Search results for: hardy cross networks accuracy

Authors: Ayse Ozge Demir, Nihat Mert

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This study has been carried out in order to determine the polymorphic traits of various biochemical parameters in goat breeds which are native to Turkey. For this purpose, Angora and Hair goats breeds were chosen as live materials. Two different herds for each breed were selected from Ankara and Antalya, respectively. Blood samples were taken from a total of 120 goats aged between 2 and 4 which was made up of 60 Angora goats and 60 Hair goats. All which derived equally from 4 lots of herds. Analyses were performed for the polymorphic determination of the Haemoglobin (Hb), Transferrine (Tf), Ceruloplasmin (Cp) and Glutathione (GSH). Hb types were determined by starch gel electrophoresis and Tf types were detected by SDS-PAGE electrophoresis. Furthermore, Cp and GSH analyses were performed by spectrophotometrically. Following the analysis, Hb types were found as 3 genotypes (AA, AB, BB) controlled by 2 allel genes. Tf types were found as 6 genotypes (AA, AB, AC, BB, BC, CC) controlled by 3 allele genes. Findings for Hb was in line with the Hardy-Weinberg Equilibrium (HWE) in Angora goats while the Hair goat was not found to be in line. Moreover, Tf was found in line with the HWE for 2 separate goat breeds. The levels of Cp and GSH of two breeds were significantly different from other (P<0.0001). The findings are recorded as a source of reference for prospective polymorphism studies.

Keywords: electrophoresis, genetic resources, goats, spectrophotometer

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Authors: Ibrahim Suliman Hanaish

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Disaggregation daily rainfall using stochastic models formulated based on multivariate approach (MuDRain) is discussed in this paper. Seven rain gauge stations are considered in this study for different distances from the referred station starting from 4 km to 160 km in Peninsular Malaysia. The hourly rainfall data used are covered the period from 1973 to 2008 and July and November months are considered as an example of dry and wet periods. The cross-correlation among the rain gauges is considered for the available hourly rainfall information at the neighboring stations or not. This paper discussed the applicability of the MuDRain model for disaggregation daily rainfall to hourly rainfall for both sources of cross-correlation. The goodness of fit of the model was based on the reproduction of fitting statistics like the means, variances, coefficients of skewness, lag zero cross-correlation of coefficients and the lag one auto correlation of coefficients. It is found the correlation coefficients based on extracted correlations that was based on daily are slightly higher than correlations based on available hourly rainfall especially for neighboring stations not more than 28 km. The results showed also the MuDRain model did not reproduce statistics very well. In addition, a bad reproduction of the actual hyetographs comparing to the synthetic hourly rainfall data. Mean while, it is showed a good fit between the distribution function of the historical and synthetic hourly rainfall. These discrepancies are unavoidable because of the lowest cross correlation of hourly rainfall. The overall performance indicated that the MuDRain model would not be appropriate choice for disaggregation daily rainfall.

Keywords: rainfall disaggregation, multivariate disaggregation rainfall model, correlation, stochastic model

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Authors: Sophie Mills, Leila L. Touil, Richard Sisson

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Introduction: The TNM classification is the global standard for the staging of head and neck cancers. Accurate clinical-radiological staging of tumours (cTNM) is essential to predict prognosis, facilitate surgical planning and determine the need for other therapeutic modalities. This study aims to determine the accuracy of pre-operative cTNM staging using pathological TNM (pTNM) and consider possible causes of TNM stage migration, noting any variation throughout the COVID-19 pandemic. Materials and Methods: A retrospective cohort study examined records of patients with surgical management of head and neck cancer at a tertiary head and neck centre from November 2019 to November 2020. Data was extracted from Somerset Cancer Registry and histopathology reports. cTNM and pTNM were compared before and during the first wave of COVID-19, as well as with other potential prognostic factors such as tumour site and tumour stage. Results: 119 cases were identified, of which 52.1% (n=62) were male, and 47.9% (n=57) were female with a mean age of 67 years. Clinical and pathological staging differed in 54.6% (n=65) of cases. Of the patients with stage migration, 40.4% (n=23) were up-staged and 59.6% (n=34) were down-staged compared with pTNM. There was no significant difference in the accuracy of cTNM staging compared with age, sex, or tumour site. There was a statistically highly significant (p < 0.001) correlation between cTNM accuracy and tumour stage, with the accuracy of cTNM staging decreasing with the advancement of pTNM staging. No statistically significant variation was noted between patients staged prior to and during COVID-19. Conclusions: Discrepancies in staging can impact management and outcomes for patients. This study found that the higher the pTNM, the more likely stage migration will occur. These findings are concordant with the oncology literature, which highlights the need to improve the accuracy of cTNM staging for more advanced tumours.

Keywords: COVID-19, head and neck cancer, stage migration, TNM staging

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Authors: Ashfaque Hossain Khan, Brian Thomson, Ratan Debnath, Abhishek Motayed, Mulpuri V. Rao

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Though the efforts had been made, the problem of cross-sensitivity for a single metal oxide-based sensor can’t be fully eliminated. In this work, a sensor array has been designed and fabricated comprising of platinum (Pt), copper (Cu), and silver (Ag) decorated TiO2 and ZnO functionalized GaN nanowires using industry-standard top-down fabrication approach. The metal/metal-oxide combinations within the array have been determined from prior molecular simulation study using first principle calculations based on density functional theory (DFT). The gas responses were obtained for both single and mixture of NO2, SO2, ethanol, and H2 in the presence of H2O and O2 gases under UV light at room temperature. Each gas leaves a unique response footprint across the array sensors by which precise discrimination of cross-sensitive gases has been achieved. An unsupervised principal component analysis (PCA) technique has been implemented on the array response. Results indicate that each gas forms a distinct cluster in the score plot for all the target gases and their mixtures, indicating a clear separation among them. In addition, the developed array device consumes very low power because of ultra-violet (UV) assisted sensing as compared to commercially available metal-oxide sensors. The nanowire sensor array, in combination with PCA, is a potential approach for precise real-time gas monitoring applications.

Keywords: cross-sensitivity, gas sensor, principle component analysis (PCA), sensor array

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Authors: Priyanka Kumari Gupta, Punya Prasanna Paltani, Shrivishal Tripathi

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This paper proposes a nonlinear photonic crystal ring resonator-based all-optical 2 × 2 switch. The nonlinear Kerr effect is used to evaluate the essential 2 x 2 components of the photonic crystal-based optical switch, including the bar and cross states. The photonic crystal comprises a two-dimensional square lattice of dielectric rods in an air background. In the background air, two different dielectric materials are used for this comparison study separately. Initially with chalcogenide glass rods, then with GaAs rods. For both materials, the operating wavelength, bandgap diagram, operating power intensities, and performance parameters, such as the extinction ratio, insertion loss, and cross-talk of an optical switch, have also been estimated using the plane wave expansion and the finite-difference time-domain method. The chalcogenide glass material (Ag20As32Se48) has a high refractive index of 3.1 which is highly suitable for switching operations. This dielectric material is immersed in an air background with a nonlinear Kerr coefficient of 9.1 x 10-17 m2/W. The resonance wavelength is at 1552 nm, with the operating power intensities at the cross-state and bar state around 60 W/μm2 and 690 W/μm2. The extinction ratio, insertion loss, and cross-talk value for the chalcogenide glass at the cross-state are 17.19 dB, 0.051 dB, and -17.14 dB, and the bar state, the values are 11.32 dB, 0.025 dB, and -11.35 dB respectively. The gallium arsenide (GaAs) dielectric material has a high refractive index of 3.4, a direct bandgap semiconductor material highly preferred nowadays for switching operations. This dielectric material is immersed in an air background with a nonlinear Kerr coefficient of 3.1 x 10-16 m2/W. The resonance wavelength is at 1558 nm, with the operating power intensities at the cross-state and bar state around 110 W/μm2 and 200 W/μm2. The extinction ratio, insertion loss, and cross-talk value for the chalcogenide glass at the cross-state are found to be 3.36.19 dB, 2.436 dB, and -5.8 dB, and for the bar state, the values are 15.60 dB, 0.985 dB, and -16.59 dB respectively. This paper proposes an all-optical 2 × 2 switch based on a nonlinear photonic crystal using a ring resonator. The two-dimensional photonic crystal comprises a square lattice of dielectric rods in an air background. The resonance wavelength is in the range of photonic bandgap. Later, another widely used material, GaAs, is also considered, and its performance is compared with the chalcogenide glass. Our presented structure can be potentially applicable in optical integration circuits and information processing.

Keywords: photonic crystal, FDTD, ring resonator, optical switch

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Authors: Zachary Bolo Awino

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The need to gain and sustain a competitive advantage is overwhelming for businesses, especially now with cut throat competition. Innovation has been suggested as one way of gaining the advantage sustainably. But innovation can only happen within certain enabling environment and cultures. This study had one hypothesis: that there is no relationship between innovation and performance. This research was a cross sectional survey in which variables of interest are not controlled or manipulated. The cross sectional survey design is also appropriate for this study as it improves accuracy in generalizing findings, since it involves detailed study of a unit. Also known as one shot study, this design enhances uniform data collection and comparison across respondents. The population of the study was the 55 publicly quoted corporations in the Nairobi Securities Exchange (NSE) as at October 2013.The number was initially envisaged to be 60 but 5 firms were delisted or suspended during the year, hence leaving 55 firms as the population of study. The rationale for the choice for these firms is because they cut across the key economic sectors in Kenyan economy which include agriculture, commercial and services, manufacturing, finance and investment. This was a census survey and targeted all the firms listed at the Nairobi Securities Exchange as of October 2013. The primary data for the study was collected through the use of a structured questionnaire. A five point type Likert scale ranging from 1 - denoting to a less event to 5 - denoting to a greater extent was used. Respondents were from senior management of NSE. From the analyses, the study established that there was a strong positive relationship between innovation and performance, and organization innovation significantly contributes to employee engagement. Also there was a moderate positive relationship between innovation and performance. The study drew expressions of interrelations between various variables, offered generalization of understanding and meaning of these relationships, thus expanding the frontiers of knowledge both theoretical and practical with respect to innovation and firm performance. Major conclusion in this study was that there is a positive strong relationship between innovation and major measures of firm performance.

Keywords: emperical, innovation, NSE, organizations, performance

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Authors: Yaxin Bi

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Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

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Authors: Vuk M. Popovic, Dunja D. Popovic

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Software maintenance phase is started once a software project has been developed and delivered. After that, any modification to it corresponds to maintenance. Software maintenance involves modifications to keep a software project usable in a changed or a changing environment, to correct discovered faults, and modifications, and to improve performance or maintainability. Software maintenance and management of software maintenance are recognized as two most important and most expensive processes in a life of a software product. This research is basing the prediction of maintenance, on risks and time evaluation, and using them as data sets for working with neural networks. The aim of this paper is to provide support to project maintenance managers. They will be able to pass the issues planned for the next software-service-patch to the experts, for risk and working time evaluation, and afterward to put all data to neural networks in order to get software maintenance prediction. This process will lead to the more accurate prediction of the working hours needed for the software-service-patch, which will eventually lead to better planning of budget for the software maintenance projects.

Keywords: laboratory information system, maintenance engineering, neural networks, software maintenance, software maintenance costs

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Authors: Mengke Zhan, Cheng-Gang Xie, Jian-Jun Shu

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A venturi flowmeter is a common device used in multiphase flow rate measurement in the upstream oil and gas industry. Having a robust computational model for multiphase flow in a venturi is desirable for understanding the gas-liquid and fluid-pipe interactions and predicting pressure and phase distributions under various flow conditions. A steady Eulerian-Eulerian framework is used to simulate upward gas-liquid flow in a vertical venturi. The simulation results are compared with experimental measurements of venturi differential pressure and chord-averaged gas holdup in the venturi throat section. The choice of turbulence model is nontrivial in the multiphase flow modelling in a venturi. The performance cross-comparison of the k-ϵ model, Reynolds stress model (RSM) and shear-stress transport (SST) k-ω turbulence model is made in the study. In terms of accuracy and computational cost, the SST k-ω turbulence model is observed to be the most efficient.

Keywords: computational fluid dynamics (CFD), gas-liquid flow, turbulence modelling, venturi

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Authors: Benjamin Bobbia, Matthias Picard

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In recent years active learning methodologies based on the representativity of the data seems more promising to limit overfitting. The presented query methodology for regression using the Wasserstein distance measuring the representativity of our labelled dataset compared to the global distribution. In this work a crucial use of GroupSort Neural Networks is made therewith to draw a double advantage. The Wasserstein distance can be exactly expressed in terms of such neural networks. Moreover, one can provide explicit bounds for their size and depth together with rates of convergence. However, heterogeneity of the dataset is also considered by weighting the Wasserstein distance with the error of approximation at the previous step of active learning. Such an approach leads to a reduction of overfitting and high prediction performance after few steps of query. After having detailed the methodology and algorithm, an empirical study is presented in order to investigate the range of our hyperparameters. The performances of this method are compared, in terms of numbers of query needed, with other classical and recent query methods on several UCI datasets.

Keywords: active learning, Lipschitz regularization, neural networks, optimal transport, regression

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Authors: José Luis Carrillo-Medina, Roberto Latorre

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Experimental evidence has revealed that different living neural systems can sign their output signals with some specific neural signature. Although experimental and modeling results suggest that neural signatures can have an important role in the activity of neural networks in order to identify the source of the information or to contextualize a message, the functional meaning of these neural fingerprints is still unclear. The existence of cellular mechanisms to identify the origin of individual neural signals can be a powerful information processing strategy for the nervous system. We have recently built different models to study the ability of a neural network to process information based on the emission and recognition of specific neural fingerprints. In this paper we further analyze the features that can influence on the information processing ability of this kind of networks. In particular, we focus on the role that the duration of a refractory period in each neuron after emitting a signed message can play in the network collective dynamics.

Keywords: neural signature, neural fingerprint, processing based on signal identification, self-organizing neural network

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Authors: Paulu Saramge Shalika Nirupani Seram

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The study delves into the complex dynamics of social networks and how they affect paddy farmers’ adoption of agricultural technologies, which are included in Yaya Development program, Weedy rice program and Good Agricultural Practices (GAP) program in Matara district. Identify the social networks among the farmers of ongoing Extension Programs in Matara district, examine the farmers’ adoption level to the ongoing extension programs in Matara district, analyze the impacts of social networks for the adoption to the technologies of ongoing extension programs and give suggestions and recommendations to improve the social network of paddy farmers in Matara District for ongoing extension programs are the objectives of this research. A structured questionnaire survey was conducted with 25 farmers from Matara-North (Wilpita), 25 farmers from Matara-Central (Kamburupitiya), and 25 farmers from Matara-South (Malimbada). UCINET (Version -6.771) software was used for social network analysis, and other than that, descriptive statistics and inferential statistics were used to analyze the findings. Matara-North has the highest social network density, and Matara-South has the lowest social network density according to the social network analysis. Dissemination of intensive technologies requires the most prominent actors of the social network, and in Matara district, agricultural instructors have the highest ability to disseminate technologies. The influence of actors in the social network, the trustworthiness of AI officers, and the trust of indigenous knowledge about paddy cultivation have a significant effect on the technology adoption of farmers. The research endeavors to contribute a nuanced understanding of the social networks and agricultural technology adoption in Matara District, offering practical insights for stakeholders involved in agricultural extension services.

Keywords: agricultural extension, paddy cultivation, social network, technology adoption

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Authors: Chirawat Wattanapanich, Hong Wei

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This study presents new gait representations for improving gait recognition accuracy on cross gait appearances, such as normal walking, wearing a coat and carrying a bag. Based on the Gait Energy Image (GEI), two ideas are implemented to generate new gait representations. One is to append lower knee regions to the original GEI, and the other is to apply convolutional operations to the GEI and its variants. A set of new gait representations are created and used for training multi-class Support Vector Machines (SVMs). Tests are conducted on the CASIA dataset B. Various combinations of the gait representations with different convolutional kernel size and different numbers of kernels used in the convolutional processes are examined. Both the entire images as features and reduced dimensional features by Principal Component Analysis (PCA) are tested in gait recognition. Interestingly, both new techniques, appending the lower knee regions to the original GEI and convolutional GEI, can significantly contribute to the performance improvement in the gait recognition. The experimental results have shown that the average recognition rate can be improved from 75.65% to 87.50%.

Keywords: convolutional image, lower knee, gait

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Authors: Navab Karimi, Tohid Alizadeh

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An intelligent vision-based system was designed to measure the quality and purity of raisins. A machine vision setup was utilized to capture the images of bulk raisins in ranges of 5-50% mixed pure-impure berries. The textural features of bulk raisins were extracted using Grey-level Histograms, Co-occurrence Matrix, and Local Binary Pattern (a total of 108 features). Genetic Algorithm and neural network regression were used for selecting and ranking the best features (21 features). As a result, the GLCM features set was found to have the highest accuracy (92.4%) among the other sets. Followingly, multiple feature combinations of the previous stage were fed into the second regression (linear regression) to increase accuracy, wherein a combination of 16 features was found to be the optimum. Finally, a Support Vector Machine (SVM) classifier was used to differentiate the mixtures, producing the best efficiency and accuracy of 96.2% and 97.35%, respectively.

Keywords: sun-dried organic raisin, genetic algorithm, feature extraction, ann regression, linear regression, support vector machine, south azerbaijan.

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Authors: Adine Barett, Jermel Watson, Anteneh Girma, Kacem Thabet

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In information security, secure communication of data across networks has always been a problem at the forefront. Transfer of information across networks is susceptible to being exploited by attackers engaging in malicious activity. In this paper, we leverage steganography and cryptography to create a layered security solution to protect the information being transmitted. The first layer of security leverages crypto- graphic techniques to scramble the information so that it cannot be deciphered even if the steganography-based layer is compromised. The second layer of security relies on steganography to disguise the encrypted in- formation so that it cannot be seen. We consider three cryptographic cipher methods in the cryptography layer, namely, Playfair cipher, Blowfish cipher, and Hills cipher. Then, the encrypted message is passed through the least significant bit (LSB) to the steganography algorithm for further encryption. Both encryption approaches are combined efficiently to help secure information in transit over a network. This multi-layered encryption is a solution that will benefit cloud platforms, social media platforms and networks that regularly transfer private information such as banks and insurance companies.

Keywords: cryptography, steganography, layered security, Cipher, encryption

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Authors: Rominkumar Busa, Shahira K. C., Lijiya A.

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Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, aims to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images.

Keywords: small text extraction, OCR, scene text recognition, CRNN

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Zhihao Zheng, Zhilin Wang, Linxin Liu

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In the research of automated knowledge question-answering systems, accuracy and efficiency are critical challenges. This paper proposes a knowledge graph-enhanced Retrieval-Augmented Generation (RAG) method, combined with a Graph Neural Network (GNN) structure, to automatically determine the correctness of knowledge competition questions. First, a domain-specific knowledge graph was constructed from a large corpus of academic journal literature, with key entities and relationships extracted using Natural Language Processing (NLP) techniques. Then, the RAG method's retrieval module was expanded to simultaneously query both text databases and the knowledge graph, leveraging the GNN to further extract structured information from the knowledge graph. During answer generation, contextual information provided by the knowledge graph and GNN is incorporated to improve the accuracy and consistency of the answers. Experimental results demonstrate that the knowledge graph and GNN-enhanced RAG method perform excellently in determining the correctness of questions, achieving an accuracy rate of 95%. Particularly in cases involving ambiguity or requiring contextual information, the structured knowledge provided by the knowledge graph and GNN significantly enhances the RAG method's performance. This approach not only demonstrates significant advantages in improving the accuracy and efficiency of automated knowledge question-answering systems but also offers new directions and ideas for future research and practical applications.

Keywords: knowledge graph, graph neural network, retrieval-augmented generation, NLP

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Authors: Wei Zhang, Chye Kiang Heng, John Chye Fung

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Background: The world’s demographics is currently undergoing the largest wave of both rapid ageing and dramatic urbanisation in human history. As one of the most rapidly ageing countries, Singapore will see about one in four residents aged 65 years and above by 2030 in its high-rise and high-density urban environment. Research questions: To support urban seniors ageing in place and interdependence among senior residents and their informal caregivers, this study argues a community-based care model with bottom-up mutual help networks and asks how neighbourhood built-environment influences the formulation and facilitation of bottom-up mutual help networks in Singapore. Methods: Two public housing communities with different physical environment and rich age-friendly neighbourhood initiatives were chosen as the case studies. The categories, participants and places of bottom-up mutual help activities will be obtained via field observation, non-structural interviews of participants, service providers and managers of care facilities, and documents. Mapping and content analysis will be used to explore the influences of neighbourhood built-environment on the formulation and facilitation of bottom-up mutual help networks. Results and conclusions: The results showed that neighbourhood design, place programming, and place governance have a confluence on the bottom-up mutual help networks for senior residents. Significance: The outcomes of this study will provide fresh evidence for paradigm shifts of community-based care for the elderly and neighbourhood planning. In addition, the research findings will shed light on meaningful implications of urban planners and policy makers as they tackle with the issues arising from the ageing society.

Keywords: Built environment, Mutual help, Neighbourhood, Senior residents, Singapore

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Authors: Mohsen Solimani Babarsad, Mohammad Rastgoo, Payam Taheri

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One of the important applications of hydraulic jets is used for discharge industrial, agricultural and urban wastewater into the rivers or other ambient water to reduce negative effects of pollutant water. Submerged jets due to turbulent condition can mix large amount of dense pollutant water with ambient flow. This study is conducted to investigate the distribution and length of the mixing zone in hydraulic jet's flow field with change in cross section shapes of nozzle. Toward this end, three shapes of cross section (square, circle and rectangular) and three saline densities current with different concentration are considered in a flume with 600 cm as long, 100 cm as high and 150 cm in width. Various discharges were used to evaluate mixing length for a wide range of densimetric Froude numbers, Frd, from 100 to 550 that is defined at the nozzle. Consequently, the circular nozzle, in comparison with other sections, has a densimetric Froude number 11% higher than square nozzle and 26% higher than rectangular nozzle.

Keywords: hydraulic jet, mixing zone, densimetric Froude number, nozzle

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Authors: Anurag Sharma, Dinesh Kumar, Rahul Malhotra, Manoj Kumar

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Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

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Authors: Loice K. Gudukeya, Charles Mbohwa

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The research investigates hydropower plant efficiency with a view to improving the power output while keeping the overall project cost per kilowatt produced within an acceptable range. It reviews the commonly used Pelton and Cross-flow turbines which are employed in the region for micro-hydro power plants. Turbine parameters such as surface texture, material used and fabrication processes are dealt with the intention of increasing the efficiency by 20 to 25 percent for the micro hydro-power plants.

Keywords: hydro, power plant, efficiency, manufacture

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Authors: Dweepabiswa Bagchi, D. V. Senthilkumar

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Numerous studies have typically demonstrated the devastation of invasions on an isolated ecosystem or, at most, a network of dispersively coupled similar ecosystem patches. Using such a simplistic 2-D network model, one can only consider dispersal coupling and inter-species trophic interactions. However, in a realistic ecosystem, numerous species co-exist and interact trophically and non-trophically in groups of 2 or more. Even different types of dispersal can introduce complexity in an ecological network. Therefore, a more accurate representation of actual ecosystems (or ecological networks) is a complex network consisting of motifs formed by two or more interacting species. Here, the apropos structure of the network should be multiplex or multi-layered. Motifs between different patches or species should be identical within the same layer and vary from one layer to another. This study investigates three distinct ecological multiplex networks facing invasion from one or more external species. This work determines and quantifies the criteria for the increased extinction risk of these networks. The dynamical states of the network with high extinction risk, i.e., the danger states, and those with low extinction risk, i.e., the resistive network states, are both subsequently identified. The analysis done in this study further quantifies the persistence of the entire network corresponding to simultaneous changes in the strength of invasive dispersal and higher-order trophic and non-trophic interactions. This study also demonstrates that the ecosystems enjoy an inherent advantage against invasions due to their multiplex network structure.

Keywords: increased ecosystem persistence, invasion on ecosystems, multiplex networks, non-trophic interactions

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Authors: Ashmita Mukherjee, Yogesh Harishchandra Kabutare, Suritra Bandyopadhyay, Paulomi Ghosh

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Uncontrolled bleeding in the battlefield and the operation rooms can lead to serious injuries, trauma and even be lethal. Keratin was reported to be a haemostatic material which rapidly activates thrombin followed by activation of fibrinogen leading to the formation of insoluble fibrin. Also platelets, the main initiator of haemostasis are reported to adhere to keratin. However, the major limitation of pure keratin as a biomaterial is its poor physical property and corresponding low mechanical strength. To overcome this problem, keratin was cross-linked with alginate to increase its mechanical stability. In our study, Keratin extracted from feather waste showed yield of 80.5% and protein content of 8.05 ± 0.43 mg/mL (n=3). FTIR and CD spectroscopy confirmed the presence of the essential functional groups and preservation of the secondary structures of keratin. The keratin was then cross-linked with alginate to make a dope. The dope was used to draw fibers of desired diameters in a suitable coagulation bath using a customized wet spinning setup. The resultant morphology of keratin fibers was observed under a brightfield microscope. The FT-IR analysis implied that there was a presence of both keratin and alginate peaks in the fibers. The cross-linking was confirmed in the keratin alginate fibers by a shift of the amide A and amide B peaks towards the right and disappearance of the peak for N-H stretching (1534.68 cm-1). Blood was drawn in citrate vacutainers for whole blood clotting test and blood clotting kinetics, which showed that the keratin fibers could accelerate blood coagulation compared to that of alginate fibers and tissue culture plate. Additionally, cross-linked keratin-alginate fiber was found to have lower haemolytic potential compared to alginate fiber. Thus, keratin cross-linked fibers can have potential applications to combat unrestrained bleeding.

Keywords: biomaterial, biowaste, fiber, keratin

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Authors: A. Sathiya Susuman, Hamisi F. Hamisi

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Background: Many socio-economic and demographic data are age-sex attributed. However, a variety of irregularities and misstatement are noted with respect to age-related data and less to sex data because of its biological differences between the genders. Noting the misstatement/misreporting of age data regardless of its significance importance in demographics and epidemiological studies, this study aims at assessing the quality of 2012 Tanzania Population and Housing Census Results. Methods: Data for the analysis are downloaded from Tanzania National Bureau of Statistics. Age heaping and digit preference were measured using summary indices viz., Whipple’s index, Myers’ blended index, and Age-Sex Accuracy index. Results: The recorded Whipple’s index for both sexes was 154.43; male has the lowest index of about 152.65 while female has the highest index of about 156.07. For Myers’ blended index, the preferences were at digits ‘0’ and ‘5’ while avoidance were at digits ‘1’ and ‘3’ for both sexes. Finally, Age-sex index stood at 59.8 where sex ratio score was 5.82 and age ratio scores were 20.89 and 21.4 for males and female respectively. Conclusion: The evaluation of the 2012 PHC data using the demographic techniques has qualified the data inaccurate as the results of systematic heaping and digit preferences/avoidances. Thus, innovative methods in data collection along with measuring and minimizing errors using statistical techniques should be used to ensure accuracy of age data.

Keywords: age heaping, digit preference/avoidance, summary indices, Whipple’s index, Myer’s index, age-sex accuracy index

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Authors: Fadhl Alakwaa

Abstract:

One of the sustainable goals of the system biology is understanding gene-gene interactions. Hence, gene regulatory networks (GRN) need to be constructed for understanding the disease ontology and to reduce the cost of drug development. To construct gene regulatory from gene expression we need to overcome many challenges such as data denoising and dimensionality. In this paper, we develop an integrated system to reduce data dimension and remove the noise. The generated network from our system was validated via available interaction databases and was compared to previous methods. The result revealed the performance of our proposed method.

Keywords: gene regulatory network, biclustering, denoising, system biology

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Authors: Sandra Hartl, Meena Chavan

Abstract:

This paper presents the outcome of a four year Ph.D. research on cross-cultural conflict management in transnational business relationships. An important and complex problem about managing conflicts that arise across cultures in business relationships is investigated, and conflict resolution strategies are identified. This paper particularly focuses on transnational relationships within a Chinese, German and Middle Eastern framework. Unlike many papers on this issue which have been built on experiments with international MBA students, this research provides real-life cases of cross-cultural conflicts which are not easy to capture. Its uniqueness is underpinned as the real case data was gathered by interviewing top executives at management positions in large multinational corporations through a qualitative case study method approach. This paper makes a valuable contribution to the theory of cross-cultural conflicts, and despite the sensitivity, this research primarily presents real-time business data about breaches of contracts between two counterparties engaged in transnational operating organizations. The overarching aim of this research is to identify the degree of significance for the cultural factors and the communication factors embedded in cross-cultural business conflicts. It questions from a cultural perspective what factors lead to the conflicts in each of the cases, what the causes are and the role of culture in identifying effective strategies for resolving international disputes in an increasingly globalized business world. The results of 20 face to face interviews are outlined, which were conducted, recorded, transcribed and then analyzed using the NVIVO qualitative data analysis system. The outcomes make evident that the factors leading to conflicts are broadly organized under seven themes, which are communication, cultural difference, environmental issues, work structures, knowledge and skills, cultural anxiety and personal characteristics. When evaluating the causes of the conflict it is to notice that these are rather multidimensional. Irrespective of the conflict types (relationship or task-based conflict or due to individual personal differences), relationships are almost always an element of all conflicts. Cultural differences, which are a critical factor for conflicts, result from different cultures placing different levels of importance on relationships. Communication issues which are another cause of conflict also reflect different relationships styles favored by different cultures. In identifying effective strategies for solving cross-cultural business conflicts this research identifies that solutions need to consider the national cultures (country specific characteristics), organizational cultures and individual culture, of the persons engaged in the conflict and how these are interlinked to each other. Outcomes identify practical dispute resolution strategies to resolve cross-cultural business conflicts in reference to communication, empathy and training to improve cultural understanding and cultural competence, through the use of mediation. To conclude, the findings of this research will not only add value to academic knowledge of cross-cultural conflict management across transnational businesses but will also add value to numerous cross-border business relationships worldwide. Above all it identifies the influence of cultures and communication and cross-cultural competence in reducing cross-cultural business conflicts in transnational business.

Keywords: business conflict, conflict management, cross-cultural communication, dispute resolution

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Authors: Walid Abdallah, Noureddine Boudriga

Abstract:

Wireless sensor networks have shown their effectiveness in the deployment of many critical applications especially in the military domain. Border surveillance is one of these applications where a set of wireless sensors are deployed along a country border line to detect illegal intrusion attempts to the national territory and report this to a control center to undergo the necessary measures. Regarding its nature, this wireless sensor network can be the target of many security attacks trying to compromise its normal operation. Particularly, in this application the deployment and location of sensor nodes are of great importance for detecting and tracking intruders. This paper proposes a location-based authentication and key distribution mechanism to secure wireless sensor networks intended for border surveillance where the key establishment is performed using elliptic curve cryptography and identity-based public key scheme. In this scheme, the public key of each sensor node will be authenticated by keys that depend on its position in the monitored area. Before establishing a pairwise key between two nodes, each one of them must verify the neighborhood location of the other node using a message authentication code (MAC) calculated on the corresponding public key and keys derived from encrypted beacon messages broadcast by anchor nodes. We show that our proposed public key authentication and key distribution scheme is more resilient to node capture and node replication attacks than currently available schemes. Also, the achievement of the key distribution between nodes in our scheme generates less communication overhead and hence increases network performances.

Keywords: wireless sensor networks, border surveillance, security, key distribution, location-based

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Authors: Mohsen Azarmjoo, Mehdi Mehdizadeh Koupaei, Maryam Mehdizadeh Koupaei, Asghar Mahdlouei Azar

Abstract:

The increasing prevalence of wireless networks in our daily lives has made them indispensable. The aim of this research is to investigate the management of information transfer in wireless networks and the integration of renewable solar energy resources in a residential building. The focus is on the transmission of electricity and information through wireless networks, as well as the utilization of sensors and wireless fire alarm systems. The research employs a descriptive approach to examine the transmission of electricity and information on a wireless network with electric and optical telephone lines. It also investigates the transmission of signals from sensors and wireless fire alarm systems via radio waves. The methodology includes a detailed analysis of security, comfort conditions, and costs related to the utilization of wireless networks and renewable solar energy resources. The study reveals that it is feasible to transmit electricity on a network cable using two pairs of network cables without the need for separate power cabling. Additionally, the integration of renewable solar energy systems in residential buildings can reduce dependence on traditional energy carriers. The use of sensors and wireless remote information processing can enhance the safety and efficiency of energy usage in buildings and the surrounding spaces.

Keywords: renewable energy, intelligentization, wireless sensors, fire alarm system

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Authors: Amin AminiKhafri, James M. Dawson-Edwards, Ryan M. Simpson, Simaan M. AbouRizk

Abstract:

Earned value management (EVM) has been introduced as an integrated method to combine schedule, budget, and work breakdown structure (WBS). EVM provides various indices to demonstrate project performance including the cost performance index (CPI). CPI is also used to forecast final project cost at completion based on the cost performance during the project execution. Knowing the final project cost during execution can initiate corrective actions, which can enhance project outputs. CPI, however, is not constant during the project, and calculating the final project cost using a variable index is an inaccurate and challenging task for practitioners. Since CPI is based on the cumulative progress values and because of the learning curve effect, CPI variation dampens and stabilizes as project progress. Although various definitions for the CPI stability have been proposed in literature, many scholars have agreed upon the definition that considers a project as stable if the CPI at 20% completion varies less than 0.1 from the final CPI. While 20% completion point is recognized as the stability point for military development projects, construction projects stability have not been studied. In the current study, an empirical study was first conducted using construction project data to determine the stability point for construction projects. Early findings have demonstrated that a majority of construction projects stabilize towards completion (i.e., after 70% completion point). To investigate the effect of CPI stability on cost forecast accuracy, the correlation between CPI stability and project cost at completion forecast accuracy was also investigated. It was determined that as projects progress closer towards completion, variation of the CPI decreases and final project cost forecast accuracy increases. Most projects were found to have 90% accuracy in the final cost forecast at 70% completion point, which is inlined with findings from the CPI stability findings. It can be concluded that early stabilization of the project CPI results in more accurate cost at completion forecasts.

Keywords: cost performance index, earned value management, empirical study, final project cost

Procedia PDF Downloads 153