Search

The article focuses on the importance of graphing data before statistical analysis to avoid misleading results due to outliers, illustrated through a regression analysis example where an outlier was identified and removed after graphing the data, leading to a statistically significant relationship. It highlights three essential graphs for identifying statistical outliers and emphasizes the significance of visualizing data to ensure the accuracy of statistical analysis.

The present study compares the seasonal relationship of land surface temperature (LST) with six vegetation indices (DVI, EVI, FV, NDVI, RVI, and SAVI) and three other land surface indices (MNDWI, NDBI, and NMDI) in Imphal City, Northeast India, using the summer and winter Landsat 8 satellite image of 2021. These land surface indices respond differently to changes in LST in an urban landscape. The relationships between LST and these indices were presented spatially using Pearson correlation coefficient method, scatter plot graphs, box plot graphs, and cross-sectional drawings. The study found that LST had a moderate negative relationship (less than -0.45) with all six vegetation indices in summer and winter. Among the other land surface indices, MNDWI had a weak negative relationship (-0.25) in summer and a moderate negative relationship (-0.45) in winter. NDBI generates a moderate positive relation (0.56) in summer and a strong positive relation (0.69) in winter. NMDI forms a weak positive relation (0.08) in summer and a moderate positive relation (0.35) in winter. From the cross-section analysis, it was observed that LST was low along the high values of vegetation indices and vice versa. Moreover, built-up and bare surfaces generated a high LST along the cross-section. [ABSTRACT FROM AUTHOR]

Pretreatments for delignification are required for the enzymatic saccharification of lignocellulosic biomasses. However, in the current literature, various pretreatment approaches have been applied for the same kinds of biomass. To find the optimum pretreatments for biomaterials containing various lignin contents, in this study, a quantitative comparison was carried out on the delignification performance of 15 categories of pretreatments. In total, 1729 sets of biomass, cellulose, hemicellulose, and lignin recovery data were collected from 214 relevant studies. Box plots and Cate–Nelson-like graphs were applied for analyses. The results showed that alkali, oxidation, organic solvent, and multistep pretreatments generally were better at removing lignin and recovering cellulose. Moreover, among these four categories, alkali pretreatments had the best performance, increasing the saccharification efficiency by approximately five-fold. Considering both delignification performance and saccharification improvement, alkali pretreatments are currently considered to be the optimum pretreatment methods for enzymatic saccharification. [ABSTRACT FROM AUTHOR]

Despite the existence of sophisticated statistical methods, systematic reviews regularly indicate that single-case experimental designs (SCEDs) are predominantly analyzed through visual tools. For the quantitative aggregation of results, different meta-analytical techniques are available, but specific visual tools for the meta-analysis of SCEDs are lacking. The present article therefore describes the use of violin plots as visual tools to represent the raw data. We first describe the underlying rationale of violin plots and their main characteristics. We then show how the violin plots can complement the statistics obtained in a quantitative meta-analysis. The main advantages of violin plots as visual tools in meta-analysis are (a) that they preserve information about the raw data from each study, (b) that they have the ability to visually represent data from different designs in one graph, and (c) that they enable the comparison of score distributions from different experimental phases from different studies. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Risk assessment of river water quality using long-memory processes subject to divergence or Wasserstein uncertainty." The correction addresses an error in Figure 2 of the original article, where the horizontal axis was incorrect due to the misplacement of the number "1." The authors have provided a corrected version of Figure 2. The document also includes a note from the publisher, Springer Nature, stating their neutrality regarding jurisdictional claims and institutional affiliations. The correction notice was reported by Hidekazu Yoshioka and Yumi Yoshioka. [Extracted from the article]

Context: Lung cancer is the leading cause of cancer-related deaths in the world. Computed tomography perfusion (CTP) parameters can be used to evaluate the vascular flow dynamics of lung tumours. We set out to evaluate the CTP parameters in lung cancer and correlate them with histopathological subtype and other characteristics of patients with Lung Cancer. Settings and Design: This prospective study was conducted at a tertiary care referral hospital in western India. Methods: Between January 2019 and July 2020, CTP was performed in 46 patients of lung cancer with histopathological confirmation. The CTP parameters were evaluated in detail and correlated with histopathological subtypes, staging and immunohistochemistry (IHC) markers. Analysis of variance (ANOVA) test, receiver operator characteristic (ROC) curve, Box and whiskers plot graph and Pearson correlation tests were used for statistical analysis. Results: The most common subtype was adenocarcinoma (AC) in 21 patients, followed by squamous cell carcinoma (SCC) in 15 patients and others in 10 patients. Statistically significant difference in blood flow (BF) (f = 5.563, P = 0.007), blood volume (BV) (f = 3.548, P = 0.038) and permeability/flow extraction (FE) (f = 3.617, P = 0.036) were seen in different histopathological subtypes of lung cancer. BF is the main perfusion parameter for differentiation of AC from SCC. P63 positive lesions showed statistically significant lower BF, BV and FE parameters compared to P63 negative lesions (P = 0.013, 0.016 and 0.014, respectively). Different T stages showed statistically significant differences in BF (f = 3.573, P = 0.037), BV (f = 5.145, P = 0.010) and in FE (f = 4.849, P = 0.013). Conclusion: CTP is a non-invasive imaging method to assess the vascular flow dynamics of the tumours that may predict the histopathological subtypes in lung cancer. It can be used to target large-sized lesions during biopsy and to predict the chemotherapy response. [ABSTRACT FROM AUTHOR]

Introduction: Oral health is an integral component of general health. Many oral systemic diseases manifest orally, and general medical practitioners are the primary health-care providers in society, so understanding oral health and its significance is critical for medical students and practitioners. The present study was conducted to assess the oral health awareness among MBBS students and interns. Materials and Methods: After obtaining institutional ethical committee approval, the present cross-sectional study was conducted among 318 subjects who were second, third, and final year MBBS students and interns of ESIC medical college and PGIMSR. A validated, self-structured questionnaire comprising of 20 questions pertaining to oral health awareness were prepared using Google forms and was sent to the study participants tthrough WhatsApp and the findings were analysed using proper statistical methods. Results: In the present study, 19% of the study population had good oral health awareness, 49% and 32% had fair and poor oral health awareness, respectively. The final year students had a good oral health awareness compared to the other years of students and interns. Oral health awareness scores did not differ significantly between male and female study participants. Conclusion: The present study found a fair oral health awareness amongst the study population. Clinical Significance: A proper knowledge of oral health is very essential among the medical students as they would be approached by the general population for most of the primary health-care needs. [ABSTRACT FROM AUTHOR]

Botia dario is categorized as endangered due to considerable drop in population over past two decades creates data thin condition where the nonparametric statistical methods are superior alternative approach for data drafting. Small data set necessitate graphical display reducing the chance of data compression by numerical analyses. Monthly mass and length density estimates, location, and spread were compared with R computing environment through charts and plots; proposing a graphical method for single discrete and continuous data analyses. Through graphicacy the novel method reveals the pattern for mass and length of B. dario by depicting the modes and skews of the kernel density estimates suggested wide fluctuations during pre-monsoon months; whereas the spreads and locations of boxplots draping dot-whiskers infer the Gaussian kernel by pairwise comparisons. The boxplot widths, notches of the boxplots and red dot-whiskers illustrate comprehensive variations. The novel method and suggestive narratives appeal for inclusive use excluding the limitations. [ABSTRACT FROM AUTHOR]

Rhizomes of the Polygonatum species are well-known in traditional Chinese medicine. The 2020 edition of Chinese Pharmacopoeia includes three different species that possess different pharmacological effects. Due to the lack of standardized discriminant compounds there has often been inadvertently incorrect prescriptions given for these medicines, resulting in serious consequences. Therefore, it is critical to accurately distinguish these herbal Polygonatum species. For this study, UPLC-Q-TOF-MS/MS based metabolomics was employed for the first time to discriminate between three Polygonatum species. Partial least squares discriminant analysis (PLS-DA) models were utilized to select the potential candidate discriminant compounds, after which MS/MS fragmentation patterns were used to identify them. Meanwhile, metabolic correlations were identified using the R language package corrplot, and the distribution of various metabolites was analyzed by box plot and the Z-score graph. As a result, we found that adenosine, sucrose, and pyroglutamic acid were suitable for the identification of different Polygonatum species. In conclusion, this study articulates how various herbal Polygonatum species might be more accurately and efficiently distinguished. [ABSTRACT FROM AUTHOR]

The article discusses various statistical procedures for parametric comparison of Two Means in Independent Groups with R with a focus on data matrix technique, parametric and nonparametric tests for statistical data analysis. The article also focuses on linear regression models, model comparison tests and robust statistical tests for defining the p-value.

Regression testing is a necessary but costly process. It involves re-running all of the test cases each time the software is updated. The resources and time needed for retesting can be decreased by minimizing redundancy and prioritizing the test cases. Furthermore, optimization procedures enhance the efficacy of test case prioritization and minimization. In this research, we have proposed a discrete and combinatorial dragonfly algorithm. In addition, its hybrid version is created with a particle swarm optimization algorithm. The suggested approaches are compared to the random search, genetic algorithm, particle swarm optimization and the bat algorithm. The assessment is done on four subject programs of differing sizes. The simulation results show that the proposed methods are more efficient and effective than the compared algorithms. Furthermore, the hybrid algorithm has a compact distribution as seen by boxplots and interval plots of the average percentage of fault detection and the test minimization percentage. [ABSTRACT FROM AUTHOR]

Optimization methods are used to solve many problems and, under certain constraints, can provide the best possible results. They are inspired by the behavior of living things in nature and called metaheuristic algorithms. The population-based tree–seed algorithm (TSA) is an example of these algorithms and is used to solve continuous optimization problems that have recently emerged. This method, inspired by the relationship between trees and seeds, produces a certain number of seeds for each tree during each iteration. In this study, during seed formation in the TSA, trees were selected using the tournament selection method rather than by random means. Efforts were also made to enhance high-dimensional solutions, utilizing problem dimensions, D, of 20, 50, 100 and 1000 by optimizing the search tendency parameter within the structure of the algorithm, resulting in a modified TSA (MTSA). Empirical test data, convergence graphs and box plots were obtained by applying the MTSA to numerical benchmark functions. In addition, the results of the current algorithms in the literature were compared with the MTSA and the statistical test results were presented. The results from this analysis demonstrated that the MTSA could achieve superior results to the original TSA. [ABSTRACT FROM AUTHOR]

The aim of this clinical study was to evaluate bacterial colonization, marginal bone loss, and optical alveolar density in implants with Morse taper (MT) and external hexagon (EH) connections. Thirty-five implants were installed in 7 patients (mean age: 65.8 ± 6.7 years). Implants were divided into 2 groups, according to platform design: G1 - MT, installed 2mm infra-osseous and G2 - EH, positioned according to Branemark protocol. Patients were evaluated at baseline (T0), 21 days (T1), 3 months (T2), 6 months (T3), and 12 months (T4) after installations. Bone loss and alveolar density were evaluated by standardized periapical radiographs and bacterial profile with checkerboard DNA–DNA hybridization. Statistical analyses were performed using SPSS 23.0. To present the results, boxplots and a line graph of mean were used. P-values ≤.05 were statistically significant. After 3 months, alveolar bone loss was significantly higher in the G2 (T2-T0: P =.006; T3-T0: P =.003; and T4-T0: P =.005). No significant differences between G1 and G2 groups were observed for optical alveolar density. Microbiological analysis showed similar profiles between studied groups; however, there were significantly higher counts of Tannerella forsythia (P =.048), Campylobacter showae (P =.038), and Actinomyces naeslundii (P =.027) in G1 after 12 months. Based on the results of this study, it can be concluded that there was less peri-implant bone loss in MT compared to EH connections, but microbiological profile did not seem to influence bone changes. [ABSTRACT FROM AUTHOR]

To achieve effective oil management, it is critical to disclose the laws of oil supply, consumption, and natural loss through data analysis. However, the accuracy of data analysis is often suppressed by the mistakes and irrelevance of the input data, which are inevitable due to the large size and diversity of the data collected from the oil depots. To solve the problem, this paper proposes an abnormal oil data detection approach based on feature selection (AODDFS). In the AODDFS, the format of the input data was preprocessed to satisfy the requirements of feature selection; the fisher score was then employed to compute the relevance of each entry with normal features; finally, the abnormal entries were located based on the relevance values. Then, the AODDFS results were analyzed with boxplot and standard deviation. Finally, the AODDFS was verified through a case study on the data collected from several large oil depots. The results show that the AODDFS can effectively detect abnormal oil data with a precision of 85.00% and a recall of 80.94%. [ABSTRACT FROM AUTHOR]

The standard boxplot is one of the most popular nonparametric tools for detecting outliers in univariate datasets. For Gaussian or symmetric distributions, the chance of data occurring outside of the standard boxplot fence is only 0.7%. However, for skewed data, such as telemetric rain observations in a real-time flood forecasting system, the probability is significantly higher. To overcome this problem, a medcouple (MC) that is robust to resisting outliers and sensitive to detecting skewness was introduced to construct a new robust skewed boxplot fence. Three types of boxplot fences related to MC were analyzed and compared, and the exponential function boxplot fence was selected. Operating on uncontaminated as well as simulated contaminated data, the results showed that the proposed method could produce a lower swamping rate and higher accuracy than the standard boxplot and semi-interquartile range boxplot. The outcomes of this study demonstrated that it is reasonable to use the new robust skewed boxplot method to detect outliers in skewed rain distributions. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the performance of the 12 entropy-based features for the monitoring and detection of bearing faults. These entropy measures were proposed both in time, frequency and time–frequency domain. Probability mass function (PMF) was extracted from the time waveforms using four different methods: (i) via power spectral density, (ii) via ordinal pattern distribution, (iii) via wavelet packet tree and iv) ensemble empirical mode decomposition. Three different entropy measures were used in the article: (i) Shannon entropy, (ii) Rényi entropy and (iii) Jensen–Rényi divergence. A new bearing produces a vibration time series characterised by random noise without prominent periodic content. As soon as a fault develops, impulses are produced, what excites structural resonances generating a train of impulse responses. As defect grows, it becomes a distributed fault, and then no sharp impulses are generated but rather an amplitude modulated random noise signal. The proposed methodology has been applied to detect bearing faults by the analysis of two real bearing datasets, from run-to-failure experiments. Three bearings that presented different defects in the test (inner race fault, rolling elements fault and outer race fault) were analysed to validate the performance of the entropy-based features. The modified Z-score has been implemented and used as an index to detect changes of the entropy features. The results clearly demonstrate that the proposed approach represents a valuable non-parametric tool for early detection of anomalies in bearings vibration signals. Highlights • We study 12 entropy-based features for monitoring and detection of bearing faults. • The proposed methodology is tested on two real bearing vibration signal datasets. • Entropy is shown to be a valuable tool for early detection of anomalies in bearings. [ABSTRACT FROM AUTHOR]

Intravascular large B-cell lymphoma (IVLBCL) is a very rare subtype of extranodal malignant B-cell lymphoma.1,2 A useful method to diagnose IVLBCL is random skin biopsy, taken from healthy-appearing skin.3 However, no consensus exists regarding random skin biopsy methods, and there is insufficient data regarding its accuracy.4 Thus, we aimed to determine potential correlations between positive diagnoses and biopsy techniques, serum lactate dehydrogenase (LDH) levels, as well as serum-soluble interleukin 2 receptor (sIL-2R) levels. Conversely, when patients have sIL-2R <500 U/mL and normal serum LDH levels, we do not recommend random skin biopsy because these patients had no lymphoproliferative disorders in our study. Incisional random skin biopsy, not punch biopsy, is an appropriate method for diagnosis of intravascular large B-cell lymphoma: a clinicopathological study of 25 patients. [Extracted from the article]

The box plot has been used for a very long time since 70s in checking the existence of outliers and the asymmetrical shape of data. The existing box plot is constructed using five values of statistics calculated from either the discrete or continous data. Many improvement of box plots have deviated from the elegant and simplier approach of exploratory data analysis by incorporating many other statistic values resulting the turning back of the noble philosophy behind the creation of box plot. The modification using range value with the minimum and maximum values are being incorporated to suit the need of selected discrete distribution when outliers is not an important criteria anymore. The new modification of box plot is not based on the asymmetrical shape of distribution but more on the spreading and partitioning data into range measure. The new propose name for the box plot with only three values of statistics is called range-box plot. [ABSTRACT FROM AUTHOR]

The animation of articulated characters is a central problem in the computer graphics field. Skeletal animation techniques define a workflow which has proven to be effective for boundary representations (B-Reps). This paper extends the classical skeletal animation pipeline to deal with characters internal tissues. In contrast to most common approaches, the proposed technique automates all the stages of this workflow. Well known skinning algorithms, such as Linear Blending Skinning, Dual Quaternion Skinning or Optimized Centers of Rotation were adapted to allow the use of our technique in applications where interactivity is required. The pipeline proposed in this paper can be used in many computer graphics systems such as games or educational applications to visualize and animate the internal anatomy of a virtual character at interactive rates. [ABSTRACT FROM AUTHOR]

Background Insecure attachment and maladaptive coping are important predisposing vulnerabilities for Functional Neurological Disorders (FND)/Conversion Disorder, yet no prior structural neuroimaging studies have investigated biomarkers associated with these risk factors in FND populations. This magnetic resonance imaging study examined cortical thickness and subcortical volumes associated with self-reported attachment and coping styles in patients with FND. We hypothesized that insecure attachment and maladaptive coping would relate to limbic-paralimbic structural alterations. Methods FreeSurfer cortical thickness and subcortical volumetric analyses were performed in 26 patients with motor FND (21 women; 5 men) and 27 healthy controls (22 women; 5 men). For between-group comparisons, patients with FND were stratified by Relationship Scales Questionnaire, Ways of Coping Scale-Revised, and Connor-Davidson Resilience Scale scores. Within-group analyses were also performed in patients with FND. All analyses were performed in the complete cohort and separately in women only to evaluate for gender-specific effects. Cortical thickness analyses were whole-brain corrected at the cluster-wise level; subcortical analyses were Bonferroni corrected. Results In women with FND, dismissing attachment correlated with reduced left parahippocampal cortical thickness. Confrontive coping was associated with reduced right hippocampal volume, while accepting responsibility positively correlated with right precentral gyrus cortical thickness. These findings held adjusting for anti-depressant use. All FND-related findings were within the normal range when compared to healthy women. Conclusion These observations connect individual-differences in limbic-paralimbic and premotor structures to attachment and coping styles in FND. The relationship between parahippocampal thickness and dismissing attachment may indicate aberrant social-emotional and contextual appraisal in women with FND. [ABSTRACT FROM AUTHOR]

Purpose To develop and evaluate a noise-robust method for reconstruction of water and fat images for spoiled gradient multi-echo sequences. Methods The proposed method performs water-fat separation by using a graph cut to minimize an energy function consisting of unary and binary terms. Spatial smoothing is incorporated to increase robustness to noise. The graph cut can fail to find a solution covering the entire image, in which case the relative weighting of the unary term is iteratively increased until a complete solution is found. The proposed method was compared to two previously published methods. Reconstructions were performed on 16 cases taken from the 2012 ISMRM water-fat reconstruction challenge dataset, for which reference reconstructions were provided. Robustness towards noise was evaluated by reconstructing images with different levels of noise added. The percentage of water-fat swaps were calculated to measure performance. Results At low noise levels the proposed method produced similar results to one of the previously published methods, while outperforming the other. The proposed method significantly outperformed both of the previously published methods at moderate and high noise levels. Conclusion By incorporating spatial smoothing, an increased robustness towards noise is achieved when performing water-fat reconstruction of spoiled gradient multi-echo sequences. [ABSTRACT FROM AUTHOR]

Horizontal Cooperation is emerging as a way to increase competitiveness in logistics and transportation. Its implementation, however, may be hindered by conflicts and opportunist behavior among the members of the coalition. This paper develops an agent-based simulation model studying the evolution of a coalition over time taking into account various trust-related issues. Different degrees of cooperation, rules for enlarging the coalition with new members as well as a Shapley-based methodology for allocating savings are implemented. To calculate such savings, vehicle routing solution procedures are further integrated. This enables an extensive investigation of the effects of Horizontal Cooperation from both an economic and environmental perspective. Experimental results highlight that significant savings can be achieved with the degree of cooperation and trust-related issues indicating the highest importance. [ABSTRACT FROM AUTHOR]

This article introduces a new graphical tool to summarize data which possess a mixture structure. Computation of the required summary statistics makes use of posterior probabilities of class membership which can be obtained from a fitted mixture model. Real and simulated data are used to highlight the usefulness of this tool for the visualization of mixture data in comparison to the traditional boxplot. [ABSTRACT FROM AUTHOR]

Flexible systems and services require a solid approach for modeling and enacting dynamic behavior. Declarative process models gained plenty of traction lately as they have proven to provide a good fit for the problem at hand, i.e. visualizing and executing flexible business processes. These models are based on constraints that impose behavioral restrictions on process behavior. Essentially, a declarative model is a set of constraints defined over the set of activities in a process. While allowing for very flexible process specifications, a major downside is that the combination of constraints can lead to behavioral restrictions not explicitly visible when reading a model. These restrictions, so-called hidden dependencies, make the models much more difficult to understand. This paper presents a technique for discovering hidden dependencies and making them explicit by means of dependency structures. Experiments with novice process modelers demonstrate that the proposed technique lowers the cognitive effort necessary to comprehend a constraint-based process model. [ABSTRACT FROM AUTHOR]

Improving the accuracy of material feeding for printed circuit board (PCB) template orders can reduce the overall cost for factories. In this paper, a data mining approach based on multivariate boxplot, multiple structural change model (MSCM), neighborhood component feature selection (NCFS), and artificial neural networks (ANN) was developed for the prediction of scrap rate and material feeding optimization. Scrap rate related variables were specified and 30,117 samples of the orders were exported from a PCB template production company. Multivariate boxplot was developed for outlier detection. MSCM was employed to explore the structural change of the samples that were finally partitioned into six groups. NCFS and ANN were utilized to select scrap rate related features and construct prediction models for each group of the samples, respectively. Performances of the proposed model were compared to manual feeding, ANN, and the results indicate that the approach exhibits obvious superiority to the other two methods by reducing surplus rate and supplemental feeding rate simultaneously and thereby reduces the comprehensive cost of raw material, production, logistics, inventory, disposal, and delivery tardiness compensation. [ABSTRACT FROM AUTHOR]

The management of infrastructure involves accounting for factors which vary in space over the system domain and in time as the system changes. Effective system management should be guided by models which account for uncertainty in these influencing factors as well as for information gathered to reduce this uncertainty. In this paper, we address the problem of optimal information collection for spatially distributed dynamic infrastructure systems. Based on prior information, a monitoring scheme can be designed, including placement and scheduling of sensors. This scheme can be adapted during the management process, as more information becomes available. Optimality can be defined in terms of the value of information (VoI), which provides a rational metric for quantifying the benefits of data gathering efforts to support system management decision-making. However, the computation of this metric in spatially and temporally extensive systems can present a practical impediment to its implementation. We describe this complexity, and investigate a special case of system topology, termed as a temporally decomposable system with uncontrolled evolution, in which the complexity of assessing VoI grows at a manageable rate with respect to the system management time duration. We demonstrate the evaluation and optimization of the VoI in an example of such a system. [ABSTRACT FROM AUTHOR]

Independent components analysis (ICA) may be considered as one of the most established blind source separation techniques for the treatment of complex data sets in analytical chemistry. Like other similar methods, the determination of the optimal number of latent variables, in this case, independent components (ICs), is a crucial step before any modeling. Therefore, validation methods are required in order to decide about the optimal number of ICs to be used in the computation of the final model. In this paper, three new validation methods are formally presented. The first one, called Random_ICA, is a generalization of the ICA_by_blocks method. Its specificity resides in the random way of splitting the initial data matrix into two blocks, and then repeating this procedure several times, giving a broader perspective for the selection of the optimal number of ICs. The second method, called KMO_ICA_Residuals is based on the computation of the Kaiser-Meyer-Olkin (KMO) index of the transposed residual matrices obtained after progressive extraction of ICs. The third method, called ICA_corr_y, helps to select the optimal number of ICs by computing the correlations between calculated proportions and known physico-chemical information about samples, generally concentrations, or between a source signal known to be present in the mixture and the signals extracted by ICA. These three methods were tested using varied simulated and experimental data sets and compared, when necessary, to ICA_by_blocks. Results were relevant and in line with expected ones, proving the reliability of the three proposed methods. [ABSTRACT FROM AUTHOR]

Excess water production is a major problem in water-flooded mature oilfields. Recently, using microgel treatment as a conformance control method to reduce water production has drawn increasing attention in oil industry. The success of microgel treatments highly depends on in-depth understanding where and when microgel can be successfully applied, how to design the injection parameters, and how well the performances of microgel treatments in different reservoir conditions. To better solve these problems, a total of 154 field application data from microgel-treated injection wells was collected from 2005 to 2016. These data are statistically analyzed by using histograms, boxplots, and scatterplots. Histograms are used to analyze the reservoir and well characteristic data in order to find out where and when microgels are generally used. Scatterplots are used to uncover some general trends that can guide field engineers to design injection parameters and improve the treatment performances. Boxplots compare the injection parameters among different types of reservoirs and outline special cases that are far different from the majority of the cases. These special cases are further studied because they contain additional information during the treatment. Throughout the comprehensively data analysis, the application guidelines for microgel treatments are presented, which includes screening guidelines, design considerations, and treatment performances. The instructional guidelines can help to maximize the likelihood of microgel treatment projects being technically successful. [ABSTRACT FROM AUTHOR]

The ability to interpret graphs is highly important in modern society, but has proven to be a challenge for many people. In this paper, two teaching methods were used to remediate one specific misinterpretation: the area misinterpretation of box plots. First, we used refutational text to explicitly state and invalidate the area misinterpretation of box plots. Second, we used multiple external representations (MERs): Histograms were used as an overlay on box plots in order to give students a better insight in the way box plots represent data distributions. Third, we combined refutational text and MERs. We found that refutational text was successful in improving students’ interpretation of box plots, but that the use of MERs did not improve students’ interpretation of box plots. The addition of MERs also did not increase the effect of refutational text. [ABSTRACT FROM PUBLISHER]

Graphicacy is an important skill in today's society; however, the interpretation of graphs proofs to be more difficult than it might seem. In this study, we focus on one specific misinterpretation, the area misinterpretation of box plots, which is caused by incorrect heuristic processing of salient features of the box plot. In this study, we tried to eradicate the area misinterpretation by the use of refutational text. We tested whether participants exposed to refutational text solved items on the area misinterpretation better than participants in a control condition and also whether, when correct answers were given, the heuristic processing was completely eradicated. Both reaction time and accuracy rate patterns showed that, although not completely eradicated, participants in the refutation condition scored significantly better than participants in the control condition. On top of that, there were more participants with (near) perfect performance in the refutation than in the control condition. We conclude that refutational text can help in remediating the area misinterpretation. However, it did not completely eradicate this incorrect heuristic processing. [ABSTRACT FROM AUTHOR]

A framework is presented for the probabilistic modelling of non-stationary coastal storm event sequences. Such modelling is required to integrate seasonal, climatic and long-term non-stationarities into coastal erosion hazard assessments. The framework is applied to a study site on the East Australian Coast where storm waves are found to exhibit non-stationarities related to El Niño-Southern Oscillation (ENSO) and seasonality. The impact of ENSO is most prominent for storm wave direction, long term mean sea level (MSL) and the rate of storms, while seasonal non-stationarity is more ubiquitous, affecting the latter variables as well as storm wave height, duration, period and surge. The probabilistic framework herein separates the modelling of ENSO and seasonal non-stationarity in the storm wave properties from the modelling of their marginal distributions, using copulas. The advantage of this separation is that non-stationarities can be straightforwardly modelled in all storm wave variables, irrespective of whether parametric or non-parametric techniques are used to model their marginal distributions. Storm wave direction and steepness are modelled with non-parametric distributions whereas storm wave height, duration and surge are modelled parametrically using extreme value mixture distributions. The advantage of the extreme value mixture distributions, compared with the standard extreme value distribution for peaks-over-threshold data (Generalized Pareto), is that the statistical threshold becomes a model parameter instead of being fixed, and so uncertainties in the threshold can be straightforwardly integrated into the analysis. Robust quantification of uncertainties in the model predictions is crucial to support hazard applications, and herein uncertainties are quantified using a novel mixture of parametric percentile bootstrap and Bayesian techniques. Percentile bootstrap confidence intervals are shown to non-conservatively underestimate uncertainties in the extremes (e.g. 1% annual exceedance probability wave heights), both in an idealized setting and in our application. The Bayesian approach is applied to the extreme value models to remedy this shortcoming. The modelling framework is applicable to any site where multivariate storm wave properties and timings are affected by seasonal, climatic and long-term non-stationarities, and can be used to account for such non-stationarities in coastal hazard assessments. [ABSTRACT FROM AUTHOR]

We propose a new method for the construction and visualization of boxplot-type displays for functional data. We use a recent functional data analysis framework, based on a representation of functions called square-root slope functions, to decompose observed variation in functional data into three main components: amplitude, phase, and vertical translation. We then construct separate displays for each component, using the geometry and metric of each representation space, based on a novel definition of the median, the two quartiles, and extreme observations. The outlyingness of functional data is a very complex concept. Thus, we propose to identify outliers based on any of the three main components after decomposition. We provide a variety of visualization tools for the proposed boxplot-type displays including surface plots. We evaluate the proposed method using extensive simulations and then focus our attention on three real data applications including exploratory data analysis of sea surface temperature functions, electrocardiogram functions, and growth curves. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Boxplots are useful displays that convey rough information about the distribution of a variable. Boxplots were designed to be drawn by hand and work best for small datasets, where detailed estimates of tail behavior beyond the quartiles may not be trustworthy. Larger datasets afford more precise estimates of tail behavior, but boxplots do not take advantage of this precision, instead presenting large numbers of extreme, though not unexpected, observations. Letter-value plots address this problem by including more detailed information about the tails using “letter values,” an order statistic defined by Tukey. Boxplots display the first two letter values (the median and quartiles); letter-value plots display further letter values so far as they are reliable estimates of their corresponding quantiles. We illustrate letter-value plots with real data that demonstrate their usefulness for large datasets. All graphics are created using the R packagelvplot, and code and data are available in the supplementary materials. [ABSTRACT FROM PUBLISHER]

This paper employs non-radial and non-oriented super-efficiency SBM model under the assumption of a variable return to scale to analyse performance of twenty-two Czech and Slovak domestic commercial banks in 2015. The banks were ranked according to asset-oriented and profit-oriented intermediation approach. We pooled the cross-country data and used them to define a common best-practice efficiency frontier. This allowed us to focus on determining relative differences in efficiency across banks. The average efficiency was evaluated separately on the "national" and "international" level. Based on the results of analysis can be seen that in Slovak banking sector the level of super-efficiency was lower compared to Czech banks. Also, the number of super-efficient banks was lower in a case of Slovakia under both approaches. The boxplot analysis was used to determine the outliers in the dataset. The results suggest that the exclusion of outliers led to the better statistical characteristic of estimated efficiency. [ABSTRACT FROM AUTHOR]

Taking into account the needs of decision makers inside the companies, marketing research is meant to provide the best information that really can to help the adoption of the best decisions. In this respect a lot of methods of data analysis can be used but the researcher has to choose those results that minimize the errors. This paper proposes an instrumental research regarding the using of Boxplot analysis to identify certain outliers that can alter the information. In order to attain the objective of this research, we exemplified the Boxplot analysis on data related to the GDP recorded in 2014 by Romanian counties. [ABSTRACT FROM AUTHOR]

The article traces the key enablers to improve the quality of higher education in all university-affiliated degree colleges in the North-East of India. The seven North-East Indian states, popularly known as ‘Seven Sisters’ are distinctly different from the other parts of the country in terms of the hostility of the geographic and socio-economic environment. As a pioneering attempt we have used the metrics of Measure phase under the Six Sigma Define Measure Analyze Improvement and Control (DMAIC) approach to identify the critically weak areas that need immediate attention in the higher education sector in a backward region. The study was conducted in two phases – Phase 1 determines the sampling plan. In the second phase of the study, using the Box plot technique, the study identifies the possible weak areas (sub-enablers) and weakest areas (critical domain) for improvement. In particular, while the entire North-East part of the country is characterised by adverse topography, a skewed rural-urban divide as well as erratic socio-political factors, tribal unrests, militant activities, our study focuses on providing a theoretical dimension to the abysmal quality and passing rates in such a hostile environment and suggested possible improvements. [ABSTRACT FROM AUTHOR]

Micro- and nano-topographies of scaffold surfaces play a pivotal role in tissue engineering applications, influencing cell behavior such as adhesion, orientation, alignment, morphology and proliferation. In this study, a novel microfabrication method based on the combination of soft-lithography and electrospinning for the production of micro-patterned electrospun scaffolds was proposed. Subsequently, a 3D screening device for electrospun meshes with different micro-topographies was designed, fabricated and biologically validated. Results indicated that the use of defined patterns could induce specific morphological variations in human mesenchymal stem cell cytoskeletal organization, which could be related to differential activity of signaling pathways. Statement of Significance We introduce a novel and time saving method to fabricate 3D micropatterns with controlled micro-architectures on electrospun meshes using a custom made collector and a PDMS mold with the desired topography. A possible application of this fabrication technique is represented by a 3D screening system for patterned electrospun meshes that allows the screening of different scaffold/electrospun parameters on cell activity. In addition, what we have developed in this study could be modularly applied to existing platforms. Considering the different patterned geometries, the cell morphological data indicated a change in the cytoskeletal organization with a close correspondence to the patterns, as shown by phenoplot and boxplot analysis, and might hint at the differential activity of cell signaling. The 3D screening system proposed in this study could be used to evaluate topographies favoring cell alignment, proliferation and functional performance, and has the potential to be upscaled for high-throughput. [ABSTRACT FROM AUTHOR]

Background: Analyses of molecular high-throughput data often lack in robustness, i.e. results are very sensitive to the addition or removal of a single observation. Therefore, the identification of extreme observations is an important step of quality control before doing further data analysis. Standard outlier detection methods for univariate data are however not applicable, since the considered data are high-dimensional, i.e. multiple hundreds or thousands of features are observed in small samples. Usually, outliers in high-dimensional data are solely detected by visual inspection of a graphical representation of the data by the analyst. Typical graphical representation for high-dimensional data are hierarchical cluster tree or principal component plots. Pure visual approaches depend, however, on the individual judgement of the analyst and are hard to automate. Existing methods for automated outlier detection are only dedicated to data of a single experimental groups. Results: In this work we propose to use bagplots, the 2-dimensional extension of the boxplot, to automatically identify outliers in the subspace of the first two principal components of the data. Furthermore, we present for the first time the gemplot, the 3-dimensional extension of boxplot and bagplot, which can be used in the subspace of the first three principal components. Bagplot and gemplot surround the regular observations with convex hulls and observations outside these hulls are regarded as outliers. The convex hulls are determined separately for the observations of each experimental group while the observations of all groups can be displayed in the same subspace of principal components. We demonstrate the usefulness of this approach on multiple sets of artificial data as well as one set of gene expression data from a next-generation sequencing experiment, and compare the new method to other common approaches. Furthermore, we provide an implementation of the gemplot in the package 'gemPlot' for the R programming environment. Conclusions: Bagplots and gemplots in subspaces of principal components are useful for automated and objective outlier identification in high-dimensional data from molecular high-throughput experiments. A clear advantage over other methods is that multiple experimental groups can be displayed in the same figure although outlier detection is performed for each individual group. [ABSTRACT FROM AUTHOR]

In the European Union ( EU) potato production is surveyed for Clavibacter michiganensis subsp. sepedonicus (potato ring rot) and Ralstonia solanacearum (potato brown rot) under Commission Directives 93/85/EEC with its amendment 2006/56/ EC and 98/57/EEC with its amendment 2006/63/ EC. A regular update of the Directives is required in view of developments in understanding of the biology of these organisms and the diagnostics recommended for their detection and identification. Three inter-laboratory tests ( ILT1, ILT2 and ILT3) were performed from 2009 to 2012 as part of a Euphresco Phytosanitary ERA- NET project to assess performance of current official methods for C. michiganensis subsp. sepedonicus and R. solanacearum. A major aim of the ILTs was to generate data on the performance of real-time PCR protocols to support their introduction as primary (core) screening tests for both pathogens. In ILT1, 29 laboratories from 23 countries participated, in ILT2, 23 laboratories from 18 countries and in ILT3 42 laboratories from 24 countries. Relative accuracies for real-time PCR tests averaged 92% for R. solanacearum and 96% for C. michiganensis subsp. sepedonicus) and compared with existing primary (core) screening tests (immunofluorescence, conventional PCR, semi-selective plating and bioassay) in terms of analytical sensitivity, analytical specificity and robustness. It was concluded that all methods tested, including real-time PCR, can be considered as equivalent. Therefore TaqMan ® real-time PCR is recommended for inclusion in EU Directives and EPPO Standards as a reliable primary (core) screening method. [ABSTRACT FROM AUTHOR]

Graphs are powerful and versatile data structures that can be used to represent a wide range of different types of information. In this article, we introduce a method to analyze and then visualize an important class of data described over a graph—namely, ensembles of paths. Analysis of such path ensembles is useful in a variety of applications, in diverse fields such as transportation, computer networks, and molecular dynamics. The proposed method generalizes the concept ofband depthto an ensemble of paths on a graph, which provides a center-outward ordering on the paths. This ordering is, in turn, used to construct a generalization of the conventional boxplot or whisker plot, called apath boxplot, which applies to paths on a graph. The utility of path boxplot is demonstrated for several examples of path ensembles including paths defined over computer networks and roads. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]