Search results for: error estimation

Authors: Navdeep Goel, Salvador Gabarda

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Digital images are widely used in computer applications. To store or transmit the uncompressed images requires considerable storage capacity and transmission bandwidth. Image compression is a means to perform transmission or storage of visual data in the most economical way. This paper explains about how images can be encoded to be transmitted in a multiplexing time-frequency domain channel. Multiplexing involves packing signals together whose representations are compact in the working domain. In order to optimize transmission resources each 4x4 pixel block of the image is transformed by a suitable polynomial approximation, into a minimal number of coefficients. Less than 4*4 coefficients in one block spares a significant amount of transmitted information, but some information is lost. Different approximations for image transformation have been evaluated as polynomial representation (Vandermonde matrix), least squares + gradient descent, 1-D Chebyshev polynomials, 2-D Chebyshev polynomials or singular value decomposition (SVD). Results have been compared in terms of nominal compression rate (NCR), compression ratio (CR) and peak signal-to-noise ratio (PSNR) in order to minimize the error function defined as the difference between the original pixel gray levels and the approximated polynomial output. Polynomial coefficients have been later encoded and handled for generating chirps in a target rate of about two chirps per 4*4 pixel block and then submitted to a transmission multiplexing operation in the time-frequency domain.

Keywords: chirp signals, image multiplexing, image transformation, linear canonical transform, polynomial approximation

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Authors: Douglas J. Kube

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At the beginning of the COVID-19 pandemic, the Canadian Red Cross deployed workers into long-term care homes across Canada to support our most vulnerable citizens. It began by recruiting and training small teams of workers to provide non-clinical services for facilities in outbreak. Deployed workers were trained on an approach based on successful Red Cross deployments used with Ebola in which zones were established, levels of protection used, and strict protocols followed to prevent exposure. This paper addresses aspects of human performance through a safety culture lens. The Red Cross deployments highlight valuable insights and are an excellent case study in the principles of human performance and organizational culture. This paper looks at human performance principles, including human fallibility, predictability of error-likely situations, avoiding events by understanding reasons mistakes occur, and the influence on behaviour by organizational factors. This study demonstrates how the Red Cross’s organizational culture and work design positively influenced performance to protect workers and residents/clients. Lastly, this paper shares lessons that can be applied in many workplaces to improve worker health and safety and safety culture. This critical examination is based on the author’s experience as a Senior Occupational Health and Safety Advisor with the Red Cross during the pandemic as part of the team responsible for developing and implementing biological safety practices in long-term care deployments.

Keywords: COVID, human performance, organizational culture, work design

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Authors: Lai Wenfang

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Tthis study will describe how to use artificial intelligence (AI) technology to build the user-oriented platform for integrated archival service. The platform will be launched in 2020 by the National Archives Administration (NAA) in Taiwan. With the progression of information communication technology (ICT) the NAA has built many systems to provide archival service. In order to cope with new challenges, such as new ICT, artificial intelligence or blockchain etc. the NAA will try to use the natural language processing (NLP) and machine learning (ML) skill to build a training model and propose suggestions based on the data sent to the platform. NAA expects the platform not only can automatically inform the sending agencies’ staffs which records catalogues are against the transfer or destroy rules, but also can use the model to find the details hidden in the catalogues and suggest NAA’s staff whether the records should be or not to be, to shorten the auditing time. The platform keeps all the users’ browse trails; so that the platform can predict what kinds of archives user could be interested and recommend the search terms by visualization, moreover, inform them the new coming archives. In addition, according to the Archives Act, the NAA’s staff must spend a lot of time to mark or remove the personal data, classified data, etc. before archives provided. To upgrade the archives access service process, the platform will use some text recognition pattern to black out automatically, the staff only need to adjust the error and upload the correct one, when the platform has learned the accuracy will be getting higher. In short, the purpose of the platform is to deduct the government digital transformation and implement the vision of a service-oriented smart government.

Keywords: artificial intelligence, natural language processing, machine learning, visualization

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Authors: Mubarek Alpkiray, Tan Nguyen, Arild Saasen

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Geothermal drilling operations contain numerous challenges that are encountered to increase the well cost and nonproductive time. Fluid loss is one of the most undesirable troublesome that can cause well abandonment in geothermal drilling. Lost circulation can be seen due to natural fractures, high mud weight, and extremely high formation temperatures. This challenge may cause wellbore stability problems and lead to expensive drilling operations. Wellbore stability is the main domain that should be considered to mitigate or prevent fluid loss into the formation. This paper describes the causes of fluid loss in the Pamukoren geothermal field in Turkey. A geomechanics approach integration and assessment is applied to help the understanding of fluid loss problems. In geothermal drillings, geomechanics is primarily based on rock properties, in-situ stress characterization, the temperature of the rock, determination of stresses around the wellbore, and rock failure criteria. Since a high-temperature difference between the wellbore wall and drilling fluid is presented, temperature distribution through the wellbore is estimated and implemented to the wellbore stability approach. This study reviewed geothermal drilling data to analyze temperature estimation along the wellbore, the cause of fluid loss and stored electric capacity of the reservoir. Our observation demonstrates the geomechanical approach's significant role in understanding safe drilling operations on high-temperature wells. Fluid loss is encountered due to thermal stress effects around the borehole. This paper provides a wellbore stability analysis for a geothermal drilling operation to discuss the causes of lost circulation resulting in nonproductive time and cost.

Keywords: geothermal wells, drilling, wellbore stresses, drilling fluid loss, thermal stress

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Authors: Mahsa Jalili, Trude Johansen, Signe Dille Lovmo, Turid Rustad, Rolf Erik Olsen, Atle M. Bones

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The quality of fish muscle is a key factor in fish industry, and dietary ingredients can influence fish quality. The aim of this study was to examine the impact of krill meal, soybean meal, Bactocell® and butyrate fortified feeds and control diet on characteristics of salmon fillet. Thirty Atlantic salmon (6 per each group) were farmed for 12 weeks. All the fish were killed and frozen immediately. The white muscle from top posterior part of dorsal fin was dissected to analyze fat content, carotenoid content, content of water-soluble and salt-soluble proteins, cathepsin B and cathepsin B-L activities. ANOVA test was used to analyze mean and standard error of mean values at 0.05 significance level. There were significant difference in cathepsin B activity, water-soluble proteins and salt-soluble proteins (p-value= 0.005, 0.009 and 0.002). The mean values of other factors were not significantly different among the groups. Cathepsin B activity was higher in soymeal group. Water-soluble proteins were reported higher in soy meal and krill groups and salt-soluble proteins were significantly higher in soy meal and butyrate rich diets. Although soy meal has proven effect on enteritis, it results in higher percentage of protein in fillets. On the other hand, this feeding may have role in textural deterioration of fillets owing to higher values of endogenous cathepsin B in soymeal group.

Keywords: aquaculture, food quality, Krill protein extract, prebiotics, probiotics, Salmo salar, soy

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Authors: E. A. Krasikov

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Influence of neutron irradiation on RPV steel degradation are examined with reference to the possible reasons of the substantial experimental data scatter and furthermore – nonstandard (non-monotonous) and oscillatory embrittlement behavior. In our glance, this phenomenon may be explained by presence of the wavelike component in the embrittlement kinetics. We suppose that the main factor affecting steel anomalous embrittlement is fast neutron intensity (dose rate or flux), flux effect manifestation depends on state-of-the-art fluence level. At low fluencies, radiation degradation has to exceed normative value, then approaches to normative meaning and finally became sub normative. Data on radiation damage change including through the ex-service RPVs taking into account chemical factor, fast neutron fluence and neutron flux were obtained and analyzed. In our opinion, controversy in the estimation on neutron flux on radiation degradation impact may be explained by presence of the wavelike component in the embrittlement kinetics. Therefore, flux effect manifestation depends on fluence level. At low fluencies, radiation degradation has to exceed normative value, then approaches to normative meaning and finally became sub normative. Moreover as a hypothesis we suppose that at some stages of irradiation damaged metal have to be partially restored by irradiation i.e. neutron bombardment. Nascent during irradiation structure undergo occurring once or periodically transformation in a direction both degradation and recovery of the initial properties. According to our hypothesis, at some stage(s) of metal structure degradation neutron bombardment became recovering factor. As a result, oscillation arises that in turn leads to enhanced data scatter.

Keywords: annealing, embrittlement, radiation, RPV steel

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Authors: Risikat Oladoyin S. Dauda, Onyebuchi Iwegbu

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This study investigates the response of human development outcomes to selected macroeconomic indicators in Nigeria. Human development outcomes is measured by human development index while the selected macroeconomic variables are inflation rate, real interest rate, government capital expenditure, real exchange rate, current account balance, and savings. Structural Vector Autoregression (SVAR) technique is employed in examining the response of human development index to the macroeconomic shocks. The result from the forecast error variance decomposition and Impulse-Response analysis reveals that fiscal policy (government capital expenditure) shock is the greatest determinant of human development outcomes. This result reiterates the role which the government plays in improving the welfare of the citizenry. The fiscal policy tool is pivotal in human development which comes in the form of investment in education, health, housing, and infrastructure. Further conclusion drawn from this study is that human development outcome positively and significantly responds to shocks from real interest rate, a monetary policy transmission variable and is felt greatly in the short run period. The policy implication of this study is that if capital budget implementation falls below expectations, human development will be engendered. Hence, efforts should be made to ensure that full implementation and appraisal of government capital expenditure is taken sacrosanct as any shock from such plan, engenders human development outcome.

Keywords: human development outcome, macroeconomic outcomes, structural vector autoregression, SVAR

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Authors: Thamir K. Ibrahim, M. M. Rahman, Marwah Noori Mohammed

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This study deals with modeling and performance enhancements of a gas-turbine combined cycle power plant. A clean and safe energy is the greatest challenges to meet the requirements of the green environment. These requirements have given way the long-time governing authority of steam turbine (ST) in the world power generation, and the gas turbine (GT) will replace it. Therefore, it is necessary to predict the characteristics of the GT system and optimize its operating strategy by developing a simulation system. The integrated model and simulation code for exploiting the performance of gas turbine power plant are developed utilizing MATLAB code. The performance code for heavy-duty GT and CCGT power plants are validated with the real power plant of Baiji GT and MARAFIQ CCGT plants the results have been satisfactory. A new technology of correlation was considered for all types of simulation data; whose coefficient of determination (R2) was calculated as 0.9825. Some of the latest launched correlations were checked on the Baiji GT plant and apply error analysis. The GT performance was judged by particular parameters opted from the simulation model and also utilized Adaptive Neuro-Fuzzy System (ANFIS) an advanced new optimization technology. The best thermal efficiency and power output attained were about 56% and 345MW respectively. Thus, the operation conditions and ambient temperature are strongly influenced on the overall performance of the GT. The optimum efficiency and power are found at higher turbine inlet temperatures. It can be comprehended that the developed models are powerful tools for estimating the overall performance of the GT plants.

Keywords: gas turbine, optimization, ANFIS, performance, operating conditions

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Authors: Khalifa Maha, Ben Othman Hakim, Khaled Hussainey

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Prior empirical studies have investigated the economic consequences of accounting conservatism by examining its impact on the cost of equity capital (COEC). However, findings are not conclusive. We assume that inconsistent results of such association may be attributed to the regression models used in data analysis. To address this issue, we re-examine the effect of different dimension of accounting conservatism: unconditional conservatism (U_CONS) and conditional conservatism (C_CONS) on the COEC for a sample of listed firms from Middle Eastern and North Africa (MENA) countries, applying quantile regression (QR) approach developed by Koenker and Basset (1978). While classical ordinary least square (OLS) method is widely used in empirical accounting research, however it may produce inefficient and bias estimates in the case of departures from normality or long tail error distribution. QR method is more powerful than OLS to handle this kind of problem. It allows the coefficient on the independent variables to shift across the distribution of the dependent variable whereas OLS method only estimates the conditional mean effects of a response variable. We find as predicted that U_CONS has a significant positive effect on the COEC however, C_CONS has a negative impact. Findings suggest also that the effect of the two dimensions of accounting conservatism differs considerably across COEC quantiles. Comparing results from QR method with those of OLS, this study throws more lights on the association between accounting conservatism and COEC.

Keywords: unconditional conservatism, conditional conservatism, cost of equity capital, OLS, quantile regression, emerging markets, MENA countries

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Authors: S. Khakurel, R. P. Dhakal, T. Z. Yeow

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Downtime is one of the major sources (alongside damage and injury/death) of financial loss incurred by a structure in an earthquake. The length of downtime associated with a building after an earthquake varies depending on the time taken for the reaction (to the earthquake), decision (on the future course of action) and execution (of the decided course of action) phases. Post-earthquake assessment of buildings is a key step in the decision making process to decide the appropriate safety placarding as well as to decide whether a damaged building is to be repaired or demolished. The aim of the present study is to develop a model to quantify downtime associated with the post-earthquake building-assessment phase in terms of two parameters; i) duration of the different assessment phase; and ii) probability of different colour tagging. Post-earthquake assessment of buildings includes three stages; Level 1 Rapid Assessment including a fast external inspection shortly after the earthquake, Level 2 Rapid Assessment including a visit inside the building and Detailed Engineering Evaluation (if needed). In this study, the durations of all three assessment phases are first estimated from the total number of damaged buildings, total number of available engineers and the average time needed for assessing each building. Then, probability of different tag colours is computed from the 2010-11 Canterbury earthquake Sequence database. Finally, a downtime model for the post-earthquake building inspection phase is proposed based on the estimated phase length and probability of tag colours. This model is expected to be used for rapid estimation of seismic downtime within the Loss Optimisation Seismic Design (LOSD) framework.

Keywords: assessment, downtime, LOSD, Loss Optimisation Seismic Design, phase length, tag color

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Authors: Daniel Pinheiro Da Silva Junior, Aline Paes, Daniel De Oliveira, Christiano Lacerda Ghuerren, Marcio Duran

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The Courts of Accounts are institutions responsible for overseeing and point out irregularities of Public Administration expenses. They have a high demand for processes to be analyzed, whose decisions must be grounded on severity laws. Despite the existing large amount of processes, there are several cases reporting similar subjects. Thus, previous decisions on already analyzed processes can be a precedent for current processes that refer to similar topics. Identifying similar topics is an open, yet essential task for identifying similarities between several processes. Since the actual amount of topics is considerably large, it is tedious and error-prone to identify topics using a pure manual approach. This paper presents a tool based on Machine Learning and Natural Language Processing to assists in building a labeled dataset. The tool relies on Topic Modelling with Latent Dirichlet Allocation to find the topics underlying a document followed by Jensen Shannon distance metric to generate a probability of similarity between documents pairs. Furthermore, in a case study with a corpus of decisions of the Rio de Janeiro State Court of Accounts, it was noted that data pre-processing plays an essential role in modeling relevant topics. Also, the combination of topic modeling and a calculated distance metric over document represented among generated topics has been proved useful in helping to construct a labeled base of similar and non-similar document pairs.

Keywords: courts of accounts, data labelling, document similarity, topic modeling

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Authors: Takeshi Takemoto, Naoya Suzuki, Naohisa Takagaki, Satoru Komori, Masako Terui, George Truscott

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Drag coefficient is an important parameter in order to correctly estimate the air-sea momentum flux. However, The parameterization of the drag coefficient hasn’t been established due to the variation in the field data. Instead, a number of drag coefficient model formulae have been proposed, even though almost all these models haven’t discussed the extreme wind speed range. With regards to such models, it is unclear how the drag coefficient changes in the extreme wind speed range as the wind speed increased. In this study, we investigated the effect of the drag coefficient models concerning the air-sea momentum flux in the extreme wind range on a global scale, comparing two different drag coefficient models. Interestingly, one model didn’t discuss the extreme wind speed range while the other model considered it. We found that the difference of the models in the annual global air-sea momentum flux was small because the occurrence frequency of strong wind was approximately 1% with a wind speed of 20m/s or more. However, we also discovered that the difference of the models was shown in the middle latitude where the annual mean air-sea momentum flux was large and the occurrence frequency of strong wind was high. In addition, the estimated data showed that the difference of the models in the drag coefficient was large in the extreme wind speed range and that the largest difference became 23% with a wind speed of 35m/s or more. These results clearly show that the difference of the two models concerning the drag coefficient has a significant impact on the estimation of a regional air-sea momentum flux in an extreme wind speed range such as that seen in a tropical cyclone environment. Furthermore, we estimated each air-sea momentum flux using several kinds of drag coefficient models. We will also provide data from an observation tower and result from CFD (Computational Fluid Dynamics) concerning the influence of wind flow at and around the place.

Keywords: air-sea interaction, drag coefficient, air-sea momentum flux, CFD (Computational Fluid Dynamics)

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

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This paper presents an approach for the easy creation of an institutional risk profile for endangerment analysis of file formats. The main contribution of this work is the employment of data mining techniques to support risk factors set up with just the most important values that are important for a particular organisation. Subsequently, the risk profile employs fuzzy models and associated configurations for the file format metadata aggregator to support digital preservation experts with a semi-automatic estimation of endangerment level for file formats. Our goal is to make use of a domain expert knowledge base aggregated from a digital preservation survey in order to detect preservation risks for a particular institution. Another contribution is support for visualisation and analysis of risk factors for a requried dimension. The proposed methods improve the visibility of risk factor information and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and automatically aggregated file format metadata from linked open data sources. To facilitate decision-making, the aggregated information about the risk factors is presented as a multidimensional vector. The goal is to visualise particular dimensions of this vector for analysis by an expert. The sample risk profile calculation and the visualisation of some risk factor dimensions is presented in the evaluation section.

Keywords: digital information management, file format, endangerment analysis, fuzzy models

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Authors: Hamid Kazemi, Abbasali Sadeghi

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Assessment of seismic behavior of frame structures is just done for evaluating life and financial damages or lost. The new structural seismic behavior assessment methods have been proposed, so it is necessary to define a formulation as a damage index, which the damage amount has been quantified and qualified. In this paper, four new steel moment-resisting frames with intermediate ductility and different height (2, 5, 8, and 12-story) with regular geometry and simple rectangular plan were supposed and designed. The three existing groups’ damage indices were studied, each group consisting of local index (Drift, Maximum Roof Displacement, Banon Failure, Kinematic, Banon Normalized Cumulative Rotation, Cumulative Plastic Rotation and Ductility), global index (Roufaiel and Meyer, Papadopoulos, Sozen, Rosenblueth, Ductility and Base Shear), and story (Banon Failure and Inter-story Rotation). The necessary parameters for these damage indices have been calculated under the effect of far-fault ground motion records by Non-linear Dynamic Time History Analysis. Finally, prioritization of damage indices is defined based on more conservative values in terms of more damageability rate. The results show that the selected damage index has an important effect on estimation of the damage state. Also, failure, drift, and Rosenblueth damage indices are more conservative indices respectively for local, story and global damage indices.

Keywords: damage index, far-fault ground motion records, non-linear time history analysis, SeismoStruct software, steel moment-resisting frame

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Authors: Jeries J. Abou-Hanna

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Several techniques exist for determining stress-intensity factors in linear elastic fracture mechanics analysis. These techniques are based on analytical, numerical, and empirical approaches that have been well documented in literature and engineering handbooks. However, not all techniques share the same merit. In addition to overly-conservative results, the numerical methods that require extensive computational effort, and those requiring copious user parameters hinder practicing engineers from efficiently evaluating stress-intensity factors. This paper investigates the prospects of reducing the complexity and required variables to determine stress-intensity factors through the utilization of the stress gradient and a weighting function. The heart of this work resides in the understanding that fracture emanating from stress concentration locations cannot be explained by a single maximum stress value approach, but requires use of a critical volume in which the crack exists. In order to understand the effectiveness of this technique, this study investigated components of different notch geometry and varying levels of stress gradients. Two forms of weighting functions were employed to determine stress-intensity factors and results were compared to analytical exact methods. The results indicated that the “exponential” weighting function was superior to the “absolute” weighting function. An error band +/- 10% was met for cases ranging from a steep stress gradient in a sharp v-notch to the less severe stress transitions of a large circular notch. The incorporation of the proposed method has shown to be a worthwhile consideration.

Keywords: fracture mechanics, finite element method, stress intensity factor, stress gradient

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Authors: Meenakshi Verma, Mandeep Singh Bakshi, Kultar Singh

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Magnetic nanoparticles have received incredible importance in view of their diverse applications, which arise primarily due to their response to the external magnetic field. The magnetic behaviour of magnetic nanoparticles (NPs) helps them in numerous different ways. The most important amongst them is the ease with which they can be purified and also can be separated from the media in which they are present merely by applying an external magnetic field. This exceptional ease of separation of the magnetic NPs from an aqueous media enables them to use for extracting/removing metal pollutants from complex aqueous medium. Functionalized magnetic NPs can be subjected for the metallic impurities extraction if are favourably adsorbed on the NPs surfaces. We have successfully used the magnetic NPs as vehicles for gold and silver NPs removal from the complex fluids. The NPs loaded with gold and silver NPs pollutant fractions has been easily removed from the aqueous media by using external magnetic field. Similarly, we have used the magnetic NPs for extraction of protein from complex media and then constantly washed with pure water to eliminate the unwanted surface adsorbed components for quantitative estimation. The purified and protein loaded magnetic NPs are best analyzed with SDS Page to not only for characterization but also for separating the protein fractions. A collective review of the results indicates that we have synthesized surfactant coated iron oxide NPs and then functionalized these with selected materials. These surface active magnetic NPs work very well for the extraction of metallic NPs from the aqueous bulk and make the whole process environmentally sustainable. Also, magnetic NPs-Au/Ag/Pd hybrids have excellent protein extracting properties. They are much easier to use in order to extract the magnetic impurities as well as protein fractions under the effect of external magnetic field without any complex conventional purification methods.

Keywords: magnetic nanoparticles, protein, functionalized, extraction

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Authors: V. Nigam, V. Nambiar

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Aegle Marmelos leaf has been used as a remedy for various gastrointestinal infections and lowering blood sugar level in traditional system of medicine in India due to the presence of various constituents such as flavonoids, tannins and alkaloids (eg. Aegelin, Marmelosin, Luvangetin).The objective of the present study was to evaluate the total antioxidant activity, total and individual phenol content of the wild and cultivated variety of Aegle marmelos leaves to assess the role of this plant in ethanomedicine in India. The methanolic extracts of the leaves were screened for total antioxidant capacity through Ferric Reducing Antioxidant Potential (FRAP) and 1, 1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging assay; Total Phenol content (TPC) through spectrophotometric technique based on Folin Ciocalteau assay and for qualitative estimation of phenols, High performance Liquid Chromatography was used. The TPC of wild and cultivated variety was 7.6% and 6.5% respectively whereas HPLC analysis for quantification of individual polyphenol revealed the presence of gallic acid, chlorogenic acid and Ferullic acid in wild variety whereas gallic acid, Ferullic acid and pyrocatechol in cultivated variety. FRAP values and IC 50 value (DPPH) for wild and cultivated variety was 14.65 μmol/l and 11.80μmol/l; 437 μg/ml and 620μg/ml respectively and thus it can be used as potential inhibitor of free radicals. The wild variety was having more antioxidant capacity than the cultivated one it can be exploited further for its therapeutic application. As Aegle marmelos is rich in antioxidant, it can be used as food additives to delay the oxidative deterioration of foods and as nutraceutical in medicinal formulation against degenerative diseases like diabetes.

Keywords: antioxidant activity, aegle marmelos, antidiabetic, nutraceutical

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Authors: S. Agheb, G. Ledwich, G.Walker, Z.Tong

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Frequency control has become more of concern for reliable operation of interconnected power systems due to the integration of low inertia renewable energy sources to the grid and their volatility. Also, in case of a sudden fault, the system has less time to recover before widespread blackouts. Electric Vehicles (EV)s have the potential to cooperate in the Emergency Frequency Regulation (EFR) by a nonlinear control of the power system in case of large disturbances. The time is not adequate to communicate with each individual EV on emergency cases, and thus, an aggregate model is necessary for a quick response to prevent from much frequency deviation and the occurrence of any blackout. In this work, an aggregate of EVs is modelled as a big virtual battery in each area considering various aspects of uncertainty such as the number of connected EVs and their initial State of Charge (SOC) as stochastic variables. A control law was proposed and applied to the aggregate model using Lyapunov energy function to maximize the rate of reduction of total kinetic energy in a two-area network after the occurrence of a fault. The control methods are primarily based on the charging/ discharging control of available EVs as shunt capacity in the distribution system. Three different cases were studied considering the locational aspect of the model with the virtual EV either in the center of the two areas or in the corners. The simulation results showed that EVs could help the generator lose its kinetic energy in a short time after a contingency. Earlier estimation of possible contributions of EVs can help the supervisory control level to transmit a prompt control signal to the subsystems such as the aggregator agents and the grid. Thus, the percentage of EVs contribution for EFR will be characterized in the future as the goal of this study.

Keywords: emergency frequency regulation, electric vehicle, EV, aggregation, Lyapunov energy function

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Authors: Mona Hejazi, Ali Motie Nasrabadi

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Epilepsy is a persistent neurological disorder that affects more than 50 million people worldwide. Hence, there is a necessity to introduce an efficient prediction model for making a correct diagnosis of the epileptic seizure and accurate prediction of its type. In this study we consider how the Effective Connectivity (EC) patterns obtained from intracranial Electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict imminent seizures, as this will enable the patients (and caregivers) to take appropriate precautions. We use this definition because we believe that effective connectivity near seizures begin to change, so we can predict seizures according to this feature. Results are reported on the standard Freiburg EEG dataset which contains data from 21 patients suffering from medically intractable focal epilepsy. Six channels of EEG from each patients are considered and effective connectivity using Directed Transfer Function (DTF) and Granger Causality (GC) methods is estimated. We concentrate on effective connectivity standard deviation over time and feature changes in five brain frequency sub-bands (Alpha, Beta, Theta, Delta, and Gamma) are compared. The performance obtained for the proposed scheme in predicting seizures is: average prediction time is 50 minutes before seizure onset, the maximum sensitivity is approximate ~80% and the false positive rate is 0.33 FP/h. DTF method is more acceptable to predict epileptic seizures and generally we can observe that the greater results are in gamma and beta sub-bands. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.

Keywords: effective connectivity, Granger causality, directed transfer function, epilepsy seizure prediction, EEG

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Authors: Azadeh Rouhandeh, Chris Joslin, Zhen Qu, Yuu Ono

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The centre of rotation of the hip joint is needed for an accurate simulation of the joint performance in many applications such as pre-operative planning simulation, human gait analysis, and hip joint disorders. In human movement analysis, the hip joint center can be estimated using a functional method based on the relative motion of the femur to pelvis measured using reflective markers attached to the skin surface. The principal source of errors in estimation of hip joint centre location using functional methods is soft tissue artefacts due to the relative motion between the markers and bone. One of the main objectives in human movement analysis is the assessment of soft tissue artefact as the accuracy of functional methods depends upon it. Various studies have described the movement of soft tissue artefact invasively, such as intra-cortical pins, external fixators, percutaneous skeletal trackers, and Roentgen photogrammetry. The goal of this study is to present a non-invasive method to assess the displacements of the markers relative to the underlying bone using optical motion capture data and tissue thickness from ultrasound measurements during flexion, extension, and abduction (all with knee extended) of the hip joint. Results show that the artefact skin marker displacements are non-linear and larger in areas closer to the hip joint. Also marker displacements are dependent on the movement type and relatively larger in abduction movement. The quantification of soft tissue artefacts can be used as a basis for a correction procedure for hip joint kinematics.

Keywords: hip joint center, motion capture, soft tissue artefact, ultrasound depth measurement

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Authors: Jinhoa Lee

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The relationships between environmental quality, energy use and economic output have created growing attention over the past decades among researchers and policy makers. Focusing on the empirical aspects of the role of CO2 emissions and energy use in affecting the economic output, this paper is an effort to fulfil the gap in a comprehensive case study at a country level using modern econometric techniques. To achieve the goal, this country-specific study examines the short-run and long-run relationships among energy consumption, carbon dioxide (CO2) emissions and gross domestic product (GDP) for Oman using time series analysis from the year 1980-2010. To investigate the relationships between the variables, this paper employs the Augmented Dickey Fuller (ADF) test for stationary, Johansen maximum likelihood method for co-integration and a Vector Error Correction Model (VECM) for both short- and long-run causality among the research variables for the sample. All the variables in this study show very strong significant effects on GDP in the country for the long term. The long-run equilibrium in the VECM suggests positive long-run causalities from CO2 emissions to GDP. Conversely, negative impacts of energy consumption on GDP are found to be significant in Oman during the period. In the short run, there exist negative unidirectional causalities among GDP, CO2 emissions and energy consumption running from GDP to CO2 emissions and from energy consumption to CO2 emissions. Overall, the results support arguments that there are relationships among environmental quality, energy use and economic output in Oman over of period 1980-2010.

Keywords: CO2 emissions, energy consumption, GDP, Oman, time series analysis

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Authors: N. Bourayou, A. -H. Meniai, A. Gouaoura

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The presence of salt can either raise or lower the distribution coefficient of a solute acetic acid in liquid- liquid equilibria. The coefficient of solute is defined as the ratio of the composition of solute in solvent rich phase to the composition of solute in diluents (water) rich phase. The phenomena are known as salting–out or salting-in, respectively. The effect of monovalent salt, sodium chloride and the bivalent salt, sodium sulfate on the distribution of acetic acid between 1-butanol and water at 298.15K were experimentally shown to be effective in modifying the liquid-liquid equilibrium of water/acetic acid/1-butanol system in favour of the solvent extraction of acetic acid from an aqueous solution with 1-butanol, particularly at high salt concentrations of both salts. All the two salts studied are found to have to salt out effect for acetic acid in varying degrees. The experimentally measured data were well correlated by Eisen-Joffe equation. NRTL model for solvent mixtures containing salts was able to provide good correlation of the present liquid-liquid equilibrium data. Using the regressed salt concentration coefficients for the salt-solvent interaction parameters and the solvent-solvent interaction parameters obtained from the same system without salt. The calculated phase equilibrium was in a quite good agreement with the experimental data, showing the ability of NRTL model to correlate salt effect on the liquid-liquid equilibrium.

Keywords: activity coefficient, Eisen-Joffe, NRTL model, sodium chloride

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Authors: Velázquez Alejandro, Pradhan Sujan, Yoon Rokhyun, Sanada Yasushi

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The utilization of new materials as an alternative solution to decrease the environmental impact of the construction industry has been gaining more relevance in the architectural design and construction industry. One such material is cross-laminated timber (CLT), an engineered timber solution that excels for its faster construction times, workability, lightweight, and capacity for carbon storage. This material is usually used alone for the entire structure or combined with steel frames, but a hybrid with reinforced concrete (RC) is rarer. Since RC is one of the most used materials worldwide, a hybrid with CLT would allow further utilization of the latter, and in the process, it would help reduce the environmental impact of RC construction to achieve a sustainable society, but first, the structural performance of such hybrids must be understood. This paper focuses on proposing a model to predict the seismic performance of RC frames with CLT panels as infills. A series of static horizontal cyclic loading experiments were conducted on two 40% scale specimens of reinforced concrete frames with and without CLT panels at Osaka University, Japan. An analytical model was created to simulate the seismic performance of the RC frame with CLT infill based on the experimental results. The proposed model was verified by comparing the experimental and analytical results, showing that the load-deformation relationship and the failure mechanism agreed well with limited error. Hence, the proposed analytical model can be implemented for the seismic performance evaluation of the RC frames with CLT infill.

Keywords: analytical model, multi spring, performance evaluation, reinforced concrete, rocking mechanism, wooden wall

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Authors: Md. S. Ansari, S. S. Motsa

Abstract:

In this paper, an analysis has been made on the flow of non-Newtonian viscoelastic nanofluid over a linearly stretching sheet under the influence of uniform magnetic field. Heat transfer characteristics is analyzed taking into the effect of nonlinear radiation and non-uniform heat source/sink. Transport equations contain the simultaneous effects of Brownian motion and thermophoretic diffusion of nanoparticles. The relevant partial differential equations are non-dimensionalized and transformed into ordinary differential equations by using appropriate similarity transformations. The transformed, highly nonlinear, ordinary differential equations are solved by spectral local linearisation method. The numerical convergence, error and stability analysis of iteration schemes are presented. The effects of different controlling parameters, namely, radiation, space and temperature-dependent heat source/sink, Brownian motion, thermophoresis, viscoelastic, Lewis number and the magnetic force parameter on the flow field, heat transfer characteristics and nanoparticles concentration are examined. The present investigation has many industrial and engineering applications in the fields of coatings and suspensions, cooling of metallic plates, oils and grease, paper production, coal water or coal–oil slurries, heat exchangers’ technology, and materials’ processing and exploiting.

Keywords: magnetic field, nonlinear radiation, non-uniform heat source/sink, similar solution, spectral local linearisation method, Rosseland diffusion approximation

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Authors: Elhem Zaatir, Taher Hamza

Abstract:

Several governments seek to implement gender parity on boards, but the results of doing so are not clear and could harm corporations and economies. The present paper aims to investigate the relationship between women’s presence on boards and firms’ performance in the context of the French listed firms during the quota period. A dynamic panel generalized method of moment estimation is applied to control the endogenous effect of board structure and reverse the causality impact of the financial performance. Our results show that the impact of gender diversity manifests in conflicting directions, positively affecting accounting performance and negatively influencing market performance. These results suggest that female directors create economic value, but the market discounts their impact. Apparently, they are subject to a biased evaluation by the market, which undervalues their presence on boards. Added to that, our results confirm a twofold nature of female representation in the French market. The effect of female directorship on firm performance varies with the affiliation of the directors. In other words, the positive impact of gender diversity on return on assets primarily originates from the positive effect of non-family-affiliated women directors on market performance rather than on the effect of family-affiliated women directors on ROA. Finally, according to our results, women’s demographic attributes namely the level of education and multiple directorships strongly and positively impact firm performance as measured by return on assets (ROA). Obviously, women directors seem to be appointed to the business case rather than as token directors.

Keywords: corporate governance, board of directors, women, gender diversity, demographic attributes, firm performance

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Authors: Navneet Kaur, S. D. Tiwari

Abstract:

Magnetic nanoparticles possess fascinating properties which make their behavior unique in comparison to corresponding bulk materials. Superparamagnetism is one such interesting phenomenon exhibited only by small particles of magnetic materials. In this state, the thermal energy of particles become more than their magnetic anisotropy energy, and so particle magnetic moment vectors fluctuate between states of minimum energy. This situation is similar to paramagnetism of non-interacting ions and termed as superparamagnetism. The magnetization of such systems has been described by Langevin function. But, the estimated fit parameters, in this case, are found to be unphysical. It is due to non-consideration of particle size distribution. In this work, analysis of magnetization data on NiO nanoparticles is presented considering the effect of particle size distribution. Nanoparticles of NiO of two different sizes are prepared by heating freshly synthesized Ni(OH)₂ at different temperatures. Room temperature X-ray diffraction patterns confirm the formation of single phase of NiO. The diffraction lines are seen to be quite broad indicating the nanocrystalline nature of the samples. The average crystallite size are estimated to be about 6 and 8 nm. The samples are also characterized by transmission electron microscope. Magnetization of both sample is measured as function of temperature and applied magnetic field. Zero field cooled and field cooled magnetization are measured as a function of temperature to determine the bifurcation temperature. The magnetization is also measured at several temperatures in superparamagnetic region. The data are fitted to an appropriate expression considering a distribution in particle size following a least square fit procedure. The computer codes are written in PYTHON. The presented analysis is found to be very useful for estimating the particle size distribution present in the samples. The estimated distributions are compared with those determined from transmission electron micrographs.

Keywords: anisotropy, magnetization, nanoparticles, superparamagnetism

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Authors: Asmamaw Yehun, Tsegaye Kassa, Addisu Hunegnaw, Martin Vermeer

Abstract:

There are various models to estimate the neutral atmospheric parameter values, such as in-suite and reanalysis datasets from numerical models. Accurate estimated values of the atmospheric parameters are useful for weather forecasting and, climate modeling and monitoring of climate change. Recently, Global Navigation Satellite System (GNSS) measurements have been applied for atmospheric sounding due to its robust data quality and wide horizontal and vertical coverage. The Global Positioning System (GPS) solutions that includes tropospheric parameters constitute a reliable set of data to be assimilated into climate models. The objective of this paper is, to estimate the neutral atmospheric parameters such as Wet Zenith Delay (WZD), Precipitable Water Vapour (PWV) and Total Zenith Delay (TZD) using six selected GPS stations in the equatorial regions, more precisely, the Ethiopian GPS stations from 2012 to 2015 observational data. Based on historic estimated GPS-derived values of PWV, we forecasted the PWV from 2015 to 2030. During data processing and analysis, we applied GAMIT-GLOBK software packages to estimate the atmospheric parameters. In the result, we found that the annual averaged minimum values of PWV are 9.72 mm for IISC and maximum 50.37 mm for BJCO stations. The annual averaged minimum values of WZD are 6 cm for IISC and maximum 31 cm for BDMT stations. In the long series of observations (from 2012 to 2015), we also found that there is a trend and cyclic patterns of WZD, PWV and TZD for all stations.

Keywords: atmosphere, GNSS, neutral atmosphere, precipitable water vapour

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Authors: Muhammad Ejaz1

Abstract:

Rework has been defined as: "The unnecessary effort of re-doing a process or activity that was incorrectly implemented the first time." A great threat to the construction industry is rework. By and large due attention has not been given to avoid the causes of reworks, resulting time and cost over runs, in civil engineering projects. Besides these direct consequences, there might also be indirect consequences, such as stress, de-motivation or loss of future clients. When delivered products do not meet the requirements or expectations, work often has to be redone. Rework occurs in various phases of the construction process or in various divisions of a company. Rework can occur on the construction site or in a management department due to for example bad materials management. Rework can also have internal or external origins. Changes in clients’ expectations are an example of an external factor that might lead to rework. Rework can cause many costs to be higher than calculated at the start of the project. Rework events can have many different origins and for this research they have been categorized into four categories; changes, errors, omissions, and damages. The research showed that the major source of reworks were non professional attitude from technical hands and ignorance of total quality management principals by stakeholders. It also revealed that sources of reworks have not major differences among project categories. The causes were further analyzed by interviewing employees. Based on existing literature an extensive list of rework causes was made and during the interviews the interviewees were asked to confirm or deny statements regarding rework causes. The causes that were most frequently confirmed can be grouped into the understanding categories. 56% (max) of the causes are change-related, 30% (max) is error-related and 18% (max) falls into another category. Therefore, by recognizing above mentioned factors, reworks can be reduced to a great extent.

Keywords: total quality management, construction industry, cost overruns, rework, material management, client’s expectations

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Authors: Gizem Eser Erdek

Abstract:

This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: Ahmad Rabby

Abstract:

The demographic trend analyses of Bombay-duck from time series catch data using CMSY and BSM for the first time in Bangladesh. During 2000-2018, CMSY indicates average lowest production in 2000 and highest in 2018. This has been used in the estimation of prior biomass by the default rules. Possible 31030 viable trajectories for 3422 r-k pairs were found by the CMSY analysis and the final estimates for intrinsic rate of population increase (r) was 1.19 year-1 with 95% CL= 0.957-1.48 year-1. The carrying capacity(k) of Bombay-duck was 283×103 tons with 95% CL=173×103 - 464×103 tons and MSY was 84.3×103tons year-1, 95% CL=49.1×103-145×103 tons year-1. Results from Bayesian state-space implementation of the Schaefer production model (BSM) using catch & CPUE data, found catchabilitiy coefficient(q) was 1.63 ×10-6 from lcl=1.27×10-6 to ucl=2.10×10-6 and r= 1.06 year-1 with 95% CL= 0.727 - 1.55 year-1, k was 226×103 tons with 95% CL=170×103-301×103 tons and MSY was 60×103 tons year-1 with 95% CL=49.9 ×103- 72.2 ×103 tons year-1. Results for Bombay-duck fishery management based on BSM assessment from time series catch data illustrated that, Fmsy=0.531 with 95% CL =0.364 - 0.775 (if B > 1/2 Bmsy then Fmsy =0.5r); Fmsy=0.531 with 95% CL =0.364-0.775 (r and Fmsy are linearly reduced if B < 1/2Bmsy). Biomass in 2018 was 110×103 tons with 2.5th to 97.5th percentile=82.3-155×103 tons. Relative biomass (B/Bmsy) in last year was 0.972 from 2.5th percentile to 97.5th percentile=0.728 -1.37. Fishing mortality in last year was 0.738 with 2.5th-97.5th percentile=0.525-1.37. Exploitation F/Fmsy was 1.39, from 2.5th to 97.5th percentile it was 0.988 -1.86. The biological reference points of B/BMSY was smaller than 1.0, while F/FMSY was higher than 1.0 revealed an over-exploitation of the fishery, indicating that more conservative management strategies are required for Bombay-duck fishery.

Keywords: biological reference points, catchability coefficient, carrying capacity, intrinsic rate of population increase

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