Boxplot Analysis Research Articles

Classification of flame extinction based on acoustic oscillations using artificial intelligence methods

Fire, one of the most serious disasters threatening human life, is a chemical event that can destroy forests, buildings, and machinery within minutes. For this reason, there have been numerous methods developed to extinguish the fire. Within the scope of this study, a sound wave flame extinction system was developed in order to extinguish the flames at an early stage of the fire. The data used in the study were obtained as a result of experiments conducted with the developed system. The created dataset consists of data obtained from 17,442 experiments. It is aimed to classify the fuel type, flame size, decibel, frequency, airflow and distance features, and the extinction-non-extinction status of the flame through rule-based machine learning methods. In the study, rule-based machine learning methods, ANFIS (Adaptive-Network Based Fuzzy Inference Systems), CN2 Rule and DT (Decision Tree) were used. The methods of Box Plot, Scatter Plot and Correlation Analysis were utilized for statistical analysis of the data. As a result of the classifications, respectively, 94.5%, 99.91%, and 97.28% success were achieved with the ANFIS, CN2 Rule, and DT methods. As a result of the evaluations made by using Box Plot, Scatter Plot and Correlation Analysis.

Predictive Models to Estimate Carbon Stocks in Agroforestry Systems

This study aims to assess the carbon stock in a pasture area and fragment of forest in natural regeneration, given the importance of agroforestry systems in mitigating gas emissions which contribute to the greenhouse effect, as well as promoting the maintenance of agricultural productivity. Our other goal was to predict the carbon stock, according to different land use systems, from physical and chemical soil variables using the Random Forest algorithm. We carried out our study at an Entisols Quartzipsamments area with a completely randomized experimental design: four treatments and six replites. The treatments consisted of the following: (i) an agroforestry system developed for livestock, (ii) an agroforestry system developed for fruit culture, (iii) a conventional pasture, and (iv) a forest fragment. Deformed and undeformed soil samples were collected in order to analyze their physical and chemical properties across two consecutive agricultural years. The response variable, carbon stock, was subjected to a boxplot analysis and all the databases were used for a predictive modeling which in turn used the Random Forest algorithm. Results led to the conclusion that the agroforestry systems developed both for fruit culture and livestock, are more efficient at stocking carbon in the soil than the pasture area and forest fragment undergoing natural regeneration. Nitrogen stock and land use systems are the most important variables to estimate carbon stock from the physical and chemical variables of soil using the Random Forest algorithm. The predictive models generated from the physical and chemical variables of soil, as well as the Random Forest algorithm, presented a high potential for predicting soil carbon stock and are sensitive to different land use systems.

HSWOA: An ensemble of hunger games search and whale optimization algorithm for global optimization

The search for food stimulated by hunger is a common phenomenon in the animal world. Mimicking the concept, recently, an optimization algorithm Hunger Games Search (HGS) has been proposed for global optimization. On the other side, the Whale Optimization Algorithm (WOA) is a commonly utilized nature-inspired algorithm portrayed by a straightforward construction with easy parameters imitating the hunting behavior of humpback whales. However, due to minimum exploration of the search space, WOA has a high chance of trapping into local solutions, and more exploitation leads it towards premature convergence. The concept of hunger from HGS is merged with the food searching techniques of the whale to lessen the inherent drawbacks of WOA. Two weights of HGS are adaptively designed for every whale using the respective hunger level for balancing search strategies. Performance verification of the proposed hunger search-based whale optimization algorithm (HSWOA) is done by comparing it with 10 state-of-the-art algorithms, including three very recently developed algorithms on 30 classical benchmark functions. Comparison with some basic algorithms, recently modified algorithms, and WOA variants is performed using IEEE CEC 2019 function set. Statistical performance of the proposed algorithm is verified with Friedman's test, boxplot analysis, and Nemenyi multiple comparison test. The operating speed of the algorithm is determined and tested with complexity analysis and convergence analysis. Finally, seven real-world engineering problems are solved and compared with a list of metaheuristic algorithms. Numerical and statistical performance comparison with state-of-the-art algorithms confirms the efficacy of the newly designed algorithm.

Automated assessment for Alzheimer's disease diagnosis from MRI images: Meta‐heuristic assisted deep learning model

AbstractAlzheimer's disease (AD) is a widespread neurodegenerative disease that causes 60–80% of all dementias and has a large economic impact in developed countries. For early‐stage AD detection, volumetric measures of magnetic resonance imaging (MRI) have proven to be a benchmark method. To detect potential cases, existing methods combine health records, neuropsychological testing, and MRI, although learning implementation is inconsistently used and has low sensitivity and specificity. Furthermore, numerous classification approaches for diagnosing AD have been suggested with differing complexity. Thus, we have introduced our novel AD diagnosis model with two main phases such as proposed feature extraction and classification. In the first phase, the gray‐level co‐occurrence matrix (GLCM), Haralick features as well as proposed geometric Haralick features known as geometric correlation and variance are extracted. In the second phase, an optimized deep convolutional neural network (DCNN) is utilized for classification. To make the prediction more accurate, the weight and the activation function of DCNN are optimally chosen by a new hybrid model termed as Combined Gray Wolf and Dragon Updating (CG‐DU). At last, the superiority of the adopted scheme is validated in terms of performance analysis, convergence analysis, box plot analysis, and computation time analysis. Especially, the proposed model achieves a mean accuracy of 0.98795, sensitivity of 0.98671, and specificity of 0.99429. Moreover, the computation time of the CG‐DU model is 2.92%, and 0.14% superior to existing GWO and DA methods respectively.

Research on Visual Monitoring System of Line Loss in Power Internet of Things and Power Distribution Network

In order to reduce the comprehensive line loss of the power grid, a visual monitoring technology of line loss in the distribution network based on the Internet of Things is proposed. The paper incorporates the monitoring and management of line loss during the same period into the visualization platform of the distribution network to monitor and manage the effectiveness of the whole process, and solve the problems of incomplete information, untimely filing, and inadequate updates, and fully support "one data source, one grid map, and one business line" The construction and application of the ubiquitous power Internet of Things. Based on the Internet of Things technology, the article screens the distribution network line loss data for the same period and uses Tableau software to make a visual worksheet, visualizes the line loss data, analyses the relationship between the distribution network line loss data for the same period, and conducts anti-theft analysis on the same period distribution network. The paper then performs K-means algorithm clustering analysis, visual analysis and box plot analysis on the line loss data. Summarize the threshold range of each line loss index corresponding to different conditions such as abnormal station area, to-be-upgraded station area and normal station area. Finally, build a visual interactive platform for the analysis of line loss abnormalities in the station area. Experiments have proved that the line loss visual monitoring system can control the overall situation of line loss and the processing of abnormal line loss in real time, which greatly improves work efficiency.

Gas Chromatography-Mass Spectrometry Analysis of Compounds Emitted by Pepper Yellow Leaf Curl Virus-Infected Chili Plants: A Preliminary Study

Pepper yellow leaf curl virus (PYLCV) is a threat to chili plants and can significantly reduce yields. This study aimed as a pilot project to detect PYLCV by analyzing compounds emitted by chili plants using gas chromatography-mass spectrometry (GC-MS). The samples investigated in this research were PYLCV-infected and PYLCV-undetected chili plants taken from commercial chili fields. The infection status was validated by using a polymerase chain reaction (PCR) test. A headspace technique was used to extract the volatile organic compounds emitted by plants. The analysis of GC-MS results began with pre-processing, analyzing sample compound variability with a boxplot analysis, and sample classification by using a multivariate technique. Unsupervised multivariate technique principal component analysis (PCA) was performed to discover whether GC-MS could identify PYLCV-infected or not. The results showed that PYLCV-infected and PYLCV-undetected chili plants could be differentiated, with a total percent variance of the first three principal components reaching 91.32%, and successfully discriminated between PYLCV-infected and PYLCV-undetected chili plants. However, more comprehensive studies are needed to find the potential biomarkers of the infected plants.

Investigation of Particulate Matter (PM10) Pollution in Ipoh City, Malaysia

Air pollution is one of the phenomena that adversely affect the environment and negatively impact human health. Air pollution occurs when there are excessive air pollutants in the atmosphere. This study aims to identify and discuss the sources of PM10 emissions according to land use around Ipoh using descriptive and boxplot analysis. There are five areas selected as a sampling location for this study: Ipoh, Lahat, Chemor, Tanjung Rambutan, and Simpang Pulai that located in Hulu Kinta and Sungai Raya district. This sampling location's primary focus is located around Ipoh city, concentrated in the industrial area. Ten land use-based sampling stations are selected to measure. PM10 data were recorded for one week during working and non-working days in July 2020 on 10 sampling stations around Ipoh. The result shows PM10 concentrations consist of industrial, infrastructure and utility, commercial, residential, and recreational areas, as shown PM10 values were recorded for one week during working days and non-working days on 10 observation stations using Portable Laser Aerosol Spectrometer and Dust Monitor Model 1.108. Based on the observations, two stations show the daily average mean of PM10 exceeding 100 μg/m3 outlined in the Malaysian Ambient Air Quality Standard (MAAQS) at S6 (175.78 μg/m3) and S10 (103.79 μg/m3). This situation is driven by the presence of limestone-based industrial areas as well as quarrying activities. The findings also show that PM10 concentration is higher during working days rather than non-working days. Overall, PM10 concentrations that exceed the limit will have a detrimental effect on the environment and human health.

Determining context of association rules by using machine learning

ABSTRACT Association rule mining is typically used to uncover the enthralling interdependencies between the set of variables and reveals the hidden pattern within the dataset. The associations are identified based on co-occurring variables with high frequencies. These associations can be positive (A→B) or negative (A→⌐B). The number of these association rules in larger databases are considerably higher which restricted the extraction of valuable insights from the dataset. Some rule pruning strategies are used to reduce the number of rules that can sometimes miss an important, or include an unimportant rule into the final rule set because of not considering the context of the rule. Context-based positive and negative association rule mining (CBPNARM) for the first time included context variable in the algorithms of association rule mining for selection/ de-selection of such rules. In CBPNARM, the selection of context variable and its range of values are done by the user/expert of the system which demands unwanted user interaction and may add some bias to the results. This paper proposes a method to automate the selection of context variable and selection of its value range. The context variable is chosen by using the diversity index and chi-square test, and the range of values for the context variable is set by using box plot analysis. The proposed method on top of it added conditional-probability increment ratio (CPIR) for further pruning uninteresting rules. Experiments show the system can select the context variable automatically and set the right range for the selected context variable. The performance of the proposed method is compared with CBPNARM and other state of the art methods.

Subjective well being in adolescences on Minangkabau ethnic; an analysis based of dimension and gender

This study aims to analyze the subjective well-being of adolescents in Minangkabau ethnicity. There are 182 participants of Minang adolescencets. The method in this study used a quantitative descriptive approach. The research instrument uses a subjective well being scale that has been tested for validity and reliability. The data analysis technique used descriptive statistics and boxplot analysis used the JAPS (Jeffrey's Amazing Statistics Program) application. The result showed that the subjective well being Minangkabau adolescents were in the high category, based on the subjective well being dimension, the dominant life statistic was in the high category and the affective dimension was in the medium category. Then based on gender, there is no significant difference in subjective well being of adolescent men and woman. This shows that the subjective well-being condition of Minangkabau adolescents is high as well as in each of its dimensions, both cognitive and affective. This condition needs to be optimized through various parties so that Minangkabau adolescents have good subjective well-being and avoid maladaptive behavior.

Optimization of process conditions using RSM and ANFIS for the removal of Remazol Brilliant Orange 3R in a packed bed column

The current investigation explored the potential of biochar produced from Ulva lactuca to continuously adsorb Remazol Brilliant Orange 3R (RBO3R) in a packed bed column. The influencing parameters namely sorbent bed depth, the flowrate of the solute and the initial RBO3R concentration were studied. The process conditions were optimized using the Box Behnken method Response Surface Methodology (RSM) and also the experiment results were predicted using Adaptive Neuro-Fuzzy Interface System (ANFIS). A comparison study was analyzed using statistical error analysis, boxplot and cluster analysis. The experimental results concluded that a correlation coefficient of 0.9981 and 0.9999 was obtained for RSM and ANFIS predictive models. The removal efficiency of 68.6% was obtained at optimum conditions of 15 ​cm sorbent depth, 0.45 ​L/h flowrate and 0.25 ​mmol/L initial RBO3R concentration.

Analyzing the Role of Filler Interface on the Erosion Performance of Filled RTV Silicone Rubber under DC Dry-band Arcing

This study investigates the role of filler interface on suppressing the erosion of room temperature vulcanized silicone rubber composites filled with fumed silica, nano alumina trihydrate and sub-micron hexagonal boron nitride fillers during DC dry-band arcing. Simultaneous thermogravimetric-differential thermal analyses indicate a superior effect for fumed silica in suppressing the depolymerization of silicone rubber and promoting radical based crosslinking. This can be attributed to favorable interactions at the fumed silica-silicone interface tethering the siloxane chains. At low filler loading of alumina trlhydrate, water of hydration has insignificant effects on suppressing depolymerization compared to that influenced by fumed silica's interface at equal filler loading. Similarly, incorporating thermally conductive boron nitride filler in silicone rubber does not show improvement in the depolymerization rate compared to that influenced by fumed silica. These findings correlate with the +DC inclined plane tracking and erosion test outcomes indicating superior erosion performance for the fumed silica filled composite. This accordingly supports the influential role of the filler interface over the water of hydration and thermal conductivity enhancement in suppressing the DC erosion of silicone rubber composites under the test conditions of this study. A statistical boxplot analysis technique is introduced to elucidate the inception of the stable dry-band arc in terms of the change in leakage current randomness during the +DC inclined plane tracking and erosion test. The boxplots reveal a slow inception of the stable dry-band arc with the fumed silica filled composite delaying the erosion of silicone rubber during the test. This finding confirms the influence of the filler interface over composite thermal conductivity in suppressing erosion of silicone rubber under DC dry-band arcing.

Constriction coefficient based particle swarm optimization and gravitational search algorithm for multilevel image thresholding

AbstractImage segmentation is one of the pivotal steps in image processing. Actually, it deals with the partitioning of the image into different classes based on pixel intensities. This work introduces a new image segmentation method based on the constriction coefficient‐based particle swarm optimization and gravitational search algorithm (CPSOGSA). The random samples of the image act as searcher agents of the CPSOGSA algorithm. The optimal number of thresholds is determined using Kapur's entropy method. The effectiveness and applicability of CPSOGSA in image segmentation is accomplished by applying it to five standard images from the USC‐SIPI image database, namely Aeroplane, Cameraman, Clock, Lena, and Pirate. Various performance metrics are employed to investigate the simulation outcomes, including optimal thresholds, standard deviation, MSE (mean square error), run time analysis, PSNR (peak signal to noise ratio), best fitness value calculation, convergence maps, segmented image graphs, and box plot analysis. Moreover, image accuracy is benchmarked by utilizing SSIM (structural similarity index measure) and FSIM (feature similarity index measure) metrics. Also, a pairwise non‐parametric signed Wilcoxon rank‐sum test is utilized for statistical verification of simulation results. In addition, the experimental outcomes of CPSOGSA are compared with eight different algorithms including standard PSO, classical GSA, PSOGSA, SCA (sine cosine algorithm), SSA (salp swarm algorithm), GWO (grey wolf optimizer), MFO (moth flame optimizer), and ABC (artificial bee colony). The simulation results clearly indicate that the hybrid CPSOGSA has successfully provided the best SSIM, FSIM, and threshold values to the benchmark images.

METTL3 involves the progression of osteoarthritis probably by affecting ECM degradation and regulating the inflammatory response

We aimed to identify RNA N6-methyladenosine methylation associated genes in osteoarthritis (OA), and to explore possible regulatory mechanisms of these RNA methylation associated genes. Bioinformatics analyses, including differential expression analysis, functional enrichment analysis, verification analysis, and box plot analysis, were conducted based on different datasets from OA and non-OA patients. Gene expression at mRNA and protein levels was determined by quantitative reverse transcription PCR, western blot and immunofluorescence. Interleukin 1β (IL-1β)-treated SW1353 cells was used as cell model. Lentiviral vector was used for over-expression METTL3 in vitro. CCK-8 assay kit was used to determine cell viability and inflammatory cytokines (IL-1α, IL-6, IL-8, IL-10 and TNF-α) was detected using ELISA kits. Bioinformatics analysis showed that METTL3 expression was decreased in OA group, which was confirmed in clinical samples. Expression of METTL3 was also reduced in IL-1β-treated cells. Levels of inflammatory cytokines were obviously reduced in the METTL3 overexpression group, while IL-1β treatment reversed such decrease caused by METTL3 overexpression (p < 0.05). Both METTL3 overexpression and IL-1β treatment promoted expression of p65 protein and p-ERK (p < 0.01). Additionally, increased expression of MMP1 and MMP3, and decreased expression of MMP13, TIMP-1, and TIMP-2 at both mRNA and protein levels were observed in the METTL3 overexpression group when compared with the control group (p < 0.01). Expression of m6A methylation gene METTL3 was reduced in OA. METTL3 is involved in OA probably by regulating the inflammatory response. METTL3 overexpression may affect extracellular matrix degradation in OA by adjusting the balance between TIMPs and MMPs.

Noise Reduction of Ceiling Fans in Mass Production Applying DMAIC-Six Sigma Approach

Abstract Success and business reputation depend upon the quality of products where product quality relies on the capability of a process, yield value, sigma score, etc. The poor quality of ceiling fans was determined from the Pareto analysis of the define, measure, analyze, improve and control (DMAIC) model, which was humming noise during running, and further actions were undertaken regarding the reduction of the humming noise. Noise data were collected from random samples to determine the current sigma score, which was 3.14. Also, the weak value (1.05) of the Process Potential Index implied that the process condition was below the standard (&amp;lt; 1.33) and needed to be improved badly. Then, root cause analysis and relationship diagram were used to identify the possible causes. With the design of experiments and correlation analysis, it was clear that the air gap between the stator and rotor was the main culprit behind the humming noise. The suitable range of air gap was determined from boxplot analysis, which was 0.2 mm–0.225 mm and the corresponding amount of sound level in dB (37.5–40.3 dB) for a 0.225-mm air gap with the watt consumption (83 w) from the hypothesis test for the corresponding air gap. Finally, the updated sigma score and process capability analysis were performed with control charts to show the comparison after applying the DMAIC-Six Sigma methodology. The final sigma score was 5.1, which indicates a significant improvement of the process with the capability of saving 23,438 USD/year caused by the poor quality of ceiling fans.

Análise Espaço-Temporal da Pluma de Dispersão dos Sedimentos Dragados do Porto de Maceió, Alagoas

O entendimento da dinâmica sedimentar é importante na caracterização dos impactos causados pelas dragagens e este conhecimento torna-se decisivo para o discernimento dos mesmos O objetivo deste artigo foi analisar a pluma de dispersão dos sedimentos do Porto de Maceió. As análises foram realizadas em três campanhas amostrais, antes, durante e após as obras portuárias, na qual foram plotados os pontos de coleta nas áreas de dragagem e descarte. Foram analisados o material particulado em suspensão (MPS) pelo método gravimétrico e realizadas amostragens in situ dos parâmetros turbidez, oxigênio dissolvido e porcentagem de saturação, salinidade e condutividade elétrica com sonda multiparâmetros e de transparência da água com disco de Secchi. Os parâmetros MPS, turbidez e transparência foram analisados por interpolação linear, enquanto as demais varáveis foram submetidas à análise em curva de calibração polinomial. Os multiparâmetros trabalhados foram submetidos à análise em box plot de modo a avaliar a variância entre as campanhas. A variação de MPS nas campanhas amostrais mostrou comportamento espacial e temporal dinâmicos; A interpolação promoveu melhor visualização dos parâmetros físicos que expressam diretamente a pluma de dispersão; As variáveis suplementares apresentaram padrões de tendência em relação à carga de sedimentos; A análise em box plot mostrou-se ser pertinentemente útil para avaliar e interpretar os resultados de forma integrada, principalmente devido ao grande número de parâmetros analisados; As obras de dragagem tiveram gestão equilibrada entre a quantidade de sedimentos dragados e a disposição espacial dos mesmos no local de descarte quando comparado a área de dragagem. Spatio-Temporal Analysis of the Dredged Sediment Scattering Plume in the Port of Maceió, Alagoas A B S T R A C TThe understanding of the sedimentary dynamics is important in the characterization of impacts caused by dredging and this knowledge becomes decisive for their discernment. The aim of this article was to analyze the plume of dispersion of the sediments of the Port of Maceió. The analyzes were carried out in three sampling campaigns, before, during and after the port works, in which the collection points in the dredging and discard areas were plotted. The suspended particulate material (SPM) was analyzed using the gravimetric method and in situ sampling of the parameters turbidity, dissolved oxygen and percentage of saturation, salinity and electrical conductivity with a multiparameter probe and water transparency with Secchi disk. The parameters SPM, turbidity and transparency were analyzed by linear interpolation, while the other variables were subjected to analysis on a polynomial calibration curve. The multiparameters worked were submitted to box plot analysis in order to assess the variance between campaigns. The variation of SPM in the sample campaigns showed dynamic spatial and temporal behavior; The interpolation promoted a better visualization of the physical parameters that directly express the plume of dispersion; The supplementary variables showed trend patterns in relation to the sediment charge; Box plot analysis proved to be pertinently useful for evaluating and interpreting the results in an integrated manner, mainly due to the large number of parameters analyzed; The dredging works had a balanced management between the amount of dredged sediments and their spatial disposition at the discard site when compared to the dredging area.Keywords: Dredging. Suspend particulate material. Multiparametric variation.

Study of drought stress using PEG-6000 on germination stage of sesame germplasm (Sesamum indicum L.) under laboratory conditions

Sesame is a drought tolerant oilseed crop, but it is sensitive to drought at germination stage. This study was conducted to determine the effect of drought stress on germination of Eight hundred twenty nine sesame germplasm. The experiment was conducted in the laboratory of the Project Co-ordinating Unit, All India Coordinated Research Project on Sesame and Niger (Indian Council of Agricultural Research), JNKVV Campus, Jabalpur (MP). Eight hundred twenty nine sesame germplasm and two levels of drought (0.0 (control) and -4.9 bar) were the treatment tested in the experiment. Study of boxplot analysis results which is used to visually summarize and compare groups of data. On considering the values of germination percent were found maximum in SI-1116, followed by IS-158-3-84, PCU-130, PCU134, NIC-17257, NIC-8399-I, S-0534, RJS-194, JLT-8, IS-130, NIC-17868, NIC-7795-A, ES-10-A and EC-138836-A at -4.9 bar and being identified as the most tolerant germplasm among those tested in this experiment. Distinction of significant difference in germination of germplasm in induced water stress suggested that this parameter could be used as criteria in to screen germplasm to identify those most drought stress at germination stage.

High performance teams: an investigation of the effect on self-management towards performance

Abstract Paper aims Work design is being constantly transformed by technology and requirements for polyvalent workers piloting the work system instead of operating it. This paper investigates self-management teams’ implementation and its effects on performance. A Brazilian cosmetics company case study of team development was used as a guidance for this investigation. Originality Consistent connection between team development theory and practice, exploring sociotechnical approach and empowerment best practices. Research method Boxplot analysis is performed over team building stages along with One-way Variance Analysis to test average performance differences among stages’ transitions. Tukey test was sequentially applied to identify its statistic differences in pairs. Descriptive analysis and Analysis of Means are complementarily applied for shop-floor accidents performance assessment. Main findings Team development reached the expected performance over team stages. Forming to storming, though, was the only transition with no performance average gains. Storming to norming was the highest improvement, which meets sociotechnical principles. Moreover, mean of accidents was inferior at performing stage comparing to the overall mean of accidents during the whole project, highlighting additional shop-floor safety improvements enhanced by the new work design. Implications for theory and practice This paper is restricted to a single sociotechnical environment. Therefore, generic conclusions imply further investigation.

Utilização de ferramentas estatísticas para verificação e controle do processo de enriquecimento isotópico de hexafluoreto de urânio enriquecido a no máximo 5% de 235U

Este artigo tem como objetivo, utilizar ferramentas estatísticas para verificação do comportamento e controle do processo de enriquecimento isotópico de hexafluoreto de urânio enriquecido a no máximo 5% de 235U. Analisar o comportamento do processo assim como o estabelecimento de um método estatístico para acompanhar e controlar a produção com a busca de aumentar a produtividade e diminuir os gastos. A metodologia utilizada para o desenvolvimento deste trabalho foram os fundamentos relacionados à estatística descritiva como o teste de normalidade de Anderson-Darling, verificação de médias e desvios padrão assim como gráficos de histograma, de probabilidade e de valores individuais de teores, além da análise boxplot para duas campanhas de enriquecimento distintas.

Quality of GNSS Traces from VGI: A Data Cleaning Method Based on Activity Type and User Experience

VGI (Volunteered Geographic Information) refers to spatial data collected, created, and shared voluntarily by users. Georeferenced tracks are one of the most common components of VGI, and, as such, are not free from errors. The cleaning of GNSS (Global Navigation Satellite System) tracks is usually based on the detection and removal of outliers using their geometric characteristics. However, according to our experience, user profile differentiation is still a novelty, and studies delving into the relationship between contributor efficiency, activity, and quality of the VGI produced are lacking. The aim of this study is to design a procedure to filter GNSS traces according to their quality, the type of activity pursued, and the contributor efficiency with VGI. Source data are obtained Wikiloc. The methodology includes tracks classification according mobility types, box plot analysis to identify outliers, bivariate user segmentation according to level of activity and efficiency, and the study of its spatial behavior using kernel-density maps. The results reveal that out of 44,326 tracks, 8096 (18.26%) are considered erroneous, mainly (73.02%) due to contributors’ poor practices and the remaining being due to bad GNSS reception. The results also show a positive correlation between data quality and the author’s efficiency collecting VGI.

Heterogeneity of MYC Abnormalities in Multiple Myeloma

Heterogeneity of MYC Abnormalities in Multiple Myeloma