Simple Series Research Articles

Técnicas estadísticas de análisis multivariante aplicadas a la interpretación de variables del cambio climático

Los métodos multivariados son una herramienta de mucha aplicación en series de datos con gran cantidad de variables, las cuales muchas veces no tienen una correlación directa, pero de los cuales se necesita hacer interpretaciones y estimaciones. Un ejemplo son todos los datos que pueden estar relacionados con el cambio climático. Los países hacen mediciones de muchos factores que pueden ser causa o son consecuencia del mismo. Esto aporta bases de datos muy grandes, que son de difícil interpretación. Los métodos de análisis como el de componentes principales o el factorial, ayudan a la interpretación y agrupamiento de gran número de parámetros en series más sencillas. Para este estudio se utilizaron datos del Banco Mundial, en específico para los países de América Latina. Se eligieron datos sobre tierras agrícolas, área selvática, áreas terrestres protegidas, crecimiento de la población, población total, crecimiento de la población urbana y población urbana. Todas estas parecen tener cierta correlación, pero la misma no es tan evidente y en especial cuando se trata de mediciones en diferentes unidades. Sin embargo con el método de componentes principales se lograron encontrar grupos que se pueden relacionar con la necesidad de alimento, con la necesidad de tierra para vivienda y con la pérdida de ecosistemas. En el caso del análisis factorial, los grupos en los factores encontrados muestran conceptos como el uso de la tierra, las poblaciones totales y los crecimientos poblacionales. En ambos análisis se evidencia la utilidad de estos métodos para interpretación de grandes grupos de datos.

Free vibration analysis of moderately thick isotropic homogeneous open cylindrical shells using improved Fourier series method

In this paper an Improved Fourier series method has been employed to study the free vibrations of isotropic homogeneous moderately thick open cylindrical shells with arbitrary subtended angle and general elastic restraints. In this method, regardless of the boundary conditions, each of the displacement components of open shell is invariably expressed as a simple trigonometric series with accelerated and uniform convergence over the solution domain. Distributed elastic restraints are used to specify the elastic boundary conditions along the shell edges and therefore, arbitrary boundary restraints can be achieved by varying the values of spring’s stiffness. All the unknown expansion coefficients are treated as the generalized coordinates and solved using the Rayleigh-Ritz technique. A considerable number of new vibration results for isotropic open cylindrical shells with various geometric parameters and boundary conditions are presented. The effects of boundary stiffness, thickness to radius ratio and subtended angle on the vibration characteristics are also discussed in detail.

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

BackgroundInertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development.ObjectiveThe study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network.MethodsWe applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the proposed method to automatically classify the exercise being completed was assessed using this dataset. For comparative purposes, classification using the same dataset was also performed using the more conventional approach of feature-extraction and classification using random forest classifiers.ResultsWith the collected dataset and the proposed method, the different exercises could be recognized with a 95.89% (3827/3991) accuracy, which is competitive with current state-of-the-art techniques in ED.ConclusionsThe high level of accuracy attained with the proposed approach indicates that the waveform morphologies in the time-series plots for each of the exercises is sufficiently distinct among the participants to allow the use of machine vision approaches. The use of high-level machine learning frameworks, coupled with the novel use of machine vision techniques instead of complex manually crafted features, may facilitate access to research in the HAR field for individuals without extensive digital signal processing or machine learning backgrounds.

Midinfrared radiation energy harvesting device

The International Energy Agency reports a 17.6% annual growth rate in sustainable energy production. However, sustainable power generation based on environmental conditions (wind and solar) requires an infrastructure that can handle intermittent power generation. An electromagnetic thermoelectric (EMTE) device to overcome the intermittency problems of current sustainable energy technologies, providing the continuous supply unachievable by photovoltaic cells with portability impossible for traditional thermoelectric (TE) generators, is proposed. The EMTE converts environmental electromagnetic waves to a voltage output without requiring additional input. A single cell of this TE-inspired broadband EMTE can generate a 19.50 nV output within a 7.2- μ m 2 area, with a verified linear scalability of the output voltage through cell addition. This idea leads to a challenge: the electrical polarity of each row of cells is the same but may require additional routing to combine output from each row. An innovative layout is proposed to overcome this issue through switching the electrical polarity every other row. In this scheme, the EM wave absorption spectrum is not altered, and a simple series connection can be implemented to boost the total voltage output by 1 order within a limited area.

Towards an improved gain scheduling predictive control strategy for a solar thermal power plant

This paper improves a recently proposed gain scheduling predictive control strategy for the ACUREX distributed solar collector field at the Plataforma Solar de Almeria, in south-east Spain. Measured disturbances are an integral part of the plant and while simple classical, series and parallel, feedforward approaches have been proposed and used extensively in the literature, the proposed approach incorporates a feedforward systematically into the predictive control strategy by including the effects of the measured disturbances of the ACUREX plant into the predictions of future outputs. Models of the measured disturbances are estimated around a family of operating points directly from input-output data and using a subspace identification method while taking into account the frequency response of the plant. Input-output data are obtained from a validated nonlinear simulation model of the plant rather than the plant itself. The nonlinear simulation model is validated here against measured data obtained from the ACUREX plant and the effectiveness of the proposed control approach is evaluated in the same nonlinear simulation environment. The paper also considers related issues like the significance of sufficient modelling of the measured disturbances of the ACUREX plant and the impact of incorporating the expected future behaviour of a measured disturbance along a given prediction horizon, a theme which has received little attention in the literature.

WITHDRAWN: Forecasting of nonlinear time series using artificial neural network

Abstract When forecasting time series, it is important to classify them according to linearity behavior; the linear time series remains at the forefront of academic and applied research. It has often been found that simple linear time series models usually leave certain aspects of economic and financial data unexplained. The dynamic behavior of most of the time series in our real life, with its autoregressive and inherited moving average terms, pose the challenge to forecast nonlinear times series that contain inherited moving average terms using computational intelligence methodologies such as neural networks. It is rare to find studies that concentrate on forecasting nonlinear times series that contain moving average terms. In this study, we demonstrate that the common neural networks are not efficient for recognizing the behavior of nonlinear or dynamic time series which has moving average terms and hence low forecasting capability. This leads to the importance of formulating new models of neural networks such as Deep Learning neural networks with or without hybrid methodologies such as Fuzzy Logic.

A New Discrete Model for the Simulation of the Dynamics of a Simple Series Circuit

A New Discrete Model for the Simulation of the Dynamics of a Simple Series Circuit

Dialysis, Albumin Binding, and Competitive Binding: A Laboratory Lesson Relating Three Chemical Concepts to Healthcare

Introductory Chemistry laboratories must go beyond “cookbook” methods to illustrate how chemistry concepts apply to complex, real-world problems. In our case, we are preparing students to use their chemistry knowledge in the healthcare profession. The experiment described here explicitly models three important chemical concepts: dialysis of small molecules (dye), reversible binding (dye binding to albumin), and competitive binding (dye and a competitor binding to albumin). Moreover, each concept is intimately related to a physiological phenomenon: dialysis is used to treat renal failure, drugs travel in the blood bound to albumin, and competitive albumin binding is a common drug–drug interaction. In the context of this simple series of experiments, students create models, use evidence to validate their models, and finally use their understanding to describe physiological phenomena. This laboratory experiment was implemented in a 100-level course for predominantly prenursing majors. Student pre- and postla...

A Novel Fuzzy Document Based Information Retrieval Model for Forecasting

Information retrieval systems are generally used to find documents that are most appropriate according to some query that comes dynamically from users. In this paper a novel Fuzzy Document based Information Retrieval Model (FDIRM) is proposed for the purpose of Stock Market Index forecasting. The novelty of proposed approach is a modified tf-idf scoring scheme to predict the future trend of the stock market index. The contribution of this paper has two dimensions, 1) In the proposed system the simple time series is converted to an enriched fuzzy linguistic time series with a unique approach of incorporating market sentiment related information along with the price and 2) A unique approach is followed while modeling the information retrieval (IR) system which converts a simple IR system into a forecasting system. From the performance comparison of FDIRM with standard benchmark models it can be affirmed that the proposed model has a potential of becoming a good forecasting model. The stock market data provided by Standard & Poor’s CRISIL NSE Index 50 (CNX NIFTY-50 index) of National Stock Exchange of India (NSE) is used to experiment and validate the proposed model. The authentic data for validation and experimentation is obtained from http://www.nseindia.com which is the official website of NSE. A java program is under construction to implement the model in real-time with graphical users’ interface.

Forecasting of nonlinear time series using ANN

When forecasting time series, it is important to classify them according linearity behavior that the linear time series remains at the forefront of academic and applied research, it has often been found that simple linear time series models usually leave certain aspects of economic and financial data unexplained. The dynamic behavior of most of the time series in our real life with its autoregressive and inherited moving average terms issue the challenge to forecast nonlinear times series that contain inherited moving average terms using computational intelligence methodologies such as neural networks. It is rare to find studies that concentrate on forecasting nonlinear times series that contain moving average terms. In this study, we demonstrate that the common neural networks are not efficient for recognizing the behavior of nonlinear or dynamic time series which has moving average terms and hence low forecasting capability. This leads to the importance of formulating new models of neural networks such as Deep Learning neural networks with or without hybrid methodologies such as Fuzzy Logic.

On the relationship between indentation hardness and modulus, and the damage resistance of biological materials.

Instrumented indentation is a widespread tool for characterising the mechanical properties of biological materials. Here, we show that the ratio between indentation hardness and modulus is approximately constant in biological materials. A simple elastic-plastic series deformation model is employed to rationalise part of this correlation, and criteria for a meaningful comparison of indentation hardness across biological materials are proposed. The ratio between indentation hardness and modulus emerges as the key parameter characterising the relative amount of irreversible deformation during indentation. Despite their comparatively high hardness to modulus ratio, biological materials are susceptible to quasiplastic deformation, due to their high toughness: quasi-plastic deformation is hence hypothesised to be a frequent yet poorly understood phenomenon, highlighting an important area of future research.

Yield analysis as a function of stochastic plant architecture: Case of Spilanthes acmella in the wet and dry season

The number of organs produced by a plant varies among the individuals of a population. Taking these variations into account is an important step in understanding phenotypic variability. The aim of this study was to simulate stochastic development and growth in response to environmental change using GreenLab, an organ level functional-structural model. An annual herbaceous species, Spilanthes acmella L., was grown in pots in two climatic conditions corresponding to a wet and a dry season. Detailed records of plant development, plant architecture and organ growth were kept throughout the growing period. The concept of simple and compound organic series was introduced to target data for fitting. The model was calibrated using a mathematical model of stochastic plant development and growth. Here we describe (1) how a stochastic Functional Structural Plant Model is calibrated in two steps by first assessing the functioning parameters of meristems, and second the source-sink parameters of organs by fitting them on average organic series; (2) how dry conditions trigger the response of the plant both in the development of the inflorescence and in the allocation of biomass, quantified by model parameters. The calibration of a stochastic plant model opens a large window of opportunity to capture the common features of plant development and growth among stochastic individuals in a plant population, especially those with a branching structure. This extends the area of application of FSPM to analyzing food plants, or assisting breeding.

Transient Response of Negative Capacitance in P(VDF0.75-TrFE0.25) Organic Ferroelectric Capacitor

The transient response of negative capacitance (NC) in an organic ferroelectric capacitor is experimentally investigated. To observe the transient response of the NC, a simple series network, consisting of an external series resistor and an organic ferroelectric capacitor, is built. The Landau coefficients of the organic ferroelectric capacitor were extracted from the measured polarization versus applied electric field data. A circuit level simulation was provided with the extracted parameters based on the Landau–Khalatnikov equation. To explore the impact of the RC time constant on the NC time duration in the RC series circuit, the external series resistor is altered as a tuning parameter. Also, the effect of various temperatures of the ferroelectric capacitor on the NC time duration is experimented. This paper demonstrates that either the external resistor or the ambient temperature can change the NC time duration in an organic ferroelectric capacitor.

Area-Efficient Decimation Filter with 50/60Hz Power-Line Noise Suppression for ΔΣ A/D Converters

This paper introduces a new digital filter utilizing a SINC filter used in ΔΣ A/D converters and featuring 50/60Hz power-line noise rejection. Compared with a simple series connection o...

Kinetic Modelling of Hydrogenation of Cardanol over Pd/C Catalyst

The catalytic hydrogenation of cardanol was studied in a stirred three-phase-slurry reactor using palladium on carbon, often referred to as the Pd/C catalyst (5% loading of palladium is supported on activated carbon powder). The effects of various process parameters were examined. The kinetics of hydrogenation, which proceeds through three parallel–series reactions, was investigated. The reaction was observed to be of first order with respect to both hydrogen and cardanol. A simple second-order series reaction model was well fitted to the experimental data. Also, the Langmuir–Hinshelwood–Hougen–Watson approach was used to interpret the possible mechanistic behaviour of the reaction. It was observed that the reaction follows dual site, competitive and dissociative type of adsorption of hydrogen. Surface reaction of the adsorbed reactants was identified as the overall rate controlling step. Various rate constants and adsorption equilibrium constants for the different steps were also estimated. Different thermodynamic parameters associated with these constants were obtained to assess their physical significance. Also, these constants were evaluated statistically using the F-test and t-test.

A Nivat theorem for weighted picture automata and weighted MSO logics

Picture languages have been investigated by several research groups. Here, we define weighted two-dimensional on-line tessellation automata (w2ota) taking weights from a new weight structure called picture valuation monoid. The behavior of this automaton model is a picture series mapping pictures to values. As our first main result, we prove a Nivat theorem for w2ota. It shows a connection of mappings and recognizability of certain simple series. Then, we introduce a weighted MSO logic which models average density of pictures. In the second main result, we show that w2ota and a fragment of our weighted MSO logics are expressively equivalent.

Model and Analysis of Multi Level Multi Frequency RF Rectifier Energy System for Low Power Supply Application Device

Sustainable energy for the mobile electronic devices always needed during the energy storage batteries capacity in the mobile electronic devices are limited for a few hours for the usage time. To get a long lasting operating time from the mobile electronics equipment sets, the energy source should always be connected into the device. In this paper, we were proposed a charging energy method via wireless operation supply using the microwaves (RF) radiated by the air multi-frequency. The RF to DC rectifier circuit is a major component for changing the RF wave to an electronic current (DC). The Dickson models were used as an approach to superiority includes a simple series, low DC ripple factor, etc. The design, analysis, and the experimentation from the rectifier circuit have been conducted and presented in this paper. In the measurement, the mobile electronic devices placed at a distance about 5 meters from the energy source with the system voltage DC 3.7V, and have been obtained at the working frequency between 825 - 960MHz with the PCE values 12-33%, and a ripple factor of ± 0,01%. The charging time energy is needed about 4 hours at the research trial room, and about 11 hours outdoor had been observed. Based on these results, the wirelessly energy charging method for the mobile electronic devices is a potential methods to resolve the sustainable energy issue and the green technology supporting with the most programs.

Benefit on motor and non-motor behavior in a specialized unit for Parkinson's disease.

Treatment of patients with Parkinson's disease in specialized units is quite common in Germany. Data on the benefit of this hospitalization of patients with Parkinson's disease on motor and non-motor symptoms in conjunction with standardized tests are rare. Objective was to determine the efficacy of this therapeutic setting. We scored disease severity and performed clinical tests, respectively, instrumental procedures under standardized conditions in consecutively referred in-patients initially and at the end of their hospital stay. There was a decrease of motor and non-motor symptoms. The extent of improvement of non-motor and motor symptoms correlated to each other. Performance of complex movement sequences became better, whereas execution of simple movement series did not ameliorate. The interval for the timed up and go test went down. We demonstrate the effectiveness of an in-patient stay in a specialized unit for Parkinson's disease. Objective standardized testing supplements subjective clinical scoring with established rating scales.

Probing the confining effect of clay particles on an amorphous intercalated dendritic polyester

The fourth generation of a hydroxylated dendritic hyperbranched polyester (HBP) was combined with sodium montmorillonite clay (Na+MMT) using water to generate a broad range of polymer clay nanocomposites from 0 to 100% wt/wt Na+MMT. Analysis with differential scanning calorimetry (DSC) showed a deviation in heat capacity, ΔCp, with clay content at the Tg from a two-phase trend which was attributed to the formation of an immobilized rigid amorphous fraction (RAF) in the interlayer spacing of the intercalated system. This deviation occurred in a step-like fashion which we attributed to 0.5 nm incremental changes in the interlayer spacing, previously observed through X-ray diffraction analysis. A simple series model was utilized to quantify these interlayer spacings based on the ΔCp values and showed good correspondence with the X-ray results. The RAF was quantified from changes in heat capacity with clay content and was verified by an alternative novel positron annihilation lifetime spectroscopy (PALS) approach. The PALS quantification of the RAF was possible through an analysis of changes in the hole size thermal expansivity of the nanocomposites as a function of clay composition. Results indicated that as much as 32% by weight of the system is made up of the RAF at its maximum.

Infrared Spectroscopy of Methanol and Methanol/Water Clusters in Helium Nanodroplets: The OH Stretching Region.

Infrared (IR) spectra of methanol clusters in helium nanodroplets are reported in the OH stretching region for the first time. A simple series of intense bands are seen which almost perfectly match previous gas phase studies of these clusters and which are consistent with cyclic structures for the trimer and larger clusters. This finding differs from an earlier report of (CH3OH)n clusters in helium nanodroplets, which focused on the CO stretching region and concluded that while the trimer was cyclic, the tetramer and pentamer adopted branched structures based on a cyclic trimer core. We also present preliminary data for small (CH3OH)n(H2O) clusters, and in particular, we report the first IR spectra for (CH3OH)2(H2O) and (CH3OH)3(H2O). Supporting ab initio calculations suggest that, like the pure methanol clusters, cyclic structures are adopted by these mixed solvent clusters in helium droplets.