Search results

Distributions of index terms have been used in modelling information retrieval systems and databases. Most previous models used some form of the Zipf distribution. This work uses a probability model of the occurrence of index terms to derive discrete distributions which are mixtures of Poisson and negative binomial distributions. These distributions, the generalised inverse Gaussian‐Poisson and the Generalised Waring give better fits than the simpler Zipf distribution, particularly in the tails of the distribution where the high frequency terms are found. They have the advantage of being more explanatory and can incorporate a time parameter if necessary.

This research aims to integrate two theories of reference price formation and to test the resulting exemplar‐prototype hybrid (EPH) model's predictions. Study 1 tests the predictions of the EPH model regarding price attractiveness ratings. Study 2 helps to document the process by which the EPH model operates.

The investigation consists of a pair of laboratory experiments that manipulate the skew (positive, negative) of historic price data, and the frequency of the modal price (high, low) in the price history.

Both the skew of prices to which consumers are exposed and the frequency with which the modal price occurs affect recall of the modal price and evaluations of the attractiveness of subsequent prices.

Consumers rely on information about both the price range and individual price points to form reference prices. Common models of reference price effects may be improved by including a non‐linear estimate of the effect of modal price frequency.

Managers are advised to offer a consistent regular price from which occasional discounts of varying value are offered to create a strong memory trace in consumers' minds for the higher “regular” price and avoid such a trace for any one discounted price.

Prior studies detail aspects of the relationship between reference prices and the attractiveness of market prices. This is the first attempt to integrate, rather than contrast, the two predominant types of theories (range‐based, price‐point based) of reference price formation.

An unreliable supply of grid electricity has a strong negative impact on industrial and commercial profitability as well as on household activities and government services that rely on electricity supply. This unreliable grid electricity could be a result of technical and security factors affecting the grid network. Therefore, this study aims to investigate the effects of technical and security factors on the transmission and distribution of grid electricity in Uganda.

This study used the ordinary least squares (OLS) and autoregressive distributed lag (ARDL) models to examine the effects of technical and security factors on grid electricity reliability in Uganda. The study draws upon secondary time series monthly data sourced from the Uganda Electricity Transmission Company Limited (UETCL) government utility, which transmits electricity to both distributors and grid users. Additionally, data from Umeme Limited, the largest power distribution utility in Uganda, were incorporated into the analysis.

The findings revealed that technical faults, failed grid equipment, system overload and theft and vandalism affected grid electricity reliability in the transmission and distribution subsystems of the Ugandan power grid network. The effect was computed both in terms of frequency and duration of power outages. For instance, the number of power outages was 116 and 2,307 for transmission and distribution subsystems, respectively. In terms of duration, the power outages reported on average were 1,248 h and 5,826 h, respectively, for transmission and distribution subsystems.

This paper investigates the effects of technical and security factors on the transmission and distribution grid electricity reliability, specifically focusing on frequency and duration of power outages, in the Ugandan context. It combines both OLS and ARDL models for analysis and adopts the systems reliability theory in the area of grid electricity reliability research.

Despite the growing availability of scanner-panel data, surveys remain the most common and inexpensive method of gathering marketing metrics. The purpose of this paper is to explore the size, direction and correction of response errors in retrospective reports of category buying.

Self-reported purchase frequency data were validated using British household panel records and the negative binomial distribution (NBD) in six packaged goods categories. The log likelihood theory and the fit of the NBD model were used to test an approach to adjusting the errors post-data collection.

The authors found variations in systematic response errors according to buyer type. Specifically, lighter buyers tend to forward telescope their buying episodes. Heavier buyers tend either to over-use a rate-based estimation of once-a-month buying and over-report purchases at multiples of six or to use round numbers. These errors lead to overestimates of penetration and average purchase frequency. Adjusting the aggregate data for the NBD, however, improves the accuracy of these metrics.

In light of the importance of purchase data for decision making, the authors describe the inaccuracy problem in frequency reports and offer practical suggestions regarding the correction of survey data.

Two novel contributions are offered here: an investigation of errors in different buyer groups and use of the NBD in survey accuracy research.

Aims to measure syllable aggregation consistency of Romanized Chinese data in the title fields of bibliographic records. Also aims to verify if the term frequency distributions satisfy conventional bibliometric laws.

Uses Cooper's interindexer formula to evaluate aggregation consistency within and between two sets of Chinese bibliographic data. Compares the term frequency distributions of polysyllabic words and monosyllabic characters (for vernacular and Romanized data) with the Lotka and the generalised Zipf theoretical distributions. The fits are tested with the Kolmogorov‐Smirnov test.

Finds high internal aggregation consistency within each data set but some aggregation discrepancy between sets. Shows that word (polysyllabic) distributions satisfy Lotka's law but that character (monosyllabic) distributions do not abide by the law.

The findings are limited to only two sets of bibliographic data (for aggregation consistency analysis) and to one set of data for the frequency distribution analysis. Only two bibliometric distributions are tested. Internal consistency within each database remains fairly high. Therefore the main argument against syllable aggregation does not appear to hold true. The analysis revealed that Chinese words and characters behave differently in terms of frequency distribution but that there is no noticeable difference between vernacular and Romanized data. The distribution of Romanized characters exhibits the worst case in terms of fit to either Lotka's or Zipf's laws, which indicates that Romanized data in aggregated form appear to be a preferable option.

Provides empirical data on consistency and distribution of Romanized Chinese titles in bibliographic records.

This study aimed to solve the engineering problem of free vibration disturbance and local mesh refinement induced by microcrack damage in circularly curved beams. The accurate identification of the crack damage depth, number and location depends on high-precision frequency and vibration mode solutions; therefore, it is critical to obtain these reliable solutions. The high-precision finite element method for the free vibration of cracked beams needs to be developed to grasp and control error information in the conventional solutions and the non-uniform mesh generation near the cracks. Moreover, the influence of multi-crack damage on the natural frequency and vibration mode of a circularly curved beam needs to be detected.

A scheme for cross-sectional damage defects in a circularly curved beam was established to simulate the depth, location and the number of multiple cracks by implementing cross-section reduction induced by microcrack damage. In addition, the h-version finite element mesh adaptive analysis method of the Timoshenko beam was developed. The superconvergent solution of the vibration mode of the cracked curved beam was obtained using the superconvergent patch recovery displacement method to determine the finite element solution. The superconvergent solution of the frequency was obtained by computing the Rayleigh quotient. The superconvergent solution of the eigenfunction was used to estimate the error of the finite element solution in the energy norm. The mesh was then subdivided to generate an improved mesh based on the error. Accordingly, the final optimised meshes and high-precision solution of natural frequency and mode shape satisfying the preset error tolerance can be obtained. Lastly, the disturbance behaviour of multi-crack damage on the vibration mode of a circularly curved beam was also studied.

Numerical results of the free vibration and damage disturbance of cracked curved beams with cracks were obtained. The influences of crack damage depth, crack damage number and crack damage distribution on the natural frequency and mode of vibration of a circularly curved beam were quantitatively analysed. Numerical examples indicate that the vibration mode and frequency of the beam would be disturbed in the region close to the crack damage, and a greater crack depth translates to a larger frequency change. For multi-crack beams, the number and distribution of cracks also affect the vibration mode and natural frequency. The adaptive method can use a relatively dense mesh near the crack to adapt to the change in the vibration mode near the crack, thus verifying the efficacy, accuracy and reliability of the method.

The proposed combination of methodologies provides an extremely robust approach for free vibration of beams with cracks. The non-uniform mesh refinement in the adaptive method can adapt to changes in the vibration mode caused by crack damage. Moreover, the proposed method can adaptively divide a relatively fine mesh at the crack, which is applied to investigating free vibration under various curved beam angles and crack damage distribution conditions. The proposed method can be extended to crack damage detection of 2D plate and shell structures and three-dimensional structures with cracks.

The Bradford law is explored theoretically by means of a very mixed Poisson model which, it is claimed, elucidates the uncertainties surrounding the law and its applications. It is argued that Bradford succeeded in formulating an empirical regularity which has pure and hybrid forms but that all the variants can be subsumed under a simple logarithmic law which, for reasons explained, escapes exact expression in conventional frequency terms. The theoretical aspects discussed include the hybridity of form, estimations, sampling problems, the stability of ranks, homogeneity of data, and tests of significance. Some numerical examples, some simulated and some drawn from social contexts outside bibliography, are used both to illustrate theoretical issues and also to indicate the wide generality of the Bradford law. Possible applications and developments of the theory are indicated.

The microseismic monitoring technique has great advantages on identifying the location, extent and the mechanism of damage process occurring in rock mass. This study aims to analyze distribution characteristics and the evolution law of excavation damage zone of surrounding rock based on microseismic monitoring data.

In situ test using microseismic monitoring technique is carried out in the large-span transition tunnel of Badaling Great Wall Station of Beijing-Zhangjiakou high-speed railway. An intelligent microseismic monitoring system is built with symmetry monitoring point layout both on the mountain surface and inside the tunnel to achieve three-dimensional and all-round monitoring results.

Microseismic events can be divided into high density area, medium density area and low density area according to the density distribution of microseismic events. The positions where the cumulative distribution frequencies of microseismic events are 60 and 80% are identified as the boundaries between high and medium density areas and between medium and low density areas, respectively. The high density area of microseismic events is regarded as the high excavation damage zone of surrounding rock, which is affected by the grade of surrounding rock and the span of tunnel. The prediction formulas for the depth of high excavation damage zone of surrounding rock at different tunnel positions are given considering these two parameters. The scale of the average moment magnitude parameters of microseismic events is adopted to describe the damage degree of surrounding rock. The strong positive correlation and multistage characteristics between the depth of excavation damage zone and deformation of surrounding rock are revealed. Based on the depth of high excavation damage zone of surrounding rock, the prestressed anchor cable (rod) is designed, and the safety of anchor cable (rod) design parameters is verified by the deformation results of surrounding rock.

The research provides a new method to predict the surrounding rock damage zone of large-span tunnel and also provides a reference basis for design parameters of prestressed anchor cable (rod).

Cyber risk refers to risk affecting information and technology assets of a corporation or government institution. As cyber risk management become important, insurance is one possible solution. However, lack of data and severe information asymmetries increase the difficulties in pricing-related insurance products. In this chapter, we discuss first-party insurance that indemnifies the loss when the insured encounters virus attack and provide pricing model for the policy using copula methodology. Simulation results show that model risk may exist in the distribution of server downtime hours and is minor in the distribution of incident frequency and number of personal computers (PCs) infected.

About 13 to 44 per cent of motor faults are caused by bearing failures in induction motors (IMs), where lubrication plays a significant role in maintaining rotating equipment because it minimizes friction and prevents wear by separating parts that move next to each other, and more than 35 per cent of bearing failures can be attributed to improper lubrication. An excessive amount of grease causes the rollers or balls to slide along the race instead of turning, and the grease will actually churn. This churning action will eventually wear down the base oil of the grease and all that will be left to lubricate the bearing is a thickener system with little or no lubricating properties. The heat generated from the churning, insufficient lubricating oil will begin to harden the grease, and this will prevent any new grease added to the bearing from reaching the rolling elements, with the consequence of bearing failure and equipment downtime. Regarding the case of grease excess in bearings, this case has not been sufficiently studied. This work aims to present an effective methodology applied to the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the Margenau-Hill distribution (MHD) and artificial neural networks (ANNs), where the obtained results demonstrate the correct classification of the studied cases.

This work proposed an effective methodology applied to the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the MHD and ANNs.

In this paper, three cases of study for a bearing in an IM are studied, detected and classified correctly by combining some methods. The marginal frequency is obtained from the MHD, which in turn is achieved from the stator current signal, and a total of six features are estimated from the power spectrum, and these features are forwarded to the designed ANN with three output neurons, where each one represents a condition in the IM: healthy bearing, mechanical bearing fault and excessively lubricated bearing.

The proposed methodology can be applied to other applications; it could be useful to use a time–frequency representation through the MHD for obtaining the energy density distribution of the signal frequency components through time for analysis, evaluation and identification of faults or conditions in the IM for example; therefore, the proposed methodology has a generalized nature that allows its application for detecting other conditions or even multiple conditions under different working conditions by a proper calibration.

The lubrication plays a significant role in maintaining rotating equipment because it minimizes friction and prevents wear by separating parts that move next to each other, and more than 35 per cent of bearing failures can be attributed to improper lubrication and it negatively affects the efficiency of the motor, resulting in higher operating costs. Therefore, in this work, a new methodology is proposed for the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the MHD and ANNs. The proposed methodology uses a total of six features estimated from the power spectrum, and these features are sent to the designed ANN with three output neurons, where each one represents a condition in the IM: healthy bearing, mechanical bearing fault and excessively lubricated bearing. From the obtained results, it was demonstrated that the proposed approach achieves higher classification performance, compared to short-time Fourier transform, Gabor transform and Wigner-Ville distribution methods, allowing to identify mechanical bearing faults and bearing excessively lubricated conditions in an IM, with a remarkable 100 per cent effectiveness during classification for treated cases. Also, the proposed methodology has a generalized nature that allows its application for detecting other conditions or even multiple conditions under different working conditions by a proper calibration.