Search results for: Thermal images.

Authors: Gustavo J. Costa, Felipe J. Costa, Bruno L. Coelho, Ronaldo L. Cardoso, Rafael O. Santos, Israel S. Rêgo, Marco A. S. Minucci, Antonio C. Oliveira, Paulo G. P. Toro

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The Prof. Henry T. Nagamatsu Aerothermodynamics and Hipersonics Laboratory, of the Institute for Advanced Studies (IEAv) conducts research and development (R&D) of the Technological Demonstrator scramjet 14-X S, aiming atmospheric flight at 30 km altitude with the speed correspondent to Mach number 7, using scramjet technology providing hypersonic propulsion system based on supersonic combustion. Hypersonic aerospace vehicles with air-breathing supersonic propulsion system face extremal environments for super/hypersonic flights in terms of thermal and aerodynamic loads. Thus, it is necessary to use aerodynamic protection at the scramjet engine inlet to face the thermal and aerodynamic loads without compromising the efficiency of scramjet engine, taking into account: i) inlet design (boundary layer, oblique shockwave and reflected oblique shockwave); ii) wall temperature of the cowl and of the compression ramp; iii) supersonic flow into the combustion chamber. The aerodynamic protection of the scramjet engine inlet will act to prevent the engine unstart and match the predictions made by theoretical-analytical, numerical analysis and experimental research, during the atmospheric flight of the Technological Demonstrator scramjet 14-X S.

Keywords: 14-X, hypersonic, scramjet, supersonic combustion

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Authors: S. Benkraouda, Z. Djelloul-Khedda, B. Yagoubi

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we present a method for early detection of forest fires from a thermal infrared satellite image, using the image matrix of the probability of belonging. The principle of the method is to compare a theoretical mathematical model to an experimental model. We considered that each line of the image matrix, as an embodiment of a non-stationary random process. Since the distribution of pixels in the satellite image is statistically dependent, we divided these lines into small stationary and ergodic intervals to characterize the image by an adequate mathematical model. A standard deviation was chosen to generate random variables, so each interval behaves naturally like white Gaussian noise. The latter has been selected as the mathematical model that represents a set of very majority pixels, which we can be considered as the image background. Before modeling the image, we made a few pretreatments, then the parameters of the theoretical Gaussian model were extracted from the modeled image, these settings will be used to calculate the probability of each interval of the modeled image to belong to the theoretical Gaussian model. The high intensities pixels are regarded as foreign elements to it, so they will have a low probability, and the pixels that belong to the background image will have a high probability. Finally, we did present the reverse of the matrix of probabilities of these intervals for a better fire detection.

Keywords: forest fire, forest fire detection, satellite image, normal distribution, theoretical gaussian model, thermal infrared matrix image

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Authors: Syed Mamoon Siyar, Fayaz Ali, Sajjad Ahmad, Samina Jahandad, George Kontakiotis, Hammad T. Janjuhah, Assimina Antonarakou, Waqas Naseem

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The Cretaceous Chichali Formation in the Chanda-01, Chanda-02, Chanda-03 and Mela-05 wells and the oil samples from Chanda-01 and Chanda-01 wells located in the Kohat Basin, Pakistan, were analyzed with the objectives of evaluating the hydrocarbon generation potential, source, thermal maturity and depositional of organic matter, and oil-source correlation by employing geochemical screening techniques and biomarker studies. The total organic carbon (TOC) values in Chanda-02, Chanda-03 and Mela-05 indicate, in general, poor to fair, fair and fair to good source rock potential with low genetic potential, respectively. The nature of organic matter has been determined by standard cross plots of Rock Eval pyrolysis parameters, indicating that studied cuttings from the Chichali Formation dominantly contain type III kerogen at present and show maturity for oil generation in the studied wells. The organic petrographic study also confirmed the vitrinite (type III) as a major maceral in the investigated Chichali Shales and its reflectance values show maturity for oil. The different ratios of non-biomarkers and biomarkers i.e., steranes, terpenes and aromatics parameters, indicate the marine source of organic matter deposited in the anoxic environment for the Chichali Formation in Chanda-01 and Chanda-02 wells and mixed source input of organic matter deposited in suboxic conditions for oil in the same wells. The CPI, and different biomarkers parameters such as C29 S/S+R, ββ/αα+ββ), M29/H30, Ts/Ts+Tm, H31 (S/S+R) and aromatic compounds methyl phenanthrene index (MPI) and organic petrographic analysis (vitrinite reflectance) suggest mature stage of oil generation for Chichali Shales and oil samples in the study area with little high thermal maturity in case of oils. Based on source and thermal maturity biomarkers and non-biomarkers parameters, the produced oils have no correlation with the Cretaceous Chichali Formation in the studied Chanda-01 and Chanda-02 wells in Kohat Basin, Pakistan, but it has been suggested that these oils have been generated by the strata containing high terrestrial organic input compare to Chichali Shales.

Keywords: Organic geochemistry, Chichali Shales and crude oils, Kohat Basin, Pakistan

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Authors: Jae Ni Jang, SoYoon Park, Sukhee Park, Yumin Song, Jae Won Kim, Keum Nae Kang, Young Uk Kim

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Objectives: Patellofemoral pain syndrome (PFPS) is a common clinical condition characterized by anterior knee pain. Here, we investigated the quadriceps tendon cross-sectional area (QTCSA) as a novel predictor for the diagnosis of PFPS. By examining the association between the QTCSA and PFPS, we aimed to provide a more valuable diagnostic parameter and more equivocal assessment of the diagnostic potential of PFPS by comparing the QTCSA with the quadriceps tendon thickness (QTT), a traditional measure of quadriceps tendon hypertrophy. Patients and Methods: This retrospective study included 30 patients with PFPS and 30 healthy participants who underwent knee magnetic resonance imaging. T1-weighted turbo spin echo transverse magnetic resonance images were obtained. The QTCSA was measured on the axial-angled phases of the images by drawing outlines, and the QTT was measured at the most hypertrophied quadriceps tendon. Results: The average QTT and QTCSA for patients with PFPS (6.33±0.80 mm and 155.77±36.60 mm², respectively) were significantly greater than those for healthy participants (5.77±0.36 mm and 111.90±24.10 mm2, respectively; both P<0.001). We used a receiver operating characteristic curve to confirm the sensitivities and specificities for both the QTT and QTCSA as predictors of PFPS. The optimal diagnostic cutoff value for QTT was 5.98 mm, with a sensitivity of 66.7%, a specificity of 70.0%, and an area under the curve of 0.75 (0.62–0.88). The optimal diagnostic cutoff value for QTCSA was 121.04 mm², with a sensitivity of 73.3%, a specificity of 70.0%, and an area under the curve of 0.83 (0.74–0.93). Conclusion: The QTCSA was found to be a more reliable diagnostic indicator for PFPS than QTT.

Keywords: patellofemoral pain syndrome, quadriceps muscle, hypertrophy, magnetic resonance imaging

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Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

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In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

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Authors: Pamela R. Jackson, Andrea Hawkins-Daarud, Cassandra R. Rickertsen, Kamala Clark-Swanson, Scott A. Whitmire, Kristin R. Swanson

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Glioblastoma Multiforme (GBM), the most common primary brain tumor, often presents with hyperintensity on T2-weighted or T2-weighted fluid attenuated inversion recovery (T2/FLAIR) magnetic resonance imaging (MRI). This hyperintensity corresponds with vasogenic edema, however there are likely many infiltrating tumor cells within the hyperintensity as well. While MRIs do not directly indicate tumor cells, MRIs do reflect the microenvironmental water abnormalities caused by the presence of tumor cells and edema. The inherent heterogeneity and resulting MRI features of GBMs complicate assessing disease response. To understand how hyperintensity on T2/FLAIR MRI may correlate with edema in the extracellular space (ECS), a multi-compartmental MRI signal equation which takes into account tissue compartments and their associated volumes with input coming from a mathematical model of glioma growth that incorporates edema formation was explored. The reasonableness of two possible extracellular space schema was evaluated by varying the T2 of the edema compartment and calculating the possible resulting T2s in tumor and peripheral edema. In the mathematical model, gliomas were comprised of vasculature and three tumor cellular phenotypes: normoxic, hypoxic, and necrotic. Edema was characterized as fluid leaking from abnormal tumor vessels. Spatial maps of tumor cell density and edema for virtual tumors were simulated with different rates of proliferation and invasion and various ECS expansion schemes. These spatial maps were then passed into a multi-compartmental MRI signal model for generating simulated T2/FLAIR MR images. Individual compartments’ T2 values in the signal equation were either from literature or estimated and the T2 for edema specifically was varied over a wide range (200 ms – 9200 ms). T2 maps were calculated from simulated images. T2 values based on simulated images were evaluated for regions of interest (ROIs) in normal appearing white matter, tumor, and peripheral edema. The ROI T2 values were compared to T2 values reported in literature. The expanding scheme of extracellular space is had T2 values similar to the literature calculated values. The static scheme of extracellular space had a much lower T2 values and no matter what T2 was associated with edema, the intensities did not come close to literature values. Expanding the extracellular space is necessary to achieve simulated edema intensities commiserate with acquired MRIs.

Keywords: extracellular space, glioblastoma multiforme, magnetic resonance imaging, mathematical modeling

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Authors: M. A. Alqahtani, D. P. Coleman, N. D. Pugh, L. D. M. Nokes

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In diagnostic ultrasound, the echo graphic B-scan texture is an important area of investigation since it can be analyzed to characterize the histological state of internal tissues. An important factor requiring consideration when evaluating ultrasonic tissue texture is the depth. The effect of attenuation with depth of ultrasound, the size of the region of interest, gain, and dynamic range are important variables to consider as they can influence the analysis of texture features. These sources of variability have to be considered carefully when evaluating image texture as different settings might influence the resultant image. The aim of this study is to investigate the effect of depth on the texture features in-vivo using a 3D ultrasound probe. The left leg medial head of the gastrocnemius muscle of 10 healthy subjects were scanned. Two regions A and B were defined at different depth within the gastrocnemius muscle boundary. The size of both ROI’s was 280*20 pixels and the distance between region A and B was kept constant at 5 mm. Texture parameters include gray level, variance, skewness, kurtosis, co-occurrence matrix; run length matrix, gradient, autoregressive (AR) model and wavelet transform were extracted from the images. The paired t –test was used to test the depth effect for the normally distributed data and the Wilcoxon–Mann-Whitney test was used for the non-normally distributed data. The gray level, variance, and run length matrix were significantly lowered when the depth increased. The other texture parameters showed similar values at different depth. All the texture parameters showed no significant difference between depths A and B (p > 0.05) except for gray level, variance and run length matrix (p < 0.05). This indicates that gray level, variance, and run length matrix are depth dependent.

Keywords: ultrasound image, texture parameters, computational biology, biomedical engineering

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Authors: Rodrigo T. Sepulveda-Hirose, Ana B. Carrera-Aguilar, Magnolia G. Martinez-Rivera, Pablo de J. Angeles-Salto, Carlos Herrera-Ventosa

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In order to diminish health risks, it is of major importance to monitor air quality. However, this process is accompanied by the high costs of physical and human resources. In this context, this research is carried out with the main objective of developing a predictive model for concentrations of inhalable particles (PM10-2.5) using remote sensing. To develop the model, satellite images, mainly from Landsat 8, of the Mexico City’s Metropolitan Area were used. Using historical PM10 and PM2.5 measurements of the RAMA (Automatic Environmental Monitoring Network of Mexico City) and through the processing of the available satellite images, a preliminary model was generated in which it was possible to observe critical opportunity areas that will allow the generation of a robust model. Through the preliminary model applied to the scenes of Mexico City, three areas were identified that cause great interest due to the presumed high concentration of PM; the zones are those that present high plant density, bodies of water and soil without constructions or vegetation. To date, work continues on this line to improve the preliminary model that has been proposed. In addition, a brief analysis was made of six models, presented in articles developed in different parts of the world, this in order to visualize the optimal bands for the generation of a suitable model for Mexico City. It was found that infrared bands have helped to model in other cities, but the effectiveness that these bands could provide for the geographic and climatic conditions of Mexico City is still being evaluated.

Keywords: air quality, modeling pollution, particulate matter, remote sensing

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Authors: Sireetorn Kuharat, Anwar Beg

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In this study, multi-mode heat transfer characteristics of spacecraft solar collectors are investigated computationally. Two-dimensional steady-state incompressible laminar Newtonian viscous convection-radiative heat transfer in a rectangular solar collector geometry. The ANSYS FLUENT finite volume code (version 17.2) is employed to simulate the thermo-fluid characteristics. Several radiative transfer models are employed which are available in the ANSYS workbench, including the classical Rosseland flux model and the more elegant P1 flux model. Mesh-independence tests are conducted. Validation of the simulations is conducted with a computational Harlow-Welch MAC (Marker and Cell) finite difference method and excellent correlation. The influence of aspect ratio, Prandtl number (Pr), Rayleigh number (Ra) and radiative flux model on temperature, isotherms, velocity, the pressure is evaluated and visualized in color plots. Additionally, the local convective heat flux is computed and solutions are compared with the MAC solver for various buoyancy effects (e.g. Ra = 10,000,000) achieving excellent agreement. The P1 model is shown to better predict the actual influence of solar radiative flux on thermal fluid behavior compared with the limited Rosseland model. With increasing Rayleigh numbers the hot zone emanating from the base of the collector is found to penetrate deeper into the collector and rises symmetrically dividing into two vortex regions with very high buoyancy effect (Ra >100,000). With increasing Prandtl number (three gas cases are examined respectively hydrogen gas mixture, air and ammonia gas) there is also a progressive incursion of the hot zone at the solar collector base higher into the solar collector space and simultaneously a greater asymmetric behavior of the dual isothermal zones. With increasing aspect ratio (wider base relative to the height of the solar collector geometry) there is a greater thermal convection pattern around the whole geometry, higher temperatures and the elimination of the cold upper zone associated with lower aspect ratio.

Keywords: thermal convection, radiative heat transfer, solar collector, Rayleigh number

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Blessing Ojeme, Frederick Quinn, Russell Karls, Shannon Quinn

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The detection and segmentation of mitochondria from fluorescence microscopy are crucial for understanding the complex structure of the nervous system. However, the constant fission and fusion of mitochondria and image distortion in the background make the task of detection and segmentation challenging. In the literature, a number of open-source software tools and artificial intelligence (AI) methods have been described for analyzing mitochondrial images, achieving remarkable classification and quantitation results. However, the availability of combined expertise in the medical field and AI required to utilize these tools poses a challenge to its full adoption and use in clinical settings. Motivated by the advantages of automated methods in terms of good performance, minimum detection time, ease of implementation, and cross-platform compatibility, this study proposes a fully automated framework for the detection and segmentation of mitochondria using both image shape information and descriptive statistics. Using the low-cost, open-source python and openCV library, the algorithms are implemented in three stages: pre-processing, image binarization, and coarse-to-fine segmentation. The proposed model is validated using the mitochondrial fluorescence dataset. Ground truth labels generated using a Lab kit were also used to evaluate the performance of our detection and segmentation model. The study produces good detection and segmentation results and reports the challenges encountered during the image analysis of mitochondrial morphology from the fluorescence mitochondrial dataset. A discussion on the methods and future perspectives of fully automated frameworks conclude the paper.

Keywords: 2D, binarization, CLAHE, detection, fluorescence microscopy, mitochondria, segmentation

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Authors: Areej Almalkawi, Sameer Hamadna, Parviz Soroushian, Nalin Darsana

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The work on indigenous binders in this paper focused on the following indigenous raw materials: red clay, red lava and pumice (as primary aluminosilicate precursors), wood ash and gypsum (as supplementary minerals), and sodium sulfate and lime (as alkali activators). The experimental methods used for evaluation of these indigenous raw materials included laser granulometry, x-ray fluorescence (XRF) spectroscopy, and chemical reactivity. Formulations were devised for transforming these raw materials into alkali aluminosilicate-based hydraulic cements. These formulations were processed into hydraulic cements via simple heating and milling actions to render thermal activation, mechanochemical and size reduction effects. The resulting hydraulic cements were subjected to laser granulometry, heat of hydration and reactivity tests. These cements were also used to prepare mortar mixtures, which were evaluated via performance of compressive strength tests. The measured values of strength were correlated with the reactivity, size distribution and microstructural features of raw materials. Some of the indigenous hydraulic cements produced in this reporting period yielded viable levels of compressive strength. The correlation trends established in this work are being evaluated for development of simple and thorough methods of qualifying indigenous raw materials for use in production of indigenous hydraulic cements.

Keywords: one-part geopolymer cement, aluminosilicate precursors, thermal activation, mechanochemical

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Authors: Mahamuda Sk, Srinivasa Rao Allam, Vijaya Prakash G.

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The different concentrations of Eu3+ ions doped in Oxy-fluoroborate glasses of composition 60 B2O3-10 BaF2-10 CaF2-15 CaF2- (5-x) Al2O3 -x Eu2O3 where x = 0.1, 0.5, 1.0 and 2.0 mol%, have been prepared by conventional melt quenching technique and are characterized through absorption and photoluminescence (PL), decay, color chromaticity and Confocal measurements. The absorption spectra of all the glasses consists of six peaks corresponding to the transitions 7F0→5D2, 7F0→5D1, 7F1→5D1, 7F1→5D0, 7F0→7F6 and 7F1→7F6 respectively. The experimental oscillator strengths with and without thermal corrections have been evaluated using absorption spectra. Judd-Ofelt (JO) intensity parameters (Ω2 and Ω4) have been evaluated from the photoluminescence spectra of all the glasses. PL spectra of all the glasses have been recorded at excitation wavelengths 395 nm (conventional excitation source) and 410 nm (diode laser) to observe the intensity variation in the PL spectra. All the spectra consists of five emission peaks corresponding to the transitions 5D0→7FJ (J = 0, 1, 2, 3 and 4). Surprisingly no concentration quenching is observed on PL spectra. Among all the glasses the glass with 2.0 mol% of Eu3+ ion concentration possesses maximum intensity for the transition 5D0→7F2 (612 nm) in bright red region. The JO parameters derived from the photoluminescence spectra have been used to evaluate the essential radiative properties such as transition probability (A), radiative lifetime (τR), branching ratio (βR) and peak stimulated emission cross-section (σse) for the 5D0→7FJ (J = 0, 1, 2, 3 and 4) transitions of the Eu3+ ions. The decay rates of the 5D0 fluorescent level of Eu3+ ions in the title glasses are found to be single exponential for all the studied Eu3+ ion concentrations. A marginal increase in lifetime of the 5D0 level has been noticed with increase in Eu3+ ion concentration from 0.1 mol% to 2.0 mol%. Among all the glasses, the glass with 2.0 mol% of Eu3+ ion concentration possesses maximum values of branching ratio, stimulated emission cross-section and quantum efficiency for the transition 5D0→7F2 (612 nm) in bright red region. The color chromaticity coordinates are also evaluated to confirm the reddish luminescence from these glasses. These color coordinates exactly fall in the bright red region. Confocal images also recorded to confirm reddish luminescence from these glasses. From all the obtained results in the present study, it is suggested that the glass with 2.0 mol% of Eu3+ ion concentration is suitable to emit bright red color laser.

Keywords: Europium, Judd-Ofelt parameters, laser, luminescence

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Authors: Kubra Sayin, Derya Arslan

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Red pepper (Capsicum annum L.) has long been recognized as a good source of antioxidants, being rich in ascorbic acid and other phytochemicals. In Turkish cuisine red pepper is sometimes consumed raw in salads and baked as a garnish, but its most wide consumption type is red pepper paste. The processing of red pepper into pepper paste includes various thermal treatment steps such as heating and pasteurizing. There are reports demonstrating an enhancement or reduction in antioxidant activity of vegetables after thermal treatment. So this study was conducted to investigate the total phenolics, ascorbic acid and total carotenoids as well as free radical scavenging activity of raw red pepper and various red pepper pastes obtainable on the market. The samples were analyzed for radical-scavenging activity (RSA) and total polyphenol (TP) content using 1,1-diphenyl-2-picrylhydrazyl (DPPH) and Folin-Ciocalteu methods, respectively. They were also evaluated for ascorbic acid content (AsA) by HPLC. Total carotenoids content was determined spectrophotometrically. Results suggest that there is no significant (P > 0.05) difference in RSA, TP, AsA and total carotenoids content between various red pepper paste products. However, red pepper paste showed marked differences (P < 0.05) in the RSA, TP and AsA contents compared with raw red pepper. It is concluded that the red pepper paste, that has a wide range of consumption in Turkish cuisine, presents a good dose of phenolic compounds and antioxidant capacity and it should be regarded as a functional food.

Keywords: red pepper paste, antioxidant properties, total carotenoids, total phenolics

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Authors: Michael Karnes, Alper Yilmaz

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This paper presents the application of the omni-modeler towards pedestrian redetection. The pedestrian redetection task creates several challenges when applying deep neural networks (DNN) due to the variety of pedestrian appearance with camera position, the variety of environmental conditions, and the specificity required to recognize one pedestrian from another. DNNs require significant training sets and are not easily adapted for changes in class appearances or changes in the set of classes held in its knowledge domain. Pedestrian redetection requires an algorithm that can actively manage its knowledge domain as individuals move in and out of the scene, as well as learn individual appearances from a few frames of a video. The Omni-Modeler is a dynamically learning few-shot visual recognition algorithm developed for tasks with limited training data availability. The Omni-Modeler adapts the knowledge domain of pre-trained deep neural networks to novel concepts with a calculated localized language encoder. The Omni-Modeler knowledge domain is generated by creating a dynamic dictionary of concept definitions, which are directly updatable as new information becomes available. Query images are identified through nearest neighbor comparison to the learned object definitions. The study presented in this paper evaluates its performance in re-identifying individuals as they move through a scene in both single-camera and multi-camera tracking applications. The results demonstrate that the Omni-Modeler shows potential for across-camera view pedestrian redetection and is highly effective for single-camera redetection with a 93% accuracy across 30 individuals using 64 example images for each individual.

Keywords: dynamic learning, few-shot learning, pedestrian redetection, visual recognition

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Authors: Luma Al-Tamimi, Hassan Farhat, Wessam Hasan

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A three-dimensional (3D) hybrid quasi-steady thermal lattice Boltzmann model is developed to couple the effects of surfactant, temperature, interfacial tension, and contact angle. This 3D model is an extended scheme of a previously introduced two-dimensional (2D) hybrid lattice Boltzmann model. The 3D model is used to study the combined multi-physics effects on emulsion systems flowing in rectangular microchannels with and without confinements, where the suspended phase is made of droplets, plugs, or a mixture of both. The simulation results show that emulsion systems with plugs as the suspended phase are more efficient than with droplets, whereas mixed systems that form large plugs through coalescence have even greater efficiency. The 3D contact angle model generates matching results to those of the 2D model, which were validated with experiments. Furthermore, the effects of various confinements on adhering single drop systems are investigated for delineating their influence on the power required for transporting the suspended phase through the channel. It is shown that the deeper the constriction is, the lower the system efficiency. Increasing the surfactant concentration or fluid temperature in a channel with confinement carries a substantial positive effect on oil droplet transportation.

Keywords: lattice Boltzmann method, thermal, contact angle, surfactants, high viscosity ratio, porous media

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Authors: Parisa Mansour

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Cystic fibrosis (CF) is one of the most common autosomal recessive diseases among whites. It mostly affects the lungs, causing infections and inflammation that account for 90% of deaths in CF patients. Because of this high variability in clinical presentation and organ involvement, investigating treatment responses and evaluating lung changes over time is critical to preventing CF progression. High-resolution computed tomography (HRCT) greatly facilitates the assessment of lung disease progression in CF patients. Recently, artificial intelligence was used to analyze chest CT scans of CF patients. In this paper, we propose a convolutional neural network (CNN) approach to classify CF lung patterns in HRCT images. The proposed network consists of two convolutional layers with 3 × 3 kernels and maximally connected in each layer, followed by two dense layers with 1024 and 10 neurons, respectively. The softmax layer prepares a predicted output probability distribution between classes. This layer has three exits corresponding to the categories of normal (healthy), bronchitis and inflammation. To train and evaluate the network, we constructed a patch-based dataset extracted from more than 1100 lung HRCT slices obtained from 45 CF patients. Comparative evaluation showed the effectiveness of the proposed CNN compared to its close peers. Classification accuracy, average sensitivity and specificity of 93.64%, 93.47% and 96.61% were achieved, indicating the potential of CNNs in analyzing lung CF patterns and monitoring lung health. In addition, the visual features extracted by our proposed method can be useful for automatic measurement and finally evaluation of the severity of CF patterns in lung HRCT images.

Keywords: HRCT, CF, cystic fibrosis, chest CT, artificial intelligence

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Authors: Luis Marquez, Ge Zhu, Vikas Srivastava

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High-density polyethylene (HDPE) is one of the most commonly used thermoplastic polymer materials for water and gas pipelines. Slow crack growth failure is a well-known phenomenon in high-density polyethylene material and causes brittle failure well below the yield point with no obvious sign. The failure of transportation pipelines can cause catastrophic environmental and economic consequences. Using the non-destructive testing method to predict slow crack growth failure behavior is the primary preventative measurement employed by the pipeline industry but is often costly and time-consuming. Phenomenological slow crack growth models are useful to predict the slow crack growth behavior in the polymer material due to their ability to evaluate slow crack growth under different temperature and loading conditions. We developed a quantitative method to assess the slow crack growth behavior in the high-density polyethylene pipeline material under different thermal conditions based on existing physics-based phenomenological models. We are also working on developing an experimental protocol and quantitative model that can address slow crack growth behavior under different chemical exposure conditions to improve the safety, reliability, and resilience of HDPE-based pipeline infrastructure.

Keywords: mechanics of materials, physics-based modeling, civil engineering, fracture mechanics

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Authors: Ziani Zakarya, Mahdad Moustafa Yassine

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Greenhouse farming has greatly contributed to the development of modern agriculture by optimizing crops, especially market gardening, ornamental horticulture, and recently, fruit species ... Greenhouse cultivation has enabled farmers to produce fruits and vegetables out of season while guaranteeing them a good production, and therefore a considerable gain throughout the year. However, this mode of production has shown its limits, especially in extreme conditions, such as the continental steppe climate and the Saharan climate, which are characterized by significant thermal amplitudes and strong winds, making it impossible to use conventional greenhouses for several months, of the year. In Algeria and precisely in the highlands, the use of greenhouses by farmers is very rare or occasional, especially in spring, because the limiting factors mentioned above are frequent there, causing significant damage to the plant product and to the environment. infrastructure. The same observation is observed in the Saharan regions but with less frequencies. Certainly, the use of controlled multi-chapel greenhouses would solve the problem, but at what cost? These hi-tech infrastructures are very expensive to purchase but also to maintain, so few farmers have the financial means to obtain them. In addition, the existence of intelligent and less expensive polymer films, whose properties could control greenhouse production parameters, in particular, the temperature parameter, maybe a judicious solution for the development of new generations of greenhouses that can be used in extreme conditions and normal.

Keywords: greenhouse, polymer film, modern agriculture, optimizing crops

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Authors: Özge Alptoğa, Nuray Uçar, Nilgün Karatepe Yavuz, Ayşen Önen

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Sulfur dioxide (SO₂) is a very toxic air pollutant gas and it causes the greenhouse effect, photochemical smog, and acid rain, which threaten human health severely. Thus, the capture of SO₂ gas is very important for the environment. Graphene which is two-dimensional material has excellent mechanical, chemical, thermal properties, and many application areas such as energy storage devices, gas adsorption, sensing devices, and optical electronics. Further, graphene oxide (GO) is examined as a good adsorbent because of its important features such as functional groups (epoxy, carboxyl and hydroxyl) on the surface and layered structure. The SO₂ adsorption properties of the fibers are usually investigated on carbon fibers. In this study, potential adsorption capacity of GO fibers was researched. GO dispersion was first obtained with Hummers’ method from graphite, and then GO fibers were obtained via wet spinning process. These fibers were converted into a disc shape, dried, and then subjected to SO₂ gas adsorption test. The SO₂ gas adsorption capacity of GO fiber discs was investigated in the fields of utilization of different coagulation baths and reduction by hydrazine hydrate. As coagulation baths, single and triple baths were used. In single bath, only ethanol and CaCl₂ (calcium chloride) salt were added. In triple bath, each bath has a different concentration of water/ethanol and CaCl₂ salt, and the disc obtained from triple bath has been called as reference disk. The fibers which were produced with single bath were flexible and rough, and the analyses show that they had higher SO₂ adsorption capacity than triple bath fibers (reference disk). However, the reduction process did not increase the adsorption capacity, because the SEM images showed that the layers and uniform structure in the fiber form were damaged, and reduction decreased the functional groups which SO₂ will be attached. Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffraction (XRD) analyzes were performed on the fibers and discs, and the effects on the results were interpreted. In the future applications of the study, it is aimed that subjects such as pH and additives will be examined.

Keywords: coagulation bath, graphene oxide fiber, reduction, SO2 gas adsorption

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Authors: Maíra O. Palm, Cintia Marangoni, Ozair Souza, Noeli Sellin

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Wastes from a vegetal extracts industry (cocoa, oak, Guarana and mate) were characterized by particle size, proximate and ultimate analysis, lignocellulosic fractions, high heating value, thermal analysis (Thermogravimetric analysis – TGA, and Differential thermal analysis - DTA) and energy density to evaluate their potential as biomass in the form of briquettes for power generation. All wastes presented adequate particle sizes to briquettes production. The wastes showed high moisture content, requiring previous drying for use as briquettes. Cocoa and oak wastes had the highest volatile matter contents with maximum mass loss at 310 ºC and 450 ºC, respectively. The solvents used in the aroma extraction process influenced in the moisture content of the wastes, which was higher for mate due to water has been used as solvent. All wastes showed an insignificant loss mass after 565 °C, hence resulting in low ash content. High carbon and hydrogen contents and low sulfur and nitrogen contents were observed ensuring a low generation of sulfur and nitrous oxides. Mate and cocoa exhibited the highest carbon and lignin content, and high heating value. The dried wastes had high heating value, from 17.1 MJ/kg to 20.8 MJ/kg. The results indicate the energy potential of wastes for use as fuel in power generation.

Keywords: agro-industrial waste, biomass, briquettes, combustion

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Authors: Kenan Morani, Esra Kaya Ayana

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This study aimed to develop a novel pipeline for COVID-19 detection using a large and rigorously annotated database of computed tomography (CT) images. The pipeline consists of UNet-based segmentation, lung extraction, and a classification part, with the addition of optional slice removal techniques following the segmentation part. In this work, a batch normalization was added to the original UNet model to produce lighter and better localization, which is then utilized to build a full pipeline for COVID-19 diagnosis. To evaluate the effectiveness of the proposed pipeline, various segmentation methods were compared in terms of their performance and complexity. The proposed segmentation method with batch normalization outperformed traditional methods and other alternatives, resulting in a higher dice score on a publicly available dataset. Moreover, at the slice level, the proposed pipeline demonstrated high validation accuracy, indicating the efficiency of predicting 2D slices. At the patient level, the full approach exhibited higher validation accuracy and macro F1 score compared to other alternatives, surpassing the baseline. The classification component of the proposed pipeline utilizes a convolutional neural network (CNN) to make final diagnosis decisions. The COV19-CT-DB dataset, which contains a large number of CT scans with various types of slices and rigorously annotated for COVID-19 detection, was utilized for classification. The proposed pipeline outperformed many other alternatives on the dataset.

Keywords: classification, computed tomography, lung extraction, macro F1 score, UNet segmentation

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Authors: Palash Soni, Vivek Kumar Gaba, Shubhankar Bhowmick, Bidyut Mazumdar

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Refrigeration technology is a fast developing field at the present era since it has very wide application in both domestic and industrial areas. It started from the usage of simple ice coolers to store food stuffs to the present sophisticated cold storages along with other air conditioning system. A variety of techniques are used to bring down the temperature below the ambient. Adsorption refrigeration technology is a novel, advanced and promising technique developed in the past few decades. It gained attention due to its attractive property of exploiting unlimited natural sources like solar energy, geothermal energy or even waste heat recovery from plants or from the exhaust of locomotives to fulfill its energy need. This will reduce the exploitation of non-renewable resources and hence reduce pollution too. This work is aimed to develop a model for a solar adsorption refrigeration system and to simulate the same for different operating conditions. In this system, the mechanical compressor is replaced by a thermal compressor. The thermal compressor uses renewable energy such as solar energy and geothermal energy which makes it useful for those areas where electricity is not available. Refrigerants normally in use like chlorofluorocarbon/perfluorocarbon have harmful effects like ozone depletion and greenhouse warming. It is another advantage of adsorption systems that it can replace these refrigerants with less harmful natural refrigerants like water, methanol, ammonia, etc. Thus the double benefit of reduction in energy consumption and pollution can be achieved. A thermodynamic model was developed for the proposed adsorber, and a universal MATLAB code was used to simulate the model. Simulations were carried out for a different operating condition for the silicagel-methanol working pair. Various graphs are plotted between regeneration temperature, adsorption capacities, the coefficient of performance, desorption rate, specific cooling power, adsorption/desorption times and mass. The results proved that adsorption system could be installed successfully for refrigeration purpose as it has saving in terms of power and reduction in carbon emission even though the efficiency is comparatively less as compared to conventional systems. The model was tested for its compliance in a cold storage refrigeration with a cooling load of 12 TR.

Keywords: adsorption, refrigeration, renewable energy, silicagel-methanol

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Authors: Mariangela Viviani, Daniele Germano, Simone Colace, Agostino Forestiero, Giuseppe Papuzzo, Sara Laurita

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Nowadays, people spend most of their time in closed environments, in offices, or at home. Therefore, secure and highly livable environmental conditions are needed to reduce the probability of aerial viruses spreading. Also, to lower the human impact on the planet, it is important to reduce energy consumption. Heating, Ventilation, and Air Conditioning (HVAC) systems account for the major part of energy consumption in buildings [1]. Devising systems to control and regulate the airflow is, therefore, essential for energy efficiency. Moreover, an optimal setting for thermal comfort and air quality is essential for people’s well-being, at home or in offices, and increases productivity. Thanks to the features of Artificial Intelligence (AI) tools and techniques, it is possible to design innovative systems with: (i) Improved monitoring and prediction accuracy; (ii) Enhanced decision-making and mitigation strategies; (iii) Real-time air quality information; (iv) Increased efficiency in data analysis and processing; (v) Advanced early warning systems for air pollution events; (vi) Automated and cost-effective m onitoring network; and (vii) A better understanding of air quality patterns and trends. We propose AIR SAFE, an IoT-based infrastructure designed to optimize air quality and thermal comfort in indoor environments leveraging AI tools. AIR SAFE employs a network of smart sensors collecting indoor and outdoor data to be analyzed in order to take any corrective measures to ensure the occupants’ wellness. The data are analyzed through AI algorithms able to predict the future levels of temperature, relative humidity, and CO₂ concentration [2]. Based on these predictions, AIR SAFE takes actions, such as opening/closing the window or the air conditioner, to guarantee a high level of thermal comfort and air quality in the environment. In this contribution, we present the results from the AI algorithm we have implemented on the first s et o f d ata c ollected i n a real environment. The results were compared with other models from the literature to validate our approach.

Keywords: air quality, internet of things, artificial intelligence, smart home

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Authors: Rachid El Aidani, Phuong Nguyen-Tri, Toan Vu-Khanh

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The filter media in synthetic fibre is the most geotextile materials used in aerosol and drainage filtration, particularly for buildings soil reinforcement in civil engineering due to its appropriated properties and its low cost. However, the current understanding of the durability and stability of this material in real service conditions, especially under severe long-term conditions are completely limited. This study has examined the effects of the chemical aging of a filter media in polyester non-woven under different temperatures (50, 70 and 80˚C) and pH (2. 7 and 12). The effect of aging conditions on mechanical properties, morphology, permeability, thermal stability and molar weigh changes is investigated. The results showed a significant reduction of mechanical properties in term of tensile strength, puncture force and tearing forces of the filter media after chemical aging due to the chemical degradation. The molar mass and mechanical properties changes in different temperature and pH showed a complex dependence of material properties on environmental conditions. The SEM and AFM characterizations showed a significant impact of the thermal aging on the morphological properties of the fibers. Based on the obtained results, the lifetime of the material in different temperatures was determined by the use of the Arrhenius model. These results provide useful information to better understand phenomena occurring during chemical aging of the filter media and may help to predict the service lifetime of this material in real used conditions.

Keywords: nonwoven membrane, chemical aging, mechanical properties, lifetime, filter media

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Authors: N. Hatraf, l. Merabeti, M. Abbas

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The interest in air conditioning using renewable energies is increasing. The Thermal energy produced from the solar energy can be transformed to useful cooling and heating through the thermo chemical or thermo physical processes by using thermally activated energy conversion system. Solid desiccant conditioning systems can represent a reliable alternative solution compared with other thermal cooling technologies. Their basic characteristics refer to the capability to regulate both temperature and humidity of the conditioned space in one side and to its potential in electrical energy saving in the other side. The ambient air contains so much water that very high dehumidification rates are required. For a continuous dehumidification of the process air the water adsorbed on the desiccant material has to be removed, which is done by allowing hot air to flow through the desiccant material (regeneration). Basically, solid desiccant cooling system transfers moisture from the inlet air to the silica gel by using two processes: absorption process and the regeneration process; The silica gel in the desiccant wheel which is the most important device in the system absorbs the moisture from the incoming air to the desiccant material in this case the silica gel, then it changes the heat with an rotary heat exchanger, after that the air passes through an humidifier to have the humidity required before entering to the local. The main aim of this paper is to study how the dehumidification rate, the generation temperature and many other factors influence the efficiency of a solid desiccant system by using TRNSYS software.

Keywords: desiccation, dehumidification, TRNSYS, efficiency

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Authors: Anas Rao, Hao Duan, Fanhua Ma

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The dependency on the fossil fuels can be minimized by using the hydrogen enriched compressed natural gas (HCNG) in the transportation vehicles. However, the NOx emissions of HCNG engines are significantly higher, and this turned to be its major drawback. Therefore, the study of NOx emission of HCNG engines is a very important area of research. In this context, the experiments have been performed at the different hydrogen percentage, ignition timing, air-fuel ratio, manifold-absolute pressure, load and engine speed. Afterwards, the simulation has been accomplished by the quasi-dimensional combustion model of HCNG engine. In order to investigate the NOx emission, the NO mechanism has been coupled to the quasi-dimensional combustion model of HCNG engine. The three NOx mechanism: the thermal NOx, prompt NOx and N2O mechanism have been used to predict NOx emission. For the validation purpose, NO curve has been transformed into NO packets based on the temperature difference of 100 K for the lean-burn and 60 K for stoichiometric condition. While, the width of the packet has been taken as the ratio of crank duration of the packet to the total burnt duration. The combustion chamber of the engine has been divided into three zones, with the zone equal to the product of summation of NO packets and space. In order to check the accuracy of the model, the percentage error of NOx emission has been evaluated, and it lies in the range of ±6% and ±10% for the lean-burn and stoichiometric conditions respectively. Finally, the percentage contribution of each NO formation has been evaluated.

Keywords: quasi-dimensional combustion , thermal NO, prompt NO, NO packet

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Authors: Berhanu Terfa, Nengcheng C.

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The study deals with the analysis of urban expansion and land transformation of Hawass City using remote sensing data and landscape metrics during last three decades (1987–2017). Remote sensing data from Various multi-temporal satellite images viz., TM (1987), TM (1995), ETM+ (2005) and OLI (2017) were used to examine the urban expansion, growth types, and spatial isolation within the urban landscape to develop an understanding the trends of built-up growth in Hawassa City, Ethiopia. Landscape metrics and built-up density were employed to analyze the pattern, process and overall growth status. The area under investigation was divided into concentric circles with a consecutive circle of 1 km incremental radius from the central pixel (Central Business District) for analysis. The result exhibited that the built-up area had increased by 541.32% between 1987 and 2017and an extension growth types (more than 67 %) was observed. The major growth took place in north-west direction followed by north direction in haphazard manner during 1987–1995 period, whereas predominant built-up development was observed in south and southwest direction during 1995–2017 period. Land scape metrics result revealed that the of urban patches density, total edge and edge density increased, while mean nearest neighbors’ distance decreased showing the tendency of sprawl.

Keywords: landscape metrics, spatial patterns, remote sensing, multi-temporal, urban sprawl

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Authors: Noreen Sajjad Ghulam Hussain

Abstract:

Catalysts are prepared by simple physical mixing and thermal treatment of support and metal acetate precursors.The effect of metal ratio and metal loading to produce highly active catalyst for the oxidation of benzyl alcohol are studied.

Keywords: catalyst, acetates, benzyl alcohols

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Authors: Hiroki Takiguchi, Masahiro Furuya, Takahiro Arai

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In order to quantitatively comprehend thermal flow for some industrial applications such as nuclear and chemical reactors, detailed measurements for temperature and abundance (concentration) of materials at high temporal and spatial resolution are required. Additionally, rigorous evaluation of the size effect is also important for practical realization. This paper introduces a real-time spectroscopic imaging method in micro scale field, which visualizes temperature and concentration distribution of a liquid or mix liquids with near-infrared (NIR) wavelength region. This imaging principle is based on absorption of pre-selected narrow band from absorption spectrum peak or its dependence property of target liquid in NIR region. For example, water has a positive temperature sensitivity in the wavelength at 1905 nm, therefore the temperature of water can be measured using the wavelength band. In the experiment, the real-time imaging observation of concentration distribution in micro channel was demonstrated to investigate the applicability of micro-scale diffusion coefficient and temperature measurement technique using this proposed method. The effect of thermal diffusion and binary mutual diffusion was evaluated with the time-series visualizations of concentration distribution.

Keywords: near-infrared spectroscopic imaging, micro fluid channel, concentration distribution, diffusion phenomenon

Procedia PDF Downloads 161