Search results for: series coupling

Authors: Baya Takhedmit, Karim Abbas, Sofiane Ouazine

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The vacations queues are often employed to model many real situations such as computer systems, communication networks, manufacturing and production systems, transportation systems and so forth. These queueing models are solved at fixed parameters values. However, the parameter values themselves are determined from a finite number of observations and hence have uncertainty associated with them (epistemic uncertainty). In this paper, we consider the M/G/1/N queue with server vacation and exhaustive discipline where we assume that the vacation parameter values have uncertainty. We use the Taylor series expansions approach to estimate the expectation and variance of model output, due to epistemic uncertainties in the model input parameters.

Keywords: epistemic uncertainty, M/G/1/N queue with vacations, non-parametric sensitivity analysis, Taylor series expansion

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Authors: Luofu Liu, Fei Xiao Jr., Fei Xiao

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Through the analysis technology of gas chromatography-mass spectrometry (GC-MS), the composition and distribution characteristics of aromatic hydrocarbons in the Chepaizi area of the Junggar Basin were analyzed in detail. Based on that, the biological input, maturity of crude oils and sedimentary environment of the corresponding source rocks were determined and the origin types of crude oils were divided. The results show that there are three types of crude oils in the study area including Type I, Type II and Type III oils. The crude oils from the 1st member of the Neogene Shawan Formation are the Type I oils; the crude oils from the 2nd member of the Neogene Shawan Formation are the Type II oils; the crude oils from the Cretaceous Qingshuihe and Jurassic Badaowan Formations are the Type III oils. For the Type I oils, they show a single model in the late retention time of the chromatogram of total aromatic hydrocarbons. The content of triaromatic steroid series is high, and the content of dibenzofuran is low. Maturity parameters related to alkyl naphthalene, methylphenanthrene and alkyl dibenzothiophene all indicate low maturity for the Type I oils. For the Type II oils, they have also a single model in the early retention time of the chromatogram of total aromatic hydrocarbons. The content of naphthalene and phenanthrene series is high, and the content of dibenzofuran is medium. The content of polycyclic aromatic hydrocarbon representing the terrestrial organic matter is high. The aromatic maturity parameters indicate high maturity for the Type II oils. For the Type III oils, they have a bi-model in the chromatogram of total aromatic hydrocarbons. The contents of naphthalene series, phenanthrene series, and dibenzofuran series are high. The aromatic maturity parameters indicate medium maturity for the Type III oils. The correlation results of triaromatic steroid series fingerprint show that the Type I and Type III oils have similar source and are both from the Permian Wuerhe source rocks. Because of the strong biodegradation and mixing from other source, the Type I oils are very different from the Type III oils in aromatic hydrocarbon characteristics. The Type II oils have the typical characteristics of terrestrial organic matter input under oxidative environment, and are the coal oil mainly generated by the mature Jurassic coal measure source rocks. However, the overprinting effect from the low maturity Cretaceous source rocks changed the original distribution characteristics of aromatic hydrocarbons to some degree.

Keywords: oil source, geochemistry, aromatic hydrocarbons, crude oils, chepaizi area, Junggar Basin

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

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Floods have huge environmental and economic impact. Therefore, flood prediction is given a lot of attention due to its importance. This study analysed the annual maximum streamflow (discharge) (AMS or AMD) of Karkheh River in Karkheh River Basin for flood predicting using ARIMA model. For this purpose, we use the Box-Jenkins approach, which contains four-stage method model identification, parameter estimation, diagnostic checking and forecasting (predicting). The main tool used in ARIMA modelling was the SAS and SPSS software. Model identification was done by visual inspection on the ACF and PACF. SAS software computed the model parameters using the ML, CLS and ULS methods. The diagnostic checking tests, AIC criterion, RACF graph and RPACF graphs, were used for selected model verification. In this study, the best ARIMA models for Annual Maximum Discharge (AMD) time series was (4,1,1) with their AIC value of 88.87. The RACF and RPACF showed residuals’ independence. To forecast AMD for 10 future years, this model showed the ability of the model to predict floods of the river under study in the Karkheh River Basin. Model accuracy was checked by comparing the predicted and observation series by using coefficient of determination (R²).

Keywords: time series modelling, stochastic processes, ARIMA model, Karkheh river

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Authors: Aysegul Sener, Gonca Tuncel Memis, Mirac Kamislioglu

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Chaos theory is one of the new paradigms of science since the last century. After determining chaos in the weather systems by Edward Lorenz the popularity of the theory was increased. Chaos is observed in many natural systems and studies continue to defect chaos to other natural systems. Acid rain is one of the environmental problems that have negative effects on environment and acid rains values are monitored continuously. In this study, we aim that analyze the chaotic behavior of acid rains in Turkey with the chaotic defecting approaches. The data of pH degree of rain waters, concentration of sulfate and nitrate data of wet rain water samples in the rain collecting stations which are located in different regions of Turkey are provided by Turkish State Meteorology Service. Lyapunov exponents, reconstruction of the phase space, power spectrums are used in this study to determine and predict the chaotic behaviors of acid rains. As a result of the analysis it is found that acid rain time series have positive Lyapunov exponents and wide power spectrums and chaotic behavior is observed in the acid rain time series.

Keywords: acid rains, chaos, chaotic analysis, Lypapunov exponents

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Authors: Salim Belouettar, Kodjo Attipou

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The thermal wrinkling behavior of thin membranes is investigated. The Fourier double scale series are used to deduce the macroscopic membrane wrinkling equations. The obtained equations account for the global and local wrinkling modes. Numerical examples are conducted to assess the validity of the approach developed. Compared to the finite element full model, the present model needs only few degrees of freedom to recover accurately the bifurcation curves and wrinkling paths. Different parameters such as membrane’s aspect ratio, wave number, pre-stressed membranes are discussed from a numerical point of view and the properties of the wrinkles (critical load, wavelength, size and location) are presented.

Keywords: wrinkling, thermal stresses, Fourier series, thin membranes

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Authors: Jinee Gogoi, Som K. Sharma, Kalyan Bhuyan

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The atmospheric layers are coupled to each other with the different dynamical, electrical, radiative and chemical processes. A large scale thermodynamical phenomenon in winter polar regions which affects the middle atmosphere vigorously is Sudden Stratospheric Warming (SSW). Two major SSW events were occurred during 1998-1999; one in December 1998 which is associated with vortex displacement and another in February- March 1999 associated with vortex splitting. Lidar study of these two major events from Mt. Abu (24.36⁰N, 72.45⁰E, ~1670 m amsl) has shown that though SSWs are mostly observed over high and mid latitudes, their effects can also be seen over India. We have studied ionospheric variations (primarily fₒF₂, h’F and hpF₂) over Ahmedabad (23.1⁰N, 72.58⁰E) during these events. Ionospheric disturbances have been found after four-five days of peak temperature. An increase (decrease) in critical frequency (fₒF₂) during morning (afternoon) has been noticed which may be in response to the updrift (down drift). Effects are stronger during displacement event (1998) than during the splitting event (1999). We have also studied some recent events occurred during 2006 (January), 2009 (January) and 2013 (January) using temperature data from Sounding of Atmosphere using Broadband Emission Radiometry (SABER) satellite. Though some modeling work supports the hypothesis that planetary waves are responsible for atmosphere-ionosphere coupling, there is still more significant works to do to understand how exactly the coupling can take place.

Keywords: sudden stratospheric warming (SSW), polar vortex, ionosphere, critical frequency

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Authors: Kumud Singh, Janvin Itteera, Priti Ukarde, Sanjay Malhotra, P. PMarathe, Ayan Bandyopadhay, Rakesh Meena, Vikram Rawat, L. M. Joshi

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Application of Permanent magnet technology to high frequency miniature klystron tubes to be utilized for space applications improves the efficiency and operational reliability of these tubes. But nevertheless the task of generating magnetic focusing forces to eliminate beam divergence once the beam crosses the electrostatic focusing regime and enters the drift region in the RF section of the tube throws several challenges. Building a high quality magnet focusing lens to meet beam optics requirement in cathode gun and RF interaction region is considered to be one of the critical issues for these high frequency miniature tubes. In this paper, electromagnetic design and particle trajectory studies in combined electric and magnetic field for optimizing the magnetic circuit using 3D finite element method (FEM) analysis software is presented. A rectangular configuration of the magnet was constructed to accommodate apertures for input and output waveguide sections and facilitate coupling of electromagnetic fields into the input klystron cavity and out from output klystron cavity through coupling loops. Prototype lenses have been built and have been tested after integration with the klystron tube. We discuss the design requirements and challenges, and the results from beam transmission of the prototype lens.

Keywords: beam transmission, Brillouin, confined flow, miniature klystron

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Authors: Seok Hong Min, Tae Kwon Ha

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In this study, thermal fatigue properties of 400 series ferritic stainless steels have been evaluated in the temperature ranges of 200-800oC and 200-900oC. Systematic methods for control of temperatures within the predetermined range and measurement of load applied to specimens as a function of temperature during thermal cycles have been established. Thermal fatigue tests were conducted under fully constrained condition, where both ends of specimens were completely fixed. It has been revealed that load relaxation behavior at the temperatures of thermal cycle was closely related with the thermal fatigue property. Thermal fatigue resistance of 430J1L stainless steel is found to be superior to the other steels.

Keywords: ferritic stainless steel, automotive exhaust, thermal fatigue, microstructure, load relaxation

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Authors: Deepanjali Gurav, Kun Qian

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In-vitro metabolic diagnosis relies on designed materials-based analytical platforms for detection of selected metabolites in biological samples, which has a key role in disease detection and therapeutic evaluation in clinics. However, the basic challenge deals with developing a simple approach for metabolic analysis in bio-samples with high sample complexity and low molecular abundance. In this work, we report a designer plasmonic nanoshells based platform for direct detection of small metabolites in clinical samples for in-vitro metabolic diagnostics. We first synthesized a series of plasmonic core-shell particles with tunable nanoshell structures. The optimized plasmonic nanoshells as new matrices allowed fast, multiplex, sensitive, and selective LDI MS (Laser desorption/ionization mass spectrometry) detection of small metabolites in 0.5 μL of bio-fluids without enrichment or purification. Furthermore, coupling with isotopic quantification of selected metabolites, we demonstrated the use of these plasmonic nanoshells for disease detection and therapeutic evaluation in clinics. For disease detection, we identified patients with postoperative brain infection through glucose quantitation and daily monitoring by cerebrospinal fluid (CSF) analysis. For therapeutic evaluation, we investigated drug distribution in blood and CSF systems and validated the function and permeability of blood-brain/CSF-barriers, during therapeutic treatment of patients with cerebral edema for pharmacokinetic study. Our work sheds light on the design of materials for high-performance metabolic analysis and precision diagnostics in real cases.

Keywords: plasmonic nanoparticles, metabolites, fingerprinting, mass spectrometry, in-vitro diagnostics

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Authors: Manit Pollar

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Identifying the dengue epidemic periods early would be helpful to take necessary actions to prevent the dengue outbreaks. Providing an accurate prediction on dengue epidemic seasons will allow sufficient time to take the necessary decisions and actions to safeguard the situation for local authorities. This study aimed to develop a forecasting model on number of dengue incidences in Nakhon Si Thammarat Province, Southern Thailand using time series analysis. We develop Seasonal Autoregressive Moving Average (SARIMA) models on the monthly data collected between 2003-2011 and validated the models using data collected between January-September 2012. The result of this study revealed that the SARIMA(1,1,0)(1,2,1)12 model closely described the trends and seasons of dengue incidence and confirmed the existence of dengue fever cases in Nakhon Si Thammarat for the years between 2003-2011. The study showed that the one-step approach for predicting dengue incidences provided significantly more accurate predictions than the twelve-step approach. The model, even if based purely on statistical data analysis, can provide a useful basis for allocation of resources for disease prevention.

Keywords: SARIMA, time series model, dengue cases, Thailand

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Authors: Hanyu Wang, Guangdong Wang

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County-level areas serve as vital spatial units for advancing new urbanization and implementing the principles of low-carbon development, representing critical regions where conflicts between the two are pronounced. Using the 142 county-level units in the Chengdu-Chongqing city clusters as a case study, a coupled coordination model is employed to investigate the coupled coordination relationship and its spatiotemporal evolution between county-level new urbanization and low-carbon development levels. Results indicate that (1) from 2005 to 2020, the overall levels of new urbanization and low-carbon development in the Chengdu-Chongqing city clusters showed an upward trend but with significant regional disparities. The new urbanization level exhibited a spatial differentiation pattern of "high in the suburban areas, low in the distant suburbs, and some counties standing out." The temporal change in low-carbon development levels was not pronounced, yet spatial disparities were notable. (2) The overall coupling coordination degree between new urbanization and low-carbon development is transitioning from barely coordinated to moderately coordinated. The lag in new urbanization levels serves as a primary factor constraining the coordinated development of most counties. (3) Based on the temporal evolution of development states, all county units can be categorized into four types: coordinated demonstration areas, synergistic improvement areas, low-carbon transformation areas, and development lag areas. The research findings offer crucial reference points for spatial planning and the formulation of low-carbon development policies.

Keywords: county units, coupling coordination, low-carbon development, new urbanization

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Authors: Hajar Hajmohammadi, Paul Pfeffer, Anna De Simoni, Jim Cole, Chris Griffiths, Sally Hull, Benjamin Heydecker

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Background: There is strong interest in the relationship between short-term air pollution exposure and human health. Most studies in this field focus on serious health effects such as death or hospital admission, but air pollution exposure affects many people with less severe impacts, such as exacerbations of respiratory conditions. A lack of quantitative analysis and inconsistent findings suggest improved methodology is needed to understand these effectsmore fully. Method: We developed a time series regression model to quantify the relationship between daily NOₓ concentration and Asthma exacerbations requiring oral steroids from primary care settings. Explanatory variables include daily NOₓ concentration measurements extracted from 8 available background and roadside monitoring stations in east London and daily ambient temperature extracted for London City Airport, located in east London. Lags of NOx concentrations up to 21 days (3 weeks) were used in the model. The dependent variable was the daily number of oral steroid courses prescribed for GP registered patients with asthma in east London. A mixed distribution model was then fitted to the significant lags of the regression model. Result: Results of the time series modelling showed a significant relationship between NOₓconcentrations on each day and the number of oral steroid courses prescribed in the following three weeks. In addition, the model using only roadside stations performs better than the model with a mixture of roadside and background stations.

Keywords: air pollution, time series modeling, public health, road transport

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Authors: Shalaka Kadam, Dinesh Apte, Sagar Mainkar

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The life cycle of products is becoming shorter and shorter due to increased competition in market, shorter product development time and increased product diversity. Short life cycles are normal in retail industry, style business, entertainment media, and telecom and semiconductor industry. The subject of accurate forecasting for demand of short lifecycle products is of special enthusiasm for many researchers and organizations. Due to short life cycle of products the amount of historical data that is available for forecasting is very minimal or even absent when new or modified products are launched in market. The companies dealing with such products want to increase the accuracy in demand forecasting so that they can utilize the full potential of the market at the same time do not oversupply. This provides the challenge to develop a forecasting model that can forecast accurately while handling large variations in data and consider the complex relationships between various parameters of data. Many statistical models have been proposed in literature for forecasting time series data. Traditional time series forecasting models do not work well for short life cycles due to lack of historical data. Also artificial neural networks (ANN) models are very time consuming to perform forecasting. We have studied the existing models that are used for forecasting and their limitations. This work proposes an effective and powerful forecasting approach for short life cycle time series forecasting. We have proposed an approach which takes into consideration different scenarios related to data availability for short lifecycle products. We then suggest a methodology which combines statistical analysis with structured judgement. Also the defined approach can be applied across domains. We then describe the method of creating a profile from analogous products. This profile can then be used for forecasting products with historical data of analogous products. We have designed an application which combines data, analytics and domain knowledge using point-and-click technology. The forecasting results generated are compared using MAPE, MSE and RMSE error scores. Conclusion: Based on the results it is observed that no one approach is sufficient for short life-cycle forecasting and we need to combine two or more approaches for achieving the desired accuracy.

Keywords: forecast, short life cycle product, structured judgement, time series

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Authors: Lia Matchavariani

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Water reservoir construction on the rivers flowing into the sea complicates the coast protection, seashore starts to degrade causing coast erosion and disaster on the backdrop of current climate change. The instruments of the impact of a water reservoir on the climate and coastal areas are its contact surface with the atmosphere and the area irrigated with its water or humidified with infiltrated waters. The Black Sea coastline is characterized by the highest ecological vulnerability. The type and intensity of the water reservoir impact are determined by its morphometry, type of regulation, level regime, and geomorphological and geological characteristics of the adjoining area. Studies showed the impact of the water reservoir on the climate, on its comfort parameters is positive if it is located in the zone of insufficient humidity and vice versa, is negative if the water reservoir is found in the zone with abundant humidity. There are many natural and anthropogenic factors determining the peculiarities of the impact of the water reservoir on the climate, which can be assessed with maximum accuracy by the so-called “long series” method, which operates on the meteorological elements (temperature, wind, precipitations, etc.) with the long series formed with the stationary observation data. This is the time series, which consists of two periods with statistically sufficient duration. The first period covers the observations up to the formation of the water reservoir and another period covers the observations accomplished during its operation. If no such data are available, or their series is statistically short, “an analog” method is used. Such an analog water reservoir is selected based on the similarity of the environmental conditions. It must be located within the zone of the designed water reservoir, under similar environmental conditions, and besides, a sufficient number of observations accomplished in its coastal zone.

Keywords: coast-constituent sediment, eustasy, meteorological parameters, seashore degradation, water reservoirs impact

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Authors: Renato Morbidelli, Carla Saltalippi, Alessia Flammini, Tommaso Picciafuoco, Corrado Corradini

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A considerable part of rainfall data to be used in the hydrological practice is available in aggregated form within constant time intervals. This can produce undesirable effects, like the underestimate of the annual maximum rainfall depth, Hd, associated with a given duration, d, that is the basic quantity in the development of rainfall depth-duration-frequency relationships and in determining if climate change is producing effects on extreme event intensities and frequencies. The errors in the evaluation of Hd from data characterized by a coarse temporal aggregation, ta, and a procedure to reduce the non-homogeneity of the Hd series are here investigated. Our results indicate that: 1) in the worst conditions, for d=ta, the estimation of a single Hd value can be affected by an underestimation error up to 50%, while the average underestimation error for a series with at least 15-20 Hd values, is less than or equal to 16.7%; 2) the underestimation error values follow an exponential probability density function; 3) each very long time series of Hd contains many underestimated values; 4) relationships between the non-dimensional ratio ta/d and the average underestimate of Hd, derived from continuous rainfall data observed in many stations of Central Italy, may overcome this issue; 5) these equations should allow to improve the Hd estimates and the associated depth-duration-frequency curves at least in areas with similar climatic conditions.

Keywords: central Italy, extreme events, rainfall data, underestimation errors

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Authors: N. Sopipan

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In this paper, we study of rainfall time series of weather stations in Nakhon Ratchasima province in Thailand using various statistical methods enabled to analyse the behaviour of rainfall in the study areas. Time-series analysis is an important tool in modelling and forecasting rainfall. ARIMA and Holt-Winter models based on exponential smoothing were built. All the models proved to be adequate. Therefore, could give information that can help decision makers establish strategies for proper planning of agriculture, drainage system and other water resource applications in Nakhon Ratchasima province. We found the best perform for forecasting is ARIMA(1,0,1)(1,0,1)12.

Keywords: ARIMA Models, exponential smoothing, Holt-Winter model

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Authors: Smita Sonker, Uaday Singh

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Various investigators have determined the degree of approximation of functions belonging to the classes W(L r , ξ(t)), Lip(ξ(t), r), Lip(α, r), and Lipα using different summability methods with monotonocity conditions. Recently, Lal has determined the degree of approximation of the functions belonging to Lipα and W(L r , ξ(t)) classes by using Ces`aro-N¨orlund (C 1 .Np)- summability with non-increasing weights {pn}. In this paper, we shall determine the degree of approximation of 2π - periodic functions f belonging to the function classes Lipα and W(L r , ξ(t)) by C 1 .T - means of Fourier series of f. Our theorems generalize the results of Lal and we also improve these results in the light off. From our results, we also derive some corollaries.

Keywords: Lipschitz classes, product matrix operator, signals, trigonometric Fourier approximation

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Authors: Serkan Aras, Emrah Gulay

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Combining various forecasting models is an important tool for researchers to attain more accurate forecasts. A great number of papers have shown that selecting single models as dissimilar models, or methods based on different information as possible leads to better forecasting performances. However, there is not a certain rule regarding the number of single models to be used in any combining methods. This study focuses on determining the optimal or near optimal number for single models with the help of statistical tests. An extensive experiment is carried out by utilizing some well-known time series data sets from diverse fields. Furthermore, many rival forecasting methods and some of the commonly used combining methods are employed. The obtained results indicate that some statistically significant performance differences can be found regarding the number of the single models in the combining methods under investigation.

Keywords: combined forecast, forecasting, M-competition, time series

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Authors: Rajendra Kumar

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We study the Electronic spectral density of a double coupled quantum dots sandwich between superconducting leads, where one of the superconducting leads (QD1) are connected with left superconductor lead and (QD1) also connected right superconductor lead. (QD1) and (QD2) are coupling to each other. The electronic spectral density through a quantum dots between superconducting leads having s-wave symmetry of the superconducting order parameter. Such junction is called superconducting –quantum dot (S-QD-S) junction. For this purpose, we have considered a renormalized Anderson model that includes the double coupled of the superconducting leads with the quantum dots level and an attractive BCS-type effective interaction in superconducting leads. We employed the Green’s function technique to obtain superconducting order parameter with the BCS framework and Ambegaoker-Baratoff formalism to analyze the electronic spectral density through such (S-QD-S) junction. It has been pointed out that electronic spectral density through such a junction is dominated by the attractive the paring interaction in the leads, energy of the level on the dot with respect to Fermi energy and also on the coupling parameter of the two in an essential way. On the basis of numerical analysis we have compared the theoretical results of electronic spectral density with the recent transport existing theoretical analysis. QDs is the charging energy that may give rise to effects based on the interplay of Coulomb repulsion and superconducting correlations. It is, therefore, an interesting question to ask how the discrete level spectrum and the charging energy affect the DC and AC Josephson transport between two superconductors coupled via a QD. In the absence of a bias voltage, a finite DC current can be sustained in such an S-QD-S by the DC Josephson effect.

Keywords: quantum dots, S-QD-S junction, BCS superconductors, Anderson model

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Authors: F. Messaoudi, O. Haddad, R. Bouras, S. Kaci

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The development of self-compacting concrete (SCC) marks a huge step towards improved efficiency and working conditions on construction sites and in the precast industry. SCC flows easily into more complex shapes and through reinforcement bars, reduces the manpower required for the placement; no vibration is required to ensure correct compaction of concrete. This concrete contains a high volume of binder which is controlled by their rheological behavior. The paste consists of binders (Portland cement with or without supplementary cementitious materials), water, chemical admixtures and fillers. In this study, two series of tests were performed on self-compacting cement pastes made with marble waste additions as the mineral addition. The first series of this investigation was to determine the flow time of paste using Marsh cone, the second series was to determine the rheological parameters of the same paste namely yield stress and plastic viscosity using the rheometer Haake RheoStress 1. The results of this investigation allowed us to study the evolution of the yield stress, viscosity and the flow time Marsh cone paste as a function of the composition of the paste. A correlation between the results obtained on the flow test Marsh cone and those of the plastic viscosity on the mottled different cement pastes is proposed.

Keywords: adjuvant, rheological parameter, self-compacting cement pastes, waste marble

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Authors: Himanshu Chandrakar, Krishnapriya S., Rama Komaragiri, Suja K. J.

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Micro coils are significant components for micro magnetic sensors and actuators especially in biomedical devices. Non-spiral planar microcoils of square, hexagonal and octagonal shapes are introduced for the first time in this paper. Comparison between different planar spiral and non-spiral coils are also discussed. The fabrication advantages and low power dissipation of non-spiral structures make them a strong alternative for conventional spiral planar coils. Series resistance of non-spiral coil is lesser than that of spiral coils though magnetic field is slightly lesser for non-spiral coils. Comparison of different planar microcoils shows that the proposed square non-spiral coil gives better performance than other structures.

Keywords: non-spiral planar microcoil, power dissipation, series resistance, spiral

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Authors: Wieslaw Marszalek, Zdzislaw Trzaska

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Interesting properties of various one-period loops of singularly perturbed memristive circuits with mixed-mode oscillations (MMOs) are analyzed in this paper. The analysis is mixed, both analytical and numerical and focused on the properties of pinched hysteresis of the memristive element and other one-period loops formed by pairs of time-series solutions for various circuits' variables. The memristive element is the only nonlinear element in the two circuits. A theorem on periods of mixed-mode oscillations of the circuits is formulated and proved. Replacements of memristors by parallel G-C or series R-L circuits for a MMO response with equivalent RMS values is also discussed.

Keywords: mixed-mode oscillations, memristive circuits, pinched hysteresis, one-period loops, singularly perturbed circuits

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Authors: Smita Pareek, Ratna Dahiya

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The power generated by solar photovoltaic (PV) module depends on surrounding irradiance, temperature, shading conditions, and shading pattern. This paper presents a simulation of photovoltaic module using Matlab/Simulink. PV Array is also simulated by series and parallel connections of modules and their characteristics curves are given. Further PV module topology/configuration are proposed for 5.5kW inverter available in the literature. Shading of a PV array either complete or partial can have a significant impact on its power output and energy yield; therefore, the simulated model characteristics curves (I-V and P-V) are drawn for uniform shading conditions (USC) and then output power, voltage and current are calculated for variation in insolation for shading conditions. Additionally the characteristics curves are also given for a predetermined shadowing condition.

Keywords: array, series, parallel, photovoltaic, partial shading

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Authors: Daumantas Zidanavicius

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In order to investigate the bond between concrete and steel in the circular steel tube column filled with concrete, the 7 series of specimens were tested with the same geometrical parameters but different concrete properties. Two types of specimens were chosen. For the first type, the expansive additives to the concrete mixture were taken to increase internal forces. And for the second type, mechanical components were used. All 7 series of the short columns were modeled by FEM and tested experimentally. In the work, big attention was taken to the bond-slip models between steel and concrete. Results show that additives to concrete let increase the bond strength up to two times and the mechanical anchorage –up to 6 times compared to control specimens without additives and anchorage.

Keywords: concrete filled steel tube, push-out test, bond slip relationship, bond stress distribution

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Andrei Tiberiu Cucuruz, Ecaterina Andronescu, Cristina Daniela Ghitulica, Andreia Cucuruz

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In modern dentistry, the application of resin-based composites continues to increase and in the majority of countries has completely replaced mercury amalgams. Alumina (Al2O3) is a representative bioinert ceramic with a variety of applications in industry as well as in medicine. Alumina has the potential to improve electrical resistivity and thermal conductivity of polymers. The application of poly(methacrylic acid) (PMAA) in medicine was poorly investigated in the past but can lead to good results by the incorporation of alumina particles that can bring bioinertness to the composite. However, because of the differences related to chemical bonding of these materials, the interaction is very weak at the interface leading to no significant values in practical situations. The aim of this work was to modify the structure of alumina with silane coupling agents and to study the influence of silanization on the physicomechanical properties of the resulting composite materials. Two silanes were used in this study: 3-aminopropyl-trimethoxysilane (APTMS) and dichlorodimethylsilane (DCDMS). Both silanes proved to have a significant effect on the overall performance of composites by establishing bonds with the polymer matrix and the filler. All these improvements in dental adhesive systems made for bonding resin composites to tooth structure have enhanced the clinical application of polymeric restorative materials to the position that they are now considered the material of choice for esthetic restoration.

Keywords: alumina, compressive strength, dental materials, silane coupling agents, poly(methacrylic acid)

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Authors: Harold Vásquez, Cesar Hernández, Ingrid Páez

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This paper approach cognitive radio technic and applied pure proactive handoff Model to decrease interference between PU and SU and comparing it with reactive handoff model. Through the study and analysis of multivariate models SAW and TOPSIS join to 3 dynamic prediction techniques AR, MA ,and ARMA. To evaluate the best model is taken four metrics: number failed handoff, number handoff, number predictions, and number interference. The result presented the advantages using this type of pure proactive models to predict changes in the PU according to the selected channel and reduce interference. The model showed better performance was TOPSIS-MA, although TOPSIS-AR had a higher predictive ability this was not reflected in the interference reduction.

Keywords: cognitive radio, spectrum handoff, decision making, time series, wireless networks

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Authors: Harish Rajak, Preeti Patel

Abstract:

HDAC inhibitors can reactivate gene expression and inhibit the growth and survival of cancer cells. The 3D-QSAR and Pharmacophore modeling studies were performed to identify important pharmacophoric features and correlate 3D-chemical structure with biological activity. The pharmacophore hypotheses were developed using e-pharmacophore script and phase module. Pharmacophore hypothesis represents the 3D arrangement of molecular features necessary for activity. A series of 55 compounds with well-assigned HDAC inhibitory activity was used for 3D-QSAR model development. Best 3D-QSAR model, which is a five PLS factor model with good statistics and predictive ability, acquired Q2 (0.7293), R2 (0.9811) and standard deviation (0.0952). Molecular docking were performed using Histone Deacetylase protein (PDB ID: 1t69) and prepared series of hydroxamic acid based HDAC inhibitors. Docking study of compound 43 show significant binding interactions Ser 276 and oxygen atom of dioxine cap region, Gly 151 and amino group and Asp 267 with carboxyl group of CONHOH, which are essential for anticancer activity. On docking, most of the compounds exhibited better glide score values between -8 to -10.5. We have established structure activity correlation using docking, energetic based pharmacophore modelling, pharmacophore and atom based 3D QSAR model. The results of these studies were further used for the design and testing of new HDAC analogs.

Keywords: Docking, e-pharmacophore, HDACIs, QSAR, Suberoylanilidehydroxamic acid.

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Authors: Alexander N. Pisarchik, Parth Chholak

Abstract:

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: Selin Guney, Andres Riquelme

Abstract:

Among the main purposes of optimal and efficient forecasts of agricultural commodity prices is to guide the firms to advance the economic decision making process such as planning business operations and marketing decisions. Governments are also the beneficiaries and suppliers of agricultural price forecasts. They use this information to establish a proper agricultural policy, and hence, the forecasts affect social welfare and systematic errors in forecasts could lead to a misallocation of scarce resources. Various empirical approaches have been applied to forecast commodity prices that have used different methodologies. Most commonly-used approaches to forecast commodity sectors depend on classical time series models that assume values of the response variables are precise which is quite often not true in reality. Recently, this literature has mostly evolved to a consideration of fuzzy time series models that provide more flexibility in terms of the classical time series models assumptions such as stationarity, and large sample size requirement. Besides, fuzzy modeling approach allows decision making with estimated values under incomplete information or uncertainty. A number of fuzzy time series models have been developed and implemented over the last decades; however, most of them are not appropriate for forecasting repeated and nonconsecutive transitions in the data. The modeling scheme used in this paper eliminates this problem by introducing Markov modeling approach that takes into account both the repeated and nonconsecutive transitions. Also, the determination of length of interval is crucial in terms of the accuracy of forecasts. The problem of determining the length of interval arbitrarily is overcome and a methodology to determine the proper length of interval based on the distribution or mean of the first differences of series to improve forecast accuracy is proposed. The specific purpose of this paper is to propose and investigate the potential of a new forecasting model that integrates methodologies for determining the proper length of interval based on the distribution or mean of the first differences of series and Fuzzy Time Series- Markov Chain model. Moreover, the accuracy of the forecasting performance of proposed integrated model is compared to different univariate time series models and the superiority of proposed method over competing methods in respect of modelling and forecasting on the basis of forecast evaluation criteria is demonstrated. The application is to daily corn and soybean prices observed at three commercially important North Carolina markets; Candor, Cofield and Roaring River for corn and Fayetteville, Cofield and Greenville City for soybeans respectively. One main conclusion from this paper is that using fuzzy logic improves the forecast performance and accuracy; the effectiveness and potential benefits of the proposed model is confirmed with small selection criteria value such MAPE. The paper concludes with a discussion of the implications of integrating fuzzy logic and nonarbitrary determination of length of interval for the reliability and accuracy of price forecasts. The empirical results represent a significant contribution to our understanding of the applicability of fuzzy modeling in commodity price forecasts.

Keywords: commodity, forecast, fuzzy, Markov

Procedia PDF Downloads 197