Validation Rules Research Articles

Impact of material property variations and sensor positioning on the coating thickness determination of steel sheets using eddy current testing

Purpose This paper aims to investigate the reliable thickness, and more generally, the geometric and material parameter determination of thin electrically conductive and diamagnetic coatings on conductive and ferromagnetic substrates, e.g. steel, using eddy current testing (ECT). Design/methodology/approach The analytical model of an ECT coil arrangement known from the literature is analyzed to evaluate the numerical simulation performed by a Finite Element (FE) program. The latter is used to investigate the influence of the sheet edge on the measurement result. Finally, a measurement setup is presented and the unknown geometric and material parameters are estimated from measurement data of different sample sheets at different air gaps. Findings Generally, valid mesh rules are found for a very accurate FE analysis of eddy current problems with large air gaps. The influence of large air gaps on the parameter estimation is emphasized. Moreover, the formulated hypotheses can be widely confirmed by measurements. Research limitations/implications In this paper, electrical steel sheets coated with a conductive oven-cured ink are used. This sample configuration creates a discrete transition between the substrate and the coating as present in the analytical modeling approaches. Furthermore, the ferromagnetic substrate’s nonlinear B-H curve is not considered in the analytical model so far. Originality/value The analytical model is known from the literature. However, real practical measurements have not been carried out with the discussed setup. Furthermore, well-known literature on eddy current measurements usually only considers constant and very small air gaps.

Development of Targeted Safety Hazard Management Plans Utilizing Multidimensional Association Rule Mining

Investigating hidden hazards and implementing closed-loop management are essential strategies for accident prevention in the mining industry. This study tackles a key challenge in applying association rule mining to the development of hazard management plans for underground mines. The current approach mainly focuses on hazard description data, often underutilizing critical information such as hazard time and location. To address this, we integrate topic mining with association rule mining to uncover intrinsic association patterns among various attributes of mine safety hazards. Through a systematic analysis of standardized mining hazard attributes, five key analytical dimensions were identified: Hazard Type, Level, Time, Location, and Responsible Units. A topic mining model, utilizing the Biterm Topic Model, was constructed to reduce dimensionality and aggregate hazard description data. Evaluation indicators such as Standard Lift and Difference Degree were proposed, resulting in a multidimensional association rule mining model for mining safety hazards. In this research, 1,387 valid rules were extracted based on hazard inspection data from an underground gold mine in China. The analysis revealed relatively strong associations between hazard location and hazard type, responsible unit, as well as hazard level, with association degrees of 1.934, 1.412, and 1.240, respectively. Additionally, 15 rules with a high degree of differentiation were identified to explore interesting correlations among different attributes. Based on this, corresponding control measures and improvement plans were developed for 19 locations. The results demonstrate that a multidimensional partition-based association rule mining approach for mining safety hazards can significantly enhance the specificity of safety training and improve the efficiency of safety hazard investigation.

SCALE FOR PRE-SERVICE SCIENCE TEACHERS’ KNOWLEDGE IN DESIGNING INCLUSIVE LEARNING: CONSTRUCTION, VALIDATION, AND RELIABILITY

Scale development is essential in testing constructs and variables in social research, including education. Scales that meet the rules of validity and reliability are developed using standardized procedures. This research focuses on scale production to create reliable and valid construct measures to assess teacher knowledge in designing inclusive learning, using three frameworks, namely Technological Pedagogical and Content Knowledge, Backward Design, and Universal Design for Learning. Research on this issue involves measuring organizational and psychological constructs, which present unique challenges because they are generally unobservable, very abstract, often complex, and may consist of several different components. As a result of this complexity, developing this scale was challenging. The production of this scale followed Boateng et al., and the field test involved 259 pre-service teachers. The Rasch Rating Scale Model analysis was used to test the scale and produced 18 items that have high reliability (0.96). This scale can be used nationally to assess teachers' ability to implement inclusive education. The scale also applies as a teacher self-assessment tool for developing their competence.

Rekonstruksi Konsep Syaż Dalam Kajian Hadis: Telaah Pemikiran Syaikh Abdullah Al-Ghumari

This research is based on the opinion of several Ulama who stated that researching the syaż of a hadiṡ is not an easy activity. The implication is that studies on syaż in the treasury of hadiṡ science have not experienced encouraging developments. This causes books that discuss hadiṡ containing syaż to be very difficult to find. Until finally the work of Sheikh Abdullah al-Ghumari appeared, which became the antithesis of this opinion. The purpose of this study is to determine the concept of syaż and the steps taken by Sheikh Abdullah Al-Ghumari in determining the syaż of a hadiṡ. This research is a descriptive qualitative type that compiles and explains the results of the study through words and sentences, not numbers. This study uses a library research method in data collection. The main source of information for this study is Sabil al-Tawfiq fi Tarjamati Abdullah bin al-Siddiq al-Ghumari. The data analysis used in this study is the thematic analysis. The results of this study, related to the steps taken by Sheikh Abdullah al-Ghumari in criticizing the hadiṡs that are considered syaż in general, are as follows: 1) Mentioning the mukharrij of the Hadiṡ; 2) Mentioning the status of the Hadiṡ as syaż and not allowed to be practiced; 3) Mentioning the reasons for its syaż either because it contradicts the mutawatir Hadiṡ, verses of the Qur'an, ijma', valid rules in the science of hadiṡ,, the weakness of the narrator, or because the Hadiṡ is munqathi' or mursal.

The Establishment of the Spiking Method to Evaluate the Rapid Diagnostic Test Antigen (Ag-RDT) Product for COVID-19 Detection

The quality control of the COVID-19 Rapid Diagnostic Test (Ag-RDT) product is regarded as one of the government’s responsibilities. The Indonesian government establishes rules for Ag-RDT post-market validation, where it should be performed by two designated laboratories, using the spiking technique. The usage of this technique raises concerns, especially if it does not represent the precise product quality, due to the sample dilution. In addition, the requisite of using fresh samples that should be prepared for less than 48 hours is considered costly and time-consuming. In response to this, we tested two samples from different age groups on the Ag-RDT brand recommended by the World Health Organization (WHO); Panbio™ Covid-19 Ag Rapid Test (Abbott) and standard Q Ag-RDT (SD Biosensor, Roche). In both Ag-RDT products, the samples observed in the cycle threshold (Ct) values≤25 groups exhibit >80%sensitivity and >97% specificity as in compliance with the WHO recommendation. Meanwhile, as observed in the Ct>25 groups, the sensitivity of the two Ag-RDT products was below 25%, which was not in compliance with the WHO recommendation. Overall, this study indicated that the Spiking technique is eligible to be used for evaluating the performance of Ag-RDT, especially at Ct≤25. Additionally, the samples’ life span of up to 2 weeks of storage at -80oC can be used for post-market validation of Ag-RDT. Furthermore, the quality control assay for longer sample storage is interesting to be carried out.

Association Relationship Analysis in Finding Sales of Goods With Apriori Algorithm

Technology can be designed to help human life from all aspects ranging from agriculture, health science, industry and daily life. Toko Intan, a business engaged in the sale of basic and daily necessities. Every day, Toko Intan records every sales transaction in an archive stored in Microsoft Excel, containing data on goods sold every day. The purpose of this research is to find out what items are bought simultaneously by consumers to manage inventory, with the data mining method used in this research is the Association Rules method. Association Rules is one of the data mining techniques from the a priori algorithm which functions to find a combination pattern of an item. Tests carried out to process data in this study using the RapidMiner application, from tests carried out with the specified parameters, namely minimum support 30% and minimum confidence 65%, resulted in a lift ratio validation rule of 1.206. Personal Care Biscuits with 30.8% support and 90.9% confidence with a validation lift ratio of 1.206. Sales transaction data analysis can be applied well, and is able to generate a new association rule from the sales transaction dataset. With this research, it is hoped that it can provide information to the owner of the Intan store in providing the stock of goods needed by consumers and to find out the combination of item sets from the sales transaction dataset.

Predicting hazard degree levels of metro operation accidents based on ordered constraint Apriori-RF method

To explore the non-linear relationship between risk sources and the hazard degree levels of accidents, and to precisely predict the hazard impact of metro operation accidents, we propose the ordered constraint Apriori-RF method for forecasting metro operation accident hazard degree levels. Firstly, the hazard degree of metro operation accidents is quantified from three dimensions: casualties, train delays, and facility damages. K-means clustering is then applied to categorize hazard degree levels. Secondly, the ordered constraint Apriori algorithm is employed to mine valid association rules between metro operation risk sources and accident hazard degree levels. These valid association rules are subsequently employed in the random forest algorithm for training, establishing a reliable and accurate prediction model. Finally, the method is validated using metro accident data from a city in China. The research results indicate that the ordered constraint Apriori-RF method enhances the effectiveness of association rule mining by 74.9% and exhibits higher computational efficiency. The predicted values of the ordered constraint Apriori-RF method have small errors. Compared to traditional Random Forest algorithms, the root mean square error is reduced by 14%, and the weighted root mean square error is reduced by 36%, demonstrating the higher accuracy of the ordered constraint Apriori-RF method and its clear advantages. The research findings provide a precise and effective method for quantitatively predicting the hazard degree levels of metro operation accidents, holding significant theoretical and practical value in ensuring metro operation safety and implementing accident mitigation and prevention measures.

Empowering Model‐Based Systems Engineering Through Metamodeling

AbstractA critical enabler for Model‐Based Systems Engineering (MBSE) and Digital Engineering (DE) is the generation of coherent and consistent views of a system‐of‐interest based on information within a system model. In practice, system model development is facilitated through domain‐specific profiles, style guides, reference models, and low‐fidelity meta‐models to create coherent and consistent system information. Each of these approaches are useful but are insufficient for robust and automated verification of system models to an ideal. Furthermore, the expression of domain‐specific concepts and semantics relies on the proliferation of non‐standard, domain‐specific profiles as standard system modeling languages like the Systems Modeling Language (SysML) are general purpose. This paper proposes a novel approach to creating precise, machine‐interpretable metamodels implemented as a lightweight Unified Modeling Language (UML) profile. The profile includes numerous features that allow model architects to quickly specify context and domain‐specific modeling constructs without creating non‐standard stereotypes to apply domain‐specific meaning and usage rules. Three kinds of constraints can be inferred based on the relationships between meta‐model elements: type, multiplicity, and default value. Applications of well‐formed metamodels include a shared understanding of the intended model format and structure, as well as the one‐time programmatic generation of an encompassing suite of validation rules to evaluate a system model against the inferred constraints, thus ensuring consistency. Additional applications include programmatic generation of model analysis metrics, system models from metamodels, metamodels from reference models and element finding queries, and the ability to update a system model based upon the updated metamodel automatically. Use of the approach results in reduced time in system model development and analysis and ensures coherency and consistency of information thus increasing stakeholder use and confidence in the system model.

Wittgenstein and the liar

Ludwig Wittgenstein’s remarks on contradictions and paradoxes have been met with incomprehension and have fueled the widespread and long-standing prejudice that his later thoughts on the foundations of logic and mathematics are the “surprisingly insignificant product of a sparkling mind” (Kreisel, British Journal for the Philosophy of Science 9:135–158, 1959, p. 158). This paper disagrees; it argues that Wittgenstein’s remarks on semantic paradoxes suggest an account of the Liar and its kin that is not only of historical interest but also represents a hitherto unnoticed paraconsistent alternative to established approaches to the Liar. In what follows, a reading of Wittgenstein’s remarks will be offered according to which Wittgenstein subscribes to a form of dialetheism (that is, the view that there are sentences that are both true and false). In contrast to modern dialetheist approaches to the Liar, however, some of Wittgenstein’s remarks suggest combining a dialetheist position with what is called ‘logical nihilism’ (that is, the view that there are no universally valid inference rules).

Detection and Exclusion of False-Positive Molecular Formula Assignments via Mass Error Distributions in UHR Mass Spectra of Natural Organic Matter.

Ultrahigh resolution mass spectrometry (UHRMS) routinely detects and identifies thousands of mass peaks in complex mixtures, such as natural organic matter (NOM) and petroleum. The assignment of several chemically plausible molecular formulas (MFs) for a single accurate mass still poses a major problem for the reliable interpretation of NOM composition in a biogeochemical context. Applying sensible chemical rules for MF validation is often insufficient to eliminate multiple assignments (MultiAs)─especially for mass peaks with low abundance or if ample heteroatoms or isotopes are included - and requires manual inspection or expert judgment. Here, we present a new approach based on mass error distributions for the identification of true and false assignments among MultiAs. To this end, we used the mass error in millidalton (mDa), which was superior to the commonly used relative mass error in ppm. We developed an automatic workflow to group MultiAs based on their shared formula units and Kendrick mass defect values and to evaluate the mass error distribution. In this way, the number of valid assignments of chlorinated disinfection byproducts was increased by 8-fold as compared to only applying 37Cl/35Cl isotope ratio filters. Likewise, phosphorus-containing MFs can be differentiated against chlorine-containing MFs with high confidence. Further, false assignments of highly aromatic sulfur-containing MFs ("black sulfur") to sodium adducts in negative ionization mode can be excluded by applying our approach. Overall, MFs for mass peaks that are close to the detection limit or where naturally occurring isotopes are rare (e.g., 15N) or absent (e.g., P and F) can now be validated, substantially increasing the reliability of MF assignments and broadening the applicability of UHRMS analysis to even more complex samples and processes.

Integrative taxonomic revision of the African thicket rats (Murinae: Grammomys): how genomics decreases the number of currently recognized species

Abstract Thicket rats of the genus Grammomys are partly arboreal rodents distributed across a large part of sub-Saharan Africa. Previous work showed that their highest diversity is in forests and woodlands of Eastern Africa, that the evolutionary history of the genus roughly mirrors the evolution of African forests, and that the currently delimited species (names) do not correspond to major genetic clades. We used thousands of single nucleotide polymorphisms to perform phylogenomic analysis and to delimit genomic pools (i.e. candidate biological species). The resulting molecular operational taxonomic units were then compared with the most comprehensive sets of other available data (mitochondrial DNA variability, ecological requirements, morphology, karyotypes, sequences from the type material, etc.) and used for an integrative solution to the taxonomy of the genus. Our revision delimited seven species, which is less than the 12 recognized in the most recent literature on African rodents. We conclude that the African thicket rats represent a classic example of challenging problems in current integrative taxonomy. It is now relatively easy to obtain genomic data and to delimit candidate species, but it is much more complicated to give them species names using the valid rules of zoological nomenclature.

Recommendations for the Model-Based Systems Engineering Modeling Process Based on the SysML Model and Domain Knowledge

Model-based systems engineering (MBSE) is a modeling approach used in industry to support the formalization, analysis, design, checking and verification of systems. In MBSE modeling, domain knowledge is the basis of the modeling. However, modeling does not happen overnight; it requires systematic training and a significant investment of resources. Unfortunately, many domain experts lack the expertise required for modeling, even though they know the domain well. The question arises about how to provide system modelers with domain knowledge at the right time to support the efficient completion of modeling. Since MBSE research that integrates AI is just beginning to take off, no public dataset is available. In this paper, aerospace SysML models are constructed based on spacecraft-related domain knowledge to form SysML model data. The validation rules are studied to validate the SysML model data, and combined with the concept of the recommended system, a recommendation method for the MBSE modeling process based on the knowledge and SysML model is proposed. A GLOVE language model is pre-trained by using domain knowledge and general knowledge; the model data are also used to fine-tune the GLOVE language model combined with the pre-training to recommend some domain development processes. The recommendation list is manually quality-verified and fed into the pre-training phase, while new requirement texts are continuously added in the fine-tuning phase, resulting in a more relevant and accurate recommendation list. Experiments show that the incremental recommender system can not only effectively recommend SysML models, but also improve the quality and efficiency of MBSE development.

Trends in Adverse Event Reporting Before and After the Introduction of the Med Safety App in Nigeria.

Spontaneous reporting of adverse events (AEs) is a mainstay of pharmacovigilance, and an ongoing challenge is how to ensure that more high-quality reports are collected for comprehensive information provision. The Med Safety App, a smartphone-based application, was launched in Nigeria in November 2020 to provide an electronic platform for users to seamlessly report AEs. There has been a paucity of evidence on the use of this application or other mobile applications for reporting adverse drug reactions/AEs following immunization in the Nigerian environment. The aim of this study was to evaluate the trends in adverse event reporting before and after the introduction of the Med Safety App in Nigeria. This was a retrospective, observational study using data from the VigiFlow database to compare adverse event reporting in Nigeria before and after the deployment of the Med Safety App. The baseline period was 1st April 2019to 30th October 2020 and the comparison period was 1st November 2020to 31st May 2022. We used Vigilance Hub, the back-end system for the Med Safety App, to extract data on App downloads and de-identified user statistics. Data were summarized using descriptive statistics, frequencies and proportions. Quality was assessed by assigning a completeness score to each individual case safety report. The Kruskal-Wallis test was used to test for differences in medians between groups. Following deployment of the App, the Nigerian National Pharmacovigilance Centre recorded an increase in the total number of adverse event reports received in VigiFlow, from 2051 in the baseline period to 18,995 following deployment of the App, with 81.7% of those reported via the Med Safety App. There was a reduction in the proportion of paper-based reporting from 98.4 to 15.7% post-deployment, and direct reporting by consumers increased from 2.7 to 17.6%. Of the 15,526 reports submitted via the App, 15,111 (97.3%) had a completeness score above 70% and 6993 (45%) had a completeness score of 100%. The median completeness score of adverse event reports on the Med Safety App was 6 out of 7. On bivariate analysis using the Kruskal-Wallis test, there was an association between means of reporting and completeness score, and this association was significant, with a p value of 0.0001, which may reflect the validation rules that are applied within the App. Deployment of the Med Safety App increased both the number and quality of adverse event reports; however, more awareness and capacity building are needed to strengthen and sustain reporting on the tool by all categories of healthcare professionals and consumers/patients.

Dynamic behaviour of bridges under critical conventional and regular trains: Review of some regulations included in EN 1991-2

In the field of structural analysis dedicated to the study of vibrations of high-speed railway bridges, one reference load model is the well-known HSLM-A, which limits of validity are stated in Eurocode EN 1991-2, Annex E. In a recent paper published in the Journal of Rail and Rapid Transit, the authors investigated the degree of coverage provided by HSLM-A to critical articulated trains. Now in the present article, the authors have extended those analyses to critical conventional and regular trains as well. This is an important aspect because HSLM-A as such is an articulated-type model, so it is of interest to understand how it deals with covering the various resonance phenomena generated by other train types. Therefore, the main goal of this work is to establish whether the conventional and regular trains that stem from the validity rules given in Annex E/EN 1991-2, produce vibratory effects that are duly covered by HSLM-A. Following the aforementioned validity rules, one first aspect analysed is the importance of near-to-integer wheelbase ratios in the coupled vibrations produced by conventional trains. Subsequently, seven realistic, conventional and regular high-speed train models have been synthesised; these models have been made publicly available in Mendeley Data, and comprise almost 3800 different sequences of axle loads. Finally, the response of simply-supported bridges has been analysed with a view to compare the seven synthesised models versus HSLM-A. The exceedance and required speed increase have been computed for both displacements and accelerations, in a comprehensive ensemble of spans and speeds. The results provide a diagnosis of the degree of coverage of HSLM-A with respect to those conventional and regular trains compliant with Annex E/EN 1991-2.

Two-photon lithography for customized microstructured surfaces and their influence on wettability and bacterial load

BackgroundDevice-related bacterial infections account for a large proportion of hospital-acquired infections. The ability of bacteria to form a biofilm as a protective shield usually makes treatment impossible without removal of the implant. Topographic surfaces have attracted considerable attention in studies seeking antibacterial properties without the need for additional antimicrobial substances. As there are still no valid rules for the design of antibacterial microstructured surfaces, a fast, reproducible production technique with good resolution is required to produce test surfaces and to examine their effectiveness with regard to their antibacterial properties.MethodsIn this work various surfaces, flat and with microcylinders in different dimensions (flat, 1, 3 and 9 μm) with a surface area of 7 × 7 mm were fabricated with a nanoprinter using two-photon lithography and evaluated for their antibiofilm effect. The microstructured surfaces were cultured for 24 h with different strains of Pseudomonas aeruginosa and Staphylococcus aureus to study bacterial attachment to the patterned surfaces. In addition, surface wettability was measured by a static contact angle measurement.ResultsContact angles increased with cylinder size and thus hydrophobicity. Despite the difference in wettability, Staphylococcus aureus was not affected by the microstructures, while for Pseudomonas aeruginosa the bacterial load increased with the size of the cylinders, and compared to a flat surface, a reduction in bacteria was observed for one strain on the smallest cylinders.ConclusionsTwo-photon lithography allowed rapid and flexible production of microcylinders of different sizes, which affected surface wettability and bacterial load, however, depending on bacterial type and strain.

LEI2JSON: Schema-based validation and conversion of livestock event information

Livestock producers often need help in standardising (i.e., converting and validating) their livestock event data. This article introduces a novel solution, LEI2JSON (Livestock Event Information To JSON). The tool is an add-on for Google Sheets, adhering to the livestock event information (LEI) schema. The core objective of LEI2JSON is to provide livestock producers with an efficient mechanism to standardise their data, leading to substantial savings in time and resources. This is achieved by building the spreadsheet template with the appropriate column headers, notes, and validation rules, converting the spreadsheet data into JSON format, and validating the output against the schema. LEI2JSON facilitates the seamless storage of livestock event information locally or on Google Drive in JSON. Additionally, we have conducted an extensive experimental evaluation to assess the effectiveness of the tool.

Forecasting spare part extractions from returned systems in a closed-loop supply chain

In a closed-loop supply chain, the reuse of spare parts from returned systems is a recovery process referred to as spare parts harvesting. The unpredictability of the parts supply capacity from returned systems is a challenge in healthcare industry as product returns depend on several factors and regulatory and legal requirements must be respected. The focus of this paper is to provide a forecasting method of harvested parts supply capacity in healthcare industry that combines statistical methods with field information and business knowledge to provide an informed forecast. We propose a dynamic forecasting process that gets updated monthly employing TSB-Croston, 12-month moving average, ARIMA, ARIMA with seasonality, and a new business knowledge based model. A prediction method of the inventory state changes is introduced. The forecasters judgment is transformed into validation rules for an automatic forecast adjustment. This method is evaluated on more than 1400 time series with intermittent behaviour representing General Electric Healthcare spare parts harvesting history. We evaluate the performance of our method compared to each tested model using a modified MAPE, MAE, MSE, and RMSE. By means of the designed method, the forecast performance is improved compared to all tested models.

Implementation of thyroid eye disease registry in Iran: rationale and research protocol.

To describe the implementation of a registry system for patients with thyroid eye disease (TED) in Iran to obtain more information about its nature, prevalence, and annual incidence, as well as extend insight into the etiology, pathogenesis, and eventually make an accurate prognosis of different medical or surgical treatment methods. After receiving approval from the Disease Registry Committee of Iran University of Medical Sciences (IUMS) in 2019 and the Ministry of Health and Medical Education (MOHME) in 2020, the protocol was introduced in three consecutive phases at regional, provincial and national levels. The establishment of a registry committee in Rassoul Akram Hospital, one of the medical centers affiliated to IUMS, was the first step to organizing the registry project's main core. The steering committee included six subgroups of required subject fields. The members are experts in developing a guideline, providing a new dataset, drawing an outline for the next steps, and structuring user-friendly software through several panel discussion meetings. The data is collected from clinical and para-clinical/imaging findings, laboratory evaluations, and their selected treatment strategy, retrospectively and prospectively. The purpose is to broaden our knowledge about the profile of TED; accordingly, data related to patients' demographics, thyroid gland disease (status, duration, treatments, and function tests), general medical and ocular history, along with visual/ocular exams resulting TED status are collected and recorded in a 2- language software. The web-based software system is accessible at https://orc.iums.ac.ir . To maintain data security, prioritized user access was defined for different members. Furthermore, diverse methods, such as employing trained staff and utilizing software validation rules, were implemented to control data quality in every step of data collection, entry, and registration. Medical records of retrospective subjects were also evaluated and entered after accuracy verification. Iran's TED registry provides practitioners with comprehensive data on natural history and phenotype variations in clinical features and outcomes. It facilitates patient recruitment and, consequently, earlier diagnosis on a large scale which helps improve treatment and quality of life for patients.

Frequent Errors in Modeling by Machine Learning: A Prototype Case of Predicting the Timely Evolution of COVID-19 Pandemic

Background: The development and application of machine learning (ML) methods have become so fast that almost nobody can follow their developments in every detail. It is no wonder that numerous errors and inconsistencies in their usage have also spread with a similar speed independently from the tasks: regression and classification. This work summarizes frequent errors committed by certain authors with the aim of helping scientists to avoid them. Methods: The principle of parsimony governs the train of thought. Fair method comparison can be completed with multicriteria decision-making techniques, preferably by the sum of ranking differences (SRD). Its coupling with analysis of variance (ANOVA) decomposes the effects of several factors. Earlier findings are summarized in a review-like manner: the abuse of the correlation coefficient and proper practices for model discrimination are also outlined. Results: Using an illustrative example, the correct practice and the methodology are summarized as guidelines for model discrimination, and for minimizing the prediction errors. The following factors are all prerequisites for successful modeling: proper data preprocessing, statistical tests, suitable performance parameters, appropriate degrees of freedom, fair comparison of models, and outlier detection, just to name a few. A checklist is provided in a tutorial manner on how to present ML modeling properly. The advocated practices are reviewed shortly in the discussion. Conclusions: Many of the errors can easily be filtered out with careful reviewing. Every authors’ responsibility is to adhere to the rules of modeling and validation. A representative sampling of recent literature outlines correct practices and emphasizes that no error-free publication exists.

INFORMATION TECHNOLOGY OF SOFTWARE DATA SECURITY MONITORING

The article proposes an overview of an information technology of software data security monitoring with the aim of increasing the security of auxiliary and end-user software in run-time using the designed universal architecture with the capabilities of synchronized data security validation processes in client-server interactions and integration into software development technologies using unified interfaces for specifying extensible sets of validation rules. An analysis of the subject area was conducted, during which it was established that the tasks of software data security validation are included in the requirements of well-known data security standards, and the related vulnerabilities are noted as highly important. It was also established that the traditional monitoring of only software input data is not enough, therefore the problem of security monitoring of various categories of software data is relevant. The capabilities of existing tools to monitor security and correctness of software data are incomplete: focus on certain categories of data, platform dependency, narrow integration capabilities with other software development tools, limited usage, complex or limited extensibility, difficult reuse of well-known verified solutions etc. Based on the analysis of the existing software data security monitoring tools, the cons of the existing implementations were identified, and a universal architecture of the software framework was proposed as a solution. The analysis of the vulnerabilities of various categories of software data was conducted along with the recommended methods of implementation of data security. Discovered methods of data security implementation were used in the proposed solution. The article examines the structure of the proposed information technology, provides the universal architecture of the software framework, demonstrates the verification of the work of the developed tool, and provides the assessment of the effectiveness of usage of the data security monitoring framework in software development. The proposed architecture and directions of further improvements of the framework allow significant extension of its functionality and easy integration into popular software development technologies. It is assumed that the developed information technology of software data security monitoring will be widely used in commercial software development as well as in educational and scientific appliance.