia Vu
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Estimation of isoform-level gene expression from RNA-seq data depends on simplifying assumptions, such as uniform reads distribution, that are easily violated in real data. Such violations typically lead to biased estimates. Most existing methods provide a bias correction step(s), which is based on biological considerations, such as GC content–and applied in single samples separately. The main problem is that not all biases are known. For example, new technologies such as single-cell RNA-seq (scRNA-seq) may introduce new sources of bias not seen in bulk-cell data. This study introduces a method called XAEM based on a more flexible and robust statistical model. Existing methods are essentially based on a linear model Xβ, where the design matrix X is known and derived based on the simplifying assumptions. In contrast, XAEM considers Xβ as a bilinear model with both X and β unknown. Joint estimation of X and β is made possible by simultaneous analysis of multi-sample RNA-seq data. Compared to existing methods, XAEM automatically performs empirical correction of potentially unknown biases. XAEM implements an alternating expectation-maximization (AEM) algorithm, alternating between estimation of X and β. For speed XAEM utilizes quasi-mapping for read alignment, thus leading to a fast algorithm. Overall XAEM performs favorably compared to other recent advanced methods. For simulated datasets, XAEM obtains higher accuracy for multiple-isoform genes, particularly for paralogs. In a differential-expression analysis of a real scRNA-seq dataset, XAEM achieves substantially greater rediscovery rates in an independent validation set.Keywords: alternating EM algorithm, bias correction, bilinear model, gene expression, RNA-seq
Procedia PDF Downloads 135441 A Method for Multimedia User Interface Design for Mobile Learning
Authors: Shimaa Nagro, Russell Campion
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Mobile devices are becoming ever more widely available, with growing functionality, and are increasingly used as an enabling technology to give students access to educational material anytime and anywhere. However, the design of educational material user interfaces for mobile devices is beset by many unresolved research issues such as those arising from emphasising the information concepts then mapping this information to appropriate media (modelling information then mapping media effectively). This report describes a multimedia user interface design method for mobile learning. The method covers specification of user requirements and information architecture, media selection to represent the information content, design for directing attention to important information, and interaction design to enhance user engagement based on Human-Computer Interaction design strategies (HCI). The method will be evaluated by three different case studies to prove the method is suitable for application to different areas / applications, these are; an application to teach about major computer networking concepts, an application to deliver a history-based topic; (after these case studies have been completed, the method will be revised to remove deficiencies and then used to develop a third case study), an application to teach mathematical principles. At this point, the method will again be revised into its final format. A usability evaluation will be carried out to measure the usefulness and effectiveness of the method. The investigation will combine qualitative and quantitative methods, including interviews and questionnaires for data collection and three case studies for validating the MDMLM method. The researcher has successfully produced the method at this point which is now under validation and testing procedures. From this point forward in the report, the researcher will refer to the method using the MDMLM abbreviation which means Multimedia Design Mobile Learning Method.Keywords: human-computer interaction, interface design, mobile learning, education
Procedia PDF Downloads 238440 Artificial Neural Network Based Approach in Prediction of Potential Water Pollution Across Different Land-Use Patterns
Authors: M.Rüştü Karaman, İsmail İşeri, Kadir Saltalı, A.Reşit Brohi, Ayhan Horuz, Mümin Dizman
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Considerable relations has recently been given to the environmental hazardous caused by agricultural chemicals such as excess fertilizers. In this study, a neural network approach was investigated in the prediction of potential nitrate pollution across different land-use patterns by using a feedforward multilayered computer model of artificial neural network (ANN) with proper training. Periodical concentrations of some anions, especially nitrate (NO3-), and cations were also detected in drainage waters collected from the drain pipes placed in irrigated tomato field, unirrigated wheat field, fallow and pasture lands. The soil samples were collected from the irrigated tomato field and unirrigated wheat field on a grid system with 20 m x 20 m intervals. Site specific nitrate concentrations in the soil samples were measured for ANN based simulation of nitrate leaching potential from the land profiles. In the application of ANN model, a multi layered feedforward was evaluated, and data sets regarding with training, validation and testing containing the measured soil nitrate values were estimated based on spatial variability. As a result of the testing values, while the optimal structures of 2-15-1 was obtained (R2= 0.96, P < 0.01) for unirrigated field, the optimal structures of 2-10-1 was obtained (R2= 0.96, P < 0.01) for irrigated field. The results showed that the ANN model could be successfully used in prediction of the potential leaching levels of nitrate, based on different land use patterns. However, for the most suitable results, the model should be calibrated by training according to different NN structures depending on site specific soil parameters and varied agricultural managements.Keywords: artificial intelligence, ANN, drainage water, nitrate pollution
Procedia PDF Downloads 301439 Validation of the Recovery of House Dust Mites from Fabrics by Means of Vacuum Sampling
Authors: A. Aljohani, D. Burke, D. Clarke, M. Gormally, M. Byrne, G. Fleming
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Introduction: House Dust Mites (HDMs) are a source of allergen particles embedded in textiles and furnishings. Vacuum sampling is commonly used to recover and determine the abundance of HDMs but the efficiency of this method is less than standardized. Here, the efficiency of recovery of HDMs was evaluated from home-associated textiles using vacuum sampling protocols.Methods/Approach: Living Mites (LMs) or dead Mites (DMs) House Dust Mites (Dermatophagoides pteronyssinus: FERA, UK) were separately seeded onto the surfaces of Smooth Cotton, Denim and Fleece (25 mites/10x10cm2 squares) and left for 10 minutes before vacuuming. Fabrics were vacuumed (SKC Flite 2 pump) at a flow rate of 14 L/min for 60, 90 or 120 seconds and the number of mites retained by the filter (0.4μm x 37mm) unit was determined. Vacuuming was carried out in a linear direction (Protocol 1) or in a multidirectional pattern (Protocol 2). Additional fabrics with LMs were also frozen and then thawed, thereby euthanizing live mites (now termed EMs). Results/Findings: While there was significantly greater (p=0.000) recovery of mites (76% greater) in fabrics seeded with DMs than LMs irrespective of vacuuming protocol or fabric type, the efficiency of recovery of DMs (72%-76%) did not vary significantly between fabrics. For fabrics containing EMs, recovery was greatest for Smooth Cotton and Denim (65-73% recovered) and least for Fleece (15% recovered). There was no significant difference (p=0.99) between the recovery of mites across all three mite categories from Smooth Cotton and Denim but significantly fewer (p=0.000) mites were recovered from Fleece. Scanning Electron Microscopy images of HMD-seeded fabrics showed that live mites burrowed deeply into the Fleece weave which reduced their efficiency of recovery by vacuuming. Research Implications: Results presented here have implications for the recovery of HDMs by vacuuming and the choice of fabric to ameliorate HDM-dust sensitization.Keywords: allergy, asthma, dead, fabric, fleece, live mites, sampling
Procedia PDF Downloads 133438 Web Data Scraping Technology Using Term Frequency Inverse Document Frequency to Enhance the Big Data Quality on Sentiment Analysis
Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan
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Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of Big Data Technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centers or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through Vader and Roberta model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and TFIDF Vectorization, and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.Keywords: counter vectorization, convolutional neural network, crawler, data technology, long short-term memory, web scraping, sentiment analysis
Procedia PDF Downloads 81437 Classifying Affective States in Virtual Reality Environments Using Physiological Signals
Authors: Apostolos Kalatzis, Ashish Teotia, Vishnunarayan Girishan Prabhu, Laura Stanley
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Emotions are functional behaviors influenced by thoughts, stimuli, and other factors that induce neurophysiological changes in the human body. Understanding and classifying emotions are challenging as individuals have varying perceptions of their environments. Therefore, it is crucial that there are publicly available databases and virtual reality (VR) based environments that have been scientifically validated for assessing emotional classification. This study utilized two commercially available VR applications (Guided Meditation VR™ and Richie’s Plank Experience™) to induce acute stress and calm state among participants. Subjective and objective measures were collected to create a validated multimodal dataset and classification scheme for affective state classification. Participants’ subjective measures included the use of the Self-Assessment Manikin, emotional cards and 9 point Visual Analogue Scale for perceived stress, collected using a Virtual Reality Assessment Tool developed by our team. Participants’ objective measures included Electrocardiogram and Respiration data that were collected from 25 participants (15 M, 10 F, Mean = 22.28 4.92). The features extracted from these data included heart rate variability components and respiration rate, both of which were used to train two machine learning models. Subjective responses validated the efficacy of the VR applications in eliciting the two desired affective states; for classifying the affective states, a logistic regression (LR) and a support vector machine (SVM) with a linear kernel algorithm were developed. The LR outperformed the SVM and achieved 93.8%, 96.2%, 93.8% leave one subject out cross-validation accuracy, precision and recall, respectively. The VR assessment tool and data collected in this study are publicly available for other researchers.Keywords: affective computing, biosignals, machine learning, stress database
Procedia PDF Downloads 135436 Strategic Shear Wall Arrangement in Buildings under Seismic Loads
Authors: Akram Khelaifia, Salah Guettala, Nesreddine Djafar Henni, Rachid Chebili
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Reinforced concrete shear walls are pivotal in protecting buildings from seismic forces by providing strength and stiffness. This study highlights the importance of strategically placing shear walls and optimizing the shear wall-to-floor area ratio in building design. Nonlinear analyses were conducted on an eight-story building situated in a high seismic zone, exploring various scenarios of shear wall positioning and ratios to floor area. Employing the performance-based seismic design (PBSD) approach, the study aims to meet acceptance criteria such as inter-story drift ratio and damage levels. The results indicate that concentrating shear walls in the middle of the structure during the design phase yields superior performance compared to peripheral distributions. Utilizing shear walls that fully infill the frame and adopting compound shapes (e.g., Box, U, and L) enhances reliability in terms of inter-story drift. Conversely, the absence of complete shear walls within the frame leads to decreased stiffness and degradation of shorter beams. Increasing the shear wall-to-floor area ratio in building design enhances structural rigidity and reliability regarding inter-story drift, facilitating the attainment of desired performance levels. The study suggests that a shear wall ratio of 1.0% is necessary to meet validation criteria for inter-story drift and structural damage, as exceeding this percentage leads to excessive performance levels, proving uneconomical as structural elements operate near the elastic range.Keywords: nonlinear analyses, pushover analysis, shear wall, plastic hinge, performance level
Procedia PDF Downloads 41435 Evaluation of a Method for the Virtual Design of a Software-based Approach for Electronic Fuse Protection in Automotive Applications
Authors: Dominic Huschke, Rudolf Keil
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New driving functionalities like highly automated driving have a major impact on the electrics/electronics architecture of future vehicles and inevitably lead to higher safety requirements. Partly due to these increased requirements, the vehicle industry is increasingly looking at semiconductor switches as an alternative to conventional melting fuses. The protective functionality of semiconductor switches can be implemented in hardware as well as in software. A current approach discussed in science and industry is the implementation of a model of the protected low voltage power cable on a microcontroller to calculate its temperature. Here, the information regarding the current is provided by the continuous current measurement of the semiconductor switch. The signal to open the semiconductor switch is provided by the microcontroller when a previously defined limit for the temperature of the low voltage power cable is exceeded. A setup for the testing of the described principle for electronic fuse protection of a low voltage power cable is built and successfullyvalidated with experiments afterwards. Here, the evaluation criterion is the deviation of the measured temperature of the low voltage power cable from the specified limit temperature when the semiconductor switch is opened. The analysis is carried out with an assumed ambient temperature as well as with a measured ambient temperature. Subsequently, the experimentally performed investigations are simulated in a virtual environment. The explicit focus is on the simulation of the behavior of the microcontroller with an implemented model of a low voltage power cable in a real-time environment. Subsequently, the generated results are compared with those of the experiments. Based on this, the completely virtual design of the described approach is assumed to be valid.Keywords: automotive wire harness, electronic fuse protection, low voltage power cable, semiconductor-based fuses, software-based validation
Procedia PDF Downloads 99434 A Segmentation Method for Grayscale Images Based on the Firefly Algorithm and the Gaussian Mixture Model
Authors: Donatella Giuliani
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In this research, we propose an unsupervised grayscale image segmentation method based on a combination of the Firefly Algorithm and the Gaussian Mixture Model. Firstly, the Firefly Algorithm has been applied in a histogram-based research of cluster means. The Firefly Algorithm is a stochastic global optimization technique, centered on the flashing characteristics of fireflies. In this context it has been performed to determine the number of clusters and the related cluster means in a histogram-based segmentation approach. Successively these means are used in the initialization step for the parameter estimation of a Gaussian Mixture Model. The parametric probability density function of a Gaussian Mixture Model is represented as a weighted sum of Gaussian component densities, whose parameters are evaluated applying the iterative Expectation-Maximization technique. The coefficients of the linear super-position of Gaussians can be thought as prior probabilities of each component. Applying the Bayes rule, the posterior probabilities of the grayscale intensities have been evaluated, therefore their maxima are used to assign each pixel to the clusters, according to their gray-level values. The proposed approach appears fairly solid and reliable when applied even to complex grayscale images. The validation has been performed by using different standard measures, more precisely: the Root Mean Square Error (RMSE), the Structural Content (SC), the Normalized Correlation Coefficient (NK) and the Davies-Bouldin (DB) index. The achieved results have strongly confirmed the robustness of this gray scale segmentation method based on a metaheuristic algorithm. Another noteworthy advantage of this methodology is due to the use of maxima of responsibilities for the pixel assignment that implies a consistent reduction of the computational costs.Keywords: clustering images, firefly algorithm, Gaussian mixture model, meta heuristic algorithm, image segmentation
Procedia PDF Downloads 211433 Establishment and Validation of Correlation Equations to Estimate Volumetric Oxygen Mass Transfer Coefficient (KLa) from Process Parameters in Stirred-Tank Bioreactors Using Response Surface Methodology
Authors: Jantakan Jullawateelert, Korakod Haonoo, Sutipong Sananseang, Sarun Torpaiboon, Thanunthon Bowornsakulwong, Lalintip Hocharoen
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Process scale-up is essential for the biological process to increase production capacity from bench-scale bioreactors to either pilot or commercial production. Scale-up based on constant volumetric oxygen mass transfer coefficient (KLa) is mostly used as a scale-up factor since oxygen supply is one of the key limiting factors for cell growth. However, to estimate KLa of culture vessels operated with different conditions are time-consuming since it is considerably influenced by a lot of factors. To overcome the issue, this study aimed to establish correlation equations of KLa and operating parameters in 0.5 L and 5 L bioreactor employed with pitched-blade impeller and gas sparger. Temperature, gas flow rate, agitation speed, and impeller position were selected as process parameters and equations were created using response surface methodology (RSM) based on central composite design (CCD). In addition, the effects of these parameters on KLa were also investigated. Based on RSM, second-order polynomial models for 0.5 L and 5 L bioreactor were obtained with an acceptable determination coefficient (R²) as 0.9736 and 0.9190, respectively. These models were validated, and experimental values showed differences less than 10% from the predicted values. Moreover, RSM revealed that gas flow rate is the most significant parameter while temperature and agitation speed were also found to greatly affect the KLa in both bioreactors. Nevertheless, impeller position was shown to influence KLa in only 5L system. To sum up, these modeled correlations can be used to accurately predict KLa within the specified range of process parameters of two different sizes of bioreactors for further scale-up application.Keywords: response surface methodology, scale-up, stirred-tank bioreactor, volumetric oxygen mass transfer coefficient
Procedia PDF Downloads 199432 Potential Effects of Climate Change on Streamflow, Based on the Occurrence of Severe Floods in Kelantan, East Coasts of Peninsular Malaysia River Basin
Authors: Muhd. Barzani Gasim, Mohd. Ekhwan Toriman, Mohd. Khairul Amri Kamarudin, Azman Azid, Siti Humaira Haron, Muhammad Hafiz Md. Saad
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Malaysia is a country in Southeast Asia that constantly exposed to flooding and landslide. The disaster has caused some troubles such loss of property, loss of life and discomfort of people involved. This problem occurs as a result of climate change leading to increased stream flow rate as a result of disruption to regional hydrological cycles. The aim of the study is to determine hydrologic processes in the east coasts of Peninsular Malaysia, especially in Kelantan Basin. Parameterized to account for the spatial and temporal variability of basin characteristics and their responses to climate variability. For hydrological modeling of the basin, the Soil and Water Assessment Tool (SWAT) model such as relief, soil type, and its use, and historical daily time series of climate and river flow rates are studied. The interpretation of Landsat map/land uses will be applied in this study. The combined of SWAT and climate models, the system will be predicted an increase in future scenario climate precipitation, increase in surface runoff, increase in recharge and increase in the total water yield. As a result, this model has successfully developed the basin analysis by demonstrating analyzing hydrographs visually, good estimates of minimum and maximum flows and severe floods observed during calibration and validation periods.Keywords: east coasts of Peninsular Malaysia, Kelantan river basin, minimum and maximum flows, severe floods, SWAT model
Procedia PDF Downloads 256431 DMBR-Net: Deep Multiple-Resolution Bilateral Networks for Real-Time and Accurate Semantic Segmentation
Authors: Pengfei Meng, Shuangcheng Jia, Qian Li
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We proposed a real-time high-precision semantic segmentation network based on a multi-resolution feature fusion module, the auxiliary feature extracting module, upsampling module, and atrous spatial pyramid pooling (ASPP) module. We designed a feature fusion structure, which is integrated with sufficient features of different resolutions. We also studied the effect of side-branch structure on the network and made discoveries. Based on the discoveries about the side-branch of the network structure, we used a side-branch auxiliary feature extraction layer in the network to improve the effectiveness of the network. We also designed upsampling module, which has better results than the original upsampling module. In addition, we also re-considered the locations and number of atrous spatial pyramid pooling (ASPP) modules and modified the network structure according to the experimental results to further improve the effectiveness of the network. The network presented in this paper takes the backbone network of Bisenetv2 as a basic network, based on which we constructed a network structure on which we made improvements. We named this network deep multiple-resolution bilateral networks for real-time, referred to as DMBR-Net. After experimental testing, our proposed DMBR-Net network achieved 81.2% mIoU at 119FPS on the Cityscapes validation dataset, 80.7% mIoU at 109FPS on the CamVid test dataset, 29.9% mIoU at 78FPS on the COCOStuff test dataset. Compared with all lightweight real-time semantic segmentation networks, our network achieves the highest accuracy at an appropriate speed.Keywords: multi-resolution feature fusion, atrous convolutional, bilateral networks, pyramid pooling
Procedia PDF Downloads 141430 Assessing Denitrification-Disintegration Model’s Efficacy in Simulating Greenhouse Gas Emissions, Crop Growth, Yield, and Soil Biochemical Processes in Moroccan Context
Authors: Mohamed Boullouz, Mohamed Louay Metougui
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Accurate modeling of greenhouse gas (GHG) emissions, crop growth, soil productivity, and biochemical processes is crucial considering escalating global concerns about climate change and the urgent need to improve agricultural sustainability. The application of the denitrification-disintegration (DNDC) model in the context of Morocco's unique agro-climate is thoroughly investigated in this study. Our main research hypothesis is that the DNDC model offers an effective and powerful tool for precisely simulating a wide range of significant parameters, including greenhouse gas emissions, crop growth, yield potential, and complex soil biogeochemical processes, all consistent with the intricate features of environmental Moroccan agriculture. In order to verify these hypotheses, a vast amount of field data covering Morocco's various agricultural regions and encompassing a range of soil types, climatic factors, and crop varieties had to be gathered. These experimental data sets will serve as the foundation for careful model calibration and subsequent validation, ensuring the accuracy of simulation results. In conclusion, the prospective research findings add to the global conversation on climate-resilient agricultural practices while encouraging the promotion of sustainable agricultural models in Morocco. A policy architect's and an agricultural actor's ability to make informed decisions that not only advance food security but also environmental stability may be strengthened by the impending recognition of the DNDC model as a potent simulation tool tailored to Moroccan conditions.Keywords: greenhouse gas emissions, DNDC model, sustainable agriculture, Moroccan cropping systems
Procedia PDF Downloads 58429 Ground Surface Temperature History Prediction Using Long-Short Term Memory Neural Network Architecture
Authors: Venkat S. Somayajula
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Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical featuresKeywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle
Procedia PDF Downloads 124428 On-Line Super Critical Fluid Extraction, Supercritical Fluid Chromatography, Mass Spectrometry, a Technique in Pharmaceutical Analysis
Authors: Narayana Murthy Akurathi, Vijaya Lakshmi Marella
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The literature is reviewed with regard to online Super critical fluid extraction (SFE) coupled directly with supercritical fluid chromatography (SFC) -mass spectrometry that have typically more sensitive than conventional LC-MS/MS and GC-MS/MS. It is becoming increasingly interesting to use on-line techniques that combine sample preparation, separation and detection in one analytical set up. This provides less human intervention, uses small amount of sample and organic solvent and yields enhanced analyte enrichment in a shorter time. The sample extraction is performed under light shielding and anaerobic conditions, preventing the degradation of thermo labile analytes. It may be able to analyze compounds over a wide polarity range as SFC generally uses carbon dioxide which was collected as a by-product of other chemical reactions or is collected from the atmosphere as it contributes no new chemicals to the environment. The diffusion of solutes in supercritical fluids is about ten times greater than that in liquids and about three times less than in gases which results in a decrease in resistance to mass transfer in the column and allows for fast high resolution separations. The drawback of SFC when using carbon dioxide as mobile phase is that the direct introduction of water samples poses a series of problems, water must therefore be eliminated before it reaches the analytical column. Hundreds of compounds analysed simultaneously by simple enclosing in an extraction vessel. This is mainly applicable for pharmaceutical industry where it can analyse fatty acids and phospholipids that have many analogues as their UV spectrum is very similar, trace additives in polymers, cleaning validation can be conducted by putting swab sample in an extraction vessel, analysing hundreds of pesticides with good resolution.Keywords: super critical fluid extraction (SFE), super critical fluid chromatography (SFC), LCMS/MS, GCMS/MS
Procedia PDF Downloads 385427 Improved Distance Estimation in Dynamic Environments through Multi-Sensor Fusion with Extended Kalman Filter
Authors: Iffat Ara Ebu, Fahmida Islam, Mohammad Abdus Shahid Rafi, Mahfuzur Rahman, Umar Iqbal, John Ball
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The application of multi-sensor fusion for enhanced distance estimation accuracy in dynamic environments is crucial for advanced driver assistance systems (ADAS) and autonomous vehicles. Limitations of single sensors such as cameras or radar in adverse conditions motivate the use of combined camera and radar data to improve reliability, adaptability, and object recognition. A multi-sensor fusion approach using an extended Kalman filter (EKF) is proposed to combine sensor measurements with a dynamic system model, achieving robust and accurate distance estimation. The research utilizes the Mississippi State University Autonomous Vehicular Simulator (MAVS) to create a controlled environment for data collection. Data analysis is performed using MATLAB. Qualitative (visualization of fused data vs ground truth) and quantitative metrics (RMSE, MAE) are employed for performance assessment. Initial results with simulated data demonstrate accurate distance estimation compared to individual sensors. The optimal sensor measurement noise variance and plant noise variance parameters within the EKF are identified, and the algorithm is validated with real-world data from a Chevrolet Blazer. In summary, this research demonstrates that multi-sensor fusion with an EKF significantly improves distance estimation accuracy in dynamic environments. This is supported by comprehensive evaluation metrics, with validation transitioning from simulated to real-world data, paving the way for safer and more reliable autonomous vehicle control.Keywords: sensor fusion, EKF, MATLAB, MAVS, autonomous vehicle, ADAS
Procedia PDF Downloads 32426 Electrochemical Biosensor for the Detection of Botrytis spp. in Temperate Legume Crops
Authors: Marzia Bilkiss, Muhammad J. A. Shiddiky, Mostafa K. Masud, Prabhakaran Sambasivam, Ido Bar, Jeremy Brownlie, Rebecca Ford
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A greater achievement in the Integrated Disease Management (IDM) to prevent the loss would result from early diagnosis and quantitation of the causal pathogen species for accurate and timely disease control. This could significantly reduce costs to the growers and reduce any flow on impacts to the environment from excessive chemical spraying. Necrotrophic fungal disease botrytis grey mould, caused by Botrytis cinerea and Botrytis fabae, significantly reduce temperate legume yield and grain quality during favourable environmental condition in Australia and worldwide. Several immunogenic and molecular probe-type protocols have been developed for their diagnosis, but these have varying levels of species-specificity, sensitivity, and consequent usefulness within the paddock. To substantially improve speed, accuracy, and sensitivity, advanced nanoparticle-based biosensor approaches have been developed. For this, two sets of primers were designed for both Botrytis cinerea and Botrytis fabae which have shown the species specificity with initial sensitivity of two genomic copies/µl in pure fungal backgrounds using multiplexed quantitative PCR. During further validation, quantitative PCR detected 100 spores on artificially infected legume leaves. Simultaneously an electro-catalytic assay was developed for both target fungal DNA using functionalised magnetic nanoparticles. This was extremely sensitive, able to detect a single spore within a raw total plant nucleic acid extract background. We believe that the translation of this technology to the field will enable quantitative assessment of pathogen load for future accurate decision support of informed botrytis grey mould management.Keywords: biosensor, botrytis grey mould, sensitive, species specific
Procedia PDF Downloads 166425 Biomechanical Prediction of Veins and Soft Tissues beneath Compression Stockings Using Fluid-Solid Interaction Model
Authors: Chongyang Ye, Rong Liu
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Elastic compression stockings (ECSs) have been widely applied in prophylaxis and treatment of chronic venous insufficiency of lower extremities. The medical function of ECS is to improve venous return and increase muscular pumping action to facilitate blood circulation, which is largely determined by the complex interaction between the ECS and lower limb tissues. Understanding the mechanical transmission of ECS along the skin surface, deeper tissues, and vascular system is essential to assess the effectiveness of the ECSs. In this study, a three-dimensional (3D) finite element (FE) model of the leg-ECS system integrated with a 3D fluid-solid interaction (FSI) model of the leg-vein system was constructed to analyze the biomechanical properties of veins and soft tissues under different ECS compression. The Magnetic Resonance Imaging (MRI) of the human leg was divided into three regions, including soft tissues, bones (tibia and fibula) and veins (peroneal vein, great saphenous vein, and small saphenous vein). The ECSs with pressure ranges from 15 to 26 mmHg (Classes I and II) were adopted in the developed FE-FSI model. The soft tissue was assumed as a Neo-Hookean hyperelastic model with the fixed bones, and the ECSs were regarded as an orthotropic elastic shell. The interfacial pressure and stress transmission were simulated by the FE model, and venous hemodynamics properties were simulated by the FSI model. The experimental validation indicated that the simulated interfacial pressure distributions were in accordance with the pressure measurement results. The developed model can be used to predict interfacial pressure, stress transmission, and venous hemodynamics exerted by ECSs and optimize the structure and materials properties of ECSs design, thus improving the efficiency of compression therapy.Keywords: elastic compression stockings, fluid-solid interaction, tissue and vein properties, prediction
Procedia PDF Downloads 109424 Assessment of Multi-Domain Energy Systems Modelling Methods
Authors: M. Stewart, Ameer Al-Khaykan, J. M. Counsell
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Emissions are a consequence of electricity generation. A major option for low carbon generation, local energy systems featuring Combined Heat and Power with solar PV (CHPV) has significant potential to increase energy performance, increase resilience, and offer greater control of local energy prices while complementing the UK’s emissions standards and targets. Recent advances in dynamic modelling and simulation of buildings and clusters of buildings using the IDEAS framework have successfully validated a novel multi-vector (simultaneous control of both heat and electricity) approach to integrating the wide range of primary and secondary plant typical of local energy systems designs including CHP, solar PV, gas boilers, absorption chillers and thermal energy storage, and associated electrical and hot water networks, all operating under a single unified control strategy. Results from this work indicate through simulation that integrated control of thermal storage can have a pivotal role in optimizing system performance well beyond the present expectations. Environmental impact analysis and reporting of all energy systems including CHPV LES presently employ a static annual average carbon emissions intensity for grid supplied electricity. This paper focuses on establishing and validating CHPV environmental performance against conventional emissions values and assessment benchmarks to analyze emissions performance without and with an active thermal store in a notional group of non-domestic buildings. Results of this analysis are presented and discussed in context of performance validation and quantifying the reduced environmental impact of CHPV systems with active energy storage in comparison with conventional LES designs.Keywords: CHPV, thermal storage, control, dynamic simulation
Procedia PDF Downloads 234423 Hydrodynamics of Periphyton Biofilters in Recirculating Aquaculture
Authors: Adam N. Bell, Sarina J. Ergas, Michael Nystrom, Nathan P. Brennan, Kevan L. Main
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Integrated Multi-Trophic Aquaculture systems (IMTA) have the potential to improve the sustainability of seafood production, generate organic fertilizer and feed, remove waste discharges and reduce energy use. IMTA can include periphyton biofilters where algae and microbes grow on surfaces, along with caught detritus and amphipods. Periphyton biofilters provide many advantages: nitrification, denitrification, primary production and ecological diversity. The goal of this study was to determine how biofilter hydraulic residence time (τ) effects periphyton biomass production, dissolved oxygen (DO) and nutrient removal. A pilot scale recirculating aquaculture system (RAS) was designed, constructed and operated at different hydraulic residence times (τ= 1, 2, 4, 6, 8 hours per tank). For each τ, a conservative tracer study was conducted to investigate system hydrodynamics. Data on periphyton weights, pH, nitrogen species, phosphorus, temperature and DO were collected. The tracer study for τ =1 hour revealed that the normalized time < τ, indicating short-circuiting. Periphyton biomass production rate was relatively unaffected by τ (R_e<1 for all τ). Average ammonia nitrogen removal was > 75% for all trials. Nitrate and nitrite did not accumulate in the RAS for τ≥4 hours due to enhanced denitrification in anoxic zones. For τ≥4 hours DO concentration was at a maximum of 4 mg L-1 after 14:00, and decreased to 0 mg L-1 during nighttime. At τ=1 hour, the RAS stayed > 2 mg L-1 and DO was more evenly distributed. For the validation trial, the culture tank was stocked with Centropomus undecimalis (common snook) and the system was operated at τ= 1 hr. Preliminary results showed that a RAS with an integrated periphyton biofilter could support fish health with low nutrient concentrations DO > 6 mg L-1.Keywords: sustainable aquaculture, resource recovery, nitrogen, microalgae, hydrodynamics, integrated multi-trophic aquaculture
Procedia PDF Downloads 125422 AI-Driven Solutions for Optimizing Master Data Management
Authors: Srinivas Vangari
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In the era of big data, ensuring the accuracy, consistency, and reliability of critical data assets is crucial for data-driven enterprises. Master Data Management (MDM) plays a crucial role in this endeavor. This paper investigates the role of Artificial Intelligence (AI) in enhancing MDM, focusing on how AI-driven solutions can automate and optimize various stages of the master data lifecycle. By integrating AI (Quantitative and Qualitative Analysis) into processes such as data creation, maintenance, enrichment, and usage, organizations can achieve significant improvements in data quality and operational efficiency. Quantitative analysis is employed to measure the impact of AI on key metrics, including data accuracy, processing speed, and error reduction. For instance, our study demonstrates an 18% improvement in data accuracy and a 75% reduction in duplicate records across multiple systems post-AI implementation. Furthermore, AI’s predictive maintenance capabilities reduced data obsolescence by 22%, as indicated by statistical analyses of data usage patterns over a 12-month period. Complementing this, a qualitative analysis delves into the specific AI-driven strategies that enhance MDM practices, such as automating data entry and validation, which resulted in a 28% decrease in manual errors. Insights from case studies highlight how AI-driven data cleansing processes reduced inconsistencies by 25% and how AI-powered enrichment strategies improved data relevance by 24%, thus boosting decision-making accuracy. The findings demonstrate that AI significantly enhances data quality and integrity, leading to improved enterprise performance through cost reduction, increased compliance, and more accurate, real-time decision-making. These insights underscore the value of AI as a critical tool in modern data management strategies, offering a competitive edge to organizations that leverage its capabilities.Keywords: artificial intelligence, master data management, data governance, data quality
Procedia PDF Downloads 7421 Validity of a Timing System in the Alpine Ski Field: A Magnet-Based Timing System Using the Magnetometer Built into an Inertial Measurement Units
Authors: Carla Pérez-Chirinos Buxadé, Bruno Fernández-Valdés, Mónica Morral-Yepes, Sílvia Tuyà Viñas, Josep Maria Padullés Riu, Gerard Moras Feliu
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There is a long way to explore all the possible applications inertial measurement units (IMUs) have in the sports field. The aim of this study was to evaluate the validity of a new application on the use of these wearable sensors, specifically it was to evaluate a magnet-based timing system (M-BTS) for timing gate-to-gate in an alpine ski slalom using the magnetometer embedded in an IMU. This was a validation study. The criterion validity of time measured by the M-BTS was assessed using the 95% error range against actual time obtained from photocells. The experiment was carried out with first-and second-year junior skiers performing a ski slalom on a ski training slope. Eight alpine skiers (17.4 ± 0.8 years, 176.4 ± 4.9 cm, 67.7 ± 2.0 kg, 128.8 ± 26.6 slalom FIS-Points) participated in the study. An IMU device was attached to the skier’s lower back. Skiers performed a 40-gate slalom from which four gates were assessed. The M-BTS consisted of placing four bar magnets buried into the snow surface on the inner side of each gate’s turning pole; the magnetometer built into the IMU detected the peak-shaped magnetic field when passing near the magnets at a certain speed. Four magnetic peaks were detected. The time compressed between peaks was calculated. Three inter-gate times were obtained for each system: photocells and M-BTS. The total time was defined as the time sum of the inter-gate times. The 95% error interval for the total time was 0.050 s for the ski slalom. The M-BTS is valid for timing gate-to-gate in an alpine ski slalom. Inter-gate times can provide additional data for analyzing a skier’s performance, such as asymmetries between left and right foot.Keywords: gate crossing time, inertial measurement unit, timing system, wearable sensor
Procedia PDF Downloads 178420 Transcriptome Analysis Reveals Role of Long Non-Coding RNA NEAT1 in Dengue Patients
Authors: Abhaydeep Pandey, Shweta Shukla, Saptamita Goswami, Bhaswati Bandyopadhyay, Vishnampettai Ramachandran, Sudhanshu Vrati, Arup Banerjee
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Background: Long non-coding RNAs (lncRNAs) are the important regulators of gene expression and play important role in viral replication and disease progression. The role of lncRNA genes in the pathogenesis of Dengue virus-mediated pathogenesis is currently unknown. Methods: To gain additional insights, we utilized an unbiased RNA sequencing followed by in silico analysis approach to identify the differentially expressed lncRNA and genes that are associated with dengue disease progression. Further, we focused our study on lncRNAs NEAT1 (Nuclear Paraspeckle Assembly Transcript 1) as it was found to be differentially expressed in PBMC of dengue infected patients. Results: The expression of lncRNAs NEAT1, as compared to dengue infection (DI), was significantly down-regulated as the patients developed the complication. Moreover, pairwise analysis on follow up patients confirmed that suppression of NEAT1 expression was associated with rapid fall in platelet count in dengue infected patients. Severe dengue patients (DS) (n=18; platelet count < 20K) when recovered from infection showing high NEAT1 expression as it observed in healthy donors. By co-expression network analysis and subsequent validation, we revealed that coding gene; IFI27 expression was significantly up-regulated in severe dengue cases and negatively correlated with NEAT1 expression. To discriminate DI from dengue severe, receiver operating characteristic (ROC) curve was calculated. It revealed sensitivity and specificity of 100% (95%CI: 85.69 – 97.22) and area under the curve (AUC) = 0.97 for NEAT1. Conclusions: Altogether, our first observations demonstrate that monitoring NEAT1and IFI27 expression in dengue patients could be useful in understanding dengue virus-induced disease progression and may be involved in pathophysiological processes.Keywords: dengue, lncRNA, NEAT1, transcriptome
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