Erroneous Decisions Research Articles

A Cross-Sectional Study of Serum Levels of Several Vitamins in Healthy and Unhealthy Periodontal Conditions

Vitamins are examples of micronutrients, which are nutrients needed in very minute quantities. They're crucial to metabolism and keeping tissues working properly. The purpose of this study was to determine whether vitamin deficiencies are associated with an increased risk of periodontitis. There was a total of 110 participants: 55 with chronic periodontitis spread throughout the mouth and 55 periodontally healthy individuals. Clinical parameters that were assessed included: Clinical attachment loss (in millimetres) and gingival index (in millimetres). Vitamin B12, vitamin D, folate, as well as vitamin E levels in serum samples were measured. Also measured were Beta-carotene and Beta-cryptoxanthin levels. The acquired data was aggregated and analysed using statistical programmes. All periodontal status clinical measures in the periodontitis group were statistically significantly different from those in the healthy volunteers group (p < 0.0001). Vitamin A precursors Beta-carotene and Beta-cryptoxanthin, folate, D, vitamins B12, and E, and vitamin E were all significantly lower in the periodontitis group compared to the healthy volunteers, but the difference was only statistically significant for vitamins B12 as well as D (p < 0.05). Finally, we conclude that serum micronutrient levels, particularly those of Vitamin A, Vitamin B12, and Vitamin D, may be modifiable risk factors for periodontal disease. In addition to practicing good dental hygiene on a regular basis, eating a diet rich in the right kinds of vitamins may play a significant part in warding off periodontitis.

Problems and prospects of the application of artificial intelligence in the combat of criminal offenses by The Security Service of Ukraine at critical infrastructure facilities

The article is devoted to the study of the problems and prospects of the use of artificial intelligence in countering criminal offenses by the Security Service of Ukraine at critical infrastructure facilities. The peculiarities of state policy, the current state of legal regulation of the use of innovative technology in Ukraine are considered, and problematic issues of the use of artificial intelligence in the course of the tasks of prevention, detection, termination, and pre­trial investigation of criminal proceedings in the relevant field are analyzed. The main areas of application of artificial intelligence in combating criminal offenses at critical infrastructure facilities have been worked out, such as: intelligent detection of threats in the process of countermeasures; automation of algorithms for combating illegal encroachments; information and analytical support. The key methods and technologies of artificial intelligence that can be used in the process of combating criminal offenses at critical infrastructure facilities by the Security Service of Ukraine are highlighted, in particular: deep learning, machine learning, natural language processing and computer vision. The potential risks and problematic issues of introducing artificial intelligence into the automated process of combating criminal offenses at critical infrastructure facilities are outlined. Attention is drawn to compliance with the principles of transparency and accountability of activity, legality, responsibility for erroneous decisions, ensuring human rights and freedoms, prevention of risks of discrimination, excessive reliance on artificial intelligence. The need to improve the legal basis for the use of artificial intelligence in combating criminal offenses at critical infrastructure facilities, as well as introducing changes and additions to the legislation of Ukraine concerning the criminal-procedural activity of the Security Service of Ukraine, was noted. The introduction of artificial intelligence technologies into the system of combating criminal offenses at critical infrastructure facilities is identified as a promising direction for further research.

A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes

BackgroundPrecision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs.MethodsTo address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the “borrowing of information” across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model.ResultsWe demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analyses demonstrate the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs.ConclusionThe study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

ASSESSMENT OF THE IMPACT OF WELL GRID DENSITY ON OIL RECOVERY UNDER CONDITIONS OF UNCERTAINTY

The density of the well grid is one of the most important parameters that has a significant impact on the rate and amount of oil reserves extraction. As practice shows, existing approaches to the reliable assessment and identification of the degree of influence of this indicator on oil recovery do not allow us to adequately form an idea of the dynamics of reservoir processes and their role in making managerial decisions in conditions of uncertainty. Taking into account the decrease in the number of hydrodynamic and geophysical studies conducted in relation to the total number of wells in operation at fields that are being developed for a long time or just being put into development, the issue of predicting the oil recovery coefficient according to the list of geological and field parameters remains open and requires an urgent solution, including those related to the use of unique complex algorithms. The purpose of the work is to determine the relationship between the density of the well grid and the final coefficient of oil recovery in the development of fields in natural conditions using computer modeling systems. The research methodology consists of the use of existing scientific and methodological approaches to the processing of geological and commercial information in conditions of different densities in combination with the use of various advanced systems for identifying hidden patterns. Geological and statistical models have been defined for the oil deposits of the Famensk tier of the South Tatar arch of the Volga-Ural oil and gas province, allowing for the forecasting procedure for groups of objects identified during differentiation, subject to the dynamic density of geological and field information and various technical and economic indicators of field development. A comprehensive interpretation of the nature and degree of influence of geological and technological parameters on the amount of oil reserves production has been carried out. The need for preliminary differentiation of deposits based on identification with the subsequent use of the presented geological and statistical models to reduce the risks of making erroneous decisions during activities aimed at optimizing the development of analog facilities and rational extraction of residual oil reserves is reliably substantiated.

Divergence of ESG Ratings: International and Russian Experience

ESG rating is an important indicator of a company’s social responsibility, which is taken into account by investors and regulators. However, if the ESG rating is calculated incorrectly, investors and regulators will make erroneous decisions and companies will be given improper guidance about how to modify their operations. Currently, there is a serious methodological problem in that ESG ratings from various rating agencies can be significantly different for the same company. Two of the most urgent questions in current ESG research are why these differences come about and how critical the divergence of ratings is. First, the article reviews international research on rating divergence and summarizes the findings. A similar analysis is then performed to compare the methodologies and rating results of three leading Russian ESG rating providers — RSPP, RAEX, and NRC — for 2020-2021. The results indicate that Russian rating agencies are somewhat more divergent in their assessments of companies than international ones (Krippendorff’s alpha for Russian ratings is 0.4, but for non-Russian ratings it is 0.55), and Russian agencies’ coverage of ESG components is less comprehensive than that of the other rating agencies (although coverage in the RAEX methodology is close to the average of the international equivalents). The article also analyzes the distribution of companies across different gradations of the ratings by the three Russian agencies and attributes the distinctions that emerge to the way the ratings are calculated and the general ideology of the agency. Comparison of average ratings for different industries by different agencies indicates that these ratings are also rather inconsistent.

Nonconvexity and computational effort in the problem of Hydro-Power spillage policy assessment

This paper aims to assess the spillage policies in the Day-Ahead Hydro-Power Plant operation. For this purpose, a combination technique is proposed to prevent erroneous spillage decisions. Given that the developed mathematical modeling lacks the convexity property, we examine the multiple solutions obtained from the Hydro-Power production optimization subject to different power output formulations. In addition, a nonlinear approach to consider friction losses in the modeling is introduced. The modeling is described by Mixed-Integer Linear (MILP) and Integer Nonlinear (MINLP) Programming models. The optimal solutions are provided by Outer Approximation (OA), Branch-and-Bound (BB), and Interior Point algorithms. The simulations show that the water is managed distinctly depending on the optimizer and solution algorithms. Finally, the findings of the spillage policy obtained by the Mixed-Integer Nonlinear model under the cubic function for power generation encourage and indicate its adequacy for a more realistic operation and management of the Mexican Hydro-Power Plant water resources.

Sequential investment decisions for mining projects using compound multiple volatility real options approach

Managers evaluating investment decisions in mining projects have reported using the net present value approach (NPV). However, NPV has the drawback of collapsing variations from uncertainties in future project cash flows into a fixed expected project value today. Ignoring future uncertainties and contingencies can lead managers to make incorrect up-front binary decisions, such as investing in or abandoning the project now. The real options analysis approach (ROA) instead captures variations from uncertainties as volatilities in project value and allows flexibility in investment decisions to be contingent on information as it is revealed. Managers we consulted agreed that ROA is superior to NPV in principle, but too complex to apply in practice. The advanced mathematics and restrictive assumptions required by analytical ROAs make them especially impractical for real-world projects exposed to multiple uncertainties. Furthermore, real-world projects are often multistage and involve valuing a compound option, which is an option on an underlying option, for sequential investment decisions. Extant numerical compound ROAs have been applied to such projects but suffer from various drawbacks. Amongst other issues, they combine variations from multiple uncertainties into a consolidated volatility of project value. This conceals the impact of each source of uncertainty, resulting in inaccurate project valuation and incorrect investment decisions. We present an innovative compound multiple volatility real options approach (C-MVR) to value a multistage project while accommodating separate volatilities of project value arising from multiple uncertainties for making sequential investment decisions. The resultant value is termed the compound enhanced net present value. Implementing C-MVR for a real coal mining project demonstrates how other ROAs can be seen as inaccurate simplifications that produce erroneous investment decisions. C-MVR provides a rigorous and versatile approach that can be applied to many investment decisions across various industries.

A dependent circular-linear model for multivariate biomechanical data: Ilizarov ring fixator study.

Biomechanical and orthopaedic studies frequently encounter complex datasets that encompass both circular and linear variables. In most cases (i) the circular and linear variables are considered in isolation with dependency between variables neglected and (ii) the cyclicity of the circular variables is disregarded resulting in erroneous decision making. Given the inherent characteristics of circular variables, it is imperative to adopt methods that integrate directional statistics to achieve precise modelling. This paper is motivated by the modelling of biomechanical data, that is, the fracture displacements, that is used as a measure in external fixator comparisons. We focus on a dataset, based on an Ilizarov ring fixator, comprising of six variables. A modelling framework applicable to the six-dimensional joint distribution of circular-linear data based on vine copulas is proposed. The pair-copula decomposition concept of vine copulas represents the dependence structure as a combination of circular-linear, circular-circular and linear-linear pairs modelled by their respective copulas. This framework allows us to assess the dependencies in the joint distribution as well as account for the cyclicity of the circular variables. Thus, a new approach for accurate modelling of mechanical behaviour for Ilizarov ring fixators and other data of this nature is imparted.

Interpreting PPV and NPV of Diagnostic Tests with Uncertain Prevalence.

Medical decision-making is often uncertain. The positive predictive value (PPV) and negative predictive value (NPV) are conditional probabilities characterizing diagnostic tests and assessing diagnostic interventions in clinical medicine and epidemiology. The PPV is the probability that a patient has a specified disease, given a positive test result for that disease. The NPV is the probability that a patient does not have the disease, given a negative test result for that disease. Both values depend on disease incidence or prevalence, which may be highly uncertain for unfamiliar diseases, epidemics, etc. Probability distributions for this uncertainty are usually unavailable. We develop a non-probabilistic method for interpreting PPV and NPV with uncertain prevalence. Uncertainty in PPV and NPV is managed with the non-probabilistic concept of robustness in info-gap theory. Robustness of PPV or NPV estimates is the greatest uncertainty (in prevalence) at which the estimate's error is acceptable. Four properties are demonstrated. Zeroing: best estimates of PPV or NPV have no robustness to uncertain prevalence; best estimates are unreliable for interpreting diagnostic tests. Trade-off: robustness increases as error increases; this trade-off identifies robustly reliable error in PPV or NPV. Preference reversal: sometimes sub-optimal PPV or NPV estimates are more robust to uncertain incidence or prevalence than optimal estimates, motivating reversal of preference from the putative optimum to the sub-optimal estimate. Trade-off between specificity and robustness to uncertainty: the robustness increases as test-specificity decreases. These four properties underlie the interpretation of PPV and NPV. The PPV and NPV assess diagnostic tests, but are sensitive to lack of knowledge that generates non-probabilistic uncertain prevalence and must be supplemented with robustness analysis. When uncertainties abound, as with unfamiliar diseases, assessing robustness is critical to avoiding erroneous decisions.

An Overview of Deep Learning Methods for Exudate Detection in Diabetic Retinopathy

Background: This comprehensive review aims to provide a thorough overview of exudate detection techniques with a focus on their application in diagnosing diabetic retinopathy(DR) early. Main body of the abstract: This review employs a systematic analysis of peer-reviewed articles, investigating the utilization of deep learning techniques in exudate detection. These techniques encompass convolutional neural networks, fuzzy c-means clustering, neural networks, and more. The precise detection and quantification of exudates are pivotal in monitoring the progression of DR, as they serve as crucial indicators for assessing the risk factors associated with vision-threatening complications. Conventional methods are prone to erroneous clinical decisions due to factors like observer fatigue and subjectivity during interpretation. Consequently, an increasing number of deep learning-based approaches have emerged to address these limitations. Short conclusion: The techniques for detecting diabetic retinopathy exudates demonstrate considerable promise in terms of accuracy and efficiency. Nonetheless, further research is imperative to develop more robust and reliable methods, facilitating early diagnosis and timely intervention in cases of DR.

Development and initial validation of a data quality evaluation tool in obstetrics real-world data through HL7-FHIR interoperable Bayesian networks and expert rules.

The increasing prevalence of electronic health records (EHRs) in healthcare systems globally has underscored the importance of data quality for clinical decision-making and research, particularly in obstetrics. High-quality data is vital for an accurate representation of patient populations and to avoid erroneous healthcare decisions. However, existing studies have highlighted significant challenges in EHR data quality, necessitating innovative tools and methodologies for effective data quality assessment and improvement. This article addresses the critical need for data quality evaluation in obstetrics by developing a novel tool. The tool utilizes Health Level 7 (HL7) Fast Healthcare Interoperable Resources (FHIR) standards in conjunction with Bayesian Networks and expert rules, offering a novel approach to assessing data quality in real-world obstetrics data. A harmonized framework focusing on completeness, plausibility, and conformance underpins our methodology. We employed Bayesian networks for advanced probabilistic modeling, integrated outlier detection methods, and a rule-based system grounded in domain-specific knowledge. The development and validation of the tool were based on obstetrics data from 9 Portuguese hospitals, spanning the years 2019-2020. The developed tool demonstrated strong potential for identifying data quality issues in obstetrics EHRs. Bayesian networks used in the tool showed high performance for various features with area under the receiver operating characteristic curve (AUROC) between 75% and 97%. The tool's infrastructure and interoperable format as a FHIR Application Programming Interface (API) enables a possible deployment of a real-time data quality assessment in obstetrics settings. Our initial assessments show promised, even when compared with physicians' assessment of real records, the tool can reach AUROC of 88%, depending on the threshold defined. Our results also show that obstetrics clinical records are difficult to assess in terms of quality and assessments like ours could benefit from more categorical approaches of ranking between bad and good quality. This study contributes significantly to the field of EHR data quality assessment, with a specific focus on obstetrics. The combination of HL7-FHIR interoperability, machine learning techniques, and expert knowledge presents a robust, adaptable solution to the challenges of healthcare data quality. Future research should explore tailored data quality evaluations for different healthcare contexts, as well as further validation of the tool capabilities, enhancing the tool's utility across diverse medical domains.

Reducing the risks of making erroneous decisions when managing the development of deposits in carbonate reservoirs

To reduce the risks of making erroneous decisions aimed at improving the efficiency of oil deposits development in the carbonate reservoirs of the Volga-Ural oil and gas province, it is proposed to use the analogy method. An algorithm has been developed that allows using analog objects or groups of them to solve various fishing tasks. The search is proposed to be carried out using 20 parameters characterizing the geological, physical and physico-chemical properties of the formations and the fluids saturating them. The deep differentiation of objects allows to increase the degree of identification of objects. Keywords: Volga-Ural oil and gas province; analogy method; differentiation; grouping; carbonate reservoirs.

Analysis of the peculiarities of the method of combining an electronic cartographic navigation information system with a ship's radar station

The article analyses the importance of radar image overlay on the screen of an electronic charting navigation information system (ECNIS) for improving safety and efficiency of navigation. Based on a review of scientific publications and professional literature, the article discusses the technical aspects of the overlay function, its advantages and limitations, and the impact on the decision-making process of a navigator. The overlay of radar information on an electronic navigation chart ensures the integration of planned and actual data on the environment, which contributes to the increase of situational awareness of the navigator. The combination of the systems makes it possible to visually compare radar targets with cartographic objects, clarify the vessel's location, control drift and orientation, and detect navigation hazards. The overlay function is especially valuable when sailing in confined waters and in conditions of limited visibility. At the same time, the use of overlay technology is associated with a number of technical difficulties and limitations related to ensuring the accuracy of overlap, harmonisation of data formats, and synchronisation of information updates. Errors in the operation of positioning systems, direction guidance, and cartography errors can lead to inconsistencies in image layers and incorrect interpretation of the navigation situation. The issues of equipment reliability, display parameters settings, competence and training of navigators are of critical importance. It is emphasised that radar image overlay on ECDIS is an effective tool for improving safety of navigation, but it does not replace traditional navigation practices and does not eliminate the need for critical evaluation of data. Over-reliance on technology, ignoring its limitations and disregard for the principles of good maritime practice can lead to erroneous decisions and have a negative impact on safety. Further prospects for the development of technology can be associated with the improvement of information processing algorithms, optimisation of human-machine interaction, and the development of training methods. The article emphasises the need for an integrated approach combining the improvement of technical systems and the human factor to fully realise the potential of the overlay function in ensuring safety of navigation. Only a balanced use of technological innovations and traditional navigational practices can guarantee the reliability and efficiency of modern navigation

The Assessment Model of Robotic Process Automation (RPA) Project Using Benefit Study and Balanced Scorecard (BSC) Approach

Assessing Robotic Process Automation (RPA) projects is challenging, especially for small and medium-sized enterprises (SMEs) with limited resources. The most common approach, cost-benefit analysis, measures the financial success of a project, but it does not capture all RPA benefits, which can lead to erroneous decisions about starting, continuing, delaying, or stopping an RPA project. This research presents a novel RPA project assessment model that combines a benefits approach with the balanced scorecard (BSC) framework. The model identifies RPA benefits from the scientific literature and classifies them into four BSC indicators: financial, business process improvement, customer satisfaction, and learning and development. SMEs can easily use the model by comparing expected and actual benefits to make quick and holistic decisions. The model's simplicity, ease of use, and ability to reveal both monetary and non-monetary RPA benefits make it a valuable tool for SMEs.

The application of artificial intellect in health care: prospects and challenges for science and clinical medicine

The paper analyses publications data related to issues of application of AI and machine learning systems in medical science and practice. The particular attention is paid to key points of AI application in health care: diagnostics, telemedicine, development of new medications, medical rehabilitation and management decision-making process. Despite broad perspectives of applying the given systems in clinical practice and pharmaceutical industry, there are a number of such unsolved problems as ensuring information security, risk of making erroneous decisions and necessity to change existing normative legal base of health care.

Effect of sampling locations on reliability of pile groups

Geotechnical site investigations represent an imperative prerequisite in the pursuit of reliable foundation designs. However, site investigations are frequently restricted to a limited number of locations owing to the constraints imposed by budget and time considerations, thereby potentially yielding a range of adverse consequences. Hence, the development of an efficient site investigation plan — one that optimally selects the number and location of tests, is important to gain adequate information for a given cost. This paper proposes a framework to identify optimal investigation locations by minimizing the probability of erroneous decisions (i.e., error types I and II). A pile group was adopted for the demonstration. The best locations corresponding to various numbers of tests were identified based on the derived probabilities of type I and II errors.

Strata-constrained Gaussian window weighted-constrained long short-term memory network for logging lithology prediction

Precisely identifying rock lithology from logging curves is critically important for reservoir characterization and exploration risk assessment. Although traditional knowledge-based lithology interpretation by a well-logging interpreter has achieved success, the interpreter-dominated lithology prediction process, in turn, could lead to a biased prediction or erroneous decision making. Deep neural network indicates the most advanced performance in various domains such as medical science, computer vision, or even geosciences. Therefore, a potential strata-constrained long short-term memory (LSTM) strategy is developed. By combining Gaussian windows to characterize the weighted stratigraphic sequence information on the target formation, rock lithology can be intelligently identified from the input logging curves. This weighted stratigraphic sequence constrain-based LSTM workflow can predict the rock lithology precisely, even for the thinner layers. Here, the F1 score and confusion matrix demonstrate that considering the rock strata sequence features, the predicted lithology by the Gaussian window weighted-constrained LSTM model and rectangular window weighted-constrained LSTM model have superior performance than those of the conventional LSTM model.

Operational Elements Influencing the Occurrence of Unfavorable Incidents in the Railway System.

The article delves into the intricacies of rail transport, addressing the repercussions of adverse events such as delays, incidents, and accidents within the railway system. It explores the impact of disruptions on the day-to-day responsibilities of railway employees, with a primary focus on Station managers. The study employs two distinct approaches: first, an experimental analysis involving traffic management personnel, where factors contributing to failures are examined through a questionnaire survey. Second, a comprehensive perspective is taken by leveraging expert knowledge, combined with Fault Tree Analysis and Event Tree Analysis methodologies. This combination is used to illustrate how alterations in the system, including erroneous decisions, can lead to subsequent undesirable events.

Accuracy and reproducibility of forensic tire examination decisions

Tire impression evidence can be a valuable tool during a crime scene investigation—it can link vehicles to scenes or secondary locations, and reveal information about the series of events surrounding a crime. The interpretation of tire impression evidence relies on the expertise of forensic tire examiners. To date, there have not been any studies published that empirically evaluate the accuracy and reproducibility of decisions made by tire impression examiners. This paper presents the results of a study in which 17 tire impression examiners and trainees conducted 238 comparisons on 77 distinct questioned impression-known tire comparison sets (QKsets). This study was conducted digitally and addressed examinations based solely upon the characteristics of the tire impression images provided. The quality and characteristics of the impressions were selected to be broadly representative of those encountered in casework. Participants reported their decisions using a multi-level conclusion scale: 68% of responses were class associations (Association of Class Characteristics or Limited Association of Class), 21% were definitive decisions (ID or Exclusion), 8% were probable decisions (High Degree of Association or Indications of Non-Association), and 3% were neutral responses (Not Suitable or Inconclusive). Although class associations were the most reported response type, when definitive decisions were reported, they were often correct: 96% of IDs and 89% of Exclusions were consistent with ground truth regarding the source of the known tire in the QKset. Overall, we observed 4 erroneous definitive decisions (3 Exclusions on mated QKsets; 1 ID on a nonmated QKset) and 1 incorrect probable decision (Indications of Non-Association on a mated QKset). Decision rates were notably associated with both quality (lower quality questioned impressions were more likely to result in class associations) and dimensionality (2D questioned impressions were more likely to result in definitive decisions), which were correlated factors. Although the study size limits the precision of the measured rates, the results of this study remain valuable to the forensic science and legal communities and provide empirical data regarding examiner performance for a discipline that previously did not have any such estimates.

Sensor attack detection based on active excitation response with uncertain delays

Cyber–physical systems face the threat of sensor attacks, which can result in incorrect measurements of the physical state for the controller and lead to erroneous decisions and control instructions. To address this issue, our paper introduces a sensor attack detection method involving the active injection of excitation into the system and a comparison of actual sensor data with the expected response. First, we address challenges posed by sensing noise and uncertain delays by compensating for them and presenting a probability interval derived from the convolution of noise and delay. Second, we create an anticipated response spectrum by adjusting the probability interval within a defined time window under specific active excitation. Third, the disparity between the expected response spectrum and the actual sensor data within the time window is utilized to calculate the probability of a sensor being attacked. Finally, we evaluate the effectiveness of our method through extensive simulations in a vehicle-platoon case study.