Statistical Knowledge Research Articles

Determinants Of Preventing Diarrhea in Children in Siulak Gedang Puskesmas In 2022

Background: Diarrhea is a condition marked by changes in the shape and content of watery feces. It can also be accompanied by vomiting and bloody stools. Especially in the first three years of life, when the child may experience one to three episodes of severe diarrhea, this disease frequently affects children under the age of five. Aims: This study's objective was to determine the relationship between education level, knowledge, and attitudes of mothers in preventing diarrhea in toddlers in the working area of the Siulak Gedang Public Health Center, Kerinci Regency, 2022. Method: This kind of study is quantitative, cross-sectional, and uses 96 samples as its sample size. The Siulak Gedang Health Center in Kerinci Regency's operational region served as the site of this study. Proportional Random Sampling is the method of sampling. Without taking into account the strata in the population, proportional random sampling is a technique for taking samples at random. Results: According to the study's findings, 56.3% of people engage in effective actions for preventing diarrhea. According to statistical analyses, knowledge (0.000) and attitude (0.000) are the two factors that mothers' conduct in preventing diarrhea is connected with (0.000). Additionally, there is no connection between behavior used to prevent diarrhea and education level (0.338). Conclusion: There are relationship between the knowledge and attitudes of mothers with the prevention of diarrhea in children under five in the working area of the Siulak Gedang Health Center, Kerinci Regency.

Assessment of the Potential of Concentrating Cancer Care in Hospitals With Certification Through Survival Analysis.

Certification programs seek to improve the quality of complex interdisciplinary models of care such as cancer treatment through structuring the process of care in accordance with evidence-based guidelines. In Germany, the German Cancer Society (Deutsche Krebsgesellschaft, DKG) provides a certification programme for cancer care that covers more than one thousand centers. In a recent retrospective cohort study, it has been shown on a large, nationwide data set based on data from a statutory health insurance and selected clinical cancer registries, that there is a benefit in survival for cancer patients who have received initial treatment in hospitals certified by the DKG. Here, we deduce two absolute measures from the relative benefit in survival with the aim to quantify this benefit if all patients had been treated in a certified center. The WiZen study analysed survival of adult patients insured by the AOK with a cancer diagnosis between 2009 and 2017 in certified hospitals vs. non-certified hospitals. Besides Kaplan-Meier-estimators, Cox regression with shared frailty was used for 11 types of cancer in total, adjusting for patient-specific information such as demographic characteristics and comorbidities as well as hospital characteristics and temporal trend. Based on this regression, we predict adjusted survival curves that directly address the certification effect. From the adjusted survivals, we calculated years of life lost (YLL) and number needed to treat (NNT), along with a difference in deaths 5 years after diagnosis. Based on our estimate for the 537,396 patients that were treated in a non-certified hospital included in the WiZen study, corresponding to 68,7% of the study population, we find a potential of 33,243 YLL per year in Germany based on the size of the German population as of 2017. The potential to avoid death cases 5 years from diagnosis totals 4,729 per year in Germany. While Cox regression is an important tool to evaluate the benefit that arises from variables with a potential impact on survival such as certification, its direct results are not well suited to quantify this benefit for decision makers in health care. The estimated years of life lost and the number of deaths that could have been avoided 5 years from diagnosis avoid mis-interpretation of the hazard ratios commonly used in survival analysis and should help to inform key stakeholders in health care without specialist background knowledge in statistics. Our measures, directly adressing the effect of certification, can furthermore be used as a starting point for health-economic calculations. Steering the care of cancer patients primarily to certified hospitals would have a high potential to improve outcomes.

Building skills for the future of work: Students’ perspectives on emerging jobs in the Data and AI Cluster through artificial intelligence in education

The main goal of this paper is to supplement the existing literature with new knowledge in the field of artificial intelligence and education, which relates to the importance of courses in statistics, quantitative methods, and students’ perspectives about emerging jobs in the Data and AI Cluster. A multidimensional model of the perceived usefulness of artificial intelligence in students’ perspective about emerging jobs in the Data and AI Cluster was formed; it includes constructs students’ knowledge of the meaning of “artificial intelligence”, their perception of its usefulness in their studies, the perceived ease of use of AI, the perceived usefulness of statistics and quantitative methods, students’ perspective on work skills for the future, and their perspective on emerging jobs in the Data and AI Cluster. The empirical research included 197 undergraduate and postgraduate students from the University of Maribor, Faculty of Economics and Business in Slovenia, who had prior knowledge of statistics obtained during their studies. The data were analyzed using structural equation modeling. The main findings of our research are important for curricula development and stress these implications: emphasis on teaching the meaning and importance of AI, integration of AI in coursework, strengthening quantitative skills and developing future work skills that are aligned with emerging trends in the Data and AI Cluster.

DTAmetasa: An R shiny application for meta-analysis of diagnostic test accuracy and sensitivity analysis of publication bias.

Meta-analysis of diagnostic test accuracy (DTA) is a powerful statistical method for synthesizing and evaluating the diagnostic capacity of medical tests and has been extensively used by clinical physicians and healthcare decision-makers. However, publication bias (PB) threatens the validity of meta-analysis of DTA. Some statistical methods have been developed to deal with PB in meta-analysis of DTA, but implementing these methods requires high-level statistical knowledge and programming skill. To assist non-technical users in running most routines in meta-analysis of DTA and handling with PB, we developed an interactive application, DTAmetasa. DTAmetasa is developed as a web-based graphical user interface based on the R shiny framework. It allows users to upload data and conduct meta-analysis of DTA by "point and click" operations. Moreover, DTAmetasa provides the sensitivity analysis of PB and presents the graphical results to evaluate the magnitude of the PB under various publication mechanisms. In this study, we introduce the functionalities of DTAmetasa and use the real-world meta-analysis to show its capacity for dealing with PB.

An examination of descriptive statistical knowledge of 12th-grade secondary school students - comparing and analysing their answers to closed and open questions

In this article, we examine the conceptual knowledge of 12th-grade students in the field of descriptive statistics (hereafter statistics), how their knowledge is aligned with the output requirements, and how they can apply their conceptual knowledge in terms of means, graphs, and dispersion indicators. What is the proportion and the result of their answers to (semi-)open questions for which they have the necessary conceptual knowledge, but which they encounter less frequently (or not at all) in the classroom and during questioning? In spring 2020, before the outbreak of the pandemic in Hungary, a traditional-classroom, “paper-based” survey was conducted with 159 graduating students and their teachers from 3 secondary schools. According to the results of the survey, the majority of students have no difficulties in solving the type of tasks included in the final exam. Solving more complex, open-ended tasks with longer texts is more challenging, despite having all the tools to solve them, based on their conceptual knowledge and comprehension skills. A valuable supplement to the analysis and interpretation of the results is the student attitudes test, also included in the questionnaire.
 Subject Classification: 97K40, 97-11, 97D60

QUESTIONS OF SELF-CALIBRATION OF SATELLITE TOOLS FOR DETERMINING THE VOLUME OF BURNED ASSOCIATED GAS IN FLARE

The possibility of carrying out mutual calibration of satellite means for determining the volume of associated gas burned in flares was analyzed. Existing satellite methods for estimating the total amount of associated hydrocarbon gas flared, implemented on the basis of MODIS and VIIRS data, are considered. Methods have been developed for inter sensor calibration of satellite meters, implemented by introducing an additive calibration correction for the measured temperature of objects outside the plume in the first case, when the MODIS data are calibrated according to the VIIRS readings and for the plume emissivity, in the case when the VİİRS readings are calibrated according to the MODIS readings. The problem of the optimal choice of the temperature of the gas torches, at which the emission of an aerosol such as elemental carbon (BC) can be reduced in comparison with the maximum generation level, is considered and solved. With the full knowledge of the frequency statistics of the appearance of low-temperature flares and the appearance of high-temperature flares, the proposed mathematical model allows us to estimate the maximum formation of a dangerous aerosol to prevent the formation of a hazard and allows us to calculate the remaining quantities if the values of five quantities are known from the set of variables participating in the model. The proposed model makes it possible to determine the maximum emission and its average temperature expectation. Keywords: Associated gas, calibration, satellite measurements, flame, temperature.

Knowledge-transfer-enabled porosity prediction for new part geometry in laser metal deposition

Data-driven porosity prediction in Laser Metal Deposition (LMD) is mainly done with supervised machine learning methods. These methods require labeled thermal signatures for model training, with the “labels” being post-process evaluations of porosity. In practice, acquiring porosity records for newly printed parts is expensive and time-consuming; matching thermal signatures from the printing process with the porosity records is subject to data registration errors. To enable convenient porosity prediction for new part geometry, this study proposes a “knowledge transfer” method to transfer prior statistical knowledge about printed parts to new printing processes. The prior knowledge is leveraged to evaluate the statistical property of new thermal signatures and assign them labels. Supervised machine learning methods can be readily trained with labeled data. The effort for post-process porosity inspection is therefore saved, and the efficiency of data-driven porosity prediction is significantly improved. The proposed method is validated with datasets from an LMD machine, specifically an OPTOMEC LENS 750 system. The statistical inference knowledge about a Ti-6Al-4V thin wall is transferred to two different Ti-6Al-4V cylinders, respectively, to label their thermal signatures and train Convolutional Neural Networks (CNNs) for in-situ porosity prediction. The case study results demonstrate the effectiveness of the proposed method.

Phospho-Analyst: An Interactive, Easy-to-Use Web Platform To Analyze Quantitative Phosphoproteomics Data.

Phosphoproteomics is nowadays the method of choice to comprehensively identify and quantify thousands of phosphorylated peptides and their associated proteins with the goal of interrogating changes in signal transduction pathways and other cellular processes. One of the most popular software suites to analyze phosphoproteomic data sets is MaxQuant, which converts mass spectrometric raw data into quantitative information on phosphopeptides and proteins. However, despite the increased utilization of phosphoproteomics in biomedical research, simple and user-friendly tools supporting downstream statistical analysis and interpretation of these highly complex outputs are still lacking. We have therefore developed Phospho-Analyst, which─similar to its sibling LFQ-Analyst─is an easy-to-use, interactive web application specifically designed to reproducibly perform differential expression analyses with "one click" and to visualize phosphoproteomic results in a meaningful and practical manner. Furthermore, if quantitative total proteomic information is available for the same samples, Phospho-Analyst automatically normalizes all phosphoproteomic results to underlying protein abundance levels, thereby ensuring that only genuine changes in phosphorylation events are considered. As such, Phospho-Analyst can not only be used by experienced proteomic veterans but also by researchers without any prior knowledge in (phospho)proteomics, statistics, or bioinformatics. Phospho-Analyst, including a detailed manual, is freely available at https://analyst-suites.org/apps/phospho-analyst/.

Assessing Psychology Student Applied Knowledge of Statistics via Open-book Multiple Choice Online Exams

Assessing Psychology Student Applied Knowledge of Statistics via Open-book Multiple Choice Online Exams

Controlled mixing during colloidal quantum dot synthesis: A proxy-concept based on equivalent parameters

Mixing is a key process parameter during liquid phase particle synthesis that requires special attention, in particular for scalable process and reactor development. This issue is especially pressing for advanced materials where on the one hand multifunctional properties require products of utmost quality, while on the other hand only few strategies for process design are yet developed. One material class in this context is quantum dot (QD) nanocrystals. By making use of automation and high throughout (HT) experimentation, we could recently demonstrate qualitatively the role of mixing on focusing and defocusing during QD synthesis by hot injection. In this work, we demonstrate the application of HT methodologies to investigate the effect of mixing as an important process parameter during process optimization and scale up. We performed reaction optimization by screening a multi-parameter design space using design of experiments (DoE) and machine learning (ML). We developed a so-called cold model as proxy that is based on equivalent mixing times (EMTs) derived from a competing parallel reaction system. Experimentally, we realized a fully automated approach using a HT platform for particle synthesis. The final dataset (128 experiments for model development including repeats and 29 experiments for prediction testing) was combined with DoE and complimented with machine learning. By doing so, with DoE, we could reduce the number of experiments required to develop a predictive, statistical knowledge base for EMTs at ambient conditions. Finally, we demonstrated the applicability of EMTs for two defined stirrer geometries to fine tune the widths of the particle size distribution and photoluminescence of QDs at increasing reaction volumes from 20 mL to 80 mL. This opens the door towards scalable processes for QDs at increased reaction volumes while maintaining product quality.

Bi‐directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL‐C model with Rayleigh fading distribution

SummaryIn this work, a deep learning (DL)‐based massive multiple‐input multiple‐output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL‐C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi‐directional long short‐term memory (Bi‐LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi‐LSTM‐based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function‐trained Bi‐LSTM‐based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi‐LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short‐term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.

Deep Learning Peephole LSTM Neural Network-Based Channel State Estimators for OFDM 5G and Beyond Networks

This study uses deep learning (DL) techniques for pilot-based channel estimation in orthogonal frequency division multiplexing (OFDM). Conventional channel estimators in pilot-symbol-aided OFDM systems suffer from performance degradation, especially in low signal-to-noise ratio (SNR) regions, due to noise amplification in the estimation process, intercarrier interference, a lack of primary channel data, and poor performance with few pilots, although they exhibit lower complexity and require implicit knowledge of the channel statistics. A new method for estimating channels using DL with peephole long short-term memory (peephole LSTM) is proposed. The proposed peephole LSTM-based channel state estimator is deployed online after offline training with generated datasets to track channel parameters, which enables robust recovery of transmitted data. A comparison is made between the proposed estimator and conventional LSTM and GRU-based channel state estimators using three different DL optimization techniques. Due to the outstanding learning and generalization properties of the DL-based peephole LSTM model, the suggested estimator significantly outperforms the conventional least square (LS) and minimum mean square error (MMSE) estimators, especially with a few pilots. The suggested estimator can be used without prior information on channel statistics. For this reason, it seems promising that the proposed estimator can be used to estimate the channel states of an OFDM communication system.

Comparison of canonical correlation and discriminant analysis

Learning from data is at the heart of the statistical discipline known as statistics. Understanding statistics, you will be able to use the right techniques to obtain the data, carry out the relevant analysis, and present the results in an effective way if you have a working knowledge of statistics. The process of creating scientific discoveries, as well as decisions and projections that are founded on data, involves the use of statistics, which is a key element in the process.

Pengenalan dan Sosialisasi Penggunaan Aplikasi Statistika untuk Penelitian Tindakan Kelas Bagi Guru-Guru SMA

The aim of this activity is to provide knowledge about basic statistics and the application of statistical software package for classroom action research. Senior high school teachers from 4 schools participated in this workshop. They were provided with the basic knowledge of statistics, the use of statistics and research methodology for classroom action research and how to apply Minitab to help in data calculation and interpretation. During workshop, the participants were demonstrated the use of Minitab in the laboratory of Statistics and, Industrial Engineering Department, Atma Jaya Catholic University of Indonesia.

Robust Fisher-Regularized Twin Extreme Learning Machine with Capped L1-Norm for Classification

Twin extreme learning machine (TELM) is a classical and high-efficiency classifier. However, it neglects the statistical knowledge hidden inside the data. In this paper, in order to make full use of statistical information from sample data, we first come up with a Fisher-regularized twin extreme learning machine (FTELM) by applying Fisher regularization into TELM learning framework. This strategy not only inherits the advantages of TELM, but also minimizes the within-class divergence of samples. Further, in an effort to further boost the anti-noise ability of FTELM method, we propose a new capped L1-norm FTELM (CL1-FTELM) by introducing capped L1-norm in FTELM to dwindle the influence of abnormal points, and CL1-FTELM improves the robust performance of our FTELM. Then, for the proposed FTELM method, we utilize an efficient successive overrelaxation algorithm to solve the corresponding optimization problem. For the proposed CL1-FTELM, an iterative method is designed to solve the corresponding optimization based on re-weighted technique. Meanwhile, the convergence and local optimality of CL1-FTELM are proved theoretically. Finally, numerical experiments on manual and UCI datasets show that the proposed methods achieve better classification effects than the state-of-the-art methods in most cases, which demonstrates the effectiveness and stability of the proposed methods.

Hybrid UWB-Inertial TDoA-Based Target Tracking With Concentrated Anchors

In this paper, hybrid radio/inertial mobile target tracking for accurate and smooth path estimation is considered. The proposed tracking approach builds upon an Ultra WideBand (UWB)-based positioning algorithm, based on the Linear Hyperbolic Positioning System (LinHPS), with Time Difference of Arrival (TDoA) processing and anchors concentrated on a single hotspot at the center of the environment where the target moves. First, we design an Adaptive Radio-based Extended Kalman Filter (AREKF), which does not require a priori statistical knowledge of the noise in the target movement model and estimates the measurement noise covariance, at each sampling time, according to a proper LookUp Table (LUT). In order to improve the performance of AREKF, we incorporate inertial data collected from the target and propose three “hybrid” radio/inertial algorithms, denoted as Hybrid Inertial Measurement Unit (IMU)-aided Radio-based EKF (HIREKF), Hybrid Noisy Control EKF (HNCEKF), and Hybrid Control EKF (HCEKF). Our results on experimentally acquired paths show that the proposed algorithms achieve an average instantaneous position estimation error on the order of a few centimeters. Moreover, the minimum target path length estimation error, obtained with HCEKF, is on the order of 6% and 1% for two paths with lengths equal to approximately 17 m and 46 m, respectively.

Efektif Pengetahuan dan Sikap Masyarakat Dalam Upaya Pencegahan Tuberkulosis

Knowledge directly affects a person; attitudes arise due to the presence of knowledge from the individual. Tuberculosis is an infectious disease that causes death. Tuberculosis is transmitted through the air and is influenced by risk factors that play a role in transmission, such as people's attitudes, knowledge, and behaviour. This study aims to determine the influence of knowledge and community attitudes in preventing tuberculosis. The research design uses a descriptive survey with an observational approach. Accidental sampling method with a total of 52 respondents. Research instruments using questionnaires and observation sheets. Data analysis with multivariate linear regression test. The results of the statistical knowledge test with a p-value: of 0.811, an attitude of 0.599 and a multivariate logistic regression test showed that among the variables of understanding and attitude, the knowledge variable was more significantly related to efforts to prevent tuberculosis, as evidenced by the sig value. P value: 0.997 and Exp (B) 2.256. It can be concluded that the attitude and towards tuberculosis prevention efforts, but knowledge has partial value to Tubercollusis prevention efforts. Suggestions need to be made to educate them massively and continuously about tuberculosis, which are main causes, ways of transmission, and prevention.

Asymptotic Learning Requirements for Stealth Attacks on Linearized State Estimation

Information-theoretic stealth attacks are data injection attacks that minimize the amount of information acquired by the operator about the state variables, while simultaneously limiting the Kullback-Leibler divergence between the distribution of the measurements under attack and the distribution under normal operation with the aim of controling the probability of attack detection. For Gaussian distributed state variables, attack construction requires knowledge of the second order statistics of the state variables, which is estimated from a finite number of past realizations using a sample covariance matrix. Within this framework, the attack performance is studied for the attack construction with the sample covariance matrix. This results in an analysis of the amount of data required to learn the covariance matrix of the state variables used on the attack construction. The ergodic attack performance is characterized using asymptotic random matrix theory tools, and the variance of the attack performance is bounded. The ergodic performance and the variance bounds are assessed with simulations on IEEE test systems.

«Santé publique et libertés»: Considérations sur la peste dans «The Free-Thinker» (1718-1721)

In June 1720, a plague epidemic broke out in Marseille, killed half its population and, spread to Provence and Languedoc, thus arousing fears of contagion in England, where the memory of the Great Plague of London in 1664/5 was still vivid. While the English newspapers published short news on the mortality and measures taken by the French authorities, a leisure periodical, «The Free-Thinker», a bi-weekly essay paper published in London and circulated throughout the country between March 1718 and July 1721, chose to devote a series of twenty-five papers to the contagion between December 1720 and June 1721. The present study analyses the rational and liberal discourse of this periodical. It will show that at a time when the plague was the subject of many providentialist sermons, «The Free-Thinker» offered its readers an exclusively rational discourse based on the popularisation of medical, statistical and historical knowledge about the plague. «The Free-Thinker»’s arguments also echoed both the essentially preventive approach of the English government and medical polemics concerning two contagionist conceptions of the disease. Finally, although the periodical justified its series by using the argument of public good and put forward public health policies, its aim was primarily to counter a radical contagionist discourse which was considered harmful to the economic and political freedoms of its readers.

Virtual Learning Object (VLO) for Teaching and Learning Non-Parametric Statistical Methods

nterpreting, understanding, and applying statistical knowledge, presents, in many cases, some difficulties for students in the training process. For this reason, and thanks to the rise of information and communication technologies; a virtual object was developed for learning the statistical methods of Kruskal Wallis, Mann Whitney U and Wilcoxon, which are included in non-parametric statistics. The objective of this quasi-experimental design study was to apply the virtual object as a teaching-learning strategy for these three statistical methods after its creation and validation in order to support the training of students in biostatistics. The virtual learning object was evaluated by experts through the LORI instrument (tool that allows to evaluate learning objects based on nine variables), granting a quality level in the medium-high range according to the final weighting. The evaluation instrument and the comparative statistical analysis used in this process showed that the learning object is adequate for the purpose and objective set, concluding that there is a significant difference in the academic results of the students to whom this digital tool was applied.