Search

To reduce the adverse effects of inevitable error and random error in the fire data obtained by multiple sensors, in this paper, we propose a fire judgment method that uses Swarm intelligence optimization techniques and Evidence fusion. First, three sensors (CO, smoke and temperature) are used to obtain fire data which are processed by the method of the interval number processing, and the distances between the fire data and the characteristic value of fire grade are calculated. The reliability coefficient N k is optimized by Swarm intelligence algorithms to complete the modification of the mass function. Then, the combination rule of interval evidence and the modified mass function are synthesized to obtain the comprehensive interval evidence. Finally, the fire grade is judged according to the decision rule. We study the usability of these techniques for fire judgment and compare the optimization performance of the important Swarm intelligence algorithms, including traditional Particle Swarm Optimization (PSO ) and its improved algorithm (IPSO ), the latest algorithms, Black Widow Optimization (BWO ) and its improved algorithm (IBWO ), Bald Eagle Search Algorithm (BES ) and its improved algorithm (IBES ). The experimental results show that the average probabilities of IBWO , IBES and IPSO for obtaining the correct fire grades are 0.96, 0.88, and 0.86, respectively, the performance of three improved algorithms in fire judgment have been greatly increased, compared to traditional D - S evidence fusion method, the increase ratios of IBWO , IBES and IPSO are 43.3%, 31.3%, 28.4%. Therefore, the D - S evidence fusion method optimized by Swarm intelligence algorithms are better than that of traditional D - S evidence method for fire detection, which provides a new idea for fire detection. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of the dynamic goniometer, the influence of its bearing unit on the measurement error has been studied. The spectral characteristics of the random error of two dynamic goniometers with similar optical angle sensors and different bearings (air and angular contact ball bearings) have been studied. It is shown that the use of a ball bearing in the goniometer leads to nonstationarity of the random error of angular measurements. Using wavelet analysis and the Allan variation, the random error of the dynamic goniometer has been analyzed, the sources of nonstationarity have been identified, and its potential accuracy has been determined. It has been established that the use of an air bearing allows increasing the accuracy of a dynamic goniometer and bringing its value closer to the accuracy of a static goniometer, in which the influence of the bearing unit is minimized. The results obtained are of interest to users and developers of dynamic goniometers. [ABSTRACT FROM AUTHOR]

To achieve better observation for sea surface, a new generation of wide-swath interferometric altimeter satellites is proposed. Before satellite launch, it is particularly important to study the data processing methods and carry out the detailed error analysis of ocean satellites, because it is directly related to the ultimate ability of satellites to capture ocean information. For this purpose, ocean eddies are considered a specific case of ocean signals, and it can cause significant changes in sea surface elevation. It is suitable for theoretical simulation of the sea surface and systematic simulation of the altimeter. We analyzed the impacts of random error and baseline error on the sea surface and ocean signals and proposed a combined strategy of low-pass filtering, empirical orthogonal function (EOF) decomposition, and linear fitting to remove the errors. Through this strategy, sea surface anomalies caused by errors were considerably improved, and the capability of satellite for capturing ocean information was enhanced. Notably, we found that the baseline error in sea surface height data was likely to cause inaccuracy in eddy boundary detection, as well as false eddy detection. These abnormalities could be prevented for "clean" sea surface height after the errors removal. [ABSTRACT FROM AUTHOR]

We perform theoretical and experimental investigations of the methods of measurement of resonance frequencies according to the amplitude-frequency and phase-frequency characteristics of resonators (amplitude and phase methods, respectively). For the evaluation of the resonance frequency, we used the procedure of digital frequency scanning. On the basis of the probability theory, we deduce analytic expressions that describe the dependences of the systematic and random errors on the location of the resonance frequency in the interval between the closest discrete frequencies and on the level of noise. The reliability of these expressions is confirmed in the course of virtual experiments performed with a computer model of the resonator. It is also shown that the errors of the amplitude and phase methods practically coincide for the noise level at which we record at most two discrete frequencies but are noticeably different if the number of recorded discrete frequencies is greater than two. In this case, the errors of the resonance frequency measured by the phase method are practically independent of the steps of frequency tuning, as these step decrease and linearly depend on the level of phase noise. In measuring the resonance frequency by the amplitude method, the measurement errors decrease as the step of frequency tuning becomes smaller. For this case, we propose an empirical formula that describes the dependence of the systematic and random errors on the step of frequency tuning and the level of amplitude noise. The accumulated results can be used in the construction of digital resonance gauges. [ABSTRACT FROM AUTHOR]

Background: Digital image correlation (DIC) has become a superior tool for surface deformation measurement so far. In order to improve its measurement accuracy, different error models have been developed to reveal the cause of various error. Objective: An error model unifying the random error and systematic error should be valuable. Method: In this paper, a frequency-domain error model for 2D-DIC is established based on Fourier analysis. Using the model, the error caused by random noise, subpixel interpolation and the combination of the two can be analyzed and predicted. Results: Numerical experiments were conducted to verify the effectiveness of the proposed models. The results from theoretical models and numerical experiments are in a good agreement. Conclusions: It is demonstrated that the proposed model is a generalization of several previously established models. [ABSTRACT FROM AUTHOR]

Error is not a mistake in science. A mistake can be due to an incorrect entry of data in an experiment or an incorrect calculation. Error refers to the precision (uncertainty) of measurements, which one obtains by estimating the standard deviation of repetitive measurements of a certain parameter. We often use "the method of error propagation" to determine uncertainty (error) in a dependent variable from the measured uncertainty in the independent variables. Here, we discuss the origin of the error propagation equation and the assumptions considered to derive it. Intuitional notion of error propagation in statistics suggests that random relative error in the dependent variable cannot be less than the sum of those in the independent variable(s). In this article, we explain that some transcendental functions (such as trigonometric and log functions), however, do not follow this notion of error propagation because their first partial derivatives are usually small in magnitude and sometimes vanish completely at certain points. We further explain and discuss the behaviour of such a function. We have made suggestions for estimating errors in such non-linear functions. [ABSTRACT FROM AUTHOR]

History matching is the process of modifying a numerical model (representing a reservoir) in the light of observed production data. In the oil and gas industry, production data are employed during a history matching exercise in order to reduce the uncertainty in associated reservoir models. However, production data, normally measured using commercial flowmeters that may or may not be accurate depending on factors such as maintenance schedules, or estimated using mathematical equations, inevitably has inherent errors. In other words, the data which are used to reduce the uncertainty of the model may have considerable uncertainty in itself. This problem is exacerbated for gas condensate and wet gas reservoirs as there are even greater errors associated with measuring small fractions of liquid. The influence of this uncertainty in the production data on history matching has not been addressed in the literature so far. In this paper, the effect of systematic and random flow measurement errors on history matching is investigated. Initially, 14 production data sets with different ranges of systematic and random errors, from 0 to 10%, have been employed in a history matching exercise for an oil reservoir and the results have later been evaluated based on a reference model. Subsequently, 23 data sets with errors ranging from 0 to 20% have been employed in the same process for a wet gas reservoir. The results show that for both cases systematic errors considerably affect history matching, while the effect of random errors on the considered scenarios is seen to be insignificant. Although reservoir model parameters in the wet gas reservoir were not as sensitive to the flow measurement errors as in the oil reservoir, for both cases, the future production forecast was significantly affected by the errors. Permeability was seen to be the most sensitive history matching parameter to the flow measurement errors in the oil reservoir, while for the wet gas reservoir, the most sensitive parameter was the forecast of future oil and gas production. Finally, considering the noticeable effect of systematic errors on both cases, it is suggested that flowmeter calibration and regular maintenance is prioritised, although the subsequent economic cost needs to be considered. [ABSTRACT FROM AUTHOR]

To improve the measurement and calibration accuracy of Portable Coordinate Measuring Arm, it is very important to accurately identify the system and random errors of the measuring machine. From the kinematic error model, system errors can be identified during calibration. But identifying random errors remains a difficult problem. First, we use the SolidWorks software to analyze the structure of each joint. Second, CETOL 6σ tolerance analysis software is used to calculate the random error of the probe generated by the clearance of bearings in each joint. Finally, the random error due to the systematic uncertainty of the rotary encoder is calculated. The experimental results show that the total value of the random error due to the bearing clearance, the systematic uncertainty of the rotary encoder and the thermal expansion of the mechanical structure according to the temperature variation does not exceed 0.1112 mm. This value is theoretically the limit of the single point repeatability accuracy that can be achieved by this measuring machine. The use of the CETOL 6σ not only allows the design to be carried out scientifically, but also reduces production costs. [ABSTRACT FROM AUTHOR]

The influence of the inadequacy of a priori knowledge of the properties of the results of measurements and of standards used in estimation of the probabilistic characteristics of errors is considered. A computer program of a procedure for determining the dependence of the reliability of a verification of the results of measurements on the inadequacy of the form of the probability distribution of the new estimate, where the residual systematic errors of the results of measurements and the quantity reproducible by the standard have been excluded from consideration, as well as on the volume of the sample employed is presented. [ABSTRACT FROM AUTHOR]

In view of the current error separation method cannot successfully separate the dissimilar systematic error components from the whole surface machining data, a method for machining errors decomposition based on blind signal separation is proposed which can distinguish those dissimilar systematic error components. Firstly, the error transfer model to describe the errors synthesizing from the single error component caused by corresponding individual error source to the synthesis machining surface error is conducted. To determine the number of the systematic error components, the principal component analysis (PCA) method is used. Then according to the theory of blind source separation, negative entropy based fixed point algorithm is proposed to fulfill the machining error components separation, which can realize the separation of the systematic error components even in close frequency scale. Finally, a shaft surface finish turning data and a flat surface milling data are used as examples to verify the proposed method. The result shows that the proposed error separation method can effectively realize the machining error separation in close frequency scale. [ABSTRACT FROM AUTHOR]

Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous meteorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear crossprediction error (NCPE) model, and their stability in the prediction validity period in 10-30-day extended range forecasting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small (10-10), minor variation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random error has minimal effect on the prediction. When the ratio is in the range of 10-2 (i.e., random error dominates), attention should be paid to the random error instead of only the initial error. When the ratio is around 10-10, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecasting, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depicted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect ( m > 4) is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperature or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water. [ABSTRACT FROM AUTHOR]

The parameters of an equivalent circuit of power transmission lines in the UNPG single-line model are estimated from synchrophasor measurements of nodal voltages and currents. Systematic and random errors in PMU measurements that lead to errors in the estimates of the transmission line parameters are considered. [ABSTRACT FROM AUTHOR]

The enthalpy of fusion of indium was measured at the heating rates of 3, 9, and 15 K min according to two procedures, with and without replacing the crucible with the sample in DSC cell. Repeatability in the enthalpies after these two procedures differs by ten times. Placing the crucible with a sample into its position inside DSC was concluded to generate a random error of about 0.5 % in the enthalpy due to the irreproducibility in the thermal conduction. The error in a single value measured after single run can be as high as 2-3 %. If that value is used for the evaluation of the calibration coefficient, the random error of the sample positioning becomes systematic error of the calibration. The ways of escaping such a flaw are discussed. [ABSTRACT FROM AUTHOR]

The random errors of subset-based digital image correlation due to the use of matched or overmatched shape functions are investigated. Governing formulations that can quantitatively predict these random errors are derived. Theoretical results reveal that all these random errors are in linear proportion to the noise level of the speckle images and inverse proportion to the sum of square of subset intensity gradients of the interrogated subsets. However, for matched or overmatched cases, zero-order and first-order shape functions give rise to the same magnitude of random errors, while the random errors induced by second-order shape functions are approximately twice that resulted from the regularly used first-order shape functions. The correctness of the derived theoretical formulations is verified using numerical tests, and also shows good accordance with previously reported experimental results. [ABSTRACT FROM AUTHOR]

Shannon's seminal approach to estimating information capacity is widely used to quantify information processing by biological systems. However, the Shannon information theory, which is based on power spectrum estimation, necessarily contains two sources of error: time delay bias error and random error. These errors are particularly important for systems with relatively large time delay values and for responses of limited duration, as is often the case in experimental work. The window function type and size chosen, as well as the values of inherent delays cause changes in both the delay bias and random errors, with possibly strong effect on the estimates of system properties. Here, we investigated the properties of these errors using white-noise simulations and analysis of experimental photoreceptor responses to naturalistic and white-noise light contrasts. Photoreceptors were used from several insect species, each characterized by different visual performance, behavior, and ecology. We show that the effect of random error on the spectral estimates of photoreceptor performance (gain, coherence, signal-to-noise ratio, Shannon information rate) is opposite to that of the time delay bias error: the former overestimates information rate, while the latter underestimates it. We propose a new algorithm for reducing the impact of time delay bias error and random error, based on discovering, and then using that size of window, at which the absolute values of these errors are equal and opposite, thus cancelling each other, allowing minimally biased measurement of neural coding. [ABSTRACT FROM AUTHOR]

Evaluation of pest abundance is an important task of integrated pest management. It has recently been shown that evaluation of pest population size from discrete sampling data can be done by using the ideas of numerical integration. Numerical integration of the pest population density function is a computational technique that readily gives us an estimate of the pest population size, where the accuracy of the estimate depends on the number of traps installed in the agricultural field to collect the data. However, in a standard mathematical problem of numerical integration, it is assumed that the data are precise, so that the random error is zero when the data are collected. This assumption does not hold in ecological applications. An inherent random error is often present in field measurements, and therefore it may strongly affect the accuracy of evaluation. In our paper, we offer a novel approach to evaluate the pest insect population size under the assumption that the data about the pest population include a random error. The evaluation is not based on statistical methods but is done using a spatially discrete method of numerical integration where the data obtained by trapping as in pest insect monitoring are converted to values of the population density. It will be discussed in the paper how the accuracy of evaluation differs from the case where the same evaluation method is employed to handle precise data. We also consider how the accuracy of the pest insect abundance evaluation can be affected by noise when the data available from trapping are sparse. In particular, we show that, contrary to intuitive expectations, noise does not have any considerable impact on the accuracy of evaluation when the number of traps is small as is conventional in ecological applications. [ABSTRACT FROM AUTHOR]

Temperature and relative humidity profiles derived from two China-made global positioning system (GPS) radiosondes (GPS-TK and CF-06-A) during the east tropical Indian Ocean (ETIO) experiment were compared with Vaisala RS92-SGP to assess the performances of China-made radiosondes over the tropical ocean. The results show that there have relative large biases in temperature observations between the GPSTK and the RS92-SGP in the low troposphere, with a warm bias of greater than 2 K in the day and a cooling bias of 0.6 K at night. The temperature differences of the CF-06-A were small in the troposphere both in daytime and nighttime, and became large peak-to-peak fluctuations in the stratosphere. The intercomparison of the relative humidity showed that the CF-06-A had large random errors due to the limitation of sensors and the lack of correction scheme, and the GPS-TK had large systematic biases in the low troposphere which might be related to the temperature impact. GPS height measurements are clearly suitable for China-made radiosonde systems operation. At night, the CF-06-A and the GPS-TK could provide virtual potential temperature and atmospheric boundary layer height measurements of suitable quality for both weather and climate research. As a result of the intercomparison experiment, major errors in the China-made radiosonde systems were well indentified and subsequently rectified to ensure improving accuracy for historical and future radiosondes. [ABSTRACT FROM AUTHOR]

The residual dependent-variable variance in experiments is not 'random error', as it is often assumed to be, but merely 'unaccounted for variance', because what is random is inexplicable in terms of any possible set of independent-variables and this is something that ultimately is only empirically determinable. So, if there is any unaccounted for dependent-variable variance, an experiment's set of independent-variables is certainly under-specified and perhaps mis-specified because of the confounding of variables included in this set by causally relevant variables not included in the set. Thus, the proper first empirical test of any linear model is whether it leaves any residual dependent-variable variance, and if it does then none of its independent variables can yet logically justifiably be claimed to predict or causally explain any of the dependent-variable variance whatsoever. [ABSTRACT FROM AUTHOR]

The shortcomings of the ISO Guide to the Expression of Uncertainty in Measurement that most often lead to erroneous results when using the error propagation law for estimating the uncertainty of measurements are systematized. A new interpretation of the concept of random measurement error is given. The consistency of measures of uncertainty and precision of measurements is confirmed analytically. [ABSTRACT FROM AUTHOR]

Methods and means of constructing a new generation of absolute angle encoders designed with modern components are presented. The collimation and reflex circuits of the devices are described and their basic properties are analyzed. Computational algorithms for processing information and estimating the errors of the devices and results of laboratory tests of the metrological characteristics of experimental models of the devices are presented. [ABSTRACT FROM AUTHOR]

A spatially local decomposition of turbulent fluxes based on properties of spatial filters is used to develop a new method of estimating random error in turbulent moments of any order. The proposed error estimation method does not require an estimate of the integral time scale, which can be highly sensitive to the method used to calculate it. The error estimation method is validated using synthetic flux data with a known ensemble mean and intercompared with existing methods using data from the Advection Horizontal Array Turbulence Study (AHATS). Typical errors for a 27.3-min block of data collected at a height of 8 m are found to be approximately 10% for the heat flux and 7-15% for variances. The error in the momentum flux increases rapidly with increasing atmospheric instability, reaching values of 40% or greater for unstable conditions. A new method based on filtering is also proposed to estimate integral time scales of turbulent quantities. [ABSTRACT FROM AUTHOR]

The most frequently used methods for handling random error are largely misunderstood or misused by researchers. We propose a simple approach to quantify the amount of random error which does not require solid background in statistics for its proper interpretation. This method may help researchers refrain from oversimplistic interpretations relying on statistical significance. [ABSTRACT FROM AUTHOR]

The random error of the A-205 optical angle encoder, the angular scale of which is produced by holography, and which forms part of various dynamic goniometric systems, is investigated. A method of determining the random error which enables the potential and actual accuracy of the encoder to be determined is proposed. Results are presented which show that the random error of a single measurement with the encoder in an angle-measuring system with air bearings is less than 0.01″. [ABSTRACT FROM AUTHOR]

This paper considers a stochastic analysis of measurement errors in MotionNode inertial measurement units (IMUs). The sensors of this device are based on the microelectromechanical system (MEMS) technology. The use of the Allan variance algorithm for identifying the error's nature and the stochastic modeling of this error make it possible to substantially reduce the measurement errors by sensors. Based on the results of this study, we elaborated a number of recommendations for increasing the efficiency of the use of MEMS inertial sensors for problems of the navigation of moving objects. [ABSTRACT FROM AUTHOR]

An approach to the estimation of measurement uncertainty based on assumptions that are adopted in the design of information and measurement systems is presented. Arguments that attest to the possibility of increasing the precision of estimation and its simplification by comparison with the approach adopted in the Guide to the Expression of Uncertainty in Measurement are presented. [ABSTRACT FROM AUTHOR]

Investors who are more willing to accept risks when evaluating their investments less frequently are said to exhibit myopic loss aversion (MLA). Several recent experimental studies found that, on average, subjects bet significantly higher amounts on a risky lottery when they observe only a cumulative outcome of several realizations of the lottery (long evaluation period). In this article, we reexamine these empirical findings by analyzing individual rather than aggregate choice patterns. The behavior of the majority of subjects is inconsistent with the hypothesis of MLA: they bet an intermediate fraction of their initial endowment and these bets, on average, are not significantly different across two treatments with short and long evaluation period. We discuss several alternative explanations of this finding, including the Fechner model of random errors and the financial asset pricing model. [ABSTRACT FROM AUTHOR]

The objective of this study is to produce two-dimensional maps of the sensible and the latent heat fluxes from airborne measurements, based on the analysis of a flight pattern, called grid flights. A footprint model with along-wind and cross-wind components was used to project the measured fluxes onto the surface map. The method was applied to measurements over Arctic tundra during the Mackenzie Area GEWEX (Global Energy and Water Cycle Experiment) Study (MAGS) 1999. The resulting flux estimates were computed by integration of a wavelet transform, and corrected for long wavelength losses using information from 100 km long regional runs that were conducted close to the grid flights. The random flux error was estimated based on the flight length that is represented in each map element, and a map resolution of 3 × 3 km was chosen in order to keep the average random error of the latent heat flux below 25%. The random error of the sensible heat flux was smaller by a factor of 1.4 on average. An analysis of airborne flux measurements at different altitudes showed no significant increase of flux estimates for measurement heights below 90 m. Thus, the fluxes measured at heights between 48 and 64 m were not corrected for vertical flux divergence. The resulting flux maps provide quantitative two-dimensional estimates of the energy exchange between the surface and the atmosphere during the snow melt period in an Arctic environment, which are well-suited for calibration and validation of numerical models. [ABSTRACT FROM AUTHOR]

Regression Toward the Mean (RTM) in extreme-group quasi-experiments is a thoroughly theoretical matter, not a simple mathematical necessity, so results of extreme-group before-and-after treatment effectiveness studies should not be doubted simply because of the theoretical possibility of RTM. The estimation of RTM, wherever one has a treatment group with a pretreatment mean off to one side of its population’s mean, requires knowledge of the population mean, standard deviation, and regression slope. RTM ought not be presumed to be operative in studies unless its several theoretical assumptions have been justified and its implications for sample data have been corroborated on that data. RTM can properly be adjusted for only when it has been adequately estimated from the relevant population data. [ABSTRACT FROM AUTHOR]

Objective:To determine the minimum number of days of dietary intake interviews required to reduce the effects of random error (day-to-day variability in dietary intake) when using the multiple-pass, multiple-day, 24-h recall method.Design:Cross-sectional study.Setting:University research department.Subjects:A total of 50 healthy non-smoking overweight and obese (body mass index=26–40 kg/m2) adult men and women aged 39–45 years completed the study. Participants were randomly selected from volunteers for a larger unrelated study.Interventions:Each participant completed 10, multiple-pass, 24-h recall interviews on randomly chosen days over 4 weeks. The minimum number of record days was determined for each macronutrient (carbohydrate, fat, protein) and energy, for each gender, to obtain a ‘true’ (unobservable) representative intake from reported (observed) dietary intakes.Results:The greatest number of days required to obtain a ‘true’ representative intake was 8 days. Carbohydrate intakes required the greatest number of days of dietary record among males (7 days), whereas protein required the greatest number of days among females (8 days) in this cohort. Sunday was the day of the week that showed greatest variability in macronutrient intakes. Protein (P<0.05) and fat (P<0.001) intakes were significantly more variable than carbohydrate on Sundays compared with weekdays, for both men and women.Conclusion:A logistically achievable 8 days of dietary intake interviews was sufficient to minimize the effect of random error when using the multiple-pass, 24-h recall dietary intake method. Sunday should be included among the dietary interview days to ensure a ‘true’ representation of macronutrient intakes. This method can be confidently applied to small cohort studies in which dietary intakes from different groups are to be compared or to investigations of associations between nutrient intakes and disease.European Journal of Clinical Nutrition (2008) 62, 537–543; doi:10.1038/sj.ejcn.1602740; published online 21 March 2007 [ABSTRACT FROM AUTHOR]

A recent meta-analysis of meta-analyses by Møller and Jennions (2002, Oecologia 132:492–500) suggested that ecologists using statistical models are explaining between 2.5% and 5.42% of the variability in ecological studies. Although we agree that there is considerable variability in ecological systems that is not explained, we disagree with the approach and general conclusions of Møller and Jennions. As an alternate perspective, we explored the question: "How much ecological variation in relationships is not explained?" We did this by examining published studies in five different journals representative of the numerous sub-disciplines of ecology. We quantified the proportion of variance not explained in statistical models as the residual or random error compared to the total variation in the data set. Our results indicate that statistical models explain roughly half of the variation in variables of interest, vastly different from the 2.5%–5.42% reported by Møller and Jennions. This difference resulted largely from a different level of analysis: we considered the original study to be the appropriate level for quantifying variability while Møller and Jennions combined studies at different temporal and spatial scales and attempted to find universal single-factor relationships between ecological variables across study organisms or locations. Therefore, we believe that Møller and Jennions actually measured the universality of single factor effects across multiple ecological systems, not the amount of variability in ecological studies explained by ecologists. This study, combined with Møller and Jennions', illustrates importance of applying statistical models appropriately to assess ecological relationships. [ABSTRACT FROM AUTHOR]

A choice is made of a method for solving the problem of identifying the distribution laws of the random errors of measuring instruments in terms of the results of an analysis of the characteristics of three methods of utilizing experimental data to determine an analytical model for a distribution law. The defects of the chosen method are considered and particular problems are formulated for eliminating them. [ABSTRACT FROM AUTHOR]

The validity of any model depends on its ability to imagine the situation or problem to which it is applied. Further, the assumptions made in relation to the model are determining for the actual outcome. Within the field of clinical biochemistry a lot of models for analytical quality specifications, based on a variety of concepts and ’clinical settings’, have been proposed. A hierarchical structure for application of these approaches and models has been agreed on at several occasions in 1999. In this hierarchy, the highest rank is given to evaluation of analytical quality specifications based on ’clinical settings’/’clinical outcome’ models, followed by specifications based on biological variation and on ’clinicians opinions’. This contribution, deals with the problems of combining random and systematic errors and the implications of application of different models to a variety of clinical settings. [ABSTRACT FROM AUTHOR]

Using the method of mathematical modeling, we consider the transformation of a random error of light intensity measurement into an error of determining elements of the light scattering matrix. We consider a device in which the polarization properties of probing radiation change depending on the setting of the phase plate and polarization analysis of scattered radiation is performed while the second phase plate located ahead of a stationary analyzer is rotated. A set of settings of the first phase plate has been revealed, which provides a minimum random error in determining the scattered light matrix elements. [ABSTRACT FROM AUTHOR]

An automated Stokes polarimeter of the dynamic type is created to measure the polarization characteristics of radiation emitted, reflected, and scattered by objects at different angles to a surface. An analysis of the versions of processing of measurement resuls has shown that the dependence of a random error in determination of the parameters on the number of positions N of a quarter-wave plate, at which the radiation intensity is measured, is of a similar nature as the dependence on the number of independent measurements of the same quantity $$( \sim 1/\sqrt N )$$ . [ABSTRACT FROM AUTHOR]

Copyright of Annals of Telecommunications is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In point counting, the selection of a random subset from the counting grid permits random errors to be described by a simple mixed binomial model. The model is independent of any grain size considerations, and general formulas are presented giving upper bounds to the number of sections required. [ABSTRACT FROM AUTHOR]

The effects of statistically dependent random errors on the sidelobe are analyzed for the linear array. It is shown that the random errors cause a rise in the sidelobe level. The simple formulas can also be obtained for the case of independent random errors. [ABSTRACT FROM AUTHOR]