Search

This paper considers the problem of obtaining robust control charts for detecting changes in the mean µ and standard deviation σ of process observations that have a continuous distribution. The standard control charts for monitoring µ and σ are based on the assumption that the process distribution is normal. However, the process distribution may not be normal in many situations, and using these control charts can lead to very misleading conclusions. Although some control charts for µ can be tuned to be robust to non-normal distributions, the most critical problem with non-robustness is with the control chart for σ. This paper investigates the performance of two CUSUM chart combinations that can be made to be robust to non-normality. One combination consists of the standard CUSUM chart for µ and a CUSUM chart of absolute deviations from target for σ, where these CUSUM charts are tuned to detect relatively small parameter shifts. The other combination is based on using winsorized observations in the standard CUSUM chart for µ and a CUSUM chart of squared deviations from target for σ. Guidance is given for selecting the design parameters and control limits of these robust CUSUM chart combinations. When the observations are actually normal, using one of these robust CUSUM chart combination will result in some reduction in the ability to detect moderate and large changes in µ and σ, compared with using a CUSUM chart combination that is designed specifically for the normal distribution. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper considers the problem of monitoring the mean of a normally distributed process variable when the objective is to effectively detect both small and large shifts in this mean. The performance of a generalized likelihood ratio (GLR) control chart is evaluated, where the likelihood ratio is based on a moving window of past observations, The performance of the GLR chart is compared with the performance of other options, such as combinations of Shewhart and cumulative sum (CUSUM) charts and an adaptive CUSUM chart, that have been proposed for detecting a wide range of shift sizes. Performance is evaluated for sustained shifts, transient shifts, and drifts in the mean. It is shown that the overall performance of the GLR chart is at least as good as these other options. These other options have multiple control-chart parameters that allow for the charts to be tuned to be more sensitive to certain shifts that may be of interest. However, the GLR chart does not require users to specify the values of any control-chart parameters other than the size of the window and the control limit. We recommend a specific window size and provide a table of control limits corresponding to specified values of the false-alarm rate, so it is very easy to design the GLR chart for use in applications. Simulating the performance of the GLR chart is time consuming, but approximating the GLR chart with a set of CUSUM charts provides a much faster way of evaluating the performance of the GLR chart for research purposes. [ABSTRACT FROM AUTHOR]

This paper investigates control charts for detecting special causes in an ARIMA(0, 1, 1) process that is being adjusted automatically after each observation using a minimum mean-squared error adjustment policy. It is assumed that the adjustment mechanism is designed to compensate for the inherent variation due to the ARIMA(0, 1, 1) process, but it is desirable to detect and eliminate special causes that occur occasionally and produce additional process variation. It is assumed that these special causes can change the process mean, the process variance, the moving average parameter, or the effect of the adjustment mechanism. Expressions are derived for the process deviation from target for all of these process parameter changes. Numerical results are presented for sustained shifts, transient shifts, and sustained drifts in the process parameters. The objective is to find control charts or combinations of control charts that will be effective for detecting special causes that result in any of these types of parameter changes in any or all of the parameters. CUSUM charts designed for detecting specific parameter changes are considered. It is shown that combinations of CUSUM charts that include a CUSUM chart designed to detect mean shifts and a CUSUM chart of squared deviations from target give good overall performance in detecting a wide range of process changes. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background. This paper studied technical aspects and feasibility of single incision laparoscopic colectomy (SILC). Methods. Bibliographic search was carried out up to October 2009 including original articles, case reports, and technical notes. Assessed criteria were techniques, operative time, scar length, conversion, complications, and hospitalization duration. Results. The review analyzed seventeen SILCs by seven surgical teams. A single port system was used by four teams.No team used the same laparoscope. Two teams used two laparoscopes. All teams used curved instruments. SILC time was 116 ± 34 minutes. Final scar was longer than port incision (31 ± 7 versus 24 ± 8 mm; P = .036). No conversion was reported. The only complication was a bacteremia. Hospitalization was 5 ± 2 days. Conclusion. SILC is feasible. A single incision around the umbilical scar represents cosmetic progress. Comparative studies are needed to assess potential abdominal wall and recovery benefits to justify the increased cost of SILC. [ABSTRACT FROM AUTHOR]

When traditional control charts are used to monitor a proportion p, it is assumed that the binary observations are independent. This paper investigates the problem of monitoring p when there is a continuous stream of autocorrelated binary observations that follow a two-state Markov chain model with first-order dependence. It is shown that both the Shewhart p-chart and the most efficient chart for independent observations, the Bernoulli CUSUM chart, are not robust to autocorrelation, and that adjusting the control limits of these traditional charts to account for the autocorrelation is not an efficient approach. Here we construct a Markov binary CUSUM (MBCUSUM) chart based on a log-likelihood-ratio statistic and show that this chart can be well approximated by using a Markov chain model, for which exact properties are calculable. Numerical results show that the MBCUSUM chart will detect most increases in p faster than competing charts. The effect of the size of the Phase I data set used in setting up the MBCUSUM chart is also investigated. [ABSTRACT FROM AUTHOR]

Professor Frisen discusses a number of important issues concerning surveillance procedures that are used in a variety of settings. Some comments are given here on the issues of optimal surveillance procedures in complex situations, the effect of the sampling scheme on the performance of surveillance procedures, the robustness of surveillance procedures, and diagnostic procedures that can be used after a process change is detected. [ABSTRACT FROM AUTHOR]

The article presents a study which determines the compliance of patients with the guideline recommendations of colonoscopy and carcinoembryonic antigen (CEA) tests among patients who have undergone curative resection for colorectal cancer in the U.S. The authors have determined factors associated with meeting guidelines including younger age group, race and stage cancers. It is indicated that older survivor patients underwent testing below a minimum number specified by the guidelines.

When monitoring a process that has multivariate normal variables, the Shewhart-type control chart traditionally used for monitoring the process mean vector is effective for detecting large shifts, but for detecting small shifts, it is more effective to use the multivariate exponentially weighted moving average (MEWMA) control chart. It has been proposed that better overall performance in detecting small and large shifts in the mean can be obtained by using the MEWMA chart in combination with the Shewhart chart. Here we investigate the performance of this combination in the context of the more general problem of detecting changes in the mean or increases in variability. Recent investigations of combinations of the MEWMA chart for the mean and MEWMA-type charts based on the squared deviations of the observations from the target has shown that these combinations have excellent performance in detecting sustained shifts in the mean or in variability. Here we consider both sustained and transient shifts and show that a combination of two MEWMA charts has better overall performance than the combination of the MEWMA and Shewhart charts. We also consider a three-chart combination consisting of the MEWMA chart for the mean, an MEWMA-type chart of the squared deviations from target, and the Shewhart chart. When the sample size is n = 1, this three-chart combination does not seem to have better overall performance than the combination of the two MEWMA charts. When n > 1, the three-chart combination has significantly better performance for some mean shifts, but somewhat worse performance for shifts in variability. [ABSTRACT FROM AUTHOR]

It is often important to rapidly detect an increase in the incidence rate of a given disease or other medical condition. It has been shown that when disease counts are sequentially available from a single region, a univariate control chart designed to detect rate increases, such as a one-sided cumulative sum chart, is very effective. When disease counts are available from several regions at corresponding times, the most efficient monitoring method is not readily apparent. Multivariate monitoring methods have been suggested for dealing with this detection problem. Some of these approaches have shortcomings that have been recently demonstrated in the quality control literature. We discuss these limitations and suggest an alternative multivariate exponentially weighted moving average chart. We compare the average run-length performance of this chart with that of competing methods. We also evaluate the statistical performance of these charts when the actual increase in the disease count rate is different from the one that the chart was optimized to detect quickly. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Adaptive control charts allow the components of the quality-monitoring scheme to vary in order to obtain improved performance over non-adaptive control charts. Research has centered on components such as the sample size, time between samples, warning limits, and control limits and has recommended a variety of schemes, many of which are optimal in some sense. In practice, there are many other adaptive schemes that are near optimal, which will still yield considerable improvement over non-adaptive control charts. In addition, the impact of parameter estimation on adaptive control chart performance must be taken into consideration. Based on the simulation results shown here, adaptive control charts should only be used for mature processes, where a sufficient amount of Phase I data have been obtained to ensure that the estimated control limits are accurate. When evaluating control chart performance, we consider initial state performance measures for simplicity and note that the conclusions obtained here apply to steady-state performance measures. The evaluation of performance measures is easily handled by the Markov chain approach detailed in the Appendix. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Traditional control charts for process monitoring are based on taking samples of fixed size at equally spaced sampling points. As an alternative to these traditional fixed sampling rate (FSR) control charts, variable sampling rate (VSR) control charts change the sampling rate as a function of the data from the process. With VSR control charts the sampling rate is increased whenever there is some indication of a problem with the process, and decreased when there is no indication of a problem. This paper investigates a type of VSR control chart based on sequential sampling in which the sample size used at a sampling point is a function of the data from the current and past sampling points. Sequential sampling is investigated in the context of simultaneously monitoring both the mean and variability of a multivariate normal process. Multivariate exponentially weighted moving average (MEWMA) control charts based on sample means and on the sum of the squared deviations from the target are considered. When sequential sampling is used in a combination of the MEWMA charts based on sample means and on squared deviations from the target, the average performance is much better than that of the standard FSR multivariate control charts that have traditionally been used. [ABSTRACT FROM AUTHOR]

This article discusses riparian buffer width in relation to nitrogen removal efficiency, hydrological flow path, and vegetative cover in watersheds. The authors present a meta-analysis of the vegetated region adjacent to streams and wetlands which indicates that wide buffers more consistently removed significant portions of nitrogen entering a riparian zone than narrow buffers and buffers composed of herbaceous and forest/herbaceous vegetation were more effective when wider. They concluded that soil type, subsurface hydrology, and subsurface biogeochemistry also are important factors governing nitrogen removal in buffers.

The polytype and surface and defect microstructure of epitaxial layers grown on 4H(1120), 4H(0001) on-axis, 4H(0001) 8° off-axis, and 6H(0001) on-axis substrates have been investigated. High-resolution x-ray diffraction (XRD) revealed the epitaxial layers on 4H(1120) and 4H(0001) 8° off-axis to have the 4H-SiC (silicon carbide) polytype, while the 3C-SiC polytype was identified for epitaxial layers on 4H(0001) and 6H(0001) on-axis substrates. Cathodoluminescence (CL), Raman spectroscopy, and transmission electron microscopy (TEM) confirmed these results. The epitaxial surface of 4H(11̄20) films was specular with a roughness of 0.16-nm root-mean-square (RMS), in contrast to the surfaces of the other epitaxial layer-substrate orientations, which contained curvilinear boundaries, growth pits (∼3 x 104 cm-2), triangular defects >100 µm, and significant step bunching. Molten KOH etching revealed large defect densities within 4H(11̄20) films that decreased with film thickness to ∼106 cm-2 at 2.5 µm, while cross-sectional TEM studies showed areas free of defects and an indistinguishable film-substrate interface for 4H(11̄20) epitaxial layers. [ABSTRACT FROM AUTHOR]

An exponentially weighted moving average (EWMA) control chart for monitoring the process mean μ may be slow to detect large shifts in μ when the EWMA tuning parameter λ is small. An additional problem, Sometimes called the inertia problem, is that the EWMA statistic may be in a disadvantageous position on the wrong side of the target when a shift in μ occurs, which may significantly delay detection of a shift in μ. Options for improving the performance of the EWMA chart include using the EWMA chart in combination with a Shewhart chart or in combination with an EWMA chart based on squared deviations from target. The EWMA chart based on squared deviations from target is designed to detect increases in the process standard deviation σ, but it is also very effective for detecting large shifts in μ. Capizzi and Masarotto recently proposed the option of an adaptive EWMA control chart in which λ is a function of the data. With the adaptive feature, the EWMA chart behaves like a standard EWMA chart when the current observation is close to the previous EWMA statistic, and like a Shewhart chart otherwise. Here we extend the use of the adaptive feature to EWMA charts based on squared deviations from target, and also consider an alternate way of defining the adaptive feature. We discuss performance measures that we believe are appropriate for assessing the effects of inertia, and compare the performance of various charts and combinations of charts. Standard practice is to simultaneously monitor both μ, and σ, so we consider control chart performance when the objective is to detect small or large changes in μ or increases in λ. We find that combinations of EWMA control charts that include a chart based on squared deviations from target give good overall performance whether or not these charts have the adaptive feature. [ABSTRACT FROM AUTHOR]

Although psychotherapy has been and continues to be a face-to-face activity primarily, a growing minority of therapists are conducting text-based (i.e. e-mail) psychotherapy over the Internet. This study compared the session impact (measured by the Session Evaluation Questionnaire, SEQ; Stiles, Gordon, & Lani, 2002) and the client-therapist alliance (measured by the Agnew Relationship Measure, ARM; Agnew-Davies, Stiles, Hardy, Barkham, & Shapiro, 1998) of the exchanges between clients and therapists who are engaged in e-mail therapy with previously published results on face-to-face therapy. According to preliminary results, the online clients provided similar session impact and therapeutic alliance ratings compared to face-to-face clients. Although online therapists followed this general trend, they evaluated the depth, smoothness, and positivity aspects of session impact and confidence aspect of therapeutic alliance more highly than face-to-face therapists. [ABSTRACT FROM AUTHOR]

Multivariate control charts are considered for the simultaneous monitoring of the mean vector and covariance matrix when the joint distribution of process variables is multivariate normal. Emphasis is on the use of combinations of multivariate exponentially weighted moving average (MEWMA) control charts based on sample means and on the sum of the squared deviations from target. The performance of these combinations is compared with the performance of standard multivariate Shewhart charts and to combinations of univariate EWMA charts applied to each of the variables. The performance of these control charts with and without the use of Hawkins' (1991) method of regression adjustment of the variables is investigated. The performance of many of the control charts depends on the direction of the shift in the mean vector or covariance matrix, so performance is investigated for specific shift directions and also for averages over all directions. The best overall performance is achieved using a combination of MEWMA charts based on the sample means and on the sum of squared regression adjusted deviations from target. [ABSTRACT FROM AUTHOR]

Chironomid pupal exuviae were sampled monthly using drift nets and hand sieves over an annual cycle from Hendrick Mill Branch (HMB; Blount County, AL) and Payne Creek (PC; Hale County, AL) Taxon richness, community composition, and emergence phenologies were similar despite marked differences in physical/chemical characteristics of the study streams. The highest emergence rates were observed in spring (PC) or both spring and fall (HMB). However, there was no significant relationship between emergence/emergent biomass and date. Estimates of daily emergence and emergent biomass were much higher in HMB than in PC. These patterns may have resulted from a more consistent flow regime, higher substrate stability, tower variation in temperature/chemical characteristics, greater channel surface area, and a more significant hyporheic zone in HMB than in PC. [ABSTRACT FROM AUTHOR]

Traditional Shewhart control charts are usually considered effective for detecting large shifts in process parameters, but ineffective for detecting small shifts. For detecting small parameter shifts, it is much better to use exponentially weighted moving average (EWMA) control charts or cumulative sum (CUSUM) control charts, but these charts are not considered as effective as Shewhart charts for large parameter shifts. It is frequently recommended that EWMA or CUSUM charts be used in combination with a Shewhart chart to gain the benefits of both types of charts, so that both small and large shifts can be detected quickly. Here we consider the problem of process monitoring when a continuous process variable is being observed and the objective is to detect small or large shifts in either the process mean μ or the process standard deviation σ. In this situation it is customary to use a combination of two control charts, one chart designed to monitor μ, and the other designed to monitor σ. For this situation, the best EWMA or CUSUM chart for monitoring μ. is based on sample means, and the best chart for monitoring σ is based on squared deviations from target. An EWMA or CUSUM chart combination based on sample means and squared deviations from target is very effective for detecting small or large shifts in μ or σ. We show that this type of combination is more effective in terms of overall performance than other combinations that do not include the chart based on squared deviations from target and generally are at least as effective as any of the combinations that include the Shewhart chart. Thus we conclude that it is not really necessary to use a Shewhart chart with an EWMA or CUSUM chart to obtain the best overall performance, but it is necessary to use the EWMA or CUSUM chart based on squared deviations from target. [ABSTRACT FROM AUTHOR]

When multivariate control charts are used to monitor a process, it is usually assumed that the same sample size is used for each variable. In this paper, we investigate the MEWMA control chart for monitoring the process mean vector when the sample sizes are unequal. Unequal sample sizes may arise because of sampling constraints or because the sample sizes of some variables are increased to provide more information about variables that are considered to be more important than other variables. Here we focus on the situation in which one variable is considered to be more important than the other variables, so that the sample size for this variable is increased and the sample sizes of the other variables are decreased. When the correlation between the variables is not high, increasing the sample size of the important variable improves the ability to detect shifts in this variable. However, the ability to detect shifts in the other variables is decreased. We compare the approach of using unequal sample sizes with the approach of using control charts that are designed to be sensitive to the specific shift direction of interest. We also investigate the use of unequal sample sizes along with control charts designed to be sensitive to the shift direction of interest. [ABSTRACT FROM AUTHOR]

Multivariate control charts are used to monitor a process when more than one quality variable associated with the process is being observed. The standard practice when using any control chart to monitor a process is to take samples of fixed size at regular sampling intervals. Here we investigate multivariate exponentially weighted moving average (MEWMA) control charts based on sequential sampling. When sequential sampling is used, observations at a sampling point are taken in groups of one or more observations, and the number of groups actually taken is a random variable that depends on the data. The basic idea is that sampling starts with a small initial group of observations, and no additional sampling is done at this point if there is no indication of a problem with the process. If there is some indication of a problem with the process, then an additional group or groups of observations is taken at this sampling point. A signal is given after any group if there is a strong indication of a problem with the process. The performance of the MEWMA control chart based on sequential sampling is compared with the performance of standard control charts. It is shown that the MEWMA chart based on sequential sampling is much more efficient in detecting changes in the process mean vector than standard control charts based on nonsequential sampling. Sequential sampling performs best when there is the possibility of many groups at each sampling point, but sequential sampling is still substantially better than nonsequential sampling when only two groups are allowed. [ABSTRACT FROM AUTHOR]

When control charts are used to monitor processes to detect special causes, it is usually assumed that a special cause will produce a sustained shift in a process parameter that lasts until the shift is detected and the cause is removed. However, some special causes may produce a transient shift in a process parameter that lasts only for a short period of time. Control charts are usually based on samples of n ≥ 1 observations using a sampling interval of fixed length, say d. When n > 1, the usual practice, based on the so-called rational subgroups concept, is to take a concentrated sample at one time point at the end of the sampling interval d, but another option is to disperse the sample over the interval d. In this paper, we investigate the question of whether it is better to use n = 1, or to use n ≥ 1 and either concentrated or dispersed samples. The objective of monitoring is assumed to be the detection of special causes that may produce either a sustained or transient shift in the process mean μ and/or process standard deviation σ. It is assumed that the sampling rate in terms of the number of observations per unit time is fixed, so that the ratio n/d is fixed. The best sampling strategy depends on the type of control chart being used, so Shewhart and cumulative sum (CUSUM) charts are considered. For both types of control charts, a combination of two charts is investigated; one chart is designed to monitor μ, and the other is designed to monitor σ. The overall conclusion is that it is best to take samples of n = 1 and use a CUSUM chart combination. The Shewhart chart combination with the best overall performance is based on n > 1, but this combination has inferior statistical performance compared with the CUSUM chart combination. [ABSTRACT FROM AUTHOR]

Control charts for monitoring the process mean μ and process standard deviation σ are often based on samples of n > 1 observations, but in many applications individual observations are used (n = 1). In this article we investigate the question of whether it is better, from the perspective of statistical performance, to use n = 1 or n > 1. We assume that the sampling rate in terms of the number of observations per unit time is fixed, so using n = 1 means that samples can be taken more frequently than when n > 1. The best choice for n depends on the type of control chart being used, so we consider Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) charts. For each type of control chart we investigate a combination of two charts, one chart designed to monitor μ and the other designed to monitor σ. Most control chart comparisons in the literature assume that a special cause produces a sustained shift in a process parameter that lasts until the shift is detected. We also consider transient shifts in process parameters, which are of a short duration, and drifts in which a parameter moves away from its in-control value at a constant rate. We evaluate control chart combinations using the expected detection time for the various types of process changes and a quadratic loss function. When a signal is generated, it is important to know which parameters have changed, so the ability of control chart combinations to correctly indicate the type of parameter change is also evaluated. Our overall conclusion is that it is best to take samples of n = 1 observations and use an EWMA or CUSUM chart combination. The Shewhart chart combination with the best overall performance is based on n > 1, but this combination is inferior to the EWMA and CUSUM chart combinations on almost all performance characteristics (the exception being simplicity). This conclusion seems to contradict the conventional wisdom about some of the... [ABSTRACT FROM AUTHOR]

Planar, high-index interphase boundaries developed during the α→γm transformation in a Ti–46.54% Al alloy were re-examined with a 3-D near-coincidence site analysis. Pairs of low-index planes, fulfilling the Zhang and Purdy [Philos. Magn. A 68 (1993) 279] minimum mismatch criterion, meet along their edges. Atoms at these edges commingle almost linearly, thereby producing a kinetic barrier to boundary migration. [Copyright &y& Elsevier]

A theoretical study of the subband occupation in an intersubband staircase laser has been performed within the density-matrix approach by applying a canonical transformation to a tight-binding model of a superlattice with four quantum wells per unit cell. The field-induced carrier redistribution in a superlattice structure is investigated, for which recently mid-infrared laser operation has been demonstrated. An intrinsic population inversion occurs, which is due to resonant tunneling. [ABSTRACT FROM AUTHOR]

Presents information on a study which examined the effects of mechanical grinding on the nature of disorder in kaolinites. Mixtures contained in kaolinites; Methodology; Results and discussion.

Details the direct measurement of lateral carrier leakage in indium-gallium-arsenide-phosphorous multiple-quantum-well capped mesa buried heterostructure lasers. Current dependence of the modal gain spectra for mesa; Dependence of the inverse slope efficiency on cavity length.

Investigates Cumulative Sum (CUSUM) control charts based on residuals or on the original observation. AR(1) process with an additional random error; Comparison of CUSUM and Exponentially Weighted Moving Average (EWMA) charts; Issues in applying control charts in the presence of autocorrelation.

Traditional control charts for process monitoring are based on taking samples of fixed size from the process using a fixed sampling interval. Variable Sample Size (VSS) and Variable Sampling Interval (VSI) control charts vary the sampling rate from the process as a function of the data from the process. VSS and VSI control charts sample at a higher rate when there is evidence of a change in the process, and are thus able to detect process changes faster than traditional control charts. This paper considers general VSS and VSI control charts and develops integral equation and Markov chain methods for finding the statistical properties of these charts. EWMA charts with the VSS and/or the VSI features are studied in detail, and different ways of defining the EWMA control statistic are investigated. It is shown that using either the VSS or VSI feature in an EWMA control chart substantially improves the ability to detect all but very large shifts in the process mean. The VSI feature usually gives more improvement in detection ability than the VSS feature, and using both features together sometimes gives more improvement than using either one separately. Guidelines are given for choosing the possible sample sizes and the possible sampling intervals for these charts. EWMA charts with the VSS and/or YSI feature are compared to CUSUM charts and Shewhart &Xbar; charts with the VSS and/or VSI features. [ABSTRACT FROM AUTHOR]

Presents information on a study which investigated control charts for monitoring a process to detect changes in the mean and/or variance of a normal quality variable in sampling intervals. Process mean and variance; Type of parameter shift; Summary and conclusions.

Presents a study that calculated the frequency and severity of mouthpart deformities in larval midges in correlation with zinc, cadmium and lead concentrations in sediments of the Empire Lake. Information on trace metals; Frequency of morphological deformities in chironomids; Background on benthic sediments.

The links between 13 auditory and visual behavioral cues, measured intelligence, and observer judgments of intelligence in a zero-acquaintance context were examined in a lens model study. Auditory-plus-visual, auditory-only, and visual-only information conditions, in addition to a transcript-only control condition, were employed to determine whether auditory or visual cues encode measured intelligence more strongly and which are used more in judgments of intelligence. Five cues (of both types) accounted for nearly half the variance in measured intelligence, but it was much more strongly associated with auditory than visual cues. Observers' judgments of intelligence were also much more strongly related to auditory than visual cues. Visual cues may even depress accuracy; accuracy was higher in an auditory-only condition than in an auditory-plus-visual condition. [ABSTRACT FROM AUTHOR]

Considers CUSUM charts with the variable sample size (VSS) feature and with both the VSS and variable sampling interval features. Two ways of developing the control statistic of the charts; Guidelines for choosing the possible sample sizes and the possible sampling intervals for the charts; Methods for setting up the charts for practical applications; Conclusions.

Federal agencies are important players in the formulation of statutes. As an object of study and as part of the governmental process, academic public administration has shown little interest in the nature and extent of civil servants' involvement in this phenomenon, or in its ramifications. Making use of some data from survey research questionnaires as well as recently published histories of congressional enactments, this article seeks to open the door a little bit on this subject. Preliminary findings suggest that administrators are a significant influence from the earliest stages of statutes' evolution, impacting both important and run-of-the-mine enactments. That influence is evident in the frequency with which many agencies initiate and pursue their own legislative agendas year by year, draft their own bills, and participate in shaping policy objectives arising within Congress and among interest groups. By law, agencies are supposed to clear proposals through OMB before going to Congress, but as we will not, clearance can be a relative thing. Few proposed initiatives emanate directly from the White House. Tax and spending bills were excluded from the study, as were private bills. [ABSTRACT FROM AUTHOR]

A 1993 issue of the International Journal of Public Administration (IJPA) addressed the question of how public policy fits into the teaching curricula of Schools of Public Administration/ Public Affairs. A number of suggestions have emerged. Since then, turmoil among scholars within the perceived discipline of public administration, as well as changing attitudes within the public at large as to what role the government should take, has brought forth new but unclear signposts regarding the future of the public administration. Public policy will surely be a legitimate, as well as a conspicuous, component among the numerous claimants vying for inclusion in the evolving field of study. Like the previous issue of IJPA, the present compendium addresses ties between public administration and public policy in rather conventional terms. Viewed from the perspective of public administration, public policy intersects at a number of places in the policy process: 1) bringing issues into the public sector; 2) setting agendas in the public arena; 3) analyzing policy with respect to public problems and formulating alternative strategies to deal with them; 4) enacting an agreed-upon strategy and implementing it; and 5) evaluating results. Unlike the preceding issue of IJPA. However, this compendium leans more toward process and substance in action; it seeks to avoid deliberations at length about definitions and the finer points of topical sequencing. The articles in this issue of IJPA seek to identify and assess developments in a number of areas in which public policy and administration intersect. There are many such intersections; limitations of space, rather than material, impose constraints on the topics addressed, but to what extent? Ethics, evaluation, implementation, intergovernmental relations, and certain aspects of policy formulation, substantive as well as budgetary, provide the content of this issue of JJPA. There are clearly other points of intersection between public policy and administration (e.g., public organization, planning, the effects of racial and gender diversification on formulating and implementing policy). Topics like these provide fodder for future compendia seeding to highlight or assess policy administration ties. It is hoped that this issue of IJPA will inspire emulations of this genre in the years to come. [ABSTRACT FROM AUTHOR]

This article discusses the state of statistical process control as we proceed into the 21st century. SPC refers to some statistical methods used extensively to monitor and improve the quality and productivity of manufacturing processes and service operations. SPC primarily involves the implementation of control charts, which are used to detect any change in a process that may affect the quality of the output. The first control charts were developed by Walter A. Shewhart in the 1920s. These simple Shewhart charts have dominated applications to date. The process-monitoring problem can be described in general terms. Murphy's law explains the purpose monitoring: over time, something will inevitably change and possibly cause deterioration in process quality. The Shewhart charts were designed to make it relatively easy for process personnel without statistical training to set up, apply, and interpret the charts using only a pencil and paper for calculations. Bayesian procedures appear to be naturally suited for process monitoring. The standard approach to sampling for a control chart is to use a fixed sampling rate in which samples of fixed size are obtained using a fixed-length sampling interval.

Studies the effects of non-normality and autocorrelation on the performances of various individuals control charts for monitoring the process mean and/or variance. Shewhart X chart; Moving range chart; Exponentially weighted moving average charts; Control charts.

Presents information on a study which examined the physical and affective bases of the differences between architects' and laypersons' aesthetic evaluations of building facades. Research approaches in design aesthetics; Lens model framework used; Methods; Results; Discussion.

The forward I-V characteristics of InGaN-based light emitting diodes heteroepitaxially grown have previously been shown to be dominated by tunneling over a wide range of bias, as indicated by unrealistic values for the ideality factor. Comparison of the electrical characteristics in different bias regimes for InGaN light emitting diodes (LEDs) to expressions for the tunneling current enables one to achieve an understanding of the tunneling entities involved. At low bias for LEDs grown on sapphire, data are consistent with electron tunneling to deep levels in the vicinity of mixed/screw dislocations and are characterized by an energy near 200 meV. This component is absent for those devices homoepitaxially grown. In the intermediate bias regime, LEDs both heteroepitaxially and homoepitaxially grown exhibit a tunneling component that is proposed to be associated with the heavy hole tunneling via intermediate states and to have a characteristic energy near 80 meV. [ABSTRACT FROM AUTHOR]