Search results for: conjugated gradient method

Authors: Amelia Tero-Vescan, Camil-Eugen Vari, Laura Ciulea, Cristina Filip, Silvia Imre

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A HPLC –UV method for the identification of ephedrine (EPH), sibutramine (SB), yohimbine (Y) and caffeine (CF) was developed. Separation was performed on a Kromasil 100-RP8, 150 mm x 4.6 mm, 5 mm column equipped with a precolumn Kromasil RP 8. Mobile phase was a gradient of 80-35 % sodium dihydrogen phosphate pH=5 with NH4OH and acetonitrile over 15 minutes time of analysis. Based on the responses of 113 athletes about dietary supplements (DS) consumed for "fat burning" and weight loss which have a legal status in Romania, 28 supplements have been selected and investigated for their content in CF, Y, legal substances, and SB, EPH (prohibited substances in DS). The method allows quantitative determination of the four substances in a short analysis time and with minimum cost. The presence of SB and EPH in the analyzed DS was not detected while the content in CF and Y considering the dosage recommended by the manufacturer does not affect the health of the consumers. DS labeling (plant extracts with CF and Y content) allows manufacturers to avoid declaring correct and exact amounts per pharmaceutical form (pure CF or equivalent and Y, respectively).

Keywords: dietary supplements, sibutramine, ephedrine, yohimbine, caffeine, HPLC

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Authors: Khaled M. Mohamed

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Background: P-Phenylenediamine (PPD) is used in the manufacture of hair dyes and skin decoration. In some developing countries, suicidal, homicidal and accidental cases by PPD were recorded. In this work, a sensitive LC-MS/MS method for determination of PPD and its metabolites N-acetyl-p-phenylenediamine (MAPPD) and N,N-diacetyl-p-phenylenediamine (DAPPD) in human urine has been developed and validated. Methods: PPD, MAPPD and DAPPD were extracted from urine by methylene chloride at alkaline pH. Acetanilide was used as internal standard (IS). The analytes and IS were separated on an Eclipse XDB- C18 column (150 X 4.6 mm, 5 µm) using a mobile phase of acetonitrile-1% formic acid in gradient elution. Detection was performed by LC-MS/MS using electrospray positive ionization under multiple reaction-monitoring mode. The transition ions m/z 109 → 92, m/z 151 → 92, m/z 193 → 92, and m/z 136 → 77 were selected for the quantification of PPD, MAPPD, DAPPD, and IS, respectively. Results: Calibration curves were linear in the range 10–2000 ng/mL for all analytes. The mean recoveries for PPD, MAPPD and DAPPD were 57.62, 74.19 and 50.99%, respectively. Intra-assay and inter-assay imprecisions were within 1.58–9.52% and 5.43–9.45% respectively for PPD, MAPPD and DAPPD. Inter-assay accuracies were within -7.43 and 7.36 for all compounds. PPD, MAPPD and DAPPD were stable in urine at –20 degrees for 24 hours. Conclusions: The method was successfully applied to the analysis of PPD, MAPPD and DAPPD in urine samples collected from suicidal cases.

Keywords: p-Phenylenediamine, metabolites, urine, LC-MS/MS, validation

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Authors: Frida Carrillo Morales, Maria Maricela Carrasco Yépez, Saúl Rojas Hernández

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Naegleria fowleri is the causative agent of primary amebic meningoencephalitis, this disease is acute and fulminant that affects humans. It has been reported that despite the existence of therapeutic options against this disease, its mortality rate is 97%. Therefore, the need arises to have vaccines that confer protection against this disease and, in addition to developing adjuvants to enhance the immune response. In this regard, in our work group, we obtained a peptide designed from the membrane protein MP2CL5 of Naegleria fowleri called Smp145 that was shown to be immunogenic; however, it would be of great importance to enhance its immunological response, being able to co-administer it with a non-toxic adjuvant. Therefore, the objective of this work was to carry out the bioinformatic design of a peptide of the Naegleria fowleri membrane protein MP2CL5 conjugated with a non-toxic modified adjuvant from the native A1 structure of Cholera Toxin. For which different bioinformatics tools were used to obtain a model with a modification in amino acid 61 of the A1 subunit of the CT (CTA1), to which the Smp145 peptide was added and both molecules were joined with a 13-glycine linker. As for the results obtained, the modification in CTA1 bound to the peptide produces a reduction in the toxicity of the molecule in in silico experiments, likewise, the prediction in the binding of Smp145 to the receptor of B cells suggests that the molecule is directed in specifically to the BCR receptor, decreasing its native enzymatic activity. The stereochemical evaluation showed that the generated model has a high number of adequately predicted residues. In the ERRAT test, the confidence with which it is possible to reject regions that exceed the error values was evaluated, in the generated model, a high score was obtained, which determines that the model has a good structural resolution. Therefore, the design of the conjugated peptide in this work will allow us to proceed with its chemical synthesis and subsequently be able to use it in the mouse meningitis protection model caused by N. fowleri.

Keywords: immunology, vaccines, pathogens, infectious disease

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Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

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This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

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Authors: Layla El Gaini, Majdouline Belaqziz, Meriem Outaki, Mariam Minhaj

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In this study, it introduce and validate a high-performance liquid chromatography method with diode-array detection (HPLC-DAD) specifically designed for the accurate quantification of caffeic acid in phenolic extracts obtained from olive mill wastewater. The separation process of caffeic acid was effectively achieved through the use of an Acclaim Polar Advantage column (5µm, 250x4.6mm). A meticulous multi-step gradient mobile phase was employed, comprising water acidified with phosphoric acid (pH 2.3) and acetonitrile, to ensure optimal separation. The diode-array detection was adeptly conducted within the UV–VIS spectrum, spanning a range of 200–800 nm, which facilitated precise analytical results. The method underwent comprehensive validation, addressing several essential analytical parameters, including specificity, repeatability, linearity, as well as the limits of detection and quantification, alongside measurement uncertainty. The generated linear standard curves displayed high correlation coefficients, underscoring the method's efficacy and consistency. This validated approach is not only robust but also demonstrates exceptional reliability for the focused analysis of caffeic acid within the intricate matrices of wastewater, thus offering significant potential for applications in environmental and analytical chemistry.

Keywords: high-performance liquid chromatography (HPLC-DAD), caffeic acid analysis, olive mill wastewater phenolics, analytical method validation

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Authors: Wanhyun Cho, Seongchae Seo, Soonja Kang

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In this work, we propose a variational technique for image contrast enhancement which utilizes global and local information around each pixel. The energy functional is defined by a weighted linear combination of three terms which are called on a local, a global contrast term and dispersion term. The first one is a local contrast term that can lead to improve the contrast of an input image by increasing the grey-level differences between each pixel and its neighboring to utilize contextual information around each pixel. The second one is global contrast term, which can lead to enhance a contrast of image by minimizing the difference between its empirical distribution function and a cumulative distribution function to make the probability distribution of pixel values becoming a symmetric distribution about median. The third one is a dispersion term that controls the departure between new pixel value and pixel value of original image while preserving original image characteristics as well as possible. Second, we derive the Euler-Lagrange equation for true image that can achieve the minimum of a proposed functional by using the fundamental lemma for the calculus of variations. And, we considered the procedure that this equation can be solved by using a gradient decent method, which is one of the dynamic approximation techniques. Finally, by conducting various experiments, we can demonstrate that the proposed method can enhance the contrast of colour images better than existing techniques.

Keywords: color image, contrast enhancement technique, variational approach, Euler-Lagrang equation, dynamic approximation method, EME measure

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Authors: Archana Gupta, Rajesh Kumar

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The chemistry of chalcones has generated intensive scientific studies throughout the world. Especially, interest has been focused on the synthesis and biodynamic activities of chalcones. The blue light transmittance, excellent crystallizability and the two planar rings connected through a conjugated double bond show that chalcone derivatives are superior nonlinear organic compounds. 3-(2-Chloro-6-fluoro¬phen¬yl)-1-(2-thien¬yl) prop-2-en-1-one, 3-(2, 4- Dichlorophenyl) – 1 - (4-methylphenyl) – prop -2-en-1-one, (2E)-3-[4-(methylsulfanyl) phenyl]-1-(4-nitrophenyl) prop-2-en-1-one are some chalcone derivatives exhibiting non linear optical (NLO) properties. NLO materials have been extensively investigated in recent years as they are the key elements for photonic technologies of optical communication, optical interconnect oscillator, amplifier, frequency converter etc. Due to their high molecular hyperpolarizabilities, organic materials display a number of significant NLO properties. Experimental measurements and theoretical calculations on molecular hyperpolarizability β have become one of the key factors in the design of second order NLO materials. Theoretical determination of hyperpolarizability is quite useful both in understanding the relationship between the molecular structure and NLO properties. It also provides a guideline to experimentalists for the design and synthesis of organic NLO materials. Quantum-chemical calculations have made an important contribution to the understanding of the electronic polarization underlying the molecular NLO processes and the establishment of structure–property relationships. In the present investigation, the detailed vibrational analysis of some chalcone derivatives is taken up to understand the correlation of the charge transfer interaction and the NLO activity of the molecules based on density functional theory calculations. The vibrational modes contributing toward the NLO activity have been identified and analyzed. Rather large hyperpolarizability derived by theoretical calculations suggests the possible future use of these compounds for non-linear optical applications. The study suggests the importance of π - conjugated systems for non-linear optical properties and the possibility of charge transfer interactions. We hope that the results of the present study of chalcone derivatives are of assistance in development of new efficient materials for technological applications.

Keywords: hyperpolarizability, molecular structure, NLO material, quantum chemical calculations

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Authors: Y. Yang, Dian Ming Xing, Qiu Hua Du

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Based on the Motorized Momentum Exchange Tether (MMET), with the principle of momentum exchange, the three dimension flexible dynamics of continuous cislunar payloads transferring system (CCPTS) is built by Lagrange method and its numerical solution is solved by Mathematica software. In the derivation precession of potential energy, this paper uses the Tylor expansion method to simplify the Lagrange equation. Furthermore, the tension coming from the centripetal load is considered in the elastic potential energy. The comparison simulation results between the 3D rigid model and 3D flexible model of CCPTS shows that the tether flexibility has important influence on CCPTS’s orbital parameters (such as radius of CCPTS’s COM and the true anomaly) and the tether’s rotational movement, the relative deviation of radius and the true anomaly between the two dynamic models is about 0.00678% and 0.00259%, the relative deviation of the angle of tether-span and local gravity gradient is about 3.55%. Additionally, the external torque has an apparent influence on the tether’s axial vibration.

Keywords: cislunar transfer, dynamics, momentum exchange, tether

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Authors: James Ladzekpo

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Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: Sajid Hussain

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The complex domain approach for image segmentation based on active contour has been designed, which deforms step by step to partition an image into numerous expedient regions. A novel region-based trigonometric complex pressure force function is proposed, which propagates around the region of interest using image forces. The signed trigonometric force function controls the propagation of the active contour and the active contour stops on the exact edges of the object accurately. The proposed model makes the level set function binary and uses Gaussian smoothing kernel to adjust and escape the re-initialization procedure. The working principle of the proposed model is as follows: The real image data is transformed into complex data by iota (i) times of image data and the average iota (i) times of horizontal and vertical components of the gradient of image data is inserted in the proposed model to catch complex gradient of the image data. A simple finite difference mathematical technique has been used to implement the proposed model. The efficiency and robustness of the proposed model have been verified and compared with other state-of-the-art models.

Keywords: image segmentation, active contour, level set, Mumford and Shah model

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Authors: Ehsan Mehryaar, Reza Bushehri

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One of the principal parameters defining the clay soil dynamic response is the strain-shear modulus relation. Predicting the strain and, subsequently, shear modulus reduction of the soil is essential for performance analysis of structures exposed to earthquake and dynamic loadings. Many soil properties affect soil’s dynamic behavior. In order to capture those effects, in this study, a database containing 1193 data points consists of maximum shear modulus, strain, moisture content, initial void ratio, plastic limit, liquid limit, initial confining pressure resulting from dynamic laboratory testing of 21 clays is collected for predicting the shear modulus vs. strain curve of soil. A model based on an extreme gradient boosting technique is proposed. A tree-structured parzan estimator hyper-parameter tuning algorithm is utilized simultaneously to find the best hyper-parameters for the model. The performance of the model is compared to the existing empirical equations using the coefficient of correlation and root mean square error.

Keywords: XGBoost, hyper-parameter tuning, soil shear modulus, dynamic response

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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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Authors: Romisaa Ali

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This paper presents a comparison between twin-delayed Deep Deterministic Policy Gradient (TD3) and Soft Actor-Critic (SAC) reinforcement learning algorithms in the context of training robust navigation policies for Jackal robots. By leveraging an open-source framework and custom motion control environments, the study evaluates the performance, robustness, and transferability of the trained policies across a range of scenarios. The primary focus of the experiments is to assess the training process, the adaptability of the algorithms, and the robot’s ability to navigate in previously unseen environments. Moreover, the paper examines the influence of varying environmental complexities on the learning process and the generalization capabilities of the resulting policies. The results of this study aim to inform and guide the development of more efficient and practical reinforcement learning-based navigation policies for Jackal robots in real-world scenarios.

Keywords: Jackal robot environments, reinforcement learning, TD3, SAC, robust navigation, transferability, custom environment

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Authors: Farshad Zeinali, Sajedeh Norouzi, Nader Mokari, Eduard Jorswieck

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The capacity of fifth-generation (5G) vehicle-to-everything (V2X) networks poses significant challenges. To ad- dress this challenge, this paper utilizes New Radio (NR) and New Radio Unlicensed (NR-U) networks to develop a heterogeneous vehicular network (HetNet). We propose a new framework, named joint BS assignment and resource allocation (JBSRA) for mobile V2X users and also consider coexistence schemes based on flexible duty cycle (DC) mechanism for unlicensed bands. Our objective is to maximize the average throughput of vehicles while guaranteeing the WiFi users' throughput. In simulations based on deep reinforcement learning (DRL) algorithms such as deep deterministic policy gradient (DDPG) and deep Q network (DQN), our proposed framework outperforms existing solutions that rely on fixed DC or schemes without consideration of unlicensed bands.

Keywords: vehicle-to-everything (V2X), resource allocation, BS assignment, new radio (NR), new radio unlicensed (NR-U), coexistence NR-U and WiFi, deep deterministic policy gradient (DDPG), deep Q-network (DQN), joint BS assignment and resource allocation (JBSRA), duty cycle mechanism

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Authors: Muhammad Jawwad, Riffat Iqbal

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The Socratic method, also known as the dialectical method and elenctic method, has significant relevance in the contemporary educational system. It can be incorporated into modern-day educational systems theoretically as well as practically. Being interactive and dialogue-based in nature, this teaching approach is followed by critical thinking and innovation. The pragmatic value of the Dialectical Method has been discussed in this article, and the limitations of the Socratic method have also been highlighted. The interactive Method of Socrates can be used in many subjects for students of different grades. The Limitations and delimitations of the Method have also been discussed for its proper implementation. This article has attempted to elaborate and analyze the teaching method of Socrates with all its pre-suppositions and Epistemological character.

Keywords: Socratic method, dialectical method, knowledge, teaching, virtue

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Authors: Wenxiao Liu, Kun Zhang, Yongqing Li

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Bovine herpesvirus 1, a member of the genus Variellovirus of the subfamily Alphaherpesvirinae, has caused severe economic cost to the bovine industry. In this study, BoHV-1 glycerol protein gD was expressed in insect cells, and the purified gD was immunized in the Balb/C mice to generate monoclonal antibodies. Based on hybridoma cell fusion techniques, 20 monoclonal antibodies against Bovine herpesvirus 1 have been obtained. Further, mAb 3F8 with neutralizing activity and gD were applied to develop a blocking enzyme-linked immunosorbent assay (Elisa) for detecting neutralizing antibodies against BoHV-1, which shows a significant correlation between the blocking Elisa and VNT. The sensitivity and specificity of the test were estimated to be 94.59% and 93.42%, respectively. Furthermore, antibody pairing tests revealed that mAb 1B6 conjugated to fluorescence microspheres was used as the capture antibody, and mAb 3F9 was used as the detectable antibody to establish the immunochromatographic assay (ICS). The ICS was conducted to detect BoHV-1 in bovine samples with high sensitivity, specificity, and good stability. Clinical sample testing revealed that the results of ICS and real-time PCR have a coincidence rate of 95.42%. Our research confirmed that the ICS is a rapid and reliable method for the diagnosis of BoHV-1. In conclusion, our results lay a solid foundation for the prevention and control of BoHV-1 infection.

Keywords: bovine disease, BoHV-1, ELISA, ICS assay

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Authors: Dmitry V. Pasynkov, Ivan A. Egoshin, Alexey A. Kolchev, Ivan V. Kliouchkin

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In most cases, typical cysts are easily recognized at ultrasonography. The specificity of this method for typical cysts reaches 98%, and it is usually considered as gold standard for typical cyst diagnosis. However, it is necessary to have all the following features to conclude the typical cyst: clear margin, the absence of internal echoes and dorsal acoustic enhancement. At the same time, not every breast cyst is typical. It is especially characteristic for protein-contained cysts that may have significant internal echoes. On the other hand, some solid lesions (predominantly malignant) may have cystic appearance and may be falsely accepted as cysts. Therefore we tried to develop the automatic method of cystic and solid breast lesions differentiation. Materials and methods. The input data were the ultrasonography digital images with the 256-gradations of gray color (Medison SA8000SE, Siemens X150, Esaote MyLab C). Identification of the lesion on these images was performed in two steps. On the first one, the region of interest (or contour of lesion) was searched and selected. Selection of such region is carried out using the sigmoid filter where the threshold is calculated according to the empirical distribution function of the image brightness and, if necessary, it was corrected according to the average brightness of the image points which have the highest gradient of brightness. At the second step, the identification of the selected region to one of lesion groups by its statistical characteristics of brightness distribution was made. The following characteristics were used: entropy, coefficients of the linear and polynomial regression, quantiles of different orders, an average gradient of brightness, etc. For determination of decisive criterion of belonging to one of lesion groups (cystic or solid) the training set of these characteristics of brightness distribution separately for benign and malignant lesions were received. To test our approach we used a set of 217 ultrasonic images of 107 cystic (including 53 atypical, difficult for bare eye differentiation) and 110 solid lesions. All lesions were cytologically and/or histologically confirmed. Visual identification was performed by trained specialist in breast ultrasonography. Results. Our system correctly distinguished all (107, 100%) typical cysts, 107 of 110 (97.3%) solid lesions and 50 of 53 (94.3%) atypical cysts. On the contrary, with the bare eye it was possible to identify correctly all (107, 100%) typical cysts, 96 of 110 (87.3%) solid lesions and 32 of 53 (60.4%) atypical cysts. Conclusion. Automatic approach significantly surpasses the visual assessment performed by trained specialist. The difference is especially large for atypical cysts and hypoechoic solid lesions with the clear margin. This data may have a clinical significance.

Keywords: breast cyst, breast solid lesion, differentiation, ultrasonography

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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: Naren Bag, S. Bhattacharyya, Partha P. Gopmandal

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In this study, we have analyzed the transport of analytes under a two dimensional steady incompressible flow of power-law fluids through rectangular nanochannel. A mathematical model based on the Cauchy momentum-Nernst-Planck-Poisson equations is considered to study the combined effect of mixed electroosmotic (EO) and pressure driven (PD) flow. The coupled governing equations are solved numerically by finite volume method. We have studied extensively the effect of key parameters, e.g., flow behavior index, concentration of the electrolyte, surface potential, imposed pressure gradient and imposed electric field strength on the net average flow across the channel. In addition to study the effect of mixed EOF and PD on the analyte distribution across the channel, we consider a nonlinear model based on general convective-diffusion-electromigration equation. We have also presented the retention factor for various values of electrolyte concentration and flow behavior index.

Keywords: electric double layer, finite volume method, flow behavior index, mixed electroosmotic/pressure driven flow, non-Newtonian power-law fluids, numerical simulation

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Authors: Atitallah Sofien, Bouyahia Olfa, Attar Souleima, Missaoui Nada, Ben Rabeh Rania, Yahyaoui Salem, Mazigh Sonia, Boukthir Samir

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Introduction: Ciliopathies are a spectrum of diseases that have in common a defect in the synthesis of ciliary proteins. It is a rare cause of neonatal cholestasis. Clinical presentation varies extremely, and the main affected organs are the kidneys, liver, and pancreas. Methodology: This is a descriptive case report of a newborn who was admitted for exploration of neonatal cholestasis in the Paediatric Department C at the Children’s Hospital of Tunis, where the investigations concluded with a rare genetic mutation. Results: This is the case of a newborn male with no family history of hepatic and renal diseases, born to consanguineous parents, and from a well-monitored uneventful pregnancy. He developed jaundice on the second day of life, for which he received conventional phototherapy in the neonatal intensive care unit. He was admitted at 15 days for mild bronchiolitis. On clinical examination, intense jaundice was noted with normal stool and urine colour. Initial blood work showed an elevation in conjugated bilirubin and a high gamma-glutamyl transferase level. Transaminases and prothrombin time were normal. Abdominal sonography revealed hepatomegaly, splenomegaly, and undifferentiated renal cortex with bilateral medullar micro-cysts. Kidney function tests were normal. The infant received ursodeoxycholic acid and vitamin therapy. Genetic testing showed a homozygous mutation in the DCDC2 gene that hadn’t been documented before confirming the diagnosis of renal-hepatic ciliopathy. The patient has regular follow-ups, and his conjugated bilirubin and gamma-glutamyl transferase levels have been decreasing. Conclusion: Genetic testing has revolutionized the approach to etiological diagnosis in pediatric cholestasis. It enables personalised treatment strategies to better enhance the quality of life of patients and prevent potential complications following adequate long-term monitoring.

Keywords: cholestasis, newborn, ciliopathy, DCDC2, genetic

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Authors: Cyril Deroy, Agata Rumianek, David R. Greaves, Peter R. Cook, Edmond J. Walsh

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Various microfluidic platforms have been developed in the past couple of decades offering experimental methods for the study of cell migration; however, their implementation in the laboratory has remained limited. Some reasons cited for the lack of uptake include the technical complexity of the devices, high failure rate associated with gas-bubbles, biocompatibility concerns with the use of polydimethylsiloxane (PDMS) and equipment/time/expertise requirements for operation and manufacture. As sample handling remains challenging due to the closed format of microfluidic devices, open microfluidic systems have been developed offering versatility and simplicity of use. Rather than confining fluids by solid walls, samples can be accessed directly over the open platform, by removing at least one of the solid boundaries, such as the cover. In this paper, a method for the fabrication of open fluid-walled microfluidic circuits for cell migration studies is introduced, where only materials commonly used by the life-science community are required; tissue culture dishes and cell media. The simplicity of the method, and ability to retrieve cells of interest are two key features of the method. Both passive and active flow-devices can be created in this way. To demonstrate the versatility of the method a cell migration assay is performed, which requires fabricating circuits for establishing chemical gradients, loading cells and incubating, creating chemical gradients, real time imaging of cell migration and finally retrieval of cells. The open architecture has high fidelity as it eliminates air bubble related failures and enables the precise control of gradients. The ability to fabricate custom microfluidic designs in minutes should make this method suitable for use in a wide range of cell migration studies.

Keywords: chemotaxis, fluid walls, gradient generation, open microfluidics

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Authors: Xiangxu Chai, Bin Feng, Ping Li, Deyan Zhu, Liquan Wang, Guanzhong Wang, Yukun Jing

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In high power large-aperture laser systems, such as the inertial confinement fusion project, the Nd: glass laser (1053nm) is usually needed to be converted to ultraviolet (UV) light and the fourth harmonic generation (FHG) is one of the most favorite candidates to achieve UV light. Deuterated potassium dihydrogen phosphate (DKDP) crystal is an optimal choice for converting the Nd: glass radiation to the fourth harmonic laser by noncritical phase matching (NCPM). To reduce the damage probability of focusing lens, the DKDP crystal is suggested to be set before the focusing lens. And a converging beam enters the FHG crystal consequently. In this paper, we simulate the process of FHG in the scheme and the dependence of FHG efficiency on the lens’ F is derived. Besides, DKDP crystal with gradient deuterium is proposed to realize the NCPM FHG of the converging beam. At every position, the phase matching is achieved by adjusting the deuterium level, and the FHG efficiency increases as a result. The relation of the lens’ F with the deuterium gradient is investigated as well.

Keywords: fourth harmonic generation, laser induced damage, converging beam, DKDP crystal

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Authors: Du Yanxia, Stephan E. Bansmer, Gui Yewei, Xiao Guangming, Yang Xiaofeng

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The heat and mass transfer characteristics of icing coupled with film flow is studied and the coupled model of the thermal behavior with the flow simulation by single-step method is developed. The behavior of ice and water was analyzed. The results show that under supercooled large droplet (SLD) icing conditions, the film flow is an important phonomena in icing accretion process. The pressure gradient, gravity and shear stress are the main factors affecting the film flow on icing surface, which has important influence on the shape and rate of icing. To predict SLD ice accretion accurately, the heat and mass transfer of ice and film flow should be taken into account.

Keywords: SLD, aircraft, icing, heat and mass transfer

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Authors: Sourav Ghosh, Antony Gomes

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Since 1894 anti-snake venom serum (ASVS) is the only available treatment against snake envenomation, although there are many side effects and limitations. The need for a supportive treatment was felt for a long time to overcome the side effects and limitations of ASVS. Andrographolide conjugated with gold nanoparticle (A-GNP) has been found to antagonize viper venom-induced local damages. The present study was aimed to study the protective efficacy of A-GNP against Viper venom-induced inflammatory response and organ toxicity in animal model. Ethical clearance was obtained from animal experiments. Physico-chemical characterization of A-GNP was done by DLS (diameter and zeta potential), FE-SEM and XRD. Swiss albino male mice were divided into 4 groups: Gr.1-Sham control, Gr.2- Russell’s Viper venom (RVV) control, Gr.3- andrographolide treated and Gr.4- A-GNP treated. The 1/5th minimum lethal dose of RVV (500µg/kg, s.c.) was induced in animals of group 2, 3 & 4 animals, followed by treatment with andrographolide (100mg/kg, i.p.) and A-GNP (100mg/kg, i.v.) in group 3 & 4 animals, respectively. Blood was collected after 18 h, serum was prepared, and inflammatory markers (IL 1β, 6, 17a, 10, TNF α) and biochemical markers (AST, ACP, LDH, urea, creatinine) were assessed. Values were expressed as mean±SEM (n=4), one way ANOVA was done, P<0.05 was considered as statistically significant. DLS size showed the hydrodynamic diameter of A-GNP to be 230-260nm with polydispersity index of 0.103 and zeta potential was -18.32mV. XRD data confirmed the presence of crystalline gold in A-GNP, and FESEM indicated the presence of nearly spherical particle with size18-24nm.Treatment with A-GNP significantly decreased viper venom-induced proinflammatory markers (IL 1β, 6, 17, TNF α) increased anti-inflammatory markers (IL 10) and decreased organ toxicity markers (AST, ACP, LDH, urea, creatinine) in animal model. Venom neutralization efficacy of A-GNP was > andrographolide, which confirmed the increased efficacy of andrographolide after gold nanoparticle conjugation. Venom neutralization by A-GNP was due to anti-oxidant/anti-inflammatory activity of andrographolide, which showed increased efficacy after gold nanoparticle tagging. Thus, A-GNP may serve as a supportive therapy in snake-bite (against inflammatory response and organ toxicity) subject to further detail studies.

Keywords: andrographolide, gold nanoparticle, inflammatory response, organ toxicity, snake venom, snake venom neutralization, viper venom

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Authors: B. Bouadjemi, S. Bentata, W. Benstaali, A. Souidi, A. Abbad, T. Lantri, Z. Aziz, A. Zitouni

Abstract:

The electronic and magnetic structures of double perovskite Ba2MnMoO6 are systematically investigated using the first principle method of the Full Potential Linear Augmented Plane Waves Plus the Local Orbitals (FP-LAPW+LO) within the Local Spin Density Approximation (LSDA) and the Generalized Gradient Approximation (GGA). In order to take into account the strong on-site Coulomb interaction, we included the Hubbard correlation terms: LSDA+U and GGA+U approaches. Whereas half-metallic ferromagnetic character is observed due to dominant Mn spin-up and Mo spin-down contributions insulating ground state is obtained. The LSDA+U and GGA+U calculations yield better agreement with the theoretical and the experimental results than LSDA and GGA do.

Keywords: electronic structure, double perovskite, first principles, Ba2MnMoO6, half-metallic

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Authors: Jaegu Choi, Jae-Mean Koo, Chang-Sung Seok, Byungwoo Moon

Abstract:

Since massive earthquakes in the world have been reported recently, the safety of nuclear power plants for seismic loading has become a significant issue. Seismic loading is the reverse cyclic loading, consisting of repeated tensile and compression by longitudinal and transverse wave. Up to this time, the study on characteristics of fracture toughness under reverse cyclic loading has been unsatisfactory. Therefore, it is necessary to obtain the fracture toughness under reverse cyclic load for the integrity estimation of nuclear power plants under seismic load. Fracture resistance (J-R) curves, which are used for determination of fracture toughness or integrity estimation in terms of elastic-plastic fracture mechanics, can be derived by the fracture resistance test using single specimen technique. The objective of this paper is to study the effects of reverse cyclic loading on a fracture resistance curve of ESG specimen, having a similar stress gradient compared to the crack surface of the real pipe. For this, we carried out the fracture toughness test under the reverse cyclic loading, while changing incremental plastic displacement. Test results showed that the J-R curves were decreased with a decrease of the incremental plastic displacement.

Keywords: reverse cyclic loading, j-r curve, ESG specimen, incremental plastic displacement

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

Abstract:

The interpretation of medical images benefits from anatomical and physiological priors to optimize computer- aided diagnosis applications. Segmentation of liver and liver lesion is regarded as a major primary step in computer aided diagnosis of liver diseases. Precise liver segmentation in abdominal CT images is one of the most important steps for the computer-aided diagnosis of liver pathology. In this papers, a semi- automated method for medical image data is presented for the liver and liver lesion segmentation data using mathematical morphology. Our algorithm is currency in two parts. In the first, we seek to determine the region of interest by applying the morphological filters to extract the liver. The second step consists to detect the liver lesion. In this task; we proposed a new method developed for the semi-automatic segmentation of the liver and hepatic lesions. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to improve the quality of the original image and image gradient by applying the spatial filter followed by the morphological filters. The second step consists to calculate the internal and external markers of the liver and hepatic lesions. Thereafter we proceed to the liver and hepatic lesions segmentation by the watershed transform controlled by markers. The validation of the developed algorithm is done using several images. Obtained results show the good performances of our proposed algorithm

Keywords: anisotropic diffusion filter, CT images, hepatic lesion segmentation, Liver segmentation, morphological filter, the watershed algorithm

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Authors: Farhad Alizadeh, Alireza Faregh Gharamaleki, Mojtaba Jalilzadeh, Houshang Gholami, Ali Akhoundzadeh

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This study presented hybrid pre-processing approach along with a conceptual model to enhance the accuracy of river discharge prediction. In order to achieve this goal, Ensemble Empirical Mode Decomposition algorithm (EEMD), Discrete Wavelet Transform (DWT) and Mutual Information (MI) were employed as a hybrid pre-processing approach conjugated to Least Square Support Vector Machine (LSSVM). A conceptual strategy namely multi-station model was developed to forecast the Souris River discharge more accurately. The strategy used herein was capable of covering uncertainties and complexities of river discharge modeling. DWT and EEMD was coupled, and the feature selection was performed for decomposed sub-series using MI to be employed in multi-station model. In the proposed feature selection method, some useless sub-series were omitted to achieve better performance. Results approved efficiency of the proposed DWT-EEMD-MI approach to improve accuracy of multi-station modeling strategies.

Keywords: river stage-discharge process, LSSVM, discrete wavelet transform, Ensemble Empirical Decomposition Mode, multi-station modeling

Procedia PDF Downloads 148

Authors: Habtamu Garoma Debela, Gemechis File Duressa

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In this work, our primary interest is to provide ε-uniformly convergent numerical techniques for solving singularly perturbed semilinear boundary value problems with non-local boundary condition. These singular perturbation problems are described by differential equations in which the highest-order derivative is multiplied by an arbitrarily small parameter ε (say) known as singular perturbation parameter. This leads to the existence of boundary layers, which are basically narrow regions in the neighborhood of the boundary of the domain, where the gradient of the solution becomes steep as the perturbation parameter tends to zero. Due to the appearance of the layer phenomena, it is a challenging task to provide ε-uniform numerical methods. The term 'ε-uniform' refers to identify those numerical methods in which the approximate solution converges to the corresponding exact solution (measured to the supremum norm) independently with respect to the perturbation parameter ε. Thus, the purpose of this work is to develop, analyze, and improve the ε-uniform numerical methods for solving singularly perturbed problems. These methods are based on nonstandard fitted finite difference method. The basic idea behind the fitted operator, finite difference method, is to replace the denominator functions of the classical derivatives with positive functions derived in such a way that they capture some notable properties of the governing differential equation. A uniformly convergent numerical method is constructed via nonstandard fitted operator numerical method and numerical integration methods to solve the problem. The non-local boundary condition is treated using numerical integration techniques. Additionally, Richardson extrapolation technique, which improves the first-order accuracy of the standard scheme to second-order convergence, is applied for singularly perturbed convection-diffusion problems using the proposed numerical method. Maximum absolute errors and rates of convergence for different values of perturbation parameter and mesh sizes are tabulated for the numerical example considered. The method is shown to be ε-uniformly convergent. Finally, extensive numerical experiments are conducted which support all of our theoretical findings. A concise conclusion is provided at the end of this work.

Keywords: nonlocal boundary condition, nonstandard fitted operator, semilinear problem, singular perturbation, uniformly convergent

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Authors: Yue Yan, Chang Nyung Kim

Abstract:

The numerical investigation of the three-dimensional liquid-metal (LM) magnetohydrodynamic (MHD) flows in a curved duct with flow channel insert (FCI) is presented in this paper, based on the computational fluid dynamics (CFD) method. A uniform magnetic field is applied perpendicular to the duct. The interdependency of the flow variables is examined in terms of the flow velocity, current density, electric potential and pressure. The electromagnetic characteristics of the LM MHD flows are reviewed with an introduction of the electric-field component and electro-motive component of the current. The influence of the existence of the FCI on the fluid flow is investigated in detail. The case with FCI slit located near the side layer yields smaller pressure gradient with stable flow field.

Keywords: curved duct, flow channel insert, liquid-metal, magnetohydrodynamic

Procedia PDF Downloads 454