Search results for: Fuzzy Logic estimation

Authors: G. M. Hamada, A. A. Al-Gathe, A. M. Al-Khudafi

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Determination of water saturation in sandstone is a vital question to determine the initial oil or gas in place in reservoir rocks. Water saturation determination using electrical measurements is mainly on Archie’s formula. Consequently accuracy of Archie’s formula parameters affects water saturation values rigorously. Determination of Archie’s parameters a, m, and n is proceeded by three conventional techniques, Core Archie-Parameter Estimation (CAPE) and 3-D. This work introduces the hybrid system of parallel self-organizing neural network (PSONN) targeting accepted values of Archie’s parameters and, consequently, reliable water saturation values. This work focuses on Archie’s parameters determination techniques; conventional technique, CAPE technique, and 3-D technique, and then the calculation of water saturation using current. Using the same data, a hybrid parallel self-organizing neural network (PSONN) algorithm is used to estimate Archie’s parameters and predict water saturation. Results have shown that estimated Arche’s parameters m, a, and n are highly accepted with statistical analysis, indicating that the PSONN model has a lower statistical error and higher correlation coefficient. This study was conducted using a high number of measurement points for 144 core plugs from a sandstone reservoir. PSONN algorithm can provide reliable water saturation values, and it can supplement or even replace the conventional techniques to determine Archie’s parameters and thereby calculate water saturation profiles.

Keywords: water saturation, Archie’s parameters, artificial intelligence, PSONN, sandstone reservoir

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Authors: Anuja Askhedkar, G. H. Agrawal, Madhu Gudgunti

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Direct Charge Transfer Switched Capacitor (DCT-SC) DAC is the internal DAC used in Delta-Sigma (∆∑) DAC which works on Over-Sampling concept. The Switched Capacitor DAC mainly suffers from mismatch among capacitors. Mismatch among capacitors in DAC, causes non linearity between output and input. Dynamic Element Matching (DEM) technique is used to match the capacitors. According to element selection logic there are many types. In this paper, Data Weighted Averaging (DWA) technique is used for mismatch shaping. In this paper, the 4 bit DCT-SC-DAC with DWA-DEM technique is implemented using WINSPICE simulation software in 180nm CMOS technology. DNL for DAC with DWA is ±0.03 LSB and INL is ± 0.02LSB.

Keywords: ∑-Δ DAC, DCT-SC-DAC, mismatch shaping, DWA, DEM

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Authors: F. C. Amadi, G. C. Enyi, G. Nasr

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Relative permeabilities are practical factors that are used to correct the single phase Darcy’s law for application to multiphase flow. For effective characterisation of large-scale multiphase flow in hydrocarbon recovery, relative permeability and capillary pressures are used. These parameters are acquired via special core flooding experiments. Special core analysis (SCAL) module of reservoir simulation is applied by engineers for the evaluation of these parameters. But, core flooding experiments in shale core sample are expensive and time consuming before various flow assumptions are achieved for instance Darcy’s law. This makes it imperative for the application of coreflooding simulations in which various analysis of relative permeabilities and capillary pressures of multiphase flow can be carried out efficiently and effectively at a relative pace. This paper presents a Sendra software simulation of core flooding to achieve to relative permeabilities and capillary pressures using different correlations. The approach used in this study was three steps. The first step, the basic petrophysical parameters of Marcellus shale sample such as porosity was determined using laboratory techniques. Secondly, core flooding was simulated for particular scenario of injection using different correlations. And thirdly the best fit correlations for the estimation of relative permeability and capillary pressure was obtained. This research approach saves cost and time and very reliable in the computation of relative permeability and capillary pressures at steady or unsteady state, drainage or imbibition processes in oil and gas industry when compared to other methods.

Keywords: relative permeabilty, porosity, 1-D black oil simulator, capillary pressures

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Authors: Mohammad Mahdi Hajiali

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Many of construction projects estimated in Iran have been influenced by the limitations of financial resources. As for Iran, a country that is developing, and to follow this development-oriented approach which many numbers of projects each year run in, if we can reduce the cost of projects by applying a method we will help greatly to minimize the cost of major construction projects and therefore projects will finish faster and more efficiently. One of the components of transportation infrastructure are roads that are considered to have a considerable share of the country budget. In addition, major budget of the related ministry is spending to repair, improve and maintain roads. Value Engineering is a simple and powerful methodology over the past six decades that has been successful in reducing the cost of many projects. Specific solution for using value engineering in the stage of project implementation is called value engineering change proposal (VECP). It was tried in this research to apply VECP in one of the road-building projects in Iran in order to enhance the value of this kind of projects and reduce their cost. In this case study after applying VECP, an idea was raised. It was about use of concrete pavement instead of hot mixed asphalt (HMA) and also using fiber in order to improve concrete pavement performance. VE group team made a decision that for choosing the best alternatives, get expert’s opinions in pavement systems and use Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) for ranking opinions of the experts. Finally, Jointed Plain Concrete Pavement (JPCP) was selected. Group also experimented concrete samples with available fibers in Iran and the results of experiments showed a significant increment in concrete specifications such as flexural strength. In the end, it was shown that by using of fiber-reinforced concrete pavement instead of asphalt pavement, we can achieve a significant saving in cost, time and also increment in quality, durability, and longevity.

Keywords: road-building projects, value engineering change proposal (VECP), Jointed Plain Concrete Pavement (JPCP), Fuzzy TOPSIS, fiber-reinforced concrete

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Authors: Moustafa Osman Mohammed

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This paper introduces Luhmann’s autopoietic social systems starting with the original concept of autopoiesis by biologists and scientists, including the modification of general systems based on socialized medicine. A specific type of autopoietic system is explained in the three existing groups of the ecological phenomena: interaction, social and medical sciences. This hypothesis model, nevertheless, has a nonlinear interaction with its natural environment ‘interactional cycle’ for the exchange of photon energy with molecular without any changes in topology. The external forces in the systems environment might be concomitant with the natural fluctuations’ influence (e.g. radioactive radiation, electromagnetic waves). The cantilever sensor deploys insights to the future chip processor for prevention of social metabolic systems. Thus, the circuits with resonant electric and optical properties are prototyped on board as an intra–chip inter–chip transmission for producing electromagnetic energy approximately ranges from 1.7 mA at 3.3 V to service the detection in locomotion with the least significant power losses. Nowadays, therapeutic systems are assimilated materials from embryonic stem cells to aggregate multiple functions of the vessels nature de-cellular structure for replenishment. While, the interior actuators deploy base-pair complementarity of nucleotides for the symmetric arrangement in particular bacterial nanonetworks of the sequence cycle creating double-stranded DNA strings. The DNA strands must be sequenced, assembled, and decoded in order to reconstruct the original source reliably. The design of exterior actuators have the ability in sensing different variations in the corresponding patterns regarding beat-to-beat heart rate variability (HRV) for spatial autocorrelation of molecular communication, which consists of human electromagnetic, piezoelectric, electrostatic and electrothermal energy to monitor and transfer the dynamic changes of all the cantilevers simultaneously in real-time workspace with high precision. A prototype-enabled dynamic energy sensor has been investigated in the laboratory for inclusion of nanoscale devices in the architecture with a fuzzy logic control for detection of thermal and electrostatic changes with optoelectronic devices to interpret uncertainty associated with signal interference. Ultimately, the controversial aspect of molecular frictional properties is adjusted to each other and forms its unique spatial structure modules for providing the environment mutual contribution in the investigation of mass temperature changes due to pathogenic archival architecture of clusters.

Keywords: autopoiesis, nanoparticles, quantum photonics, portable energy, photonic structure, photodynamic therapeutic system

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Authors: Harri Jalonen

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Social media has changed the ways we communicate, collaborate and connect with each other. It has also influenced our habits of consuming sports. Social media has allowed direct interaction between sponsoring companies, athletes/players and fans. Drawing on the service dominant logic of value co-creation, the conceptual paper identifies three operant resources which are beneficial for value co-creation: i) social identity and sense of community, ii) congruence and brand personality, and iii) participatory culture and fan activation. The paper contributes to the theoretical discussion on how social can be media used for value co-creation purposes in the sports industry.

Keywords: sports, value co-creation, social media, service

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Authors: Keisuke Takahata, Hiroshi Suetsugu

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Forest height is an important parameter for forest biomass estimation, and precise elevation data is essential for accurate forest height estimation. There are several globally or nationally available digital elevation models (DEMs) like SRTM and ASTER. However, its accuracy is reported to be low particularly in mountainous areas where there are closed canopy or steep slope. Recently, space-borne LiDAR, such as the Global Ecosystem Dynamics Investigation (GEDI), have started to provide sparse but accurate ground elevation and canopy height estimates. Several studies have reported the high degree of accuracy in their elevation products on their exact footprints, while it is not clear how this sparse information can be used for wider area. In this study, we developed a digital terrain model correction algorithm by spatially interpolating the difference between existing DEMs and GEDI elevation products by using Gaussian Process (GP) regression model. The result shows that our GP-based methodology can reduce the mean bias of the elevation data from 3.7m to 0.3m when we use airborne LiDAR-derived elevation information as ground truth. Our algorithm is also capable of quantifying the elevation data uncertainty, which is critical requirement for biomass inventory. Upcoming satellite-LiDAR missions, like MOLI (Multi-footprint Observation Lidar and Imager), are expected to contribute to the more accurate digital terrain model generation.

Keywords: digital terrain model, satellite LiDAR, gaussian processes, uncertainty quantification

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Authors: Gautier Viaud, Paul-Henry Cournède

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Plant growth models have been used extensively for the prediction of the phenotypic performance of plants. However, they remain most often calibrated for a given genotype and therefore do not take into account genotype by environment interactions. One way of achieving such an objective is to consider Bayesian hierarchical models. Three levels can be identified in such models: The first level describes how a given growth model describes the phenotype of the plant as a function of individual parameters, the second level describes how these individual parameters are distributed within a plant population, the third level corresponds to the attribution of priors on population parameters. Thanks to the Bayesian framework, choosing appropriate priors for the population parameters permits to derive analytical expressions for the full conditional distributions of these population parameters. As plant growth models are of a nonlinear nature, individual parameters cannot be sampled explicitly, and a Metropolis step must be performed. This allows for the use of a hybrid Gibbs--Metropolis sampler. A generic approach was devised for the implementation of both general state space models and estimation algorithms within a programming platform. It was designed using the Julia language, which combines an elegant syntax, metaprogramming capabilities and exhibits high efficiency. Results were obtained for Arabidopsis thaliana on both simulated and real data. An organ-scale Greenlab model for the latter is thus presented, where the surface areas of each individual leaf can be simulated. It is assumed that the error made on the measurement of leaf areas is proportional to the leaf area itself; multiplicative normal noises for the observations are therefore used. Real data were obtained via image analysis of zenithal images of Arabidopsis thaliana over a period of 21 days using a two-step segmentation and tracking algorithm which notably takes advantage of the Arabidopsis thaliana phyllotaxy. Since the model formulation is rather flexible, there is no need that the data for a single individual be available at all times, nor that the times at which data is available be the same for all the different individuals. This allows to discard data from image analysis when it is not considered reliable enough, thereby providing low-biased data in large quantity for leaf areas. The proposed model precisely reproduces the dynamics of Arabidopsis thaliana’s growth while accounting for the variability between genotypes. In addition to the estimation of the population parameters, the level of variability is an interesting indicator of the genotypic stability of model parameters. A promising perspective is to test whether some of the latter should be considered as fixed effects.

Keywords: bayesian, genotypic differentiation, hierarchical models, plant growth models

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Authors: Ali Mashayekh, Mahdiye Khorasani, Thomas Weyh

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The tendency to use the Battery-Electric vehicle (BEV) for the low and medium driving range or even high driving range has been growing more and more. As a result, higher safety, reliability, and durability of the battery pack as a component of electric vehicles, which has a great share of cost and weight of the final product, are the topics to be considered and investigated. Battery aging can be considered as the predominant factor regarding the reliability and durability of BEV. To better understand the aging process, offline battery characterization has been widely used, which is time-consuming and needs very expensive infrastructures. This paper presents the substitute method for the conventional battery characterization methods, which is based on battery Modular Multilevel Management (BM3). According to this Topology, the battery cells can be drained and charged concerning their capacity, which allows varying battery pack structures. Due to the integration of the power electronics, the output voltage of the battery pack is no longer fixed but can be dynamically adjusted in small steps. In other words, each cell can have three different states, namely series, parallel, and bypass in connection with the neighbor cells. With the help of MATLAB/Simulink and by using the BM3 modules, the battery string model is created. This model allows us to switch two cells with the different SoC as parallel, which results in the internal balancing of the cells. But if the parallel switching lasts just for a couple of ms, we can have a perturbation pulse which can stimulate the cells out of the relaxation phase. With the help of modeling the voltage response pulse of the battery, it would be possible to characterize the cell. The Online EIS method, which is discussed in this paper, can be a robust substitute for the conventional battery characterization methods.

Keywords: battery characterization, SoH estimation, RLS, BEV

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Authors: Kawsar Ahmed, Hong Wang

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The study's goal was to estimate the causal mediation impact of tobacco tax before and after price hikes among low-income male smokers, with a particular emphasis on the effect estimating pathways framework for continuous and dichotomous variables. From July to December 2021, a cross-sectional investigation of observational data (n=739) was collected from Bangladeshi low-wage smokers. The Quasi-Bayesian technique, binomial probit model, and sensitivity analysis using a simulation of the computational tools R mediation package had been used to estimate the effect. After a price rise for tobacco products, the average number of cigarettes or bidis sticks taken decreased from 6.7 to 4.56. Tobacco product rising prices have a direct effect on low-income people's decisions to quit or lessen their daily smoking habits of Average Causal Mediation Effect (ACME) [effect=2.31, 95 % confidence interval (C.I.) = (4.71-0.00), p<0.01], Average Direct Effect (ADE) [effect=8.6, 95 percent (C.I.) = (6.8-0.11), p<0.001], and overall significant effects (p<0.001). Tobacco smoking choice is described by the mediated proportion of income effect, which is 26.1% less of following price rise. The curve of ACME and ADE is based on observational figures of the coefficients of determination that asses the model of hypothesis as the substantial consequence after price rises in the sensitivity analysis. To reduce smoking product behaviors, price increases through taxation have a positive causal mediation with income that affects the decision to limit tobacco use and promote low-income men's healthcare policy.

Keywords: causal mediation analysis, directed acyclic graphs, tobacco price policy, sensitivity analysis, pathway estimation

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Authors: Diego Liberati

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Standard molecular modeling is traditionally done through Schroedinger equations via the help of powerful tools helping to manage them atom by atom, often needing High Performance Computing. Here, a full portfolio of new tools, conjugating statistical inference in the so called eXplainable Artificial Intelligence framework (in the form of Machine Learning of understandable rules) to the more traditional modeling and simulation control theory of mixed dynamic logic hybrid processes, is offered as quite a general purpose even if making an example to a popular chemical physics set of problems.

Keywords: understandable rules ML, k-means, PCA, PieceWise Affine Auto Regression with eXogenous input

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Authors: Rupali Verma, Maitreyee Dutta, Renu Vig

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Encryption and decryption in RSA are done by modular exponentiation which is achieved by repeated modular multiplication. Hence, efficiency of modular multiplication directly determines the efficiency of RSA cryptosystem. This paper designs a Modified Montgomery Modular multiplication in which addition of operands is computed by 4:2 compressor. The basic logic operations in addition are partitioned over two iterations such that parallel computations are performed. This reduces the critical path delay of proposed Montgomery design. The proposed design and RSA are implemented on Virtex 2 and Virtex 5 FPGAs. The two factors partitioning and parallelism have improved the frequency and throughput of proposed design.

Keywords: RSA, montgomery modular multiplication, 4:2 compressor, FPGA

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Authors: Georgiana Onicescu, Yuqian Shen

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Due to the complex nature of geo-referenced data, multicollinearity of the risk factors in public health spatial studies is a commonly encountered issue, which leads to low parameter estimation accuracy because it inflates the variance in the regression analysis. To address this issue, we proposed a two-stage variable selection method by extending the least absolute shrinkage and selection operator (Lasso) to the Bayesian spatial setting, investigating the impact of risk factors to health outcomes. Specifically, in stage I, we performed the variable selection using Bayesian Lasso and several other variable selection approaches. Then, in stage II, we performed the model selection with only the selected variables from stage I and compared again the methods. To evaluate the performance of the two-stage variable selection methods, we conducted a simulation study with different distributions for the risk factors, using geo-referenced count data as the outcome and Michigan as the research region. We considered the cases when all candidate risk factors are independently normally distributed, or follow a multivariate normal distribution with different correlation levels. Two other Bayesian variable selection methods, Binary indicator, and the combination of Binary indicator and Lasso were considered and compared as alternative methods. The simulation results indicated that the proposed two-stage Bayesian Lasso variable selection method has the best performance for both independent and dependent cases considered. When compared with the one-stage approach, and the other two alternative methods, the two-stage Bayesian Lasso approach provides the highest estimation accuracy in all scenarios considered.

Keywords: Lasso, Bayesian analysis, spatial analysis, variable selection

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Authors: Ece Cigdem Mutlu, Burak Alakent

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Maintaining the quality of manufactured products at a desired level depends on the stability of process dispersion and location parameters and detection of perturbations in these parameters as promptly as possible. Shewhart control chart is the most widely used technique in statistical process monitoring to monitor the quality of products and control process mean and variability. In the application of Xbar control charts, sample standard deviation and sample mean are known to be the most efficient conventional estimators in determining process dispersion and location parameters, respectively, based on the assumption of independent and normally distributed datasets. On the other hand, there is no guarantee that the real-world data would be normally distributed. In the cases of estimated process parameters from Phase I data clouded with outliers, efficiency of traditional estimators is significantly reduced, and performance of Xbar charts are undesirably low, e.g. occasional outliers in the rational subgroups in Phase I data set may considerably affect the sample mean and standard deviation, resulting a serious delay in detection of inferior products in Phase II. For more efficient application of control charts, it is required to use robust estimators against contaminations, which may exist in Phase I. In the current study, we present a simple approach to construct robust Xbar control charts using average distance to the median, Qn-estimator of scale, M-estimator of scale with logistic psi-function in the estimation of process dispersion parameter, and Harrell-Davis qth quantile estimator, Hodge-Lehmann estimator and M-estimator of location with Huber psi-function and logistic psi-function in the estimation of process location parameter. Phase I efficiency of proposed estimators and Phase II performance of Xbar charts constructed from these estimators are compared with the conventional mean and standard deviation statistics both under normality and against diffuse-localized and symmetric-asymmetric contaminations using 50,000 Monte Carlo simulations on MATLAB. Consequently, it is found that robust estimators yield parameter estimates with higher efficiency against all types of contaminations, and Xbar charts constructed using robust estimators have higher power in detecting disturbances, compared to conventional methods. Additionally, utilizing individuals charts to screen outlier subgroups and employing different combination of dispersion and location estimators on subgroups and individual observations are found to improve the performance of Xbar charts.

Keywords: average run length, M-estimators, quality control, robust estimators

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Authors: Kaushal Kishore

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Immense amount of imported fuels are used in Fiji for electricity generation, transportation and for carrying out miscellaneous household work. To alleviate its dependency on fossil fuel, paramount importance has been given to instigate the utilization of renewable energy sources for power generation and to reduce the environmental dilapidation. Amongst the many renewable energy sources, wind has been considered as one of the best identified renewable sources that are comprehensively available in Fiji. In this study the wind resource assessment for three locations in Rakiraki, Fiji has been carried out. The wind resource estimation at Rokavukavu, Navolau and at Tuvavatu has been analyzed. The average wind speed at 55 m above ground level (a.g.l) at Rokavukavu, Navolau, and Tuvavatu sites are 5.91 m/s, 8.94 m/s and 8.13 m/s with the turbulence intensity of 14.9%, 17.1%, and 11.7% respectively. The moment fitting method has been used to estimate the Weibull parameter and the power density at each sites. A high resolution wind resource map for the three locations has been developed by using Wind Atlas Analysis and Application Program (WAsP). The results obtained from WAsP exhibited good wind potential at Navolau and Tuvavatu sites. A wind farm has been proposed at Navolau and Tuvavatu site that comprises six Vergnet 275 kW wind turbines at each site. The annual energy production (AEP) for each wind farm is estimated and an economic analysis is performed. The economic analysis for the proposed wind farms at Navolau and Tuvavatu sites showed a payback period of 5 and 6 years respectively.

Keywords: annual energy production, Rakiraki Fiji, turbulence intensity, Weibull parameter, wind speed, Wind Atlas Analysis and Application Program

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Authors: Hesam Gholami

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Elevated suspended sediment loads in riverine systems resulting from accelerated erosion due to human activities are a serious threat to the sustainable management of watersheds and ecosystem services therein worldwide. Therefore, mitigation of deleterious sediment effects as a distributed or non-point pollution source in the catchments requires reliable provenance information. Sediment tracing or sediment fingerprinting, as a combined process consisting of sampling, laboratory measurements, different statistical tests, and the application of mixing or unmixing models, is a useful technique for discriminating the sources of sediments. From 1996 to the present, different aspects of this technique, such as grouping the sources (spatial and individual sources), discriminating the potential sources by different statistical techniques, and modification of mixing and unmixing models, have been introduced and modified by many researchers worldwide, and have been applied to identify the provenance of fine materials in agricultural, rural, mountainous, and coastal catchments, and in large catchments with numerous lakes and reservoirs. In the last two decades, efforts exploring the uncertainties associated with sediment fingerprinting results have attracted increasing attention. The frameworks used to quantify the uncertainty associated with fingerprinting estimates can be divided into three groups comprising Monte Carlo simulation, Bayesian approaches and generalized likelihood uncertainty estimation (GLUE). Given the above background, the primary goal of this study was to apply geochemical fingerprinting within the GLUE framework in the estimation of sub-basin spatial sediment source contributions in the arid Mehran River catchment in southern Iran, which drains into the Persian Gulf. The accuracy of GLUE predictions generated using four different sets of statistical tests for discriminating three sub-basin spatial sources was evaluated using 10 virtual sediments (VS) samples with known source contributions using the root mean square error (RMSE) and mean absolute error (MAE). Based on the results, the contributions modeled by GLUE for the western, central and eastern sub-basins are 1-42% (overall mean 20%), 0.5-30% (overall mean 12%) and 55-84% (overall mean 68%), respectively. According to the mean absolute fit (MAF; ≥ 95% for all target sediment samples) and goodness-of-fit (GOF; ≥ 99% for all samples), our suggested modeling approach is an accurate technique to quantify the source of sediments in the catchments. Overall, the estimated source proportions can help watershed engineers plan the targeting of conservation programs for soil and water resources.

Keywords: sediment source tracing, generalized likelihood uncertainty estimation, virtual sediment mixtures, Iran

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Authors: Pedro Acevedo, Carlos Díaz, Mónica Vázquez, Joel Durán

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This paper describes the design of a pulse generator based on the Programmable System-on-Chip (PSoC) module. In this module, using programmable logic is possible to implement different pulses which are required for ultrasonic applications, either in a single channel or multiple channels. This module can operate with programmable frequencies from 3-74 MHz; its programming may be versatile covering a wide range of ultrasonic applications. It is ideal for low-power ultrasonic applications where PZT or PVDF transducers are used.

Keywords: PSoC, pulse generator, PVDF, ultrasonic transducer

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Authors: Ahmed Z. Mazen, Nejat Rahmanian, Iqbal Mujtaba, Ali Hassanpour

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In oil and gas well drilling, the drill bit is an important part of the Bottom Hole Assembly (BHA), which is installed and designed to drill and produce a hole by several mechanisms. The efficiency of the bit depends on many drilling parameters such as weight on bit, rotary speed, and mud properties. When the bit is pulled out of the hole, the evaluation of the bit damage must be recorded very carefully to guide engineers in order to select the bits for further planned wells. Having a worn bit for hole drilling may cause severe damage to bit leading to cutter or cone losses in the bottom of hole, where a fishing job will have to take place, and all of these will increase the operating cost. The main factor to reduce the cost of drilling operation is to maximize the rate of penetration by analyzing real-time data to predict the drill bit wear while drilling. There are numerous models in the literature for prediction of the rate of penetration based on drilling parameters, mostly based on empirical approaches. One of the most commonly used approaches is Bourgoyne and Young model, where the rate of penetration can be estimated by the drilling parameters as well as a wear index using an empirical correlation, provided all the constants and coefficients are accurately determined. This paper introduces a new methodology to estimate the eight coefficients for Bourgoyne and Young model using the gPROMS parameters estimation GPE (Version 4.2.0). Real data collected form similar formations (12 ¼’ sections) in two different fields in Libya are used to estimate the coefficients. The estimated coefficients are then used in the equations and applied to nearby wells in the same field to predict the bit wear.

Keywords: Bourgoyne and Young model, bit wear, gPROMS, rate of penetration

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Authors: Abdeljalil Beniiche

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The limitation with smart contracts is that they cannot access external data that might be required to control the execution of business logic. Oracles can be used to provide external data to smart contracts. An oracle is an interface that delivers data from external data outside the blockchain to a smart contract to consume. Oracle can deliver different types of data depending on the industry and requirements. In this paper, we study and describe the widely used blockchain oracles. Then, we elaborate on his potential role, technical architecture, and design patterns. Finally, we discuss the human oracle and its key role in solving the truth problem by reaching a consensus about a certain inquiry and tasks.

Keywords: blockchain, oracles, oracles design, human oracles

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Authors: R. Sharma, S. Kumar, C. Sharma

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A number of toxic chlorophenolic compounds are formed during pulp bleaching. The nature and concentration of these chlorophenolic compounds largely depends upon the amount and nature of bleaching chemicals used. These compounds are highly recalcitrant and difficult to remove but are partially removed by the biochemical treatment processes adopted by the paper industry. Identification and estimation of these chlorophenolic compounds has been carried out in the primary and secondary clarified effluents from the paper mill by GCMS. Twenty-six chorophenolic compounds have been identified and estimated in paper mill waste waters. Electrochemical treatment is an efficient method for oxidation of pollutants and has successfully been used to treat textile and oil waste water. Electrochemical treatment using less expensive anode material, stainless steel electrodes has been tried to study their removal. The electrochemical assembly comprised a DC power supply, a magnetic stirrer and stainless steel (316 L) electrode. The optimization of operating conditions has been carried out and treatment has been performed under optimized treatment conditions. Results indicate that 68.7% and 83.8% of cholorphenolic compounds are removed during 2 h of electrochemical treatment from primary and secondary clarified effluent respectively. Further, there is a reduction of 65.1, 60 and 92.6% of COD, AOX and color, respectively for primary clarified and 83.8%, 75.9% and 96.8% of COD, AOX and color, respectively for secondary clarified effluent. EC treatment has also been found to increase significantly the biodegradability index of wastewater because of conversion of non- biodegradable fraction into biodegradable fraction. Thus, electrochemical treatment is an efficient method for the degradation of cholorophenolic compounds, removal of color, AOX and other recalcitrant organic matter present in paper mill waste water.

Keywords: chlorophenolics, effluent, electrochemical treatment, wastewater

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Authors: R. Santos Alimi

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Using time-series techniques, this study empirically tested the validity of existing theory which stipulates there is a nonlinear relationship between government size and economic growth; such that government spending is growth-enhancing at low levels but growth-retarding at high levels, with the optimal size occurring somewhere in between. This study employed three estimation equations. First, for the size of government, two measures are considered as follows: (i) share of total expenditures to gross domestic product, (ii) share of recurrent expenditures to gross domestic product. Second, the study adopted real GDP (without government expenditure component), as a variant measure of economic growth other than the real total GDP, in estimating the optimal level of government expenditure. The study is based on annual Nigeria country-level data for the period 1970 to 2012. Estimation results show that the inverted U-shaped curve exists for the two measures of government size and the estimated optimum shares are 19.81% and 10.98%, respectively. Finally, with the adoption of real GDP (without government expenditure component), the optimum government size was found to be 12.58% of GDP. Our analysis shows that the actual share of government spending on average (2000 - 2012) is about 13.4%.This study adds to the literature confirming that the optimal government size exists not only for developed economies but also for developing economy like Nigeria. Thus, a public intervention threshold level that fosters economic growth is a reality; beyond this point economic growth should be left in the hands of the private sector. This finding has a significant implication for the appraisal of government spending and budgetary policy design.

Keywords: public expenditure, economic growth, optimum level, fully modified OLS

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Authors: Alexander N. Pisarchik, Parth Chholak

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Intrinsic brain noise was estimated via magneto-encephalograms (MEG) recorded during perception of flickering visual stimuli with frequencies of 6.67 and 8.57 Hz. First, we measured the mean phase difference between the flicker signal and steady-state event-related field (SSERF) in the occipital area where the brain response at the flicker frequencies and their harmonics appeared in the power spectrum. Then, we calculated the probability distribution of the phase fluctuations in the regions of frequency locking and computed its kurtosis. Since kurtosis is a measure of the distribution’s sharpness, we suppose that inverse kurtosis is related to intrinsic brain noise. In our experiments, the kurtosis value varied among subjects from K = 3 to K = 5 for 6.67 Hz and from 2.6 to 4 for 8.57 Hz. The majority of subjects demonstrated leptokurtic kurtosis (K < 3), i.e., the distribution tails approached zero more slowly than Gaussian. In addition, we found a strong correlation between kurtosis and brain complexity measured as the correlation dimension, so that the MEGs of subjects with higher kurtosis exhibited lower complexity. The obtained results are discussed in the framework of nonlinear dynamics and complex network theories. Specifically, in a network of coupled oscillators, phase synchronization is mainly determined by two antagonistic factors, noise, and the coupling strength. While noise worsens phase synchronization, the coupling improves it. If we assume that each neuron and each synapse contribute to brain noise, the larger neuronal network should have stronger noise, and therefore phase synchronization should be worse, that results in smaller kurtosis. The described method for brain noise estimation can be useful for diagnostics of some brain pathologies associated with abnormal brain noise.

Keywords: brain, flickering, magnetoencephalography, MEG, visual perception, perception time

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Authors: Nelson F. F. Ebecken, Gilberto C. Pereira, Lucio P. de Andrade

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Bays, estuaries and coastal ecosystems are some of the most used and threatened natural systems globally. Its deterioration is due to intense and increasing human activities. This paper aims to monitor the socio-ecological in Brazil, model and simulate it through a multilayer network representing a DPSIR structure (Drivers, Pressures, States-Impacts-Responses) considering the concept of Management based on Ecosystems to support decision-making under the National/State/Municipal Coastal Management policy. This approach considers several interferences and can represent a significant advance in several scientific aspects. The main objective of this paper is the coupling of three different types of complex networks, the first being an ecological network, the second a social network, and the third a network of economic activities, in order to model the marine ecosystem. Multilayer networks comprise two or more "layers", which may represent different types of interactions, different communities, different points in time, and so on. The dependency between layers results from processes that affect the various layers. For example, the dispersion of individuals between two patches affects the network structure of both samples. A multilayer network consists of (i) a set of physical nodes representing entities (e.g., species, people, companies); (ii) a set of layers, which may include multiple layering aspects (e.g., time dependency and multiple types of relationships); (iii) a set of state nodes, each of which corresponds to the manifestation of a given physical node in a layer-specific; and (iv) a set of edges (weighted or not) to connect the state nodes among themselves. The edge set includes the intralayer edges familiar and interlayer ones, which connect state nodes between layers. The applied methodology in an existent case uses the Flow cytometry process and the modeling of ecological relationships (trophic and non-trophic) following fuzzy theory concepts and graph visualization. The identification of subnetworks in the fuzzy graphs is carried out using a specific computational method. This methodology allows considering the influence of different factors and helps their contributions to the decision-making process.

Keywords: marine ecosystems, complex systems, multilayer network, ecosystems management

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Authors: S. J. Matlan, M. Mukhlisin, M. R. Taha

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Characterization of the engineering behavior of unsaturated soil is dependent on the soil-water characteristic curve (SWCC), a graphical representation of the relationship between water content or degree of saturation and soil suction. A reasonable description of the SWCC is thus important for the accurate prediction of unsaturated soil parameters. The measurement procedures for determining the SWCC, however, are difficult, expensive, and time-consuming. During the past few decades, researchers have laid a major focus on developing empirical equations for predicting the SWCC, with a large number of empirical models suggested. One of the most crucial questions is how precisely existing equations can represent the SWCC. As different models have different ranges of capability, it is essential to evaluate the precision of the SWCC models used for each particular soil type for better SWCC estimation. It is expected that better estimation of SWCC would be achieved via a thorough statistical analysis of its distribution within a particular soil class. With this in view, a statistical analysis was conducted in order to evaluate the reliability of the SWCC prediction models against laboratory measurement. Optimization techniques were used to obtain the best-fit of the model parameters in four forms of SWCC equation, using laboratory data for relatively coarse-textured (i.e., sandy) soil. The four most prominent SWCCs were evaluated and computed for each sample. The result shows that the Brooks and Corey model is the most consistent in describing the SWCC for sand soil type. The Brooks and Corey model prediction also exhibit compatibility with samples ranging from low to high soil water content in which subjected to the samples that evaluated in this study.

Keywords: soil-water characteristic curve (SWCC), statistical analysis, unsaturated soil, geotechnical engineering

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Authors: Michael Smalenberger

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Intelligent tutoring systems (ITS) are computer-based platforms which can incorporate artificial intelligence to provide step-by-step guidance as students practice problem-solving skills. ITS can replicate and even exceed some benefits of one-on-one tutoring, foster transactivity in collaborative environments, and lead to substantial learning gains when used to supplement the instruction of a teacher or when used as the sole method of instruction. A common facet of many ITS is their use of Bayesian Knowledge Tracing (BKT) to estimate parameters necessary for the implementation of the artificial intelligence component, and for the probability of mastery of a knowledge component relevant to the ITS. While various techniques exist to estimate these parameters and probability of mastery, none directly and reliably ask the user to self-assess these. In this study, 111 undergraduate students used an ITS in a college-level introductory statistics course for which detailed transaction-level observations were recorded, and users were also routinely asked direct questions that would lead to such a self-assessment. Comparisons were made between these self-assessed values and those obtained using commonly used estimation techniques. Our findings show that such self-assessments are particularly relevant at the early stages of ITS usage while transaction level data are scant. Once a user’s transaction level data become available after sufficient ITS usage, these can replace the self-assessments in order to eliminate the identifiability problem in BKT. We discuss how these findings are relevant to the number of exercises necessary to lead to mastery of a knowledge component, the associated implications on learning curves, and its relevance to instruction time.

Keywords: Bayesian Knowledge Tracing, Intelligent Tutoring System, in vivo study, parameter estimation

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Authors: Allan F. F. Alves, Fernando A. Bacchim Neto, Guilherme Giacomini, Marcela de Oliveira, Ana L. M. Pavan, Maria E. D. Rosa, Diana R. Pina

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Stroke is a worldwide concern, only in Brazil it accounts for 10% of all registered deaths. There are 2 stroke types, ischemic (87%) and hemorrhagic (13%). Early diagnosis is essential to avoid irreversible cerebral damage. Non-enhanced computed tomography (NECT) is one of the main diagnostic techniques used due to its wide availability and rapid diagnosis. Detection depends on the size and severity of lesions and the time spent between the first symptoms and examination. The Alberta Stroke Program Early CT Score (ASPECTS) is a subjective method that increases the detection rate. The aim of this work was to implement an image segmentation system to enhance ischemic stroke and to quantify the area of ischemic and hemorrhagic stroke lesions in CT scans. We evaluated 10 patients with NECT examinations diagnosed with ischemic stroke. Analyzes were performed in two axial slices, one at the level of the thalamus and basal ganglion and one adjacent to the top edge of the ganglionic structures with window width between 80 and 100 Hounsfield Units. We used different image processing techniques such as morphological filters, discrete wavelet transform and Fuzzy C-means clustering. Subjective analyzes were performed by a neuroradiologist according to the ASPECTS scale to quantify ischemic areas in the middle cerebral artery region. These subjective analysis results were compared with objective analyzes performed by the computational algorithm. Preliminary results indicate that the morphological filters actually improve the ischemic areas for subjective evaluations. The comparison in area of the ischemic region contoured by the neuroradiologist and the defined area by computational algorithm showed no deviations greater than 12% in any of the 10 examination tests. Although there is a tendency that the areas contoured by the neuroradiologist are smaller than those obtained by the algorithm. These results show the importance of a computer aided diagnosis software to assist neuroradiology decisions, especially in critical situations as the choice of treatment for ischemic stroke.

Keywords: ischemic stroke, image processing, CT scans, Fuzzy C-means

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Authors: Yuli Paola Cifuentes Sanabria, Lina Paola Beltrán Beltrán, Leonardo Juan Ramírez López

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The availability to deploy mobile applications for healthcare is increasing daily thru different mobile app stores. But within these capabilities the number of hacking attacks has also increased, in particular into medical mobile applications. The security vulnerabilities in medical mobile apps can be triggered by errors in code, incorrect logic, poor design, among other parameters. This is usually used by malicious attackers to steal or modify the users’ information. The aim of this research is to analyze the vulnerabilities detected in mobile medical apps according to risk factor standards defined by OWASP in 2014.

Keywords: mHealth apps, OWASP, protocols, security vulnerabilities, risk factors

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Authors: Aid Abdelkrim

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A method of fatigue damage accumulation based upon application of energy parameters of the fatigue process is proposed in the paper. Using this model is simple, it has no parameter to be determined, it requires only the knowledge of the curve W–N (W: strain energy density N: number of cycles at failure) determined from the experimental Wöhler curve. To examine the performance of nonlinear models proposed in the estimation of fatigue damage and fatigue life of components under random loading, a batch of specimens made of 6082 T 6 aluminium alloy has been studied and some of the results are reported in the present paper. The paper describes an algorithm and suggests a fatigue cumulative damage model, especially when random loading is considered. This work contains the results of uni-axial random load fatigue tests with different mean and amplitude values performed on 6082T6 aluminium alloy specimens. The proposed model has been formulated to take into account the damage evolution at different load levels and it allows the effect of the loading sequence to be included by means of a recurrence formula derived for multilevel loading, considering complex load sequences. It is concluded that a ‘damaged stress interaction damage rule’ proposed here allows a better fatigue damage prediction than the widely used Palmgren–Miner rule, and a formula derived in random fatigue could be used to predict the fatigue damage and fatigue lifetime very easily. The results obtained by the model are compared with the experimental results and those calculated by the most fatigue damage model used in fatigue (Miner’s model). The comparison shows that the proposed model, presents a good estimation of the experimental results. Moreover, the error is minimized in comparison to the Miner’s model.

Keywords: damage accumulation, energy model, damage indicator, variable loading, random loading

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Authors: Shorena Pharjiani

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The Present empirical paper investigates the relationship between FDI and economic growth by 10 selected industries in 10 Central and Eastern European countries from the period 1995 to 2012. Different estimation approaches were used to explore the connection between FDI and economic growth, for example OLS, RE, FE with and without time dummies. Obtained empirical results leads to some main consequences: First, the Central and East European countries (CEEC) attracted foreign direct investment, which raised the productivity of industries they entered in. It should be concluded that the linkage between FDI and output growth by industries is positive and significant enough to suggest that foreign firm’s participation enhanced the productivity of the industries they occupied. There had been an endogeneity problem in the regression and fixed effects estimation approach was used which partially corrected the regression analysis in order to make the results less biased. Second, it should be stressed that the results show that time has an important role in making FDI operational for enhancing output growth by industries via total factor productivity. Third, R&D positively affected economic growth and at the same time, it should take some time for research and development to influence economic growth. Fourth, the general trends masked crucial differences at the country level: over the last 20 years, the analysis of the tables and figures at the country level show that the main recipients of FDI of the 11 Central and Eastern European countries were Hungary, Poland and the Czech Republic. The main reason was that these countries had more open door policies for attracting the FDI. Fifth, according to the graphical analysis, while Hungary had the highest FDI inflow in this region, it was not reflected in the GDP growth as much as in other Central and Eastern European countries.

Keywords: central and East European countries (CEEC), economic growth, FDI, panel data

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Authors: Adhinarayanan Venkatasubramanian

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For design of high speed digital circuit wave pipeline is the best approach this can be operated at higher operating frequencies by adjusting clock periods and skews so as latch the o/p of combinational logic circuit at the stable period. In this paper, there are two methods are proposed in automation task one is BIST (Built in self test) and second method is Reconfigurable technique. For the above two approaches dedicated AND gate (multiplier) by applying wave pipeline technique. BIST approach is implemented by Xilinx Spartan-II device. In reconfigurable technique done by ASIC. From the results, wave pipeline circuits are faster than nonpipeline circuit and area, power dissipation are reduced by reconfigurable technique.

Keywords: SOC, wave-pipelining, FPGA, self-testing, reconfigurable, ASIC

Procedia PDF Downloads 429