Search results for: ecological binary data

Authors: A. Brener, B. Ismailov, G. Berdalieva

Abstract:

In the paper we submit the modification of kinetic Smoluchowski equation for binary aggregation applying to systems with chemical reactions of first and second orders in which the main product is insoluble. The goal of this work is to create theoretical foundation and engineering procedures for calculating the chemical apparatuses in the conditions of joint course of chemical reactions and processes of aggregation of insoluble dispersed phases which are formed in working zones of the reactor.

Keywords: binary aggregation, clusters, chemical reactions, insoluble phases

Procedia PDF Downloads 277

Authors: Dong Won Kim, Hye Ji Kim, Hyun Young Jung

Abstract:

Improved energy storage is inevitably needed to improve energy efficiency and to be environmentally friendly to chemical processes. Ionic liquids (ILs) can play a crucial role in addressing these needs due to inherent adjustable properties including low volatility, low flammability, inherent conductivity, wide liquid range, broad electrochemical window, high thermal stability, and recyclability. Here, binary mixtures of ILs were prepared with fumed silica nanoparticles and characterized to obtain ILs with conductivity and electrochemical properties optimized for use in energy storage devices. The solutes were prepared by varying the size and the weight percent concentration of the nanoparticles and made up 10 % of the binary mixture by weight. We report on the physical and electrochemical properties of the individual ILs and their binary mixtures.

Keywords: ionic liquid, silica nanoparticle, energy storage, electrochemical properties

Procedia PDF Downloads 176

Authors: P. Shrivastava, A. Shukla, K. Verma, S. Rungta

Abstract:

Parkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Gait serve as a primary outcome measure for studies aiming at early recognition of disease. Using gait techniques, this paper implements efficient binary bat algorithm for an early detection of Parkinson's disease by selecting optimal features required for classification of affected patients from others. The data of 166 people, both fit and affected is collected and optimal feature selection is done using PSO and Bat algorithm. The reduced dataset is then classified using neural network. The experiments indicate that binary bat algorithm outperforms traditional PSO and genetic algorithm and gives a fairly good recognition rate even with the reduced dataset.

Keywords: parkinson, gait, feature selection, bat algorithm

Procedia PDF Downloads 509

Authors: Raquel C. A. G. Mota, Livia R. Menezes, Emerson O. da Silva

Abstract:

Both silver nanoparticles and titanium dioxide have been extensively used in tissue engineering since they’ve been approved by the Food and Drug Administration (FDA), and present a bactericide effect when added to a polymeric matrix. In this work, the focus is on fabricating binary systems with both nanoparticles so that the synergistic effect can be investigated. The systems were tested by Nuclear Magnetic Resonance (NMR), Thermogravimetric Analysis (TGA), Fourier-Transformed Infrared (FTIR), and Differential Scanning Calorimetry (DSC), and X-ray Diffraction (XRD), and had both their bioactivity and bactericide effect tested. The binary systems presented different properties than the individual systems, enhancing both the thermal and biological properties as was to be expected. The crystallinity was also affected, as indicated by the finding of the DSC and XDR techniques, and the NMR showed a good dispersion of both nanoparticles in the polymer matrix. These findings indicate the potential of combining TiO₂ and silver nanoparticles in biomedicine.

Keywords: metallic nanoparticles, nanotechnology, polymer nanocomposites, polymer science

Procedia PDF Downloads 105

Authors: Kibret Berhanu Getinet

Abstract:

Off-farm income diversification by farm rural households has gained the attention of researchers and policymakers due to the fact that agriculture failed to meet the needs of people in developing countries like Ethiopia. The objective of this study was to investigate income diversification strategies into off-farm activities among rural households in Hawassa Zuria Woreda, Sidama National Regional State, Ethiopia. The study used primary and secondary data sources for the primary data collection questionnaire employed as a data collection instrument. A multistage sampling technique was used to collect data from a total of 197 sample households from four kebeles of the study area. Descriptive statistics, as well as econometrics methods of data analysis, were employed. The descriptive statistics result indicates that the majority of sample rural households (68.53 %) have engaged in off-farm income diversification activities while the remaining 31.47% of households did not participate in the diversification in the study area. The choice of participants among the strategies indicates that 6.60% of respondents participated in off-farm wage employment, 30.46% participated in off-farm self-employment, and about 31.47% of them participated in both off-farm wage employment. The study revealed that the share of off-farm income in total annual earnings of households was about 48.457%, and thus, the off-farm diversification significantly contributes to the rural household income. Moreover, binary and multinomial logistic regression models were employed to identify factors that affect the participation and the choices of the off-farm income diversification strategies, respectively. The binary logit model result indicated that agro-ecological zone, education status of the households, available technical skills of the household, household saving, total livestock owned by the households, access to electricity, road access and being married of household head were significant and positively affected the chance of diversification in off-farm activities while the on-farm income of households is negatively affected the chance of diversification. Similarly, the multinomial logistic regression model estimate revealed that agroecological zone, on-farm income, available technical skills, household savings, and access to electricity are positively related and significantly influenced the household’s choice of employment into off-farm wage employment. The off-farm self-employment diversification choice is significantly influenced by on-farm income, available technical skills, household savings, total livestock owned, and access to electricity. Moreover, the result showed that the factors that affect the choice of farm households to engage in both off-farm wage and self-employment are ecological zone, education status, on-farm income, available technical skills, household own saving, market access, total livestock owned, access to electricity and road access. Thus, due attention should be given to addressing the demographic, socio-economic, and institutional constraints to strengthen off-farm income diversification strategies to improve the income of rural households.

Keywords: off-farm, incoem, diversification, logit model

Procedia PDF Downloads 14

Authors: S. Chami, J. Chauvin, T. Demarest, Stan Ng, M. Straus, W. Jahner

Abstract:

Biometrics tools such as fingerprint and iris are widely used in industry to protect critical assets. However, their vulnerability and lack of robustness raise several worries about the protection of highly critical assets. Biometrics based on Electrocardiographic (ECG) signals is a robust identification tool. However, most of the state-of-the-art techniques have worked on clinical signals, which are of high quality and less noisy, extracted from wearable devices like a smartwatch. In this paper, we are presenting a complete machine learning pipeline that identifies people using ECG extracted from an off-person device. An off-person device is a wearable device that is not used in a medical context such as a smartwatch. In addition, one of the main challenges of ECG biometrics is the variability of the ECG of different persons and different situations. To solve this issue, we proposed two different approaches: per person classifier, and one-for-all classifier. The first approach suggests making binary classifier to distinguish one person from others. The second approach suggests a multi-classifier that distinguishes the selected set of individuals from non-selected individuals (others). The preliminary results, the binary classifier obtained a performance 90% in terms of accuracy within a balanced data. The second approach has reported a log loss of 0.05 as a multi-class score.

Keywords: biometrics, electrocardiographic, machine learning, signals processing

Procedia PDF Downloads 111

Authors: Elmira Ghanbari, Jan Van Esch, Stephen J. Picken, Sahil Aggarwal

Abstract:

Low-molecular-weight organogelators form gels by self-assembly into the crystalline network which immobilizes the organic solvent. For single bisamide organogelator systems, the effect of the molecular structure on the molecular interaction and their self-assembly behavior has been explored. The spatial arrangement of bisamide molecules in the gel-state is driven by a combination of hydrogen bonding and Van der Waals interactions. The hydrogen-bonding pattern between the amide groups of bisamide molecules is regulated by the number of methylene spacers; the even number of methylene spacers between two amide groups, in even-spaced bisamides, leads to the antiparallel position of amide groups within a molecule. An even-spaced bisamide molecule with antiparallel amide groups can make two pairs of hydrogen bonding with the molecules on the same plane. The odd-spaced bisamide with a parallel directionality of amide groups can form four independent hydrogen bonds with four other bisamide molecules on different planes. The arrangement of bisamide molecules in the crystalline state and the interaction of these molecules depends on the molecular structure, particularly the parity of the spacer length between the amide groups in the bisamide molecule. In this study, the directionality of amide groups has been exploited as a structural characteristic to affect the arrangement of molecules in the crystalline state and produce different binary bisamide gelators with different degrees of crystallinities. Single odd- and even-spaced single bisamides were synthesized and blended to produce binary bisamide organogelators to be characterized in order to understand the effect of the different directionality of amide groups on the molecular interaction in the crystalline state. The pattern of molecular interactions between these blended molecules, mixing or phase separation, has been monitored via differential scanning calorimetry (DSC) and crystallography techniques; X-ray powder diffraction (XRD) and Small-angle X-ray scattering (SAXS). The formation of lamellar structures for odd- and even-spaced bisamide gelators was confirmed by using SAXS and XRD techniques. DSC results have shown that binary bisamide organogelators with different parity of methylene spacers (odd-even binary blends) have a higher tendency for phase separation compared to the binary bisamides with the same parity (odd-odd or even-even binary blends). Phase separation in binary odd-even bisamides was confirmed by the presence of individual (100) reflections of odd and even lamellar structures. The structural characteristic of bisamide organogelators, the parity of spacer length in binary systems, is a promising tool to control the arrangement of molecules and their crystalline structure.

Keywords: binary bisamide organogelators, crystalline structure, phase separation, self-assembly behavior

Procedia PDF Downloads 156

Authors: Ma. Gracia Corazon Cayanan, Kai Yuen Cheong, Li Sha

Abstract:

Training a language model for a minority language has been a challenging task. The lack of available corpora to train and fine-tune state-of-the-art language models is still a challenge in the area of Natural Language Processing (NLP). Moreover, the need for high computational resources and bulk data limit the attainment of this task. In this paper, we presented the following contributions: (1) we introduce and used a translation pair set of Tagalog and English (TL-EN) in pre-training a language model to a minority language resource; (2) we fine-tuned and evaluated top-ranking and pre-trained semantic textual similarity binary task (STSB) models, to both TL-EN and STS dataset pairs. (3) then, we reduced the size of the model to offset the need for high computational resources. Based on our results, the models that were pre-trained to translation pairs and STS pairs can perform well for STSB task. Also, having it reduced to a smaller dimension has no negative effect on the performance but rather has a notable increase on the similarity scores. Moreover, models that were pre-trained to a similar dataset have a tremendous effect on the model’s performance scores.

Keywords: semantic matching, semantic textual similarity binary task, low resource minority language, fine-tuning, dimension reduction, transformer models

Procedia PDF Downloads 171

Authors: Filippo Portera

Abstract:

Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we present several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vextor Classification and a regression net for Multylayer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better.

Keywords: loss, binary-classification, MLP, weights, regression

Procedia PDF Downloads 58

Authors: Javad Rahimipour Anaraki, Saeed Samet, Mahdi Eftekhari, Chang Wook Ahn

Abstract:

Feature selection and attribute reduction are crucial problems, and widely used techniques in the field of machine learning, data mining and pattern recognition to overcome the well-known phenomenon of the Curse of Dimensionality. This paper presents a feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset. It consists of two components: 1) a measure for feature subset evaluation, and 2) a search strategy. For the evaluation measure, we have employed the fuzzy-rough dependency degree (FRFDD) of the lower approximation-based fuzzy-rough feature selection (L-FRFS) due to its effectiveness in feature selection. As for the search strategy, a modified version of a binary shuffled frog leaping algorithm is proposed (B-SFLA). The proposed feature selection method is obtained by hybridizing the B-SFLA with the FRDD. Nine classifiers have been employed to compare the proposed approach with several existing methods over twenty two datasets, including nine high dimensional and large ones, from the UCI repository. The experimental results demonstrate that the B-SFLA approach significantly outperforms other metaheuristic methods in terms of the number of selected features and the classification accuracy.

Keywords: binary shuffled frog leaping algorithm, feature selection, fuzzy-rough set, minimal reduct

Procedia PDF Downloads 183

Authors: M. El Hajji, A. Hallaoui, L. Bazzi, A. Benlhachemi, O. Jbara, A. Tara, B. Bakiz, L. Bazzi, M. Hilali

Abstract:

The objective of this study was the development of zinc-copper binary oxide "Cu2O-ZnO" thin films by the electrochemical method "cathodic electrodeposition" and their uses for the degradation of a basic dye "Congo Red" by direct anodic oxidation. The anode materials synthesized were characterized by X-ray diffraction "XRD" and by scanning electron microscopy "SEM" coupled to EDS.

Keywords: Cu2O-ZnO thin films, cathodic electrodeposition, electrodegradation, Congo Red, BDD

Procedia PDF Downloads 317

Authors: Georgios Charizanos, Haydar Demirhan, Duygu Icen

Abstract:

Two of the most impactful issues in classical logistic regression are class imbalance and complete separation. These can result in model predictions heavily leaning towards the imbalanced class on the binary response variable or over-fitting issues. Fuzzy methodology offers key solutions for handling these problems. However, most studies propose the transformation of the binary responses into a continuous format limited within [0,1]. This is called the possibilistic approach within fuzzy logistic regression. Following this approach is more aligned with straightforward regression since a logit-link function is not utilized, and fuzzy probabilities are not generated. In contrast, we propose a method of fuzzifying binary response variables that allows for the use of the logit-link function; hence, a probabilistic fuzzy logistic regression model with the Monte Carlo method. The fuzzy probabilities are then classified by selecting a fuzzy threshold. Different combinations of fuzzy and crisp input, output, and coefficients are explored, aiming to understand which of these perform better under different conditions of imbalance and separation. We conduct numerical experiments using both synthetic and real datasets to demonstrate the performance of the fuzzy logistic regression framework against seven crisp machine learning methods. The proposed framework shows better performance irrespective of the degree of imbalance and presence of separation in the data, while the considered machine learning methods are significantly impacted.

Keywords: fuzzy logistic regression, fuzzy, logistic, machine learning

Procedia PDF Downloads 33

Authors: Cromwell F. Gopo, Joy L. Picar

Abstract:

This study aimed to predict the employability of senior high school graduates for S.Y. 2018- 2019 in the Davao del Norte Division through quantitative research design using the descriptive status and predictive approaches among the indicated parameters, namely gender, school type, academics, academic award recipient, skills, values, and strand. The respondents of the study were the 33 secondary schools offering senior high school programs identified through simple random sampling, which resulted in 1,530 cases of graduates’ secondary data, which were analyzed using frequency, percentage, mean, standard deviation, and binary logistic regression. Results showed that the majority of the senior high school graduates who come from large schools were females. Further, less than half of these graduates received any academic award in any semester. In general, the graduates’ performance in academics, skills, and values were proficient. Moreover, less than half of the graduates were not employed. Then, those who were employed were either contractual, casual, or part-time workers dominated by GAS graduates. Further, the predictors of employability were gender and the Information and Communications Technology (ICT) strand, while the remaining variables did not add significantly to the model. The null hypothesis had been rejected as the coefficients of the predictors in the binary logistic regression equation did not take the value of 0. After utilizing the model, it was concluded that Technical-Vocational-Livelihood (TVL) graduates except ICT had greater estimates of employability.

Keywords: employability, senior high school graduates, Davao del Norte, Philippines

Procedia PDF Downloads 105

Authors: Chang Liu, Nuria Fiol, Isabel Villaescusa, Jordi Poch

Abstract:

Over the last decades, biosorption has been an alternative to costly wastewaters treatment for metal removal. Most of the literature on metal biosorption was devoted to studying of single metal ions but nowadays studies on multi-components biosorption are booming. Hexavalent chromium is usually found in mixtures with divalent metal ions in industries wastewaters. However, studies on the simultaneous removal of Cr(VI) and divalent metals are hardly found and the cooperative or competitive mechanism governing each metal ions sorption is still unclear. In this work, simultaneous sorption of Cr(VI) and Cu(II) from their binary mixtures by using sweet potato vine (SPV) was investigated. Sweet potato is one of the four major grain crops in China. Each year about 2000 tons of SPV are generated as by-products. SPV could be a low-cost biosorbent for metal ions due to its rich in cellulose and lignin. In this work, the sorption of Cr(VI) and Cu(II) from their binary mixtures solutions was studied by using SPV sorbent. Equilibrium studies were carried out in binary mixtures in which Cr(VI) and Cu(II) concentration was both varied between 0.1 mM and 0.3 mM, Cr(VI) and Cu(II) single solutions were also prepared as comparison. All the experiments were performed at pH 3±0.05 under 30±2°C for 7 days to make sure sorption achieved equilibrium. Results showed that (i) chromium was partially (10.93%-42.04%) eliminated under studied conditions through reduction and sorption of hexavalent and trivalent forms. The presence of Cu(II) exerts a synergistic effect on the overall sorption process in all the cases of the 0.1-0.3 mM binary mixtures concentration range. (ii) Cr(VI) removal by SPV is favoured by the presence of Cu(II) in solution, because more protons needed for Cr(VI) reduction are available due to Cu(II)-proton competition; however sorption of the formed Cr(III) is unfavoured as a result of the competition between Cr(III) and Cu(II) for protons and sorbent active sites. (iii) Copper was partially (9.26%-13.91%) sorbed onto SPV under studied conditions. The presence of Cr(VI) in binary mixtures also exerts a synergistic effect on the Cu(II) removal in all the cases of the 0.1-0.3 mM binary mixtures concentration range. The results of the present work indicate that sweet potato vine can be successfully employed for the simultaneously removal of Cr(VI) and Cu(II) in binary mixtures, taking advantage of the synergistic effect provoked by one of the metal ion to each other, even though the acquisition of higher removal yields has to be further investigated. Acknowledgements—This work has been financially supported by Ministry of Human Resources and Social Security of PRC (Anhui15), Education Department of Anhui Province (KJ2016A270) and Anhui Normal University (2015rcpy33, 2014bsqdjj53).

Keywords: sweet potato vine, chromium reduction, divalent metal, synergistic sorption

Procedia PDF Downloads 140

Authors: Khelifi Mustapha, A. Moulay lakhdar, I. Elawady

Abstract:

We will study the unequal error protection for VQ image. We have used the Reed Solomon (RS) Codes as Channel coding because they offer better performance in terms of channel error correction over a binary output channel. One such channel (binary input and output) should be considered if it is the case of the application layer, because it includes all the features of the layers located below and on the what it is usually not feasible to make changes.

Keywords: vector quantization, channel error correction, Reed-Solomon channel coding, application

Procedia PDF Downloads 325

Authors: Végh Ádám, Mekler Csaba, Dezső András, Szabó Dávid, Stomp Dávid, Kaptay György

Abstract:

Grain boundary segregation transition (GBST) has been calculated by a thermodynamic model in binary alloys. The method is used on cementite (Fe3C) segregation in base-centered cubic (ferrite) iron (Fe) in the Fe-C binary system. The GBST line is shown in the Fe3C lacking part of the phase diagram with high solvent (Fe) concentration. At a lower solute content (C) or at higher temperature the grain boundary is composed mostly of the solvent atoms (Fe). On higher concentration compared to the GBST line or at lower temperature a phase transformation occurs at the grain boundary, the latter mostly composed of the associates (Fe3C). These low-segregation and high-segregation states are first order interfacial phase transitions of the grain boundary and can be transformed into each other reversibly. These occur when the GBST line is crossed by changing the bulk composition or temperature.

Keywords: GBST, cementite, segregation, Fe-C alloy

Procedia PDF Downloads 551

Authors: Nikolay Konukhov

Abstract:

This paper to report on a new analytic model for predicting microcontact resistance and the design, fabrication, and testing of microelectromechanical systems (MEMS) metal contact switches with sputtered bimetallic (i.e., gold (Au)-on-Au-platinum (Pt), (Au-on-Au-(6.3at%)Pt)), binary alloy (i.e., Au-palladium (Pd), (Au-(3.7at%)Pd)), and ternary alloy (i.e., Au-Pt-copper (Cu), (Au-(5.0at%)Pt-(0.5at%)Cu)) electric contacts. The microswitches with bimetallic and binary alloy contacts resulted in contact resistance values between 1–2

Keywords: alloys, electric contacts, microelectromechanical systems (MEMS), microswitch

Procedia PDF Downloads 135

Authors: Annalakshmi G., Sakthivel Murugan S.

Abstract:

This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

Procedia PDF Downloads 132

Authors: Kassoul Ilyes

Abstract:

The field of error correcting codes has been revolutionized by the introduction of iteratively decoded codes. Among these, LDPC codes are now a preferred solution thanks to their remarkable performance and low complexity. The binary version of LDPC codes showed even better performance, although it’s decoding introduced greater complexity. This thesis studies the performance of binary LDPC codes using simplified weighted decisions. Information is transported between a transmitter and a receiver by digital transmission systems, either by propagating over a radio channel or also by using a transmission medium such as the transmission line. The purpose of the transmission system is then to carry the information from the transmitter to the receiver as reliably as possible. These codes have not generated enough interest within the coding theory community. This forgetfulness will last until the introduction of Turbo-codes and the iterative principle. Then it was proposed to adopt Pearl's Belief Propagation (BP) algorithm for decoding these codes. Subsequently, Luby introduced irregular LDPC codes characterized by a parity check matrix. And finally, we study simplifications on binary LDPC codes. Thus, we propose a method to make the exact calculation of the APP simpler. This method leads to simplifying the implementation of the system.

Keywords: LDPC, parity check matrix, 5G, BER, SNR

Procedia PDF Downloads 124

Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

Abstract:

The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection

Procedia PDF Downloads 130

Authors: Hanjie Xie, Raphael Semiat, Ziyi Zhong

Abstract:

It is well known that many oxidation reactions in nature are closely related to the origin and life activities. One of the features of these natural reactions is that they can proceed under mild conditions employing the oxidant of molecular oxygen (O₂) in the air and enzymes as catalysts. Catalysis is also a necessary part of life for human beings, as many chemical and pharmaceutical industrial processes need to use catalysts. However, most heterogeneous catalytic reactions must be run at high operational reaction temperatures and pressures. It is not strange that, in recent years, research interest has been redirected to green catalysis, e.g., trying to run catalytic reactions under relatively mild conditions as much as possible, which needs to employ green solvents, green oxidants such O₂, particularly air, and novel catalysts. This work reports the efficient binary Fe-Mn metal oxide catalysts for low-temperature formaldehyde (HCHO) oxidation, a toxic pollutant in the air, particularly in indoor environments. We prepared a series of nanosized FeMn oxide catalysts and found that when the molar ratio of Fe/Mn = 1:1, the catalyst exhibited the highest catalytic activity. At room temperature, we realized the complete oxidation of HCHO on this catalyst for 20 h with a high GHSV of 150 L g⁻¹ h⁻¹. After a systematic investigation of the catalyst structure and the reaction, we identified the reaction intermediates, including dioxymethylene, formate, carbonate, etc. It is found that the oxygen vacancies and the derived active oxygen species contributed to this high-low-temperature catalytic activity. These findings deepen the understanding of the catalysis of these binary Fe-Mn metal oxide catalysts.

Keywords: oxygen vacancy, catalytic oxidation, binary transition oxide, formaldehyde

Procedia PDF Downloads 98

Authors: Khalid H. Abass

Abstract:

Al-Ni-Cr alloy contains different ratios of Ni and Cr was prepared by mixing Al, Ni and Cr at 800oC under an argon atmosphere. The prepared alloys were heated for 1300 hr to 560oC, and then cooled rapidly by water at the ambient temperature. Surface morphology for alloys is studied by scanning electron microscope (SEM). The resultant homogeneous surface is a result of heat treatment. The X-ray diffraction patterns showed (111), (200), and (220) diffraction lines from cubic Al crystal structure, and suggested that the intensity of peak (111) orientation is predominant. Three binary phases were observed and grown in alloys: Al3Ni (Orthorhombic, a = 6.598Ǻ, b = 7.352 Ǻ, c = 4.802 Ǻ), Cr9Al17 (Rhombohedra, a = 12.910 Ǻ, c = 15.677), and Ni2Cr3 (Tetragonal, a = 8.82 Ǻ, c = 4.58 Ǻ). The average crystallite sizes of the prepared samples were found to be from 3000 to 3094 nm by SEM, which is much smaller than that estimated from XRD data. Corrosion resistance increases with increasing Ni-Cr content in Al alloys. The electrical volume resistivity decreased with increasing Ni-Cr content at low frequency. This behavior can be seen generally at 50Hz, where the electrical volume resistivity reached the value of 3.98×10-8Ω.cm for the ratio Al-1.8 at.%Ni-0.18at.%Cr.

Keywords: Al-Ni-Cr alloy, corrosion current, electrical volume resistivity, binary phase, homogeneous surface

Procedia PDF Downloads 361

Authors: Maria Fernanda Ordoñez Martinez, Alvaro Mauricio Montenegro

Abstract:

This work presents a statistical methodology for measuring and founding constructs in Latent Semantic Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations present on Item Response Theory. More precisely, we propose initially reducing dimensionality with specific use of Principal Component Analysis for the linguistic data and then, producing axes of groups made from a clustering analysis of the semantic data. This approach allows the user to give meaning to previous clusters and found the real latent structure presented by data. The methodology is applied in a set of real semantic data presenting impressive results for the coherence, speed and precision.

Keywords: semantic analysis, factorial analysis, dimension reduction, penalized logistic regression

Procedia PDF Downloads 407

Authors: Tak-Chung Fu, Ying-Kit Hung, Fu-Lai Chung

Abstract:

In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is first introduced in 2001. This process originally works for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful cases, it is worth to further explore the opportunity to apply PIP in time series ‘Big Data’. However, the performance of PIP identification is always considered as the limitation when dealing with ‘Big’ time series data. In this paper, two distributed versions of PIP identification based on the Specialized Binary (SB) Tree are proposed. The proposed approaches solve the bottleneck when running the PIP identification process in a standalone computer. Improvement in term of speed is obtained by the distributed versions.

Keywords: distributed computing, performance analysis, Perceptually Important Point identification, time series data mining

Procedia PDF Downloads 392

Authors: K. Thiagarajan, K. Saranya, A. Veeraiah, B. Sudha

Abstract:

This paper presents a novel algorithm for secure, reliable and flexible transmission of big data in two hop wireless networks using cooperative jamming scheme. Two hop wireless networks consist of source, relay and destination nodes. Big data has to transmit from source to relay and from relay to destination by deploying security in physical layer. Cooperative jamming scheme determines transmission of big data in more secure manner by protecting it from eavesdroppers and malicious nodes of unknown location. The novel algorithm that ensures secure and energy balance transmission of big data, includes selection of data transmitting region, segmenting the selected region, determining probability ratio for each node (capture node, non-capture and eavesdropper node) in every segment, evaluating the probability using binary based evaluation. If it is secure transmission resume with the two- hop transmission of big data, otherwise prevent the attackers by cooperative jamming scheme and transmit the data in two-hop transmission.

Keywords: big data, two-hop transmission, physical layer wireless security, cooperative jamming, energy balance

Procedia PDF Downloads 450

Authors: Georgiana Onicescu, Yuqian Shen

Abstract:

Due to the complex nature of geo-referenced data, multicollinearity of the risk factors in public health spatial studies is a commonly encountered issue, which leads to low parameter estimation accuracy because it inflates the variance in the regression analysis. To address this issue, we proposed a two-stage variable selection method by extending the least absolute shrinkage and selection operator (Lasso) to the Bayesian spatial setting, investigating the impact of risk factors to health outcomes. Specifically, in stage I, we performed the variable selection using Bayesian Lasso and several other variable selection approaches. Then, in stage II, we performed the model selection with only the selected variables from stage I and compared again the methods. To evaluate the performance of the two-stage variable selection methods, we conducted a simulation study with different distributions for the risk factors, using geo-referenced count data as the outcome and Michigan as the research region. We considered the cases when all candidate risk factors are independently normally distributed, or follow a multivariate normal distribution with different correlation levels. Two other Bayesian variable selection methods, Binary indicator, and the combination of Binary indicator and Lasso were considered and compared as alternative methods. The simulation results indicated that the proposed two-stage Bayesian Lasso variable selection method has the best performance for both independent and dependent cases considered. When compared with the one-stage approach, and the other two alternative methods, the two-stage Bayesian Lasso approach provides the highest estimation accuracy in all scenarios considered.

Keywords: Lasso, Bayesian analysis, spatial analysis, variable selection

Procedia PDF Downloads 104

Authors: Elsadig Naseraddeen Ahmed Mohamed

Abstract:

In contrast to the uncertainty and complementary principle, it will be shown in the present paper that the probability of the simultaneous occupation event of any definite values of coordinates by any definite values of momentum and energy at any definite instance of time can be described by a binary definite function equivalent to the difference between their numbers of occupation and evacuation epochs up to that time and also equivalent to the number of exchanges between those occupation and evacuation epochs up to that times modulus two, these binary definite quantities can be defined at all point in the time’s real-line so it form a binary signal represent a complete mechanical description of physical reality, the time of these exchanges represent the boundary of occupation and evacuation epochs from which we can calculate these binary signals using the fact that the time of universe events actually extends in the positive and negative of time’s real-line in one direction of extension when these number of exchanges increase, so there exists noninvertible transformation matrix can be defined as the matrix multiplication of invertible rotation matrix and noninvertible scaling matrix change the direction and magnitude of exchange event vector respectively, these noninvertible transformation will be called actual transformation in contrast to information transformations by which we can navigate the universe’s events transformed by actual transformations backward and forward in time’s real-line, so these information transformations will be derived as an elements of a group can be associated to their corresponded actual transformations. The actual and information model of the universe will be derived by assuming the existence of time instance zero before and at which there is no coordinate occupied by any definite values of momentum and energy, and then after that time, the universe begin its expanding in spacetime, this assumption makes the need for the existence of Laplace’s demon who at one moment can measure the positions and momentums of all constituent particle of the universe and then use the law of classical mechanics to predict all future and past of universe’s events, superfluous, we only need for the establishment of our analog to digital converters to sense the binary signals that determine the boundaries of occupation and evacuation epochs of the definite values of coordinates relative to its origin by the definite values of momentum and energy as present events of the universe from them we can predict approximately in high precision it's past and future events.

Keywords: binary-signal mechanics, actual-information model of the universe, actual-transformation, information-transformation, uncertainty principle, Laplace's demon

Procedia PDF Downloads 135

Authors: Robin C. Ladwig

Abstract:

Diversity Management, as a substantial element of Human Resource Management, aims to secure the economic benefit that assumingly comes with a diverse workforce. Consequently, diversity managers focus on the protection of employees and securing equality measurements to assure organisational gender diversity. Gender diversity as one aspect of Diversity Management seems to adhere to gender binarism and cis-normativity. Workplaces are gendered spaces which are echoing the binary gender-normativity presented in Diversity Management, sold under the label of gender diversity. While the expectation of Diversity Management implies the inclusion of a multiplicity of marginalised groups, such as trans and gender diverse people, in current literature and practice, the reality is curated by gender binarism and cis-normativity. The qualitative multi-method research showed a lack of knowledge about trans and gender diverse matters within the profession of Diversity Management and Human Resources. The semi-structured interviews with trans and gender diverse individuals from various backgrounds and occupations in Australia exposed missing considerations of trans and gender diverse experiences in the inclusivity and gender equity of various workplaces. Even if practitioners consider trans and gender diverse matters under gender diversity, the practical execution is limited to gender binary structures and cis-normative actions as the photo-elicit questionnaire with diversity managers, human resource officers, and personnel management demonstrates. Diversity Management should approach a broader source of informed practice by extending their business focus to the knowledge of humanity studies. Humanity studies could include diversity, queer, or gender studies to increase the inclusivity of marginalised groups such as trans and gender diverse employees and people. Furthermore, the definition of gender diversity should be extended beyond the gender binary and cis-normative experience. People may lose trust in Diversity Management as a supportive ally of marginalised employees if the understanding of inclusivity is limited to a gender binary and cis-normativity value system that misrepresents the richness of gender diversity.

Keywords: cis-normativity, diversity management, gender binarism, trans and gender diversity

Procedia PDF Downloads 162

Authors: N. F. F. Ebecken, G. C. Pereira

Abstract:

Although networks provide a powerful approach to the study of a wide variety of ecological systems, their formulation usually does not include various types of interactions, interactions that vary in space and time, and interconnected systems such as networks. The emerging field of 'multilayer networks' provides a natural framework for extending ecological systems analysis to include these multiple layers of complexity as it specifically allows for differentiation and modeling of intralayer and interlayer connectivity. The structure provides a set of concepts and tools that can be adapted and applied to the ecology, facilitating research in high dimensionality, heterogeneous systems in nature. Here, ecological multilayer networks are formally defined based on a review of prior and related approaches, illustrates their application and potential with existing data analyzes, and discusses limitations, challenges, and future applications. The integration of multilayer network theory into ecology offers a largely untapped potential to further address ecological complexity, to finally provide new theoretical and empirical insights into the architecture and dynamics of ecological systems.

Keywords: ecological networks, multilayered networks, sea ecology, Brazilian Coastal Area

Procedia PDF Downloads 116

Authors: Jan Henrik Wosnitza

Abstract:

The logistic regression has developed into a standard approach for estimating conditional probabilities in a wide range of applications including credit risk prediction. The article at hand contributes to the current literature on logistic regression fourfold: First, it is demonstrated that the binary logistic regression automatically meets its model assumptions under very general conditions. This result explains, at least in part, the logistic regression's popularity. Second, the requirement of homoscedasticity in the context of binary logistic regression is theoretically substantiated. The variances among the groups of defaulted and non-defaulted obligors have to be the same across the level of the aggregated default indicators in order to achieve linear logits. Third, this article sheds some light on the question why nonlinear logits might be superior to linear logits in case of a small amount of data. Fourth, an innovative methodology for estimating correlations between obligor-specific log-odds is proposed. In order to crystallize the key ideas, this paper focuses on the example of credit risk prediction. However, the results presented in this paper can easily be transferred to any other field of application.

Keywords: correlation, credit risk estimation, default correlation, homoscedasticity, logistic regression, nonlinear logistic regression

Procedia PDF Downloads 389