Individual Data Points Research Articles

Analyzing the United Nations Speeches with a Neutrosophic Approach to Text Mining in The Context of Türkiye’s Foreign Policy

Document clustering is an integral and important part of text mining. In case of classical clustering, data item belongs to only one cluster, whereas in Plithogenic approach to fuzzy clustering, data point may fall into more than one cluster. Thus, Plithogenic approach fuzzy clustering leads to wherein each data point is associated with more than one membership function that expresses the degree to which individual data points belong to the cluster. Additionally, his speeches at the UN will be analyzed with a neutrosphic approach. With the help of this approaches, in this study, speeches of the UN sessions attended by Turkish diplomats in the United Nations (UN) will be analyzed to understand the priorities of the country in international politics, the change and continuity in these policies. The texts of the UN sessions attended by Turkish diplomats between 2015 and 2023 were taken as data set. Such a large volume of data was analyzed with the help of text mining. For this purpose, each year's data was analyzed separately using word frequencies and clustering analysis, and then topic modeling was performed for each year's data using the Latent Dirichlet Allocation (LDA) method.

PS-P02-3: DIETARY RISK FACTORS FOR HYPERTENSION- INSIGHTS FROM THE TESCO 1.0 DATASET

Background: Tesco published an open-source dataset containing area-aggregated product purchase information from 420m transactions across 16m individual Clubcard users in the Greater London area, at a borough level. This dataset is presented as a typical product purchase for each borough, divided into 220 individual datapoints. Purpose: We wanted to explore dietary correlates with the prevalence of hypertension, utilising the London Borough wide Quality and Outcomes Framework (QOF) for GP reported hypertension diagnoses. Methods: The Tesco 1.0 dataset (2015) was accessed on 1/11/21 and all data downloaded. Hypertension data was accessed from the QOF reporting database (gpcontract.co.uk) for the last year available presented by Borough (2017). HYP001: the number of established hypertension patients as a total of registered patients, and HYP006: the % of hypertensive patients with a reading < 150/90 mmHg were selected, representing the prevalence and control of hypertension respectively. To explore for relevant associated factors, the average deprivation index (IMD) for each borough was accessed from Public Health England, and demographic data accessed from the Greater London Authority database. Univariable correlation using Spearmans Rho was calculated in using scipy in Python (3.0) and significance value of < = 0.01. Results: Highly significant risk factors for hypertension were the consumption of sugar, carbohydrate and sweets. Diabetes mellitus and obesity prevalence were also highly correlated with hypertension. Salt consumption was not a significant risk factor. Highly significant protective factors included consumption of eggs, fish, fruit and vegetables. Conclusions: The Big Data approach of utilising supermarket data allows a unique insight into dietary habits at a population level. We have demonstrated here an unexpected finding, the consumption of sugar and carbohydrate are highly significant risk factors for hypertension prevalence, while salt consumption is not. While not the first time the sugar versus salt question has been asked, this is the first time it has been answered in this manner.

Vehicle theft detection by generative adversarial networks on driving behavior

Human driving behavior can be a unique fingerprint to identify individual drivers and can be used for vehicle theft detection. Prior research often uses supervised learning to classify drivers’ behaviors. However, that method is not suitable for vehicle theft detection because it is not possible to derive a training dataset which comprehends all possible thieves. A few studies have instead leveraged unsupervised learning, such as k-means, but such approaches cannot achieve acceptable accuracy or provide a method of tolerating the false outcomes from classifying individual data points. Moreover, the influence of environment and road conditions on the driver classification was not included further limiting its usability. In this work, we propose a redesigned Generative Adversarial Network (GAN) model, Convolutional Long short-term GAN (CLGAN), which comprises long short-term memory (LSTM) and a convolutional neural network (CNN). This model ensures the ability of feature extraction from the convolutional layer and feature preservation from the LSTM layer, while simultaneously minimizing overfitting. It is instructive to analyze and benchmark CLGAN with the various GAN models, such as DCGAN, RNN-GAN, and AAE. Applying a public dataset which was generated by an electronic control unit (ECU) and collected through in-vehicle Controller Area Network (CAN) bus, our model achieves high accuracy of 98.5%, and is more robust against various driving conditions on multiple types of roads. In addition, we leverage threshold random walk to ensure the reliability of detection as well as adopting Principal Component Analysis (PCA) on feature pre-processing to improve the accuracy by approximately 20%. This work sheds light on a feasible and practical way of implementing vehicle theft detection.

International interlaboratory compilation of trace element concentrations in the CUP-2 uranium ore concentrate standard

A nuclear forensics investigation involving a uranium ore concentrate relies on accurate and precise analysis of impurities. Analytical data defensibility requires the use of reference materials as part of quality control. This study presents a compilation of trace element concentration results of the CUP-2 Uranium Ore Concentrate Standard measured by 11 different laboratories. The laboratories employed various dissolution methods, analytical preparation methods, and instrumental platforms. The data presented here contain concentrations of 66 impurities with up to 138 individual data points for each impurity. Consensus values have been assigned to each impurity following a statistical analysis of the data set.

Individual-Specific Relationship Between External Training and Match Load and Creatine-Kinase Response in Youth National Team Soccer Players.

Previous studies have examined the relationship between external load and creatine-kinase (CK) response at the team level. This study aimed to build individualized CK prediction models for elite youth national team soccer players. The CK response of youth soccer players can be categorized as being sensitive to micromovements (MM), high-velocity (HV) parameters, or the combination of both, measured during training sessions and matches. Prospective cohort study. Level 4. A total of 25 U16-U17 youth national team soccer players were monitored during training sessions and matches using global positioning system (GPS) units. Individual CK values were measured every morning from whole blood. The data set consisted of 57 ± 17 individual datapoints per player. Individual prediction models were used to examine the relationship between external load and consecutive CK changes. Numerous models were built for each player using MM, HV parameters, or the combination of both. The performance of the models was described by the R2 and the root mean square error (RMSE, U/l for CK values). The MM models were superior for 8 players (R2 = 0.68; RMSE = 113 U/l), followed by HV (8 players; R2 = 0.69; RMSE = 88 U/l) and the combined models (2 players; R2 = 0.64; RMSE = 141 U/l). For the remaining 7 players, the R2 of the models was <0.5. The recovery time between efforts was more important in the HV model. Players could be categorized on sensitivity to MM, HV movements, or the combination of both. These findings can be used to individualize postmatch recovery strategies and to optimize weekly training periodization to maximize match performance.

Show and tell: approaches for effective figures

Show and tell: approaches for effective figures

Towards Hierarchical Cluster Analysis Heatmaps as Visual Data Analysis of Entire Student Cohort Longitudinal Trajectories and Outcomes from Grade 9 through College

Abstract: Research on data use and school Early Warning Systems (EWS) notes a central practice of researchers and practitioners is to search for patterns in student data to predict outcomes so schools can support success when students experience challenges. Yet, the domain lacks a means to visualize the rich longitudinal data that schools collect. Here, we use visual data analytic hierarchical cluster analysis (HCA) heatmaps to pattern and visualize entire longitudinal grading histories of a national sample of n =14,290 students from grade 9 to college in every enrolled subject and year, visualizing 6,728,920 individual datapoints. We provide both the open access code in R and an open-access online tool allowing anyone to upload their data and create a HCA heatmap, providing support for visual data analytic and data science practice for both education researchers and schooling organizations.

Evaluation of Big Data Analytics and Cognitive Computing in Smart Health Systems

The current era of Big Data has led to the use of advanced AI programs that can learn from individual data points, providing intelligent feedback in real time. Their ability to learn and grow more complex is particularly important when they are applied to smart health systems. These systems carry out an analysis of patient data through the use of machine learning and deep learning methods with customizable models designed using the latest advancements in artificial intelligence software. Furthermore, Big Data Analytics has also allowed for a new form of computational intelligence, which is cognitive computing. Experimental results: the average query performance acceleration ratio of the designed complex network big data overlapping information detection method and the other two detection methods is 11.585, 17.411 and 17.642, which proves that the detection method integrates the advantages of cloud computing platform has a higher performance.

Optimization and application of the distribution of relaxation times based on characteristic frequency resolution and hyperparameters

Distribution of relaxation times (DRT) is a promising method for interpreting electrochemical impedance spectroscopy (EIS) data. Contemporary studies have made significant progress in theory, algorithm, implementation, and application of DRT. However, the DRT deconvolution is often difficult to interpret because of the pseudo-peaks, peaks deviation or peaks overlapping. In this paper, a comprehensive regularization DRT method based on characteristic frequency resolution and hyperparameters is proposed. Based on quantitative analysis of synthetic simulation data, the characteristic frequency resolution optimization is applied to avoid the peaks overlapping and improve the accuracy of DRT. Meanwhile, the hyperparameters optimization which assigns weights to each individual data point is verified to improve the robustness of DRT. Then, the optimized DRT is applied to the EIS experimental data analysis of proton exchange membrane fuel cell (PEMFC), and the polarization processes of PEMFC are identified. Moreover, a fast EIS measurement based on characteristic frequency of DRT is proposed and verified by the impedance prediction which the deviations are within 10%, but the EIS measurement time is shortened from 6 min to 5 s. Moreover, this method can provide a new direction and idea for fault diagnosis based on EIS data, especially for on-board applications.

Algorithmic Trading Stock Price Model

Abstract: Stock market price prediction is a difficult undertaking that generally requires a lot of human-computer interaction. Traditional batch processing methods cannot be used effectively for stock market analysis due to the linked nature of stock prices. This project presents an online learning technique that employs a recurrent neural network of some sort (RNN) called Long Short-Term Memory (LSTM), which uses stochastic gradient descent to update the weights for individual data points. When compared to existing stock price prediction systems, this will yield more accurate results. With varying sizes of data, the network is trained and evaluated for accuracy, and the results are tallied. A comparison with respect to accuracy is then performed against an Artificial Neural Network. Traditional approaches to securities market analysis and stock value prediction embrace basic analysis, that appearance at a stock's past performance and therefore the general believability of the corporate itself, and applied mathematics analysis, that is exclusively involved with computation and distinguishing patterns available value variation.

A K-Nearest Neighbors Algorithm in Python for Visualizing the 3D Stratigraphic Architecture of the Llobregat River Delta in NE Spain

The k-nearest neighbors (KNN) algorithm is a non-parametric supervised machine learning classifier; which uses proximity and similarity to make classifications or predictions about the grouping of an individual data point. This ability makes the KNN algorithm ideal for classifying datasets of geological variables and parameters prior to 3D visualization. This paper introduces a machine learning KNN algorithm and Python libraries for visualizing the 3D stratigraphic architecture of sedimentary porous media in the Quaternary onshore Llobregat River Delta (LRD) in northeastern Spain. A first HTML model showed a consecutive 5 m-equispaced set of horizontal sections of the granulometry classes created with the KNN algorithm from 0 to 120 m below sea level in the onshore LRD. A second HTML model showed the 3D mapping of the main Quaternary gravel and coarse sand sedimentary bodies (lithosomes) and the basement (Pliocene and older rocks) top surface created with Python libraries. These results reproduce well the complex sedimentary structure of the LRD reported in recent scientific publications and proves the suitability of the KNN algorithm and Python libraries for visualizing the 3D stratigraphic structure of sedimentary porous media, which is a crucial stage in making decisions in different environmental and economic geology disciplines.

A data variability index: Quantifying complexity of models and analyzing adversarial data

In system modeling arises a fundamental question about the level of difficulty one may encounter when designing a model on a basis of some training data. In this study, we advocate that such level of difficulty inherently depends upon the variability of the available function (data). If for a pair of input data which exhibits small differences, the differences of the corresponding outputs are substantial then building a model in the presence of such data becomes more challenging than in cases of data where the differences in the output data are far more limited. Dwelling on this observation, we introduce a variability index quantifying the nature of data in terms of variability observed in input and output data, respectively. The proposed index is model-neutral (model agnostic), namely describes and quantifies the modeling challenge implied by the data irrespectively of the specific model to be constructed. In case of functions, we show that the Lipschitz constant plays a similar role as the variability index computed for experimental data. An original way of reducing values of the variability index through a nonlinear transformation of original data completed by a fuzzy rule-based model is introduced. It is shown that such rule-based architecture gives rise to a piecewise linear transformation (multipoint linear approximation) exhibiting required contraction-dilation characteristics. The optimization of this transformation is carried out with the use of a Particle Swarm Optimization algorithm. We also demonstrate that the index can be used to quantify a concept of adversarial data. Along this line, we introduce a granular characterization of adversarial feature of individual data points. A series of experiments is provided to offer a thorough illustration and detailed insight into the nature and a thorough characterization of publicly available data.

Affective Responses to Both Climbing and Nordic Walking Exercise Are Associated With Intermediate-Term Increases in Physical Activity in Patients With Anxiety and Posttraumatic Stress Disorder - A Randomized Longitudinal Controlled Clinical Pilot Trial.

BackgroundExercise programs have shown anxiolytic effects in psychiatric patients. Adherence to exercise programs and subsequent long-term lifestyle change is influenced by acute affective responses of the exercise programs. This research aimed to assess acute affective responses of two different exercise modalities compared to a non-exercise control program and its effects on persisting physical activity behavior change.MethodsSixty-six outpatients diagnosed with an anxiety disorder or posttraumatic stress disorder were randomly allocated to one of three groups in a randomized longitudinal controlled clinical pilot trial: climbing (n = 26), nordic walking (n = 19), social contact control (n = 21). Affective responses were assessed pre, during, and post activity. General physical activity behavior was recorded prior to participation in the program, post program, and at follow-ups three and six months after the program.ResultsMultilevel modeling analyzes of 1,066 individual data points revealed increases in affective valence in the exercise sessions compared to the social contact sessions. State anxiety decreased in the climbing group compared to the social contact group. Physical activity behavior was increased immediately following the program as well as at six months follow-up in both exercise groups. A larger increase in affective valence during and after the sessions was associated with higher physical activity post program.ConclusionsClimbing and conventional nordic walking exercise sessions revealed positive affective changes in outpatients indicating therapeutic potential of both modalities for acute emotion regulation. In accordance with theoretical models of human behavior change, it was judged that the experience of a more pleasant affective state following the exercise sessions induced more persisting effects on physical activity behavior after the exercise programs.Trial Registrationhttps://www.clinicaltrials.gov/ct2/show/NCT03758599, identifier: NCT03758599.

Blood chemistry and rectal temperature changes in a population of healthy, fall-born, suckling beef calves from birth to 72 h of age

To determine changes in suckling neonatal beef calf blood chemistry and body temperature during the first 72 h of life, jugular blood samples and rectal temperatures were obtained from 24 healthy, fall-born Angus-cross and Hereford calves (average calving date = September 11) at 0 (after standing, but pre-suckling), 6, 12, 24, 48, and 72 h postnatally. Serum chemistry panels were conducted, and plasma triglycerides and serum non-esterified fatty acids (NEFA) were also determined. Data were analyzed using sampling hour as a repeated effect, and individual data points were compared to adult bovine reference intervals. All serum chemistry measures were affected by hour of age except bicarbonate (P = 0.48). Serum glucose, total protein, and globulin concentrations increased (P < 0.001) from 0 to 24 h. Plasma triglycerides increased (P < 0.001) from 0 to 6 h and 24 to 72 h. Serum urea nitrogen increased (P < 0.001) from 0 to 6 h and decreased (P = 0.01) from 48 to 72 h. Serum NEFA and creatine kinase (CK) increased (P < 0.001) from 0 to 6 h, but decreased (P ≤ 0.02) from 12 to 72 h. Serum magnesium, aspartate aminotransferase (AST), and total and direct bilirubin increased (P < 0.001) during the first 12 h, then decreased (P < 0.001) from 24 to 72 h. Gamma-glutamyl transpeptidase (GGT) increased (P < 0.001) from 0 to 12, and then decreased (P < 0.003) from 12 to 48 h. Serum creatinine decreased (P < 0.001) from 0 to 72 h, and albumin decreased (P < 0.01) from 0 to 12 h but increased (P < 0.001) from 24 to 48 h. Anion gap decreased (P ≤ 0.05) from 6 to 24 h. No serum components measured were within adult bovine reference intervals for all calves at all sampling times. All serum albumin concentrations were outside of reference intervals, and the majority of serum glucose, calcium, phosphorus, potassium, CK, GGT, and total bilirubin were outside of reference intervals. More serum chemistry measures diverged from reference intervals in the first 24 h of neonatal life. Rectal temperature decreased (P = 0.003) from 0 to 6 h of age, then increased (P ≤ 0.02) from 12 to 48 h. In summary, blood chemistry profiles of healthy, suckling neonatal beef calves change over the first 72 h of life, indicating rapid changes in metabolism and physiology. Bovine reference intervals based on adults do not appear to represent neonatal calves; thus, neonatal status and animal age should be taken into consideration when interpreting serum chemistry in a clinical or research setting.

Cardiovascular risk factor insights into the Tesco 1.0 Dataset

Abstract Funding Acknowledgements Type of funding sources: None. Background Tesco published an open-source dataset containing area-aggregated product purchase information from 420m transactions across 16m individual Clubcard users in the Great London area, at a borough level. This dataset is presented as a ‘typical product’ purchase for each borough, divided into 220 individual datapoints. Purpose We wanted to explore dietary correlates with cardiovascular outcomes including age-standardised mortality rates for myocardial infarct, stroke, cardiac arrhythmia and COVID-19. Methods The Tesco 1.0 dataset was accessed on 1/11/21 and a sample of traditional dietary clinical risk and protective factor datapoints for each London borough were selected. Data for age-standardised mortality rates (ASMR) in each London borough were downloaded from the Office of National Statistics for 2019 for myocardial infarction, stroke, cardiac arrhythmia and 2020 COVID-19 deaths (March-July 2020). To explore for relevant associated factors, the average deprivation index (IMD) for each borough was accessed from Public Health England, and demographic data accessed from the Greater London Authority database. Statistics using Spearman’s Rho calculator were derived in Excel and calculated using a web-based statistics calculator. Results Myocardial infarction (MI) ASMR closely correlated with increased levels of salt (g), fat and oils (% of total product), and weight of purchase (g). Increased fibre (g) was correlated with lower MI mortality. An important associated risk factor was the deprivation index, a important co-variable to consider when assessing diet-health relationships. Stroke ASMR was more closely correlated with typical product constituents, significant risk factors were weight (g) of fat, carbohydrate, sugar and ready meals (%) and sweets (%), while % of fish and both % and total weight (g) of fruit and veg were protective factors. Importantly salt content was NOT a statistically significant factor. Cardiac arrhythmia ASMR did not correlate with any dietary constituents reviewed. COVID ASMR was closely correlated with myocardial infarction ASMR risk factors (salt, % of fat and oils and total weight of purchase) and total fibre (g) was again a protective factor. Interestingly, the % of ready meals was a protective factor for COVID ASMR, possibly associated with cultural and ethnicity-related behaviours, with higher BAME communities less likely to purchase ready meals. Conclusions This unique dataset offers a Big Data insight into the relationship between diet and health but is limited by a lack of individual granularity and heterogenous intra-borough populations. This research offers a starting point to further exploring in-depth food purchasing habits with healthcare outcomes, and further partnership in the future may unlock this.

MO050: The First Report from the National COVID-19 Autopsy Registry in Germany

Abstract BACKGROUND AND AIMS Autopsies are an invaluable tool in medicine for studying the pathophysiology of diseases, including kidney diseases and causes of death. To facilitate multicenter autopsy-based studies and to provide a single point of contact for data analysis and reporting, the German COVID-19 Autopsy Registry (DeRegCOVID) was launched in April 2020 to uncover effects on pulmonary (micro)vasculature, systemic viral spread including the kidneys or interaction with the immune system. METHOD The electronic registry uses a web-based electronic data collection system. Participation is voluntary and biomaterial remains at each site (decentralized biobanking). As of October 2021, the registry included N = 1129 autopsy cases with 69 271 individual data points containing information on 18 674 available biospecimens, including kidneys as part of the standard sampling protocol, collected at 29 German sites. RESULTS In the N = 1095 eligible records, the male-to-female ratio was 1–8:1, with peaks at 65–69 and 80–84 years in men and &amp;gt;85 years in women. Analysis of the chain of events leading directly to death showed that in 86% of autopsy cases, COVID-19 was the actual cause of death, whereas in 14% of cases, COVID-19 was a concomitant disease. The most common immediate cause of death was diffuse alveolar injury/acute respiratory distress syndrome, followed by multiorgan failure, including the kidneys. The registry supports several scientific projects, informs the public and provides reports to federal health authorities, which led to a change in the German Infection Protection Act and facilitated autopsies during pandemics. CONCLUSION A national autopsy registry can provide multicenter quantitative information on COVID-19 deaths at the national level to support medical research, policy decisions and public discussions. Previous and ongoing studies also showed the potential of autopsies for nephrology research.

STOCK PRICE PREDICTION

Machine learning has many important applications in the stock price prediction. Here, we will discuss about predicting the returns on stocks. This has uncertainties and it is a very complex task. This project will be developed into two parts: First, we will learn how to predict stock price using the Long Short-Term Memory neural networks. Predicting stock market prices involves human-computer interaction. For stock market analysis, conventional batch processing methods cannot be utilized efficiently due to the correlated nature of stock prices. We suggest an algorithm that utilizes a kind of recurrent neural network (RNN) called Long Short-Term Memory (LSTM), where using stochastic gradient descent the weights are adjusted for individual data points

Integrating annotations into multidimensional visual dashboards

Multidimensional data is often visualized using coordinated multiple views in an interactive dashboard. However, unlike in infographics where text is often a central part of the presentation, there is currently little knowledge of how to best integrate text and annotations in a visualization dashboard. In this paper, we explore a technique called FacetNotes for presenting these textual annotations on top of any visualization within a dashboard irrespective of the scale of data shown or the design of visual representation itself. FacetNotes does so by grouping and ordering the textual annotations based on properties of (1) the individual data points associated with the annotations, and (2) the target visual representation on which they should be shown. We present this technique along with a set of user interface features and guidelines to apply it to visualization interfaces. We also demonstrate FacetNotes in a custom visual dashboard interface. Finally, results from a user study of FacetNotes show that the technique improves the scope and complexity of insights developed during visual exploration.

Long-term, infection-acquired immunity against the SARS-CoV-2 Delta variant in a hamster model.

The emergence of the SARS-CoV-2 Delta variant (B.1.617.2) raises concerns about potential reduced sensitivity of the virus to antibody neutralization and subsequent vaccine breakthrough infections. Here, we use a live virus neutralization assay with sera from Pfizer- and Moderna-vaccinated individuals to examine neutralizing antibody titers against SARS-CoV-2 and observe a 3.9- and 2.7-fold reduction, respectively, in neutralizing antibody titers against the Delta variant compared with an early isolate bearing only a D614G substitution in its spike protein. We observe similar reduced sensitivity with sera from hamsters that were previously infected with an early isolate of SARS-CoV-2. Despite this reduction in neutralizing antibody titers against the Delta variant, hamsters previously infected (up to 15 months earlier) with an early isolate are protected from infection with the Delta variant, suggesting that the immune response to the first infection is sufficient to provide protection against subsequent infection with the Delta variant.

Investigating Explainability Methods in Recurrent Neural Network Architectures for Financial Time Series Data

Statistical methods were traditionally primarily used for time series forecasting. However, new hybrid methods demonstrate competitive accuracy, leading to increased machine-learning-based methodologies in the financial sector. However, very little development has been seen in explainable AI (XAI) for financial time series prediction, with a growing mandate for explainable systems. This study aims to determine if the existing XAI methodology is transferable to the context of financial time series prediction. Four popular methods, namely, ablation, permutation, added noise, and integrated gradients, were applied to a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU) network trained on S&amp;P 500 stocks data to determine the importance of features, individual data points, and specific cells in each architecture. The explainability analysis revealed that GRU displayed the most significant ability to retain long-term information, while the LSTM disregarded most of the given input and instead showed the most notable granularity to the considered inputs. Lastly, the RNN displayed features indicative of no long-term memory retention. The applied XAI methods produced complementary results, reinforcing paradigms on significant differences in how different architectures predict. The results show that these methods are transferable in the financial forecasting sector, but a more sophisticated hybrid prediction system requires further confirmation.