Goodness Of Fit Statistics Research Articles

Validation and Reliability of Healthcare Workers' Knowledge, Attitude, and Practice Instrument for Uncomplicated Malaria by Rasch Measurement Model.

Background: This study assessed the validity and reliability of healthcare workers’ knowledge, attitudes, and practices instrument for uncomplicated malaria (HKAPIUM) for evaluation of healthcare workers’ knowledge, attitudes, and practices (KAP) on uncomplicated malaria management in primary healthcare (PHC) facilities in Plateau state, Nigeria. Methods: Relevant variables from literature, malaria treatment guidelines for Nigeria, and World Health Organization (WHO) were used to generate and present the items for the draft HKAPIUM scale, which was first screened by six experts before administered to 121 respondents who filled and returned immediately. The data were sorted and analyzed using Rasch measurement model (Bond & Fox software®). Results: The outcome of the initial screening showed high items content validity indices (I-CVI) (0.83–1.00) and high scale-CVI (S-CVI) {universal agreement (UA) within the experts (S-CVI/UA) (0.67–0.89) and the average CVI [S-CVI/Ave (0.94–0.98)]} for relevance, clarity, simplicity, and comprehensiveness. The Rasch analysis outputs showed good items’ reliability for the three factors (KAP) > 0.9 with high separation index values of > 2.0; however person reliability were poor (< 0.6) which were confirmed by their low separation values. Goodness of fit statistics indicated nine items not fitting the model based on the suggested fit index values of 0.6 to 1.5, and ± 2 for mean square (MNSQ) and standardized Z-score (Zstds) respectively, and 0.3 to 0.7 for “point-measure correlation coefficients” (PTMEA Corr). Deletion of misfit items resulted in the items and persons’ reliabilities falling above the minimum accepted limit of 0.6, with their separation values were all in the range of 1 and 2 which were acceptable. Similarly, fit index values for MNSQ infit and outfit, and Zstd parameters items in the new scale were all within the acceptable range of 0.6 to 1.5, and ±2 respectively, in addition to the positive PTMEA Corr as further confirmation of the items’ fitness to the model. Conclusion: The reduction of 27-items draft HKAPIUM scale to 18 items was successful with good reliability and fitness to the model.

Application of business intelligence technique to manufacturing system with stochastic process (a case study of “product-based” manufacturing company in nigeria with make-to-order strategy)

Manufacturing outfit in Nigeria with make-to-order strategy continually contend with the problem of long waiting time of customers for orders, which arose through inability to predict order quantity amount for customers. This indicates the need for the application of business intelligence technique on large amount of unused data in manufacturing databases, to evaluate and validate probability distribution model that is best suitable for prediction of customers’ order values and subsequently using the best model to predict daily and yearly customers order amount. Many models were evaluated for data extracted from a manufacturing outfit and the best model was selected based on statistical goodness of fit. The normal distribution model was found most appropriate for the prediction of daily and monthly order quantity amount. Based on this, the application of business intelligence technique on extracted data has made the prediction of customers order for a product-based manufacturing outfit with make-to-order strategy possible.

Simple to Use Microsoft Excel Template for Estimating the Parameters of Some Selected Probability Distribution Model by Method of L-Moment

The focus of this research was to design a simple to use Microsoft excel algorithm that will aid in the estimation of the parameters of generalized extreme value probability distribution (GEV), generalized logistics probability distribution (GLO) and generalized pareto probability distribution (GPA), calculate the predicted rainfall/discharge based on L-moment procedures and compute the quantile estimates at various return periods.The algorithm was design based on the underlying mathematics of L-moment and has the capacity to handle forty (40) year’s annual maximum series of either rainfall or discharge data which must first be ranked in ascending order of magnitude. Basic descriptive statistics such as the sample mean, variance, standard deviation, skewness, kurtosis, coefficient of variation have been built into the algorithm. Other exciting features include; the computation of Probability weighted moment parameters (b0, b1, b2 and b3), L-Moment values (ƛ1, ƛ2, ƛ3 and ƛ4) , L-Moment ratio values (Ʈ2, Ʈ3 and Ʈ4), and goodness of fit statistics (RRMSE, RMSE, MAE, MADI and PPCC). Others include; the shape (k), scale (α) and location (ξ) parameters of GEV, GPA and GLO probability distributions. To test the performance of the algorithm, forty (40) year’s annual maximum rainfall data from Benin City was used. Basic time series analysis such as test of normality, test of homogeneity and outlier detection was conducted to ensure that the data used are adequate and suitable.Results obtained revealed that generalized logistics probability distribution GLO was the best fit distribution model for analyzing the annual maximum rainfall series at the study site. The predicted rainfall quantile magnitude (Qt) based on the GLO model ranges from 425.877mm at 2years return period to 762.759mm at 200years return period. The coefficient of determination (r 2 ) for the observed versus predicted rainfall based on the best fit model was observed to be 0.9793. It was thereafter concluded that L-moments and L –moment ratios are useful summary statistics for analyzing rainfall data. Keywords: L-moments, probability distribution, normality test, goodness of fit statistics, coefficient of variation. DOI : 10.7176/CER/11-9-05 Publication date :October 31 st 2019

Analysis of corrosion performance of rebar in Red mud blended concrete

In this paper, electrochemical technique was employed to evaluate the performance of Red mud (RM) on the reinforcement corrosion. This paper also reports the effect of chloride, sulphate and the combined chloride-sulphate solutions on the corrosivity of rebar embedded in Ordinary Portland Cement (OPC) and RM blended concrete (95% OPC & 5% RM) slab specimens. The corrosion performance was evaluated by Linear polarisation resistance sweep test on the reinforced slab specimens made with mix water contaminated with 5% NaCl, 2% MgSO4 and the combination of both salts. The corrosion performance was monitored for every 30 days over a period of 180 days, in terms of corrosion rate values. In order to investigate the overall performance of experimental test results they were analysed as per the specifications given by ASTM G16-13. The analysis of the corrosion data includes the descriptive statistics of the Normal, Weibull lognormal, and Smallest Extreme Value probability distribution functions (Pdfs) and the test of fit significance by the Anderson-Darling (AD) goodness of fit statistics. In addition, the analysis of variance was also carried out to determine the influence of each variable on the corrosion data. Further, the analysis of variance is also carried out to evaluate the influence of cementitious material and type of salt on corrosion data.

Chemometrics approach for the prediction of chemical compounds’ toxicity degree based on quantum inspired optimization with applications in drug discovery

Chemometrics, the application of mathematical and statistical methods to the analysis of chemical data, is finding ever widening applications in the chemical process environment. The reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug discovery, food safety, and the manufacturing of chemical compounds. Toxicity prediction requires several new approaches for knowledge discovery from data to paradigm composite associations between the modules of the chemical compound; the computational demands of such techniques increase greatly with the number of chemical compounds involved. State-of-the-art prediction methods such as neural networks and multi-layer regression require either tuning parameters or complex transformations of predictor or outcome variables and do not achieve highly accurate results. This paper proposes a Quantum Inspired Genetic Programming “QIGP” model to improve prediction accuracy. Genetic Programming is utilized to give a linear equation for calculating the degree of toxicity more accurately. Quantum computing is employed to improve the selection of the best-of-run individuals and handles parsimony pressure to reduce the complexity of solutions. The results of the internal validation analysis indicated that the QIGP model has better goodness of fit statistics then, and significantly outperforms, the Neural Network model.

Abstract 962: Validation of breast cancer risk model incorporating classical risk factors and polygenic risk scores in 14 prospective cohort studies in 6 countries

Abstract Background: Prospective validation of breast cancer risk models integrating classical risk factors and genetic variants is required for risk-stratified prevention and screening strategies. The objective of this study was to validate a breast cancer risk model integrating classical risk factors and a 313-variant polygenic risk score (PRS) in multiple prospective cohort studies, and to project five-year risk of breast cancer in six different countries. Methods: The study population included 7,529 cases and 230,103 controls from 14 prospective cohort studies in Australia, Germany, the Netherlands, Sweden, UK, and USA. We used the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building, validation, and risk projection. Expected five-year risk of invasive or in situ breast cancer was compared to observed risk, overall and within deciles of expected risk using goodness of fit statistics. We evaluated calibration of the relative risk through meta-analysis across cohorts, and of the absolute risk within each cohort. Model discrimination was evaluated using the area under the curve (AUC), and percentages of women crossing risk thresholds. Projections of five-year risk distributions were estimated for women of European ancestry aged 50-70 years in the general populations of these six countries. Results: Analysis showed overall good calibration of the integrated iCARE-based model relative risk for both women younger than 50 years (χ2=14.9, P=0.09) and aged 50 years or older (χ2=14.6, P=0.10), with a small overestimation of risk for women in the highest decile of expected risk (RR = 3.5 expected vs 2.3 (95% CI 1.6 to 3.2) observed for women &amp;lt;50 years; and 2.8 expected vs 2.3 (95% CI 2.0 to 2.7) observed for women 50+ years). The age-adjusted AUCs for the integrated model were 63.1 (95% CI 60.9 to 65.3) and 62.9 (95% CI 61.8 to 64.0), for the two age groups respectively. The calibration of absolute risk showed substantial variation across cohorts, particularly for the older group, but had no systematic bias. Model based projections in the general populations showed that compared to the population average, women in the 1st and 99th percentiles of the integrated risk score had relative risks 0.19 and 3.56 respectively. The proportion of women of European ancestry aged 50-70 years with a five-year risk greater than 3% (threshold for consideration of risk-lowering drugs by U.S. Preventive Services Task Force) ranged from 7.1% in Germany to 18.2% in the US, which corresponds to ~5.5 million women in the US. Conclusions: Five-year risk predictions from a model with classical risk factors and PRS are well calibrated and provide substantial risk stratification across multiple cohorts in six different countries. Further studies are needed to evaluate the clinical utility of the validated model for risk stratified screening and prevention of breast cancer. Citation Format: Parichoy Pal Choudhury, Amber Wilcox, Chi Gao, Brian Carter, Anika Husing, Mark Brook, Mikael Eriksson, Kara Martin, Chris Scott, Min Shi, Thomas Ahearn, Michael Jones, Nick Orr, Minouk Schoemaker, Kamila Czene, Jenny Chang-Claude, Jacques Simard, Doug Easton, Marjanka K. Schmidt, Dale Sandler, Clarice R. Weinberg, Celine Vachon, Roger Milne, Per Hall, Anthony Swerdlow, Rudolph Kaaks, Myrto Barrdahl, Mia Gaudet, Antonis Antoniou, Peter Kraft, Montserrat Garcia-Closas, Nilanjan Chatterjee. Validation of breast cancer risk model incorporating classical risk factors and polygenic risk scores in 14 prospective cohort studies in 6 countries [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 962.

Cost-Effectiveness of brentuximab vedotin with chemotherapy in frontline treatment of CD30-expressing peripheral T-cell lymphoma.

e19060 Background: The most common frontline treatment for peripheral T-cell lymphoma (PTCL) is cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) or a CHOP-like regimen. In the recently reported phase 3 ECHELON-2 trial, brentuximab vedotin (BV) in combination with CHP (A+CHP) demonstrated significant improvement in progression-free survival (PFS) and overall survival (OS) compared to CHOP, with a manageable safety profile. The objective of this analysis was to evaluate the cost-effectiveness of A+CHP in the frontline setting for CD30-expressing PTCL. Methods: A partitioned survival model consisting of 3 health states (PFS, post progression survival, and death), was constructed using clinical and quality of life data from ECHELON-2 from a US payer perspective over a lifetime time horizon. PFS and OS observed from ECHELON-2 were extrapolated using standard parametric distributions. The best-fitting distributions (log-normal for both arms) were selected based on statistical goodness of fit and clinical plausibility of the long-term projections. Health utilities were derived from the European Quality of Life 5-Dimensions (EQ-5D) data collected in ECHELON-2. The average utility scores for the pre- and post-progression periods were estimated via a repeated-measures mixed-effects model. Medical resource use and costs were from literature. Results: The model predicted A+CHP extended undiscounted PFS by 2.92 and OS by 3.38 years over CHOP. These survival gains drive the value in the model. After adjusting for quality of life and discounting, A+CHP was associated with 1.79 quality-adjusted life years (QALYs) gained at an incremental cost of $176,842, yielding an incremental cost-effectiveness ratio (ICER) of $98,987. Sensitivity analyses of alternative model assumptions provided ICERs from $64,000 to $154,000. The estimated probability that A+CHP is cost-effective compared with CHOP was 75% at a willingness-to-pay threshold of $150,000. Conclusions: Based on the ECHELON-2 trial data, this analysis showed for patients with previously untreated CD30-expressing PTCL, treatment with BV in combination with CHP is likely to be cost-effective in comparison to CHOP.

Restricted Recalibration of Item Response Theory Models.

In item response theory (IRT), it is often necessary to perform restricted recalibration (RR) of the model: A set of (focal) parameters is estimated holding a set of (nuisance) parameters fixed. Typical applications of RR include expanding an existing item bank, linking multiple test forms, and associating constructs measured by separately calibrated tests. In the current work, we provide full statistical theory for RR of IRT models under the framework of pseudo-maximum likelihood estimation. We describe the standard error calculation for the focal parameters, the assessment of overall goodness-of-fit (GOF) of the model, and the identification of misfitting items. We report a simulation study to evaluate the performance of these methods in the scenario of adding a new item to an existing test. Parameter recovery for the focal parameters as well as Type I error and power of the proposed tests are examined. An empirical example is also included, in which we validate the pediatric fatigue short-form scale in the Patient-Reported Outcome Measurement Information System (PROMIS), compute global and local GOF statistics, and update parameters for the misfitting items.

The psychometric properties of the CFSS-DS for schoolchildren in Saudi Arabia: A confirmatory factor analytic approach.

To test the psychometric properties of an Arabic version of the Child Fear Survey Schedule-Dental Subscale (CFSS-DS) a using confirmatory factor analysis. Two convenience samples were obtained: Sample [1]: 600 boys (33%) and girls attending 4 public schools in Onizah and Sample [2] 800 girls attending 8 public schools in Buridah. The questionnaire asked the participant's age, gender, and completion of the CFSS-DS. The data were subjected to exploratory factor analysis (EFA), parallel factor analysis, Cronbach's alpha, confirmatory factor analysis (CFA), and goodness of fit statistics. A total of 513 children in Sample [1] and 503 children in Sample [2] participated giving a valid response rate of 86% and 67%, respectively. From the EFA, 3 factors were identified and confirmed statistically using parallel factor analysis. The internal consistency of the 3 factors, Dental Fear Subscale (0.86), Hospital Fear Subscale (0.77), and Stranger Fear Subscale (0.71), was good. The CFA showed that the current EFA model was an equivalent fit to the El Housseiny etal (BMC Oral Health 2016;16:49). model; however, the solution using El Housseiny etal's structure was distorted. A 3-factor structure with acceptable reliability exists for this Arabic version of the CFSS-DS, confirmed by a CFA using an additional data set.

Validity of four measures of child care quality in a national sample of centers in Ecuador.

This paper assesses the psychometric properties of four child care quality instruments administered in 404 child care centers in Ecuador: the Classroom Assessment Scoring System for Toddlers, the Infant/Toddler Environment Rating Scale–Revised Edition, the Child Care Infant/Toddler Home Observation for Measurement of the Environment, and the Missouri Infant/Toddler Responsive Caregiving Checklist. We examined their internal consistency, tested the underlying subscale structure by means of confirmatory factor analysis (CFA), verified construct validity by testing associations with quality-related factors (e.g., child-caregiver ratio), and checked concurrent validity of the instruments’ total scores. We found high internal consistency of the instruments at the full scale level and moderate to high at the subscale/domain level. CFA showed high factor loadings, but goodness of fit statistics were low. Construct validity results varied from low to very low depending on the quality-related factor, and concurrent validity from low to very high depending on the instruments compared. This validity exercise provides useful information for policy-makers and researchers interested in using these instruments in the Ecuadorian context or elsewhere in the region. The findings will also inform future research and development of affordable and culturally-appropriate tools for monitoring process quality in child care centers in Latin American countries.

Spatial and Temporal Analysis of Precipitation and Effective Rainfall Using Gauge Observations, Satellite, and Gridded Climate Data for Agricultural Water Management in the Upper Colorado River Basin

Accurate spatial and temporal precipitation estimates are important for hydrological studies of irrigation depletion, net irrigation requirement, natural recharge, and hydrological water balances in defined areas. This analysis supports the verification of water savings (reduced depletion) from deficit irrigation of pastures in the Upper Colorado River Basin. The study area has diverse topography with scattered fields and few precipitation gauges that are not representative of the basin. Gridded precipitation products from TRMM-3B42, PRISM, Daymet, and gauge observations were evaluated on two case studies located in Colorado and Wyoming during the 2014–2016 irrigation seasons. First, the resolution at the farm level is discussed. Next, bias occurrence at different time scales (daily to monthly) is evaluated and addressed. Then, the coverage area of the gauge station, along with the impact of the dominant wind direction on the shape of the coverage area, is evaluated. Ultimately, available actual ET maps derived from the METRIC model are used to estimate spatial effective rainfall. The results show that the spatial resolutions of TRMM and PRISM are not adequate at the farm level, while Daymet is a better fit but lacks the adequate latency versus TRMM and PRISM. When compared against local weather station records, all three spatial datasets were found to have a bias that decreases at coarser temporal intervals. However, the performance of Daymet and PRISM at the monthly time step is acceptable, and they can be used for water resource management at the farm level. The adequacy of an existing gauge station for a given farm location depends on the willingness to accept the risk of the bias associated with a non-persistent, non-symmetric gauge coverage area that is highly correlated with the dominant wind direction. Among all goodness of fit statistics considered in the study, the interpretation of the summation of error makes more sense for quantifying the rainfall bias and risk for the user. Finally, based on the USDA-SCS model and actual spatial ET, overall, seasonal effective rainfall tends to be less than 60% of total rainfall for agricultural lands.

Abstract 353: Performance of SOFA Score to Predict Mortality at Hospital Discharge After Cardiac Arrest

Introduction: Cardiac arrest (CA) outcomes, when dichotomized as survival/non-survival, limit statistical power of interventional studies and do not acknowledge hospital-level factors independent of post-CA sequelae. We explored the Sequential Organ Failure Assessment (SOFA) score at 72 hours post-CA as a surrogate outcome measure for mortality. We also assessed methods to account for death &lt;72 hours post-CA in SOFA score computation. Methods: This was a single center retrospective study of post-CA patients from 1/08-12/17. SOFA score components were abstracted at baseline, 24, 48, and 72h post-CA. Thirteen ways of accounting for missing data were assessed. The outcome was mortality at hospital discharge. Model performance was assessed using area under the receiver-operator characteristic (AUC) curves and Hosmer-Lemeshow goodness of fit statistics. Results: Of 847 patients, 528 (62%) had complete baseline SOFA scores and 205 (24%) had complete scores at 72h. Death &lt;72h occurred in 28%; 45% survived to hospital discharge. SOFA score at 72h without accounting for death had an AUC of 0.62. The best performing SOFA model at 72h with good calibration imputed a 20% increase over the last observed SOFA score in patients who expired &lt;72h with an AUC of 0.79 (95% CI: 0.74-0.83). In terms of change in SOFA at 72h from baseline, the best performing model with good calibration imputed death &lt;72h as the highest possible score (AUC: 0.88 [95% CI: 0.84-0.92]). These results were consistent when analyzing in- and out-of-hospital CA separately, although the change from baseline model was not well calibrated in in-hospital arrests. Conclusions: Without consideration of death, SOFA scores at 72 hours post-CA perform poorly. Imputing for early mortality improved the model. If this imputation structure is validated prospectively, SOFA could provide a scoring system to predict death at hospital discharge and serve as a surrogate outcome measure in interventional studies.

The Cepheid period–luminosity–metallicity relation based on Gaia DR2 data

Aims. We use parallax data from the Gaia second data release (GDR2), combined with parallax data based on HIPPARCOS and HST data, to derive the period–luminosity–metallicity (PLZ) relation for Galactic classical cepheids (CCs) in the V, K, and Wesenheit WVK bands. Methods. An initial sample of 452 CCs are extracted from the literature with spectroscopically derived iron abundances. Reddening values, classifications, pulsation periods, and mean V- and K-band magnitudes are taken from the literature. Based on nine CCs with a goodness-of-fit (GOF) statistic smaller than 8 and with an accurate non-Gaia parallax (σπ comparable to that in GDR2), a parallax zero-point offset of −0.049 ± 0.018 mas is derived. Selecting a GOF statistic smaller than 8 removes about 40% of the sample most likely related due to binarity. Excluding first overtone and multi-mode cepheids and applying some other criteria reduces the sample to about 200 stars. Results. The derived PL(Z) relations depend strongly on the parallax zero-point offset. The slope of the PL relation is found to be different from the relations in the LMC at the 3σ level. Fixing the slope to the value found in the LMC leads to a distance modulus (DM) to the LMC of order 18.7 mag, larger than the canonical distance. The canonical DM of around 18.5 mag would require a parallax zero-point offset of order −0.1 mas. Given the strong correlation between zero point, period and metallicity dependence of the PL relation, and the parallax zero-point offset there is no evidence for a metallicity term in the PLZ relation. Conclusions. The GDR2 release does not allow us to improve on the current distance scale based on CCs. The value of and the uncertainty on the parallax zero-point offset leads to uncertainties of order 0.15 mag on the distance scale. The parallax zero-point offset will need to be known at a level of 3 μas or better to have a 0.01 mag or smaller effect on the zero point of the PL relation and the DM to the LMC.

Cryptosporidium concentrations in rivers worldwide

Cryptosporidium is a leading cause of diarrhoea and infant mortality worldwide. A better understanding of the sources, fate and transport of Cryptosporidium via rivers is important for effective management of waterborne transmission, especially in the developing world. We present GloWPa-Crypto C1, the first global, spatially explicit model that computes Cryptosporidium concentrations in rivers, implemented on a 0.5 × 0.5° grid and monthly time step. To this end, we first modelled Cryptosporidium inputs to rivers from human faeces and animal manure. Next, we use modelled hydrology from a grid-based macroscale hydrological model (the Variable Infiltration Capacity model). Oocyst transport through the river network is modelled using a routing model, accounting for temperature- and solar radiation-dependent decay and sedimentation along the way.Monthly average oocyst concentrations are predicted to range from 10−6 to 102 oocysts L−1 in most places. Critical regions (‘hotspots’) with high concentrations include densely populated areas in India, China, Pakistan and Bangladesh, Nigeria, Algeria and South Africa, Mexico, Venezuela and some coastal areas of Brazil, several countries in Western and Eastern Europe (incl. The UK, Belgium and Macedonia), and the Middle East. Point sources (human faeces) appears to be a more dominant source of pollution than diffuse sources (mainly animal manure) in most world regions.Validation shows that GloWPa-Crypto medians are mostly within the range of observed concentrations. The model generally produces concentrations that are 1.5–2 log10 higher than the observations. This is likely predominantly due to the absence of recovery efficiency of the observations, which are therefore likely too low. Goodness of fit statistics are reasonable. Sensitivity analysis showed that the model is most sensitive to changes in input oocyst loads.GloWPa-Crypto C1 paves the way for many new opportunities at the global scale, including scenario analysis to investigate the impact of global change and management options on oocysts concentrations in rivers, and risk analysis to investigate human health risk.

Statistical trend analysis and forecast modeling of air pollutants

The study provides a statistical trend analysis of different air pollutants using Mann-Kendall and Sen’s slope estimator approach on past pollutants statistics from air quality index station of Varanasi, India. Further, using autoregressive integrated moving average model, future values of air pollutant levels are predicted. Carbon monoxide, nitrogen dioxide, sulphur dioxide, particulate matter particles as PM2.5 and PM10 are the pollutants on which the study focuses. Mann-Kendall and Sen’s slope estimator tests are used on summer (February-May), monsoon (June-September) and winter (October-January) seasonal data from year 2013 to 2016 and trend results and power of the slopes are estimated. For predictive analysis, different autoregressive integrated moving average models are compared with goodness of fit statistics, and the observed results stated autoregressive integrated moving average (1,1,1) as the best-suited for forecast modeling of different pollutants in Varanasi. Autoregressive integrated moving average model (1,1,1) is also used on the annual concentration levels to predict forthcoming year's annual pollutants value. Study reveals that PM 10 shows a rising trend with predicted approximate annual concentration of 273 µg/m3 and PM2.5, carbon monoxide, nitrogen dioxide and sulphur dioxide show a reducing trend with approximate annual concentration of 139 µg/m3, 1.37 mg/ m3, 38 µg/m3 and 17 µg/m3, respectively, by the year 2030. The study predicted carbon monoxide, nitrogen dioxide andsulphur dioxide concentrations are lower and PM10 and PM2.5 concentrations are much higher to the standard permissible limits in future years also, and specific measures are required to control emissions of these pollutants in Varanasi.

MODELO COMPATIBLE ALTURA - ÍNDICE DE SITIO PARA CUATRO ESPECIES DE PINO EN SANTIAGO PAPASQUIARO, DURANGO

&#x0D; &#x0D; &#x0D; La productividad forestal es un concepto biológico que puede expresarse matemáticamente. En el presente estudio se describen modelos compatibles altura dominante - índice de sitio para Pinus arizonica, P. durangensis, P. leiophylla y P. teocote en la región de Santiago Papasquiaro, Durango, una de las zonas forestales más importantes de México. Los datos utilizados fueron obtenidos de análisis troncales de 202 árboles dominantes. Se ajustaron los modelos de Korf, Hossfeld y Bertalanffy-Richards, para lo cual se utilizó el método de Diferencias Algebraicas Generalizadas (GADA). La principal ventaja de este método es que depende de la calidad de estación y no de un parámetro de los modelos, con lo que las curvas obtenidas son polimórficas y con múltiples asíntotas. Durante el ajuste se modeló la estructura del error a través de un modelo autorregresivo de segundo orden para corregir la dependencia de datos longitudinales. Con base en los estadísticos de ajuste y el análisis gráfico, se recomienda el modelo de Bertalanffy-Richards para calificar la calidad de estación de las cuatro taxa. La ecuación de este modelo es polimórfica con múltiples asíntotas e invariante con respecto a la edad de referencia, además estima directamente la altura dominante y el índice de sitio a cualquier altura y edad. El uso de estas ecuaciones permitirá mejorar significativamente la estimación actual del potencial productivo de las especies estudiadas.&#x0D; &#x0D; &#x0D;

Quantum Inspired Genetic Programming Model to Predict Toxicity Degree for Chemical Compounds

Cheminformatics plays a vital role to maintain a large amount of chemical data. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug design, food safety, and manufacturing chemical compounds. Toxicity prediction topic requires several new approaches for knowledge discovery from data to paradigm composite associations between the modules of the chemical compound; such techniques need more computational cost as the number of chemical compounds increases. State-of-the-art prediction methods such as neural network and multi-layer regression that requires either tuning parameters or complex transformations of predictor or outcome variables are not achieving high accuracy results. This paper proposes a Quantum Inspired Genetic Programming “QIGP” model to improve the prediction accuracy. Genetic Programming is utilized to give a linear equation for calculating toxicity degree more accurately. Quantum computing is employed to improve the selection of the best-of-run individuals and handles parsimony pressure to reduce the complexity of the solutions. The results of the internal validation analysis indicated that the QIGP model has the better goodness of fit statistics and significantly outperforms the Neural Network model.

Maps From Mud—Using the Multiple Scenario Approach to Reconstruct Land Cover Dynamics From Pollen Records: A Case Study of Two Neolithic Landscapes

Pollen records contain a wide range of information about past land cover, but translation from the pollen diagram to other formats remains a challenge. In this paper, we present LandPolFlow, a software package enabling Multiple Scenario Approach (MSA) based land cover reconstruction from pollen records for specific landscapes. It has two components: a basic Geographic Information System which takes grids of landscape constraints (e.g. topography, geology) and generates possible 'scenarios' of past land cover using a combination of probabilistic and deterministic placement rules to distribute defined plant communities within the landscape, and a pollen dispersal and deposition model which simulates pollen loading at specified points within each scenario and compares that statistically with actual pollen assemblages from the same location. Goodness of fit statistics from multiple pollen site locations are used to identify which scenarios are likely reconstructions of past land cover. We apply this approach to two case studies of Neolithisation in Britain, the first from the Somerset Levels and the second from Mainland, Orkney. Both landscapes contain significant evidence of Neolithic activity, but present contrasting contexts. In Somerset, wet-preserved Neolithic remains such as trackways are abundant, but little dry land settlement archaeology is known, and the pre-Neolithic landscape was extensively wooded. In Orkney, the Neolithic archaeology includes domestic and monumental stone-built structures forming a UNESCO World Heritage Site, and the pre-Neolithic landscape was largely treeless. Existing pollen records were collated from both landscapes and correlated within a new age model framework (presented elsewhere). This allowed pollen data to be grouped into 200 year periods, or “timeslices”, for reconstruction of land cover through time using the MSA. Reconstruction suggests that subtle but clear and persistent impacts of Neolithisation on land cover occurred in both landscapes, with no reduction in impact during periods when archaeological records suggest lower activity levels. By applying the methodology to specific landscapes, we critically evaluate the strengths and weaknesses and identify potential remedies, which we then expand into consideration of how simulation can be incorporated into palynological research practice. We argue that the MSA deserves a place within the palynologist’s standard tool kit.

Distribution of rain height over subtropical region: Durban, South Africa for satellite communication systems

Rain height is one of the significant parameters for prediction of rain attenuation for Earth-space telecommunication links, especially those operating at frequencies above 10 GHz. This study examines Three-parameter Dagum distribution of the rain height over Durban, South Africa. 5-year data were used to study the monthly, seasonal, and annual variations using the parameters estimated by the maximum likelihood of the distribution. The performance estimation of the distribution was determined using the statistical goodness of fit. Three-parameter Dagum distribution shows an appropriate distribution for the modeling of rain height over Durban with the Root Mean Square Error of 0.26. Also, the shape and scale parameters for the distribution show a wide variation. The probability exceedance of time for 0.01% indicates the high probability of rain attenuation at higher frequencies.

Establishing the reliability and validity of the ASSIST questionnaire: a South African sample perspective

Introduction. The Approaches and Study Skills Inventory for Students (ASSIST Short Version) was used to better understand the students’ approaches to learning in a mathematics classes.Method. Quantitave method was used in this study. Participants were 345 first and second year mathematics students from a university in the Gauteng province of South Africa. The reliability of the ASSIST Short Version was computed by Cronbach’s alpha. The coefficient values from scores of each group are presented with respect to the preliminary data phase and the post-intervention data phase. The validity of the ASSIST questionnaire was computed using content validity which involved the computation of factor analysis and confirmatory factor analysis which involved checking whether the theory developed constructs of Deep, Strategic and Surface approaches.Results. The results show that the reliability of ASSIST Short Version, the alpha values ranged between .75 and .83 (preliminary data phase) as well as .65 and .82 (post-intervention data phase). For the content validity the results show that the value of KMO = .769 was acceptable while the Bartlett’s test of sphericity was also statistically significant (p &lt; 0.001). In determining the factor structure from the data, Maximum likelihood factor analysis with Direct oblimin rotation was specified. Construct validity was ascertained by computing through AMOS a confirmatory factor analysis (CFA) using data from the post-intervention data phase. The results show that the Chi square (χ2) was statistically significant [χ2 = 210.94, df = 132, p &lt; .0001]. The goodness of fit statistics were: TLI = .948, the CFI = .947, and the RMSEA = .054. The results also shows the standardized parameter estimates of the model from the ASSIST questionnaire.Discussion and Conclusion. In terms of reliability of scores from the instrument, the alpha values were accepted for this study because they were comparable to those reported in literature. Validity was computed through factor analysis (content validity) and confirmatory factor analysis (construct validity). Before computing the factor structure of the ASSIST, Kaiser-Meyer-Olkin (KMO) and Bartlett’s test of sphericity were determined. The two values reported in this study indicated that computing the factor analysis was appropriate for the data. Using the Direct oblimin rotation consistently as in reported literature, a three factor solution was accepted in this study. Because these factors were consistent with those reported in literature content validity was acceptable in this study.