Numerical Data Research Articles

Liquid pinching dynamics in an inertial transitioning regime

The pinching of a liquid filament at low viscosity, whether in air or liquid, shows a rich and interesting behavior. The liquid thread thins according to a capillary-inertial regime power law at the beginning, before transitioning into other regimes when approaching the pinching moment. Here we study the pinching of a pendant drop revealing the effect of internal and external viscosity on thread thinning. We show an inertial transitioning regime in which slower thinning happens by slightly increasing not only the droplet viscosity but also the viscosity of the surrounding liquid. The viscosity is found to reduce the filament neck axial velocity leading to a slower thinning. We derive a simple universal scaling law that can predict the thinning inertial prefactor value as a function of the neck axial velocity showing excellent agreement with numerical data. Finally, the breakup of satellite droplets is characterized given the defined thinning regimes. Published by the American Physical Society 2024

Enhancing Data Governance with Real-Time Analytics in Oil Production Forecasting

This article explores the transformative impact of real-time analytics on oil production forecasting in the oil and gas industry. It discusses the shift from traditional batch processing to real-time data analysis, highlighting the limitations of batch processing and the benefits of real-time analytics. The article examines how advanced data processing platforms enable more effective data governance and faster insights, leading to improved decision-making, enhanced operational efficiency, and increased production. It also delves into the role of real-time analytics in strengthening compliance and accountability through automated data collection, validation, and consistent data handling. The article presents various case studies and numerical data demonstrating the significant improvements in forecasting accuracy, anomaly detection, and regulatory compliance achieved by implementing real-time analytics systems.

V. S. Kozlov and his Tsifrar-diagrammometer

In the late 19th century a military statistician and a pioneers of the Russian cooperative movement, Viktor Semyonovich Kozlov (1842–1920), designed and built a unique mechanical analog computing device he named “tsifrar-diagrammometer”. It was intended for operational visual presentation of numerical results of observations, calculations, experiments, etc. (tsifrar) and for calculating their statistical characteristics (diagrammometer). At the 1889 Paris Exposition this device that represented Russia was awarded the bronze medal. Drawing on the archival records and the materials from the Russian and European periodicals, the history of the inventor’s work on this device is reconstructed for the first time and some facts from his biography are described. The authors also attempt to describe the design of the device and analyze the mechanism of its functioning. Since no sources contain a detailed description of the design with the diagrams, charts, etc. or the mechanism of its functioning while available information is often contradictory, the authors present their own version of the device’s description and operation. The tsifrar-diagrammometer aroused much interest among the most prominent Russian and French scientists of the time, including P. L. Chebyshev, D. I. Mendeleev, E. Lucas, and many others. They viewed Kozlov’s device as a forerunner of a future universal instrument for numerical data analysis and for discovering new laws of nature. The appraisals of the device by the contemporaries are also given and analyzed in the article.

Sprint Management in Agile Approach: Progress and Velocity Evaluation Applying Machine Learning

Nowadays, technology plays a fundamental role in data collection and analysis, which are essential for decision-making in various fields. Agile methodologies have transformed project management by focusing on continuous delivery and adaptation to change. In multiple project management, assessing the progress and pace of work in Sprints is particularly important. In this work, a data model was developed to evaluate the progress and pace of work, based on the visual interpretation of numerical data from certain graphs that allow tracking, such as the Burndown chart. Additionally, experiments with machine learning algorithms were carried out to validate the effectiveness and potential improvements facilitated by this dataset development.

Stock Market Forecasting Using a Neural Network Through Fundamental Indicators, Technical Indicators and Market Sentiment Analysis

AbstractThe objective of this research is to provide evidence that it is possible to obtain a prediction that better aligns with the future performance of a stock if a neural network model is trained with stock market analysis variables and qualitative variables. As a case study, thirty-three companies’ representative of the S&P 500 are selected, and a multilayer perceptron artificial neural network is built and trained with input parameter indicators of fundamental analysis, technical analysis, and market sentiment. By incorporating the latter as an additional variable, the model's accuracy increases by 1.5% for 66% of the companies analyzed. The results confirm the crucial role played by the selection of the neural network model and its variables depending on the type of company to be analyzed. The main contributions of this research are the identification of the best variables combination to train a neural network model depending on the market sector to be analyzed, likewise it is demonstrated that, by using market sentiment, it is possible obtain a high accuracy or increase the accuracy to an existing model.

Aerodynamic performance evaluation of subsonic compressor cascade blade with leading-edge damage

Abstract Material loss at the leading edge of in-service gas-turbine engine blades is detrimental to the aerodynamic performances of compressors. In this study, computational simulations and experiments using pneumatic probes were conducted to measure the total pressure loss of compressor cascade blades with material loss at their leading edges. The correlations between blade leading-edge damage geometries and cascade blade total pressure losses were analyzed. The geometrical features of blade leading-edge damage that most affect the cascade blade total pressure loss were distinguished, and the influences of leading-edge damage region cross-sectional shapes on the cascade blade total pressure loss were examined. Two methods for evaluating the total pressure loss of damaged cascade blades were developed using experimental and numerical data. In the first method, an attempt is made to correlate the cascade blade total pressure loss with a characteristic parameter that is defined using scale and angle parameters of the leading-edge damage region. The second method is based on the probability distribution function as well as the critical loss boundary of the geometrical parameters of the leading-edge damage region. The results of this study provide a better understanding of the influence of damaged leading edges on compressor cascade blade aerodynamic performance and can help establish low-cost methods for the estimation of compressor performance degradation.

Damage detection for railway bridges using time‐frequency decomposition and conditional generative model

AbstractA novel damage detection model, which utilizes the spatiotemporal characteristics of the acceleration data, is proposed to assess the structural integrity of railway bridges. For this, the measured acceleration data are decomposed into several intrinsic mode functions (IMFs) using the sparse random mode decomposition model. The generated IMFs are subsequently integrated into the enhanced time series conditional generative adversarial network model to identify possible damage in bridges across various frequency bands. The influence of environmental and operational variables (EOVs), particularly temperature fluctuations, was also investigated. The proposed model was verified using both numerical and experimental data from a plate girder bridge. Further validation was conducted using the Z24 bridge dataset, and damage cases under the influence of EOVs were successfully predicted. Throughout the validation process, various anomaly metrics were introduced to establish a threshold value, and a covariance‐based time domain metric was proven to be the most effective in our cases.

Influence of fuel data assumptions on wildfire exposure assessment of the built environment

Background Land cover information is routinely used to represent fuel conditions in wildfire hazard, risk and exposure assessments. Readily available land cover data options that vary in resolution, extent, cost and purpose of collection have become increasingly accessible in recent years. Aim This study investigates the sensitivity of community-scale wildfire exposure assessments to different land cover information products used to identify hazardous fuel. Methods Ten versions of a community wildfire exposure assessment were conducted for each of five case study locations in Alberta, Canada, by varying the input land cover data. Proportional and spatial distribution of hazardous fuels and classified exposure are compared across datasets and communities. Key results We found proportional and spatial variation of exposure values between datasets within each community, but the nature of this variation differed between communities. Land cover classification definitions and scale were important factors that led to inconsistencies in assessment results. Conclusions Readily available land cover information products may not be suitable for exposure assessments at a localised scale without consideration of unique context and local knowledge of the assessment area. Implications Results may inform fuel data selection considerations for improved results in various wildfire applications at localised scales.

A New Centralized Statistical Monitoring Method for Detecting Atypical Distribution of Qualitative Variables in Multicenter Randomized Controlled Trials

Centralized statistical monitoring (CSM) detects clinical trial centers in which the distribution of a variable is atypical compared to its distribution in other centers. Most proposed CSM methods concern quantitative variables. Here we propose a new hierarchical Bayesian beta-binomial (HBBB) method for categorical variables and report the results of a simulation study assessing the performance of the method and of an application study using a real database to assess its usefulness. In the simulation study, sensitivity exceeded 90% when the sample size in the atypical center (N a ) was ≥ 20 and the difference in the proportion of events between the atypical center and the other centers ( δ ) was ≥ 0.4; when N a was ≥ 40 and δ ≥ 0.3; and when N a was ≥ 150 and δ ≥ 0.2. Specificity exceeded 90% when N a was ≥ 150 in all scenarios, and remained between 75% and 90% when N a was lower. In the application study, the method detected two centers in which N a was 50 and 200, and δ was 0.12 and 0.04, respectively. The performance of the HBBB method was similar to that proposed by competing approach. The modeling is easy and specificity is good in many scenarios with a limited sample size.

Correlation-based polarity-check algorithm for instrument transformers

A polarity identification is very important for operation of transformers, measurement and protection equipment, where it is useful in analyzing of transformer connections and operation as well as testing of protective systems. Moreover, it’s essential in assessment of power systems performance during both normal and abnormal operation. Ensuring the correct polarity of the primary and secondary windings in voltage and current transformers is of paramount importance for various measurement and protection schemes in power networks. This paper proposes a digital polarity detector and tester using correlation coefficients and nine polarity indices calculated for instrument transformer signals. In order to test the performance of the proposed polarity tester algorithm, MATLAB code is imported to the LABVIEW model, and the numerical data obtained from the synchronous generator terminals via instrument transformers are interfaced with the computer through the Data Acquisition Card (DAC). The experimental system consists of a motor-generator set supplying a three-phase inductive load with instrument transformers connected to measure each phase voltage and current. The obtained results for various operating conditions and different types of abnormal conditions prove that the suggested algorithm is accurate, reliable and applicable to smart grids and substation automation systems. It can be considered as an integrated system incorporated with digital fault recorders, relays and meters.

Using Extracorporeal Shockwave Therapy in Patients with Orthopedic Conditions

Background: Among other non-invasive procedures, Extracorporeal Shockwave Treatment (ESWT) has been used to resolve many musculoskeletal ailments. The authors completed a systematic review to study articles that reported the effectiveness of focused shockwave therapy in patients with orthopedic conditions. Methods: A comprehensive search was conducted across reputable databases including Pub Med, The Cochrane Library, Science Direct, PEDro, Clinicaltrials.gov, and Google Scholar, ensuring a thorough review of the literature until October 10, 2022. Original articles of any design reporting on the use of ESWT in orthopedic patients were meticulously selected. Information related to study design, sample size, participants, intervention, and key findings of the study were extracted. The PEDro classification scale, a recognized tool, was used to evaluate the quality of the studies, further enhancing the credibility of findings. Results: Nineteen research articles (including 1157 participants) were retrieved and included for qualitative analysis. The sample size ranged from twenty-one to one hundred fifteen subjects, and the articles were published between 2001 and 2022. Out of 19 studies published, five were conducted in China, three in Germany, two in Iran and Turkey, and one in the United States, Canada, Italy, Denmark, Spain, Taiwan, and Saudi Arabia. The risk of bias was either moderate or low among the included studies. In patients with various orthopedic conditions, considerable moderate evidence of ESWT’s effectiveness and success in lowering pain levels, increasing treatment success rates, promoting patient-reported functional recovery, and raising performance-based functional outcomes was seen in the results. Conclusion: The systematic literature review provided compelling evidence of the successful application of ESWT in treating a wide range of musculoskeletal problems. These positive outcomes were achieved without severe complications and morbidities, offering a promising outlook for the future of orthopedic treatment.

Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings

Wind-structures interaction has been extensively examined in the last few decades using field measurements, full scale measurements and wind tunnel testing. These experimental approaches are considered costly and time consuming. The need for a reliable analytical approach that can be used for examining wind-effects on buildings is clear. Although Computational Fluid Dynamics (CFD) is one of the other alternative numerical options yet might not reached the level of confidence to be reliably used to finalize the structural design. On the other hand, a limited number of studies have been carried out using soft computing methods to examine wind-induced loads on structures. However, its promising results, more work is still required towards achieving the full analytical prediction of wind effects on structures. This study investigates the use of different soft-computing techniques in predicting wind pressures on tall buildings. Two deep learning methods viz deep belief network (DBN) and deep neural network (DNN), and five machine learning methods namely feedforward neural network, extreme learning machine, weighted extreme learning machine, random forest, and gradient boosting machine were evaluated, and compared in predicting the design wind pressures on tall buildings. Wind tunnel datasets, used in the current study to develop the proposed computing models, were collected from testing three tall buildings having the same full-scale horizontal dimensions of (40 m and 80 m) and different heights of (80 m, 120 m and 160 m). The buildings were tested at a scale of 1:400 in urban terrain exposure. Mean and fluctuating wind pressure coefficients on the building with the height of 120 m are herein predicted using the seven computing methods and the results were compared to the corresponding measured pressures. Overall, the examined methods performed well in the wind pressure prediction process. Furthermore, the employed DNN was found to have the best performance in predicting mean and fluctuating wind pressures with the highest correlation coefficients. Hence, the DNN was also used in predicting the mean and fluctuating wind pressures on the two other buildings with heights of 80 m and 160 m. Experimental results indicate that the employed DNN model can be effectively used in predicting wind-induced pressures on tall buildings.

Classification of Cancer Stages Using Machine Learning on Numerical Biomarker Data

Cancer staging is a crucial aspect of determining appropriate treatment strategies and predicting patient outcomes. Traditional diagnostic methods rely heavily on invasive procedures and qualitative assessments, which can be time-consuming and prone to subjective errors. This research aims to address these limitations by leveraging machine learning (ML) techniques to classify cancer stages using numerical biomarker data, such as C-reactive protein (CRP), tumor mutation burden (TMB), and lactate dehydrogenase (LDH). By applying models like Random Forest, Support Vector Machines (SVM), Gradient Boosting, and Multi-Layer Perceptron (MLP), we aim to improve the accuracy of cancer stage classification. Feature selection through Recursive Feature Elimination (RFE) was performed to enhance model efficiency by identifying the most significant biomarkers. The results show that ML models can effectively predict cancer stages, with Random Forest achieving the highest accuracy at 85%. This method offers a non-invasive, rapid, and scalable alternative to conventional diagnostic approaches, potentially improving clinical decision-making and patient care.

Productivity of grafted papaya growing under open environment in the Mexican pacific

ABSTRACT The phytosanitary problems and the complex floral biology influence the fruit quality and productivity of papaya. Use of grafting mitigates adverse aspects, becoming a complementary activity of crop. The objective of this study was to evaluate the agronomic behaviour of grafted papaya plants. Two experiments were carried out. In the first experiment, the treatments were derived from six rootstock/scion combinations and four controls, based on commercial genotypes. In the second experiment, four rootstocks and two genotypes formed eight rootstock/scion treatments, and two controls were used. The morphological variables as well as the qualitative and productive variables of fruits were recorded. Results showed that plant height was maintained or reduced in some treatments, whereas stem girth and leaf numbers in grafted treatments were increased. The fruit quality was not influenced by grafting. In the productivity, the grafted plants showed higher fruit production. For an example, the Rootstock 'MSXJ' × Scion 'Maradona' and the Rootstock 'BS2' × Scion 'Maradol'>40 kg · plant−1 and almost 9 kg · m–2, respectively; and the Rootstock 'Robusta' × Scion 'Maradol' treatment was higher than the other treatments, reaching 38.39 kg · plant–1 and 8.53 kg · m–2, in Experiments 1 and 2, respectively. Grafting on papaya positively changed the morphological variables, increased the number of fruits per plant in grafted plant treatments, as well as yield per area, surpassing controls by 25% and 22% in both experiments, respectively.

A novel MissForest-based missing values imputation approach with recursive feature elimination in medical applications

BackgroundMissing values in datasets present significant challenges for data analysis, particularly in the medical field where data accuracy is crucial for patient diagnosis and treatment. Although MissForest (MF) has demonstrated efficacy in imputation research and recursive feature elimination (RFE) has proven effective in feature selection, the potential for enhancing MF through RFE integration remains unexplored.MethodsThis study introduces a novel imputation method, “recursive feature elimination-MissForest” (RFE-MF), designed to enhance imputation quality by reducing the impact of irrelevant features. A comparative analysis is conducted between RFE-MF and four classical imputation methods: mean/mode, k-nearest neighbors (kNN), multiple imputation by chained equations (MICE), and MF. The comparison is carried out across ten medical datasets containing both numerical and mixed data types. Different missing data rates, ranging from 10 to 50%, are evaluated under the missing completely at random (MCAR) mechanism. The performance of each method is assessed using two evaluation metrics: normalized root mean squared error (NRMSE) and predictive fidelity criterion (PFC). Additionally, paired samples t-tests are employed to analyze the statistical significance of differences among the outcomes.ResultsThe findings indicate that RFE-MF demonstrates superior performance across the majority of datasets when compared to four classical imputation methods (mean/mode, kNN, MICE, and MF). Notably, RFE-MF consistently outperforms the original MF, irrespective of variable type (numerical or categorical). Mean/mode imputation exhibits consistent performance across various scenarios. Conversely, the efficacy of kNN imputation fluctuates in relation to varying missing data rates.ConclusionThis study demonstrates that RFE-MF holds promise as an effective imputation method for medical datasets, providing a novel approach to addressing missing data challenges in medical applications.

Nursing Home Administrators' Experiences of Work Stress and Intention to Quit during the COVID-19 Pandemic.

The COVID-19 pandemic negatively impacted nursing home administrators (NHAs). This study explores licensed and employed NHA's perceptions about what work stress events would drive their turnover intentions (TI) in year three of the pandemic. As part of a larger survey-based research project collecting numerical and textual data, this study conducted content analysis with qualitative data. Narrative data from 3,859 discrete responses of 1,139 NHAs, from 50 states and Washington D.C, on two open-ended electronic survey questions asking about their work stress and TI were collected February - March 2022. Categories and subcategories were counted and discussed in context as themes. We identified 4 main narrative categories with subcategories about NHA's stress about the regulatory system (e.g., excessive, conflicting, changing regulations), staffing (e.g., insufficient, agency staff), organizational pressures (e.g., work overload), and macro system stressors (e.g. media representation of nursing homes). Three main TI categories with subcategories were identified: the LTC setting and system (e.g., regulatory demands, staffing, facility surveys); personal factors (e.g. stress, career opportunities), and others (e.g., "I would never quit," or "I already decided to quit"). Whole community disaster planning, person-centered surveyor training, improved regulatory guidance, simplified reporting, adequate guidance assistance, and adequate staff could mitigate NHA stress and TI during crises. Administrator resilience should be recognized. Solutions for supporting NHAs can include long-term care industry participation in public health disaster preparedness coalitions, consistent regulatory guidelines with established communication channels, and ongoing crisis leadership training for administrators.

Exponential Wiener index of some silicate networks

Any graph that depicts a particular molecular structure can be given a topological graph index, also known as a molecular descriptor. This index can be used to examine numerical data and discover more about specific physical properties of molecules. Silicates are hard crystals that can be sliced into little pieces. Therefore, silicon play a vital role in many real time applications. It is the authentic material to make microchips which run in PDA, PC and other game tools. In the current study, we have determined the new topological index for a few silicate structures, including cyclic, single-chain, and Pyro silicates by grouping the vertices of the corresponding molecular graphs based on their distance sums. This index is known as the exponential Wiener index. It is expanded to include their line graphs as well. The numerical values of exponential Wiener index and multiplicative exponential Wiener index for the molecular graphs of cyclic silicates, single chain silicates and their line graphs with n vertices where n = 3, 4, 5 are explored.

Enhancing Supply Chain Sustainability and Reliability Through WSM and TOPSIS: A Symmetrical Real-World Case Study

Large corporations have recently demonstrated an increasing propensity to enhance the sustainability and reliability of their supply chains in order to comply with environmental regulations and improve customer satisfaction through on-time demand fulfillment. There are two phases to this study: mathematical modeling and model solution using precise techniques. In the first step, a mixed-integer linear programming model is developed. This model is an improvement of an existing supply chain model. Further, our suggested strategy is verified by using numerical data based on three criteria and four suppliers. The goals of the proposed model are to maximize supply chain reliability, economic profit, and social responsibilities by taking suppliers’ priorities into account. Modeled as a mixed-integer linear programming problem, the constraints on the problem include budget, emission, demand, allocation, facility, and shipping capacity. Power symmetry and information symmetry are incorporated in order to perform symmetric analysis. The weighted sum method (WSM) and the technique for order of preference by similarity to ideal solution (TOPSIS) are the two methods used in the second step of solving the model to identify the best supplier. In order to evaluate how well the proposed methodology was applied, a practical case was considered and implemented.

Gender differences in reported potentially morally injurious events among post-9/11 U.S. combat veterans, using two measures.

Military experiences that violate one's sense of right and wrong (i.e., potentially morally injurious events [PMIEs]) may result in moral injury, characterized by shame, guilt, demoralization, self-condemnation, and social withdrawal. The objective of this study was to examine gender-related differences in the rate of reporting PMIEs. Secondary analyses were conducted on a previously collected longitudinal cohort study of postdeployment functioning among U.S. post-9/11 combat veterans in Central Texas (N = 406). As part of the parent study, participants completed two measures of PMIEs-the Moral Injury Events Scale (MIES; Nash et al., 2013) and the Moral Injury Questionnaire-Military Version (MIQ-M; Currier et al., 2015). Gender differences at the item response level were analyzed in a series of Bayesian multilevel item response theory models. The most frequently endorsed responses on both the MIES and MIQ-M for both men and women were never or strongly disagree. On the MIQ-M, more veteran men endorsed experiencing PMIEs to some degree. On the MIES, more veteran women endorsed strongly agree to betrayal items. Veteran men and women reported any PMIE occurring at relatively high rates. Gender differences at the item response level were found for most items on both the MIES and MIQ-M, albeit of small magnitude. Awareness of gender differences in the likelihood of experiencing a given PMIE may help inform clinical assessments and case conceptualizations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The impact of the Centers for Disease Control and Prevention new surveillance definitions of ventilator-associated event on our clinical practice

ABSTRACT Objective Surveillance of ventilator-associated pneumonia is subjective, inaccurate, time-consuming, and does not predict outcomes. The Centers for Disease Control and Prevention (CDC) recommended the use of ventilator-associated event (VAE) definitions instead of ventilator-associated pneumonia (VAP) in 2013. Therefore, we evaluated this novel surveillance algorithm by comparing the results of our VAP and VAE methods. Material Methods We evaluated mechanically ventilated adult medical and surgical patients in our 13-bed intensive care unit (ICU). Nine patients diagnosed with VAP in 2018-2019 and 11 patients diagnosed with VAE in 2022-2024 were retrospectively evaluated. The impact of the new definitions on clinical processes such as days on the mechanical ventilator, duration of antibiotic use, ICU stay in determining infectious status was monitored. Results. Statistical analysis revealed that demographic and numeric data were similar in both VAP and VAE diagnosis groups (p