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1
González-de-la-Rosa, Juan-José, Olivia Florencias-Oliveros, and Paula Remigio-Carmona. "Strategy for Visual Measurement of Power Quality Based on Higher-Order Statistics and Exploratory Big Data Analysis." Applied Sciences 15, no. 12 (2025): 6422. https://doi.org/10.3390/app15126422.
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This article proposes a strategy for the visual characterization of power quality in big data analysis contexts, culminating in the development of a visualization tool based on higher-order statistics, which exhibits an efficiency between 83.33% and 100% in detecting 50 Hz synthetic and real-life simple and hybrid events, showing its significant potential for real-world applications marked by non-linear loads and non-Gaussian behaviors and surpassing the detection of traditional tools such as boxplot by up to 50%. Efficient energy management is closely accompanied by an optimum energy data management (EDM). It implies the acquisition, analysis, and interpretation of data to make decisions regarding the best energy usage with subsequent cost reductions. Through a study of indicators, including higher-order statistics, crest factor, SNR and THD, the article establishes nominal values and behavioral patterns, expanding the previous knowledge of these parameters. The indicators are presented as vertices in a radar-type charting tool, providing a multidimensional spatial visualization from individual indices that allows the behavioral pattern associated with each type of disturbance to be characterized combined with a decision tree. In addition, boxplots reflecting data processing are included, which facilitates the comparison and discussion of both visualization instruments: radar chart and boxplot.
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Ahmad Sabri, Noor Aida Syakira, Nik Fasha Edora Nik Kamaruzaman, Nurlaila Ismail, et al. "Statistical analysis for chemical compound based on several species of aquilaria essential oil." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 4 (2024): 3663. http://dx.doi.org/10.11591/ijece.v14i4.pp3663-3673.
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The paper examines the characterization of Aquilaria essential oils from different species, namely Aquilaria malaccensis, Aquilaria beccariana, Aquilaria crassna, and Aquilaria subintegra, renowned for agarwood production in Malaysia. Gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID) were employed for extracting essential oil data, facilitating compound identification. Subsequently, a preliminary analysis focused on classifying significant chemical compounds in the samples. The study then utilized boxplot pre-processing for visualizing and interpreting data distribution. The statistical analyses were performed using MATLAB software version R2021b, considering two key parameters which are the peak area (%) of significant chemical compounds and the classification of Aquilaria species (A. beccariana, A. malaccensis, A. crassna, and A. subintegra) based on their chemical composition. The results, presented through boxplot analyses, demonstrated a clear representation of the parameters and their distribution in the data. This method not only confirmed the potential of boxplot analysis in statistical evaluation of significant compounds in Aquilaria essential oil but also suggested its applicability for further classification work.
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Aida, Syakira Ahmad Sabri Noor, Edora Nik Kamaruzaman Nik Fasha, Nurlaila Ismail, et al. "Statistical analysis for chemical compound based on several species of Aquilaria essential oil." Statistical analysis for chemical compound based on several species of Aquilaria essential oil 14, no. 4 (2024): 3663–73. https://doi.org/10.11591/ijece.v14i4.pp3663-3673.
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The paper examines the characterization of Aquilaria essential oils from different species, namely Aquilaria malaccensis, Aquilaria beccariana, Aquilaria crassna, and Aquilaria subintegra, renowned for agarwood production in Malaysia. Gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID) were employed for extracting essential oil data, facilitating compound identification. Subsequently, a preliminary analysis focused on classifying significant chemical compounds in the samples. The study then utilized boxplot pre-processing for visualizing and interpreting data distribution. The statistical analyses were performed using MATLAB software version R2021b, considering two key parameters which are the peak area (%) of significant chemical compounds and the classification of Aquilaria species (A. beccariana, A. malaccensis, A. crassna, and A. subintegra) based on their chemical composition. The results, presented through boxplot analyses, demonstrated a clear representation of the parameters and their distribution in the data. This method not only confirmed the potential of boxplot analysis in statistical evaluation of significant compounds in Aquilaria essential oil but also suggested its applicability for further classification work.
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Babura, Babangida Ibrahim, Mohd Bakri Adam, Abdul Rahim Abdul Samad, Anwar Fitrianto, and Bashir Yusif. "Analysis and Assessment of Boxplot Characters for Extreme Data." Journal of Physics: Conference Series 1132 (November 2018): 012078. http://dx.doi.org/10.1088/1742-6596/1132/1/012078.
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Hasnu Al-Hadi, Anis Hazirah 'Izzati, Aqib Fawwaz Mohd Amidon, Siti Mariatul Hazwa Mohd Huzir, et al. "Boxplot analysis of 4 grade agarwood essential oil for various grades." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (2022): 238. http://dx.doi.org/10.11591/ijeecs.v29.i1.pp238-244.
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Agarwood essential oil is used in most perfumery ingredients, as an incense and in traditional medical preparations. Agarwood essential oil, called "Black Gold," is extremely valued to the global community due to its numerous benefits. As of now, there is still no standard technique of grading different grades of agarwood essential oil. The current grading technique is inefficient since the agarwood essential oil is graded by using human sensory panel. Different people might have different perspective on grading the agarwood essential oil hence, the technique is not practical to adapt it globally. Due to the current technology, numerous intelligent techniques for verifying the grades of agarwood essential oil have been proposed and implemented. The study has conducted a statistical analysis on 4 grade agarwood essential oil using boxplot. Boxplot analysis summarizes the abundances for each chemical compounds from four different grades of agarwood essential oil with a high grade as a reference. This study shows the analysis of boxplot investigated 10-epi-δ-eudesmol, α-agarofuran, β-agarofuran, δ-eudesmol and dihydrocollumellarin as most important chemical compounds in high grade of agarwood essential oil. The chemical compounds that have been identified in high grade of agarwood essential oil can be a reference for future research studies.
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Al-Hadi, Anis Hazirah 'Izzati H., Aqib Fawwaz Mohd Amidon, Siti Mariatul Hazwa Mohd Huzir, et al. "Boxplot analysis of 4 grade agarwood essential oil for various grades." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 1 (2023): 238–44. https://doi.org/10.11591/ijeecs.v29.i1.pp238-244.
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Agarwood essential oil is used in most perfumery ingredients, as an incense and in traditional medical preparations. Agarwood essential oil, called "Black Gold," is extremely valued to the global community due to its numerous benefits. As of now, there is still no standard technique of grading different grades of agarwood essential oil. The current grading technique is inefficient since the agarwood essential oil is graded by using human sensory panel. Different people might have different perspective on grading the agarwood essential oil hence, the technique is not practical to adapt it globally. Due to the current technology, numerous intelligent techniques for verifying the grades of agarwood essential oil have been proposed and implemented. The study has conducted a statistical analysis on 4 grade agarwood essential oil using boxplot. Boxplot analysis summarizes the abundances for each chemical compounds from four different grades of agarwood essential oil with a high grade as a reference. This study shows the analysis of boxplot investigated 10-epi-δ-eudesmol, α-agarofuran, βagarofuran, δ-eudesmol and dihydrocollumellarin as most important chemical compounds in high grade of agarwood essential oil. The chemical compounds that have been identified in high grade of agarwood essential oil can be a reference for future research studies.
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Balestra, M., R. Pierdicca, L. Cesaretti, et al. "A COMPARISON OF PRE-PROCESSING APPROACHES FOR REMOTELY SENSED TIME SERIES CLASSIFICATION BASED ON FUNCTIONAL ANALYSIS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1/W1-2023 (December 5, 2023): 33–40. http://dx.doi.org/10.5194/isprs-annals-x-1-w1-2023-33-2023.
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Abstract. Satellite remote sensing has gained a key role for vegetation mapping distribution. Given the availability of multi-temporal satellite data, seasonal variations in vegetation dynamics can be used trough time series analysis for vegetation distribution mapping. These types of data have a very high variability within them and are subjected by artifacts. Therefore, a pre-processing phase must be performed to properly detect outliers, for data smoothing process and to correctly interpolate the data. In this work, we compare four pre-processing approaches for functional analysis on 4-years of remotely sensed images, resulting in four time series datasets. The methodologies presented are the results of the combination of two outlier detection methods, namely tsclean and boxplot functions in R and two discrete data smoothing approaches (Generalized Additive Model ”GAM” on daily and aggregated data). The approaches proposed are: tsclean-GAM on aggregated data (M01), boxplot-GAM on aggregated data (M02), tsclean-GAM on daily data (M03), boxplot-GAM on daily data (M04). Our results prove that the approach which involves tsclean function and GAM applied to daily data (M03) is ameliorative to the logic of the procedure and leads to better model performance in terms of Overall Accuracy (OA) which is always among the highest when compared with the others obtained from the other three different approaches.
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Fitrianto, Anwar, Wan Zuki Azman Wan Muhamad, Suliana Kriswan, and Budi Susetyo. "Comparing Outlier Detection Methods using Boxplot Generalized Extreme Studentized Deviate and Sequential Fences." Aceh International Journal of Science and Technology 11, no. 1 (2022): 38–45. http://dx.doi.org/10.13170/aijst.11.1.23809.
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Outliers identification is essential in data analysis since it can make wrong inferential statistics. This study aimed to compare the performance of Boxplot, Generalized Extreme Studentized Deviate (Generalized ESD), and Sequential Fences method in identifying outliers. A published dataset was used in the study. Based on preliminary outlier identification, the data did not contain outliers. Each outlier detection method's performance was evaluated by contaminating the original data with few outliers. The contaminations were conducted by replacing the two smallest and largest observations with outliers. The analysis was conducted using SAS version 9.2 for both original and contaminated data. We found that Sequential Fences have outstanding performance in identifying outliers compared to Boxplot and Generalized ESD.
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Kayugina, Svetlana. "Quantile analysis of the density of addition and porosity of virgin gray forest soils in the south of the Tyumen region." АгроЭкоИнфо 5, no. 59 (2023): 2. http://dx.doi.org/10.51419/202135502.
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Statistical processing of a large array of data on the density of addition and total porosity of virgin gray forest soils in the south of the Tyumen region was performed. Quantile analysis (boxplot diagrams) was used to estimate the variability of values. It is established that the type of gray forest soils has relatively favorable agrophysical properties. The average density of the humus horizon (A1) in all three subtypes has values that fall within the range of the "optimum" of most agricultural crops. The illuvial horizon of light gray and actually gray forest soils is compacted, therefore, when these soils are introduced into arable land in rainy years, there is a high probability of violation of aeration and surface waterlogging. Keywords: GRAY FOREST SOILS, ADDITION DENSITY, POROSITY, QUANTILE ANALYSIS, BOXPLOT DIAGRAM
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Thi-Tuyet-Mai, Nguyen, and Luong Thi-Bich-Thuy. "Detection Of Hotspots Of Tuberculosis Cases In Vietnam." International Journal Of Health & Medical Research 03, no. 01 (2024): 29–36. https://doi.org/10.5281/zenodo.10567350.
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Background: Tuberculosis (TB) is regarded as one of the leading causes of death globally. It remains a significant cause of morbidity and mortality in Vietnam. This study aims to identify hotspots of TB using boxplot and Getis-Ord’s G_i^* statistic-based hotspot analysis. Data used and Methods: A total of 101,438 TB cases in 2020 collected from 63 provinces/cities in Vietnam was used in study. Boxplot is first used to study distribution of TB cases. Getis-Ord’s G_i^* statistic was then employed to identify hotspots of TB cases. Finally, results and main findings will be discussed and concluded. Results: It was found that a total of 05 hotspots and 04 coldspots of TB cases were detected throughout Vietnam. Five hotspots were detected in 05 provinces in the northeastern region including Ha Nam, Nam Dinh, Hai Phong, Hai Duong, and Hung Yen. Whereas, four coldspots were mainly concentrated in 03 provinces in the northwest region (Cao Bang, Tuyen Quang and Son La), and Dak Lak in the central south region.Conclusion: It can be concluded that the combination of boxplot and Getis-Ord’s G_i^* statistic can help to effectively detect hotspots of TB cases. Findings in this study provide an insight into how to used spatial statistics and spatial analysis in the study of TB distribution.
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Zakwan, Z., Z. Lubis, and E. Julianti. "The analysis of the glyceride components on the treatment variation of refined bleached deodorized palm oil by gas chromatography method." Food Research 6, no. 1 (2022): 164–67. http://dx.doi.org/10.26656/fr.2017.6(1).773.
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The glyceride components such as glycerol, ester, mono and diglyceride are useful components for food ingredients like food emulsifiers. One of the natural resources of the glyceride component is refined bleached deodorized palm oil (RBDPO). This research was aimed to analyze the glyceride component of the treatment variation of RBDPO. The design of the research was completely randomized design (CRD) non-factorial with three variables of treatment specifically the 9 g of RBDPO with 5 g glycerol (A), RBDPO (B) and RBDPO with 0.7 g lipase enzyme Thermomyces lanuginosus immobilized (TLIM). The concentration of glycerol, ester, mono- and diglyceride was tested by the gas chromatography method. The data will be analyzed by using a descriptive method with a boxplot and histogram. The results showed that the highest concentration of glycerol, ester, mono- and diglyceride, respectively were shown in treatment B (1.5922%), C (9.5699%), C (0.1783%), C (3.3329%). The boxplot graphic described that there was statistically significant difference among the treatments.
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宋, 刚. "A Diesel Engine Fault Detection Algorithm Based on Differential Analysis and Boxplot." Mechanical Engineering and Technology 14, no. 01 (2025): 26–40. https://doi.org/10.12677/met.2025.141003.
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Zheng, Wengang, Renping Lan, Lili Zhangzhong, Linnan Yang, Lutao Gao, and Jingxin Yu. "A Hybrid Approach for Soil Total Nitrogen Anomaly Detection Integrating Machine Learning and Spatial Statistics." Agronomy 13, no. 11 (2023): 2669. http://dx.doi.org/10.3390/agronomy13112669.
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Soil total nitrogen is one of the most important basic indicators for fertiliser decision making, but tens of millions of soil total nitrogen sampling data have been accumulated, forming a huge database. In this large database, there is a large amount of anomalous data, which can interfere with data analysis, affect the construction of spatial interpolation and prediction models, and then affect the accuracy of nutrient management decisions. The traditional method of identifying soil total nitrogen anomalies based on boxplots suffers from the problems of not being able to identify local anomalies, which can easily lead to misclassification of soil total nitrogen data anomalies, and the detection efficiency is not high. We propose a method to identify soil total nitrogen outliers by combining the Isolation Forest algorithm and local spatial autocorrelation analysis, which can simultaneously detect global and local outliers from large amounts of data and combine organic matter as an auxiliary indicator in the spatial analysis to help judge local outliers. Finally, the results of global and local anomalies were combined to provide a comprehensive assessment of the soil nitrogen data, avoiding the misjudgement or omission of judgement that can occur when using a single method. Using 25,930 soil test data from Yunnan Province in 2009 as an example, we compared and analysed the typical boxplot method and the unsupervised OneClassSVM method and evaluated the performance of each method in terms of correct detection rate, false positive rate and false negative rate. The results show that the proposed method has a correct detection rate (TR) of 99.97%, a false positive rate (FPR) of 8.06% and a false negative rate (FNR) of 0.01% on the data, which shows high validity and accuracy; it is also comparable to the independent isolated forests (FNR = 4.76%), boxplot (FNR = 3.90%) and OneClassSVM (FNR = 4.77%), and the false negative rate is reduced by 4.75%, 3.89% and 4.76%, respectively.
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Mohd Huzir, Siti Mariatul Hazwa, Anis Hazirah 'Izzati Hasnu Al-Hadi, Amir Hussairi Zaidi, et al. "Pre-processing technique of Aquilaria species from Malaysia for four different qualities." Bulletin of Electrical Engineering and Informatics 13, no. 1 (2024): 152–59. http://dx.doi.org/10.11591/eei.v13i1.5577.
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The paper interprets data distribution by using boxplot pre-processing in classify the quality of Agarwood oil for eleven chemical substances into four different qualities. The varieties usage of Agarwood oil makes it considered as an expensive and valuable product on the essential oil market. Perfumes, fragrances, incense, aromatherapy, and traditional medicine are the most popular Agarwood oil applications. However, the classification of Agarwood oil grades does not yet have standard grading method. This because it has been graded manually into different qualities by using human sensory evaluation. Boxplot analysis involving eleven chemical subtances that will be focusing in this study by concerned the quality for low, medium low, medium high and high. ɤ-eudesmol, ar-curcumene, β-dihydro agarofuran, ϒ-cadinene, α-agarofuran, allo aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin compounds are the selected significant compounds that represent the input for boxplot. Agarwood oil consist 660 data samples from low, medium low, medium high, and high quality. The result in this study showed that the four selected significant compounds (ɤ-eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran, and dihydrocollumellarin) are important as a marker for Agarwood oil quality classification. The identification of chemical substances on high quality done as reference for future research studies.
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Remigio-Carmona, P., O. Florencias-Oliveros, and J. J. González-de-la-Rosa. "A Graphical method for PQ assessment: EDA tools using traditional indices and Higher-order Statistics." Renewable Energy and Power Quality Journal 21, no. 1 (2023): 551–55. http://dx.doi.org/10.24084/repqj21.390.
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This paper presents a new qualitative method for assessing the power quality (PQ) of electrical systems using both time domain traditional indices and higher-order statistics. The method employs engineering data analysis (EDA) tools to analyse and interpret the PQ data coming from real datasets. Boxplot of each index are considered an essential tool that deserves to be included and studied when an external dataset it is analysed. But this research intends to go a step further, and for this reason a new tool for the spatial visualization of supply quality based on a radar chart is proposed. Each of its vertices constitutes an index, integrating from 3rd to 6 th order statistics with the traditional indicators SNR, SINAD and crest factor. The proposed methodology is applied to the analysis of real available signals and both, boxplot and radarchart, results are compared and commented. Finally, relationships are established between the altered indicators and the type(s) of event found in the signal.
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Wu, Gangrou, Min He, Peng Liang, Chunsheng Ye, and Yue Xu. "Automated Modal Identification Based on Improved Clustering Method." Mathematical Problems in Engineering 2020 (April 25, 2020): 1–16. http://dx.doi.org/10.1155/2020/5698609.
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The automated modal identification has been playing an important role in online structural damage detection and condition assessment. This paper proposes an improved hierarchical clustering method to identify the precise modal parameters by automatically interpreting the stabilization diagram. Two major improvements are provided in the whole clustering process. The modal uncertainty is first introduced in the first stage to eliminate as many as possible mathematical modal data to produce more precise clustering threshold, which helps to produce more precise clustering results. The boxplot is introduced in the last stage to assess the precision of the clustering results from a statistical perspective. Based on an iterative analysis of boxplot, the outliers of the clustering results are found out and eliminated and the precise modal results are finally produced. The Z24 benchmark experiment data are utilized to validate the feasibility of the proposed method, and comparison between the previous method and the improved method is also provided. From the result, it can be concluded that the modal uncertainty is more effective than the other modal criteria in distinguishing the mathematical modal data. The modal results by clustering process are not precise in statistic and the boxplot can find out the outliers of the clustering results and produce more precise modal results. The improved automated modal identification method can automatically extract the physical modal data and produce more precise modal parameters.
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Tareen, Aleem Dad Khan, Malik Sajjad Ahmed Nadeem, Kimberlee Jane Kearfott, Kamran Abbas, Muhammad Asim Khawaja, and Muhammad Rafique. "Descriptive analysis and earthquake prediction using boxplot interpretation of soil radon time series data." Applied Radiation and Isotopes 154 (December 2019): 108861. http://dx.doi.org/10.1016/j.apradiso.2019.108861.
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Kosiorowski, Daniel, Jerzy P. Rydlewski, and Zygmunt Zawadzki. "Functional Outliers Detection by the Example of Air Quality Monitoring." Przegląd Statystyczny 65, no. 1 (2019): 83–100. http://dx.doi.org/10.5604/01.3001.0014.0528.
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Methods of functional outliers detection in functional setting have been discussed, i.e. shape outliers and magnitude outliers. Outliergram has been discussed, a tool for functional shape outliers detection. Robust adjusted functional boxplot has been discussed as well, a tool for functional magnitude outliers detection. „The elements of functional outliers analysis have been applied to air pollution data for Katowice and Kraków.”
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Saleh, Laila Dao, Mingzhen Wei, and Baojun Bai. "Data Analysis and Updated Screening Criteria for Polymer Flooding Based on Oilfield Data." SPE Reservoir Evaluation & Engineering 17, no. 01 (2014): 15–25. http://dx.doi.org/10.2118/168220-pa.
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Summary Enhanced-oil-recovery (EOR) screening is considered the first step in evaluating potential EOR techniques for candidate reservoirs. Therefore, as new technologies are developed, it is important to update the screening criteria. Many of the screening criteria regarding polymer flooding that have been described in the literature were based on data collected from EOR surveys of the Oil and Gas Journal. However, the data quality has not been addressed in previous research. The data set originally contained 481 polymer-flooding projects from around the world, and it contained some problems, including outliers and duplicate, inconsistent, and missing data. To ensure the quality of the data set before running analyses, boxplots and crossplots were used to detect and identify data problems. After detecting outliers and deleting duplicate and severely inconsistent data records, only 250 projects remained. Both graphical and statistical methods were used to analyze and describe the results of the data set. Two major sets of information were given after data cleaning: The first was that the majority distribution of each parameter was shown by use of a histogram distribution, and the second was that the range of each parameter and some of its statistical values were presented by use of a boxplot. Finally, the screening criteria are presented on the basis of these statistics and the defined data parameters. The developed criteria were compared with previously published criteria, and their differences were explained. The developed criteria show that a polymer-flooding project can be successfully applied in a reservoir with a temperature of less than 210°F, an oil viscosity up to 5,000 cp, and gravity lower to 12°API.
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Rodriguez, Pablo Cosa, Pere Marti-Puig, Cesar F. Caiafa, Moisès Serra-Serra, Jordi Cusidó, and Jordi Solé-Casals. "Exploratory Analysis of SCADA Data from Wind Turbines Using the K-Means Clustering Algorithm for Predictive Maintenance Purposes." Machines 11, no. 2 (2023): 270. http://dx.doi.org/10.3390/machines11020270.
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Product maintenance costs throughout the product’s lifetime can account for between 30–60% of total operating costs, making it necessary to implement maintenance strategies. This problem not only affects the economy but is also related to the impact on the environment, since breakdowns are also responsible for the delivery of greenhouse gases. Industrial maintenance is a set of measures of a technical-organizational nature whose purpose is to sustain the functionality of the equipment and guarantee an optimal state of the machines over time, with the aim of saving costs, extending the useful life of the machines, saving energy, maximising production and availability, ensuring the quality of the product obtained, providing job security for technicians, preserving the environment, and reducing emissions as much as possible. Machine learning techniques can be used to detect or predict faults in wind turbines. However, labelled data suffers from many problems in this application because alarms are usually not clearly associated with a specific fault, some labels are wrongly associated with a problem, and the imbalance between labels is evident. To avoid using labelled data, we investigate here the use of the clustering technique, more specifically K-means, and boxplot representations of the variables for a set of six different tests. Experimental results show that in some cases, the clustering and boxplot techniques allow us to determine outliers or identify erroneous behaviours of the wind turbines. These cases can then be investigated in detail by a specialist so that more efficient predictive maintenance can be carried out.
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Menéndez-García, Luis Alfonso, Paulino José García-Nieto, Esperanza García-Gonzalo, Fernando Sánchez Lasheras, Laura Álvarez-de-Prado, and Antonio Bernardo-Sánchez. "Method for the Detection of Functional Outliers Applied to Quality Monitoring Samples in the Vicinity of El Musel Seaport in the Metropolitan Area of Gijón (Northern Spain)." Mathematics 11, no. 12 (2023): 2631. http://dx.doi.org/10.3390/math11122631.
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Air pollution affects human health and is one of the main problems in the world, including in coastal cities with industrial seaports. In this sense, the city of Gijón (northern Spain) stands out as one of the 20 Spanish cities with the worst air quality. The study aims to identify outliers in air quality observations near the El Musel seaport, resulting from the emissions of six pollutants over an eight-year period (2014–2021). It compares methods based on the functional data analysis (FDA) approach and vector methods to determine the optimal approach for detecting outliers and supporting air quality control. Our approach involves analyzing air pollutant observations as a set of curves rather than vectors. Therefore, in the FDA approach, curves are constructed to provide the best fit to isolated data points, resulting in a collection of continuous functions. These functions capture the behavior of the data in a continuous domain. Two FDA approach methodologies were used here: the functional bagplot and the high-density region (HDR) boxplot. Finally, outlier detection using the FDA approach was found to be more powerful than the vector methods and the functional bagplot method detected more outliers than the HDR boxplot.
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Mambang, Mambang, Ahmad Hidayat, Finki Dona Marleny, and Johan Wahyudi. "EXPLANATORY DATA ANALISIS UNTUK MENGEVALUASI PENELUSURAN KATA KUNCI VIDEO PEMBELAJARAN DI YOUTUBE DENGAN PENDEKATAN MACHINE LEARNING." Jurnal Informatika Dan Tekonologi Komputer (JITEK) 2, no. 2 (2022): 181–89. http://dx.doi.org/10.55606/jitek.v2i2.287.
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The purpose of this study was to find correlations related to the variable number of impressions, likes, subscribers, and comments on each learning video keyword search on YouTube. This research uses quantitative methods and experiments with secondary data sources. Exploratory Data Analysis in machine learning using several libraries in Python programming produces image visualizations that provide information related to the dataset that has been processed, such as boxplot graphs, histograms, line plots, and correlation graphs. Exploratory Data Analysis with machine learning that we have done finds results on boxplot graphs on five variables showing a whisker more elongated upwards which states positive data results. The difference in this histogram chart is in the duration variable. On the line plot graph, we find the keywords learning videos learning mathematics have the advantage of four variables and the keywords of accounting learning videos one variable. 
 Exploratory Data Analysis using the correlation head map in the seaborn library shows that the like and comment variables strongly correlate with a value of 1. Duration variables have a low and negative correlation with other variables. The subscribers variable has a high correlation with the like variable 0.95. Thus, several indicators need to be considered in making learning videos, such as content or content of innovative and creative learning videos, so that the number of likes and comments becomes high. The length of time in learning videos does not affect the number of likes, subscribers, and comments.
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Lima, Alan Yago Barbosa de, Amanda Rodrigues de Souza, Giovanni Calderoni Statonato, Gabriel Tieppo Gonçalves Camacho, Andréa de Oliveira Cardoso, and María Cleofé Valverde. "Padrão e extremos de precipitação na cidade de São Paulo." E&S Engineering and Science 12, no. 1 (2023): 38–52. https://doi.org/10.18607/es20231215137.
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The objective of this study is to analyze the precipitation variability, extreme events, and trends of this variable in the city of São Paulo. The data used covered the period between 1933 and 2020, originating from the IAG/USP Meteorological Station. The monthly averages for the study of seasonality, the boxplot diagram for the investigation of rare monthly events, and the quantile technique for classifying extreme precipitation events were considered for analysis. A trend analysis was performed through the accumulated annual precipitation and linear adjustment by a simple linear regression equation. In seasonal terms, the city of São Paulo has low precipitation in winter, with a minimum in August and an average of 38.05 mm, increasing total precipitation during spring. It was observed that the highest precipitation values occurred during the summer, with a peak in January and 232.14 mm. According to the boxplot diagram, the rare events of high precipitation occur mainly in winter, with emphasis on May and June. There is a greater concentration of extreme precipitation events in the second half of the observed years. The dry months have the highest occurrences of rare precipitation events. Simple linear regression analysis indicated a positive trend, indicating an average increase in annual precipitation of 508.43 mm in the city of São Paulo. In general, there is an indication of an precipitation increase in the city, especially due to the higher frequency of monthly extreme rainy events. Keywords: Seasonality. Extreme events. Rain. Dry. Climatological norms.
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Gallegos-Erazo, Franklin Antonio, and Ana María Gallardo-Cornejo. "Technical Analysis of a Non-Globally Integrated Stock Index." Revista Venezolana de Gerencia 30, no. 110 (2025): 847–64. https://doi.org/10.52080/rvgluz.30.110.4.
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This study examines the Ecuadorian stock market index, ECUINDEX, a market that is not integrated into international financial markets, unlike others in Latin America. The objective was to determine if graphical technical analysis can describe the price behavior of the ECUINDEX, testing the weak-form efficient market hypothesis. Market stationarity was evaluated to identify suitable volatility indicators. A mixed-methods approach was applied, combining quantitative tests (Boxplot and Dickey-Fuller) with qualitative analyses (graphical observation of prices and TEMA, RVI, and MA indicators). Monthly and daily closing price data of the ECUINDEX from 2013 to 2023 were analyzed, obtained from the Quito Stock Exchange through Investing. The results reveal that the ECUINDEX is non-stationary and that technical analysis consistently describes momentum, price direction changes, and market turns, highlighting the usefulness of technical indicators for understanding price trends and market inefficiencies.
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Gallegos-Erazo, Franklin Antonio, and Ana María Gallardo-Cornejo. "Technical Analysis of a Non-Globally Integrated Stock Index." Revista Venezolana de Gerencia (RVG). Facultad de Ciencias Económicas y Sociales. Universidad del Zulia. 30, no. 110 (2025): 847–64. https://doi.org/10.52080/rvgluz.30.110.4.
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This study examines the Ecuadorian stock market index, ECUINDEX, a market that is not integrated into international financial markets, unlike others in Latin America. The objective was to determine if graphical technical analysis can describe the price behavior of the ECUINDEX, testing the weak-form efficient market hypothesis. Market stationarity was evaluated to identify suitable volatility indicators. A mixed-methods approach was applied, combining quantitative tests (Boxplot and Dickey-Fuller) with qualitative analyses (graphical observation of prices and TEMA, RVI, and MA indicators). Monthly and daily closing price data of the ECUINDEX from 2013 to 2023 were analyzed, obtained from the Quito Stock Exchange through Investing. The results reveal that the ECUINDEX is non-stationary and that technical analysis consistently describes momentum, price direction changes, and market turns, highlighting the usefulness of technical indicators for understanding price trends and market inefficiencies.
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Chen, Shoukai, Yongqiwen Fu, Lei Guo, Shifeng Yang, and Yajing Bie. "Statistical Law and Predictive Analysis of Compressive Strength of Cemented Sand and Gravel." Science and Engineering of Composite Materials 27, no. 1 (2020): 291–98. http://dx.doi.org/10.1515/secm-2020-0030.
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AbstractA data set of cemented sand and gravel (CSG) mix proportion and 28-day compressive strength was established, with outliers determined and removed based on the Boxplot. Then, the distribution law of compressive strength of CSG was analyzed using the skewness kurtosis and single-sample Kolmogorov-Smirnov tests. And with the help of Python software, a model based on Back Propagation neural network was built to predict the compressive strength of CSG according to its mix proportion. The results showed that the compressive strength follows the normal distribution law, the expected value and variance were 5.471 MPa and 3.962 MPa respectively, and the average relative error was 7.16%, indicating the predictability of compressive strength of CSG and its correlation with the mix proportion.
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WALUYO, WALUYO, ARSYAD RAMADHAN DARLIS, FEBRIAN HADIATNA, and AHMAD ABIMANYU. "Influences of Environmental Factors of a Hybrid Photovoltaic and Thermoelectric Generation System." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 12, no. 4 (2024): 1038. https://doi.org/10.26760/elkomika.v12i4.1038.
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ABSTRAKSaat ini, pemanfaatan sumber energi terbarukan menjadi salah satu pilihan dalam menjawab tantangan krisis energi. Sistem pembangkit listrik fotovoltaiktermoelektrik hibrida yang menggabungkan keunggulan konversi energi cahaya dan panas dari matahari perlu dikaji kinerjanya. Penelitian ini membahas peningkatan efisiensi pembangkit listrik tenaga surya hybrid panel-termoelektrik dan hubungannya dengan pengaruh faktor lingkungan. Akuisisi data berdasarkan analisis komputasi menganalisis statistik multivariat, seperti regresi, korelasi, boxplot, dan principal component analysis (PCA). Hasil penelitian menunjukkan daya masukan MPPT lebih tinggi dibandingkan daya keluaran, dengan efisiensi tipikal sebesar 99,66%. Peningkatan suhu udara membuat tegangan sedikit menurun, dan daya yang dihasilkan meningkat cukup besar seiring dengan naiknya suhu udara. Peda penelitian terlihat bahwa radiasi ultraviolet (UV) meningkat secara signifikan seiring dengan peningkatan suhu lingkungan.Kata kunci: efisiensi, faktor lingkungan, fotovoltaik, analisis statistik, termoelektrik ABSTRACTRecently, using renewable energy sources is one option in answering the challenge of the energy crisis. The hybrid photovoltaic-thermoelectric power generation system is a solution that combines the advantages of converting both light and heat energy from the sun, which needs to be studied. This study discussed the increase in efficiency in a hybrid solar panel-thermoelectric power generation and its relationship to the influence of environmental factors. The data acquisition is based on the computational analyses of multivariate statistics, such as regressions, correlations, boxplots and principal component analysis. It showed the input power of the MPPT was higher than the output power, with a typical efficiency of 99.66%. The rising air temperature decreased the voltages and generated power considerably, increasing as the air temperature rose. Finally, the ultraviolet (UV) radiation increased significantly as the ambient temperature rose.Keywords: efficiency, environmental factor, photovoltaic, statistical analysis, thermoelectric
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Qasrina Ann, Nurnajmin, Dwi Pebrianti, Mohd Fadhil Abas, and Luhur Bayuaji. "Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 2167. http://dx.doi.org/10.11591/ijece.v13i2.pp2167-2176.
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<p>Deep neural networks (DNNs) are very dependent on their parameterization and require experts to determine which method to implement and modify the hyper-parameters value. This study proposes an automated-tuned hyper-parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). AOA makes use of the distribution properties of mathematics’ primary arithmetic operators, including multiplication, division, addition, and subtraction. AOA is mathematically modeled and implemented to optimize processes across a broad range of search spaces. The performance of AOA is evaluated against 29 benchmark functions, and several real-world engineering design problems are to demonstrate AOA’s applicability. The hyper-parameter tuning framework consists of a set of Lorenz chaotic system datasets, hybrid DNN architecture, and AOA that works automatically. As a result, AOA produced the highest accuracy in the test dataset with a combination of optimized hyper-parameters for DNN architecture. The boxplot analysis also produced the ten AOA particles that are the most accurately chosen. Hence, AOA with ten particles had the smallest size of boxplot for all hyper-parameters, which concluded the best solution. In particular, the result for the proposed system is outperformed compared to the architecture tested with particle swarm optimization.</p>
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Dastjerdy, Behzad, Ali Saeidi, and Shahriyar Heidarzadeh. "Review of Applicable Outlier Detection Methods to Treat Geomechanical Data." Geotechnics 3, no. 2 (2023): 375–96. http://dx.doi.org/10.3390/geotechnics3020022.
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The reliability of geomechanical models and engineering designs depend heavily on high-quality data. In geomechanical projects, collecting and analyzing laboratory data is crucial in characterizing the mechanical properties of soils and rocks. However, insufficient lab data or underestimating data treatment can lead to unreliable data being used in the design stage, causing safety hazards, delays, or failures. Hence, detecting outliers or extreme values is significant for ensuring accurate geomechanical analysis. This study reviews and categorizes applicable outlier detection methods for geomechanical data into fence labeling methods and statistical tests. Using real geomechanical data, the applicability of these methods was examined based on four elements: data distribution, sensitivity to extreme values, sample size, and data skewness. The results indicated that statistical tests were less effective than fence labeling methods in detecting outliers in geomechanical data due to limitations in handling skewed data and small sample sizes. Thus, the best outlier detection method should consider this matter. Fence labeling methods, specifically, the medcouple boxplot and semi-interquartile range rule, were identified as the most accurate outlier detection methods for geomechanical data but may necessitate more advanced statistical techniques. Moreover, Tukey’s boxplot was found unsuitable for geomechanical data due to negative confidence intervals that conflicted with geomechanical principles.
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Nurnajmin, Qasrina Ann, Pebrianti Dwi, Fadhil Abas Mohammad, and Bayuaji Luhur. "Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 2167–76. https://doi.org/10.11591/ijece.v13i2.pp2167-2176.
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Deep neural networks (DNNs) are very dependent on their parameterization and require experts to determine which method to implement and modify the hyper-parameters value. This study proposes an automated-tuned hyperparameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). AOA makes use of the distribution properties of mathematics’ primary arithmetic operators, including multiplication, division, addition, and subtraction. AOA is mathematically modeled and implemented to optimize processes across a broad range of search spaces. The performance of AOA is evaluated against 29 benchmark functions, and several real-world engineering design problems are to demonstrate AOA’s applicability. The hyper-parameter tuning framework consists of a set of Lorenz chaotic system datasets, hybrid DNN architecture, and AOA that works automatically. As a result, AOA produced the highest accuracy in the test dataset with a combination of optimized hyper-parameters for DNN architecture. The boxplot analysis also produced the ten AOA particles that are the most accurately chosen. Hence, AOA with ten particles had the smallest size of boxplot for all hyper-parameters, which concluded the best solution. In particular, the result for the proposed system is outperformed compared to the architecture tested with particle swarm optimization.
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Wang, Yifei, Xiaofeng Zhao, Feng Zhang, Siyang Xie, and Zhihan Liu. "Optimal trading strategies based on time series analysis." Advances in Economics and Management Research 7, no. 1 (2023): 730. http://dx.doi.org/10.56028/aemr.7.1.730.2023.
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Quantitative investment has been widely used in the field of foreign finance, especially the rapid development of international investment in the past decade. And financial activity is an important field of national economic activity. The frequency of financial transactions is an important indicator of the complexity of a country's economy, so it is of great significance to study the optimal investment strategy. This article uses daily price streams from past investments in gold, cash, and bitcoin to determine whether traders should buy, hold, or sell assets in their portfolios. The outlier data were processed by boxplot analysis, and the EM algorithm based on maximum likelihood estimation was used to visualize the case data. The ARIMA model and GARCH model are used to establish the portfolio optimization model and obtain the best portfolio scheme. The time series prediction model is used to conduct specific quantitative analysis on gold and Bitcoin and obtain the investment forecast of the initial $1000 in the future.
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Ali, Fayyadh. "Detection and Treatment of Outliers in Experimental Design: Real Data for Completely Randomized Design." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 1 (October 1, 2021): 420–31. http://dx.doi.org/10.55562/jrucs.v46i1.93.
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The presence of outliers values in the data leads to errors in statistical analysis due to the use of traditional methods of calculation, so it is necessary to switch to new methods that deal with these outlier values so as to ensure the accuracy of the calculations to the proper statistical analysis, and in this research resorted to the method adjusted boxplot to detect outlier values and then deleted and re-statistical analysis data have been used for a realistic agricultural experiment to completely randomized design in the College of Agriculture Wasit for 2017 has shown the result of statistical analysis that there is a difference in the results before and after deleting outlier values.
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Rahman, Abdul, Razali Ismail, Umie Asyikin binti Rozali, and Khairil Anuar bin Arshad. "Statistical Analysis and Prediction of Paddy Yield Using Neural Network." Indonesian Journal of Fundamental Sciences 9, no. 1 (2023): 17. http://dx.doi.org/10.26858/ijfs.v9i1.48441.
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Abstract. This research uses boxplot, Anova and posthoc to analyse the effect of factors such as urine and phosphorous in rice paddy yield. Then an artificial neural network (ANN) is used to predict paddy yields based on those factors. ANN is also used to predict paddy yields from polybags based on the actual data of paddy yields from rice field. A total of 25 data were used in this study where 70% data were used for training while 15% data each for testing and validation. We use the training model using data from rice field to predict paddy yield in polybags. STATISTICA software was used to run the neural networks. The predictive power of constructed neural networks was measured using accuracy measurement Mean Squared Error. The result shows that prediction can be made through neural network since the performance is very encouraging. Keywords: neural networks, paddy yield, prediction, statistical analysis.
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Nugroho, Atmoko, Danny Manongga, Hindriyanto Dwi Purnomo, and Hendry Hendry. "Analysis Of Spotify Top Songs During Covid-19 Pandemic." International Journal of Marketing and Digital Creative 1, no. 2 (2023): 1–14. http://dx.doi.org/10.31098/ijmadic.v1i2.1565.
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During the COVID-19 pandemic, many behaviors or habits have changed, especially in the internet audio-visual field which has increased significantly, one example is Spotify as an audio service provider. Not all songs on Spotify are popular or in the Top Songs. This study aims to examine whether there were differences in popular songs during the pandemic and before the pandemic and to determine the relationship between factors of popular songs on Spotify during the COVID-19 pandemic. The method used is to fetch Spotify songs via the API (Application Programming Interface) with the Spotify Python library. The features obtained are compared with the boxplot. The correlation between the Danceability and Energy features is obtained which ranges from 0.5-0.7, while the other features require further preprocessing because the values are not the same and are empty. This shows that every song that is considered good Danceability and Energy ranges from 0.5 to 0.7, regardless of singer, genre, or other song features.
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Aisyah, S., H. Hidayat, and D. Verawati. "Statistical Assessment of Some Water Quality and Rainfall Data in Ciliwung River, Indonesia." IOP Conference Series: Earth and Environmental Science 1062, no. 1 (2022): 012035. http://dx.doi.org/10.1088/1755-1315/1062/1/012035.
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Abstract Ciliwung is the river that flows from upstream in the Puncak area of Bogor Regency to Jakarta Bay. Water quality parameters have been monitored each month at three stations in the Ciliwung River watershed by the Indonesian Ministry of Environmental and Forestry and Meteorological, Climatological, and Geophysical Agency. The data analyzed in this study are rainfall and water quality data for the period 2017-2020 with water quality variables including pH, temperature, Dissolved Oxygen (DO), electrical conductivity (EC), Turbidity, Total Dissolved Solids (TDS), and Nitrate-N. This paper aimed to analyze the time series data using statistical methods and describes certain chemical parameters and rainfall data, that show Ciliwung water quality during 2017-2020. Descriptive analysis was used to determine the mean, skewness, and kurtosis values based on time and location. Histogram and boxplot graphs were used to describe distribution of the data set. Test of Kolmogorov-Smirnov was used to evaluate the normality of the data set. The descriptive analysis resulted in the mean of each water quality parameter showing a value that did not show a significant difference between observation locations except for the TDS parameter. From the histogram and boxplot graphs, it can be seen that the data shows an abnormal distribution and there are many outliers. Kolmogorov-Smirnov test resulted in better normality of data distribution. The main problem of pollution is the use of oxygen by organic matter contained in river water. Ciliwung River downstream was changed into open accumulator wastewater from the food industry, livestock, and settlements.
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Liu, Junsheng, Feng Liang, Kai Wei, and Changqun Zuo. "Prediction Model for Cutterhead Rotation Speed Based on Dimensional Analysis and Elastic Net Regression." Applied Sciences 15, no. 3 (2025): 1298. https://doi.org/10.3390/app15031298.
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The development and maturation of TBM (tunnel boring machine) technology have significantly improved the accuracy and richness of excavation data, driving advancements in intelligent tunneling research. However, challenges remain in managing data noise and parameter coupling, limiting the interpretability of traditional machine learning models regarding TBM parameter relationships. This study proposes a cutterhead rotation speed prediction model based on dimensional analysis. By utilizing boxplot methods and low-pass filtering techniques, excavation data were preprocessed to select appropriate operational and mechanical parameters. A dimensionless model was established and integrated with elastic net regression to quantify parameters. Using TBM cluster data from a water diversion tunnel project in Xinjiang, the accuracy and generalizability of the model were validated. Results indicate that the proposed model achieves high prediction accuracy, effectively capturing trends in cutterhead rotation speed while demonstrating strong generalizability.
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Sharifov, Galib. "Enhancing lyceum physics education with LAB Disc technology: a comparative study." Physics Education 59, no. 4 (2024): 045025. http://dx.doi.org/10.1088/1361-6552/ad4b85.
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Abstract The present study explores the impact of LAB Disc technology on enhancing physics education among gifted ninth-grade students in lyceums. The study employed a quasi-experimental design to examine students’ academic performance and engagement levels in two groups: an experimental group that utilised LAB Discs and a control group that received conventional physics education. To ensure comparability in baseline understanding of the subject, each group consisted of 30 ninth-grade students who were selected based on their academic prowess and enthusiasm for physics. The utilisation of LAB Discs, versatile devices equipped with diverse sensors for instantaneous data gathering and examination, was designed to offer a more engaging and experiential learning encounter. The study’s methodology employed a combination of quantitative and qualitative data collection techniques, including tests, surveys, interviews, and classroom observations. The impact of LAB Disc technology on students’ learning outcomes was assessed using an independent t-test and boxplot visualisation. The results demonstrated a statistically significant enhancement in ninth-grade students’ comprehension and involvement when utilising LAB Disc technology, as evidenced by a t-statistic of 6.522 and a p-value of less than 0.001. The boxplot analysis validated that students in the experimental group exhibited a superior median score and demonstrated greater consistency in their performance. The results emphasise the capacity of interactive educational tools, like LAB Discs, to enhance cognitive abilities and establish a more captivating learning atmosphere in physics classes at the lyceum level.
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da Anunciação, Talita Andrade, Juan Diego Silva Guedes, Pedro Paulo Lordelo Guimarães Tavares, et al. "Biological Significance of Probiotic Microorganisms from Kefir and Kombucha: A Review." Microorganisms 12, no. 6 (2024): 1127. http://dx.doi.org/10.3390/microorganisms12061127.
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(1) Background: The human microbiota is essential for maintaining a healthy body. The gut microbiota plays a protective role against pathogenic bacteria. Probiotics are live microorganisms capable of preventing and controlling gastrointestinal and balancing the immune system. They also aid in better nutrients and vitamins absorption. Examples of natural probiotic cultures are kefir and kombucha. (2) Methods: Therefore, the aim of this review was to address the beneficial properties of probiotic kefir and kombucha using a Boxplot analysis to search for scientific data in the online literature up to January 2024: (Latin American and Caribbean Health Sciences (LILACS), PubMed, Medical Literature Analysis (MED-LINE), Science Direct, Google Scholar/Google Academic, Bioline Inter-national and Springer Link). Boxplots showed the summary of a set of data “Index Terms—Keywords” on kefir and kombucha in three languages (English, Portuguese and Spanish). (3) Results: Google Scholar was the database with the highest number of articles found, when the search for the keywords used in the study (containing ~4 × 106–~4 million articles available). This was Followed by the Science Direct database, containing ~3 × 106–~3 million articles available, and the BVS databases—Biblioteca Virtual de Saúde (Virtual Health Library) e Lilacs, both containing a value of ~2 × 106–~2 million articles available. The databases containing the smallest number of articles found were Nutrients and Medline, both containing a value of ≤0.1 × 106–≤100 thousand articles. (4) Conclusions: Scientific studies indicate that kefir and kombucha certainly contain various functional properties, such as antimicrobial, antitumor, anticarcinogenic and immunomodulatory activity, in addition to having a microbiological composition of probiotic bacteria and yeasts. Kefir and kombucha represent key opportunities in the food and clinic/medical fields.
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Du, YiKuan, LuLu He, XinNi Ye, et al. "To Explore the Molecular Mechanism of Acupuncture Alleviating Inflammation and Treating Obesity Based on Text Mining." BioMed Research International 2022 (September 5, 2022): 1–13. http://dx.doi.org/10.1155/2022/3133096.
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Objective. To explore the related mechanism of acupuncture affecting obesity by regulating inflammation using bioinformatics methods. Methods. The genes related to obesity, inflammation, and acupuncture and inflammation were mined using GenCLiP 3, and the intersecting genes were extracted using Venn diagram. The DAVID database was employed for pathway enrichment analysis and functional annotation of coexpressed genes. Then, the protein-protein interaction (PPI) network was constructed with the STRING database and visualized by the Cytoscape software and screened out important hub genes. Finally, the Boxplot and Survival Analysis of the hub genes in various cancers were performed by GEPIA. Results. 755 genes related to obesity and inflammation and 38 genes related to acupuncture and inflammation were identified, and 24 coexpressed genes related to obesity, inflammation, and acupuncture were extracted from the Venn diagram. Eight hub genes including interleukin-6 (IL-6), interleukin-10 (IL-10), Toll-like receptor 4 (TLR4), signal transduction and transcriptional activation factor 3 (STAT3), C-X-C motif chemokine 10 (CXCL10), interleukin-17A (IL-17A), prostaglandin peroxide synthesis-2 (PTGS2), signal transistors, and transcriptional activation factor 6 (STAT6) were identified by gene ontology (GO), Kyoto Encyclopedia of Genes (KEGG), and PPI network analysis. Among them, IL-6 is suggested to play an essential role in the treatment of obesity and inflammation by acupuncture, and IL-6 was significant in both Boxplot and Survival Analysis of pancreatic cancer (PAAD). Therefore, in this study, the core gene, IL-6 was used as the breakthrough point to explore the possible mechanism of acupuncture in treating obesity and pancreatic cancer by regulating IL-6. Conclusion. (1) Acupuncture can regulate the expression of IL-6 through the TLR4/nuclear factor-κB (NF-κB) pathway, thereby alleviating inflammation, which can be used as a potential strategy for the treatment of obesity. (2) IL-6/STAT3 is closely related to the occurrence, development, and metastasis of pancreatic cancer. Acupuncture affecting pancreatic cancer through TLR4/NF-κB/IL-6/STAT3 pathway may be a potential method for the treatment of pancreatic cancer.
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Mesquita, Glaucia Machado, Felipe Corrêa Veloso dos Santos, Anne Louise Dores, and Vladia Correchel. "SPATIAL VARIABILITY OF HYDRAULIC CONDUCTIVITY OF SATURATED SOIL IN CONSERVATION UNIT." REVISTA DE AGRICULTURA NEOTROPICAL 9, no. 1 (2022): e6532. http://dx.doi.org/10.32404/rean.v9i1.6532.
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The study aimed to evaluate the hydraulic conductivity using soil samples with undisturbed structure in the soil layers of 0-5, 5-10, 10-20, and 20-40 cm; 120 soil samples were collected. For the determination of hydraulic conductivity, the constant load permeameter was used. For geostatistical analysis, exploratory data analysis was performed using frequency histograms, determining the main measures of position and dispersion, verifying the trends for the construction of boxplot graphics, which allows the identification of discrepant points. The lowest and highest hydraulic conductivity values were found in the 20-40 cm and 0-5 cm soil layers, respectively; values commonly found in soils under forest conditions. Based on the results, we conclude when the soil sampling for analysis of hydraulic conductivity is random, the minimum distance between the points must be greater than 15.5 m.
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Musa, Zulkifli, Zuwairie Ibrahim, Mohd Ibrahim Shapiai, and Nor Azlina Ab Aziz. "Cubature Kalman Optimizer versus Teaching Learning Based Optimization: A Performance Comparison based on CEC2014 Test Suite." International Journal of Membrane Science and Technology 10, no. 3 (2023): 1872–84. http://dx.doi.org/10.15379/ijmst.v10i3.1847.
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This paper compares a new Cubature Kalman Optimizer performance against the Teaching Learning Based Optimization in solving the CEC2014 test suite. The Cubature Kalman Optimizer is inspired by the estimation algorithm named Cubature Kalman filter, while the Teaching Learning Based Optimization is inspired by the teaching-learning process in a classroom. Both algorithms can be characterized as a parameter-less nature. Graphical analysis based on convergence curve shows that Cubature Kalman Optimizer has better exploration than Teaching Learning Based Optimization in the first half of the total iteration that make it able to find better solution. On the other hand, for boxplot, both algorithms show comparative based on consistency. Meanwhile, statistical analysis shows that the Cubature Kalman Optimizer algorithm is a promising approach compared to Teaching Learning Based Optimization.
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Rovnak, Martin, Roman Novotny, and Matus Bakon. "Analytical study of selected economic-environmental indicators of waste management system in Slovakia." Economics and Environment 75, no. 4 (2020): 12. https://doi.org/10.34659/eis.2020.75.4.5.
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The main objective of the paper was to visualize and analyze the relationships between selected economic and environmental indicators in the waste management system of Slovakia, i.e., the amount of fees for municipal waste in individual districts of Slovakia in 2019 and the amount of average monthly wage and unemployment in Slovakia in the same year. Data were visualized and analyzed on a thematic map and in a boxplot, and subsequently, they were subjected to statistical testing. Based on the performed analysis, we can confirm the statistical relationship between the average wage and the amount of fees for municipal waste collection and the statistical relation between the municipal waste fee and the unemployment rate in individual districts of Slovakia.
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Nguyen, Khanh-Toan, Thanh-Ngoc Tran, and Huy-Tuan Nguyen. "Research on the Influence of Hyperparameters on the LightGBM Model in Load Forecasting." Engineering, Technology & Applied Science Research 14, no. 5 (2024): 17005–10. http://dx.doi.org/10.48084/etasr.8266.
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Electric load forecasting plays a vital role in all aspects of the electrical system, including generation, transmission, distribution, and electricity retail. The LightGBM ensemble learning method has been widely applied in load forecasting and has yielded many positive results. This study presents an algorithm combining the grid space of hyperparameters with cross-validation to evaluate the accuracy of LightGBM models across different hyperparameter values. Peak load data from Ho Chi Minh City were used to enhance the reliability of the results. Analysis of the results based on boxplot statistical charts indicated that the accuracy of the LightGBM model significantly depends on the hyperparameter values. Moreover, using default hyperparameter values may result in large errors in load forecasting.
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Alnowibet, Khalid Abdulaziz, Shalini Shekhawat, Akash Saxena, Karam M. Sallam, and Ali Wagdy Mohamed. "Development and Applications of Augmented Whale Optimization Algorithm." Mathematics 10, no. 12 (2022): 2076. http://dx.doi.org/10.3390/math10122076.
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Metaheuristics are proven solutions for complex optimization problems. Recently, bio-inspired metaheuristics have shown their capabilities for solving complex engineering problems. The Whale Optimization Algorithm is a popular metaheuristic, which is based on the hunting behavior of whale. For some problems, this algorithm suffers from local minima entrapment. To make WOA compatible with a number of challenging problems, two major modifications are proposed in this paper: the first one is opposition-based learning in the initialization phase, while the second is inculcation of Cauchy mutation operator in the position updating phase. The proposed variant is named the Augmented Whale Optimization Algorithm (AWOA) and tested over two benchmark suits, i.e., classical benchmark functions and the latest CEC-2017 benchmark functions for 10 dimension and 30 dimension problems. Various analyses, including convergence property analysis, boxplot analysis and Wilcoxon rank sum test analysis, show that the proposed variant possesses better exploration and exploitation capabilities. Along with this, the application of AWOA has been reported for three real-world problems of various disciplines. The results revealed that the proposed variant exhibits better optimization performance.
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Amalianita, Berru, and Herman Nirawana. "Subjective well being in adolescences on Minangkabau ethnic; an analysis based of dimension and gender." Jurnal Konseling dan Pendidikan 9, no. 2 (2021): 147. http://dx.doi.org/10.29210/162100.
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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.
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Hou, Dake, Wenli Zhou, Qiuxia Zhang, Kun Zhang, and Jiaqi Fang. "A comparative study of different variable selection methods based on numerical simulation and empirical analysis." PeerJ Computer Science 9 (August 16, 2023): e1522. http://dx.doi.org/10.7717/peerj-cs.1522.
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This study employs the principles of computer science and statistics to evaluate the efficacy of the linear random effect model, utilizing Lasso variable selection techniques (including Lasso, Elastic-Net, Adaptive-Lasso, and SCAD) through numerical simulation and empirical research. The analysis focuses on the model’s consistency in variable selection, prediction accuracy, stability, and efficiency. This study employs a novel approach to assess the consistency of variable selection across models. Specifically, the angle between the actual coefficient vector β and the estimated coefficient vector $\hat {\beta }$ is computed to determine the degree of consistency. Additionally, the boxplot tool of statistical analysis is utilized to visually represent the distribution of model prediction accuracy data and variable selection consistency. The comparative stability of each model is assessed based on the frequency of outliers. This study conducts comparative experiments of numerical simulation to evaluate a proposed model evaluation method against commonly used analysis methods. The results demonstrate the effectiveness and correctness of the proposed method, highlighting its ability to conveniently analyze the stability and efficiency of each fitting model.
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Jatteau, Marjolène, Jean Cauzid, Cécile Fabre, Panagiotis Voudouris, Georgios Soulamidis, and Alexandre Tarantola. "Portable Analyses of Strategic Metal-Rich Minerals Using pXRF and pLIBS: Methodology and Database Development." Data 10, no. 2 (2025): 12. https://doi.org/10.3390/data10020012.
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Strategic metals are indispensable for meeting the needs of modern society. It is then necessary to reassess the potential of such metals in Europe. For the exploration of strategic metals, portable XRF (X-Ray Fluorescence) and LIBS (Laser Induced Breakdown Spectroscopy) are powerful techniques allowing their multi-elementary analysis. This paper presents a database providing more than 2000 pXRF data and more than 4000 pLIBS spectra acquired on minerals from the Mineralogy and Petrology Museum of National and Kapodistrian University of Athens (NKUA), selected based on their potential in bearing strategic metals. The combination of these two portable techniques, along with expanding dataset on strategic metal-rich minerals, provides valuable insights into strategic metal affinities and demonstrates the effectiveness of portable tools for exploring strategic raw materials. Indeed, such database allows to strengthen the knowledge on strategic metals by producing statistic and chemometric analyses (e.g., boxplot, PCA, PLS) on their distribution.
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Țurcanu, Florin-Emilian, Cătălin-George Popovici, Marina Verdeș, Vasilică Ciocan, and Sebastian-Valeriu Hudișteanu. "Indoor Climate Modelling and Economic Analysis Regarding the Energetic Rehabilitation of a Church." Energies 13, no. 11 (2020): 2815. http://dx.doi.org/10.3390/en13112815.
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Background: The aim of our study was to identify an optimal heating system for the analyzed church. We also evaluated the energy consumption of the existing system and of those proposed in order to choose the best heating system. Methods: We analyzed the current existing heating system, a mixed system (static heaters and hot air heating) in a Romanian heritage church, build in the 16th century, and we compared it with an underfloor heating system that has been mentioned in the literature as an alternative for church heating. We used a computational fluid dynamics (CFD) analysis of the indoor climate with two turbulence models (k-ε and k-ω). Results: Comparing the two heating systems through boxplot graphs, we could highlight pertinent conclusions regarding the temperatures and velocities of the measured air currents. Thus, of all the heating systems, the underfloor heating had the lowest temperatures, but the highest air velocities, in the churchgoers area, especially under the towers zone. Conclusions: We observed that the underfloor heating system was more efficient than the existing heating system (static heaters and hot air heating), ensuring heritage conservation and high thermal comfort to the churchgoers.
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Moraes, Ramon, Marcelo Vivas, Janieli Maganha Silva Vivas, et al. "Genetic parameters and performance of papaya genotypes to black spot resistance (Asperisporium caricae)." Australian Journal of Crop Science, no. 13(05) 2019 (May 20, 2019): 649–55. http://dx.doi.org/10.21475/ajcs.19.13.05.p1097.
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Estimation of genetic parameters such as genetic variability of germplasm allows inferring genotype-enviromental interaction for a given variable. The information is important for the process of choosing the variables to be applied to the superior genotype selection. This study aimed at evaluating characteristics related to genetic resistance of papaya to black spot during time testing, as well as estimating genetic parameters associated with some characteristics. The experiment was carried out in RCBD design at Agua Limpa farm, Espirito Santo state, Brazil, using six genotypes: ‘STZ-03’, ‘SS-PT’, ‘Golden’ (‘Solo’ group) ‘Maradol’ (‘Formosa’ group) ‘STA-04’ ‘STA-10’ (landraces), and four repetitions. The 6 treatments were arranged in single row, spacing 2 m between rows and 1.5 m within plants. Nine evaluations were performed during 9 months. We quantified plants on a monthly basis for the characters such as symptom appearance of black spot (FS) on leaves; the incidence of leaves with black spot symptoms (IBS); the severity of black spot on the fifth leaf (SBS5F) and on the leaf with axil attached to the first open flower (SBSFO). By means of the evaluation values, we built a Boxplot graphic to characterize the magnitude of the variables and to describe the dispersion of the data set throughout the evaluations. Analysis of variance, genetic parameter estimate and comparative test of mean were also conducted. The Boxplot graphic allowed classification and magnitude of the variables and described the dispersion of the data set during evaluations. The results showed that SBS5F and the SBSFO were the characteristics that generated reliable results to select genotypes in all evaluations. They showed high H² (Coefficient of genotypic determination), CVg (Coefficient of genotypic variance), CVr (Coefficient of relative variance) and AS (Selective accuracy). The months July, August, September and October showed higher representativeness to evaluate attributes related to resistance to black spot in papaya leaves.
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Santosa, Raden Gunawan, Antonius Rachmat Chrismanto, and Willy Sudiarto Raharjo. "Comparison of the Accuracy of Brown's and Holt's Double Exponential Smoothing in LQ45 Stock Price Forecasting." International Journal of Information Technology and Computer Science Applications 2, no. 1 (2024): 1–11. http://dx.doi.org/10.58776/ijitcsa.v2i1.112.
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As of May 2022, 787 stocks are listed on the Indonesia Stock Exchange (IDX), and the number of stock indices in Indonesia to date is 38. One interesting and important stock index is the LQ45 index. Because this index is a very important reference index for investors, this research data focuses on stocks in the LQ45 index. There are two essential things in the forecasting process: the data and the right forecasting method. Two forecasting methods that can be used are Brown and Holt's Double Exponential Smoothing (DES). This study examines two methods with the lowest accuracy error in forecasting the LQ45 stock price data. Mean Absolute Percentage Error (MAPE) is used to measure the accuracy of the error. The analysis methods used to compare the MAPE of the two methods are the F test for variance similarity, Boxplot, t-test to test paired means with different cases of variance, and Wilcoxon signed rank test to test paired means nonparametric statistics. The result is that the MAPE average with Holt's DES method is smaller than the average MAPE with Brown's DES method. This is supported by the t-test for paired means with different cases of variance and also supported by the Wilcoxon signed exact rank test. Meanwhile, the MAPE standard deviation with Holt's DES method is smaller than the MAPE standard deviation with Brown's DES method. This is supported by the F test to test the variance similarity and is visually supported by a Boxplot diagram. From this study, LQ45 stocks with the smallest MAPE value accuracy are ICBP stocks. In general, based on the MAPE value, Holt's DES method is better than Brown's DES method in predicting the prices of stocks in the LQ45 index.