Diagnostic Check Research Articles

Beware with the Quality of Thermodynamic Data Diagnostic Check of Phase Transitions Energetics of Active Pharmaceutical Ingredients

Thermodynamics is crucial for optimizing drug synthesis, developing pharmaceutical formulations and ensuring their stability, effectiveness and safety. The thermochemical properties of active pharmaceutical ingredients with the common biphenyl motif (RS-Flurbiprofen, Diflunisal, Fenbufen and Biprofen) available in the literature were collected, combined with our own complementary experimental results and evaluated. The vapour pressures temperature dependence of RS-Flurbiprofen was measured using the Knudsen effusion method, and the enthalpy of sublimation was obtained from this measurement. The enthalpy of fusion of RS-Flurbiprofen was measured using DSC. A reliable diagnostic method has been outlined to evaluate the quality of the available experimental thermodynamic data of drugs. The data previously available in the literature for RS-flurbiprofen, diflunisal and fenbufen were analysed and diagnosed as "thick". For RS-flurbiprofen, diflunisal, fenbufen and biprofen, the consistent sets of thermodynamic data were evaluated and recommended for the calculations of the pharmaceutic processes. The "paper-and-pencil" concept presented in this paper can be extended to the diagnosis of "sick" or "healthy" experimental thermodynamic data for pharmaceuticals with a structure other than profens.

Quantum Chemistry and Pharmacy: Diagnostic Check of the Thermochemistry of Ibuprofen.

The thermodynamic data on ibuprofen available in the literature shows that the disarray of experimental results is unacceptable for this very important drug. The data on ibuprofens available in the literature were collected, combined with our complementary experimental results and evaluated. The enthalpies of combustion and formation of the crystalline RS-(±)- and S-(+)-ibuprofens were measured using high-precision combustion calorimetry. The temperature dependence of the vapour pressure of S-(+)-ibuprofen was measured using the transpiration method and the enthalpy of vaporization was derived from this measurement. The enthalpies of fusion of both compounds were measured using DSC. The G4 calculations have been carried out to determine the enthalpy of formation in the gaseous state of the most stable conformer. Thermochemical properties of the compounds studied were evaluated and tested for consistency with the "centerpiece approach". A set of reliable and consistent values of thermodynamic properties of ibuprofens at 298.15 K is recommended for thermochemical calculations of the pharmaceutical processes. The diagnostic protocol was developed to distinguish between the "sick" or "healthy" thermodynamic data. This diagnostic is also applicable to other drugs with a different structure than ibuprofen.

"In Vitro" and "In Vivo" Diagnostic Check for the Thermochemistry of Metal-Organic Compounds.

Volatile metal β-diketonates are of interest from both practical and theoretical perspectives (manufacturing of film materials, catalysis, and the nature of metal-ligand bonding). Knowledge of their reliable thermochemical properties is essential for effective applications. However, there is an unacceptable scattering of the available data on the enthalpies of formation. In this work, we proposed "in vitro" and "in vivo" diagnostic tools to verify the available enthalpies of formation in both the crystalline and gaseous states for metal tris-β-diketonates. The "in vitro" procedure involved high-level quantum-chemical calculations and was applied to define a consistent data set on the enthalpies of formation for iron(III) β-diketonates. This data set has provided the basis for "in vivo" structure-property-based diagnostics to evaluate the robustness of the thermochemical data for β-diketonate tris-complexes with metals other than iron.

Cluster system management

Cluster system management (CSM) was co-designed with the Department of Energy Labs to provide the support necessary to effectively manage the Summit and Sierra supercomputers. The CSM system administration tools provide a unified view of a large-scale cluster and the ability to examine and understand data from multiple sources. CSM consists of five components: 1) application programming interfaces (APIs) and infrastructure; 2) Big Data Store; 3) support for reliability, availability, and serviceability (RAS); 4) Diagnostic and Health Check; and 5) support for job management. APIs and infrastructure provide lightweight daemons for compute nodes, hardware and software inventory collection, job accounting, and RAS. Logs, environmental data, and performance data are collected in the Big Data Store for analysis. RAS events can trigger corrective actions by CSM. Diagnostic and Health Check are provided through a diagnostic framework and test results collection. To support job management, CSM coordinates with the Job Step Manager to provide an overlay network of JSM daemons. CSM is an open source and available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/IBM/CAST</uri> . Documentation can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://cast.readthedocs.io</uri> .

A Time Series Analysis of Federal Budgetary Allocations to Education Sector in Nigeria (1970-2018)

This work is carried out to statistically analyze federal budgetary allocations to the education sector in Nigeria (1970-2018). Time series analysis is used to analyse the data using the Box and Jenkins modeling approach. This involved identification of model, estimation of parameters, diagnostic checking of the model and forecasting. ARIMA (1, 1, 1) was identified in the course of identification of the model. ARIMA (1, 1, 0) was selected as a parsimonious model using the Bayesian Information Criterion (BIC). Also, the diagnostic check was carried out and it was found that the model is adequate. Forecasts are equally made for the year 2019, 2020, 2021 and 2022 using the model obtained. It was observed that there is an upward trend from 2019-2022 forecasts, hence using the model will bring about increment in the future budgets.

Forecasting Diabetes Patients Attendance at Al-Baha Hospitals Using Autoregressive Fractional Integrated Moving Average (ARFIMA) Models

Diabetes has become a concern in the developed and developing countries with its growing number of patients reported to the ministry of health records. This paper discusses the use of the Autoregressive Fractional Moving Average (ARFIMA) technique to modeling the diabetes patient’s attendance at Al-Baha hospitals using monthly time series data. The data used in the analysis of this paper are monthly readings of diabetes patients data covered the period January 2006-December 2016. The data were collected from the General Directorate of Health Affairs, Al-Baha region. The autoregressive fractional moving average approach was applied to the data through the model identification, estimation, diagnostic checking and forecasting. Hurst test results and ACF confirmed that there is a long memory behavior in diabetic patient’s data. Also, the fractional difference to diabetes series data revealed that (d = 0.44). Moreover, unit root tests indicated that the fractional difference of diabetes series level is stationary. Furthermore, according to AIC and BIC of model selection criteria ARFIMA (1, 0.44, 0) model shown the smallest values, hence this model was chosen as an adequate represents the data. Also, a diagnostic check confirmed that ARFIMA was appropriate and highly recommended in modeling and forecasting this type of data.

Flood Analysis in Karkheh River Basin using Stochastic Model

This study analyzed the annual streamflow of Karkheh River in Karkheh river basin in the west of Iran for flood forecasting using stochastic models. For this purpose, we collected annual stremflow (peak and maximum discharge) during the period from 1958 to 2015 in Jelogir Majin hydrometric station (upstream of Karkheh dam reservoir). A time series model (stochastic model or ARIMA) has three stages consists of: model identification, parameter estimation and diagnostic check. Model identification was done by visual inspection on the Autocorrelation and Partial Autocorrelation Function. Three types of ARIMA(p,d,q) models (0,1,1), (1,1,1) and (4,1,1) suggested for the studied series. The suggested model parameters were computed using the Maximum Likelihood (ML), Conditional Least Square (CLS) and Unconditional Least Square (ULS) methods. In model verification, the chosen criterion for model parsimony was the Akaike Information Criteria (AIC) and the diagnostic checks include independence of residuals. The best ARIMA model for this series was (4,1,1), with their AIC values of 88.9 and 77.8 for annual peak and maximum streamflow respectively. Forecast series up to a lead time of ten years future (2006 to 2015) were generated using the accepted ARIMA models. Model accuracy was checked by comparing the predicted and observation series by coefficient of determination (R2). Results show that the ARIMA model was adequate for the flood analysis in Karkheh River and the forecast of the series in short time at future.

Reliability and Validity of Autistic Diagnostic Check List in Bangladesh

The word ‘Autism’ is a new issue of concern in Bangladesh. The disorder was not well known in our country just a few years back. In the past 20 years our understanding of childhood autism has shifted to focus upon biological factors rather than environmental. The disorder has drawn extensively upon behavioral technology to treat specific behavior problems. In fact, it is a developmental disorder with unknown etiology. The present study determined the reliability and validity of Autistic Diagnostic Check List (Dr. Mallika Banerjee, 2007). For this purpose 115 already diagnosed autistic children aged from 3 to 16 years old were selected by purposive sampling technique from eight different autistic and special schools in Chittagong city. There were 60 items which were categorized in six sub-groups, namely general observation, cognition, emotion, social, communication, sensory deficiency. Results showed that Cronbach Alpha of the total scale was.92. Validation of the scale was assured by convergent validity. Finally, the six sub-groups seemed to be uniformly important in the understanding of autism symptoms.

Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey

In the present study, a seasonal and non-seasonal prediction of the Standardized Precipitation Index (SPI) time series is addressed by means of linear stochastic models. The methodology presented here is to develop adequate linear stochastic models known as autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to predict drought in the Buyuk Menderes river basin using SPI as drought index. Temporal characteristics of droughts based on SPI as an indicator of drought severity indicate that the basin is affected by severe and more or less prolonged periods of drought from 1975 to 2006. Therefore, drought prediction plays an important role for water resources management. ARIMA modeling approach involves the following three steps: model identification, parameter estimation, diagnostic checking. In model identification step, considering the autocorrelation function (ACF) and partial autocorrelation function (PACF) results of the SPI series, different ARIMA models are identified. The model gives the minimum Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) is selected as the best fit model. Parameter estimation step indicates that the estimated model parameters are significantly different from zero. Diagnostic check step is applied to the residuals of the selected ARIMA models and the results indicated that the residuals are independent, normally distributed and homoscedastic. For the model validation purposes, the predicted results using the best ARIMA models are compared to the observed data. The predicted data show reasonably good agreement with the actual data. The ARIMA models developed to predict drought found to give acceptable results up to 2 months ahead. The stochastic models developed for the Buyuk Menderes river basin can be employed to predict droughts up to 2 months of lead time with reasonably accuracy.

Stochastic approaches for time series forecasting of boron: a case study of Western Turkey

In the present study, a seasonal and non-seasonal prediction of boron concentrations time series data for the period of 1996-2004 from Büyük Menderes river in western Turkey are addressed by means of linear stochastic models. The methodology presented here is to develop adequate linear stochastic models known as autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to predict boron content in the Büyük Menderes catchment. Initially, the Box-Whisker plots and Kendall's tau test are used to identify the trends during the study period. The measurements locations do not show significant overall trend in boron concentrations, though marginal increasing and decreasing trends are observed for certain periods at some locations. ARIMA modeling approach involves the following three steps: model identification, parameter estimation, and diagnostic checking. In the model identification step, considering the autocorrelation function (ACF) and partial autocorrelation function (PACF) results of boron data series, different ARIMA models are identified. The model gives the minimum Akaike information criterion (AIC) is selected as the best-fit model. The parameter estimation step indicates that the estimated model parameters are significantly different from zero. The diagnostic check step is applied to the residuals of the selected ARIMA models and the results indicate that the residuals are independent, normally distributed, and homoscadastic. For the model validation purposes, the predicted results using the best ARIMA models are compared to the observed data. The predicted data show reasonably good agreement with the actual data. The comparison of the mean and variance of 3-year (2002-2004) observed data vs predicted data from the selected best models show that the boron model from ARIMA modeling approaches could be used in a safe manner since the predicted values from these models preserve the basic statistics of observed data in terms of mean. The ARIMA modeling approach is recommended for predicting boron concentration series of a river.

Improved Peňa–Rodriguez portmanteau test

Several problems with the diagnostic check suggested by Peňa and Rodriguez [2002. A powerful portmanteau test of lack of fit for time series. J. Amer. Statist. Assoc. 97, 601–610.] are noted and an improved Monte-Carlo version of this test is suggested. It is shown that quite often the test statistic recommended by Peňa and Rodriguez [2002. A powerful portmanteau test of lack of fit for time series. J. Amer. Statist. Assoc. 97, 601–610.] may not exist and their asymptotic distribution of the test does not agree with the suggested gamma approximation very well if the number of lags used by the test is small. It is shown that the convergence of this test statistic to its asymptotic distribution may be quite slow when the series length is less than 1000 and so a Monte-Carlo test is recommended. Simulation experiments suggest the Monte-Carlo test is usually more powerful than the test given by Peňa and Rodriguez [2002. A powerful portmanteau test of lack of fit for time series. J. Amer. Statist. Assoc. 97, 601–610.] and often much more powerful than the Ljung–Box portmanteau test. Two illustrative examples of enhanced diagnostic checking with the Monte-Carlo test are given.

Course and two-year outcome in anorexic and bulimic adolescents

Objective:The aim of the study was the descriptive analysis of the two-year course of bulimia nervosa (BN; N=32) and anorexia nervosa (AN; N=23) in adolescents who received intensive inpatient behavioral treatment. Methods:Assessments were made on the basis of expert ratings (Structured Interview for Anorexia and Bulimia Nervosa [SIAB-P], Psychiatric Status Rating Scale for Bulimia/Anorexia Nervosa [PSRSB], Morgan-Russell Outcome Assessment Scale and Munich Diagnostic Check List [MDCL]) and self-ratings (SIAB-S [self-rating], Anorexia Nervosa Inventory for self-rating [ANIS], Eating Disorder Inventory [EDI], SCL-90, and the Parental Bonding Instrument [FBI]). Results:Females with BN maintained their low normal weight. Females with AN maintained some of their weight increase induced during inpatient therapy. The BN group had a higher lifetime comorbidity with affective disorders while AN had a higher comorbidity with substance use disorders. The status at two-year follow-up based on the PSRSB expert rating was as follows: 50% of the BN patients also fulfilled the Diagnostic Statistical Manual, third edition, revised (DSM-III-R) criteria for BN at follow-up while only 3.1% of them fulfilled criteria for AN and 46.9% were below threshold of a diagnosis for AN or BN according to DSM-III-R; of those former BN patients below diagnostic threshold for an eating disorder at follow-up, 26.7% showed “marked symptoms,” 26.7% were classified as “partial remission,” 13.3% had “residual symptoms,” and 33.3% had normalized to their “usual self.” From the patients with AN on admission 30.4% were classified as AN according to DSM-III-R at the two-year follow-up and 21.7% as BN. The remaining 47.8% were below diagnostic threshold of DSM-III-R AN or BN (of those 45.5% with “marked symptoms,” 27.3% with “partial remission,” 9.1% with “residual symptoms,” and 18.2% had normalized to “usual self). Conclusions: At the two-year follow-up almost half of the patients with AN and almost half of those with BN had no major eating disorder (AN, BN). A considerable number of the AN patients developed BN in the two-year interval.

Advances in Box‐Jenkins modeling: 1. Model construction

Box‐Jenkins modeling of time series data can be improved and simplified by adhering to contemporary modeling procedures. This paper gives the theory and techniques of the application of many recent advances that have been made at the identification, estimation, and diagnostic check stages of model development. The inverse autocorrelation function and the inverse partial autocorrelation function are demonstrated to be useful identification tools for both nonseasonal and seasonal models. Parameters can be estimated more efficiently by employing the modified sum of squares technique. At the estimation stage it is also possible to obtain a maximum likelihood estimate for a Box‐Cox power transformation. The Akaike information criterion is introduced to formalize mathematically the concept of model parsimony. When checking for model adequacy, knowing the distribution of the residual autocorrelation allows for a sensitive test for residual whiteness. Diagnostic checks are given for verifying the assumption of homoscedasticity of the model residuals. In practice, heteroscedasticity and nonnormality of the residuals can often be removed by a Box‐Cox transformation.

For what use are tests of hypotheses and tests of significance

Tests of sharp null hypotheses, although frequently computed, are.rarely appropriate as the major end product of statistical analyses, except possibly in some, areas of the natural sciences. But procedures closely akin to tests, although often less formal, are needed in almost every investigation in which exploratory data analysis is used to help to decide upon the statistical model appropriate for the final analysis. The term “diagnostic check”has been suggested by Box and Jenkins for these procedures. Traditional statistical tests often suggest useful diagnostic checks -and this, in my view, is what tests are mainly good for-but visual examination and interpretation of data plots are often equally important. Biere is also much to be gained by the development of new diagnostic checks, and testing theory may be useful as one guide to this development.

Childhood psychosis

In a study of 66 former child psychiatric patients, Form E-1 of Rimland's Diagnostic Check List (DCL) was found to be an effective instrument for differentiating autistic, other psychotic, and nonpsychotic disturbed children. Follow-up study revealed more favorable outcomes in nonpsychotic cases, with significant association between DCL scores and improvement following discharge. Scores indicative of autism on the DCL were also significantly associated with being of the Jewish faith.

Kanner's syndrome or early-onset psychosis: a taxonomic analysis of 142 cases.

Case history data on 142 psychotic children were obtained using the Rimland Diagnostic Check List. A particular variety of the method known as numerical taxonomy was used to classify the children into subgroups on the basis of responses on the Check List. A comparison was made between a classification of Kanner's syndrome vs. "no Kanner's syndrome" and a classification using random classes as starting points. The "best" classification of the data was one in which 2 classes were obtained, one containing all the Kanner's syndrome children plus other early-onset, nonrelating psychotic children and the other containing children with later onset and more varied symptoms. Those attributes which significantly differentiated between the classes both in the "best" classification and in the Kanner's syndrome classification were extracted. A distinction between Kanner children and other similar children was found only for 3 specific behaviors: obsessive desire for sameness, islets of special ability, and skillful manipulation of small objects.

A reliability study of interparental agreement on the Rimland Diagnostic Check List.

A reliability study of interparental agreement on the Rimland Diagnostic Check List.

Time Series Analysis of Impulsive Noise in Power Line Communication (PLC) Networks

This paper proposes and discusses Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models for broadband power line communication (PLC) networks with impulsive noise enviroment in the frequency range of 1 – 30 MHz. In time series modelling and analysis, time series models are fitted to the acquired time series describing the system for purposes which include simulation, forecasting, trend assessment, and a better understanding of the dynamics of the impulsive noise in PLC systems. Also, because the acquired impulsive noise measurement data are observations made over time, time series models constitute important statistical tools for use in solving the problem of impulsive noise modelling and forecasting in PLC. In fact, the time series and other statistical methods presented in numerous available literature draw upon research developments from two areas of environmetrics called stochastic hydrology and statistical water quality modelling as well as research contributions from the field of statistics. In time series modelling and analysis, we determine the most appropriate stochastic or time series model to fit our acquired data set at the confirmatory data analysis stage. No matter what type of stochastic model is to be fitted to the data set, we follow the identification, estimation, and diagnostic check stages of model construction. In addition, we explore the resulting autocorrelation functions in estimating the parameters of the selected time series models. Finally, SARIMA model is found suitable for computer-based PLC systems simulations and forecasting based on the diagnostic checks.

Diagnostic Check Lists of OrganizationTwo Standardized Check Lists for the Organization of Secondary Schools: One for Junior High School Grades and One for Senior High School Grades. Orie I. Frederick

Previous articleNext article No AccessEducational WritingsDiagnostic Check Lists of Organization Two Standardized Check Lists for the Organization of Secondary Schools: One for Junior High School Grades and One for Senior High School Grades. Orie I. Frederick J. Orin PowersJ. Orin Powers Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Volume 41, Number 10Dec., 1933 Article DOIhttps://doi.org/10.1086/439579 Views: 2Total views on this site Journal History This article was published in The School Review (1893-1979), which is continued by the American Journal of Education (1979-present). PDF download Crossref reports no articles citing this article.