Data Validation Methods Research Articles

Mathematical Methods for Validation of Data on Reliability, Operation and Maintenance of Locomotives

The objective of the article is to describe the method proposed by the authors and intended for validity check of initial data for managing reliability of locomotives and the entire locomotive facilities. It is shown that probabilistic-statistical methods which constitute the basis of Edward Deming’s theory of enterprise variability allowing to control the quality of enterprises’ products, including their reliability.The averaged statistical data used in practices of railway transport and its locomotive economy are not homogeneous, representing what is called in popular publications «average temperature of hospital patients». The homogeneity of data is determined by their unimodality, i.e., the presence of a single process in the sample. Unsuccessful sampling leads to its bimodality and even multimodality.The research method proposed in the article is check for unimodality of the initial data based on the consequence of the law of large numbers, according to which, with an increase in the number of data, homogeneous samples tend to one of the laws of distribution of random variable: to normal, exponential, lognormal or other known law. Therefore, and since any unimodal sample must meet a goodness-of-fit test, the article proposes to apply Pearson’s chisquare test (χ2). The unimodality of the data is suggested to be estimated through the probability of compliance with the chosen law of distribution of a random variable considering the probability of more than 0,3 (30 %) to be sufficient.Using the example of locomotive operation data and on-board microprocessor systems data, data are shown that cannot really be unimodal, as well as the data that require changing the sampling rules to achieve unimodality. For example, when considering the average daily runs of locomotives by series at specific home depots with participation in one type of traffi (main traffi export, or shunting operations), their unimodality is achieved. An attempt to enlarge the data (to consider several series, several polygons, etc.) results in loss of unimodality.The article considers the unimodality of data of MSU-TP on-board microprocessor control systems for diesel locomotives of 2TE116U series. The expected operating time for the positions of the driver’s controller turned out to be multimodal data. Surprisingly, the current of the traction motors turned out to be unimodal, regardless of the driving position of the driver’s controller.

Online Presence System Design Based On Data Granted Validity

The presence of employees in an agency is very important for recording attendance accurately and avoiding fraud in the manual attendance process. Therefore, designing an online presence system is the right solution to overcome this problem. A good online presence system must have high accuracy and be integrated with an efficient data management system. One way to improve the accuracy of the online attendance system is to use the data validation method provided. The manual attendance system used by the company suffers from inaccuracies and fraud. Therefore, this study aims to overcome this problem by designing and implementing an online presence system based on the validity of the data provided. Information system development methods are used to design online presence systems that use blockchain technology to ensure data validity and reduce the risk of fraud. In conclusion, an online attendance system based on the validity of the data provided can be an effective and efficient alternative solution for institutions that still use manual attendance systems.

OVERVIEW OF EVALUATION OF THE IMPLEMENTATION OF DENGUE HEMORRHAGIC FEVER PREVENTION AND CONTROL PROGRAM IN THE WORK AREA PUSKESMAS TANAH BARU, DEPOK CITY, IN 2021

DHF is a vector-borne disease. This disease is one of the public health problems that invites attention. Based on the results of the study, it was found that the Tanah Baru Health Center area has the highest cases of dengue fever in Depok City, where there are cases every year, but it is still suspected that it has yet to succeed in reducing dengue cases. This study aims to determine an overview of the evaluation of the implementation of dengue disease prevention and control programs in the Tanah Baru Health Center area of Depok City in 2021. This research is qualitative research with a phenomenological approach. This study's informants were seven Depok City regional government health workers and the community. Data collection is sourced from in-depth interviews, observations, and document reviews using source triangulation and triangulation methods for data validation. In the input system, there is still a lack of need for more resources and funds, a no internalized policy SOP. The processing system has carried out activities such as counseling, PSN, and community involvement, but it still needs improvement. The achievements of the output system still need to be achieved, the morbidity rate is still high, and the ABJ figure is 94%. The description of the input system, process system on activities, and output system in implementing dengue prevention and control programs could be better, but there are still technical obstacles. This program needs good supervision and cooperation across programs and related sectors, especially the common, the community, households (families), and dengue disease in the environment.

A Comparative Study of DWT and EEMD Methods for Validation and Correction of Pyroshock Data

A Comparative Study of DWT and EEMD Methods for Validation and Correction of Pyroshock Data

Self-healing of Covid-19 survivors in building communication with the surrounding environment

COVID-19 entered Indonesia in 2020. Many victims died due to COVID-19 causing excessive fear, which impacted social relations survivors COVID-19 and limited communication with the surrounding environment. This study aims to find out how the self-healing experience of patients recovering from COVID-19 and how patients recovering from COVID-19 build communication with the surrounding environment. This study uses a qualitative method with a phenomenological approach. Informants were selected using the purposive sampling technique. Methods of data collection using observation, interviews, and documentation. The data analysis technique uses the IPA (Interpretative Phenomenology Analysis) method using the source triangulation data validation method. The results are self-healing using the power of the mind, exercise therapy, chest therapy, religious spiritual approach, self-healing with laughter, gratitude and introspection. Build communication with the surrounding environment by participating in congregational prayers at the mosque, greeting greetings, staying in touch with relatives, and participating in existing social activities. This is related to Alfred Schutz's phenomenological theory with motives that can be used to describe human or individual actions based on the experience of phenomena that have happened to someone, namely In-order-to-motive (Um-zu-Motive) and Because of motive (Weil

Diffusion of a Lifelog-Based Digital Healthcare Platform for Future Precision Medicine: Data Provision and Verification Study.

We propose a method for data provision, validation, and service expansion for the spread of a lifelog-based digital healthcare platform. The platform is an operational cloud-based platform, implemented in 2020, that has launched a tool that can validate and de-identify personal information in a data acquisition system dedicated to a center. The data acquired by the platform can be processed into products of statistical analysis and artificial intelligence (AI)-based deep learning modules. Application programming interfaces (APIs) have been developed to open data and can be linked in a programmatic manner. As a standardized policy, a series of procedures were performed from data collection to external sharing. The proposed platform collected 321.42 GB of data for 146 types of data. The reliability and consistency of the data were evaluated by an information system audit institution, with a defects ratio of approximately 0.03%. We presented definitions and examples of APIs developed in 17 functional units for data opening. In addition, the suitability of the de-identification tool was confirmed by evaluating the reduced risk of re-identification using quasi-identifiers. We presented specific methods for data verification, personal information de-identification, and service provision to ensure the sustainability of future digital healthcare platforms for precision medicine. The platform can contribute to the diffusion of the platform by linking data with external organizations and research environments in safe zones based on data reliability.

Household Waste Based Vermicompost and Fertilizer Effect on Yield and Attributes of Pot Culture Rice (Oryza sativa)

Organic matter content in soil improves the soil health with supporting large microbial population and stabilizing soil health deterioration. The soil physical, chemical parameters improve according to the significant application of organic matter in soil. Integrated application of organic and inorganic sources and analysis of these components in response to its effect on rice growth and yield attributes at different stages is of major concerned study. Application of four levels of vermicompost (0 t ha-1, 1.25 t ha-1, 2.5 t ha-1, 3.7 t ha-1) and three levels of fertilizer (0%, 100%, 50% Recommended Dose of Fertilizer) were taken in a pot culture experiment and total of 36 pots i.e. twelve treatments replicate thrice for analyzing its effects on growth and yield attributes of rice crop (Variety-Rajendra Bhagawati) with using factorial Completely Randomized Design statistical method for data analysis and validation. Application of higher doses of vermicompost 3.7 t ha-1 and higher dose of fertilizer i.e. 100% RDF content in combination produced elevated grain and straw yield and also boosted growth attributes (Panicle length, test weight, number of tillers, grains per panicle), root volume as well as chlorophyll content from initial growth up to the harvesting time over the control due to buildup of organic matter and nutrient mobilization in soil.

A systematic literature review on software defect prediction using artificial intelligence: Datasets, Data Validation Methods, Approaches, and Tools

Delivering high-quality software products is a challenging task. It needs proper coordination from various teams in planning, execution, and testing. Many software products have high numbers of defects revealed in a production environment. Software failures are costly regarding money, time, and reputation for a business and even life-threatening if utilized in critical applications. Identifying and fixing software defects in the production system is costly, which could be a trivial task if detected before shipping the product. Binary classification is commonly used in existing software defect prediction studies. With the advancements in Artificial Intelligence techniques, there is a great potential to provide meaningful information to software development teams for producing quality software products. An extensive survey for Software Defect Prediction is necessary for exploring datasets, data validation methods, defect detection, and prediction approaches and tools. The survey infers standard datasets utilized in early studies lack adequate features and data validation techniques. According to the finding of the literature survey, the standard datasets has few labels, resulting in insufficient details regarding defects. Systematic Literature Reviews (SLR) on Software Defect Prediction are limited. Hence this SLR presents a comprehensive analysis of defect datasets, dataset validation, detection, prediction approaches, and tools for Software Defect Prediction. The survey exhibits the futuristic recommendations that will allow researchers to develop a tool for Software Defect Prediction. The survey introduces the architecture for developing a software prediction dataset with adequate features and statistical data validation techniques for multi-label classification for software defects.

An Empirical Study on Data Validation Methods of Delphi and General Consensus

Data collection and review are the building blocks of academic research regardless of the discipline. The gathered and reviewed data, however, need to be validated in order to obtain accurate information. The Delphi consensus is known as a method for validating the data. However, several studies have shown that this method is time-consuming and requires a number of rounds to complete. Until now, there has been no clear evidence that validating data by a Delphi consensus is more significant than by a general consensus. In this regard, if data validation between both methods are not significantly different, then just using a general consensus method is sufficient, easier, and less time-consuming. Hence, this study aims to find out whether or not data validation by a Delphi consensus method is more significant than by a general consensus method. This study firstly collected and reviewed the data of sustainable building criteria, secondly validated these data by applying each consensus method, and finally made a comparison between both consensus methods. The results showed that seventeen of the valid criteria obtained from the general consensus and reduced by the Delphi consensus were found to be inconsistent for sustainable building assessments in Cambodia. Therefore, this study concludes that using the Delphi consensus method is more significant in validating the gathered and reviewed data. This experiment contributes to the selection and application of consensus methods in validating data, information, or criteria, especially in engineering fields.

EVALUASI MANAJEMEN ASET DALAM MENGOPTIMALKAN PENGELOLAAN ASET TETAP DAERAH KOTA SEMARANG

<p>The purpose of this study was to determine the management of BMD in Semarang City and to determine efforts to optimize the management of BMD in Semarang City. This type of research is qualitative or case study. The data collection method is carried out in natural settings (natural conditions), primary data sources, and data collection techniques are mostly on observation, interviews, and documentation. Qualitative data processing methods can be done through three stages, namely data processing, data presentation, and drawing conclusions. There are four data validity criteria, namely validity, portability, robustness and confirmability. In verifying the data, the researcher used the data validation method, namely triangulation. The results of the study concluded that the Semarang City Regional Asset management mechanism was basically in accordance with Permendagri 17 of 2021 concerning Systematic Technical Guidelines for Bookkeeping, Inventorying and Reporting of BMD. However, in practice the implementation of this regulation has not been maximized, this is due to several obstacles in its implementation. One way that must be taken to achieve accountability and transparency in the management of BMD is by publication, namely to find out information about regional assets/goods using the internet. Information about regional assets/goods owned by a government that is given to the public will increase public confidence in the control of BMD. As long as the assets are in their useful life, their use must be optimized using a rental mechanism, namely the use of regional property by other parties for a certain period of time by receiving money in return; then the build-to-hand-over (BGS) and the build-to-use (BSG) mechanisms.</p><p><strong>Keywords</strong>: Asset Management, Regional Asset Management</p>

Validation of Rainfall Reanalysis Data to Explore Changes in Oldeman Agricultural Climate Patterns Due to Variability of Surface Temperature Anomalies with Time Series Analysis Techniques (Case Study of Dumai City for 30 Years Period)

Indonesia is a country with a population that still uses the agricultural sector as the highest livelihood. The Central Statistics Agency said 38.61% were in this sector. However, the issue of climate change caused by an increase in global temperature has begun to impact the agricultural sector in several regions. Therefore, this study aims to understand the effect of increasing surface temperature anomaly on the Oldeman climate pattern in Dumai City. The data used are in-situ observation rainfall data for 1981 - 2010, rainfall reanalysis data for 1991 - 2020, surface temperature anomaly data for 1991 - 2020. The data validation methods used are spearman correlation and Kendall-tau methods. The approach to explore changes in the Oldeman climate pattern uses time series analysis techniques such as ACF, PACF, and moving average of 1 year lag-time with a period of 10 years. The rainfall data validation test result show that the average spatial correlation value is 0.86. It can be used to explore time-series climate data in 3 locations in Dumai. As a result, time series analysis found a positive trend in rainfall data significantly along with the temperature anomaly but the Oldeman's climate pattern analysis in Dumai is monitored as stable in type B1. Overall, it is concluded that the change in the surface temperature anomaly variability and trend as a manifestation of climate change in Dumai has not impacted in the Oldeman agricultural climate patterns. Keywords: Climate Change, Oldeman, Time Series, Rainfall, Temperature

Determination of the Association of Qualitative Variables Related to Juvenile Delinquency Factors

The present study describes the main associations between the occurrence of crimes of robbery, homicide, assault, and vandalism in the face of exposure to risk factors for juvenile delinquency, according to the literature consulted. For this, expert knowledge is processed through the AHP multi-criteria decision method and statistical analysis for data validation. A random selection of 350 files of minors under 21 years of age processed during the year 2021 for causes attributable to homicide, robbery, property damage, and assault was carried out, as well as a bibliographic review to determine the study variables to be analyzed. It was observed that drug consumption is associated with the occurrence of vandalism, homicides, and robberies. Likewise, a statistically significant association was observed between belonging to criminal gangs and the occurrence of vandalism and robbery. The analysis carried out revealed the existence of significant relationships in terms of exposure to violent housing environments and the performance of robbery, vandalism, and assault in the young defendants. It was possible to observe the existence of a certain degree of association between child abuse and the occurrence of robbery and assault.

Multi-Ideology ISIS/Jihadist White Supremacist (MIWS) Dataset for Multi-Class Extremism Text Classification

Social media platforms are a popular choice for extremist organizations to disseminate their perceptions, beliefs, and ideologies. This information is generally based on selective reporting and is subjective in content. However, the radical presentation of this disinformation and its outreach on social media leads to an increased number of susceptible audiences. Hence, detection of extremist text on social media platforms is a significant area of research. The unavailability of extremism text datasets is a challenge in online extremism research. The lack of emphasis on classifying extremism text into propaganda, radicalization, and recruitment classes is a challenge. The lack of data validation methods also challenges the accuracy of extremism detection. This research addresses these challenges and presents a seed dataset with a multi-ideology and multi-class extremism text dataset. This research presents the construction of a multi-ideology ISIS/Jihadist White supremacist (MIWS) dataset with recent tweets collected from Twitter. The presented dataset can be employed effectively and importantly to classify extremist text into popular types like propaganda, radicalization, and recruitment. Additionally, the seed dataset is statistically validated with a coherence score of Latent Dirichlet Allocation (LDA) and word mover’s distance using a pretrained Google News vector. The dataset shows effectiveness in its construction with good coherence scores within a topic and appropriate distance measures between topics. This dataset is the first publicly accessible multi-ideology, multi-class extremism text dataset to reinforce research on extremism text detection on social media platforms.

Application of Data Validation and Reconciliation to Improve Measurement Results in the Determination Process of Emission Characteristics in Co-Combustion of Sewage Sludge with Coal

One of the actions popularized worldwide to reduce the consumption of fossil fuels is the combustion of renewable fuels and the co-combustion of both of these fuels. To properly implement combustion and co-combustion processes in power-generation installations, operational characteristics, including emission characteristics are required. To determine these characteristics, tests must be conducted, within the scope of which, for individual operating stages of the installation’s work, the readings collected from a relatively large number of control and measurement instruments should be taken into account. All these instruments have different levels of accuracy, which, among other factors, bring about lower adequacy of the characteristics determined on the basis of these measurements. The objective of this study is to present possible adaptations of data validation and reconciliation methods to increase the adequacy of emission characteristics for the process of co-combustion of fuels. The methodology is discussed based on the example of studies on the co-combustion process of sewage sludge with coal in a grate furnace. The aforementioned characteristics were determined based on measurement tests of gaseous emissions of flue gas components. The tests were carried out for various preset operational conditions of the process, such as the thickness of fuel layer on the grate, the share of sludge in the fuel, the humidity of the sludge, the theoretical ratio of excess air to combustion, and the distribution of air stream during the process. The research object is described and detailed research results concerning two exemplary measurement tests are given, as well as the most important results referring to the whole research. The performed calculations indicate the necessity to take into account often significant corrections, which can amount to about 10% of the measured value.

Scientific Methods to Understand Fish Population Dynamics and Support Sustainable Fisheries Management

Fisheries play a significant role in the livelihoods of the world population, while the dependence on fisheries is acute in developing countries. Fisheries are consequently a critical element for meeting the sustainable development (SDG) and FAO goals to reduce poverty, hunger and improve health and well-being. However, 90% of global marine fish stocks are fully or over-exploited. The amount of biologically unsustainable stocks increased from 10% in 1975 to 33% in 2015. Freshwater ecosystems are the most endangered ecosystems and freshwater fish stocks are worldwide in a state of crisis. The continuous fish stock decline indicates that the world is still far from achieving SDG 14 (Life Below Water), FAO’s Blue Growth Initiative goal and SDG 15 (Life on Land, including freshwater systems). Failure to effectively manage world fish stocks can have disastrous effects on biodiversity and the livelihoods and socio-economic conditions of millions of people. Therefore, management strategies that successfully conserve the stocks and provide optimal sustainable yields are urgently needed. However, successful management is only possible when the necessary data are obtained and decision-makers are well informed. The main problem for the management of fisheries, particularly in developing countries, is the lack of information on the past and current status of the fish stocks. Sound data collection and validation methods are, therefore, important. Stock assessment models, which support sustainable fisheries, require life history traits as input parameters. In order to provide accurate estimates of these life history traits, standardized methods for otolith preparation and validation of the rate of growth zone deposition are essential. This review aims to assist researchers and fisheries managers, working on marine and freshwater fish species, in understanding concepts and processes related to stock assessment and population dynamics. Although most examples and case studies originate from developing countries in the African continent, the review remains of great value to many other countries.

Modeling of lead removal by living Scenedesmus obliquus using backpropagation (BP) neural network algorithm

Abstract Lead pollution in aquatic environment possessed lethal threats to public health. Heavy metal removal using microalgae have gained increasing attention as a novel biosorbent with great economic potential. However, excessive time consumption has been a limiting factor in algal adsorption experiments, while artificial neural network (ANN) prediction models could significantly improve the efficiency for experimental research. In this study, backpropagation (BP) neural network model was established for Pb 2 + removal from water by Scenedesmus obliquus with the 4:9:1 (input layer–hidden layer–output layer) construction. The experimental data of 275 groups derived from orthogonal experiments were obtained for training, validation and test data as input layer. Tangent sigmoid transfer function (tansig) was used in the hidden layer, while linear transfer function was used on the output layer (purelin). The correlation coefficient R2 reached 0.997 for the entire data set after training. Testing model results showed that the correlation between the predicted data and the experimental data was 0.997, with an accuracy rate of 95.4%, coefficient of determination of 0.999, root mean square error of 0.226, relative error of 1.47% and P value of 0.004. The predicted data of BP neural network also successfully applied in the pseudo-second-order kinetic model and Langmuir thermal dynamic model with the relative error less than 5%. Results showed that under pH 6 and algal biomass 2 g/L, Scenedesmus Obliquus achieved highest Pb 2 + removal efficiency. The successful application of BP neural network in Pb 2 + removal by Scenedesmus Obliquus provided an alternative and a more efficient method for microalgal adsorption experimental design and data validation.

The Resilience of Recovered Covid-19 Patients


 
 
 This study aims to assess the resilience of patients recovering from Covid 19, using a qualitative method with a case study research design. The subjects of this study were patients recovered from Covid 19. The data collection tool in this study was an interview. The data analysis technique in this research is content analysis technique. The data validation method in this study used data triangulation. The results showed that based on the emotional regulation aspect, the three subjects were able to control their emotions well, in the impulse control aspect the three subjects received support and motivation from family and fellow Covid 19 patients. Aspects of causal analysis of the three subjects had a feeling of fear of being shunned by people in the surrounding environment and the three subjects were worried if there were family members who contracted Covid 19. Then in the aspect of empathy, when they found out that there were Covid 19 patients who had worsened, the three subjects felt concerned and tried to show their empathy by providing motivational support. In the aspect of self-efficacy, the three subjects received support from the surrounding environment and the three subjects always thought positively. Then in the aspect of reaching out, after being declared cured of Covid 19, the three subjects felt grateful and could take lessons from what the three subjects had experienced. So, from these results it can be seen that the three subjects have both internal and external motivations that make the three subjects become resilient individuals.
 Keywords: COVID-19, resilience, pandemic, patient infected with positive covid 19
 
 

Application of the data validation and reconciliation method in the exergy analysis of technological processes

One of the main tasks of the exergy analysis of real physical and chemical processes is to detect, quantify and calculate exergy losses, which closes the exergy balance equation. Exergy balance equations contain directly measurable variables, which are known with measurement errors and when applied to principal chemical elements lead to the system of nonlinear equations. Usually, the number of equations is greater than the number of unknowns (not directly measured quantities) and because of inevitable measurement errors, the system of equations is not exactly satisfied. The measured quantities must be corrected to satisfy required energy and exergy balances. Such a correction is called data validation and reconciliation. Neglecting this process can lead to incorrect energy and exergy losses calculation. In this paper, the mathematical methods of correction of measurement results based on the least squares method are proposed.

Application of the data validation and reconciliation method in the exergy analysis of technological processes

One of the main tasks of the exergy analysis of real physical and chemical processes is to detect, quantify and calculate exergy losses, which closes the exergy balance equation. Exergy balance equations contain directly measurable variables, which are known with measurement errors and when applied to principal chemical elements lead to the system of nonlinear equations. Usually, the number of equations is greater than the number of unknowns (not directly measured quantities) and because of inevitable measurement errors, the system of equations is not exactly satisfied. The measured quantities must be corrected to satisfy required energy and exergy balances. Such a correction is called data validation and reconciliation. Neglecting this process can lead to incorrect energy and exergy losses calculation. In this paper, the mathematical methods of correction of measurement results based on the least squares method are proposed.

Development of a methodology for data validation in power systems using different types of measurements

In recent years, the technology of synchrophasor measurements has been introduced in power systems around the world. When managing a power system and predicting its operation conditions, the important task is to check the validity of power system data. At the same time, the traditional types of measurements, such as digital fault recorders and telemetry devices are still widely used. It is known that redundancy of measurements contributes to a more accurate solution of the data validation problem. It is useful to create a method for data validation in power systems, which could involve various types of measurements in order to increase the redundancy and, hence, the overall accuracy of measurements. This study presents some validity criteria that use the idea described above. The results of testing the proposed methodology on the substation model in Matlab software package are presented and discussed.