This research will be used to show how factor screening experiments can be augmented in order to improve the power of a specific factor of interest. This work is done in two phases. The first phase uses an empirical approach to study the relationship between the augmented design and statisticalpower of terms in a model. In the second phase this knowledge is leveraged to develop an R code for calculating the power of all terms in the linear regression model when provided with any augmentation strategy. The experimentaldesign is generated using JMP software. The integration of this method with the existing design of experiment framework is also attempted with the help of case studies. A schematic diagram of the method for integration is shown in Figure 8.
participants were required to yield 80% power in a single voxel for typical block design activation levels. However, in fMRI the multiple comparisons problem and the associated potential for high levels of false positives requires us to go to stricter thresholds where they demonstrated that twice the number of participants would be needed to maintain the same level of statisticalpower. This recommended number of participants is higher than the vast majority of fMRI studies but is similar to independent assessments based on empirical data from a visual/audio/motor task (18) and from an event-related cognitive task (19). The Murphy and Garavan study (19) found that statisticalpower is surprisingly low at typical sample sizes (n<20) but that voxels that were significantly active from these smaller sample sizes tended to be true positives. Although voxelwise overlap may be poor in tests of reproducibility, the locations of activated areas provide some optimism for studies with typical sample sizes. It was found that the similarity between centres-of- mass for activated regions does not increase after more than 20 participants are included in the statistics. The conclusion can be drawn from this paper that a study with fewer numbers of participants than Desmond and Glover propose is not necessarily inaccurate but it is incomplete: activated areas are likely to be true positives but there will be a sizable number of false negatives. Needless to say, the required number of participants is influenced by the effect size which, in turn, is affected by the sensitivity of the
Practical Assessment, Research & Evaluation, Vol 14, No 10 Page 2
Konstantopoulos, Power Tables
two-sample t-tests presented in Cohen (1988) to compute the power of the test of the treatment effect in two- and three-level experimental designs (Barcikowski, 1981; Hedges & Hedberg, 2007). Because power tables are easy to use, the power computations of the test of the treatment effect in nested designs are greatly simplified. To achieve this, one simply needs to select the appropriate sample size and effect size, because typically power values in power tables are provided on the basis of sample sizes and effect sizes. This paper provides ways of selecting sample sizes and effect sizes in two- and three-level cluster and block randomized designs that simplify power computations by making use of power tables. First, I discuss clustering in multilevel designs. Second, I define the effect size and sample size for a two-sample two-tailed t-test in two- and three-level cluster randomized designs. Then, I define the effect size and sample size for a one-sample two-tailed t-test in two- and three-level block randomized designs. For simplicity, I discuss balanced designs with one treatment and one control group. To illustrate the methods I use examples from education that involve students, classrooms, and schools.
This work is licensed under the Creative Commons Attribution International License (CC BY).
The StatisticalExperimentalDesign techniques are the most powerful tools for the chemical reac- tors experimental modeling. Empirical models can be formulated for representing the chemical behavior of reactors with the minimal effort in the necessary number of experimental runs, hence, minimizing the consumption of chemicals and the consumption of time due to the reduction in the number of experimental runs and increasing the certainty of the results. Four types of nonthermal plasma reactors were assayed seeking for the highest efficiency in obtaining hydrogen and ethy- lene. Three different geometries for AC high voltage driven reactors, and only a single geometry for a DC high voltage pulse driven reactor were studied. According to the fundamental principles of chemical kinetics and considering an analogy among the reaction rate and the applied power to the plasma reactor, the four reactors are modeled following the classical chemical reactors design to understand if the behavior of the nonthermal plasma reactors can be regarded as the chemical reactors following the flow patterns of PFR (Plug Flow Reactor) or CSTR (Continuous Stirred Tank Reactor). Dehydrogenation is a common elimination reaction that takes place in nonthermal plasmas. Owing to this characteristic, a paraffinic heavy oil with an average molecular weight cor- responding to C 15 was used to study the production of light olefins and hydrogen.
Investigations were carried out to find out the equilibrium, kinetics and Thermodynamic parameters for biosorption of cadmium from an aqueous solution using Psidiumguajava leaf powder Percentage removal of cadmium from the aqueous solution was increased significantly with increase in pH from 1 to 4.Thereafter percentage removal was decreased for further increase in pH. In the range of variables studied, percentage removal was increased from 74.0 % (3.33 mg/g) to 95.11 % (4.28 mg/g). The kinetic studies showed that the biosorption of cadmium was better described by pseudo second order kinetics. The percentage biosorption decreases with increase in temperature. The investigation also revealed the exothermic nature of biosorption as ∆H was negative, the reversibility of biosorption initially as ∆S was negative and biosorption tending towards irreversibility as ∆S was increasing, the spontaneity of the biosorption as ∆G was negative, and increase in ∆G value with an increase in temperature indicates that the biosorption of cadmium was less favorable.
One of common shear tests involves a Warner Bratzler or knife blade to cut the sample while measuring the maximum force. For this reason, the texturometer Stable MR hosystems Texture Technologies Texture Analyzer TA-XT2i was used to determine the share force at midpoint of each fillet of each group using Warner-Bratzler shear blade. A v-shaped blade was assembled to the TA.XT2i Texture Analyser. The blade was set to travel 30 mm beyond the point of the 1st contact with the sample; this was sufficient to perform complete shear of the sample. The samples were positioned so that the blade cut the sample block in half, perpendicular to the orientation of muscle fibers. The probe performed a single cut before returning to the start position. Downward probe speed was 2mm/s with a 5g force trigger to commence recording, the maximum peak force (N) required to shear through the sample was recorded as shear force. The maximum peak force (N) was chosen as a measurement of firmness. All reported results are based on average values of five replicates.
The third research objective combined decision making theory and Particle Swarm Optimization (PSO) and featured nested PSO algorithms and three criterion functions with application to the Michaelis-Menten model and the two parameter logistic regression model.
Comparisons were made among the quality of solutions found from the three criteria. The three criteria reflect different levels of “optimism” and “pessimism” associated with the decision making process in the PSO algorithm and may be adjusted to achieve different solutions to the design problem. For example, when using the “index of optimism” criterion, the settings of 0.3 (the decision maker is relatively pessimistic), 0.5 (the decision maker compromises between the pessimistic and optimistic case) and 0.7 (the decision maker is relatively optimistic) were used, respectively, and solution quality compared on the design objective function.
Electric power is supplied to the heater through variac. The voltage and the current are measured by digital panel meters. A K-type Chromel-Alumel thermocouple junction is soldered on the target plate at its extreme end to know about the steady state. The output of the thermocouple is measured by ‘Agronic’ milivoltmeter. Similarly,thermocouples are attached on cooled surface along the flow direction from the stagnation point with an interval of 5 mm between each ther mocouple to measure wall temperature distribution.
However, in contrast to the classical experimentaldesign ( Quinn and Keough, 2002 ) guided by certain theoretical optimality (e.g. the maximum power of a statistical test), the scRNA-seq experimentaldesign is impeded by various sources of data noises, making a rea- sonable theoretical analysis tremendously difficult ( Kolodziejczyk et al., 2015 ; Pierson and Yau, 2015 ). Especially, scRNA-seq data are characterized by excess zeros resulted from dropout events, in which a gene is expressed in a cell but its mRNA transcripts are un- detected. As a result, many commonly used statistical assumptions are not directly applicable to modeling scRNA-seq data. For ex- ample, Baran-Gale et al. proposed using a Negative Binomial model to estimate the number of cells to sequence, so that the resulting ex- periment is expected to capture at least a specified number of cells from the rarest cell type ( Baran-Gale et al., 2017 ). However, the esti- mation accuracy depends on the idealized Negative Binomial model assumption, which real scRNA-seq data usually do not closely fol- low ( Supplementary Fig. S1 ). There is also a theoretical investigation of the cell-depth trade-off based on the Poisson assumption of gene read counts and a specific list of genes of interests ( Zhang et al., 2018 ). In contrast to model-based design approaches ( Dumitrascu et al., 2018 ), multiple scRNA-seq studies used descriptive statistics to provide qualitative guidance instead of well-defined optimization criteria for experimentaldesign ( Gru¨n and van Oudenaarden, 2015 ; Rizzetto et al., 2017 ). However, because the descriptive statistics were proposed from diverse perspectives, their resulting experimen- tal designs are difficult to unify to guide practices. For example, one study reported that the sensitivity of most protocols saturates at ap- proximately one million reads per cell ( Ziegenhain et al., 2017 ), while another study found that the saturation occurs at around 4.5 million reads per cell ( Svensson et al., 2017 ). The reason for this dis- crepancy is that the two studies defined the sensitivity in different ways: the first study used the gene detection rate, while the second study used the minimum number of input RNA molecules required for confidently detecting a spike-in control ( Jiang et al., 2011 ).
manufacturing process to ensure predetermined product specifications. It helps to determine the critical material attributes (CMAs) and critical process parameters (CPPs) that influencing the predefined critical quality attribute (CQAs) 4 . Response surface methodology (RSM) is one of the desired methods in the development and optimization of drug delivery systems. Three level factorial design, Central composite design (CCD), D-optimal design and Box Behnken design are the various types of RSM designs available for statistical optimization of the formulations. Central composite design is one type of RSM design allows, all factors to be varied simultaneously, allowing quantification of the effects caused by independent variables and interactions between them. Face centered central composite design contribute relatively high quality predictions over the entire design space and do not require using points outside the original factor range. Hence
V erhoeven et al. 2006). These studies, however, ex- ploited mainly the linkage information of multiple line crosses. Genetic mapping using sequence information of a single chromosome from four mouse inbred strains has been studied recently (S hifman and D arvasi 2005). Various studies have been conducted on using flanking markers to infer the identity-by-descent (IBD) information of QTL (L ander and G reen 1987; J iang and Z eng 1997; M euwissen and G oddard 2001). In NAM, the nucleotide polymorphisms within tagging SNPs can be tested more directly because high-density SNPs on founders can be obtained and this information can be projected onto the progeny through flanking CPS SNPs. Rather than inferring multiple alleles at each testing locus as in previous methods, NAM reduced the testing to exact biallelic contrasts across the whole population. Nevertheless, these various methods of IBD estimation are useful in cases where the founder in- formation is not available or complicated pedigree or population design makes the projection of information unreliable.
Many social experiments are run in multiple waves, or are replications of earlier social ex- periments. In principle, the sampling design can be modified in later stages or replications to allow for more efficient estimation of causal effects. We consider the design of a two-stage experiment for estimating an average treatment effect, when covariate information is available for experimental subjects. We use data from the first stage to choose a conditional treatment assignment rule for units in the second stage of the experiment. This amounts to choosing the propensity score, the conditional probability of treatment given covariates. We propose to se- lect the propensity score to minimize the asymptotic variance bound for estimating the average treatment effect. Our procedure can be implemented simply using standard statistical software and has attractive large-sample properties.
Many social experiments are run in multiple waves, or are replications of earlier social experiments. In principle, the sampling design can be modified in later stages or replications to allow for more efficient estimation of causal effects. We consider the design of a two-stage experiment for estimating an average treatment effect, when covariate information is available for experimental subjects. We use data from the first stage to choose a conditional treatment assignment rule for units in the second stage of the experiment. This amounts to choosing the propensity score, the conditional probability of treatment given covariates. We propose to select the propensity score to minimize the asymptotic variance bound for estimating the average treatment effect. Our procedure can be implemented simply using standard statistical software and has attractive large-sample properties.
This paper presents the design of the Unified Power Quality Conditioner with photovoltaic cells. The UPQC is configured taking right shunt topology into account. A compensation strategy has been developed. The photovoltaic cell in this system partially provides electricity to the non-linear load. To be economically efficient, photovoltaic cell should operate at the maximum power point condition. Simulation result shows the source voltage and load current waveforms before and after compensation. The result shows the improvement of voltage unbalance after connecting the PV-UPQC. The %THD content in the source voltage and load current after compensation are very less. The advantages of using this device are more reliable, cost effective and can correct the unbalance and distortion in the source voltage and load current simultaneously whereas all other device can correct either current or voltage distortion. The future work is to implement the optimization technique with UPQC.
Keywords—Heat transfer, enhancement, turbulent, pressure drop, louvered square leaf inserts.
I. I NTRODUCTION
Conventional resources of energy are depleting at an alarming rate, which makes future sustainable development of energy use very difficult. As a result, considerable emphasis has been placed on the development of various augmented heat transfer surfaces and devices. Heat transfer augmentation techniques are generally classified into three categories namely: active techniques, passive techniques and compound techniques. Passive heat transfer techniques (ex: tube inserts) do not require any direct input of external power. Hence many researchers preferred passive heat transfer enhancement techniques for their simplicity and applicability for many applications. Tube inserts present some advantages over other enhancement techniques, such as they can be installed in existing smooth tube that exchanger, and they maintain the mechanical strength of the smooth tube. Their installation is easy and cost is low.
The natural convection without vibration in electrical enter is given to the heater in the cylinder. The water entered into the cylinder floor reaches to examine kingdom circumstance. The thermocouples located on the heater surface and it’s connected to the information acquired a device. The two successive readings of thermocouples were the identical, output readings are recorded. The convection with vibration in an electrical heater became given a random input. The demon stat turned into the first set to power-on position and varying to energy-load function, therefore beginning the vibration of the cylinder.
DOE modeling is increasingly being applied in industry and science, resulting in a powerful effective technique to identify, in an efficient way, influential variables as well as finding the correct settings to optimize the re- sponse. Whereas the application in industrial environment is targeted at identifying the optimal parameter set- tings of production equipment, and at the same time looks to minimize the number of trials for cost reasons, an indiscriminant and lazy application of statistics boundary conditions may be excusable. But this is not tolerable in an academic environment. In scientific research, the statistical relevance of response of a factor has to comply rigorously with the statistical requirements of minimizing Alpha and Beta-risk. Students have to put serious time and effort into the planning phase of the DOE to limit noise during experimentations and estimate up-front the potential power of their DOE in order not to invalidate the experimental results with a too low power. The here presented easy understandable 14-step approach with explicit focus on Beta-risk is ideally suitable for scientific investigation, guiding statistics-inexperienced students and researchers with a consistent fail-proof approach to obtain statistical significant research results.
his article is related to the experimentaldesign and implementation of an autonomous vehicle for the transport of goods or raw materials within an industry or trade. The project was developed and coordinated by Escuela de Ingeniería Eléctrica y Electrónica of ITCA-FEPADE. This vehicle is driven through a set of sensors, such as infrared, ultrasonic and LIDAR sensor; the vehicle is able to detect their environment, and based on them, reach their destination through decisions of a Raspberry, which, executing a program based on neural network, gives instructions to an Arduino microcontroller, which drives the electric motors using a stage of power based on MOSFETs transistors. The neural network is a type of adaptive control, which replaces traditional controllers; Like the human being, the neural network must be trained for optimal functioning using artificial intelligence, such as the backpropagation method, in which the neural network learns in a supervised manner, based on known input and output patterns. The vehicle is capable of carrying a weight of up to 30 kg and the loading and unloading tasks will be carried out by a human operator. Due to the electronic components on board the vehicle, its operation is recommended in dry environments and a flat surface. The level of autonomy of the vehicle, refers to transporting the load from one point to another without direct human action during its displacement. Among the fields of application, the logistics and industrial area can be considered, for the transport of raw materials, tools, electronic components, fabrics and canned foods, among others.
Cutler, A. e Windham, M. P., 1994. Information-Based Validity Functionals for Mixture Analysis”. In: H. Bozdogan (Eds.) Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modelling: An Informational Approach, pp. 149-170.
Dempster, A. P., Laird, N. M. e Rubin, D.B., 1997. Maximum Likelihood from Incomplete Data via EM-Algorithm”. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 39, 1-38.
0.1 ≤ L ≤ 6 (Control limit)
The values of time, cost, Gamma and shift parameters of the example test problem are as follows: Z 0 = 0.25 h; Z 1 = 1.00 h; D 0 = $50.00; D 1 = $950.0; W = $1100.00; Y = $500.00; a = $20.00; b = $4.22; δ = 0.50; λ = 0.05; alphaUB = 0.05; and pLB = 0.9. After the experimental study, convergence point of the test problem is identified as 77901th generation and the generation number was set to 80000. The result obtained from ESDCC- GA is compared with the result obtained for same number of solutions (3921017) from PSO (Chih et. al) and it is shown in Table 3. The result shows that EDDCC-GA is better than PSO in terms of minimum ECT and is faster than PSO in terms of elapsed time for uniform sampling interval.