Search results for: real quantum mechanics

Authors: Manoel Pereira, Alvaro Veiga, Camila Epprecht, Renato Costa

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This paper introduces an empirical version of the Esscher transform for risk-neutral option pricing. Traditional parametric methods require the formulation of an explicit risk-neutral model and are operational only for a few probability distributions for the returns of the underlying. In our proposal, we make only mild assumptions on the pricing kernel and there is no need for the formulation of the risk-neutral model for the returns. First, we simulate sample paths for the returns under the physical distribution. Then, based on the empirical Esscher transform, the sample is reweighted, giving rise to a risk-neutralized sample from which derivative prices can be obtained by a weighted sum of the options pay-offs in each path. We compare our proposal with some traditional parametric pricing methods in four experiments with artificial and real data.

Keywords: esscher transform, generalized autoregressive Conditional Heteroscedastic (GARCH), nonparametric option pricing

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Authors: Gui Yang Wu, Bo Wang, Xin Wang

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Nowadays, 5G service-based interface architecture uses text-based payload like JSON to transfer business data between network functions, which has obvious advantages as internet services but causes unnecessarily larger traffic. In this paper, a new 5G application payload size reduction method is presented to provides the mechanism to negotiate about new capability between network functions when network communication starts up and how 5G application data are reduced according to negotiated information with peer network function. Without losing the advantages of 5G text-based payload, this method demonstrates an excellent result on application payload size reduction and does not increase the usage quota of computing resource. Implementation of this method does not impact any standards or specifications and not change any encoding or decoding functionality too. In a real 5G network, this method will contribute to network efficiency and eventually save considerable computing resources.

Keywords: 5G, JSON, payload size, service-based interface

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Authors: Aleksandra Majchrzak, Katarzyna Baruch, Monika Sobolewska, Bartlomiej Chojnacki, Adam Pilch

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Scaled modeling is very common in the areas of science such as aerodynamics or fluid mechanics, since defining characteristic numbers enables to determine relations between objects under test and their models. In acoustics, scaled modeling is aimed mainly at investigation of room acoustics, sound insulation and sound absorption phenomena. Despite such a range of application, there is no method developed that would enable scaling acoustical perforated panels freely, maintaining their sound absorption coefficient in a desired frequency range. However, conducted theoretical and numerical analyses have proven that it is not physically possible to obtain given sound absorption coefficient in a desired frequency range by directly scaling only all of the physical dimensions of a perforated panel, according to a defined characteristic number. This paper is a continuation of the research mentioned above and presents practical evaluation of theoretical and numerical analyses. The measurements of sound absorption coefficient of perforated panels were performed in order to verify previous analyses and as a result find the relations between full-scale perforated panels and their models which will enable to scale them properly. The measurements were conducted in a one-to-eight model of a reverberation chamber of Technical Acoustics Laboratory, AGH. Obtained results verify theses proposed after theoretical and numerical analyses. Finding the relations between full-scale and modeled perforated panels will allow to produce measurement samples equivalent to the original ones. As a consequence, it will make the process of designing acoustical perforated panels easier and will also lower the costs of prototypes production. Having this knowledge, it will be possible to emulate in a constructed model panels used, or to be used, in a full-scale room more precisely and as a result imitate or predict the acoustics of a modeled space more accurately.

Keywords: characteristic numbers, dimensional analysis, model study, scaled modeling, sound absorption coefficient

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Authors: Vera Jancurova

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People are, in general, exposed to behavioral biases, however, the degree and impact are affected by experience, knowledge, and other characteristics. The purpose of this article is to study two of defined behavioral biases, the overconfidence and self-attribution bias, and its impact on economic and non-economic students at different stage of the study. The research method used for the purpose of this study is a controlled field study that contains questions on perception of own confidence and self-attribution and estimation of limits to analyse actual abilities. The results of the research show that economic students seem to be more overconfident than their non–economic colleagues, which seems to be caused by the fact the questionnaire was asking for predicting economic indexes and own knowledge and abilities in financial environment. Surprisingly, the most overconfidence was detected by the students at the beginning of their study (1st-semester students). However, the estimations of real numbers do not point out, that economic students have better results by the prediction itself. The study confirmed the presence of self-attribution bias at all of the respondents.

Keywords: behavioral finance, overconfidence, self-attribution, heuristics and biases

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Authors: S. Oudjemia, F. Alim, S. Seddiki

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This paper shows the application of multifractal analysis for additional help in cancer diagnosis. The medical image processing is a very important discipline in which many existing methods are in search of solutions to real problems of medicine. In this work, we present results of multifractal analysis of brain MRI images. The purpose of this analysis was to separate between healthy and cancerous tissue of the brain. A nonlinear method based on multifractal detrending moving average (MFDMA) which is a generalization of the detrending fluctuations analysis (DFA) is used for the detection of abnormalities in these images. The proposed method could make separation of the two types of brain tissue with success. It is very important to note that the choice of this non-linear method is due to the complexity and irregularity of tumor tissue that linear and classical nonlinear methods seem difficult to characterize completely. In order to show the performance of this method, we compared its results with those of the conventional method box-counting.

Keywords: irregularity, nonlinearity, MRI brain images, multifractal analysis, brain tumor

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Authors: Jooyoung Park, Junkyeong Kim, Aoqi Zhang, Seunghee Park

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Non-destructive testing on cable is in great demand due to safety accidents at sites where many equipments using cables are installed. In this paper, the quantitative change of the obtained signal was analyzed using a magnetic flux leakage (MFL) method. A two-dimensional simulation was conducted with a FEM model replicating real elevator cables. The simulation data were compared for three parameters (depth of defect, width of defect and inspection velocity). Then, an experiment on same conditions was carried out to verify the results of the simulation. Signals obtained from both the simulation and the experiment were transformed to characterize the properties of the damage. Throughout the results, a cable damage detection based on an MFL method was confirmed to be feasible. In further study, it is expected that the MFL signals of an entire specimen will be gained and visualized as well.

Keywords: magnetic flux leakage (mfl), cable damage detection, non-destructive testing, numerical simulation

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Authors: Triyanto

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Radicalism poses a real threat to Indonesia's future. The target of radicalism is the youth of Indonesia. This is proven by the majority of terrorists are young people. Radicalization is not only a repressive act but also requires educational action. One of the educational efforts is civic education. This study discusses the prevention of radicalism for students through civic education and its constraints. This is qualitative research. Data were collected through literature studies, observations and in-depth interviews. Data were validated by triangulation. The sample of this research is 30 high school students in Surakarta. Data were analyzed by the interactive model of analysis from Miles & Huberman. The results show that (1) civic education can be a way of preventing student radicalism in schools in the form of cultivating the values of education through learning in the classroom and outside the classroom; (2) The obstacles encountered include the lack of learning facilities, the limited ability of teachers and the low attention of students to the civic education.

Keywords: prevention, radicalism, senior high school student, civic education

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Authors: Juliette Harper, Yu Yang

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Biofuels have gained significant attention recently due to the new regulations and agreements regarding fossil fuels and greenhouse gases being made by countries around the globe. One of the most common types of biofuels is biodiesel, primarily made via the transesterification reaction. We model this nonlinear process in MATLAB using the standard kinetic equations. Then, a nonlinear Model predictive control (NMPC) was developed to regulate this process due to its capability to handle process constraints. The feeding flow uncertainty and kinetic disturbances are further incorporated in the model to capture the real-world operating conditions. The simulation results will show that the proposed NMPC can guarantee the final composition of fatty acid methyl esters (FAME) above the target threshold with a high chance by adjusting the process temperature and flowrate. This research will allow further understanding of NMPC under uncertainties and how to design the computational strategy for larger process with more variables.

Keywords: NMPC, biodiesel, uncertainties, nonlinear, MATLAB

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Authors: Kostas Kouvaris, Anastasia Eleftheriou, Georgios A. Sarantitis, Apostolos Chondronasios

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To achieve the objective of almost zero carbon energy solutions by 2050, the EU needs to accelerate the development of integrated, highly efficient and environmentally friendly solutions. In this direction, district heating and cooling (DHC) emerges as a viable and more efficient alternative to conventional, decentralized heating and cooling systems, enabling a combination of more efficient renewable and competitive energy supplies. In this paper, we develop a forecasting tool for near real-time local weather and thermal energy demand predictions for an entire DHC network. In this fashion, we are able to extend the functionality and to improve the energy efficiency of the DHC network by predicting and adjusting the heat load that is distributed from the heat generation plant to the connected buildings by the heat pipe network. Two case-studies are considered; one for Vransko, Slovenia and one for Montpellier, France. The data consists of i) local weather data, such as humidity, temperature, and precipitation, ii) weather forecast data, such as the outdoor temperature and iii) DHC operational parameters, such as the mass flow rate, supply and return temperature. The external temperature is found to be the most important energy-related variable for space conditioning, and thus it is used as an external parameter for the energy demand models. For the development of the forecasting tool, we use state-of-the-art deep neural networks and more specifically, recurrent networks with long-short-term memory cells, which are able to capture complex non-linear relations among temporal variables. Firstly, we develop models to forecast outdoor temperatures for the next 24 hours using local weather data for each case-study. Subsequently, we develop models to forecast thermal demand for the same period, taking under consideration past energy demand values as well as the predicted temperature values from the weather forecasting models. The contributions to the scientific and industrial community are three-fold, and the empirical results are highly encouraging. First, we are able to predict future thermal demand levels for the two locations under consideration with minimal errors. Second, we examine the impact of the outdoor temperature on the predictive ability of the models and how the accuracy of the energy demand forecasts decreases with the forecast horizon. Third, we extend the relevant literature with a new dataset of thermal demand and examine the performance and applicability of machine learning techniques to solve real-world problems. Overall, the solution proposed in this paper is in accordance with EU targets, providing an automated smart energy management system, decreasing human errors and reducing excessive energy production.

Keywords: machine learning, LSTMs, district heating and cooling system, thermal demand

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Authors: Po-Chen Chen, Tsung-Han Yang, Zhi-Wei Zheng, Shih-Tsang Tang

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Falls are the most common accidents during mountain hiking, especially in high-altitude environments with unstable terrain or adverse weather. Balance ability is a crucial factor in hiking, effectively ensuring hiking safety and reducing the risk of injuries. If balance ability can be assessed simply and effectively, hikers can identify their weaknesses and conduct targeted training to improve their balance ability, thereby reducing injury risks. With the widespread use of smartphones and their built-in inertial sensors, this project aims to develop a simple Inertial Measurement Unit (IMU) balance measurement technique based on smartphones. This will provide hikers with an easy-to-use, low-cost tool for assessing balance ability, monitoring training effects in real-time, and continuously tracking balance ability through uploading cloud data uploads, facilitating personal athletic performance.

Keywords: balance, IMU, smartphone, wearable devices

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Authors: Kamel Belammi, Houria Fatrim

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imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of imbalanced data sets. In medical diagnosis classification, we often face the imbalanced number of data samples between the classes in which there are not enough samples in rare classes. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS) algorithm that penalizes errors of different samples with different weight and some rules of thumb to determine those weights. After the balancing phase, we applythe different classifiers (support vector machine (SVM), k- nearest neighbor (KNN) and multilayer neuronal networks (MNN)) for balanced data set. We have also compared the obtained results before and after balancing method.

Keywords: multilayer neural networks, k- nearest neighbor, support vector machine, imbalanced medical data, least mean square algorithm, diabetes

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Authors: Marek Vondra, Petr Bobák

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Vacuum evaporation is a reliable and well-proven technology with a wide application range which is frequently used in food, chemical or pharmaceutical industries. Recently, numerous remarkable studies have been carried out to investigate utilization of this technology in the area of wastewater treatment. One of the most successful applications of vacuum evaporation principal is connected with seawater desalination. Since 1950’s, multi-stage flash distillation (MSF) has been the leading technology in this field and it is still irreplaceable in many respects, despite a rapid increase in cheaper reverse-osmosis-based installations in recent decades. MSF plants are conveniently operated in countries with a fluctuating seawater quality and at locations where a sufficient amount of waste heat is available. Nowadays, most of the MSF research is connected with alternative heat sources utilization and with hybridization, i.e. merging of different types of desalination technologies. Some of the studies are concerned with basic principles of the static flash phenomenon, but only few scientists have lately focused on the fundamentals of continuous multi-stage evaporation. Limited measurement possibilities at operating plants and insufficiently equipped experimental facilities may be the reasons. The aim of the presented study was to design, construct and test an up-to-date test rig with an advanced measurement system which will provide real time monitoring options of all the important operational parameters under various conditions. The whole system consists of a conventionally designed MSF unit with 8 evaporation chambers, versatile heating circuit for different kinds of feed water (e.g. seawater, waste water), sophisticated system for acquisition and real-time visualization of all the related quantities (temperature, pressure, flow rate, weight, conductivity, pH, water level, power input), access to a wide spectrum of operational media (salt, fresh and softened water, steam, natural gas, compressed air, electrical energy) and integrated transparent features which enable a direct visual control of selected physical mechanisms (water evaporation in chambers, water level right before brine and distillate pumps). Thanks to the adjustable process parameters, it is possible to operate the test unit at desired operational conditions. This allows researchers to carry out statistical design and analysis of experiments. Valuable results obtained in this manner could be further employed in simulations and process modeling. First experimental tests confirm correctness of the presented approach and promise interesting outputs in the future. The presented experimental apparatus enables flexible and efficient research of the whole MSF process.

Keywords: design of experiment, multi-stage flash distillation, test rig, vacuum evaporation

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Authors: Nusrat Masood, Vijaya Dubey, Suaib Luqman

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Caspase 3, a member of cysteine-aspartic acid protease family, is an imperative indicator for cell death particularly when substantiating apoptosis. Thus, caspase 3 is an interesting target for the discovery and development of anticancer agent. We adopted a four level assessment of both terpenoids and flavonoids and thus experimentally performed the enzymatic assay in cell free system as well as in cancer cell line which was validated through real time expression and molecular interaction studies. A significant difference was observed with both the class of natural products indicating terpenoids as better activators of caspase 3 compared to flavonoids both in the cell free system as well as in cell lines. The expression analysis, activation constant and binding energy also correlate well with the enzyme activity. Overall, terpenoids had an unswerving effect on caspase 3 in all the tested system while flavonoids indirectly affect enzyme activity.

Keywords: Caspase 3, terpenoids, flavonoids, activation constant, binding energy

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Authors: Armand Okende, Benjamin Beaumont

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Introduction: Deforestation is a change in the structure and composition of flora and fauna, which leads to a loss of biodiversity, production of goods and services and an increase in fires. It concerns vast territories in tropical zones particularly; this is the case of the territory of Bolobo in the current province of Maï- Ndombe in the Democratic Republic of Congo. Indeed, through this study between 2001 and 2018, we believe that it was important to show and analyze quantitatively the important forests changes and analyze quantitatively. It’s the overall objective of this study because, in this area, we are witnessing significant deforestation. Methodology: Mapping and quantification are the methodological approaches that we have put forward to assess the deforestation or forest changes through satellite images or raster layers. These satellites data from Global Forest Watch are integrated into the GIS software (GRASS GIS and Quantum GIS) to represent the loss of forest cover that has occurred and the various changes recorded (e.g., forest gain) in the territory of Bolobo. Results: The results obtained show, in terms of quantifying deforestation for the periods 2001-2006, 2007-2012 and 2013-2018, the loss of forest area in hectares each year. The different change maps produced during different study periods mentioned above show that the loss of forest areas is gradually increasing. Conclusion: With this study, knowledge of forest management and protection is a challenge to ensure good management of forest resources. To do this, it is wise to carry out more studies that would optimize the monitoring of forests to guarantee the ecological and economic functions they provide in the Congo Basin, particularly in the Democratic Republic of Congo. In addition, the cartographic approach, coupled with the geographic information system and remote sensing proposed by Global Forest Watch using raster layers, provides interesting information to explain the loss of forest areas.

Keywords: deforestation, loss year, forest change, remote sensing, drivers of deforestation

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Authors: Farzaneh Shayeganfar, Ali Ramazani

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Exciton (strong electronic interaction of electron-hole) and hot carriers created by surface plasmon polaritons has been demonstrated in nanoscale optoelectronic devices, enhancing the photoresponse of the system. Herein, we employ a quantum framework to consider coupled exciton- hot carriers effects on photovoltaiv energy distribution, scattering process, polarizability and light emission of 2D-semicnductor. We use density functional theory (DFT) to design computationally a semi-functionalized 2D honeycomb silicon boride (SiB) monolayer with H atoms, suitable for photovoltaics. The dynamical stability, electronic and optical properties of SiB and semi-hydrogenated SiB structures were investigated utilizing the Tran-Blaha modified Becke-Johnson (TB-mBJ) potential. The calculated phonon dispersion shows that while an unhydrogenated SiB monolayer is dynamically unstable, surface semi-hydrogenation improves the stability of the structure and leads to a transition from metallic to semiconducting conductivity with a direct band gap of about 1.57 eV, appropriate for photovoltaic applications. The optical conductivity of this H-SiB structure, determined using the random phase approximation (RPA), shows that light adsorption should begin at the boundary of the visible range of light. Additionally, due to hydrogenation, the reflectivity spectrum declines sharply with respect to the unhydrogenated reflectivity spectrum in the IR and visible ranges of light. The energy band gap remains direct, increasing from 0.9 to 1.8 eV, upon increasing the strain from -6% (compressive) to +6% (tensile). Additionally, compressive and tensile strains lead, respectively, to red and blue shifts of optical the conductivity threshold around the visible range of light. Overall, this study suggests that H-SiB monolayers are suitable as two-dimensional solar cell materials.

Keywords: surface plasmon, hot carrier, strain engineering, valley polariton

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Authors: Omar S. Sharaf

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The mitochondrial 12s rRNA (mt-12s rDNA) gene for pig-specific was developed to detect material from pork species in different products collected from Jordanian market. The amplification PCR products of 359 bp and 531 bp were successfully amplified from the cyt b gene of pig the amplification product using mt-12S rDNA gene were successfully produced a single band with a molecular size of 456 bp. In the present work, the PCR amplification of mtDNA of cytochrome b has been shown as a suitable tool for rapid detection of pig DNA. 100 samples from different dairy, gelatin and chocolate based products and 50 samples from baby food formula were collected and tested to a presence of any pig derivatives. It was found that 10% of chocolate based products, 12% of gelatin and 56% from dairy products and 5.2% from baby food formula showed single band from mt-12S rDNA gene.

Keywords: halal food, baby infant formula, chocolate based products, PCR, Jordan

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Authors: Deepika Bhalla, Raj Kumar Bansal, Hari Om Gupta

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Dissolved Gas Analysis (DGA) has been widely used for fault diagnosis in a transformer. Artificial neural networks (ANN) have high accuracy but are regarded as black boxes that are difficult to interpret. For many problems it is desired to extract knowledge from trained neural networks (NN) so that the user can gain a better understanding of the solution arrived by the NN. This paper applies a pedagogical approach for rule extraction from function approximating neural networks (REFANN) with application to incipient fault diagnosis using the concentrations of the dissolved gases within the transformer oil, as the input to the NN. The input space is split into subregions and for each subregion there is a linear equation that is used to predict the type of fault developing within a transformer. The experiments on real data indicate that the approach used can extract simple and useful rules and give fault predictions that match the actual fault and are at times also better than those predicted by the IEC method.

Keywords: artificial neural networks, dissolved gas analysis, rules extraction, transformer

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Authors: Cheng Xu, Rui Zhong

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Utilizing a preregistered randomized experiment, this study explores the effects of anonymity and curated identity on trust within the Metaverse. Participants were randomly assigned to different conditions of anonymity and identity curation and engaged in a series of tasks designed to mirror the complexities of trust in real-world social interactions. Trust was measured using the classical trust game, allowing for a nuanced understanding of how these factors interact and influence trust. The findings reveal that higher levels of anonymity negatively impact trust, while identity curation can moderate this effect. Mechanism analysis uncovers how anonymity influences perceived reciprocity and group cohesion, and how curation can moderate these relationships. The results demonstrate a nuanced interaction between anonymity and trust, with variations across different curation levels. These insights provide a multifaceted understanding of trust within virtual environments, contributing valuable knowledge to the design, policy-making, and ethical considerations of the Metaverse

Keywords: metaverse, anonymity, curated identity, social behavior, trust

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Authors: Jagbir Singh Kadyan, C. A. Suman Kadyan

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The researchers attempts to establish whether a relationship exists between the social activities undertaken & the funds that has been spent by the selected corporate organisations. Corporate listed on the (NSE) National Stock Exchange of India, under different categories shall be selected as a sample for the purpose of this study. The researches shall also study the dynamics of corporate social responsibility funding, financing & management of corporate social responsibility funds by the above selected organisations in the Indian context. The rationale behind selecting & undertaking specific corporate social responsibility activities shall be analysed & interpreted to discover the real drivers of corporate social responsibility. Besides above, an attempt shall further make an effort to understand & analyse the nature of impact on the selected corporate organisations on its overall performances due to the activities undertaken under their specific corporate social responsibility programs.

Keywords: corporate social responsibility, organisational performance, national stock exchange, sustainability, society, health, education, sanitation, environment

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Authors: Abdel-Razzaq Mugdadi, Ruqayyah Sani

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The Kernel Distribution Function Estimator (KDFE) method is the most popular method for nonparametric estimation of the cumulative distribution function. The kernel and the bandwidth are the most important components of this estimator. In this investigation, we replace the kernel in the KDFE with a linear combination of kernels to obtain a new estimator based on the linear combination of kernels, the mean integrated squared error (MISE), asymptotic mean integrated squared error (AMISE) and the asymptotically optimal bandwidth for the new estimator are derived. We propose a new data-based method to select the bandwidth for the new estimator. The new technique is based on the Plug-in technique in density estimation. We evaluate the new estimator and the new technique using simulations and real-life data.

Keywords: estimation, bandwidth, mean square error, cumulative distribution function

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Authors: Yassine Meraihi, Dalila Acheli, Rabah Meraihi

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Vehicular Ad hoc Network (VANET) is a special case of Mobile Ad hoc Network (MANET) used to establish communications and exchange information among nearby vehicles and between vehicles and nearby fixed infrastructure. VANET is seen as a promising technology used to provide safety, efficiency, assistance and comfort to the road users. Routing is an important issue in Vehicular Ad Hoc Network to find and maintain communication between vehicles due to the highly dynamic topology, frequently disconnected network and mobility constraints. This paper evaluates the performance of two most popular proactive routing protocols OLSR and DSDV in real city traffic scenario on the basis of three metrics namely Packet delivery ratio, throughput and average end to end delay by varying vehicles density and transmission range.

Keywords: DSDV, OLSR, quality of service, routing protocols, VANET

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Authors: Zuk Nechemia Turbovich

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This paper introduces DoD, a contextual spiral model that describes the lifecycle of products intended for manufacturing using Personal 3D Printers (P3DP). The study is based on a review of the desktop P3DPs market that shows that the combination of digital connectivity, coupled with the potential ownership of P3DP by home users, is radically changing the form of the product lifecycle, comparatively to familiar lifecycle paradigms. The paper presents the change in the design process, considering the characterization of product types in the P3DP market and the possibility of having a direct dialogue between end-user and product designers. The model, as an updated paradigm, provides a strategic perspective on product design and tools for success, understanding that design is subject to rapid and continuous improvement and that products are subject to repair, update, and customization. The paper will include a review of real cases.

Keywords: lifecycle, mass-customization, personal 3d-printing, user involvement

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Authors: Muhammad Nouman Qureshi, Muhammad Hanif

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Adaptive cluster sampling (ACS) is specifically developed for the estimation of highly clumped populations and applied to a wide range of situations like animals of rare and endangered species, uneven minerals, HIV patients and drug users. In this paper, we proposed a generalized semi-exponential estimator with two auxiliary variables under the framework of ACS design. The expressions of approximate bias and mean square error (MSE) of the proposed estimator are derived. Theoretical comparisons of the proposed estimator have been made with existing estimators. A numerical study is conducted on real and artificial populations to demonstrate and compare the efficiencies of the proposed estimator. The results indicate that the proposed generalized semi-exponential estimator performed considerably better than all the adaptive and non-adaptive estimators considered in this paper.

Keywords: auxiliary information, adaptive cluster sampling, clustered populations, Hansen-Hurwitz estimation

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Authors: Gang Liu, Huimin Song, Can Wang, Runnan Zhang, Lu Fang

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Currently, resource sharing and system security are critical issues. This paper proposes a POL module composed of PRIV ILEGE attribute (PA), obligation and log which improves attribute based access control (ABAC) model in dynamically granting authorizations and revoking authorizations. The following describes the new model termed PABAC in terms of the POL module structure, attribute definitions, policy formulation and authorization architecture, which demonstrate the advantages of it. The POL module addresses the problems which are not predicted before and not described by access control policy. It can be one of the subject attributes or resource attributes according to the practical application, which enhances the flexibility of the model compared with ABAC. A scenario that illustrates how this model is applied to the real world is provided.

Keywords: access control, attribute based access control, granting authorizations, privilege, revoking authorizations, system security

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Authors: M. Mohemmed Sha, T. Manesh, A. Ahmed Mohamed Mustaq

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In this corporate world, the technology of Web services has grown rapidly and its significance for the development of web based applications gradually rises over time. The success of Business to Business integration rely on finding novel partners and their services in a global business environment. But the selection of the most suitable Web service from the list of services with the identical functionality is more vital. The satisfaction level of the customer and the provider’s reputation of the Web service are primarily depending on the range it reaches the customer’s requirements. In most cases the customer of the Web service feels that he is spending for the service which is undelivered. This is because the customer always thinks that the real functionality of the web service is not reached. This will lead to change of the service frequently. In this paper, a framework is proposed to evaluate the Quality of Service (QoS) and its cost that makes the optimal correlation between each other. Also this research work proposes some management decision against the functional deviancy of the web service that are guaranteed at time of selection.

Keywords: web service, service level agreement, quality of a service, cost of a service, QoS, CoS, SOA, WSLA, WsRF

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Authors: Ehsan Pegah, Huabei Liu

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Characterizing the deformation properties of fill materials in a wide stress range always has been an important issue in geotechnical engineering. The hyperbolic Duncan-Chang model is a very popular model of stress-strain relationship that captures the nonlinear deformation of granular geomaterials in a very tractable manner. It consists of a particular set of the model parameters, which are generally measured from an extensive series of laboratory triaxial tests. This practice is both time-consuming and costly, especially in large projects. In addition, undesired effects caused by soil disturbance during the sampling procedure also may yield a large degree of uncertainty in the results. Accordingly, non-invasive geophysical seismic approaches may be utilized as the appropriate alternative surveys for measuring the model parameters based on the seismic wave velocities. To this end, the conventional seismic refraction profiles were carried out in the test sites with the granular fill materials to collect the seismic waves information. The acquired shot gathers are processed, from which the P- and S-wave velocities can be derived. The P-wave velocities are extracted from the Seismic Refraction Tomography (SRT) technique while S-wave velocities are obtained by the Multichannel Analysis of Surface Waves (MASW) method. The velocity values were then utilized with the equations resulting from the rigorous theories of elasticity and soil mechanics to evaluate the Duncan-Chang model parameters. The derived parameters were finally compared with those from laboratory tests to validate the reliability of the results. The findings of this study may confidently serve as the useful references for determination of nonlinear deformation parameters of granular fill geomaterials. Those are environmentally friendly and quite economic, which can yield accurate results under the actual in-situ conditions using the surface seismic methods.

Keywords: Duncan-Chang deformation parameters, granular fill materials, seismic waves velocity, multichannel analysis of surface waves, seismic refraction tomography

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Authors: Hafdaoui Hichem, Mehadjebia Cherifa, Benatia Djamel

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In this paper, we propose a new method for Bulk detection of an acoustic microwave signal during the propagation of acoustic microwaves in a piezoelectric substrate (Lithium Niobate LiNbO3). We have used the classification by probabilistic neural network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity in order to build a model from which we note the Bulk waves easily. These singularities inform us of presence of Bulk waves in piezoelectric materials. By which we obtain accurate values for each of the coefficient attenuation and acoustic velocity for Bulk waves. This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

Keywords: piezoelectric material, probabilistic neural network (PNN), classification, acoustic microwaves, bulk waves, the attenuation coefficient

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Authors: Dimitrios Triantakonstantis, Demetris Stathakis

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Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: artificial neural networks, CORINE, urban atlas, urban growth prediction

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Authors: Sandra Arango-Caro, Kristine Callis-Duehl

Abstract:

The Education Research and Outreach Lab at the Donald Danforth Plant Science Center established the Plant and Agriculture Augmented and Virtual Reality Learning Laboratory (PAVRLL) to promote science education through professional development, school programs, internships, and outreach events. Professional development is offered to high school and college science and agriculture educators on the use and applications of zSpace and Oculus platforms. Educators learn to use, edit, or create lesson plans in the zSpace platform that are aligned with the Next Generation Science Standards. They also learn to use virtual reality experiences created by the PAVRLL available in Oculus (e.g. The Soybean Saga). Using a cost-free loan rotation system, educators can bring the AVR units to the classroom and offer AVR activities to their students. Each activity has user guides and activity protocols for both teachers and students. The PAVRLL also offers activities for 3D plant modeling. High school students work in teams of art-, science-, and technology-oriented students to design and create 3D models of plant species that are under research at the Danforth Center and present their projects at scientific events. Those 3D models are open access through the zSpace platform and are used by PAVRLL for professional development and the creation of VR activities. Both teachers and students acquire knowledge of plant and agriculture content and real-world problems, gain skills in AVR technology, 3D modeling, and science communication, and become more aware and interested in plant science. Students that participate in the PAVRLL activities complete pre- and post-surveys and reflection questions that evaluate interests in STEM and STEM careers, students’ perceptions of three design features of biology lab courses (collaboration, discovery/relevance, and iteration/productive failure), plant awareness, and engagement and learning in AVR environments. The PAVRLL was established in the fall of 2019, and since then, it has trained 15 educators, three of which will implement the AVR programs in the fall of 2021. Seven students have worked in the 3D plant modeling activity through a virtual internship. Due to the COVID-19 pandemic, the number of teachers trained, and classroom implementations have been very limited. It is expected that in the fall of 2021, students will come back to the schools in person, and by the spring of 2022, the PAVRLL activities will be fully implemented. This will allow the collection of enough data on student assessments that will provide insights on benefits and best practices for the use of AVR technologies in the classrooms. The PAVRLL uses cutting-edge educational technologies to promote science education and assess their benefits and will continue its expansion. Currently, the PAVRLL is applying for grants to create its own virtual labs where students can experience authentic research experiences using real Danforth research data based on programs the Education Lab already used in classrooms.

Keywords: assessment, augmented reality, education, plant science, virtual reality

Procedia PDF Downloads 156

Authors: Ashwini Umale

Abstract:

Trajectory planning and optimization is a fundamental problem in articulated robotics. It is often viewed as a two phase problem of initial feasible path planning around obstacles and subsequent optimization of a trajectory satisfying dynamical constraints. An optimized trajectory of multi-axis robot is important and directly influences the Performance of the executing task. Optimal is defined to be the minimum time to transition from the current speed to the set speed. In optimization of trajectory through virtual environment explores the most suitable way to represent robot motion from virtual environment to real environment. This paper aims to review the research of trajectory optimization in virtual environment using simulation software Robcad. Improvements are to be expected in trajectory optimization to generate smooth and collision free trajectories with minimization of overall robot cycle time.

Keywords: trajectory optimization, forward kinematics and reverse kinematics, dynamic constraints, robcad simulation software

Procedia PDF Downloads 492