Search

AbstractThis study analyzes the performance of control charts using statistics to monitor the stability of the mean in paired data, in the presence of missing or censored data. To determine the control limits, extensive simulations of paired data with a normal distribution are conducted to establish an empirical distribution under different correlation scenarios, sample sizes, and percentages of censored or missing data. After calculating the control limits, additional simulations are carried out to assess the out of control average run length to detect changes in the mean. We employ the paired t-test and the paired Wilcoxon test when the population is normal as they obtain similar results, however if they differ significantly, thus it is an indication of a potential violation of the hypothesis of normality in the data. Additionally, we evaluated some proposals to minimize the negative effect of data displaying censoring and missing values. We found that the results using parametric and non-parametric approaches are practically similar when the data is complete; the non-parametric approach being more affected by missing and censored data. A practical example monitoring the most-streamed songs in the US illustrates the proposed procedure. [ABSTRACT FROM AUTHOR]

Copyright of Campus Virtuales is the property of Campus Virtuales and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Imperialist competitive algorithm (ICA) is an efficient meta-heuristic algorithm by simulating the competitive behaviour among imperialist countries. However, it still suffers from slow convergence and deficiency in exploration. To address these issues, an improved ICA is proposed by combining ICA with a quasi-opposition-based learning (QOBL) strategy, which is named QOBL-ICA. The improvements include two aspects. First, the QOBL strategy is adopted to generate a population of fitter individuals. Second, a QOBL-assisted assimilation strategy is proposed to enhance the exploration ability of ICA. As a result, the proposed QOBL-ICA has more powerful exploration ability than ICA as well as faster convergence speed. The effectiveness of the proposed QOBL-ICA is verified by testing on 20 benchmark functions and 3 engineering design problems. Experimental results show that the performance of QOBL-ICA is superior to most state-of-the-art meta-heuristic algorithms in terms of global optimum reached and convergence speed.

Imperialist competitive algorithm (ICA) is an efficient meta-heuristic algorithm by simulating the competitive behaviour among imperialist countries. However, it still suffers from slow convergence and deficiency in exploration. To address these issues, an improved ICA is proposed by combining ICA with a quasi-opposition-based learning (QOBL) strategy, which is named QOBL-ICA. The improvements include two aspects. First, the QOBL strategy is adopted to generate a population of fitter individuals. Second, a QOBL-assisted assimilation strategy is proposed to enhance the exploration ability of ICA. As a result, the proposed QOBL-ICA has more powerful exploration ability than ICA as well as faster convergence speed. The effectiveness of the proposed QOBL-ICA is verified by testing on 20 benchmark functions and 3 engineering design problems. Experimental results show that the performance of QOBL-ICA is superior to most state-of-the-art meta-heuristic algorithms in terms of global optimum reached and convergence speed. [ABSTRACT FROM AUTHOR]

This study investigates the stability of hydrological drought trends in the Euphrates Basin from 1960 to 2020 using three-dimensional (3D) graphical representations based on innovative trend analysis (ITA) and triple Wilcoxon test (WT) methods. Unlike traditional ITA and WT, which are widely used for trend identification but do not inherently provide trend stability information, this study employs a novel approach to assess and visualize trend stability. The Triple WT method divides the data into three equal segments, examining differences without altering the time series. Drought indices are calculated for 3-month, 6-month, and 12-month time scales using historical streamflow data from five stations. The research identifies trends and their stabilities across three distinct periods: 1967–1984, 1985–2002, and 2003–2020. Results show that as the time scale increases, trend differences between extreme drought conditions diminish. One station consistently exhibits significantly decreasing trends, while three stations show unstable trends with notable variations in the standardized streamflow index (SSFI). The use of 3D-ITA and Triple WT effectively captures the dynamics and stability of drought trends, offering a deeper understanding of hydrological drought in the Euphrates Basin. These findings provide a reference for future studies on drought trend mechanisms in various climatic regions. [ABSTRACT FROM AUTHOR]

Abstract Multilevel thresholding image segmentation will subdivide an image into several meaningful regions or objects, which makes the image more informative and easier to analyze. Optimal multilevel thresholding approaches are extensively used for segmentation because they are easy to implement and offer low computational cost. Multilevel thresholding image segmentation is frequently performed using popular methods such as Otsu’s between-class variance and Kapur’s entropy. Numerous researchers have used evolutionary algorithms to identify the best multilevel thresholds based on the above approaches using histogram. This paper uses the Energy Curve (EC) based thresholding method instead of the histogram. Chaotic Bidirectional Smell Agent Optimization with Adaptive Control Strategy (ChBSAOACS), a powerful evolutionary algorithm, is developed and employed in this paper to create and execute an effective method for multilevel thresholding segmentation of breast thermogram images based on energy curves. The proposed algorithm was tested for viability on standard breast thermogram images. All experimental data are examined quantitatively and qualitatively to verify the suggested method’s efficacy.

Multilevel thresholding image segmentation will subdivide an image into several meaningful regions or objects, which makes the image more informative and easier to analyze. Optimal multilevel thresholding approaches are extensively used for segmentation because they are easy to implement and offer low computational cost. Multilevel thresholding image segmentation is frequently performed using popular methods such as Otsu’s between-class variance and Kapur’s entropy. Numerous researchers have used evolutionary algorithms to identify the best multilevel thresholds based on the above approaches using histogram. This paper uses the Energy Curve (EC) based thresholding method instead of the histogram. Chaotic Bidirectional Smell Agent Optimization with Adaptive Control Strategy (ChBSAOACS), a powerful evolutionary algorithm, is developed and employed in this paper to create and execute an effective method for multilevel thresholding segmentation of breast thermogram images based on energy curves. The proposed algorithm was tested for viability on standard breast thermogram images. All experimental data are examined quantitatively and qualitatively to verify the suggested method’s efficacy. [ABSTRACT FROM AUTHOR]

A 77 GHz frequency-modulated continuous wave (FMCW) radar was utilized to extract biomechanical parameters for gait analysis in indoor scenarios. By preprocessing the collected raw radar data and eliminating environmental noise, a range–velocity–time (RVT) data cube encompassing the subjects' information was derived. The strongest signals from the torso in the velocity and range dimensions and the enveloped signal from the toe in the velocity dimension were individually separated for the gait parameters extraction. Then, six gait parameters, including step time, stride time, step length, stride length, torso velocity, and toe velocity, were measured. In addition, the Qualisys system was concurrently utilized to measure the gait parameters of the subjects as the ground truth. The reliability of the parameters extracted by the radar was validated through the application of the Wilcoxon test, the intraclass correlation coefficient (ICC) value, and Bland–Altman plots. The average errors of the gait parameters in the time, range, and velocity dimensions were less than 0.004 s, 0.002 m, and 0.045 m/s, respectively. This non-contact radar modality promises to be employable for gait monitoring and analysis of the elderly at home. [ABSTRACT FROM AUTHOR]

Copyright of Opuntia Brava is the property of Universidad de Ciencias Pedagogicas de Las Tunas, Centro de Documentacion e Informacion Pedagogica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of the paper is to examine seasonal patterns in the performance of fuel markets in the Visegrad Group (V4) countries (i.e., the Czech Republic, Hungary, Poland, and Slovakia). Unlike numerous papers that investigate global oil markets, this study focuses on regional retail fuel markets. The dataset consists of weekly Pb95 gasoline and diesel prices from January 2016 through December 2020. The methods applied cover a range of statistical and econometric tools, such as the Wilcoxon rank sum test, simple regression, and the generalized autoregressive conditional heteroscedasticity (GARCH) models. The research refers to important calendar effects such as the month‑of‑the‑year effect and the Halloween effect, but it also considers the seasonal gasoline transition effect. The empirical analysis presented in this paper does not bring clear evidence for significant seasonal patterns in the performance of fuel markets in the Visegrad Group as the application of different methods provides mixed results. Nevertheless, the findings of the Wilcoxon test are consistent with the GARCH (1, 1) estimates, which detected an April effect for gasoline and a December effect for diesel in Poland. The simple regression and GARCH (1, 1) estimates are consistent for an October effect for gasoline in Slovakia. None of the methods applied allows us to find a significant Halloween effect, a reverse Halloween effect, or a gasoline seasonal transition effect on the fuel markets of the Visegrad Group. These findings bring new insight into the V4 fuel markets and may be important for oil processing firms, retail traders, transport and distribution companies, farmers, and individual consumers.

Copyright of Comparative Economic Research is the property of Wydawnictwo Uniwersytetu Lodzkiego and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Greenhouse technology has an important role to play in minimising adverse effects of climate variability on vegetable production. Studies indicate that greenhouse farmers face several challenges, sometimes leading to the abandonment of Greenhouses. The types and levels of severity of the challenges have not been adequately documented and acted upon on a context-specific basis, yet the demand for Greenhouse fresh produce is increasing, particularly in populated urban areas. This study identified and evaluated core challenges, available opportunities, and a concatenation of the challenges with productivity perceptions among Greenhouse farmers in Kericho County. A cross-sectional survey design drawing on data from 10% of Greenhouse farmers from each of the six Sub-counties was adopted for the study. Structured and unstructured interview schedules were administered to 59 sampled farmers and 16 Extension agents. The Henry Garret ranking method was used to analyse ranked data on challenges from farmers' views and potential opportunities from Extension agents. A one-sample Wilcoxon test was used to analyse the deviation of views from 'neutral'. Challenge due to pests and diseases was significantly higher than hypothesised median of 3 (neutral); Z = 5.198, P< .01. Cost of inputs and lack of finances for maintenance were significantly higher than neutral; Z = 5.061, P< .01 and Z = 3.810, P< .01 respectively. The top five challenges based on Garrett scores were pests & diseases, cost of inputs, maintenance costs, inadequate water, and initial costs. Top-ranked opportunities were integrated pest & disease management, farm-inputs subsidy, information on crop varieties, support from extension and capacity building in water harvesting. Concatenations existed between productivity of greenhouse units with quality of produce, ease of access to markets and profitability of greenhouses. The study recommends stakeholders' concerted effort towards utilisation of the opportunities identified to enhance sustainable Greenhouse productivity among smallholder vegetable producers. [ABSTRACT FROM AUTHOR]

A nonparametric test labelled 'Rao Spacing-frequencies test' is explored and developed for testing whether two circular samples come from the same population. Its exact distribution and performance relative to comparable tests such as the Wheeler-Watson test and the Dixon test in small samples, are discussed. Although this test statistic is shown to be asymptotically normal, as one would expect, this large sample distribution does not provide satisfactory approximations for small to moderate samples. Exact critical values for small samples are obtained and tables provided here, using combinatorial techniques, and asymptotic critical regions are assessed against these. For moderate sample sizes in-between i.e. when the samples are too large making combinatorial techniques computationally prohibitive but yet asymptotic regions do not provide a good approximation, we provide a simple Monte Carlo procedure that gives very accurate critical values. As is well-known, the large number of usual rank-based tests are not applicable in the context of circular data since the values of such ranks depend on the arbitrary choice of origin and the sense of rotation used (clockwise or anti-clockwise). Tests that are invariant under the group of rotations, depend on the data through the so-called 'spacing frequencies', the frequencies of one sample that fall in between the spacings (or gaps) made by the other. The Wheeler-Watson, Dixon, and the proposed Rao tests are of this form and are explicitly useful for circular data, but they also have the added advantage of being valid and useful for comparing any two samples on the real line. Our study and simulations establish the 'Rao spacing-frequencies test' as a desirable, and indeed preferable test in a wide variety of contexts for comparing two circular samples, and as a viable competitor even for data on the real line. Computational help for implementing any of these tests, is made available online "TwoCircles" R package and is part of this paper.

The window functions, which are widely used in Finite Impulse Response (FIR) digital filter design with the Fourier Series Methods, aim to eliminate the oscillations adversely affecting the filter performance that occur during the filter design. Window functions, which were developed with different methods in the literature, were designed with three different optimization techniques in this study. By using Particle Swarm Optimization (PSO), Grey Wolf Colony Optimization (GWCO), Cuckoo Search Optimization (CSO) algorithms, which are among current metaheuristic optimization methods, new window functions are designed for FIR digital filter design such that the designed new window functions have different window coefficients and design parameters. The difference of the designed window functions was analyzed with the Friedman and Wilcoxon tests, which are statistical data analysis methods, and the originality of the designed window functions was proven. FIR digital filters that can perform the same operation as the designed window functions have been produced and the results obtained by performing the filtering application of the EEG signal with the produced filters are presented in the study. [ABSTRACT FROM AUTHOR]

STMIK Indonesia Padang seeks to improve the quality of its graduates by providing several academic training in information systems science and soft skills. One of them is training to improve graduates' English skills, including English I and II, English for career, and TOEFL training. The purpose of the study was to evaluate the success of the English language improvement program given to graduates of STMIK Indonesia Padang by comparing the scores for all English programs. This research method is explanatory. Data processing and analysis used descriptive statistics and comparative mean analysis techniques with Friedman test statistics on 4 data groups and Wilcoxon on two data groups. The sample of this study is a saturated sample (170 people) and dependent. The results of the test statistic showed that the value of sig. is less than 0.05. This shows that there is a significant difference between the average scores of English I, English II, English for Career, and TOEFL, either simultaneously or not. Based on descriptive statistics, it was found that the difference did not indicate an increase in the average score on English language skills. Several recommendations can be made in improving English, including (1) the implementation of continuous training, not only at the beginning of the semester and at the end of the semester for students, (2) Increasing the practice of communicating English such as participating in debate competitions, storytelling, and speeches.

Türkiye, which has a complex climate structure, is shown among the countries that will be most affected by climate change with the effect of global warming. These effects will differ in different regions due to their topographic structure, location, and orographic characteristics. Trend analyzes are used to determine the direction and magnitude of this variability. In this study, trend analysis was carried out by using hydrometeorological data obtained from streamflow and meteorology stations located in the Karasu Sub-Basin located in the Euphrates-Tigris Basin. Three innovative trend methods, namely the Şen-Innovative Trend Analysis, Onyutha Trend Test, Combination of Wilcoxon Test and Scatter Diagram trend tests, were used together with the classical Mann-Kendall method in the monthly scale analysis of hydrometeorological data of the 1979-2020 period. Both graphical and statistical trend analysis can be done with innovative methods. The results obtained in the study, in which trend analyses were evaluated at the a=0.05 significance level, reveal significant and insignificant decreasing trends in the parameters of mean streamflow, maximum precipitation, total precipitation, mean and minimum relative humidity. Significant and insignificant increasing trends were determined in the maximum, minimum, and mean temperature, maximum relative humidity, and mean wind speed data. The trends obtained in the methods used in general are consistent with each other. The findings of this study could lead to a better knowledge of the region's hydrology and contribute sustainable water management. The trend analysis methods used in the study are thought to be quite helpful in the analysis of hydro-meteorological time series. [ABSTRACT FROM AUTHOR]

Classification is an important activity in a variety of domains. Class imbalance problem have reduced the performance of the traditional classification approaches. An imbalance problem arises when mismatched class distributions are discovered among the instances of class of classification datasets. An advance extended binomial GLMBoost (EBGLMBoost) coupled with synthetic minority over-sampling technique (SMOTE) technique is the proposed model in the study to manage imbalance issues. The SMOTE is used to solve the proposed model, ensuring that the target variable's distribution is balanced, whereas the GLMBoost ensemble techniques are built to deal with imbalanced datasets. For the entire experiment, twenty different datasets are used, and support vector machine (SVM), Nu-SVM, bagging, and AdaBoost classification algorithms are used to compare with the suggested method. The model's sensitivity, specificity, geometric mean (G-mean), precision, recall, and F-measure resulted in percentages for training and testing datasets are 99.37, 66.95, 80.81, 99.21, 99.37, 99.29 and 98.61, 54.78, 69.88, 98.77, 96.61, 98.68, respectively. With the help of the Wilcoxon test, it is determined that the proposed technique performed well on unbalanced data. Finally, the proposed solutions are capable of efficiently dealing with the problem of class imbalance. [ABSTRACT FROM AUTHOR]

Measuring suspended sediment in fluvial systems is critical to understanding and managing water resources. Sampling suspended sediment has been the primary means of understanding fluvial suspended sediment. Specialized samplers, sampling methods, and laboratory methods developed by select U.S. Federal Agencies are more representative of river and stream conditions than commonly used grab sampling and total suspended solids (TSS) laboratory methods but are not widely used because they are expensive, time consuming, and not required as part of water quality standards in the United States. A new suspended-sediment sampling method called a depth-integrated grab (DIG) was developed by combining certain elements from both grab and depth-integrating sampling methods and suspended-sediment concentration (SSC) laboratory methods. The goal of the DIG method was to provide more accurate results than Grab-TSS while being easier and cheaper to sample than specialized samplers and methods. Approximately 50 paired comparison samples were collected at 9 sites in Minnesota from 2018 through 2019. Results showed no significant difference between the DIG and specialized sampling methods and a significant difference between both methods and the Grab-TSS method. The DIG-SSC provided an improved alternative to the Grab-TSS method, but additional research and testing is important to evaluate if this method is appropriate in different conditions than were observed in this study. [ABSTRACT FROM AUTHOR]

Abstract The Euphrates–Tigris River Basin (ETRB), one of the largest river basins in the Middle East, is also among the most risky transboundary basins in the world. ETRB has a critical importance for the region both politically and economically due to its location. Evaluating the increasing regional impacts of climate change is even more important for the sustainable management of water and soil resources, especially in transboundary basins such as ETRB. Türkiye is one of the most important riparian countries of the ETRB and the Türkiye part of ETRB constitutes the headwater of the basin. In this study, the temporal variability of the annual total precipitation data for the period 1965–2020 of eighteen stations located in the Türkiye part of the ETRB was investigated. Classical Mann–Kendall (MK) test was used to statistically determine the monotonic trend of precipitation. In addition to the MK method, analyses were carried out with three innovative trend methods, which have the ability to interpret trends both statistically and graphically. These innovative trend methods are Şen innovative trend analysis (Şen-ITA), Onyutha trend test (OTT) and trend analysis with combination of Wilcoxon test and scatter diagram (CWTSD). The results obtained show that there is a decreasing trend in annual total precipitation in ETRB according to all trend methods generally used for the examined period. In addition, the results obtained from the relatively new OTT and CWTSD methods show strong consistency with the results of the other two methods. The advantages such as performing numerical and visual trend analysis with innovative OTT and CWTSD methods, identifying trends in low–medium–high value data and detecting sub-trends have shown that these methods can be used as an alternative to the widely used MK and Şen-ITA.

Purpose. To compare various approaches to statistical analysis of fellow eyes and to describe correct analysis using nonparametric tests with R software. Material and methods. Various approaches to statistical analysis of fellow eyes are analyzed. Three general strategies are stated: 1) inclusion of both eyes in the same group using standard methods of statistical analysis; 2) inclusion in the same group only one eye of each subject using standard statistical methods; 3) inclusion of the both eyes in the same group using advanced statistical methods accounting correlation between fellow eyes. Results. The first approach leads to a significant underestimation of p-values when comparing groups and increases the risk of rejecting the correct null hypothesis. The second approach does not allow taking into account all available data and decreases the statistical power of a study. The third approach uses all available data and allows making valid inferences. Conclusion. Unreasonable use of standard statistical approaches for analyses quantitative data of fellow eyes leads to a significant distortion of p-values, does not allow taking into account all the material. Best practices for such situations are advanced statistical techniques accounting correlations between fellow eyes, such as the RGL and DS methods of package clusrank for R language.

The European Union and the whole world are facing the problem of increasing income and wealth inequalities at the country and regional levels. Inequalities are a severe obstacle to sustainable and balanced growth. Politicians perceive this problem by trying to implement different types of development models. The cohesion policy is the European Union's response to the development differences between countries and regions. It is clearly defined that development has to be sustainable and inclusive, which is reflected in the objectives of all leading EU development strategies. The aim of this paper is (1) to examine how effective the EU cohesion policy is in reducing regional disparities (2) to assess that the main cause of regional disparities is the misallocation of human capital. The authors base their study on the regions of Poland, estimating the state of their human capital and innovation between 2004 and 2018. We examine the effectiveness of cohesion policy using functional data analysis—the Wilcoxon test, which was used for the first time for this type of issue. The current question is whether to help metropolises and large cities, which will entail the development of smaller units, or whether to try to increase the rate of development of smaller and economically weaker territorial units in the first place. [ABSTRACT FROM AUTHOR]

The Euphrates–Tigris River Basin (ETRB), one of the largest river basins in the Middle East, is also among the most risky transboundary basins in the world. ETRB has a critical importance for the region both politically and economically due to its location. Evaluating the increasing regional impacts of climate change is even more important for the sustainable management of water and soil resources, especially in transboundary basins such as ETRB. Türkiye is one of the most important riparian countries of the ETRB and the Türkiye part of ETRB constitutes the headwater of the basin. In this study, the temporal variability of the annual total precipitation data for the period 1965–2020 of eighteen stations located in the Türkiye part of the ETRB was investigated. Classical Mann–Kendall (MK) test was used to statistically determine the monotonic trend of precipitation. In addition to the MK method, analyses were carried out with three innovative trend methods, which have the ability to interpret trends both statistically and graphically. These innovative trend methods are Şen innovative trend analysis (Şen-ITA), Onyutha trend test (OTT) and trend analysis with combination of Wilcoxon test and scatter diagram (CWTSD). The results obtained show that there is a decreasing trend in annual total precipitation in ETRB according to all trend methods generally used for the examined period. In addition, the results obtained from the relatively new OTT and CWTSD methods show strong consistency with the results of the other two methods. The advantages such as performing numerical and visual trend analysis with innovative OTT and CWTSD methods, identifying trends in low–medium–high value data and detecting sub-trends have shown that these methods can be used as an alternative to the widely used MK and Şen-ITA. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic changed the circumstances of the business environment, affecting almost every industry and company one way or another. The pandemic disrupted the global economy, leading to significant changes in the business landscape. The meat industry in Serbia was not an exception. The aim of this study was to examine the impact of the pandemic on the profitability of the meat industry in Serbia by using a causal comparative design. The research sample included 88 business entities with top market share from the meat industry, analyzed through five years (2016-2020) (440 financial statements were the units of observation). The normality of dataset distribution was tested by Shapiro-Wilk and Kolmogorov-Smirnov tests, while the Wilcoxon statistical tool was used to confirm the impact of the pandemic. Results showed the COVID-19 pandemic did have a positive impact on meat industry profitability in Serbia (so the null hypothesis was rejected). Findings from this paper add to the existing literature regarding the economic effects of the pandemic and could be useful for business entities' owners and investors in their decision-making processes. [ABSTRACT FROM AUTHOR]

Background: Irritable bowel syndrome (IBS) has a high worldwide prevalence and there are few effective treatment options. Patient education can influence patient behavior that subsequently may lead to changes in attitudes and skills necessary for maintenance or improvement in management of symptom severity and quality of life. However, as postdiagnostic patient education can be resource demanding, assessment of digital approaches and verification of their effectiveness is warranted., Objective: This cohort study aimed to investigate the effects of a digital web-based multidisciplinary eHealth program on the domains of symptom severity (Irritable Bowel Syndrome Symptom Severity Scale [IBS-SSS]), quality of life (irritable bowel syndrome quality of life [IBS-QOL]), anxiety and depression (Hospital Anxiety and Depression Scale), and a measure of general client satisfaction (client satisfaction questionnaire), compared with an onsite multidisciplinary 2-day group-based education program ("IBS-school"), in 2 cohorts of 255 patients with IBS., Methods: Patients diagnosed with IBS, aged 15-70 years, were enrolled after referral to the Section of Gastroenterology at Haukeland University Hospital, Norway. In total, 132 patients were recruited to the eHealth program and 123 to the IBS-school group for comparison. Data were self-reported and collected digitally at enrollment and after 3 months, between 2017 and 2019. Furthermore, 71 attending the eHealth program and 49 attending the IBS-school completed the questionnaires at 3 months. Intervention response was defined as a reduction of ≥50 points on the IBS-SSS., Results: Patients attending the eHealth program reported a significant reduction in IBS symptom severity 3 months after treatment (n=71), compared with patients attending the IBS-school (n=50). Overall, patients categorized as intervention responders in both programs showed a significant reduction in symptom severity at 3 months. Here, 41% (29/71) of patients attending the eHealth program reported a mean IBS-SSS reduction of 103 (SD 72.0) points (P<.001). In addition, these patients reported reduced anxiety (P>.001) and depression (P=.002) and enhanced quality of life (P=.03), especially the degrees of dysphoria, body image, food avoidance, health worry, interference with activity, relations, and social relations. Patients responding to the IBS-school intervention (18/50, 36%) reported a mean IBS-SSS reduction of 119 (SD 86.2) points (P<.001), and reduced depression scores (P=.046), but no difference in overall quality of life. Both groups reported the respective interventions as "good" quality health care programs, scoring them 23.5 (SD 4)-the eHealth program 23.5 (SD 4), and the IBS-school 24.2 (SD 4)-on the client satisfaction questionnaire., Conclusions: We conclude that the digital multidisciplinary eHealth program has a significant effect on IBS symptom severity in a portion of patients; it is useful as a tool in disease self-management and does not result in worse symptom scores than an onsite multidisciplinary 2-day group-based education program after 3 months. We believe these results indicate that a digital eHealth approach is preferable to an onsite multidisciplinary 2-day group-based education program covering the same topics., (© Birgitte Berentsen, Camilla Thuen, Eline Margrete Randulff Hillestad, Elisabeth Kjelsvik Steinsvik, Trygve Hausken, Jan Gunnar Hatlebakk. Originally published in JMIR Human Factors (https://humanfactors.jmir.org).)

Sequential change point tests aim at giving an alarm as soon as possible after a structural break has occurred while controlling the asymptotic false alarm rate. For such tests it is of particular importance to understand how quickly a break is detected. In this paper, we derive the asymptotic distribution of the delay times for sequential change point procedures based on U-statistics. This includes the difference-of-means (DOM) sequential test that has been discussed previously but also a new robust Wilcoxon sequential change point test. Based on these results we can compare the detection delay of both procedures instead of having to rely on simulations only. Indeed, the Wilcoxon sequential procedure has a smaller detection delay for heavier tailed distributions which is also confirmed by simulations. While in the previous literature for the DOM test only results for early change points were obtained, we derive the asymptotic distribution of the delay times for both early as well as late change points. Finally, we evaluate how well the asymptotic distribution approximates the actual stopping times for finite samples via a simulation study. • Asymptotic distribution of delay times for sequential tests of U -statistics. • This includes a robust Wilcoxon sequential procedure. • This includes late (linear and superlinear) changes. • For early changes the approximation improves upon existing approximations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Science & Technology (JCS&T) is the property of Journal of Computer Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current paper presented a stochastic integrated queueing-inventory-routing problem into a green dual-channel supply chain considering an online retailerwith a vehicle-routing problem(VRP) and a traditional retailing channelwith an M/M/C queueing system. Amixed-integer non-linear programming model (MINLP) is presented to address the integrated VRP and M/M/C queueing system. The suggestedmodel makes decisions about optimal routing, delivery time interval to customers, number of servers in traditional retailers, inventory replenishment policies, and retailers' price. For the first time, this model considers two retailing channels simultaneously under different uncertainty, including demand, delivery lead time, service time, and delivery time interval to customers. The inventory model also follows a continuous-timeMarkov chain. The small-scale test problems are solved using GAMS software. Since the problem is NP-hard, this study conducts a comprehensive comparative analysis of the performance of 13 different metaheuristics. The ant lion optimiser, dragonfly algorithm, grasshopper optimisation algorithm, Harris-hawks optimisation, moth-flame optimisation algorithm, multi-verse optimizer, sine cosine algorithm, salp-swarm algorithm, the whale optimisation algorithm, grey-wolf optimiser, genetic algorithm, differential evolution, and particle swarm optimization are algorithms that were chosen for this study. Comprehensive statistical tests were conducted to evaluate the performance of these methods. Furthermore, the model is executed for construction material producers as a case study. Finally, sensitivity analyses were conducted on crucialmodel parameters; and managerial insights were recommended. [ABSTRACT FROM AUTHOR]

Feature selection (FS) techniques generally require repeatedly training and evaluating models to assess the importance of each feature for a particular task. However, due to the increasing size of currently available databases, distributed processing has become a necessity for many tasks. In this context, the Apache Spark ML library is one of the most widely used libraries for performing classification and other tasks with large datasets. Therefore, knowing both the predictive performance and efficiency of its main algorithms before applying a FS technique is crucial to planning computations and saving time. In this work, a comparative study of four Spark ML classification algorithms is carried out, statistically measuring execution times and predictive power based on the number of attributes from a colon cancer database. Results were statistically analyzed, showing that, although Random Forest and Na¨ıve Bayes are the algorithms with the shortest execution times, Support Vector Machine obtains models with the best predictive power. The study of the performance of these algorithms is interesting as they are applied in many different problems, such as classification of pathologies from epigenomic data, image classification, prediction of computer attacks in network security problems, among others.

Groundwater management is essential in water and environmental engineering from both quantity and quality aspects due to the growing urban population. Groundwater vulnerability evaluation models play a prominent role in groundwater resource management, such as the DRASTIC model that has been used successfully in numerous areas. Several studies have focused on improving this model by changing the initial parameters or the rates and weights. The presented study investigated results produced by the DRASTIC model by simultaneously exerting both modifications. For this purpose, two land use-based DRASTIC-derived models, DRASTICA and susceptibility index (SI), were implemented in the Shiraz plain, Iran, a semi-arid region and the primary resource of groundwater currently struggling with groundwater pollution. To develop the novel proposed framework for the progressive improvement of the mentioned rating-based techniques, three main calculation steps for rates and weights are presented: (1) original rates and weights; (2) modified rates by Wilcoxon tests and original weights; and (3) adjusted rates and optimized weights using the genetic algorithm (GA) and particle swarm optimization (PSO) algorithms. To validate the results of this framework applied to the case study, the concentrations of three contamination pollutants, NO3, SO4, and toxic metals, were considered. The results indicated that the DRASTICA model yielded more accurate contamination concentrations for vulnerability evaluations than the SI model. Moreover, both models initially displayed well-matched results for the SO4 concentrations, specifically 0.7 for DRASTICA and 0.58 for SI, respectively. Comparatively, the DRASTICA model showed a higher correlation with NO3 concentrations (0.8) than the SI model (0.6) through improved steps. Furthermore, although both original models demonstrated less correlation with toxic metal concentrations (0.05) compared to SO4 and NO3 concentrations, the DRASTICA and SI models with modified rates and optimized weights exhibited enhanced correlation with toxic metals of about 0.7 and 0.2, respectively. [ABSTRACT FROM AUTHOR]

Graph-based clustering identification is a practical method to detect the communication between nodes in complex networks that has obtained considerable comments. Since identifying different communities in large-scale data is a challenging task, by understanding the communication between the behaviours of the elements in a community (a cluster), the general characteristics of clusters can be predicted. Graph-based clustering methods have played an important role in clustering gene expression data because of their ability to show the relations between the data. In order to be able to identify genes that lead to the development of diseases, the communication between the cells must be established. The communication between different cells can be indicated by the expression of different genes within them. In this study, the problem of cell-to-cell communication is expressed as a graph and the communication are extracted by recognizing the communities. The FANTOM5 dataset is used to simulate and calculate the similarity between cells. After pre-processing and normalizing the data, to convert this data into graphs, the expression of genes in different cells was examined and by considering a threshold and Wilcoxon test, the communication between them were identified through using clustering. The results of the comparisons showed that the proposed method Silhouette coefficient of 0.814 with a threshold of 0.2 for cells and 0.789 with a threshold of 0.1 performs better than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The article presents findings of the pilot empirical research on the performance of the ‘Mathematical Methods in Psychology’ e-course as an open digital educational resource for the implementation of blended learning using the ‘flipped classroom’ model at the university. The students' attitudes toward blended learning in the e-course format, their self-esteem of involvement in the learning process, as well as the educational results were examined. The main benefits for the students are the following: the accessibility of tutorials and information about their individual learning trajectory at any time; the convenience of self-preparation for classes. The students characterize the activity at seminars as active interaction with classmates and a teacher and involvement in the learning process. In general, the students positively assessed the use of digital resources in the blended learning educational process as a modern approach and expressed their wish to study other subjects or courses in this format (at other universities in Russia and abroad as well). No essential difficulties in taking the e-course were revealed. There is a correlation between the students’ positive evaluations of their own educational outcomes and their positive attitude to the new format (р < 0,001). The educational outcomes of the students after passing the e-course statistically significantly improved (р < 0,001). The blended learning group educational results are significantly higher as compared to traditional in-class education group (р < 0,001). The study was conducted at the Moscow State University of Psychology and Education. The total sample size N = 387 students of psychology departments, the experimental group size N = 78. The comparison group of traditional full-time education students (N = 309) is balanced with the experimental group in the following context parameters: gender and age, major academic disciplines, same teacher, MSUPE students.

Image thresholding is a well approved pre-processing methodology and enhancing the image information based on a chosen threshold is always preferred. This research implements the mayfly optimization algorithm (MOA) based image multi-level-thresholding on a class of benchmark images of dimension 512x512x1. The MOA is a novel methodology with the algorithm phases, such as; i) Initialization, ii) Exploration with male-mayfly (MM), iii) Exploration with female-mayfly (FM), iv) Offspring generation and, v) Termination. This algorithm implements a strict two-step search procedure, in which every Mayfly is forced to attain the global best solution. The proposed research considers the threshold value from 2 to 5 and the superiority of the result is confirmed by computing the essential Image quality measures (IQM). The performance of MOA is also compared and validated against the other procedures, such as particle-swarm-optimization (PSO), bacterial foraging optimization (BFO), firefly-algorithm (FA), bat algorithm (BA), cuckoo search (CS) and moth-flame optimization (MFO) and the attained p-value of Wilcoxon rank test confirmed the superiority of the MOA compared with other algorithms considered in this work. [ABSTRACT FROM AUTHOR]

This paper delves into Wilcoxon's sum-of-ranks $R$ statistic (and Mann-Whitney's translated equivalent $U$) for testing the difference between two groups. The first four raw and central moments and the $\gamma _1$ and $\gamma _2$ shape indices of the statistic are given, along with a general method for working them out. As for $R$'s null distribution, a fair sample of published tables is offered, plus two continuous (Beta, Normal) approximations. We provide a detailed procedure for establishing individual probabilities of $R$ using a recursive function of the partition of an integer. A worked out example serves to illustrate the mathematical contents. In a conclusive section, we examine the statistical power of the $R$ (and $U$) test against Student's t reference test, with a somewhat advantageous recommendation for the former. // La statistique $R$ (ou $W$) attribuée à Wilcoxon (1945), soit la somme des rangs associés aux $k$ observations du groupe 1, $R = r_1 + r_2 + ... + r_k$ , permet de décider si le niveau des données diffère entre les $k$ observations du groupe 1 et les $n - k$ observations du groupe 2, ce au moyen d'un test non-paramétrique. Algébriquement équivalente au $U$ de Mann et Whitney (1947), elle supplée au test $t$ de la différence entre deux moyennes indépendantes sans reposer sur la condition de normalité des observations. Nous mettons d'abord en place un exemple fictif, avec sa solution classique par le test $t$ de Student. Nous présentons ensuite une procédure d'intérêt général pour établir les moments à l'origine et les moments centraux de $R$ et de $U$, ce qui nous permet de présenter une expression algébrique simple de ses 3$^e$ et 4$^e$ moments et, grâce à eux, d'offrir une modélisation de la distribution de $R$ et $U$ par la loi Bêta symétrique. Enfin, nous nous intéressons à la distribution nulle de R, ce grâce à une variante originale de la méthode de partition d'un entier : la parenté entre l'élément $f_{n,k}(R)$ de la distribution de fréquences de $R$ et le nombre de partitions d'un entier est étudiée. Nous proposons une fonction $Q(S,k,x)$, à définition récursive, qui dénombre les $k$ partitions de l'entier $S$ formées d'éléments si tels que $1 \le s_i \le x$. Posant $S = R-\nicefrac {1}{2}k(k-1)$, $k = k$ et $x = n-k+1$, la fonction $Q$ fournit directement la fréquence $f_{n,k}(R)$. Les calculs (moments, probabilité exacte, modélisation Bêta, approximation normale) sont alors appliqués à l'exemple donné en illustration. L'exposé se conclut par des considérations sur la puissance statistique du test de Wilcoxon, sous différentes conditions.

Multivariate calibration based on partial least squares, random forest, and support vector machine methods, combined with the MissForest imputation algorithm, was used to understand the interaction between ozone and nitrogen oxides, carbon monoxide, wind speed, solar radiation, temperature, relative humidity, and others, the data of which were collected by air quality monitoring stations in the metropolitan area of Rio de Janeiro in four distinct sites between, 2014 and, 2018. These techniques provide an easy and feasible way of modeling and analyzing air pollutants and can be used when coupled with other methods. The results showed that random forest and support vector machine chemometric techniques can be used in modeling and predicting tropospheric ozone concentrations, with a coefficient of determination for making predictions up to 0.92, a root-mean square error of calibration between 4.66 and 27.15 µg m−3, and a root-mean square error of prediction between 4.17 and 22.45 µg m−3, depending on the air quality monitoring stations and season. [ABSTRACT FROM AUTHOR]