Violin Plot Research Articles

Predicting and Explaining Customer Response to Upselling in Telecommunications: A Malaysian Case Study

This research explores the predictive capabilities of XGBoost (XGB) and Random Forest (RF) models for customer upsell responses, emphasizing the use of Explainable Artificial Intelligence (XAI) techniques to gain insights. Initially trained without hyperparameter tuning, both models were later optimized using 5-fold cross-validation. While RF consistently achieved high accuracy (0.99), XGB exhibited lower accuracy (0.85) yet demonstrated superior precision and recall. Post-tuning, XGB maintained its competitive edge despite a slight decrease in ROC-AUC scores (0.76 and 0.75 versus RF's 0.67 and 0.72), indicating proficiency in classifying positive cases. XAI techniques complemented XGB’s prediction, revealing significant predictors such as inactive duration in days, race (Chinese), total communication count, age, and active period in days. Lesser predictive value was attributed to factors such as race (Indian), gender (female), and region (northern). While the feature importance plot provided a broad overview, it did not detail specific attribute relationships to predictions. To address this, a summary violin plot was employed to illustrate how feature importance varies with actual values, enhancing the understanding of each feature's impact. Results indicated that longer inactivity periods negatively influenced predictions, while non-Chinese ethnicity, higher communication frequency, and younger age were associated with positive outcomes. Dependence plots further elucidated these relationships, highlighting how older non-Chinese customers and those with shorter inactive periods and frequent communication were more likely to accept offers. Local explanations using Shapley's force plot and LIME offered deeper insights into specific instances. Overall, the study underscores the complementary use of XAI techniques to understand a model’s predictions.

Identification of biomarkers for the diagnosis in colorectal polyps and metabolic dysfunction-associated steatohepatitis (MASH) by bioinformatics analysis and machine learning

Colorectal polyps are precursors of colorectal cancer. Metabolic dysfunction associated steatohepatitis (MASH) is one of metabolic dysfunction associated fatty liver disease (MAFLD) phenotypic manifestations. Much evidence has suggested an association between MASH and polyps. This study investigated the biomarkers of MASH and colorectal polyps, and the prediction of targeted drugs using an integrated bioinformatics analysis method. Differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were performed on GSE89632 and GSE41258 datasets, 49 shared genes revealed after intersection. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses depicted they were mainly enriched in apoptosis, proliferation and infection pathways. Machine learning algorithms identified S100P, FOXO1, and LPAR1 were biomarkers for colorectal polyps and MASH, ROC curve and violin plot showed ideal AUC and stable expression patterns in both the discovery and validation sets. GSEA analysis showed significant enrichment of bile acid and fatty acid pathways when grouped by the expression levels of the three candidate biomarkers. Immune infiltration analysis showed a significant infiltration of M0 macrophages and Treg cells in the colorectal polyps group. A total of 9 small molecule compounds were considered as potential chemoprevention agents in MASH and colorectal polyps by using the CMap website. Using integrated bioinformatics analysis, the molecular mechanism between MASH and colorectal polyps has been further explored.

A Systematic Review of Data Visualization

Data visualization involves presenting data in graphical or pictorial form that in turn helps with decision support. This study addresses the importance of data visualizations in today’s data world, and its role in effective communication in various industry sectors. This study applied the PRISMA methodology to conduct the literature review. Four major themes are identified in this paper: data visualization principles, methods, contemporary chart types, and data visualization challenges. Popularly applied data visualization principles such as Gestalt Principles that help make data visualizations more effective are examined. Advanced chart types include doughnut chart, chord diagram, sankey diagram, and violin plot. Some challenges associated with data visualizations are data accuracy, complexity and uncertainty. Given the challenges, visualizations still benefit any field of study that requires interpreting and presenting complex information.

Research on Quantifying and Evaluating Tennis Match Momentum Based on Comprehensive Analytical Methods

Momentum is crucial in tennis matches, influencing player performance and match outcomes. However, quantifying and evaluating momentum has long been a challenge in sports science research. This study aims to quantify momentum in tennis matches by establishing a hierarchical analysis-based model for momentum flow. The model analyzes factors affecting momentum through five criteria: Service Level, Catch Level, Competition Result, Finishing Ability, and Winning Streak, with respective weights of approximately (0.1, 0.1, 0.2, 0.4, 0.2). Subsequently, the study assigns scores to these criteria based on different actions within a match. By multiplying the weights and scores, the momentum of each player is quantified, and the ratio of the two players' momentum provides a measure of momentum flow during the match. To assess the impact of momentum on match outcomes, the study employs k-means++ clustering analysis, violin plot analysis, and point-biserial correlation analysis. The point-biserial correlation analysis reveals a positive correlation coefficient of 0.66 between a player's momentum and their victory. Similar results are derived from the violin plot and clustering analysis. Therefore, it can be concluded that there is a significant association between the direction of momentum flow and a player's success in a match.

How to decipher the environmental resilience performance? A case study of Yangtze River Delta Urban Agglomeration

Improving environmental resilience performance (ERP) is a significant support for achieving sustainable development goals. This study introduces the three-dimensional analysis framework of "object-process-actor", capable of addressing multidimensional and multilevel issues. This framework systematically addresses the "three key questions" of what, how, and who to improve ERP. Results indicate that from 2011 to 2020, the ERP of the Yangtze River Delta Urban Agglomeration (YRDUA) was between 0.42 and 3.30. The violin plot and visualization analysis showed that its probability density and distribution status were stable, with significant regional differences. The ERP of economically developed cities such as Hangzhou and Shanghai is high, while the ERP of cities in the Yancheng-Chuzhou line is low, and the number of high-performing cities increases to 40.7% in 2020, which is a constantly improving development trend. Factors related to market actors have the greatest impact on ERP, with the explanatory power of FDI, green invention patents, and population concentration all exceeding 0.6, and the importance of government and social actors is also supported. The explanatory power of factor interactions was stronger than that of single factors, and land development degree ranked third in terms of its interaction power with other factors and was also a key factor influencing ERP. These findings provide new explanations and knowledge for improving ERP, which can provide references for policymakers and urban planners.

Employment status of older nursing staff aged 55 years and older in care facilities: A nationwide cross-sectional study in Japan.

An aging nursing workforce requires addressing shortages due to retirement. This nationwide descriptive cross-sectional study in Japan clarified the employment status of older nursing staff aged ≥ 55 years by facility type during January-March 2022. Questionnaires were sent to 8,000 nursing directors, with 1,658 valid responses (response rate: 20.7%). Descriptive statistics and Kruskal-Wallis or χ2 tests analyzed inter-facility differences. A violin plot depicted the proportion of older nursing staff across facilities by age group, and generalized estimating equation (GEE) models examined associated factors at the facility level. Older nursing staff's distribution differed significantly across age groups (p < 0.01), from 0% to 100% within the same facility type. Some facilities had high percentages of staff working beyond retirement age. GEE results showed higher percentages of full-time employees and nurses were negatively associated with the percentage of older nursing staff across most facility types (p < 0.05). For those aged ≥ 65 years, the total population was positively associated with employment in bedded clinics (coef. = 0.07, 95% CI: 0.01 to 0.14, p = 0.03), but negatively associated with the total population (coef. = -0.06, 95% CI: -0.10 to -0.01, p = 0.02) and percentage of the population aged ≥ 65 (coef. = -0.76, 95% CI: -1.43 to -0.08, p = 0.03) in long-term care insurance facilities. Working conditions and environments should be improvement to potentially retain older nursing staff. Job seekers should be matched with managers' needs in facilities with a higher proportion of older nursing staff to ensure a sustainable workforce.

Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction.

Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R2), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R2 values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data.

Development of comprehensive models for precise prognostics of ship fuel consumption

This study incorporates two unique machine learning algorithms, Huber regression and Light Gradient Boosting Machines (LGBM), for estimating ship consumption of fuel. These methods are employed to create forecasting models for ship fuel consumption during journeys, which is especially useful when interacting with non-linear data. The study then analyzes and evaluates the prediction accuracy of these two approaches compared to a baseline model generated using linear regression. The results of the investigation show that both methods establish extremely accurate predictions while handling non-linear data quickly. However, the Huber-based model outperforms the LGBM in terms of prediction accuracy, with an R-squared value of 0.979 versus 0.917 for the LGBM. In addition, the Huber-based model has a diminished prediction error, with an RMSE of 2.278, compared to the LGBM model's RMSE of 4.55. The graphical methods of the violin plot and Taylor’s diagram further established the superiority of Huber ML. These findings imply that Huber regression could be a suitable option for estimating in-route ship fuel usage in real time. As a consequence, this study emphasises the potential benefits of machine learning for accurately predicting ship fuel consumption, providing encouraging possibilities to optimise fuel usage while lowering greenhouse gas emissions.

Predicting surgical efficacy and diagnosing histological inflammation: the clinical significance of prostate exosome proteins in benign prostatic hyperplasia.

Benign prostatic hyperplasia (BPH) is one of the most common causes of lower urinary tract symptoms (LUTS) among the aging male population. Recent studies have shown that histological inflammation (HI) plays a significant role in BPH, with prostatic exosomal protein (PSEP) identified as a potential biomarker for prostate diseases. Therefore, this study aimed to explore the effect of HI on LUTS in patients with BPH, and to further explore the clinical value of PSEP as a diagnostic biomarker of BPH complicated with HI and whether PSEP could be used as an index to predict the improvement of LUTS after operation. This study was an open-label, cohort study. The study enrolled all patients who were clinical diagnosed as BPH with LUTS and prepared to receive operation of the prostate at the Department of Urology of the Second Hospital of Hebei Medical University. International Prostate Symptom Score (IPSS) were used to evaluate the LUTS of the BPH. And the enrolled patients were divided into four groups, including none, mild HI, moderate HI, and severe HI, based on postoperative pathological results. Then the relationships between HI and IPSS, the National Institutes of Health-Chronic Prostatitis Symptom Index (NIH-CPSI), as well as PSEP were analyzed. Simple and multiple linear regression analyses were performed on the preoperative IPSS and the difference of IPSS before and after surgery was examined. SPSS software version 26 was used for statistical analysis and Prism 9.0 was used to make violin plots. A total of 69 patients were enrolled in the study. The violin plot results indicated IPSS and NIH-CPSI scores exhibited significant increases in correlation with the severity levels of HI (P<0.001; P<0.001). Among BPH patients with total prostate-specific antigen (t-PSA) levels higher than 4.0 ng/mL, a significant correlation was observed between PSEP levels and HI (P=0.04). Besides, simple and multiple linear regression analysis showed that HI (P<0.001) or PSEP (P=0.03) was significantly associated with IPSS and improvement of LUTS, assessed by postoperative and preoperative IPSS differences. The study indicated that IPSS and PSEP (when t-PSA >4 ng/mL) were correlated with the severity of HI in patients with BPH. PSEP was linearly correlated with IPSS and the degree of reduction in IPSS after surgery. Consequently, PSEP may serve as a promising predictor for assessing surgical efficacy and diagnosing the severity of HI in patients with BPH.

State-of-the-art XGBoost, RF and DNN based soft-computing models for PGPN piles

ABSTRACT Machine learning (ML) has made significant advancements in predictive modelling across many engineering sectors. However, predicting the bearing capacity of pre-bored grouted planted nodular (PGPN) piles remains a relatively unexplored area due to the complexity of the load-bearing mechanism, pile-soil interactions, and multiple variables involved. The study utilises state-of-the-art ML techniques such as extreme gradient boosting (XGBoost), random forest (RF), gradient boosting machines (GBMs), and deep learning-based simulation models. The dataset fed into the model comprises 81 case histories of static pile load tests conducted in various regions of Vietnam. The data was validated using descriptive statistics, sensitivity analysis, correlation matrix displays, SHAP plot analysis, and regression curves, with predictive performance validated through k-fold cross-validation. Among all the models tested, XGBoost (R2 = 0.91, RMSE = 0.09) and RF (R2 = 0.82, RMSE = 0.09) performed the best, while the deep neural network also yielded satisfactory results. However, GBM was found not to be a robust model for this analysis. The performance of the models was visually analysed using Violin plot comparisons and Taylor diagrams. The outcome of this study facilitates the safe and economical designs of the eco-friendly pile.

온라인 마켓의 평점 제공을 위한 시각화 디자인 방식에 따른 소비자 의사결정 편향

This study investigated the biases associated with commonly used bar or star designs for visualizing product or service reviews in online markets. Previous research has shown biases in decision-making when inferring average scores using bar graphs, but it remained unclear whether similar biases exist with star shapes or other visualization designs. Therefore, it is crucial to examine whether the methods of using bar, star, and violin plot designs for product reviews actually induce biases in people's judgment of scores. A total of 257 participants took part in the study, where they were presented with product rating designs created using bar, star, and violin plot methods and asked to make decisions. The results revealed biases in bar and star designs compared to violin plot. This suggests that biases may occur due to human perceptual characteristics even when providing simple average rating designs and indicates that violin plot methods, which provide information on both average and distribution simultaneously, may help reduce biases in specific decision-making tasks.

Single-cell analysis extracted CAFs-related genes to established online app to predict clinical outcome and radiotherapy prognosis of prostate cancer.

Cancer-associated fibroblasts (CAFs) play a significant role in regulating the clinical outcome and radiotherapy prognosis of prostate cancer (PCa). The aim of this study is to identify CAFs-related genes (CAFsRGs) using single-cell analysis and evaluate their potential for predicting the prognosis and radiotherapy prognosis in PCa. We acquire transcriptome and single-cell RNA sequencing (scRNA-seq) results of PCa and normal adjacent tissues from The GEO and TCGA databases. The "MCPcounter" and "EPIC" R packages were used to assess the infiltration level of CAFs and examine their correlation with PCa prognosis. ScRNA-seq and differential gene expression analyses were used to extract CAFsRGs. We also applied COX and LASSO analysis to further construct a risk score (CAFsRS) to assess biochemical recurrence-free survival (BRFS) and radiotherapy prognosis of PCa. The predictive efficacy of CAFsRS was evaluated by ROC curves and subgroup analysis. Finally, we integrated the CAFsRS gene signature with relevant clinical features to develop a nomogram, enhancing the predictive accuracy. The abundance of CAFs is associated with a poor prognosis of PCa patients. ScRNA-seq and differential gene expression analysis revealed 323 CAFsRGs. After COX and LASSO analysis, we obtained seven CAFsRGs with prognostic significance (PTGS2, FKBP10, ENG, CDH11, COL5A1, COL5A2, and SRD5A2). Additionally, we established a risk score model based on the training set (n = 257). The ROC curve was used to confirm the performance of CAFsRS (The AUC values for 1, 3 and 5-year survival were determined to be 0.732, 0.773, and 0.775, respectively.). The testing set (n = 129), GSE70770 set (n = 199) and GSE116918 set (n = 248) revealed that the model exhibited exceptional predictive performance. This was also confirmed by clinical subgroup analysis. The violin plot demonstrated a statistically significant disparity in the CAFs infiltrations between the high-risk and low-risk groups of CAFsRS. Further analysis confirmed that both CAFsRS and T stage were independent prognostic factors for PCa. The nomogram was then established and its excellent predictive performance was demonstrated through calibration and ROC curves. Finally, we developed an online prognostic prediction app ( https://sysu-symh-cafsnomogram.streamlit.app/ ) to facilitate the practical application of the nomogram. The prognostic prediction risk score model we constructed could accurately predict BRFS and radiotherapy prognosis PCa, which can provide new ideas for clinicians to develop personalized PCa treatment and follow-up programs.

Solution gas-oil ratio estimation using histogram gradient boosting regression, machine learning, and mathematical models: a comparative analysis

ABSTRACT Quantifying gas solubility in oil under reservoir conditions is essential in reservoir engineering. Several laboratory techniques have been suggested to quantify it; nevertheless, these procedures are characterized by their demanding time requirements and high costs. Estimating the solution gas-oil ratio relies significantly on applying mathematical and artificial intelligence models. The research commenced by collecting 757 datasets from published articles. Subsequently, the data was partitioned into two distinct categories: training and test data. Following this data preparation phase, artificial intelligence models, including Histogram Gradient Boosting Regression (HGBoost), Adaptive Boosting (AdaBoost), Support Vector Regression (SVR), and the Adaptive Neuro-Fuzzy Inference System (ANFIS), were employed to model the solution gas-oil ratio. Additionally, the Memetic algorithm (MA) was employed to recompute the coefficients of the Petrosky and Farshad model, initially developed with 81 data points, using an expanded dataset of 605 training instances. Subsequently, model accuracy and performance were assessed through statistical and visual analyses. The results indicate that the HGBoost model performs best, achieving an overall R2 of 0.9886 and an overall RMSE of 41.29 scf/STB. Notably, among the mathematical models, the modified Petrosky and Farshad model stands out with an overall R2 of 0.9078 and an overall RMSE of 114.49 scf/STB. The Taylor diagram and violin plot both indicate that the HGBoost model and the modified Petrosky and Farshad model outperform other machine learning models and mathematical models, respectively. These results demonstrate the accuracy of the models described in this study.

Identification of key genes and pathways in adrenocortical carcinoma: evidence from bioinformatic analysis.

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with poor prognosis. The disease originates from the cortex of adrenal gland and lacks effective treatment. Efforts have been made to elucidate the pathogenesis of ACC, but the molecular mechanisms remain elusive. To identify key genes and pathways in ACC, the expression profiles of GSE12368, GSE90713 and GSE143383 were downloaded from the Gene Expression Omnibus (GEO) database. After screening differentially expressed genes (DEGs) in each microarray dataset on the basis of cut-off, we identified 206 DEGs, consisting of 72 up-regulated and 134 down-regulated genes in three datasets. Function enrichment analyses of DEGs were performed by DAVID online database and the results revealed that the DEGs were mainly enriched in cell cycle, cell cycle process, mitotic cell cycle, response to oxygen-containing compound, progesterone-mediated oocyte maturation, p53 signaling pathway. The STRING database was used to construct the protein-protein interaction (PPI) network, and modules analysis was performed using Cytoscape. Finally, we filtered out eight hub genes, including CDK1, CCNA2, CCNB1, TOP2A, MAD2L1, BIRC5, BUB1 and AURKA. Biological process analysis showed that these hub genes were significantly enriched in nuclear division, mitosis, M phase of mitotic cell cycle and cell cycle process. Violin plot, Kaplan-Meier curve and stage plot of these hub genes confirmed the reliability of the results. In conclusion, the results in this study provided reliable key genes and pathways for ACC, which will be useful for ACC mechanisms, diagnosis and candidate targeted treatment.

An efficient manta ray foraging optimization algorithm with individual information interaction and fractional derivative mutation for solving complex function extremum and engineering design problems

The manta ray foraging optimization algorithm (MRFO) is a recently proposed meta-heuristic algorithm that mimics the foraging process of manta rays. It has yielded good outcomes in solving some optimization problems because its mechanism is clear, no additional parameters need to be set, and the balance between global and local search is good. Nonetheless, while dealing with high-dimensional global optimization and complex engineering optimization problems, there are also issues such as premature convergence, low optimization-seeking accuracy, or unstable solutions. To this end, this article proposes an efficient manta ray foraging optimization algorithm (NIFMRFO) by incorporating individual information interaction and fractional derivative mutation. First, to prevent premature convergence of the algorithm, a nonlinear cosine adjustment parameter is presented, which is intended to make the demand relationship between global exploration and local development more reasonable. Then, an information interaction strategy among random individuals is employed to expedite the rate at which the algorithm converges. Finally, a fractional derivative mutation strategy is utilized to continually enhance individuals’ quality in each iteration, which not only increases the population diversity but also helps to improve the precision and stability of the search results. Theoretical analysis indicates that the improved NIFMRFO algorithm and basic MRFO algorithm have the same time complexity. In simulation experiments, the CEC2017 suite is used to conduct comparison tests with six superior-performance representative comparison algorithms in several dimensions. In terms of the optimization-seeking accuracy, convergence curve, violin plot, and Friedman average ranking, the analysis of these graphs and data shows that the NIFMRFO algorithm’s ameliorated strategy improves superiority-seeking power, convergence speed, and steadiness. Meanwhile, the Wilcoxon rank-sum test result illustrates significant differences between NIFMRFO and other compared algorithms. Finally, these algorithms are utilized to tackle seven realistic engineering design optimization problems. The result makes it clear that NIFMRFO is distinctly superior to the other six algorithms, showing that its solving ability is superior and has broad application prospects.

Investigating comparisons on the coal and gangue in various scenarios using multidimensional image features

Online intelligent recognition of coal and gangue is an important aspect of coal mine intelligent development. Current research focuses more on improving recognition accuracy through optimizing image processing algorithms, but there is limited research on the characteristics of coal gangue materials themselves, particularly the comparative analysis of their appearance in images under different scenarios. Therefore, this paper proposes a multi-dimensional feature-based analysis method for coal-gangue images, which can be used to evaluate the characteristics and distinguishability of coal and gangue images. Firstly, the original feature space of coal and gangue images is constructed based on 55 global image feature operators. Then, the characteristics of coal and gangue images in both the original space and the two-dimensional space after dimensionality reduction are analyzed using the similarity between the left and right sides of the split violin plot and the distance of probability distributions. At the same time, different types of features are compared and analyzed in terms of their impact on classification performance when used as inputs with various machine learning classifiers. Furthermore, by leveraging the interpretability of tree models, the contribution of different feature types to coal gangue classification tasks is analyzed. The proposed method is applied to analyze a large number of raw coal images collected from three different conditions. The experimental results demonstrate that the distinguishability of coal gangue images varies under different scenarios, and the distance between the two distributions in the reduced space effectively measures this distinguishability. In coal gangue images, the contribution of color features and texture features to the classification task is related to the type of coal, with texture features playing a more significant role in classifying coking coal compared to thermal coal. However, overall, color features dominate the classification task. These findings are further validated in experiments using machine learning classifiers, which are crucial for designing more refined classification algorithms.

Exploring the Accumulation Behavior and Heterogeneity of Perfluorooctanesulfonic Acid in Zebrafish Primary Organ Cells by Single-Cell Mass Cytometry.

Perfluorooctanesulfonic acid (PFOS) is a commonly found environmental pollutant with potential toxicity and health risks to biosystems and ecosystems. Study of the accumulation behavior and heterogeneity of PFOS in biological primary organ cells provides us significant insights to explore its cytotoxicity, carcinogenicity, and mutagenicity. Here a single-cell mass cytometry system was established for the high-throughput analysis of trace PFOS and the exploration of its accumulation behavior and heterogeneity in zebrafish primary organ cells. The single-cell mass cytometry system applied a ∼25 μm constant-inner-diameter capillary as the single-cell generation and transportation channel with an etched tip-end of 40 μm as the nanoelectrospray emitter for mass spectrometric analysis. The single-cell mass cytometry system showed satisfactory semiquantitative performance and sensitivity for analysis of PFOS in single cells, with a high detection throughput of ∼35 cells/min. Subsequently, the liver, intestine, heart, and brain from PFOS-exposed zebrafish (100 pg/μL, 28 days) were dissociated and prepared as cell suspensions, and the cell suspensions were introduced into the single-cell mass cytometry system for high-throughput analysis of PFOS in individual primary organ cells. Significant cellular accumulation heterogeneities were observed, with the highest content in liver cells, followed by intestine cells, then heart cells, and the lowest in brain cells. In addition, the dynamics of PFOS in the zebrafish liver, intestine, heart, and brain cells showed typical violin plot distributions and were well-described using a gamma (γ) function.

Development of decomposition-based model using Copula-GARCH approach to simulate instantaneous peak discharge

Estimation of instantaneous peak discharge is important in the design of hydraulic structures and reservoir management. In this study, a new approach called CEEMD-Copula-GARCH is presented for simulating instantaneous peak discharge in the Qale Shahrokh basin, upstream of Zayanderood Dam, Iran. In the developed method, the Complementary ensemble empirical mode decomposition (CEEMD) algorithm was used to analyze the observed values and generate the intrinsic mode function values and residual series. For this purpose, the intrinsic mode function values were simulated based on vine copula and its tree sequence (C-vine, D-vine, R-vine and their independent and Gaussian modes), and the residual series of the CEEMD algorithm were simulated by the GARCH model. The results of simulating instantaneous peak discharge values (m3/s) using the CEEMD-Copula-GARCH approach in the study area showed that the amount of simulation error based on the RMSE statistic compared to the CEEMD-Copula model and simulation without decomposition has improved by about 20 and 70%, respectively. The model’s efficiency was also estimated based on the Nash–Sutcliffe efficiency in the proposed approach of 0.99, and the certainty of the proposed approach was also confirmed based on the presented violin plot. According to the presented results, the proposed approach has high accuracy and efficiency in the simulation of instantaneous peak discharge (m3/s), which can be used in the flood control system design and flood management. Using the methodology proposed in this study, multivariable models can be used in simulating univariate series with high accuracy.

Application of boosted tree algorithm with new data preprocessing techniques in the forecasting one day ahead streamflow values in the Tigris basin, Türkiye

Accurate streamflow forecasting is very useful in water resources management, design of hydraulic structures, and almost all issues related to the use of water and water resources, especially in arid regions that have increased in recent years. Since water is the source of all life and the most important basic element for humanity to continue its life, streamflow prediction studies increase its importance daily. This research combined the boosted tree (BT) model with robust empirical mode decomposition, empirical mode decomposition, complete ensemble empirical mode decomposition with adaptive noise, empirical wavelet transforms and variational mode decomposition for predicting daily average streamflow data. While historical streamflow data was input in the model's setup, one-day lead-time streamflow data was used as the target. 70% of the data is reserved for training and the rest for testing. 5-fold cross-validation technique was used to solve the over-fitting problem. The coefficient of determination, mean squared error, Nash-Sutcliffe efficiency and percent bias statistical criteria and Taylor diagrams, polar plot, scattering diagram, and violin plot were used to determine the algorithm's success. At the end of the study, it was found that the most successful streamflow predictions were made with the variational mode decomposition-based BT hybrid approach.

Personal exposure from free Wi-Fi hotspots in downtown Mexico City.

In 2019, the Government of Mexico City implemented actions that allowed citizens to approach a free Wi-Fi hotspot, where more than 13000 points have been installed throughout the city. In this work, we present the results of the measurements of personal exposure to Radiofrequency Electromagnetic Fields carried out in Plaza de la Constitución, better known as Zócalo located in the center of Mexico City. The measurements were taken by one of the researchers while walking on a weekday morning and afternoon, in different microenvironments (on the street, on public transport: subway, at the Zócalo, and finally, at home). We also carry out spot measurements in the center of the Zócalo. Subsequently, we carried out a comparative analysis of the different microenvironments, through box plot and violin plot, and we elaborate georeferenced and interpolated maps with intensity levels through the Kriging method, using the Geographic Information System. The Kriging interpolation gives us a good visualization of the spatial distribution of RF-EMF exposure in the study area, showing the highest and lowest intensity levels. The mean values recorded at the measured points in the Zócalo were 326 μW/m2 in the 2.4- to 2.5-GHz Wi-Fi band and 2370 μW/m2 in the 5.15- to 5.85-GHz Wi-Fi band. In the case of the mean values recorded on the street, they were 119 μW/m2 in the 2.4- to 2.5-GHz frequency band and 31.8 μW/m2 in the 5.15- to 5.85-GHz frequency band, like the values recorded at home, 122 μW/m2 and 33.9 μW/m2, respectively. All values are well below the reference levels established by the International Commission on Non-Ionizing Radiation Protection.