Search results for: chemokines gradient

Authors: Mohamed Abdelwahed

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We propose an optimization algorithm for the geometric control of fluid flow. The used approach is based on the topological sensitivity analysis method. It consists in studying the variation of a cost function with respect to the insertion of a small obstacle in the domain. Some theoretical and numerical results are presented in 2D and 3D.

Keywords: sensitivity analysis, topological gradient, shape optimization, stokes equations

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Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Arwa Alzughaibi

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Human Motion detection is a challenging task due to a number of factors including variable appearance, posture and a wide range of illumination conditions and background. So, the first need of such a model is a reliable feature set that can discriminate between a human and a non-human form with a fair amount of confidence even under difficult conditions. By having richer representations, the classification task becomes easier and improved results can be achieved. The Aim of this paper is to investigate the reliable and accurate human motion detection models that are able to detect the human motions accurately under varying illumination levels and backgrounds. Different sets of features are tried and tested including Histogram of Oriented Gradients (HOG), Deformable Parts Model (DPM), Local Decorrelated Channel Feature (LDCF) and Aggregate Channel Feature (ACF). However, we propose an efficient and reliable human motion detection approach by combining Histogram of oriented gradients (HOG) and local phase quantization (LPQ) as the feature set, and implementing search pruning algorithm based on optical flow to reduce the number of false positive. Experimental results show the effectiveness of combining local phase quantization descriptor and the histogram of gradient to perform perfectly well for a large range of illumination conditions and backgrounds than the state-of-the-art human detectors. Areaunder th ROC Curve (AUC) of the proposed method achieved 0.781 for UCF dataset and 0.826 for CDW dataset which indicates that it performs comparably better than HOG, DPM, LDCF and ACF methods.

Keywords: human motion detection, histograms of oriented gradient, local phase quantization, local phase quantization

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Authors: Y. Baba, A. Archibong-Eso, H. Yeung

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Experimental study of high viscosity oil-gas flows in horizontal pipelines published in literature has indicated that hydrodynamic slug flow is the dominant flow pattern observed. Investigations have shown that hydrodynamic slugging brings about high instabilities in pressure that can damage production facilities thereby making it inherent to study high viscous slug flow regime so as to improve the understanding of its flow dynamics. Most slug flow models used in the petroleum industry for the design of pipelines together with their closure relationships were formulated based on observations of low viscosity liquid-gas flows. New experimental investigations and data are therefore required to validate these models. In cases where these models underperform, improving upon or building new predictive models and correlations will also depend on the new experimental dataset and further understanding of the flow dynamics in high viscous oil-gas flows. In this study conducted at the Flow laboratory, Oil and Gas Engineering Centre of Cranfield University, slug flow variables such as pressure gradient, mean liquid holdup, frequency and slug length for oil viscosity ranging from 1..0 – 5.5 Pa.s are experimentally investigated and analysed. The study was carried out in a 0.076m ID pipe, two fast sampling gamma densitometer and pressure transducers (differential and point) were used to obtain experimental measurements. Comparison of the measured slug flow parameters to the existing slug flow prediction models available in the literature showed disagreement with high viscosity experimental data thus highlighting the importance of building new predictive models and correlations.

Keywords: gamma densitometer, mean liquid holdup, pressure gradient, slug frequency and slug length

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Authors: M. Ibrahim Ali, M. El Dawi, M. A. Mohamed Ali

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In this study, the use of magnetic gradient survey, and the geoelectrical ground methods used together to explore archaeological features in Nuri’s pyramids area. Research methods used and the procedures and methodologies have taken full right during the study. The magnetic survey method was used to search for archaeological features using (Geoscan Fluxgate Gradiometer (FM36)). The study area was divided into a number of squares (networks) exactly equal (20 * 20 meters). These squares were collected at the end of the study to give a major network for each region. Networks also divided to take the sample using nets typically equal to (0.25 * 0.50 meter), in order to give a more specific archaeological features with some small bipolar anomalies that caused by buildings built from fired bricks. This definition is important to monitor many of the archaeological features such as rooms and others. This main network gives us an integrated map displayed for easy presentation, and it also allows for all the operations required using (Geoscan Geoplot software). The parallel traverse is the main way to take readings of the magnetic survey, to get out the high-quality data. The study area is very rich in old buildings that vary from small to very large. According to the proportion of the sand dunes and the loose soil, most of these buildings are not visible from the surface. Because of the proportion of the sandy dry soil, there is no connection between the ground surface and the electrodes. We tried to get electrical readings by adding salty water to the soil, but, unfortunately, we failed to confirm the magnetic readings with electrical readings as previously planned.

Keywords: archaeological features, independent grids, magnetic gradient, Nuri pyramid

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Authors: M. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N. Rishe, R. E. Curiel, D. Loewenstein, R. Duara, M. Adjouadi

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A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification.

Keywords: eXtreme gradient boosting, missing data, Alzheimer disease, early mild cognitive impairment, late mild cognitive impair, multiclass classification, ADNI, support vector machine, random forest

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Authors: Chiung-Yun Chang, Po-Chou Chen, Jiun-Shiang Tzeng, Ka-Wai Mac, Chia-Chi Hsiao, Jo-Chi Jao

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This study aimed to measure R2* relaxation rates in the liver of New Zealand White (NZW) rabbits. R2* relaxation rate has been widely used in various hepatic diseases for iron overload by quantifying iron contents in liver. R2* relaxation rate is defined as the reciprocal of T2* relaxation time and mainly depends on the composition of tissue. Different tissues would have different R2* relaxation rates. The signal intensity decay in Magnetic resonance imaging (MRI) may be characterized by R2* relaxation rates. In this study, a 1.5T GE Signa HDxt whole body MR scanner equipped with an 8-channel high resolution knee coil was used to observe R2* values in NZW rabbit’s liver and muscle. Eight healthy NZW rabbits weighted 2 ~ 2.5 kg were recruited. After anesthesia using Zoletil 50 and Rompun 2% mixture, the abdomen of rabbit was landmarked at the center of knee coil to perform 3-plane localizer scan using fast spoiled gradient echo (FSPGR) pulse sequence. Afterward, multi-planar fast gradient echo (MFGR) scans were performed with 8 various echo times (TEs) (2/4/6/8/10/12/14/16 ms) to acquire images for R2* calculations. Regions of interest (ROIs) at liver and muscle were measured using Advantage workstation. Finally, the R2* was obtained by a linear regression of ln(SI) on TE. The results showed that the longer the echo time, the smaller the signal intensity. The R2* values of liver and muscle were 44.8  10.9 s-1 and 37.4  9.5 s-1, respectively. It implies that the iron concentration of liver is higher than that of muscle. In conclusion, R2* is correlated with iron contents in tissue. The correlations between R2* and iron content in NZW rabbit might be valuable for further exploration.

Keywords: liver, magnetic resonance imaging, muscle, R2* relaxation rate

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Authors: B. Bouadjemi, S. Bentata, A. Abbad, W.Benstaali

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The full-potential linearized augmented plane wave method (FP-LAPW) within the Generalized Gradient Approximation (GGA) is used to calculate the magnetic and optical properties of quaternary GaFeMnN. The results show that the compound becomes magnetic and half metallic and there is an apparition of peaks at low frequencies for the optical properties.

Keywords: optical properties, DFT, Spintronic, wave

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Authors: Yuri Boiko, Iluju Kiringa, Tet Yeap

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We use modeling and simulation tools, to introduce a novel bias injection attack, named the ’Induced Pulsation Attack’, which targets Cyber Physical Systems with closed-loop controlled Brushless DC (BLDC) motor and Kalman filter driver in the feedback loop. This attack involves engaging a linear function with a constant gradient to distort the coefficient of the injected bias, which falsifies the Kalman filter estimates of the rotor’s angular speed. As a result, this manipulation interaction inside the control system causes periodic pulsations in a form of asymmetric sine wave of both current and voltage in the circuit windings, with a high magnitude. It is shown that by varying the gradient of linear function, one can control both the frequency and structure of the induced pulsations. It is also demonstrated that terminating the attack at any point leads to additional compensating effort from the controller to restore the speed to its equilibrium value. This compensation effort produces an exponentially decaying wave, which we call the ’attack withdrawal syndrome’ wave. The conditions for maximizing or minimizing the impact of the attack withdrawal syndrome are determined. Linking the termination of the attack to the end of the full period of the induced pulsation wave has been shown to nullify the attack withdrawal syndrome wave, thereby improving the attack’s covertness.

Keywords: cyber-attack, induced pulsation, bias injection, Kalman filter, BLDC motor, control system, closed loop, P- controller, PID-controller, saw-function, cyber-physical system

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Authors: G. Purushotham, Joel Hemanth

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An investigation has been carried out to fabricate and evaluate the strength and soundness of chilled composites consisting of nickel matrix and fused silica particles (size 40–150 μm) in the matrix. The dispersoid added ranged from 3 to 12 wt. % in steps of 3%. The resulting composites cast in moulds containing metallic and non-metallic chill blocks (MS, SiC, and Cu) were tested for their microstructure and mechanical properties. The main objective of the present research is to obtain fine grain Ni/SiO2 chilled sound composite having very good mechanical properties. Results of the investigation reveal the following: (1) Strength of the composite developed is highly dependent on the location of the casting from where the test specimens are taken and also on the dispersoid content of the composite. (2) Chill thickness and chill material, however, does significantly affect the strength and soundness of the composite. (3) Soundness of the composite developed is highly dependent on the chilling rate as well as the dispersoid content. An introduction of chilling and increase in the dispersoid content of the material both result in an increase in the ultimate tensile strength (UTS) of the material. The temperature gradient developed during solidification and volumetric heat capacity (VHC) of the chill used is the important parameters controlling the soundness of the composite. (4) Thermal properties of the end chills are used to determine the magnitude of the temperature gradient developed along the length of the casting solidifying under the influence of chills.

Keywords: metal matrix composite, mechanical properties, corrosion behavior, nickel alloy, fused silica, chills

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Authors: A. K. Borah, A. K. Singh

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In this paper we deal building blocks of the computer simulation of the multiphase flows. Whole simulation procedure can be viewed as two super procedures; The implementation of VOF method and the solution of Navier Stoke’s Equation. Moreover, a sequential code for a Navier Stoke’s solver has been studied.

Keywords: Bi-conjugate gradient stabilized (Bi-CGSTAB), ILUT function, krylov subspace, multifluid flows preconditioner, simple algorithm

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Authors: Kathy Stein, Mariola Lysson, Anja Schmidt, Beatrix Schumak, Sabine Specht, Hicham Bouabe, Jürgen Heesemann, Axel Roers, Joerg C. Kalff, Sven Wehner

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Objective: Postoperative ileus (POI) is a common complication of abdominal surgery. Monocyte infiltration is a hallmark of POI. The polarization of macrophages/monocytes in this process is not well understood. We aimed to investigate if and how M2 macrophage/monocyte differentiation is involved in POI pathogenesis. Design: POI was induced by intestinal manipulation (IM). C57Bl/6, CCR2-/-, IL-10 reporter (ITIB), IL-10-/- and LysMcre/IL-10fl/fl mice underwent IM. At various points in time leukocyte influx, gene and protein expression of cytokines, chemokines and M2 differentiation markers and intestinal motility were analyzed. Results: IM induced the postoperative expression of the M2 markers Arginase-1 and YM-1, predominantly in F4/80+Ly6C+ monocytes. Gene expression analyses indicated an IL-10-dependent, IL-4-independent M2 polarization of these monocytes. IL-10 dependency of M2 differentiation was confirmed in IL-10 deficient mice. Leukocytes, in the order of infiltrating monocytes, neutrophils, and resident macrophages were the main IL-10 producers during POI. IL-10 producing monocytes as well as M2 marker expression were almost absent in CCR2-deficient mice. However, postoperative IL-10 expression was not altered in CCR2-/- mice. The loss of M2 polarized monocytes neither protected CCR2-/- mice from nor affected resolution of POI. In contrast, IL-10 deficiency reduced postoperative neutrophil numbers and ameliorated POI. IL-10Ra expression was strongly induced in neutrophils but not in monocytes. Conclusion: We conclude that IL-10 counteracts POI resolution by activating IL-10Ra-expressing neutrophils in the late phase of disease while IL-10-dependent M2 differentiation is not pivotal to POI manifestation and resolution.

Keywords: interleukin-10, macrophages, neutrophils, postoperative ileus

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: Radoslav Stojchevski, Leonid Poretsky, Dimiter Avtanski

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Proinflammatory cytokines play an important role in cancer initiation and progression by mediating the intracellular communication between the cancer cells and tumor microenvironment. Increased tumor growth causing reduced oxygen concentration and oxygen pressure commonly result in hypoxia. Mechanistically, hypoxia is characterized by stabilization and nuclear translocation of hypoxia-inducible factor 1 alpha (HIF-1α) followed by propagation of molecular pathway cascade involving multiple downstream targets. Cobalt(II) chloride (CoCl₂) is commonly used to mimic hypoxia in experimental conditions since it directly induces the expression of HIF-1α. The aim of the present study was to investigate the in vitro effects and the molecular mechanisms by which hypoxia regulates the cytokine secretory profile of breast cancer cells. As a model for this study, we used several breast cancer cell lines bearing various molecular characteristics and metastatic potential (MDA-MB-231 (clauding low, ER-/PR-/HER²⁻), MCF-7 (luminal A, ER⁺/PR⁺/HER²⁻), and BT-474 (liminal B, ER⁺/PR⁺/HER²⁺)). We demonstrated that breast cancer cells secrete numerous cytokines and cytokine ligands, including interleukins, chemokines, and growth factors. Treatment with CoCl₂significantly modulated the breast cancer cells' cytokine expression in a concentration- and time-dependent manner. These effects were mediated via activation of several signaling pathways (JNK/SAPK1, NF-κB, STAT5A/B, and Erk/MAPK1/2). Taken together, the present data define some of the molecular mechanisms by which hypoxia affects the breast cancer cells' cytokine secretory profile, thus contributing to the development of novel therapies for metastatic breast cancer.

Keywords: breast cancer, cytokines, cobalt(II) chloride (CoCl₂), hypoxia

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Authors: Cyoung-Huei Huang, Chiu-Li Yeh, Man-Hui Pai, Sung-Ling Yeh

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This study evaluated the effect of different dietary n-6/n-3 polyunsaturated fatty acid (PUFA) ratios on modulating helper T (Th) and regulatory T (Treg) lymphocytes in mice with dextran sulfate sodium (DSS)-induced colitis. There were 3 control and 3 colitis groups in this study. Mice were fed for 24 d with an AIN-93G diet either with soybean oil (S), a mixture of soybean oil and low fish oil content (LF) or high fish oil content (HF). The ratio of n-6/n-3 PUFA in the LF diet was 4:1, and that in the HF diet was 2:1. The control groups drank distilled water while colitis groups provided 2% DSS in drinking water during day 15-19. All mice drank distilled water from day 20-24 for recovery and sacrificed on day 25. The results showed that colitis resulted in higher Th1, Th2, and Th17 and lower Treg percentages in the blood. Also, plasma haptoglobin and proinflammatory chemokines were elevated in colon lavage fluid. Colitic groups with fish oil had lower inflammatory mediators in the plasma and colon lavage fluid. Further, the percentages of Th1, Th2, and Th17 cells in the blood were lower, whereas Treg cell percentages were higher than those in the soybean oil group. The colitis group with n-6/n-3 PUFA ratio 2:1 had more pronounce effects than ratio 4:1. These results suggest that diets with an n-6/n-3 PUFA ratio of 2:1 or 4:1 regulate the Th/Treg balance and attenuate inflammatory mediator production in colitis. Compared to the n-6/n-3 PUFA ratio 4:1, the ratio of 2:1 was more effective in reducing inflammatory reactions in DSS-induced colitis.

Keywords: inflammatory bowel disease, n-3 polyunsaturated fatty acids, helper T lymphocyte, regulatory T lymphocyte

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Authors: Belgherbi Aicha, Bessaid Abdelhafid

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In the recent years a great deal of research work has been devoted to the development of semi-automatic and automatic techniques for the analysis of abdominal CT images. The first and fundamental step in all these studies is the semi-automatic liver and spleen segmentation that is still an open problem. In this paper, a semi-automatic liver and spleen segmentation method by the mathematical morphology based on watershed algorithm has been proposed. Our algorithm is currency in two parts. In the first, we seek to determine the region of interest by applying the morphological to extract the liver and spleen. The second step consists to improve the quality of the image gradient. In this step, we propose a method for improving the image gradient to reduce the over-segmentation problem by applying the spatial filters followed by the morphological filters. Thereafter we proceed to the segmentation of the liver, spleen. The aim of this work is to develop a method for semi-automatic segmentation liver and spleen based on watershed algorithm, improve the accuracy and the robustness of the liver and spleen segmentation and evaluate a new semi-automatic approach with the manual for liver segmentation. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work. The system has been evaluated by computing the sensitivity and specificity between the semi-automatically segmented (liver and spleen) contour and the manually contour traced by radiological experts. Liver segmentation has achieved the sensitivity and specificity; sens Liver=96% and specif Liver=99% respectively. Spleen segmentation achieves similar, promising results sens Spleen=95% and specif Spleen=99%.

Keywords: CT images, liver and spleen segmentation, anisotropic diffusion filter, morphological filters, watershed algorithm

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Authors: F. Farzan Nasab, H. J. M. Geijselaers, I. Baran, A. De Boer

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A method is introduced for the coupled skin-rib optimization of a wing box where mass minimization is the objective and local buckling is the constraint. The structure is made of composite materials where continuity of plies in multiple adjacent panels (blending) has to be satisfied. Blending guarantees the manufacturability of the structure; however, it is a highly challenging constraint to treat and has been under debate in recent research in the same area. To fulfill design guidelines with respect to symmetry, balance, contiguity, disorientation and percentage rule of the layup, a reference for the stacking sequences (stacking sequence table or SST) is generated first. Then, an innovative fully gradient-based optimization approach in relation to a specific SST is introduced to obtain the optimum thickness distribution all over the structure while blending is fulfilled. The proposed optimization approach aims to turn the discrete optimization problem associated with the integer number of plies into a continuous one. As a result of a wing box deflection, a rib is subjected to load values which vary nonlinearly with the amount of deflection. The bending stiffness of a skin affects the wing box deflection and thus affects the load applied to a rib. This indicates the necessity of a coupled skin-rib optimization approach for a more realistic optimized design. The proposed method is examined with the optimization of the layup of a composite stiffened skin and rib of a wing torsion box subjected to in-plane normal and shear loads. Results show that the method can successfully prescribe a valid design with a significantly cheap computation cost.

Keywords: blending, buckling optimization, composite panels, wing torsion box

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Authors: Reva Sharan Thakur, Mrinalini Tiwari, Jyoti das

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Cerebral malaria-associated over expression of pro-inflammatory cytokines and chemokines ultimately results in the up-regulation of adhesion molecules in the brain endothelium leading to sequestration of mature parasitized RBCs in the brain. The high-parasitic load subsequently results in increased mortality or development of neurological symptoms within a week of infection. Studies in the human and experimental cerebral malaria have implicated the breakdown of the integrity of blood-brain barrier during the lethal course of infection, cerebral dysfunction, and fatal organ pathologies that result in multi-organ failure. In the present study, using Plasmodium berghei Anka as a mouse model and in vitro conditions, we have investigated the effect of MSCs to attenuate cerebral malaria pathogenesis by diminishing the effect of inflammation altered organ morphology, reduced parasitemia, and increased survival of the mice. MSCs are also validated for their role in preventing BBB dysfunction and reducing malarial toxins. It was observed that administration of MSCs significantly reduced parasitemia and increased survival in Pb A infected mice. It was further demonstrated that MSCs play a significant role in reversing neurological complexities associated with cerebral malaria. Infusion of MSCs in infected mice decreased hemozoin deposition; oedema, and haemorrhagic lesions in vascular organs. MSCs administration also preserved the integrity of the blood-brain barrier and reduced neural inflammation. Taken together, our results demonstrate the potential of MSCs as an emerging anti-malarial candidate.

Keywords: cerebral malaria, mesenchymal stem cells, erythropoesis, cell death

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Authors: Khatia Tavidashvili, Nino Jamrishvili, Valerian Omsarashvili

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Studies of the vertical distribution of the air temperature in the atmosphere have great value for the solution of different problems of meteorology and climatology (meteorological forecast of showers, thunderstorms, and hail, weather modification, estimation of climate change, etc.). From the end of May 2015 in Kakheti after 25-year interruption, the work of anti-hail service was restored. Therefore, in connection with climate change, the need for the detailed study of the contemporary regime of the vertical distribution of the air temperature above this territory arose. In particular, the indicated information is necessary for the optimum selection of rocket means with the works on the weather modification (fight with the hail, the regulation of atmospheric precipitations, etc.). Construction of the detailed maps of the potential damage distribution of agricultural crops from the hail, etc. taking into account the dimensions of hailstones in the clouds according to the data of radar measurements and height of locality are the most important factors. For now, in Georgia, there is no aerological probing of atmosphere. To solve given problem we processed information about air temperature profiles above Telavi, at 27 km above earth's surface. Information was gathered during four observation time (4, 10, 16, 22 hours with local time. After research, we found vertical distribution of the average monthly values of the air temperature above Kakheti in ‎2012-2017 from January to December. Research was conducted from 0.543 to 27 km above sea level during four periods of research. In particular, it is obtained: -during January the monthly average air temperature linearly diminishes with 2.6 °C on the earth's surface to -57.1 °C at the height of 10 km, then little it changes up to the height of 26 km; the gradient of the air temperature in the layer of the atmosphere from 0.543 to 8 km - 6.3 °C/km; height of zero isotherm - is 1.33 km. -during July the air temperature linearly diminishes with 23.5 °C to -64.7 °C at the height of 17 km, then it grows to -47.5 °C at the height of 27 km; the gradient of the air temperature of - 6.1 °C/km; height of zero isotherm - is 4.39 km, which on 0.16 km is higher than in the sixties of past century.

Keywords: hail, Kakheti, meteorology, vertical distribution of the air temperature

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Authors: Zachary Blanks, Solomon Sonya

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

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

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Authors: Pierre Tize Mha, Mohammad Jahazi, Amèvi Togne, Olivier Pantalé

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Large-size forged blocks made of medium carbon high-strength steels are extensively used in the automotive industry as dies for the production of bumpers and dashboards through the plastic injection process. The manufacturing process of the large blocks starts with ingot casting, followed by open die forging and a quench and temper heat treatment process to achieve the desired mechanical properties and numerical simulation is widely used nowadays to predict these properties before the experiment. But the temperature gradient inside the specimen remains challenging in the sense that the temperature before loading inside the material is not the same, but during the simulation, constant temperature is used to simulate the experiment because it is assumed that temperature is homogenized after some holding time. Therefore to be close to the experiment, real distribution of the temperature through the specimen is needed before the mechanical loading. Thus, We present here a robust algorithm that allows the calculation of the temperature gradient within the specimen, thus representing a real temperature distribution within the specimen before deformation. Indeed, most numerical simulations consider a uniform temperature gradient which is not really the case because the surface and core temperatures of the specimen are not identical. Another feature that influences the mechanical properties of the specimen is recrystallization which strongly depends on the deformation conditions and the type of deformation like Upsetting, Cogging...etc. Indeed, Upsetting and Cogging are the stages where the greatest deformations are observed, and a lot of microstructural phenomena can be observed, like recrystallization, which requires in-depth characterization. Complete dynamic recrystallization plays an important role in the final grain size during the process and therefore helps to increase the mechanical properties of the final product. Thus, the identification of the conditions for the initiation of dynamic recrystallization is still relevant. Also, the temperature distribution within the sample and strain rate influence the recrystallization initiation. So the development of a technique allowing to predict the initiation of this recrystallization remains challenging. In this perspective, we propose here, in addition to the algorithm allowing to get the temperature distribution before the loading stage, an analytical model leading to determine the initiation of this recrystallization. These two techniques are implemented into the Abaqus finite element software via the UAMP and VUHARD subroutines for comparison with a simulation where an isothermal temperature is imposed. The Artificial Neural Network (ANN) model to describe the plastic behavior of the material is also implemented via the VUHARD subroutine. From the simulation, the temperature distribution inside the material and recrystallization initiation is properly predicted and compared to the literature models.

Keywords: dynamic recrystallization, finite element modeling, artificial neural network, numerical implementation

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Authors: Jiahui Song

Abstract:

The use of very high electric fields (~ 100kV/cm or higher) with pulse durations in the nanosecond range has been a recent development. The electric pulses have been used as tools to generate electroporation which has many biomedical applications. Most of the studies of electroporation have ignored possible thermal effects because of the small duration of the applied voltage pulses. However, it has been predicted membrane temperature gradients ranging from 0.2×109 to 109 K/m. This research focuses on thermal gradients that drives for electroporative enhancements, even though the actual temperature values might not have changed appreciably from their equilibrium levels. The dynamics of pore formation with the application of an externally applied electric field is studied on the basis of molecular dynamics (MD) simulations using the GROMACS package. Different temperatures are assigned to various regions to simulate the appropriate temperature gradients. The GROMACS provides the force fields for the lipid membranes, which is taken to comprise of dipalmitoyl-phosphatidyl-choline (DPPC) molecules. The water model mimicks the aqueous environment surrounding the membrane. Velocities of water and membrane molecules are generated randomly at each simulation run according to a Maxwellian distribution. For statistical significance, a total of eight MD simulations are carried out with different starting molecular velocities for each simulation. MD simulation shows no pore is formed in a 10-ns snapshot for a DPPC membrane set at a uniform temperature of 295 K after a 0.4 V/nm electric field is applied. A nano-sized pore is clearly seen in a 10-ns snapshot on the same geometry but with the top and bottom membrane surfaces kept at temperatures of 300 and 295 K, respectively. For the same applied electric field, the formation of nanopores is clearly demonstrated, but only in the presence of a temperature gradient. MD simulation results show enhanced electroporative effects arising from thermal gradients. The study suggests the temperature gradient is a secondary driver, with the electric field being the primary cause for electroporation.

Keywords: nanosecond, electroporation, thermal effects, molecular dynamics

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Authors: Eman Ghoneim, Amr S. Fahil

Abstract:

Many regions in Egypt are facing a reduction in crop productivity due to environmental degradation. One factor of crop deterioration includes the unsustainable drainage of surface water, leading to salinized soil conditions. Egypt has exerted time and effort to identify solutions to mitigate the surface water drawdown problem and its resulting effects by exploring renewable and sustainable sources of energy. Siwa Oasis represents one of the most favorable regions in Egypt for geothermal exploitation since it hosts an evident cluster of superficial thermal springs. Some of these hot springs are characterized by high surface temperatures and bottom hole temperatures (BHT) ranging between 20°C to 40 °C and 21 °C to 121.7°C, respectively. The depth to the Precambrian basement rock is commonly greater than 440 m, ranging from 440 m to 4724.4 m. It is this feature that makes the locality of Siwa Oasis sufficient for industrial processes and geothermal power production. In this study, BHT data from 27 deep oil wells were processed by applying the widely used Horner and Gulf of Mexico correction methods to obtain formation temperatures. BHT, commonly used in geothermal studies, remains the most abundant and readily available data source for subsurface temperature information. Outcomes of the present work indicated a geothermal gradient ranging from 18 to 42 °C/km, a heat flow ranging from 24.7 to 111.3 m.W.k⁻¹, and a thermal conductivity of 1.3–2.65 W.m⁻¹.k⁻¹. Remote sensing thermal infrared, topographic, geologic, and geothermal data were utilized to provide geothermal potential maps for the Siwa Oasis. Important physiographic variables (including surface elevation, lineament density, drainage density), geological and geophysical parameters (including land surface temperature, depth to basement, bottom hole temperature, magnetic, geothermal gradient, heat flow, thermal conductivity, and main rock units) were incorporated into GIS to produce a geothermal potential map (GTP) for the Siwa Oasis region. The model revealed that both the northeastern and southeastern sections of the study region are of high geothermal potential. The present work showed that combining bottom-hole temperature measurements and remote sensing data with the selected geospatial methodologies is a useful tool for geothermal prospecting in geologically and tectonically comparable settings in Egypt and East Africa. This work has implications for identifying sustainable resources needed to support food production and renewable energy resources.

Keywords: BHT, geothermal potential map, geothermal gradient, heat flow, thermal conductivity, satellite imagery, GIS

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Authors: S. M. Giripunje, Mohit Kumar

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Acoustic energy that exists in our everyday life and environment have been overlooked as a green energy that can be extracted, generated, and consumed without any significant negative impact to the environment. The harvested energy can be used to enable new technology like wireless sensor networks. Technological developments in the realization of truly autonomous MEMS devices and energy storage systems have made acoustic energy harvesting (AEH) an increasingly viable technology. AEH is the process of converting high and continuous acoustic waves from the environment into electrical energy by using an acoustic transducer or resonator. AEH is not popular as other types of energy harvesting methods since sound waves have lower energy density and such energy can only be harvested in very noisy environment. However, the energy requirements for certain applications are also correspondingly low and also there is a necessity to observe the noise to reduce noise pollution. So the ability to reclaim acoustic energy and store it in a usable electrical form enables a novel means of supplying power to relatively low power devices. A quarter-wavelength straight-tube acoustic resonator as an acoustic energy harvester is introduced with polyvinylidene fluoride (PVDF) and PVDF doped with ZnO nanoparticles, piezoelectric cantilever beams placed inside the resonator. When the resonator is excited by an incident acoustic wave at its first acoustic eigen frequency, an amplified acoustic resonant standing wave is developed inside the resonator. The acoustic pressure gradient of the amplified standing wave then drives the vibration motion of the PVDF piezoelectric beams, generating electricity due to the direct piezoelectric effect. In order to maximize the amount of the harvested energy, each PVDF and PVDF-ZnO piezoelectric beam has been designed to have the same structural eigen frequency as the acoustic eigen frequency of the resonator. With a single PVDF beam placed inside the resonator, the harvested voltage and power become the maximum near the resonator tube open inlet where the largest acoustic pressure gradient vibrates the PVDF beam. As the beam is moved to the resonator tube closed end, the voltage and power gradually decrease due to the decreased acoustic pressure gradient. Multiple piezoelectric beams PVDF and PVDF-ZnO have been placed inside the resonator with two different configurations: the aligned and zigzag configurations. With the zigzag configuration which has the more open path for acoustic air particle motions, the significant increases in the harvested voltage and power have been observed. Due to the interruption of acoustic air particle motion caused by the beams, it is found that placing PVDF beams near the closed tube end is not beneficial. The total output voltage of the piezoelectric beams increases linearly as the incident sound pressure increases. This study therefore reveals that the proposed technique used to harvest sound wave energy has great potential of converting free energy into useful energy.

Keywords: acoustic energy, acoustic resonator, energy harvester, eigenfrequency, polyvinylidene fluoride (PVDF)

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Authors: Christine Ysabelle G. Roman, Pauline Torres

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Background: Hypertrophic cardiomyopathy is a disease of marked heterogeneity. It is a genetically determined heart disease characterized by significant myocardium hypertrophy that results in diastolic dysfunction, left ventricular outflow tract obstruction, and an increased risk of arrhythmias. The primary treatment in patients with such conditions is negative inotropic drugs, such as beta-blockers, calcium channel antagonists, and disopyramide. However, for those who remain symptomatic and need septal reduction therapy, surgical septal myectomy or alcohol septal ablation are options. Case Summary: A 19 – year old female presented in the authors’ institution with easy fatigability. The consult was done a year prior, and 2D echocardiography was requested which showed concentric left ventricular hypertrophy, asymmetrically hypertrophied interventricular septum (IVS) with the largest diameter of 3.3cm & subaortic dynamic obstruction with a maximum gradient of 47 mmHg. A repeat echo a year later showed asymmetric septal hypertrophy (IVS measuring at 3cm) with the systolic anterior motion of anterior mitral valve leaflet and left ventricular outflow tract obstruction (peak gradient of 50mmHg). The patient then underwent alcohol septal ablation and was discharged stable after four days of admission. Conclusion: Hypertrophic obstructive cardiomyopathy, a cardiovascular genetic disease, results in various patterns of left ventricular hypertrophy and abnormality of mitral valve apparatus. The patient is managed medically initially. However, despite optimal drug therapy and significant left ventricular outflow tract obstruction, significant heart failure symptoms or syncope require invasive treatment.

Keywords: hypertrophic obstructive cardiomyopathy, left ventricular outflow tract obstruction, alcohol septal ablation, alcohol

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Authors: Hasmithaa Neha, Atul Kumar Patidar, Girish Ch Kothyari

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The northward convergence of the Indian plate has a dominating influence over the structural and geomorphic development of the Himalayan region. The highly deformed and complex stratigraphy in the area arises from a confluence of exogenic and endogenetic geological processes. This region frequently experiences natural hazards such as debris flows, flash floods, avalanches, landslides, and earthquakes due to its harsh and steep topography and fragile rock formations. Therefore, remote sensing technique-based examination and real-time monitoring of tectonically sensitive regions may provide crucial early warnings and invaluable data for effective hazard mitigation strategies. In order to identify unusual changes in the river gradients, the current study demonstrates a spatial quantitative geomorphic analysis of the upper Alaknanda River basin, Uttarakhand Himalaya, India, using gradient length anomaly analysis (GLAA). This basin is highly vulnerable to ground creeping and landslides due to the presence of active faults/thrusts, toe-cutting of slopes for road widening, development of heavy engineering projects on the highly sheared bedrock, and periodic earthquakes. The intersecting joint sets developed in the bedrocks have formed wedges that have facilitated the recurrence of several landslides. The main objective of current research is to identify abnormal gradient lengths, indicating potential landslide-prone zones. High-resolution digital elevation data and geospatial techniques are used to perform this analysis. The results of GLAA are corroborated with the historical landslide events and ultimately used for the generation of landslide susceptibility maps of the current study area. The preliminary results indicate that approximately 3.97% of the basin is stable, while about 8.54% is classified as moderately stable and suitable for human habitation. However, roughly 19.89% fall within the zone of moderate vulnerability, 38.06% are classified as vulnerable, and 29% fall within the highly vulnerable zones, posing risks for geohazards, including landslides, glacial avalanches, and earthquakes. This research provides valuable insights into the spatial distribution of landslide-prone areas. It offers a basis for implementing proactive measures for landslide risk reduction, including land-use planning, early warning systems, and infrastructure development techniques.

Keywords: landslide vulnerability, geohazard, GLA, upper Alaknanda Basin, Uttarakhand Himalaya

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Authors: Jihoon Yang, Jeong II Choi

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Mincle is expressed in macrophages and is members of immunoreceptors induced after exposure to various stimuli and stresses. Mincle receptor activation promotes the production of these substances by increasing the transcription of inflammatory cytokines and chemokines. Cytokines, which play an important role in the initiation and maintenance of such inflammatory pain diseases, have a significant effect on sensory neurons in addition to their enhancement and inhibitory effects on immune and inflammatory cells as mediators of cell interaction. Glial cells in the central nervous system play a critical role in development and maintenance of chronic pain states. Microglia are tissue-resident macrophages in the central nervous system, and belong to a group of mononuclear phagocytes. In the central nervous system, mincle receptor is present in neurons and glial cells of the brain.This study was performed to identify the Mincle receptor in the spinal cord and to investigate the effect of Mincle receptor activation on nociception and the changes of microglia. Materials and Methods: C-type lectins(Mincle) was identified in spinal cord of Male Sprague–Dawley rats. Then, mincle receptor ligand (TDB), via an intrathecal catheter. Mechanical allodynia was measured using von Frey test to evaluate the effect of intrathecal injection of TDB. Result: The present investigation shows that the intrathecal administration of TDB in the rat produces a reliable and quantifiable mechanical hyperalgesia. In addition, The mechanical hyperalgesia after TDB injection gradually developed over time and remained until 10 days. Mincle receptor is identified in the spinal cord, mainly expressed in neuronal cells, but not in microglia or astrocyte. These results suggest that activation of mincle receptor pathway in neurons plays an important role in inducing activation of microglia and inducing mechanical allodynia.

Keywords: mincle, spinal cord, pain, microglia

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Authors: Morteza Khashehchi, Kamel Hooman

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Thermal effects on the dynamics and stability of the flow past a circular cylinder operating in the mixed convection regime is studied experimentally for Reynolds number (ReD) between 1000 and 4000, and different cylinder wall temperatures (Tw) between 25 and 75°C by means of Particle Image Velocimetry (PIV). The experiments were conducted in a horizontal wind tunnel with the heated cylinder placed horizontally. With such assumptions, the direction of the thermally induced buoyancy force acting on the fluid surrounding the heated cylinder would be perpendicular to the flow direction. In each experiment, to acquire 3000 PIV image pairs, the temperature and Reynolds number of the approach flow were held constant. By adjusting different temperatures in different Reynolds numbers, the corresponding Richardson number (RiD = Gr/Re^2) was varied between 0:0 (unheated) and 10, resulting in a change in the heat transfer process from forced convection to mixed convection. With increasing temperature of the wall cylinder, significant modifications of the wake flow pattern and wake vortex shedding process were clearly revealed. For cylinder at low wall temperature, the size of the wake and the vortex shedding process are found to be quite similar to those of an unheated cylinder. With high wall temperature, however, the high temperature gradient in the wake shear layer creates a type of vorticity with opposite sign to that of the shear layer vorticity. This temperature gradient vorticity weakens the strength of the shear layer vorticity, causing delay in reaching the recreation point. In addition to the wake characteristics, the shedding frequency for the heated cylinder is determined for all aforementioned cases. It is found that, as the cylinder wall is heated, the organization of the vortex shedding is altered and the relative position of the first detached vortices with respect to the second one is changed. This movement of the first detached vortex toward the second one increases the frequency of the shedding process. It is also found that the wake closure length decreases with increasing the Richardson number.

Keywords: heated cylinder, PIV, wake, Reynolds number

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Authors: Sellidj Abdelaziz, Lebaili Soltane

Abstract:

A cobalt-based alloy type Co-Cr-Ni-WC was deposited by plasma transferred arc projection (PTA) on a stainless steel valve. The alloy is characterized at the equilibrium by a solid solution Co (γ) mainly dendritic, and eutectic carbides M₇C₃ and ηM₆C. At the deposit/substrate interface, this microstructure is modified by the fast cooling mode of the alloy when applied in the liquid state on the relatively cold steel substrate. The structure formed in this case is heterogeneous and metastable phases can occur and evolve over temperature service. Coating properties and reliability are directly related to microstructures formed during deposition. We were interested more particularly in this microstructure formed during the solidification of the deposit in the region of the interface joining the soldered couple and its evolution during cyclic heat treatments at temperatures similar to those of the thermal environment of the valve. The characterization was carried out by SEM-EDS microprobe CAMECA, XRD, and micro hardness profiles. The deposit obtained has a linear and regular appearance that is free of cracks and with little porosity. The morphology of the microstructure represents solidification stages that are relatively fast with a temperature gradient high at the beginning of the interface by forming a plane front solid solution Co (γ). It gradually changes with the decreasing temperature gradient by getting farther from the junction towards the outer limit of the deposit. The matrix takes the forms: cellular, mixed (cells and dendrites) and dendritic. Dendritic growth is done according to primary ramifications in the direction of the heat removal which takes place in the direction perpendicular to the interface, towards the external surface of the deposit, following secondary and tertiary undeveloped arms. The eutectic carbides M₇C₃ and ηM₆C formed are very thin and are located in the intercellular and interdendritic spaces of the solid solution Co (γ).

Keywords: Co-Ni-Cr-W-C alloy, solid deposit, microstructure, carbides, cyclic heat treatment

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Authors: Marius Gavrilescu, Sabina-Adriana Floria, Florin Leon, Silvia Curteanu, Costel Anton

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Natural sciences provide a wide range of experimental data whose related problems require study and modeling beyond the capabilities of conventional methodologies. Such problems have solution spaces whose complexity and high dimensionality require correspondingly complex regression methods for proper characterization. In this context, we propose an optimization method which consists in a hybrid dual optimizer setup: a global optimizer based on a modified variant of the popular Imperialist Competitive Algorithm (ICA), and a local optimizer based on a gradient descent approach. The ICA is modified such that intermediate solution populations are more quickly and efficiently pruned of low-fitness individuals by appropriately altering the assimilation, revolution and competition phases, which, combined with an initialization strategy based on low-discrepancy sampling, allows for a more effective exploration of the corresponding solution space. Subsequently, gradient-based optimization is used locally to seek the optimal solution in the neighborhoods of the solutions found through the modified ICA. We use this combined approach to find the optimal configuration and weights of a fully-connected neural network, resulting in regression models used to characterize the process of obtained bricks using silicon-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. Thus, the purpose of our approach is to determine by simulation the working conditions, including the manufacturing mix recipe with the addition of different materials, to minimize the emissions represented by CO and CH4. Our approach determines regression models which perform significantly better than those found using the traditional ICA for the aforementioned problem, resulting in better convergence and a substantially lower error.

Keywords: optimization, biologically inspired algorithm, regression models, bricks, emissions

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