Statistical Assessment Tools Research Articles

Software Implementation of the Epps-Pulley Criterion in Matlab Modeling Environment

Purpose. Modeling systems and programming platforms provide ample opportunities for the use of statistical tools in research activities. Since the normal distribution is one of the most common distribution laws, the criterion for checking the sample for normality is in high demand among statistical assessment tools, among which the Epps-Pulley test has the status as one of the most powerful tests to check the deviation of the distribution from the normal one. There are a number of implementations of this test in the R and Python languages. However, this test is not implemented in one of the most popular Matlab modeling software. Thus, the purpose of this study is to develop a software implementation of the Epps-Pulley criterion in the Matlab environment and verify the correctness of the performed calculations.Materials and Methods. We implemented the calculation of Epps-Pulley statistics by two methods – classical, using cycles, and matrix-vector, using linear algebra operations. The classical method requires calculating the intermediate values necessary to obtain the criterion statistics using two independent cycles, the second cycle being a double one, in which one cycle is nested into the other. The matrix-vector method requires fewer code by performing calculations using linear algebra operations on matrices and vectors. We obtained critical statistical values for the sample size from 8 to 1000 observations with two-dimensional linear interpolation of tabular values. We used an approximation by a beta function of the third kind for a sample of over 1000 elements.Results. An assessment of the computational efficiency of the methods showed that the cyclic approach is about three times higher than the matrix-vector approach in terms of consumed time, which is presumably due to the processing of insignificant elements in triangular matrices when performing component-by-component operations. The correctness of the software implementation of the Epps-Pulley criterion was tested on several examples, which confirmed the compliance of the calculated values of the criterion statistics, as well as the critical values of statistics, with known data. We carried out a criterion statistical evaluation based on the empirical values of the error of the first kind. We obtained the error values correspondence to the specified significance levels. We performed comparative estimates of the Epps-Pulley test with the Anders-Darling and Shapiro-Wilk tests in terms of the criterion empirical power and tabulated the evaluation results. We published the software implementation of the Epps-Pulley test on the MATLAB Central Internet resource and for free use.Conclusion. We developed software implementation of the Epps-Pulley criterion as a new research tool that was previously unavailable in the Matlab modeling environment. We used the time spent on calculations to make a reasonable choice of the calculation algorithm for the criterion statistics. We confirmed correctness of the calculation algorithms by a set of selective checks and statistical estimates that showed the compliance with well-known theoretical provisions.

Sustainable innovation in self-compacted concrete: Integrating by-products and waste rubber for green construction practices

Due to increasing concern about global climate change and its negative influence on societies, there is a specific focus on the construction sector as the greatest contributor to greenhouse gas emissions. About 8–10% of global CO2 emissions come from cement production, which releases almost a ton of CO2 for every ton of cement. Growing demand for cement and concrete is attributed to rapid urbanization, industrialization, and economic growth, which also substantially impacts the depletion of natural resources. To address these challenges, a feasible approach is to utilize by-product materials and waste rubber as alternatives to cement and natural fine aggregates in producing self-compacted concrete (SCC). The utilization of various percentages of fly ash (FA), ground granulate blast furnace slag (GGBFS) as cement replacement, and crumb rubber (CR) from waste tires as a partial substitution of fine aggregate offers a suitable choice for the development of sustainable SCC. In addition, compressive strength (CS) is a vital property when considering other characteristics of concrete. Therefore, it is vital to develop reliable models for predicting the CS of SCC to achieve cost, time, and energy savings. Therefore, this research examines the impact of various contents of FA and GGBFS as cement (C) alternatives and CR as a sand replacement on the CS of SCC. To predict the CS of rubberized SCC mixtures, four different models were employed: linear regression (LR), multi-linear regression (MLR), full quadratic (FQ), and M5P-tree. To accomplish this, a comprehensive set of data comprising approximately 436 samples was analyzed to develop the models; as input variables, various mixture proportions, and curing ages were considered. To ensure the accuracy and reliability of the predictive models, several statistical assessments were performed. Based on the statistical assessment tools conducted in this study, the FQ model is considered the most effective model for forecasting the CS of SCC. Based on the statistical assessment tools, the FQ model was also implemented to forecast the splitting tensile and flexural strengths of SCC. The sensitivity analysis indicates that CR and GGBFS content are the best criteria for forecasting the CS of SCC utilizing this data set.

Surface response regression and machine learning techniques to predict the characteristics of pervious concrete using non-destructive measurement: Ultrasonic pulse velocity and electrical resistivity

It is crucial to assess the characteristics of pervious concrete even post-construction. The quality monitoring of such a procedure is tricky in pervious concrete that it is typically avoided. As a potential means of enhancing the aforementioned quality control, the current study investigates the possibility of predicting characteristics of pervious concrete through response surface methodology and machine learning techniques using non-destructive test measurement (ultrasonic velocity and electrical resistivity). A total of 225 datasets from the experimental study were taken for this study. To recognize the best reliable model for predicting characteristics of pervious concrete, response surface methodology up to sixth order polynomial and five different machine learning techniques were used as statistical assessment tools. Using both ultrasonic pulse velocity and electrical resistivity as predictors for estimating porosity and compressive strength via response surface methodology, using a quadratic model for porosity prediction and a cubic model for compressive strength prediction are recommended. The machine learning models used in the research exhibited superior performance compared to the response surface methodology. Among the many machine learning models evaluated in this study, boosted decision tree regression model better predicted porosity (R2 = 0.92) and compressive strength (R2 = 0.92) of pervious concrete. Therefore, prediction models for the characteristics of pervious concrete are created using non-destructive measurement and machine learning techniques, which may ensure that the construction sector can utilize the offered models without any theoretical expertise.

Soft computing techniques to predict the compressive strength of groundnut shell ash-blended concrete

Using groundnut shell ash (GSA) as a component in concrete mixtures is a viable approach to achieving sustainability in building practices. This particular kind of concrete has the potential to effectively mitigate the issues associated with high levels of CO2 emissions and embodied energy, which are primarily attributed to the excessive utilization of cement in conventional construction materials. When GSA is utilized as a partial replacement for cement, the strength characteristics of concrete are influenced not only by the quantity of GSA replacement but also by several other factors, including cement content, water-to-cement ratio, coarse aggregate content, fine aggregate content, and curing length. This work demonstrates a predictive model for the compressive strength (CS) of GSA mixed concrete using ML methods. The models were constructed with 297 datasets obtained from published literature. These datasets included various input variables such as cement content, GSA content, fine aggregate content, coarse aggregate content, water need, and curing duration. The output variable included in the models was the CS of concrete. In this study, a set of seven machine learning algorithms was utilized as statistical assessment tools to identify the most precise and reliable model for predicting the CS of GSA mixed concrete. These techniques included linear regression, full quadratic model, artificial neural network, boosted decision tree regression, random forest regression, K nearest neighbors, and support vector regression. The present study evaluated several machine learning models, and it was shown that the random forest regression model had superior performance in forecasting the CS of GSA mixed concrete. The train data’s R2 is 0.91, with RMSE of 2.48 MPa. Similarly, for the test data, the R2 value is 0.89, with an RMSE of 2.42 MPa. The sensitivity analysis results of the random forest regression model indicate that the cement content primarily drives the material’s CS. Subsequently, the curing period and GSA content significantly impact the CS. This work systematically evaluates the CS of GSA mixed concrete, contributing to the existing body of knowledge and practical implementation in this domain.

Soft Computing and Machine Learning-Based Models to Predict the Slump and Compressive Strength of Self-Compacted Concrete Modified with Fly Ash

Self-compacted concrete (SCC) is a special type of concrete; it is a liquid mixture appropriate for structural elements with excessive reinforcement without vibration. SCC is commonly produced by increasing the paste volume and cement content. As cement production is one of the huge factors in releasing CO2 gas into the atmosphere, by-product materials such as fly ash are utilized as a cement replacement in concrete. In addition to the positive environmental impact, fly ash can maintain an excellent fresh and mechanical property. Incorporating fly ash into self-compacted concrete is widely applied in practice. However, its application is frequently limited by a lack of knowledge about the mixed material gained from laboratory tests. The most significant mechanical property for all concrete types is compressive strength (CS); also, the slump flow diameter (SL) in the fresh state is a crucial property for SCC. Hence, developing an accurate and reliable model for predicting the CS and SL is very important for saving time and energy, as well as lowering the cost. This research study proposed a projection of both the CS and SL of SCC modified with fly ash by three different model approaches: Nonlinear regression (NLR), Multi-Linear regression (MLR), and Artificial Neural Networks (ANN). In this regard, two different datasets were collected and analyzed for developing models: 308 data samples were used for predicting the CS, and 86 data samples for the SL. Each database included the same five independent parameters. The ranges for CS prediction were: cement (134.7–583 kg/m3), water-to-binder ratio (0.27–0.9), fly ash (0–525 kg/m3), sand (478–1180 kg/m3), coarse aggregate (578–1125 kg/m3), and superplasticizer (0–1.4%). The dependent parameter (CS) ranged from 9.7 to 81.3 MPa. On the other hand, the data ranges for the SL prediction included independent parameters such as cement (83–733 kg/m3), water-to-binder ratio (0.26–0.58), fly ash (0–468 kg/m3), sand (624–1038 kg/m3), coarse aggregate (590–966 kg/m3), and superplasticizer (0.087–21.84%). Also, the dependent parameter (SL) ranged from 615 to 800 m. Various statistical assessment tools, such as the coefficient of determination (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Objective value (OBJ), and Scatter Index (SI), were used to evaluate the performance of the developed models. The results showed that the ANN model best predicted the CS and SL of SCC mixtures modified with fly ash. Furthermore, the sensitivity analysis demonstrated that the cement content is the most effective factor in predicting the CS and SL of SCC mixtures.

Optimization of compressive strength of cementitious matrix composition of Textile Reinforced Concrete–Taguchi approach

In this paper, an experimental investigation is carried out to optimize the cementitious matrix proportions of the Textile Reinforced Concrete by using Taguchi’s Design of Experiment Methodology. For optimization, the parameters influencing the cementitious matrix were defined based on the available literatures. The experiments have been designed using an L27 Orthogonal Array with seven factors having three levels for each factor. The seven factors are cement content, fly ash, silica fume super-plasticizer and silica sand of three varying grades. The water content required for all the mixes was varied and it was calculated by conducting the flow table test. Using the suggested orthogonal array the effect of compressive strength was examined. The obtained results have been analyzed using MINITAB statistical software to draw a robust conclusion. Taguchi analysis was employed to find the optimal mix and the findings result that the optimum mix shows a maximum of 5% higher strength than the other mixes. Analysis of Variance results revealed that Cement amount (18.58%) and Superplasticizer (15.76%) are the most influencing parameter with a statistical significance at 97.6% confidence level with respect to compressive strength. The developed regression model was assessed using different statistical tools such as the coefficient of determination, mean absolute error, and root mean square error. Based on these statistical assessment tools, results revealed that the predicted values provided satisfactory results with the observed values.

Metamodel techniques to estimate the compressive strength of UHPFRC using various mix proportions and a high range of curing temperatures

• The impact of mix proportions on the compressive strength of UHPFRC. • Multi-scale models were developed to predict the compressive strength of UHPFRC. • A difference-based method for constructing the compressive strength targets is proposed. • Models of use to improve the prediction accuracy of UHPFRC learning are proposed. In order to predict the compressive strength (σ c ) of Ultra-high performance fiber reinforced concrete (UHPFRC), developing a reliable and precise technique based on all main concrete components is a cost-effective and time-consuming process. To predict the UHPFRC compressive strength, four different soft computing techniques were developed, including the nonlinear- relationship (NLR), pure quadratic, M5P-tree (M5P), and artificial neural network (ANN) models. Thus, 274 data were collected from previous studies and analyzed to evaluate the effect of 11 variables that impact the compressive strength, including curing temperature. The performance of the predicted models was evaluated using several statistical assessment tools. According to the findings, ANN results performed more suitable than other models with the lowest root mean square error (RMSE) and highest coefficient of determination (R2) value. According to the sensitivity analysis, the most variables that affect the compressive strength prediction of UHPFRC are a curing temperature with a percentage of 17.36%, the fiber content of 17.13%, and curing time of 15.13%.

Proposing several model techniques including ANN and M5P-tree to predict the compressive strength of geopolymer concretes incorporated with nano-silica.

Geopolymers are innovative cementitious materials that can completely replace traditional Portland cement composites and have a lower carbon footprint than Portland cement. Recent efforts have been made to incorporate various nanomaterials, most notably nano-silica (nS), into geopolymer concrete (GPC) to improve the composite's properties and performance. Compression strength (CS) is one of the essential properties of all types of concrete composites, including geopolymer concrete. As a result, creating a credible model for forecasting concrete CS is critical for saving time, energy, and money, as well as providing guidance for scheduling the construction process and removing formworks. This paper presents a large amount of mixed design data correlated to mechanical strength using empirical correlations and neural networks. Several models, including artificial neural network, M5P-tree, linear regression, nonlinear regression, and multi-logistic regression models, were utilized to create models for forecasting the CS of GPC incorporated with nS. In this case, about 207 tested CS values were collected from literature studies and then analyzed to promote the models. For the first time, eleven effective variables were employed as input model parameters during the modeling process, including the alkaline solution to binder ratio, binder content, fine and coarse aggregate content, NaOH and Na2SiO3 content, Na2SiO3/NaOH ratio, molarity, nS content, curing temperatures, and ages. The developed models were assessed using different statistical tools such as root mean squared error, mean absolute error, scatter index, objective function value, and coefficient of determination. Based on these statistical assessment tools, results revealed that the ANN model estimated the CS of GPC incorporated with nS more accurately than the other models. On the other hand, the alkaline solution to binder ratio, molarity, NaOH content, curing temperature, and ages were those parameters that have significant influences on the CS of GPC incorporated with nS.

Stress-stain behavior, elastic modulus, and toughness of the soilcrete modified with powder polymers

This article discusses the effect of two types of polymer on the stress–strain (σ–ε) behavior, elastic modulus and total toughness of soilcrete. Soilcrete properties modified with two types of polymer content up to 0.20% (% wt.) were tested. The σ–ε behavior of modified soilcrete with polymeric admixtures was examined for curing periods up to 28 days. Additional polymer content maintains the flowability of soilcrete with the range of 105 to 110% as recommended by ASTM C109, and the water/cement ratio (w/c) decreased by 14.5 to 25.5% with the addition of polymer. The compression strength of soilcrete increased by 72 to 153% when 0.20% of the polymer was added to the soilcrete, and therefore the compression toughness was increased with increasing polymer content. Modifying the soilcrete with polymer content increased the initial elastic modulus (Ei) of soilcrete by 55% to 195% according to polymeric admixture types, curing age (t), w/c, and polymeric admixture percent. With increasing polymer content, the soilcrete samples become brittle, and the strain at failure is reduced. Adding polymeric admixture creates an amorphous gel that fills the spaces between cement particles with working fibers net or meshes covering the sand particles, which cause a reduction in the voids, porosity and increasing the density of the soilcrete; subsequently, the mechanical properties significantly increased. The nonlinear Vipulanandan p-q model was employed to predict the σ–ε relationship of the modified soilcrete with polymer and was compared with the β model prediction. Statistical assessment tools were used to evaluate the compressive stress–strain models and nonlinear approaches to predict the compressive strength and the modulus of elasticity as a function of water/cement ratio, curing age, and amount of the polymers.

Comparison between the Classical Symphony and Self Selected Music on Muscle Endurance in Young Obese Males

Introduction: Music captures attention, triggers a wide range of emotions, regulates mood and arousal,increases work output, induces states of higher functioning, reduces inhibitions and encourages rhythmicmovement. Music has ergo-genic effect as well.Aim: To evaluate the comparison between classical symphony music and self selected music on muscularendurance levels in young obese males.Materials and Method: BMI assessment was performed for all participants and the 30 individuals fallingunder obese category were recruited in the study (BMI ›30 Kg/m2). The participants were asked to listen toclassical symphony music 15 min for 20 days, the muscle endurance test was done by Mosso’s ergographyand hand dynamometry. After a month the same participants were recalled. The participants were asked tolisten to any music of their choice and were grouped under self-selected music. Muscle endurance test wasdone by hand dynamometry and Mosso’s ergography method. Both the values of muscle endurance werecompared by using independent paired t test and SPSS software statistical assessment tools were used.Result: After using self-selected music the muscle endurance significantly increased using hand dynamometry(p<0.005) and Mosso’s ergography (p<0.05).Conclusion: There may be an improved functioning of neural system due to the influence music. Therefore,music may be used as an additional aid for obese individuals to maintain their skeletal muscle enduranceduring any physical activity and aerobic exercise

Hybrid soft computational approaches for modeling the maximum ultimate bond strength between the corroded steel reinforcement and surrounding concrete

The capacity efficiency of load carrying with the accurate serviceability performances of reinforced concrete (RC) structure is an important aspect, which is mainly dependent on the values of the ultimate bond strength between the corroded steel reinforcements and the surrounding concrete. Therefore, the precise determination of the ultimate bond strength degradation is of paramount importance for maintaining the safety levels of RC structures affected by corrosion. In this regard, hybrid intelligence and machine learning techniques are proposed to build a new framework to predict the ultimate bond strength in between the corroded steel reinforcements and the surrounding concrete. The proposed computational techniques include the multilayer perceptron (MLP), the radial basis function neural network and the genetic expression programming methods. In addition to that, the Levenberg–Marquardt (LM) deterministic approach and two meta-heuristic optimization approaches, namely the artificial bee colony algorithm and the particle swarm optimization algorithm, are employed in order to guarantee an optimum selection of the hyper-parameters of the proposed techniques. The latter were implemented based on an experimental published database consisted of 218 experimental tests, which cover various factors related to the ultimate bond strength, such as compressive strength of the concrete, concrete cover, the type steel, steel bar diameter, length of the bond and the level of corrosion. Based on their performance evaluation through several statistical assessment tools, the proposed models were shown to predict the ultimate bond strength accurately; outperforming the existing hybrid artificial intelligence models developed based on the same collected database. More precisely, the MLP-LM model was, by far, the best model with a determination coefficient (R2) as high as 0.97 and 0.96 for the training and the overall data, respectively.

«Green Economy»: Emerging National Models, Estimations, Trends in EU and CIS

Green economy as a transitional phase to sustainable development has gained impetus after adoption of the 2030 sustainable development goals.The article contains analysis of the existing conceptual foundations of the green growth together with the assessment tools thereof on the one part and the results of the green economy models under gradual formation in the EU and the CIS on the other part.Such approach allowed the authors to set forth the most urgent lines of development of the statistical assessment tools for the green economy achievements with the purpose of its efficient implementation.

Some recommendations for developing multidimensional computerized adaptive tests for patient-reported outcomes

PurposeMultidimensional item response theory and computerized adaptive testing (CAT) are increasingly used in mental health, quality of life (QoL), and patient-reported outcome measurement. Although multidimensional assessment techniques hold promises, they are more challenging in their application than unidimensional ones. The authors comment on minimal standards when developing multidimensional CATs.MethodsPrompted by pioneering papers published in QLR, the authors reflect on existing guidance and discussions from different psychometric communities, including guidelines developed for unidimensional CATs in the PROMIS project.ResultsThe commentary focuses on two key topics: (1) the design, evaluation, and calibration of multidimensional item banks and (2) how to study the efficiency and precision of a multidimensional item bank. The authors suggest that the development of a carefully designed and calibrated item bank encompasses a construction phase and a psychometric phase. With respect to efficiency and precision, item banks should be large enough to provide adequate precision over the full range of the latent constructs. Therefore CAT performance should be studied as a function of the latent constructs and with reference to relevant benchmarks. Solutions are also suggested for simulation studies using real data, which often result in too optimistic evaluations of an item bank’s efficiency and precision.DiscussionMultidimensional CAT applications are promising but complex statistical assessment tools which necessitate detailed theoretical frameworks and methodological scrutiny when testing their appropriateness for practical applications. The authors advise researchers to evaluate item banks with a broad set of methods, describe their choices in detail, and substantiate their approach for validation.

Optimization of engineering student learning and assessment by cognitive methods

This study addresses the learning objectives and the student outcomes of industrial engineering students by examining them at three different levels: course level, program level, and graduate level. Three learning domains are developed and analyzed for this purpose to assess the performance of students during and after graduation. These domains are labeled as the house of cognitive learning, which shows the level of learning, its outcome elements, and the depth of understanding. In the higher education system, the correct assessment of student learning is always considered as a challenging task. The aim of this study was to develop an integrated integer-programming algorithm to accurately determine the learning level of students. The method incorporates quality control charts and statistical assessment tools to present the findings. In this study, level of learning is calculated as a learning index that presents the contribution of a course to the respective student outcomes. Moreover, it depicts the overall achievements of students during their learning. Therefore, another aim of this study was to explore how to better utilize the collected data for the assessment of learning level. The outcomes of algorithm and statistical approaches are quite encouraging for the evaluation of students' learning, thus improving the quality of engineering program.

A Statistical Assessment Tool for Electricity Distribution Networks

Many interesting studies are being carried out globally to analyze the impact of new low carbon technologies on the energy distribution networks. Most of these studies are focused on synthetic and real grid samples. Research findings of such studies are case specific and have very limited applicability to other networks making them unsuitable to make generalized conclusions. An ensemble of realistic distribution network models with similar topological and technical/electrical properties can provide a good basis to conduct statistical studies on an electricity distribution network, opening up the opportunity to make robust conclusions in a more generalized manner. With these motivations, this paper presents a methodology to develop a statistical assessment tool to facilitate large scale simulation studies on electricity distribution networks. A simple case study is presented to demonstrate how such a framework can be used to perform a statistical study to analyze the impact of new low carbon technologies on electricity distribution networks.

An Intelligent Wireless Sensor and Actuator Network System for Greenhouse Microenvironment Control and Assessment

Purpose: As application-specific wireless sensor networks are gaining popularity, this paper discusses the development and field performance of the GHAN, a greenhouse area network system to monitor, control, and access greenhouse microenvironments. GHAN, which is an upgraded system, has many new functions. It is an intelligent wireless sensor and actuator network (WSAN) system for next-generation greenhouses, which enhances the state of the art of greenhouse automation systems and helps growers by providing them valuable information not available otherwise. Apart from providing online spatial and temporal monitoring of the greenhouse microclimate, GHAN has a modified vapor pressure deficit (VPD) fuzzy controller with an adaptive-selective mechanism that provides better control of the greenhouse crop VPD with energy optimization. Using the latest soil-matrix potential sensors, the GHAN system also ascertains when, where, and how much to irrigate and spatially manages the irrigation schedule within the greenhouse grids. Further, given the need to understand the microclimate control dynamics of a greenhouse during the crop season or a specific time, a statistical assessment tool to estimate the degree of optimality and spatial variability is proposed and implemented. Methods: Apart from the development work, the system was field-tested in a commercial greenhouse situated in the region of Punjab, India, under different outside weather conditions for a long period of time. Conclusions: Day results of the greenhouse microclimate control dynamics were recorded and analyzed, and they proved the successful operation of the system in keeping the greenhouse climate optimal and uniform most of the time, with high control performance.

Informing the judgments of fingerprint analysts using quality metric and statistical assessment tools

The aim of this research was to evaluate how fingerprint analysts would incorporate information from newly developed tools into their decision making processes. Specifically, we assessed effects using the following: (1) a quality tool to aid in the assessment of the clarity of the friction ridge details, (2) a statistical tool to provide likelihood ratios representing the strength of the corresponding features between compared fingerprints, and (3) consensus information from a group of trained fingerprint experts. The measured variables for the effect on examiner performance were the accuracy and reproducibility of the conclusions against the ground truth (including the impact on error rates) and the analyst accuracy and variation for feature selection and comparison. The results showed that participants using the consensus information from other fingerprint experts demonstrated more consistency and accuracy in minutiae selection. They also demonstrated higher accuracy, sensitivity, and specificity in the decisions reported. The quality tool also affected minutiae selection (which, in turn, had limited influence on the reported decisions); the statistical tool did not appear to influence the reported decisions.

A decision support system for environmental effects monitoring

The Environmental Effects Monitoring (EEM) Statistical Assessment Tool (SAT) Decision Support System (DSS) has been developed to provide a user-friendly data analysis, display and decision support tool for Canada's federal environmental effects monitoring program for the pulp and paper and mining industries. The target users include industries, consultants, regional EEM coordinators, National EEM Office and scientists involved in EEM-related research. The tool allows the assessment of the effects of effluent from industrial or other sources on fish and benthic populations. Effect endpoints, which are used as indicators of potentially important effluent effects, are measured at effluent-exposed sites and are compared statistically to measures at reference sites, in order to determine if changes have occurred and the magnitude of the changes. The main driver of the EEM-SAT DSS is its rule-based expert system. The results are used in assessing the adequacy of existing regulations for protecting aquatic environments.

Comparing Discriminant Analysis and Logistic Regression Model as a Statistical Assessment Tools of Arsenic and Heavy Metal Contents in Cockles

Two statistical techniques; discriminant analysis (DA) and logistic regression model were used to analyze the concentration of arsenic and heavy metal contents in cockles (Anadara granosa) from two estuaries in the state of Penang, Malaysia. This study was undertaken to understand the interrelationship between different parameters and also to identify probable source component in order to explain the pollution status. Arsenic (As), chromium (cr), cadmium (cd), zinc (zn), copper (cu) and lead (pb) were analyzed using a graphite flame atomic absorption spectrophotometer (GF-AAS) whilst mercury (Hg) was analyzed using a cold vapor atomic absorption spectrophotometer (CV-AAS). Logistic regression model showed that only two explanatory variables Zn (p < 0.01) and Cd (p

Statistical Approach of Assessing the Reliability of Glucose Sensors: The GLYCENSIT Procedure

In healthcare, patients with diabetes are instructed on how to apply intensified insulin therapy in an optimal manner. Tight blood glucose control is also performed on patients treated in the intensive care unit (ICU). Different blood glucose meters and glucose monitoring systems (GMSs) are used to achieve this goal, and some may lack reliability. The GLYCENSIT procedure is a statistical assessment tool we are proposing for evaluating the significant difference of paired glucose measurements. The performance of the GlucoDay system in the ICU is analyzed with GLYCENSIT. THE GLYCENSIT ANALYSIS COMPRISES THREE PHASES: testing possible persistent measurement behavior as a function of the glycemic range, testing the number of measurement errors with respect to a standard criterion for binary assessment of glucose sensors, and computing the tolerance intervals that indicate possible test sensor deviations for new observations. The probability of the tolerance intervals directly reflects the number of samples and additionally improves current assessment techniques. The method can be tuned according to the clinician's preferences regarding significance level, tolerance level, and glycemic range cutoff values. The measurement behavior of the GlucoDay sensor is found to be persistent but inaccurate and returns wide tolerance intervals, suggesting that the GlucoDay sensor may not be sufficiently reliable for glycemia control in the ICU. The GLYCENSIT procedure aims to serve as statistical guide for clinicians in the assessment of glucose sensor devices.