Search results for: linear eigenvalue analysis

Authors: Agada N. Ihuoma, Nagata Yasunori

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Artificial intelligence (AI) techniques play a crucial role in predicting the expected energy outcome and its performance, analysis, modeling, and control of renewable energy. Renewable energy is becoming more popular for economic and environmental reasons. In the face of global energy consumption and increased depletion of most fossil fuels, the world is faced with the challenges of meeting the ever-increasing energy demands. Therefore, incorporating artificial intelligence to predict solar radiation outcomes from the intermittent sunlight is crucial to enable a balance between supply and demand of energy on loads, predict the performance and outcome of solar energy, enhance production planning and energy management, and ensure proper sizing of parameters when generating clean energy. However, one of the major problems of forecasting is the algorithms used to control, model, and predict performances of the energy systems, which are complicated and involves large computer power, differential equations, and time series. Also, having unreliable data (poor quality) for solar radiation over a geographical location as well as insufficient long series can be a bottleneck to actualization. To overcome these problems, this study employs the anaconda Navigator (Jupyter Notebook) for machine learning which can combine larger amounts of data with fast, iterative processing and intelligent algorithms allowing the software to learn automatically from patterns or features to predict the performance and outcome of Solar Energy which in turns enables the balance of supply and demand on loads as well as enhance production planning and energy management.

Keywords: artificial Intelligence, backward elimination, linear regression, solar energy

Procedia PDF Downloads 158

Authors: Nemes Alexandra

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The aim of the research is to develop an optimum microstructure of steel coatings on aluminum surfaces for application on the crankcase cylinder bores. For the proper design of the microstructure of the coat, it is important to control the plasma gun unit properly. The maximum operating time was determined while the plasma gun could optimally work before its destruction. Objectives: The aim of the research is to determine the optimal operating time of the plasma gun between renovations (the renovation shall involve the replacement of the test components of the plasma gun: electrode, nozzle plate, powder injector. Methodology: Plasma jet and particle flux analysis with PFI (PFI is a diagnostic tool for all kinds of thermal spraying processes), CT reconstruction and analysis on the new and the used plasma guns, failure analysis of electrodes, nozzle plates, and powder injectors, microscopic examination of the microstructure of the coating. Contributions: As the result of the failure analysis detailed above, the use of the plasma gun was maximized at 100 operating hours in order to get optimal microstructure for the coat.

Keywords: APS, air plasma spray, failure analysis, electrode, nozzle plate, powder injector

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Authors: Anubhav Shrivastava Shivarudraswamy, Bhat Lakshya

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Distributed generation has indeed become a major area of interest in recent years. Distributed generation can address a large number of loads in a power line and hence has better efficiency over the conventional methods. However, there are certain drawbacks associated with it, an increase in voltage being the major one. This paper addresses the voltage control at the buses for an IEEE 30 bus system by regulating reactive power. For carrying out the analysis, the suitable location for placing distributed generators (DG) is identified through load flow analysis and seeing where the voltage profile is dipping. MATLAB programming is used to regulate the voltage at all buses within +/- 5% of the base value even after the introduction of DGs. Three methods for regulation of voltage are discussed. A sensitivity based analysis is then carried out to determine the priority among the various methods listed in the paper.

Keywords: distributed generators, distributed system, reactive power, voltage control, sensitivity analysis

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Authors: Md Ruhul Amin

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This study was conducted in the experimental field of the Department of Entomology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh during November 2017 to May 2018 with a view to understanding the seasonal abundance of the major sucking pests namely thrips, aphid and red spider mite on rose. The findings showed that the thrips started to build up their population from the middle of January with abundance 1.0 leaf⁻¹, increased continuously, reached to the peak level (2.6 leaf⁻¹) in the middle of February and then declined. Aphid started to build up their population from the second week of November with abundance 6.0 leaf⁻¹, increased continuously, reached to the peak level (8.4 leaf⁻¹) in the last week of December and then declined. Mite started to build up their population from the first week of December with abundance 0.8 leaf⁻¹, increased continuously, reached to the peak level (8.2 leaf⁻¹) in the second week of March and then declined. Thrips and mite prevailed until the last week of April, and aphid showed their abundance till last week of May. The daily mean temperature, relative humidity, and rainfall had an insignificant negative correlation with thrips and significant negative correlation with aphid abundance. The daily mean temperature had significant positive, relative humidity had an insignificant positive, and rainfall had an insignificant negative correlation with mite abundance. The multiple linear regression analysis showed that the weather parameters together contributed 38.1, 41.0 and 8.9% abundance on thrips, aphid and mite on rose, respectively and the equations were insignificant.

Keywords: aphid, mite, thrips, weather factors

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Authors: Jing Zhang, Daniel Nikovski

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We propose an approximation algorithm called LINKUMP to compute the Pan Matrix Profile (PMP) under the unnormalized l∞ distance (useful for value-based similarity search) using double-ended queue and linear interpolation. The algorithm has comparable time/space complexities as the state-of-the-art algorithm for typical PMP computation under the normalized l₂ distance (useful for shape-based similarity search). We validate its efficiency and effectiveness through extensive numerical experiments and a real-world anomaly detection application.

Keywords: pan matrix profile, unnormalized euclidean distance, double-ended queue, discord discovery, anomaly detection

Procedia PDF Downloads 248

Authors: H. N. Agiza, M. A. Sohaly, M. A. Elfouly

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Parkinson's disease (PD) is a heterogeneous disorder with common age of onset, symptoms, and progression levels. In this paper we will solve analytically the PD model as a non-linear delay differential equation using the steps method. The step method transforms a system of delay differential equations (DDEs) into systems of ordinary differential equations (ODEs). On some numerical examples, the analytical solution will be difficult. So we will approximate the analytical solution using Picard method and Taylor method to ODEs.

Keywords: Parkinson's disease, step method, delay differential equation, two delays

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Authors: Hochan Seok, Woosik Jang, Seung-Heon Han

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The stagnant global construction market has intensified competition since 2008 among firms that aim to win overseas contracts. Against this backdrop, subcontractor selection is identified as one of the most critical success factors in overseas construction project. However, it is difficult to select qualified subcontractors due to the lack of evaluation standards and reliability. This study aims to identify the problems associated with existing subcontractor evaluations using a correlations analysis and a multiple regression analysis with pre-qualification and performance evaluation of 121 firms in six countries.

Keywords: subcontractor evaluation system, pre-qualification, performance evaluation, correlation analysis, multiple regression analysis

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Authors: Peng Li, Er-xiang Song

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Numerical analysis of longitudinal tunnel seismic response due to spatial variation of earthquake ground motion is an important issue that cannot be ignored in the design and safety evaluation of tunnel structures. In this paper, numerical methods for analysis of tunnel longitudinal response under asynchronous seismic wave is extensively studied, including the improvement of the 1D time-domain finite element method, three dimensional numerical simulation technique for the site asynchronous earthquake response as well as the 3-D soil-tunnel structure interaction analysis. The study outcome will be beneficial to aid further research on the nonlinear meticulous numerical analysis and seismic response mechanism of tunnel structures under asynchronous earthquake motion.

Keywords: asynchronous input, longitudinal seismic response, tunnel structure, numerical simulation, traveling wave effect

Procedia PDF Downloads 438

Authors: Danielle Sandler dos Passos, Helder Coelho, Flávia Mori Sarti

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Complexity science seeks the understanding of systems adopting diverse theories from various areas. Network analysis has been gaining space and credibility, namely with the biological, social and economic systems. Significant part of the literature focuses only monolayer representations of connections among agents considering one level of their relationships, and excludes other levels of interactions, leading to simplistic results in network analysis. Therefore, this work aims to demonstrate the advantages of the use of multilayer networks for the representation and analysis of networks. For this, we analyzed an interbank network, composed of 42 banks, comparing the centrality measures of the agents (degree and PageRank) resulting from each method (monolayer x multilayer). This proved to be the most reliable and efficient the multilayer analysis for the study of the current networks and highlighted JP Morgan and Deutsche Bank as the most important banks of the analyzed network.

Keywords: complexity, interbank networks, multilayer networks, network analysis

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Authors: Usha Loganathan, Haresh Pandya

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Analysis and correlation of soil properties represent an important outset for precision agriculture and is currently promoted and implemented in the developed world. Establishing relationships among indices of soil salinity has always been a challenging task in salt affected soils necessitating unique approaches for their reclamation and management to sustain long term productivity of Soil. Soil salinity indices like Electrical Conductivity (EC) and Sodium Adsorption Ratio (SAR) are normally used to characterize soils as either sodic or saline sodic. Currently, Determination of Soil sodium adsorption ratio is a more accepted and reliable measure of soil salinity. However, it involves arduous and protracted laboratory investigations which demand evolving new and economical methods to determine SAR based on simple soil salinity index. A linear regression model to predict soil SAR from soil electrical conductivity has been developed and presented in this paper as per which, soil SAR could very well be worked out as a pedotransfer function of soil EC. The present study was carried out in Orathupalayam (11.09-11.11 N latitude and 74.54-77.59 E longitude) in the vicinity of Orathupalayam Reservoir of Noyyal River Basin, India, over a period of 3 consecutive years from September 2013 through February 2016 in different locations chosen randomly through different seasons. The research findings are discussed in the light of micro level farming practices in India and recommend determination of SAR as a low cost technology aiding in the effective management of salt affected agricultural land.

Keywords: electrical conductivity, orathupalayam, pedotranfer function, sodium adsorption ratio

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Authors: Luis C. Parra

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The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm.

Keywords: significant wave height, machine learning optimization, multilayer perceptron neural networks, evolutionary algorithms

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

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Authors: T. Watanabe

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Combustion analysis of suspended sodium droplet is performed by solving numerically the Navier-Stokes equations and the energy conservation equations. The combustion model consists of the pre-ignition and post-ignition models. The reaction rate for the pre-ignition model is based on the chemical kinetics, while that for the post-ignition model is based on the mass transfer rate of oxygen. The calculated droplet temperature is shown to be in good agreement with the existing experimental data. The temperature field in and around the droplet is obtained as well as the droplet shape variation, and the present numerical model is confirmed to be effective for the combustion analysis.

Keywords: analysis, combustion, droplet, sodium

Procedia PDF Downloads 211

Authors: Ayodeji Sowale, Athanasios Kolios, Beatriz Fidalgo, Tosin Somorin, Aikaterini Anastasopoulou, Alison Parker, Leon Williams, Ewan McAdam, Sean Tyrrel

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Recently, energy recovery systems are thriving and raising attention in the power generation sector, due to the request for cleaner forms of energy that are friendly and safe for the environment. This has created an avenue for cogeneration, where Combined Heat and Power (CHP) technologies have been recognised for their feasibility, and use in homes and small-scale businesses. The efficiency of combustors and the advantages of the free piston Stirling engines over other conventional engines in terms of output power and efficiency, have been observed and considered. This study presents the numerical analysis of a micro-combustor with a free piston Stirling engine in an integrated model of a Nano Membrane Toilet (NMT) unit. The NMT unit will use the micro-combustor to produce waste heat of high energy content from the combustion of human waste and the heat generated will power the free piston Stirling engine which will be connected to a linear alternator for electricity production. The thermodynamic influence of the combustor on the free piston Stirling engine was observed, based on the heat transfer from the flue gas to working gas of the free piston Stirling engine. The results showed that with an input of 25 MJ/kg of faecal matter, and flue gas temperature of 773 K from the micro-combustor, the free piston Stirling engine generates a daily output power of 428 W, at thermal efficiency of 10.7% with engine speed of 1800 rpm. An experimental investigation into the integration of the micro-combustor and free piston Stirling engine with the NMT unit is currently underway.

Keywords: free piston stirling engine, micro-combustor, nano membrane toilet, thermodynamics

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Authors: Nadezhda Obvintceva

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The article gives the analysis of the concept ‘relation’ from the point of view of its realization in Russian and English languages on the basis of dictionaries articles. The analysis reveals the main difference of representation of this concept in both languages. It is the number of lexemes that express its general meanings. At the end of the article the author gives an explanation of possible causes of the difference and touches upon the issue about analytical phenomena in the vocabulary.

Keywords: concept, comparison, lexeme, meaning, relation, semantics

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Authors: Parisa Hasanpour, Bahram Borooghani, Vahid Asadi

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In the current research, a catastrophic failure of a 304 stainless steel flange at pipeline transportation of ethylene in a petrochemical refinery was studied. Cracking was found in the flange after about 78840h service. Through the chemical analysis, tensile tests in addition to microstructural analysis such as optical microscopy and Scanning Electron Microscopy (SEM) on the failed part, it found that the fatigue was responsible for the fracture of the flange, which originated from bumps and depressions on the outer surface and propagated by vibration caused by the working condition.

Keywords: failure analysis, 304 stainless steel, fatigue, flange, petrochemical refinery

Procedia PDF Downloads 76

Authors: Wenqi Liu, Reginald Bailey

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This study proposes a comprehensive and effective approach to business-to-business (B2B) sales forecasting by integrating advanced machine learning models with a rule-based decision-making framework. The methodology addresses the critical challenge of optimizing sales pipeline performance and improving conversion rates through predictive analytics and actionable insights. The first component involves developing a classification model to predict the likelihood of conversion, aiming to outperform traditional methods such as logistic regression in terms of accuracy, precision, recall, and F1 score. Feature importance analysis highlights key predictive factors, such as client revenue size and sales velocity, providing valuable insights into conversion dynamics. The second component focuses on forecasting sales value using a regression model, designed to achieve superior performance compared to linear regression by minimizing mean absolute error (MAE), mean squared error (MSE), and maximizing R-squared metrics. The regression analysis identifies primary drivers of sales value, further informing data-driven strategies. To bridge the gap between predictive modeling and actionable outcomes, a rule-based decision framework is introduced. This model categorizes leads into high, medium, and low priorities based on thresholds for conversion probability and predicted sales value. By combining classification and regression outputs, this framework enables sales teams to allocate resources effectively, focus on high-value opportunities, and streamline lead management processes. The integrated approach significantly enhances lead prioritization, increases conversion rates, and drives revenue generation, offering a robust solution to the declining pipeline conversion rates faced by many B2B organizations. Our findings demonstrate the practical benefits of blending machine learning with decision-making frameworks, providing a scalable, data-driven solution for strategic sales optimization. This study underscores the potential of predictive analytics to transform B2B sales operations, enabling more informed decision-making and improved organizational outcomes in competitive markets.

Keywords: machine learning, XGBoost, regression, decision making framework, system engineering

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Authors: Madina Hamiane

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Modelling and simulation of an analogy function generator is presented based on a polynomial expansion model. The proposed function generator model is based on a 10th order polynomial approximation of any of the required functions. The polynomial approximations of these functions can then be implemented using basic CMOS circuit blocks. In this paper, a circuit model is proposed that can simultaneously generate many different mathematical functions. The circuit model is designed and simulated with HSPICE and its performance is demonstrated through the simulation of a number of non-linear functions.

Keywords: modelling and simulation, analog function generator, polynomial approximation, CMOS transistors

Procedia PDF Downloads 459

Authors: Benoit Leclerc

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The concept of scripts and the role that crime scripts has been playing in criminology during the last decade is reviewed. Particularly illuminating is the potential use of scripts not only to understand and disrupt offender scripts (e.g., commonly referred as crime scripts) but to capture victim and guardian scripts to increase the likelihood of preventing crime. Similarly, the concept of scripts is applied to forensic science – another field that can benefit from script analysis. First, similar to guardian scripts, script analysis can illuminate the process of completing crime scene investigations for those who investigate (crime scene investigators or other professionals involved in crime scene investigations), and as a result, provide a range of intervention-points to improve the success of these investigations. Second, script analysis can also provide valuable information on offenders’ crime-commission processes for crime scene investigators and highlight a number of ‘contact points’ that could be targeted during investigations.

Keywords: crime scripts, crime scene investigation, script analysis, situational crime prevention

Procedia PDF Downloads 275

Authors: M. A. Wanzala, L. A. Ogallo, F. J. Opijah, J. N. Mutemi

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The usefulness and limitations in climate information are due to uncertainty inherent in the climate system. For any given region to have sustainable development it is important to apply climate information into its socio-economic strategic plans. The overall objective of the study was to assess the performance of the Coupled Model Inter-comparison Project (CMIP5) over the Lake Victoria Basin. The datasets used included the observed point station data, gridded rainfall data from Climate Research Unit (CRU) and hindcast data from eight CMIP5. The methodology included trend analysis, spatial analysis, correlation analysis, Principal Component Analysis (PCA) regression analysis, and categorical statistical skill score. Analysis of the trends in the observed rainfall records indicated an increase in rainfall variability both in space and time for all the seasons. The spatial patterns of the individual models output from the models of MPI, MIROC, EC-EARTH and CNRM were closest to the observed rainfall patterns.

Keywords: categorical statistics, coupled model inter-comparison project, principal component analysis, statistical downscaling

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Authors: Mahdi Aghaei, Saeid Jamshidi, Mastaneh Hajipour

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Effective factors on the mechanical wellbore stability are divided in to two categories: 1) Controllable factors, 2) Uncontrollable factors. The purpose of geo-mechanical modeling of wells is to determine the limit of controlled parameters change based on the stress regime at each point and by solving the governing equations the pore-elastic environment around the well. In this research, the mechanical analysis of wellbore stability was carried out for Soroush oilfield. For this purpose, the geo-mechanical model of the field is made using available data. This model provides the necessary parameters for obtaining the distribution of stress around the wellbore. Initially, a basic model was designed to perform various analysis, based on obtained data, using Abaqus software. All of the subsequent sensitivity analysis such as sensitivity analysis on porosity, permeability, etc. was done on the same basic model. The results obtained from these analysis gives various result such as: with the constant geomechanical parameters, and sensitivity analysis on porosity permeability is ineffective. After the most important parameters affecting the wellbore stability and instability are geo-mechanical parameters.

Keywords: wellbore stability, movement, stress, instability

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Authors: N. R. N. Idris, S. Baharom

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A meta-analysis may be performed using aggregate data (AD) or an individual patient data (IPD). In practice, studies may be available at both IPD and AD level. In this situation, both the IPD and AD should be utilised in order to maximize the available information. Statistical advantages of combining the studies from different level have not been fully explored. This study aims to quantify the statistical benefits of including available IPD when conducting a conventional summary-level meta-analysis. Simulated meta-analysis were used to assess the influence of the levels of data on overall meta-analysis estimates based on IPD-only, AD-only and the combination of IPD and AD (mixed data, MD), under different study scenario. The percentage relative bias (PRB), root mean-square-error (RMSE) and coverage probability were used to assess the efficiency of the overall estimates. The results demonstrate that available IPD should always be included in a conventional meta-analysis using summary level data as they would significantly increased the accuracy of the estimates. On the other hand, if more than 80% of the available data are at IPD level, including the AD does not provide significant differences in terms of accuracy of the estimates. Additionally, combining the IPD and AD has moderating effects on the biasness of the estimates of the treatment effects as the IPD tends to overestimate the treatment effects, while the AD has the tendency to produce underestimated effect estimates. These results may provide some guide in deciding if significant benefit is gained by pooling the two levels of data when conducting meta-analysis.

Keywords: aggregate data, combined-level data, individual patient data, meta-analysis

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Authors: Prakash Singh

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The focus of this study is on the use of the SWEAT analysis approach to determine how effectively a school, as an organization, has implemented its curriculum. To gauge the feelings of the teaching staff, unstructured interviews were employed in this study, asking the participants for their ideas and opinions on each of the three identified aspects of the school: instructional materials, media and technology; teachers’ professional competencies; and the curriculum. This investigation was based on the five key components of the SWEAT model: strengths, weaknesses, expectations, abilities, and tensions. The findings of this exploratory study evoke the significance of the SWEAT achievement model as a tool for strategic analysis to be undertaken in any organization. The findings further affirm the usefulness of this analytical tool for human resource development. Employees have expectations, but competency gaps in their professional abilities may hinder them from fulfilling their tasks in terms of their job description. Also, tensions in the working environment can contribute to their experiences of tobephobia (fear of failure). The SWEAT analysis approach detects such shortcomings in any organization and can therefore culminate in the development of programmes to address such concerns. The strategic SWEAT analysis process can provide a clear distinction between success and failure, and between mediocrity and excellence in organizations. However, more research needs to be done on the effectiveness of the SWEAT analysis approach as a strategic analytical tool.

Keywords: SWEAT analysis, strategic analysis, tobephobia, competency gaps

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Authors: Rahmathulla Noufal E.

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Investigating the dynamic responses of high rise structures under the effect of siesmic ground motion is extremely important for the proper analysis and design of multitoried structures. Since the presence of infilled walls strongly influences the behaviour of frame systems in multistoried buildings, there is an increased need for developing guidelines for the analysis and design of infilled frames under the effect of dynamic loads for safe and proper design of buildings. In this manuscript, we evaluate the natural frequencies and natural periods of single bay single storey frames considering the effect of infill walls by using the Eigen value analysis and validating with SAP 2000 (free vibration analysis). Various parameters obtained from the diagonal strut model followed for the free vibration analysis is then compared with the Finite Element model, where infill is modeled as shell elements (four noded). We also evaluated the effect of various parameters on the natural periods of vibration obtained by free vibration analysis in SAP 2000 comparing them with those obtained by the empirical expressions presented in I.S. 1893(Part I)-2002.

Keywords: infilled frame, eigen value analysis, free vibration analysis, diagonal strut model, finite element model, SAP 2000, natural period

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Authors: Irshad U. Khan, Tanmay Paul, Murali Mohan Seepana

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This paper presents a study on synthesizing and characterizing a Copper oxide doped Carbon (CuO-C) electrocatalyst for the negative half-cell reactions of Vanadium Redox Flow Battery (VRFB). The CuO was synthesized using a microreactor. The electrocatalyst was characterized using X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and Field Emission Scanning Electron Microscopy (SEM). The electrochemical performance was assessed by linear sweep voltammetry (LSV). The findings suggest that the synthesized CuO exhibited favorable crystallinity, morphology, and surface area, which reflects improved cell performance.

Keywords: ECSA, electrocatalyst, energy storage, Tafel

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Authors: Berhan Fikru

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The objective of this study was to develop new complementary food from maize, soybean and moringa for young children. The complementary foods were formulated with linear programming (LP Nutri-survey software) and Faffa (corn soya blend) use as control. Analysis were made for formulated blends and compared with the control and recommended daily intake (RDI). Three complementary foods composed of maize, soya bean, moringa and sugar with ratio of 65:20:15:0, 55:25:15:5 and 65:20:10:5 for blend 1, 2 and 3, respectively. The blends were formulated based on the protein, energy, mineral (iron, zinc an calcium) and vitamin (vitamin A and C) content of foods. The overall results indicated that nutrient content of faffa (control) was 16.32 % protein, 422.31 kcal energy, 64.47 mg calcium, 3.8 mg iron, 1.87mg zinc, 0.19 mg vitamin A and 1.19 vitamin C; blend 1 had 17.16 % protein, 429.84 kcal energy, 330.40 mg calcium, 6.19 mg iron, 1.62 mg zinc, 6.33 mg vitamin A and 4.05 mg vitamin C; blend 2 had 20.26 % protein, 418.79 kcal energy, 417.44 mg calcium, 9.26 mg iron, 2.16 mg zinc, 8.43 mg vitamin A and 4.19 mg vitamin C whereas blend 3 exhibited 16.44 % protein, 417.42 kcal energy, 242.4 mg calcium, 7.09 mg iron, 2.22 mg zinc, 3.69 mg vitamin A and 4.72 mg vitamin C, respectively. The difference was found between all means statically significance (P < 0.05). Sensory evaluation showed that the faffa control and blend 3 were preferred by semi-trained panelists. Blend 3 had better in terms of its mineral and vitamin content than FAFFA corn soya blend and comparable with WFP proprietary products CSB+, CSB++ and fulfills the WHO recommendation for protein, energy and calcium. The suggested formulation with Moringa powder can therefore be used as a complementary food to improve the nutritional status and also help solve problems associated with protein energy and micronutrient malnutrition for young children in developing countries, particularly in Ethiopia.

Keywords: corn soya blend, proximate composition, micronutrient, mineral chelating agents, complementary foods

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Authors: Veslava Osinska, Adam Szalach, Dominik Piotrowski

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Contemporary VR technologies allow users to explore virtual 3D spaces where they can work, socialize, learn, and play. User's interaction with GUI and the pictures displayed implicate perceptual and also cognitive processes which can be monitored due to neuroadaptive technologies. These modalities provide valuable information about the users' intentions, situational interpretations, and emotional states, to adapt an application or interface accordingly. Virtual galleries outfitted by specialized assets have been designed using the Unity engine BITSCOPE project in the frame of CHIST-ERA IV program. Users interaction with gallery objects implies the questions about his/her visual interests in art works and styles. Moreover, an attention, curiosity, and other emotional states are possible to be monitored and analyzed. Natural gaze behavior data and eye position were recorded by built-in eye-tracking module within HTC Vive headset gogle for VR. Eye gaze results are grouped due to various users’ behavior schemes and the appropriate perpetual-cognitive styles are recognized. Parallelly usability tests and surveys were adapted to identify the basic features of a user-centered interface for the virtual environments across most of the timeline of the project. A total of sixty participants were selected from the distinct faculties of University and secondary schools. Users’ primary knowledge about art and was evaluated during pretest and this way the level of art sensitivity was described. Data were collected during two months. Each participant gave written informed consent before participation. In data analysis reducing the high-dimensional data into a relatively low-dimensional subspace ta non linear algorithms were used such as multidimensional scaling and novel technique technique t-Stochastic Neighbor Embedding. This way it can classify digital art objects by multi modal time characteristics of eye tracking measures and reveal signatures describing selected artworks. Current research establishes the optimal place on aesthetic-utility scale because contemporary interfaces of most applications require to be designed in both functional and aesthetical ways. The study concerns also an analysis of visual experience for subsamples of visitors, differentiated, e.g., in terms of frequency of museum visits, cultural interests. Eye tracking data may also show how to better allocate artefacts and paintings or increase their visibility when possible.

Keywords: eye tracking, VR, UX, visual art, virtual gallery, visual communication

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Authors: Jihene Jlassi, Ahmed Frikha, Wazna Kortli

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The objectives of Nurse-Patient Assignment are the minimization of the overall hospital cost and the maximization of nurses ‘preferences. This paper aims to assess nurses' satisfaction related to the implementation of patient acuity tool-based assignments. So, we used an integer linear program that assigns patients to nurses while balancing nurse workloads. Then, the proposed model is applied to the Paediatrics Department at Kasserine Hospital Tunisia. Where patients need special acuities and high-level nursing skills and care. Hence, numerical results suggested that proposed nurse-patient assignment models can achieve a balanced assignment

Keywords: nurse-patient assignment, mathematical model, logistics, pediatrics department, balanced assignment

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Authors: Shivam Patel, Abdullah Y. Usmani

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Physiological flow under rest and exercise conditions in patient-specific cerebral aneurysm models is numerically investigated. A finite-volume based code with BiCGStab as the linear equation solver is used to simulate unsteady three-dimensional flow field through the incompressible Navier-Stokes equations. Flow characteristics are first established in a healthy cerebral artery for both physiological conditions. The effect of saccular aneurysm on cerebral hemodynamics is then explored through a comparative analysis of the velocity distribution, nature of flow patterns, wall pressure and wall shear stress (WSS) against the reference configuration. The efficacy of coil embolization as a potential strategy of surgical intervention is also examined by modelling coil as a homogeneous and isotropic porous medium where the extended Darcy’s law, including Forchheimer and Brinkman terms, is applicable. The Carreau-Yasuda non-Newtonian blood model is incorporated to capture the shear thinning behavior of blood. Rest and exercise conditions correspond to normotensive and hypertensive blood pressures respectively. The results indicate that the fluid impingement on the outer wall of the arterial bend leads to abnormality in the distribution of wall pressure and WSS, which is expected to be the primary cause of the localized aneurysm. Exercise correlates with elevated flow velocity, vortex strength, wall pressure and WSS inside the aneurysm sac. With the insertion of coils in the aneurysm cavity, the flow bypasses the dilatation, leading to a decline in flow velocities and WSS. Particle residence time is observed to be lower under exercise conditions, a factor favorable for arresting plaque deposition and combating atherosclerosis.

Keywords: 3D FVM, Cerebral aneurysm, hypertension, coil embolization, non-Newtonian fluid

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Authors: Abdullahi Mohammed Auwal

Abstract:

Formation and change or decamping from one political party to another have now become a common trend in Nigeria. Many of the parties’ members who could not secure positions and or win elections in their parties or are not very much satisfied with the trends occurring in the party’s internal democratic principles and mechanisms, change their respective parties. This paper developed/presented and analyzed the used of non linear mathematical model for defections between two political parties using epidemiological approach. The whole population was assumed to be a constant and homogeneously mixed. Equilibria have been analytically obtained and their local and global stability discussed. Conditions for the co-existence of both the political parties have been determined, in the study of defections between People Democratic Party (PDP) and All Progressive Congress (APC) in Nigeria using numerical simulations to support the analytical results.

Keywords: model, political parties, deffection, stability, equilibrium, epidemiology

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