Search results for: binary bisamide organogelators

Authors: Azeem Ur Rehman, Asma Tayyaba

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This article deals with the promotional effects of CeO2 on PtPd/CeO2-OMC electro catalysts. The synthesized catalysts are characterized using different physico chemical techniques and evaluated in a formic acid oxidation fuel cell. N2 adsorption/desorption analysis shows that CeO2 modification increases the surface area of OMC from 1005 m2/g to 1119 m2/g. SEM, XRD and TEM analysis reveal that the presence of CeO2 enhances the active metal(s) dispersion on the CeO2-OMC surface. The average particle size of the dispersed metal decreases with the increase of Pt/Pd ratio on CeO2-OMC support. Cyclic voltametry measurement of Pd/CeO2-OMC gives 12 % higher anodic current activity with 83 mV negative shift of the peak potential as compared to unmodified Pd/OMC. In bimetallic catalysts, the addition of Pt improves the activity and stability of the catalysts significantly. Among the bimetallic samples, Pd3Pt1/CeO2-OMC displays superior current density (74.6 mA/cm2), which is 28.3 times higher than that of Pt/CeO2-OMC. It also shows higher stability in extended period of runs with least indication of CO poisoning effects.

Keywords: CeO2, ordered mesoporous carbon (OMC), electro catalyst, formic acid fuel cell

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Authors: J. K. Adedeji, M. O. Oyekanmi

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This paper has critically examined the use of Machine Learning procedures in curbing unauthorized access into valuable areas of an organization. The use of passwords, pin codes, user’s identification in recent times has been partially successful in curbing crimes involving identities, hence the need for the design of a system which incorporates biometric characteristics such as DNA and pattern recognition of variations in facial expressions. The facial model used is the OpenCV library which is based on the use of certain physiological features, the Raspberry Pi 3 module is used to compile the OpenCV library, which extracts and stores the detected faces into the datasets directory through the use of camera. The model is trained with 50 epoch run in the database and recognized by the Local Binary Pattern Histogram (LBPH) recognizer contained in the OpenCV. The training algorithm used by the neural network is back propagation coded using python algorithmic language with 200 epoch runs to identify specific resemblance in the exclusive OR (XOR) output neurons. The research however confirmed that physiological parameters are better effective measures to curb crimes relating to identities.

Keywords: biometric characters, facial recognition, neural network, OpenCV

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Authors: Nahla Elazab, Mohamed Aboudina, Ghada Ibrahim, Hossam Fahmy, Ahmed Khalil

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This paper presents the developed memristor based demodulators for eight circular Quadrature Amplitude Modulation (QAM) and Binary Frequency Shift Keying (BFSK) operating at relatively high frequency. In our implementations, the experimental-based ‘nonlinear’ dopant drift model is adopted along with the proposed circuits providing incorporation of all known non-idealities of practically realized memristor and gaining high operation frequency. The suggested designs leverage the distinctive characteristics of the memristor device, definitely, its changeable average memristance versus the frequency, phase and amplitude of the periodic excitation input. The proposed demodulators feature small integration area, low power consumption, and easy implementation. Moreover, the proposed QAM demodulator precludes the requirement for the carrier recovery circuits. In doing so, the designs were validated by transient simulations using the nonlinear dopant drift memristor model. The simulations results show high agreement with the theory presented.

Keywords: BFSK, demodulator, high frequency memristor applications, memristor based analog circuits, nonlinear dopant drift model, QAM

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Authors: Godswill Nwabuisi Osuafor, Sonto Maria Maputle

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Background: Abounding studies found that religiosity and social factors modulate alcohol use among university students. However, there is a scarcity of empirical studies examining the protective effects of religiosity and other social factors on alcohol use and abuse in South African universities. The aim of this study was therefore to assess the protective effects of religiosity and roles of social factors on alcohol use among university students. Methodology: A survey on the use of alcohol among 416 university students was conducted using structured questionnaire in 2014. Data were sourced on religiosity and contextual variables. Students were classified as practicing intrinsic religiosity or extrinsic religiosity based on the response to the measures of religiosity. Descriptive, chi square and binary logistic analyses were used in processing the data. Result: Results revealed that alcohol use was associated with religiosity, religion, sex, family history of alcohol use and experimenting with alcohol. Reporting alcohol abuse was significantly predicted by sex, family history of alcohol use and experimenting with alcohol. Religiosity mediated lower alcohol use whereas family history of alcohol use and experimenting with alcohol promoted alcohol use and abuse. Conclusion: Families, religious groups and societal factors may be the specific niches for intervention on alcohol use among university students.

Keywords: religiosity, alcohol use, protective factors, university students

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Authors: Harsh Vikram Singh, Ankur Rai, Anand Mohan

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In this paper, we propose secure and robust data hiding algorithm based on DCT by Arnold transform and chaotic sequence. The watermark image is scrambled by Arnold cat map to increases its security and then the chaotic map is used for watermark signal spread in middle band of DCT coefficients of the cover image The chaotic map can be used as pseudo-random generator for digital data hiding, to increase security and robustness .Performance evaluation for robustness and imperceptibility of proposed algorithm has been made using bit error rate (BER), normalized correlation (NC), and peak signal to noise ratio (PSNR) value for different watermark and cover images such as Lena, Girl, Tank images and gain factor .We use a binary logo image and text image as watermark. The experimental results demonstrate that the proposed algorithm achieves higher security and robustness against JPEG compression as well as other attacks such as addition of noise, low pass filtering and cropping attacks compared to other existing algorithm using DCT coefficients. Moreover, to recover watermarks in proposed algorithm, there is no need to original cover image.

Keywords: data hiding, watermarking, DCT, chaotic sequence, arnold transforms

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Authors: Andreas Aceranti, Simonetta Vernocchi, Federica Brondoni, Marco Colorato, Marta Primatesta

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This study entitled “LGBT+ migrants: a cultural and legislative comparison in Canada, Italy and Egypt” suggests an analysis of the living conditions of migrants who are members of the LGBT+ community in Canada, Italy and Egypt. The acronym LGBT+ refers to lesbian, gay, bisexual, transgender and all other gender identities and sexual orientations that do not fit into the male and female binary. This study aims at reflecting on the living conditions of LGBT+ migrants and the relatable difficulties they may face due to the culture and laws of their countries. Migratory flows were examined by providing a definition of "migrant" and the choices that drive a person to migrate elsewhere explained, followed by a focus on the recognition of refugee status related to sexual orientation and gender identity. Furthermore, we will deal with Canada, Italy and Egypt respectively, by analyzing for each country the history and rise of the LGBT+ community, the different laws and especially the migrants’ rights. Finally, the services and associations designed to provide a response to the needs of these people will be analyzed, highlighting the branches which nowadays operate in those areas and the importance of the cultural mediator.

Keywords: LGBTQ+, migrants, international rights, discrimination

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Authors: Mahesh Balan U, Rohith Srinivaas Mohanakrishnan, Venkat Subramanian

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Positive and unlabeled learning (PU) has gained more attention in both academic and industry research literature recently because of its relevance to existing business problems today. Yet, there still seems to be some existing challenges in terms of validating the performance of PU learning, as the actual truth of unlabeled data points is still unknown in contrast to a binary classification where we know the truth. In this study, we propose a novel PU learning technique based on the Pseudo-F measure, where we address this research gap. In this approach, we train the PU model to discriminate the probability distribution of the positive and unlabeled in the validation and spy data. The predicted probabilities of the PU model have a two-fold validation – (a) the predicted probabilities of reliable positives and predicted positives should be from the same distribution; (b) the predicted probabilities of predicted positives and predicted unlabeled should be from a different distribution. We experimented with this approach on a credit marketing case study in one of the world’s biggest fintech platforms and found evidence for benchmarking performance and backtested using historical data. This study contributes to the existing literature on semi-supervised learning.

Keywords: PU learning, semi-supervised learning, pseudo f measure, classification

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Authors: Hexiong Liu

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Powder metallurgy is the optimal method for the consolidation and preparation of W(Mo) alloys, which exhibit excellent application prospects at high temperatures. The properties of W(Mo) alloys are closely related to the sintered density. However, controlling the sintered density and porosity of these alloys is still challenging. In the past, the regulation methods mainly focused on time-consuming and costly trial-and-error experiments. In this study, the sintering data for more than a dozen W(Mo) alloys constituted a small-scale dataset, including both solid and liquid phases of sintering. Furthermore, simple descriptors were used to predict the sintered density of W(Mo) alloys based on the descriptor selection strategy and machine learning method (ML), where the ML algorithm included the least absolute shrinkage and selection operator (Lasso) regression, k-nearest neighbor (k-NN), random forest (RF), and multi-layer perceptron (MLP). The results showed that the interpretable descriptors extracted by our proposed selection strategy and the MLP neural network achieved a high prediction accuracy (R>0.950). By further predicting the sintered density of W(Mo) alloys using different sintering processes, the error between the predicted and experimental values was less than 0.063, confirming the application potential of the model.

Keywords: sintered density, machine learning, interpretable descriptors, W(Mo) alloy

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Authors: Fu Lin

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We consider the worst-case load-shedding problem in electric power networks where a number of transmission lines are to be taken out of service. The objective is to identify a prespecified number of line outages that lead to the maximum interruption of power generation and load at the transmission level, subject to the active power-flow model, the load and generation capacity of the buses, and the phase-angle limit across the transmission lines. For this nonlinear model with binary constraints, we show that all decision variables are separable except for the nonlinear power-flow equations. We develop an iterative decomposition algorithm, which converts the worst-case load shedding problem into a sequence of small subproblems. We show that the subproblems are either convex problems that can be solved efficiently or nonconvex problems that have closed-form solutions. Consequently, our approach is scalable for large networks. Furthermore, we prove the convergence of our algorithm to a critical point, and the objective value is guaranteed to decrease throughout the iterations. Numerical experiments with IEEE test cases demonstrate the effectiveness of the developed approach.

Keywords: load shedding, power system, proximal alternating linearization method, vulnerability analysis

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Authors: Jadisha Cornejo, Helio Pedrini

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Recognition of emotions based on facial expressions has received increasing attention from the scientific community over the last years. Several fields of applications can benefit from facial emotion recognition, such as behavior prediction, interpersonal relations, human-computer interactions, recommendation systems. In this work, we develop and analyze an emotion recognition framework based on facial expressions robust to occlusions through the Weber Local Descriptor (WLD). Initially, the occluded facial expressions are reconstructed following an extension approach of Robust Principal Component Analysis (RPCA). Then, WLD features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Experimental results on three public datasets demonstrated that the WLD representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other approaches available in the literature.

Keywords: emotion recognition, facial expression, occlusion, fiducial landmarks

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Authors: Endrick Barnacin, Jean-Luc Henry, Jack Molinié, Jimmy Nagau, Hélène Delatte, Gérard Lebreton

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Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach.

Keywords: pollen recognition, logistic model tree, expectation-maximization, local binary pattern

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Authors: Liya Shan, Qing Liao, Qinyue Hu, Shantao Jiang, Tao Wang

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In this paper, a Quality of Experience (QoE)-driven cross-layer resource allocation scheme for high traffic service over Open Wireless Network (OWN) downlink is proposed, and the related problem about the users in the whole cell including the users in overlap region of different cells has been solved.A method, in which assess models of the BestEffort service and the no-reference assess algorithm for video service are adopted, to calculate the Mean Opinion Score (MOS) value for high traffic service has been introduced. The cross-layer architecture considers the parameters in application layer, media access control layer and physical layer jointly. Based on this architecture and the MOS value, the Binary Constrained Particle Swarm Optimization (B_CPSO) algorithm is used to solve the cross-layer resource allocation problem. In addition,simulationresults show that the proposed scheme significantly outperforms other schemes in terms of maximizing average users’ MOS value for the whole system as well as maintaining fairness among users.

Keywords: high traffic service, cross-layer resource allocation, QoE, B_CPSO, OWN

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Authors: Sun Woo Lee, Tae Bum Lee, Yoon Hwa Park, Yoo Jeong Kim

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Cervical dentinal hypersensitivity (CDH) affects 8-30% of adults and nearly 85% of perio-treated patients. Various treatment schemes have been applied for treating CDH, among them being fluoride application, laser irradiation, and, recently, bioglass. The purpose of this study was to investigate the influence of bioglass, copper-bromide (Cu-Br) laser irradiation and their combination on dentinal tubule occlusion as a potential dentinal hypersensitivity treatment for CDH. 45 human dentin surfaces were organized into three equal groups: group A received Cu-Br laser only; group B received bioglass only; group C received bioglass followed by Cu-Br laser irradiation. Specimens were evaluated with regard to dentinal tubule occlusion under environmental scanning electron microscope. Treatment modality significantly affected dentinal tubule occlusion (p<0.001). Groups B and C scored higher dentinal tubule occlusion than group A. Binary logistic regression showed that bioglass application significantly (p<0.001) contributed to dentinal tubule occlusion, compared with other variables. Under the conditions used herein and within the limitations of this study, bioglass application, alone or combined with Cu-Br laser irradiation, is a superior method for producing dentinal tubule occlusion, and may lead to an effective treatment modality for CDH.

Keywords: bioglass, Cu-Br laser, cervical dentinal hypersensitivity, dentinal tubule occlusion

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Authors: M. Sneha Reddy, M. Arun Kumar, V. Kameswara Rao

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Chromites are binary transition metal oxides with a general formula of ACr₂O₄, where A = Mn²⁺, Fe²⁺, Co²⁺, Ni²⁺, and Cu²⁺. Chromites have a normal-type spinel structure with interesting applications in the areas of applied physics, material sciences, and geophysics. They have attracted great consideration because of their unique physicochemical properties and tremendous technological applications in nanodevices, sensor elements, and high-temperature ceramics with useful optical properties. Copper chromite is one of the most efficient spinel oxides, having pronounced commercial application as a catalyst in various chemical reactions like oxidation, hydrogenation, alkylation, dehydrogenation, decomposition of organic compounds, and hydrogen production. Apart from its usage in chemical industries, CuCr₂O₄ finds its major application as a burn rate modifier in solid propellant processing for space launch vehicles globally. Herein we synthesized the nanoparticles of copper chromite using the co-precipitation method. The synthesized nanoparticles were characterized by XRD, TEM, SEM, BET, and TG-DTA. The synthesized nanoparticles of copper chromites were used as a catalyst for the thermal decomposition of various high-energy materials.

Keywords: copper chromite, coprecipitation method, high energy materials, catalytic thermal decomposition

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Authors: Retius Chifurira

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Logistic regression model is the most used regression model to predict meteorological drought probabilities. When the dependent variable is extreme, the logistic model fails to adequately capture drought probabilities. In order to adequately predict drought probabilities, we use the generalized linear model (GLM) with the quantile function of the generalized extreme value distribution (GEVD) as the link function. The method maximum likelihood estimation is used to estimate the parameters of the generalized extreme value (GEV) regression model. We compare the performance of the logistic and the GEV regression models in predicting drought probabilities for Zimbabwe. The performance of the regression models are assessed using the goodness-of-fit tests, namely; relative root mean square error (RRMSE) and relative mean absolute error (RMAE). Results show that the GEV regression model performs better than the logistic model, thereby providing a good alternative candidate for predicting drought probabilities. This paper provides the first application of GLM derived from extreme value theory to predict drought probabilities for a drought-prone country such as Zimbabwe.

Keywords: generalized extreme value distribution, general linear model, mean annual rainfall, meteorological drought probabilities

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Authors: H. Hammoudi, S. Bendenia, I. Batonneau-Gener, J. Comparot, K. Marouf-Khelifa, A. Khelifa

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X zeolites were prepared by ion-exchange with Cu2+ and/or Zn2+ cations, at different concentrations of the exchange solution, and characterised by thermal analysis and nitrogen adsorption. The acidity of the samples was investigated by pyridine adsorption–desorption followed by in situ Fourier transform infrared (FTIR) spectroscopy. Desorption was carried out at 150, 250 and 350 °C. The objective is to estimate the nature and concentration of acid sites. A comparison between the binary (Cu(x)X, Zn(x)X) and ternary (CuZn(x)X) exchanges was also established (x = level of exchange) through the Cu(43)X, Zn(48)X and CuZn(50)X samples. Lewis acidity decreases overall with desorption temperature and the level of exchange. As the latter increases, there is a conversion of some Lewis sites into those of Brønsted during thermal treatment. In return, the concentration of Brønsted sites increases with the degree of exchange. The Brønsted acidity of CuZn(50)X at 350 °C is more important than the sum of those of Cu(43)X and Zn(48)X. The found values were 73, 32 and 15 μmol g-1, respectively. Besides, the concentration of Brønsted sites for CuZn(50)X increases with desorption temperature. These features indicate the presence of a synergistic effect amplifying the strength of these sites when Cu2+ and Zn2+ cations compete for the occupancy of sites distributed inside zeolitic cavities.

Keywords: acidity, adsorption, pyridine, zeolites

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Authors: Diriba Dereje

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Background: Cardiac and renal disease are the most prevalent chronic non-communicable diseases (CNCD) affecting the community in a significant manner. The best and recommended method in halting CNCD is by working on prevention as early as possible. This is only possible if early surrogate markers are identified. As part of the stated solution, this study will identify an association between microalbuminuria (an early surrogate marker of renal and cardiac disease) and lung function test among adult in the community. Objective: The main aim of this study was to assess an association between microalbuminuria (an early surrogate marker of renal and cardiac disease) and lung function test among adult in the community. Methodology: Community based cross sectional study was conducted among 384 adult in Jimma town. A systematic sampling technique was used in selecting participants to the study. In searching for the possible association, binary and multivariate logistic regression and t-test was conducted. Finally, the association between microalbuminuria and lung function test was well stated in the form of figures and written description. Result and Conclusion: A significant association was found between microalbuminuria and different lung function test parameters.

Keywords: microalbuminuria, lung function, association, test

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Authors: M. A. Sedghamiz, S. Raeissi

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This study presents three different approaches to estimate bubble point pressures for the binary system of CO2 and ethyl palmitate fatty acid ethyl ester. The first method involves the Peng-Robinson (PR) Equation of State (EoS) with the conventional mixing rule of Van der Waals. The second approach involves the PR EOS together with the Wong Sandler (WS) mixing rule, coupled with the Uniquac Ge model. In order to model the bubble point pressures with this approach, the volume and area parameter for ethyl palmitate were estimated by the Hansen group contribution method. The last method involved the Peng-Robinson, combined with the Wong-Sandler Method, but using NRTL as the GE model. Results using the Van der Waals mixing rule clearly indicated that this method has the largest errors among all three methods, with errors in the range of 3.96–6.22 %. The Pr-Ws-Uniquac method exhibited small errors, with average absolute deviations between 0.95 to 1.97 percent. The Pr-Ws-Nrtl method led to the least errors where average absolute deviations ranged between 0.65-1.7%.

Keywords: bubble pressure, Gibbs excess energy model, mixing rule, CO2 solubility, ethyl palmitate

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Authors: Abm Mostafa, Rumbidzai Mukono, Peijie Wang

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Using 285 respondents from two microfinance institutions, this research aims to assess the impact of microfinance on women’s economic empowerment in Bangladesh. Empirical measures of economic empowerment used in this paper are underpinned by a bargaining theory of household. Questionnaire is used for data collection following purposive sampling. Descriptive statistics, chi-square test, Kruskal-Wallis test, binary, and ordinal logistic regressions are deployed for data analysis. The findings of this study show that around three quarters of respondents have increased household income. They have increased their savings overwhelmingly; nonetheless, many of them are found to have a very small amount of savings. Still, more than half of the respondents are reported to have increased their savings when it is checked against at least 500 BDT per month. On the contrary, the percentage of women is moderate in terms of increasing control over finances. Empirical findings demonstrate the evidence of a relationship between the amount of loan and women’s household income, their savings, and control over finances. Nonetheless, no relationship is found in women’s areas. This study infers that women’s access to financial resources is fundamental to empower them in economic dimension.

Keywords: microfinance, women, economic, empowerment, Bangladesh

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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: Xu Han, Shanxiong Chen, Shiyu Zhu, Xiaoyu Lin, Fujia Zhao, Dingwang Wang

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Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books.

Keywords: CCS concepts, computing methodologies, interest point, salient region detections, image segmentation

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Authors: Zhen Sun

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The study adopts the method of visual social semiotics to analyze a sample of fashion photos that were recently published in Chinese fashion magazines that target towards both male and female readers. It identifies a new visual trend in fashion photography, which is characterized by two features. First, the photos represent young, confident, and stylish female models with lower-class sloppy old men. The visual inharmony between the sexually desirable women and the aged men has suggested an impossibly accomplished sexuality and eroticism. Though the women are still under the male gaze, they are depicted as unreachable objects of voyeurism other than sexual objects subordinated to men. Second, the represented people are usually put in the backdrop of tasteless or vulgar Chinese town life, which is congruent with the images of men but makes the modern city girls out of place. The photographers intentionally contrast the images of women with that of men and with the background, which implies an imaginary binary division of modern Orientalism and the photographers’ self-orientalization strategy. Under the theoretical umbrella of neoliberal postfeminism, this study defines a new kind of gynocentric stereotype in Chinese fashion photography, which challenges the previous observations on gender portrayals in fashion magazines.

Keywords: fashion photography, gynocentrism, neoliberal postfeminism, self-orientalization

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Authors: Ajay Kumar

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Using data from census of India, sample registration system and national family health survey (NFHS-3), this paper traces spatial pattern, trends and the factors which have played their role differently in fertility transition in Uttar Pradesh and Tamil Nadu. For the purpose spatial variation analysis, trend line and binary logistic regression analysis has been carried out. There exist considerable regional disparities in terms of fertility decline in northern and southern states. The pace of fertility decline has been faster in southern and coastal regions, and at a slow pace in backward northern state. In Tamil Nadu fertility declined substantially among the women of lower and higher age groups in comparison to Uttar Pradesh characterized by low literacy, low female age at marriage, poor health infrastructure and low status of women. The Study shows that Fertility rates have been higher among the most vulnerable and deprived sections of the society like Illiterate women, women belong to scheduled caste, scheduled tribe and women residing in rural areas.

Keywords: age specific fertility rate, fertility transition, replacement level, total fertility rate

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Authors: Sidra Naeem, Ayesha Naeem, Sahar Rahim, Nadia Nawaz Qadri

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Citrus greening is a bacterial disease that causes considerable damage to citrus fruits worldwide. Efficient method for this disease detection must be carried out to minimize the production loss. This paper presents a pattern recognition system that comprises three stages for the detection of citrus greening from Orange leaves: segmentation, feature extraction and classification. Image segmentation is accomplished by adaptive thresholding. The feature extraction stage comprises of three visual descriptors i.e. shape, color and texture. From shape feature we have used asymmetry index, from color feature we have used histogram of Cb component from YCbCr domain and from texture feature we have used local binary pattern. Classification was done using support vector machines and k nearest neighbors. The best performances of the system is Accuracy = 88.02% and AUROC = 90.1% was achieved by automatic segmented images. Our experiments validate that: (1). Segmentation is an imperative preprocessing step for computer assisted diagnosis of citrus greening, and (2). The combination of shape, color and texture features form a complementary set towards the identification of citrus greening disease.

Keywords: citrus greening, pattern recognition, feature extraction, classification

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Authors: K. K. Soundra Pandian, Bhupendra Gupta

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In the modern era, as the application data’s are massive and complex, it needs to be secured from the adversary attack. In this context, a non-recursive key based integrated spritz stream cipher with the circulant hash function using delayed feedback with carry shift register (d-FCSR) is proposed in this paper. The novelty of this proposed stream cipher algorithm is to engender the improved keystream using d-FCSR. The proposed algorithm is coded using Verilog HDL to produce dynamic binary key stream and implemented on commercially available FPGA device Virtex 5 xc5vlx110t-2ff1136. The implementation of stream cipher using d-FCSR on the FPGA device operates at a maximum frequency of 60.62 MHz. It achieved the data throughput of 492 Mbps and improved in terms of efficiency (throughput/area) compared to existing techniques. This paper also briefs the cryptanalysis of proposed circulant hash value based spritz stream cipher using d-FCSR is against the adversary attack on a hardware platform for the hardware based cryptography applications.

Keywords: cryptography, circulant function, field programmable gated array, hash value, spritz stream cipher

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Authors: P. Ajmal Koya, Tariq Ahmad Wagay, K. Ismail

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One of the fundamental property of surfactant molecules are their ability to aggregate in water or binary mixtures of water and organic solvents as an effort to minimize their unfavourable interaction with the medium. In this work, influence two co-solvents (ethylene glycol (EG) and propylene glycol (PG)) on the aggregation properties of a cationic gemini surfactant, butanediyl-1,4-bis(tetradecyldimethylammonium bromide) (14–4–14), has been studied by conductance and steady state fluorescence at 298 K. The weight percentage of two co-solvents varied in between 0 and 50 % at an interval of 5 % up to 20 % and then 10 % up to 50 %. It was found that micellization process is delayed by the inclusion of both the co-solvents; consequently, a progressive increase was observed in critical micelle concentration (cmc) and Gibbs free energy of micellization (∆G0m), whereas a rough increase was observed in the values of degree of counter ion dissociation (α) and a decrease was obtained in values of average aggregation number (Nagg) and Stern-Volmer constant (KSV). At low weight percentage (up to 15 %) of co-solvents, 14–4–14 geminis were found to be almost equally prone to micellization both in EG–water (EG–WR) and in PG–water (PG–WR) mixed media while at high weight percentages they are more prone to micellization in EG–WR than in PG–WR mixed media.

Keywords: aggregation number, gemini surfactant, micellization, non aqueous solvent

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Authors: Najwa Mimouni, Omar Rahli, Rachid Bennacer, Salah Chikh

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In the present work 2D and 3D numerical simulations of double diffusion natural convection in an elongated enclosure filled with a binary fluid saturating a porous medium are carried out. In the formulation of the problem, the Boussinesq approximation is considered and cross Neumann boundary conditions are specified for heat and mass walls conditions. The numerical method is based on the control volume approach with the third order QUICK scheme. Full approximation storage (FAS) with full multigrid (FMG) method is used to solve the problem. For the explored large range of the controlling parameters, we clearly evidenced that the increase in the depth of the cavity i.e. the lateral aspect ratio has an important effect on the flow patterns. The 2D perfect parallel flows obtained for a small lateral aspect ratio are drastically destabilized by increasing the cavity lateral dimension. This yields a 3D fluid motion with a much more complicated flow pattern and the classically studied 2D parallel flows are impossible.

Keywords: bifurcation, natural convection, heat and mass transfer, parallel flow, porous media

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Authors: Ildar Akhmadullin, Mayank Tyagi

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The subject of this paper is the design analysis of a single well power production unit from low enthalpy geothermal resources. A complexity of the project is defined by a low temperature heat source that usually makes such projects economically disadvantageous using the conventional binary power plant approach. A proposed new compact design is numerically analyzed. This paper describes a thermodynamic analysis, a working fluid choice, downhole heat exchanger (DHE) and turbine calculation results. The unit is able to produce 321 kW of electric power from a low enthalpy underground heat source utilizing n-Pentane as a working fluid. A geo-pressured reservoir located in Vermilion Parish, Louisiana, USA is selected as a prototype for the field application. With a brine temperature of 126℃, the optimal length of DHE is determined as 304.8 m (1000ft). All units (pipes, turbine, and pumps) are chosen from commercially available parts to bring this project closer to the industry requirements. Numerical calculations are based on petroleum industry standards. The project is sponsored by the Department of Energy of the US.

Keywords: downhole heat exchangers, geothermal power generation, organic rankine cycle, refrigerants, working fluids

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Authors: Radhika Ranjan Roy

Abstract:

Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classification tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets.

Keywords: Mahalanobis distance, machine learning, deep learning, NS-KDD, local intrinsic dimensionality, chi-square, positive semi-definite, area under the curve

Procedia PDF Downloads 51

Authors: Mengyue Gong, Xinyi Li, Amarjeet Bassi

Abstract:

Harvesting by-products while recovering biodiesel is considered a potentially valuable approach to increase the market feasibility of microalgae industry. Lutein is a possible by-product from microalgae that promotes eye health. The extraction efficiency and the expensive drying process of wet algae represent the major challenges for the utilization of microalgae biomass as a feedstock for lipids, proteins, and carotenoids. A wet extraction method was developed to extract lipids and lutein from microalga Chlorella vulgaris. To evaluate different solvent (mixtures) for the extraction, a quantitative analysis was established based on the polarity of solvents using Nile Red as the polarity (ETN) indicator. By the choice of binary solvent system then adding proper amount of water to achieve phase separation, lipids and lutein can be extracted simultaneously. Some other parameters for lipids and lutein production were also studied including saponification time, temperature, choice of alkali, and pre-treatment methods. The extraction efficiency with wet algae was compared with dried algae and shown better pigment recovery. The results indicated that the product pattern in each extracted phase was polarity dependent. Lutein and β-carotene were the main carotenoids extracted with ethanol while lipids come out with hexane.

Keywords: biodiesel, Chlorella vulgaris, extraction, lutein

Procedia PDF Downloads 316