Search results for: mean square error

Authors: Saleh Alagla, Murya Habbash

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The paper investigates whether the board of directors plays a monitoring role or not in CEO compensation for the UK firms during the eve of the recent financial crisis, 2004-2008. The use of heteroscedastic and autocorrelated error consistent estimation of the panel data shows, surprisingly, that four board characteristics variables are found to play a significant role in increasing the level of CEO compensation. This insightful result would suggest evidence of the managerial power theory in general and the cronyism hypothesis in particular. Moreover, the interesting evidence supporting managerial power perspective is that CEO-Chair duality reduces long-term compensation while increasing short-term compensation, thus suggesting that CEOs are risk averse who prefer short-term compensation to long-term compensation. Finally, consistent with the agency perspective board size is found to increase all compensation variables as expected.

Keywords: corporate governance, CEO compensation, board of directors, internal governance mechanisms, agency theory, managerial power theory, cronyism hypothesis

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Authors: Dhanuj M. Gandikota

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Sensor-derived three-dimensional (3D) point clouds of trees are invaluable in remote sensing analysis for the accurate measurement of key structural metrics, bio-inventory values, spatial planning/visualization, and ecological modeling. Machine learning (ML) holds the potential in addressing the restrictive tradeoffs in cost, spatial coverage, resolution, and information gain that exist in current point cloud sensing methods. Terrestrial laser scanning (TLS) remains the highest fidelity source of both canopy and below-canopy structural features, but usage is limited in both coverage and cost, requiring manual deployment to map out large, forested areas. While aerial laser scanning (ALS) remains a reliable avenue of LIDAR active remote sensing, ALS is also cost-restrictive in deployment methods. Space-borne photogrammetry from high-resolution satellite constellations is an avenue of passive remote sensing with promising viability in research for the accurate construction of vegetation 3-D point clouds. It provides both the lowest comparative cost and the largest spatial coverage across remote sensing methods. However, both space-borne photogrammetry and ALS demonstrate technical limitations in the capture of valuable below-canopy point cloud data. Looking to minimize these tradeoffs, we explored a class of powerful ML algorithms called Deep Learning (DL) that show promise in recent research on 3-D point cloud reconstruction and interpolation. Our research details the efficacy of applying these DL techniques to reconstruct accurate below-canopy point clouds from space-borne and aerial remote sensing through learned patterns of tree species fractal symmetry properties and the supplementation of locally sourced bio-inventory metrics. From our dataset, consisting of tree point clouds obtained from TLS, we deconstructed the point clouds of each tree into those that would be obtained through ALS and satellite photogrammetry of varying resolutions. We fed this ALS/satellite point cloud dataset, along with the simulated local bio-inventory metrics, into the DL point cloud reconstruction architectures to generate the full 3-D tree point clouds (the truth values are denoted by the full TLS tree point clouds containing the below-canopy information). Point cloud reconstruction accuracy was validated both through the measurement of error from the original TLS point clouds as well as the error of extraction of key structural metrics, such as crown base height, diameter above root crown, and leaf/wood volume. The results of this research additionally demonstrate the supplemental performance gain of using minimum locally sourced bio-inventory metric information as an input in ML systems to reach specified accuracy thresholds of tree point cloud reconstruction. This research provides insight into methods for the rapid, cost-effective, and accurate construction of below-canopy tree 3-D point clouds, as well as the supported potential of ML and DL to learn complex, unmodeled patterns of fractal tree growth symmetry.

Keywords: deep learning, machine learning, satellite, photogrammetry, aerial laser scanning, terrestrial laser scanning, point cloud, fractal symmetry

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Authors: Cristina S. Decano, Vitaliana U. Malamug, Melissa E. Agulto, Helen F. Gavino

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This study aimed to introduce new natural fiber to be used in the production of geotextile net for mitigation of soil erosion. Fiber extraction from the stalks was the main challenge faced during the processing of stalks to ropes. Thus, an investigation on the extraction procedures of corn (Zea mays L.) stalk under biological and chemical retting was undertaken. Results indicated significant differences among percent fiber yield as affected by the retting methods used with values of 15.07%, 12.97%, 11.60%, and 9.01%, for dew, water, chemical (1 day after harvest and15 days after harvest), respectively, with the corresponding average extracting duration of 70, 82, 89, and 94 minutes. Physical characterization of the developed corn stalk geotextile net resulted to average mass per unit area of 806.25 g/m² and 241% water absorbing capacity. The effect of corn stalk geotextile net in mitigating soil erosion was evaluated in a laboratory experiment for 30^o and 60^o inclinations with three treatments: bare soil (A₁), corn stalk geotextile net (A₂) and combined cornstalk geotextile net and vegetation cover (A₃). Results revealed that treatment A₂ and A₃ significantly decreased sediment yield and an increase in terms of soil loss reduction efficiency. The cost of corn stalk geotextile net is Php 62.41 per square meter.

Keywords: corn stalk, natural geotextile, retting, soil erosion

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Authors: Xiaohan Bookman, Xiaoyan Zhu

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Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects. Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture. However, the training algorithm of feed-forward neural network is developed and generated from Widrow-Hoff Principle that means to minimize the squared error. In this paper, we propose a new training algorithm for feed-forward neural networks based on Margin-Based Principle, which could effectively promote the accuracy and generalization ability of neural network classifiers with less labeled samples and flexible network. We have conducted experiments on four UCI open data sets and achieved good results as expected. In conclusion, our model could handle more sparse labeled and more high-dimension data set in a high accuracy while modification from old ANN method to our method is easy and almost free of work.

Keywords: Max-Margin Principle, Feed-Forward Neural Network, classifier, structural risk

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Authors: Abdelhamid Louliej, Younes Jabrane

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Satellites are becoming indispensable assets to fifth-generation (5G) new radio architecture, complementing wireless and terrestrial communication links. The combination of satellites and 5G architecture allows consumers to access all next-generation services anytime, anywhere, including scenarios, like traveling to remote areas (without coverage). Nevertheless, this solution faces several challenges, such as a significant propagation delay, Doppler frequency shift, and high Peak-to-Average Power Ratio (PAPR), causing signal distortion due to the non-linear saturation of the High-Power Amplifier (HPA). To compensate for HPA non-linearity in 5G satellite transmission, an efficient pre-distorter scheme using Neural Networks (NN) is proposed. To assess the proposed NN pre-distorter, two types of HPA were investigated: Travelling Wave Tube Amplifier (TWTA) and Solid-State Power Amplifier (SSPA). The results show that the NN pre-distorter design presents EVM improvement by 95.26%. NMSE and ACPR were reduced by -43,66 dB and 24.56 dBm, respectively. Moreover, the system suffers no degradation of the Bit Error Rate (BER) for TWTA and SSPA amplifiers.

Keywords: satellites, 5G, neural networks, HPA, TWTA, SSPA, EVM, NMSE, ACPR

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Authors: K. Assis, K. P. Chong, A. S. Idris, C. M. Ho

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Oil palm or Elaeis guineensis is considered as the golden crop in Malaysia. But oil palm industry in this country is now facing with the most devastating disease called as Ganoderma Basal Stem Rot disease. The objective of this paper is to analyze the economic loss due to this disease. There were three commercial oil palm sites selected for collecting the required data for economic analysis. Yield parameter used to measure the loss was the total weight of fresh fruit bunch in six months. The predictors include disease severity, change in disease severity, number of infected neighbor palms, age of palm, planting generation, topography, and first order interaction variables. The estimation model of yield loss was identified by using backward elimination based regression method. Diagnostic checking was conducted on the residual of the best yield loss model. The value of mean absolute percentage error (MAPE) was used to measure the forecast performance of the model. The best yield loss model was then used to estimate the economic loss by using the current monthly price of fresh fruit bunch at mill gate.

Keywords: ganoderma, oil palm, regression model, yield loss, economic loss

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Authors: Yuanhao Gao, Pin Lin, Kees Weijer

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In this paper, we will determine the external body force distribution with analysis of stokes fluid motion using mathematical modelling and numerical approaching. The body force distribution is regarded as the unknown variable and could be determined by the idea of optimal control theory. The Stokes flow motion and its velocity are generated by given forces in a unit square domain. A regularized objective functional is built to match the numerical result of flow velocity with the generated velocity data. So that the force distribution could be determined by minimizing the value of objective functional, which is also the difference between the numerical and experimental velocity. Then after utilizing the Lagrange multiplier method, some partial differential equations are formulated consisting the optimal control system to solve. Finite element method and conjugate gradient method are used to discretize equations and deduce the iterative expression of target body force to compute the velocity numerically and body force distribution. Programming environment FreeFEM++ supports the implementation of this model.

Keywords: optimal control model, Stokes equation, finite element method, conjugate gradient method

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Authors: Joseph Uche Azubike, Evelyn Ngozi Agbasi, M. I. Ogbonna

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The audit committee of the board of directors could no longer handle the enterprise's risks. Therefore, a risk management committee was created to control them. Thus, this study examined how enterprise risk management committee characteristics affected Nigerian exchange-listed banks' financial performance from 2013 to 2022. The study's hypotheses and three objectives were to determine how enterprise risk management committee size, composition, and gender diversity affect Nigerian banks' performance. An ex-post facto study design collected secondary data from bank annual reports. We used descriptive statistics, correlation analysis, and Ordinary least square regression to analyze panel data. Enterprise risk management committee size and composition had both negative and no significant effect on bank financial performance in Nigeria, whereas enterprise risk committee gender diversity has a 10% favorable effect. The report advises that adding more women with relevant knowledge to the risk committee to boost performance and allowing women to be at the lead of such risk management could improve bank performance in Nigeria since they are noted to be thorough in their tasks.

Keywords: bank, committee, enterprise, management, performance, risk

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Authors: Santiago Montoya-Ospina, Raúl E. Jiménez-Mejía, Rosa Elvira Correa Gutiérrez

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An experimental methodology is proposed for determining hemi-anechoic characteristics of an engineering acoustic room built at the facilities of Universidad Nacional de Colombia to evaluate the free-field conditions inside the chamber. Experimental results were compared with theoretical ones in both, the source and the sound propagation inside the chamber. Acoustic source was modeled by using monopole radiation pattern from punctual sources and the image method was considered for dealing with the reflective plane of the room, that means, the floor without insulation. Finite-difference time-domain (FDTD) method was implemented to calculate the sound pressure value at every spatial point of the chamber. Comparison between theoretical and experimental data yields to minimum error, giving satisfactory results for the hemi-anechoic characterization of the chamber.

Keywords: acoustic impedance, finite-difference time-domain, hemi-anechoic characterization

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Authors: Ghandehari Motlagh, Mohammad

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Purpose: We tried to find the most common etiologies for anisometropia in pre-op cataract cases: axial or refractive. Methods: In this cross-sectional study ,41 pre-op cataract eyes with more than 0.33 difference between axial lengths of 2 eyes were enrolled.Considered for each 1mm difference between axial lengths in long eyes( AXL more than 25):1.75-2.00 D of anisometropia, for normal eyes(AXL: 22- 25):2.50D and for short eyes (AXL less than 22):3.50-3.75 D as axial anisometropia. If there are more or lesser anisometropia, we recorded as refractive anisometropia. Results: Average of anisometropia :4.24 D, prevalence of PK or LK :1 (2.38%), kc:1(2.38%), glaucoma surgery: 1(2.38%), and pseudophakic status of the opposite eye 8(19.04%). Prevalence of axial anisometropia:21 (52.4%) and refractive anisometropia 20(47.6%).Then on basis of this study we can rely on the patient’s refraction exactly before phaco for evaluation of axial length differences between the 2 eyes, because most of the anisometropias are axial. Conclusion: In most cases, cataract does not induce significant change in refractive error (secondary myopia) and AXL difference between the 2 eyes are correlated with anisometropia.so it can be used for cataract patient’s ocular biometry evaluation. Pre-cataract refraction is a valuable variable should be measured and recorded in routin eye examination.

Keywords: ocular axial length, anisometropia, cataract, ophthalmology and optometry

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Authors: Vijay Pal Singh, Yogesh Chandra Kotiyal

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Structures are a combination of various load carrying members which transfer the loads to the foundation from the superstructure safely. At the design stage, the loading of the structure is defined and appropriate material choices are made based upon their properties, mainly related to strength. The strength of materials kept on reducing with time because of many factors like environmental exposure and deformation caused by unpredictable external loads. Hence, to predict the strength of materials used in structures, various techniques are used. Among these techniques, Non-Destructive Techniques (NDT) are the one that can be used to predict the strength without damaging the structure. In the present study, the compressive strength of concrete has been predicted using Artificial Neural Network (ANN). The predicted strength was compared with the experimentally obtained actual compressive strength of concrete and equations were developed for different models. A good co-relation has been obtained between the predicted strength by these models and experimental values. Further, the co-relation has been developed using two NDT techniques for prediction of strength by regression analysis. It was found that the percentage error has been reduced between the predicted strength by using combined techniques in place of single techniques.

Keywords: rebound, ultra-sonic pulse, penetration, ANN, NDT, regression

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Authors: Okorowo Cyril Agochi

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The Niger Delta Region of Nigeria is home to about 20 million people and 40 different ethnic groups. The region has an area of seventy thousand square kilometers (70,000 KM2) of wetlands, formed primarily by sediments deposition and makes up 7.5 percent of Nigeria's total landmass. The notable ecological zones in this region includes: coastal barrier islands; mangrove swamp forests; fresh water swamps; and lowland rainforests. This incredibly naturally-endowed ecosystem region, which contains one of the highest concentrations of biodiversity on the planet, in addition to supporting abundant flora and fauna, is threatened by the inhuman act known as gas flaring. Gas flaring is the combustion of natural gas that is associated with crude oil when it is pumped up from the ground. In petroleum-producing areas such as the Niger Delta region of Nigeria where insufficient investment was made in infrastructure to utilize natural gas, flaring is employed to dispose of this associated gas. This practice has impoverished the communities where it is practiced, with attendant environmental, economic and health challenges. This paper discusses the adverse environmental and health implication associated with the practice, the role of Government, Policy makers, Oil companies and the Local communities aimed at bring this inhuman practice to a prompt end.

Keywords: natural combustion, emission, environment, flaring, gas, health, Niger Delta

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Authors: Shipra Sharma, Jagat Jerath

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Human resource training is an essential element of talent management in the current era of global competitiveness and dynamic trade in the manufacturing industry. Globally, India is behind only China as the largest textile manufacturer. The major challenges faced by the Indian textile manufacturing Industry are low technology levels, growing skill gaps, unorganized structure, lower efficiencies, etc. indicating the need for constant talent up-gradation. Assessment of training needs from a strategic perspective is an essential step for the formulation of effective training. The paper established the significance of training in the Indian textile industry and to determine the training needs on various parameters as presented. 40 HR personnel/s working in the textile and apparel companies based in the industrial region of Punjab, India, were the respondents for the study. The research tool used in this case was a structured questionnaire as per five-point Likert scale. Statistical analysis through descriptive statistics and chi-square test indicated the increased need for training whenever there were technical changes in the organizations. As per the data presented in this study, most of the HR personnel/s agreed that the variables associated with organizational analysis, task analysis, and individual analysis have a statistically significant role to play in determining the need for training in an organization.

Keywords: Indian textile manufacturing industry, significance of training, training needs analysis, parameters for training needs assessment

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Authors: Riznaldi Akbar

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In this study, we compared the performance of the Artificial Neural Network (ANN) model with back-propagation algorithm in correctly predicting in-sample and out-of-sample external debt crisis in Indonesia. We found that exchange rate, foreign reserves, and exports are the major determinants to experiencing external debt crisis. The ANN in-sample performance provides relatively superior results. The ANN model is able to classify correctly crisis of 89.12 per cent with reasonably low false alarms of 7.01 per cent. In out-of-sample, the prediction performance fairly deteriorates compared to their in-sample performances. It could be explained as the ANN model tends to over-fit the data in the in-sample, but it could not fit the out-of-sample very well. The 10-fold cross-validation has been used to improve the out-of-sample prediction accuracy. The results also offer policy implications. The out-of-sample performance could be very sensitive to the size of the samples, as it could yield a higher total misclassification error and lower prediction accuracy. The ANN model could be used to identify past crisis episodes with some accuracy, but predicting crisis outside the estimation sample is much more challenging because of the presence of uncertainty.

Keywords: debt crisis, external debt, artificial neural network, ANN

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Authors: Radhika Ranjan Roy

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

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

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Authors: Vineetha Bettaiah, Heggere S. Ranganath

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This paper presents a Hierarchical Piecewise Linear Approximation (HPLA) for the representation of time series data in which the time series is treated as a curve in the time-amplitude image space. The curve is partitioned into segments by choosing perceptually important points as break points. Each segment between adjacent break points is recursively partitioned into two segments at the best point or midpoint until the error between the approximating line and the original curve becomes less than a pre-specified threshold. The HPLA representation achieves dimensionality reduction while preserving prominent local features and general shape of time series. The representation permits course-fine processing at different levels of details, allows flexible definition of similarity based on mathematical measures or general time series shape, and supports time series data mining operations including query by content, clustering and classification based on whole or subsequence similarity.

Keywords: data mining, dimensionality reduction, piecewise linear representation, time series representation

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Authors: Meziani Samir, Moussi Abderrahmane, Chaouchi Sofiane, Guendouzi Awatif, Djema Oussama

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Laser doping has a large potential for integration into silicon solar cell technologies. The ability to process local, heavily diffused regions in a self-aligned manner can greatly simplify processing sequences for the fabrication of selective emitter. The choice of laser parameters for a laser doping process with 532nm is investigated. Solid state lasers with different power and speed were used for laser doping. In this work, the aim is the formation of selective emitter solar cells with a reduced number of technological steps. In order to have a highly doped localized emitter region, we used a 532 nm laser doping. Note that this region will receive the metallization of the Ag grid by screen printing. For this, we use SOLIDWORKS software to design a single type of pattern for square silicon cells. Sheet resistances, phosphorus doping concentration and silicon bulk lifetimes of irradiated samples are presented. Additionally, secondary ion mass spectroscopy (SIMS) profiles of the laser processed samples were acquired. Scanning electron microscope and optical microscope images of laser processed surfaces at different parameters are shown and compared.

Keywords: laser doping, selective emitter, silicon, solar cells

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Authors: Moez ul Hassan, Bushra Noman, Sarmad Hameed, Shahab Mehmood, Asma Bashir

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The importance of Noble prize winning “Patch Clamp Technique” is well documented. However, Patch Clamp Technique is very expensive and hence hinders research in developing countries. In this paper, detection, processing and recording of ultra low current from induced cells by using transimpedence amplifier is described. The sensitivity of the proposed amplifier is in the range of femto amperes (fA). Capacitive-feedback is used with active load to obtain a 20MΩ transimpedance gain. The challenging task in designing includes achieving adequate performance in gain, noise immunity and stability. The circuit designed by the authors was able to measure current in the rangeof 300fA to 100pA. Adequate performance shown by the amplifier with different input current and outcome result was found to be within the acceptable error range. Results were recorded using LabVIEW 8.5®for further research.

Keywords: drug discovery, ionic current, operational amplifier, patch clamp

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Authors: Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Lidija Jevrić, Evgenija Djurendić, Jovana Ajduković

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In the present study, the molecular modeling of a series of 24 17-picolyl and 17-picolinylidene androstane derivatives whit significant anticancer activity was carried out. Modelling of studied compounds was performed by CS ChemBioDraw Ultra v12.0 program for drawing 2D molecular structures and CS ChemBio3D Ultra v12.0 for 3D molecular modelling. The obtained 3D structures were subjected to energy minimization using molecular mechanics force field method (MM2). The cutoff for structure optimization was set at a gradient of 0.1 kcal/Åmol. Full geometry optimization was done by the Austin Model 1 (AM1) until the root mean square (RMS) gradient reached a value smaller than 0.0001 kcal/Åmol using Molecular Orbital Package (MOPAC) program. The obtained physicochemical, lipophilicity and topological descriptors were used for analysis of molecular similarities and dissimilarities applying suitable chemometric methods (principal component analysis and cluster analysis). These results are the part of the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina and CMST COST Action CM1306.

Keywords: androstane derivatives, anticancer activity, chemometrics, molecular descriptors

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Authors: Mohammad S. Rouhi, Magdalena Juntikka

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Processing of polymer fiber composites has a remarkable influence on their mechanical performance. These mechanical properties are even more influenced when using recycled reinforcement. Therefore, we place particular attention on the evaluation of micromechanical models to estimate the mechanical properties and compare them against the experimental results of the manufactured composites. For the manufacturing process, an epoxy matrix and carbon fiber production cut-offs as reinforcing material are incorporated using a vacuum infusion process. In addition, continuous textile reinforcement in combination with the epoxy matrix is used as reference material to evaluate the kick-down in mechanical performance of the recycled composite. The experimental results show less degradation of the composite stiffness compared to the strength properties. Observations from the modeling also show the same trend as the error between the theoretical and experimental results is lower for stiffness comparisons than the strength calculations. Yet still, good mechanical performance for specific applications can be expected from these materials.

Keywords: composite recycling, carbon fibers, mechanical properties, micromechanics

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Authors: Kareem Abdelwahed, Ahmed Salama, Ahmed Rabie, Ahmed Hamdy, Waleed Abdelfattah, Ahmed Abd El-Rahman

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Conventional vapor-compression refrigeration systems rely on typical refrigerants, such as CFC, HCFC and ammonia. Despite of their suitable thermodynamic properties and their stability in the atmosphere, their corresponding global warming potential and ozone depletion potential raise concerns about their usage. Thus, the need for new refrigeration systems, which are environment-friendly, inexpensive and simple in construction, has strongly motivated the development of thermoacoustic energy conversion systems. A thermoacoustic refrigerator (TAR) is a device that is mainly consisting of a resonator, a stack and two heat exchangers. Typically, the resonator is a long circular tube, made of copper or steel and filled with Helium as a the working gas, while the stack has short and relatively low thermal conductivity ceramic parallel plates aligned with the direction of the prevailing resonant wave. Typically, the resonator of a standing-wave refrigerator has one end closed and is bounded by the acoustic driver at the other end enabling the propagation of half-wavelength acoustic excitation. The hot and cold heat exchangers are made of copper to allow for efficient heat transfer between the working gas and the external heat source and sink respectively. TARs are interesting because they have no moving parts, unlike conventional refrigerators, and almost no environmental impact exists as they rely on the conversion of acoustic and heat energies. Their fabrication process is rather simpler and sizes span wide variety of length scales. The viscous and thermal interactions between the stack plates, heat exchangers' plates and the working gas significantly affect the flow field within the plates' channels, and the energy flux density at the plates' surfaces, respectively. Here, the design, the manufacture and the testing of a compact refrigeration system that is based on the thermoacoustic energy-conversion technology is reported. A 1-D linear acoustic model is carefully and specifically developed, which is followed by building the hardware and testing procedures. The system consists of two harmonically-oscillating pistons driven by a simple 1-HP rotary drive mechanism operating at a frequency of 42Hz -hereby, replacing typical expensive linear motors and loudspeakers-, and a thermoacoustic stack within which the energy conversion of sound into heat is taken place. Air at ambient conditions is used as the working gas while the amplitude of the driver's displacement reaches 19 mm. The 30-cm-long stack is a simple porous ceramic material having 100 square channels per square inch. During operation, both oscillating-gas pressure and solid-stack temperature are recorded for further analysis. Measurements show a maximum temperature difference of about 27 degrees between the stack hot and cold ends with a Carnot coefficient of performance of 11 and estimated cooling capacity of five Watts, when operating at ambient conditions. A dynamic pressure of 7-kPa-amplitude is recorded, yielding a drive ratio of 7% approximately, and found in a good agreement with theoretical prediction. The system behavior is clearly non-linear and significant non-linear loss mechanisms are evident. This work helps understanding the operation principles of thermoacoustic refrigerators and presents a keystone towards developing commercial thermoacoustic refrigerator units.

Keywords: refrigeration system, rotary drive mechanism, standing-wave, thermoacoustic refrigerator

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Authors: Harisudha Kuresan, Dhanalakshmi Samiappan, T. Rama Rao

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In modern telecommunication applications, Glottal Closure Instants location finding is important and is directly evaluated from the speech waveform. Here, we study the GCI using Speech Event Detection using Residual Excitation and the Mean Based Signal (SEDREAMS) algorithm. Speech coding uses parameter estimation using audio signal processing techniques to model the speech signal combined with generic data compression algorithms to represent the resulting modeled in a compact bit stream. This paper proposes a sub-band coder SBC, which is a type of transform coding and its performance for GCI detection using SEDREAMS are evaluated. In SBCs code in the speech signal is divided into two or more frequency bands and each of these sub-band signal is coded individually. The sub-bands after being processed are recombined to form the output signal, whose bandwidth covers the whole frequency spectrum. Then the signal is decomposed into low and high-frequency components and decimation and interpolation in frequency domain are performed. The proposed structure significantly reduces error, and precise locations of Glottal Closure Instants (GCIs) are found using SEDREAMS algorithm.

Keywords: SEDREAMS, GCI, SBC, GOI

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Authors: Boukhiar Aissa, Halladj Fatima, Iguergaziz Nadia, Lamrani yasmina, Benamara Salem

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Determining the alcohol content in alcoholic fermentation bioprocess is of great importance. In fact, it is a key indicator for monitoring this bioprocess. Several methodologies (chemical, spectrophotometric, chromatographic) are used to the determination of this parameter. However, these techniques are very long and they require: rigorous preparations, sometimes dangerous chemical reagents and/or expensive equipment. In the present study, the date juice is used as the substrate of alcoholic fermentation. The extracted juice undergoes an alcoholic fermentation by Saccharomyces cerevisiae. The study of the possible use of refractometry as a sole means for the in situ control of alcoholic fermentation revealed a good correlation (R2=0.98) between initial and final °Brix: °Brixf=0.377×°Brixi. In addition, the relationship between Δ°Brix and alcoholic content of the final product (A,%) has been determined: Δ°Brix/A=1.1. The obtained results allowed us to establish iso-responses abacus, which can be used for the determination of alcohol and residual sugar content, with a mean relative error (MRE) of 5.35%.

Keywords: alcoholic fermentation, date juice, refractometry, residual sugar

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Authors: Muhammad Tariq, Hammad Tahir, Fawwad Mahmood Butt

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Purpose: This study attempts to examine the best forecasting methodologies in the time series. The time series forecasting models such as VAR, ARCH and the ARMA are considered for the analysis. Methodology: The Bench Marks or the parameters such as Adjusted R square, F-stats, Durban Watson, and Direction of the roots have been critically and empirically analyzed. The empirical analysis consists of time series data of Consumer Price Index and Closing Stock Price. Findings: The results show that the VAR model performed better in comparison to other models. Both the reliability and significance of VAR model is highly appreciable. In contrary to it, the ARCH model showed very poor results for forecasting. However, the results of ARMA model appeared double standards i.e. the AR roots showed that model is stationary and that of MA roots showed that the model is invertible. Therefore, the forecasting would remain doubtful if it made on the bases of ARMA model. It has been concluded that VAR model provides best forecasting results. Practical Implications: This paper provides empirical evidences for the application of time series forecasting model. This paper therefore provides the base for the application of best time series forecasting model.

Keywords: forecasting, time series, auto regression, ARCH, ARMA

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Authors: Sachin Saxena, Manju Srivastava

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The present study describes a stable, highly sensitive and selective analytical sensor. Fe-BTC was synthesized at room temperature using the noble Iron-trimesate system. The high surface area of as synthesized Fe-BTC proved MOFs as ideal modifiers for glassy carbon electrode. The characterization techniques such as TGA, XRD, FT-IR, BET (BET surface area= 1125 m2/gm) analysis explained the electrocatalytic behaviour of Fe-BTC towards these two drugs. The material formed is cost effective and exhibit higher catalytic behaviour towards analyte systems. The synergism between synthesized Fe-BTC and electroanalytical techniques helped in developing a highly sensitive analytical method for studying the redox fate of ARP and RZ, respectively. Cyclic voltammetry of ferricyanide system proved Fe-BTC/GCE with an increase in 132% enhancement in peak current value as compared to that of GCE. The response characteristics of cyclic voltammetry (CV) and square wave voltammetry (SWV) revealed that the ARP and RZ could be effectively accumulated at Fe-BTC/GCE. On the basis of the electrochemical measurements, electrode dynamics parameters have been evaluated. Present study opens up new field of applications of MOFs modified GCE for drug sensing.

Keywords: MOFs, anti-psychotic, electrochemical sensor, anti-migraine drugs

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Authors: Maamar Fatouma

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Mirror optical performance can experience varying levels of stress and tolerances, which can have a notable impact on optical parametric systems. to ensure proper optical figure and position of mirror mounting within design tolerances, it is crucial to have a robust support structure in place for optical systems. The optical figure tolerance determines the allowable deviation from the ideal form of the mirror and the position tolerance determines the location and orientations of the optical axis of the optical systems. A variety of factors influence the optical figure of the mirror. Included are self-weight (Deflection), excitation from temperature change, temperature gradients and dimensional instability. This study employs an analytical approach and finite element method to examine the effects of stress resulting from mirror mounting on the wavefront passing through the mirror. The combined effect of tolerance and deflection on mirror performance is represented by an error budget. Numerical mirror mounting is presented to illustrate the space application of performance techniques.

Keywords: opto-mechanical, bonded optic, tolerance, self-weight distortion, Rayleigh criteria

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Authors: Faranak Omidian

Abstract:

The aim of this research was to examine the quality of education service at master's level in the Islamic Azad University of Dezful. In terms of objective, this is an applied research and in regard to methodology, it is a descriptive analytical research. The statistical population included all students of master's degree in the Islamic Azad University of Dezful. The sample size was determined using stratified random sampling method in different fields of study. The research questionnaire is the translated version of standardized Abdullah's HEdPERF 41-item scale which is based on a 5-point Likert scale. In order to determine the validity, the translated questionnaire was given to the professors of educational sciences. The correlation among all questions has been regarded at a value of 0.644. The results showed that the quality of educational service at master's level in this university, based on chi-square goodness of fit test, was equal to 73.36 and its degree of freedom was 2 at a significant level of 0.001, indicating the low desirability of the services. According to Friedman test, academic responsiveness has been reported to be in a higher status than other dimensions with an average rank of 3.94 while accessibility, with an average rank of 2.15, has been in the lowest status from master's students' viewpoint.

Keywords: educational service quality, master's level, Iranian university

Procedia PDF Downloads 265

Authors: Budi Nurani Ruchjana, Atje Setiawan Abdullah, I. Gede Nyoman Mindra Jaya, Eddy Hermawan

Abstract:

In this paper, we propose the application package to estimate parameters of spatiotemporal model based on the multivariate time series analysis using the R open-source software. We build packages mainly to estimate the parameters of the Generalized Space Time Autoregressive (GSTAR) model. GSTAR is a combination of time series and spatial models that have parameters vary per location. We use the method of Ordinary Least Squares (OLS) and use the Mean Average Percentage Error (MAPE) to fit the model to spatiotemporal real phenomenon. For case study, we use oil production data from volcanic layer at Jatibarang Indonesia or climate data such as rainfall in Indonesia. Software R is very user-friendly and it is making calculation easier, processing the data is accurate and faster. Limitations R script for the estimation of model parameters spatiotemporal GSTAR built is still limited to a stationary time series model. Therefore, the R program under windows can be developed either for theoretical studies and application.

Keywords: GSTAR Model, MAPE, OLS method, oil production, R software

Procedia PDF Downloads 225

Authors: Triano Nurhikmat

Abstract:

Along with the progress of science and technology, the development of the industrialized world in Indonesia took place very rapidly. This leads to a process of industrialization of society Indonesia faster with the establishment of the company and the workplace are diverse. Development of the industry relates to the activity of the worker. Where in these work activities do not cover the possibility of an impending crash on either the workers or on a construction project. The cause of the occurrence of industrial accidents was the fault of electrical damage, work procedures, and error technique. The method of an association rule is one of the main techniques in data mining and is the most common form used in finding the patterns of data collection. In this research would like to know how relations of the association between the incidence of any industrial accidents. Therefore, by using methods of analysis association rule patterns associated with combination obtained two iterations item set (2 large item set) when every factor of industrial accidents with a West Jakarta so industrial accidents caused by the occurrence of an electrical value damage = 0.2 support and confidence value = 1, and the reverse pattern with value = 0.2 support and confidence = 0.75.

Keywords: association rule, data mining, industrial accidents, rules

Procedia PDF Downloads 276

Authors: Debabrata Das

Abstract:

This paper explains how to design a regulation loop for a power supply circuit. It also discusses the need of a regulation loop and the improvement of a circuit with regulation loop. A sample design is used to demonstrate how to use PSpice to design feedback loop to control output voltage of a power supply and how to check if the power supply is stable or oscillatory. A sample design is made using a specific Integrated Circuit (IC) available in the PSpice library. A designer can experiment feedback loop design using Cadence Pspice tool. PSpice is easy to use, reliable, and convenient. To test a feedback loop, generally, engineers use trial and error method with the hardware which takes a lot of time and manpower. Moreover, it is expensive because component and Printed Circuit Board (PCB) may go bad. PSpice can be used by designers to test their loop designs without using hardware circuits. A designer can save time, cost, manpower and simulate his/her power supply circuit accurately before making a real hardware using this software package.

Keywords: power electronics, feedback loop, regulation, stability, pole, zero, oscillation

Procedia PDF Downloads 333