Correctness Criterion Research Articles

Application of machine learning algorithms for optimizing the trajectory of inclined wells

Machine learning, a subset of artificial intelligence, allows computers to learn and improve from data without explicit programming. Machine learning algorithms are categorized into supervised, unsupervised, and reinforcement learning, each serving different applications such as classification, clustering, and decision-making. In the oil and gas industry, machine learning is applied to drilling processes, reservoir characterization, and exploration. It improves efficiency in predicting reservoir properties, optimizing drilling parameters, and detecting anomalies. The methodology for analyzing well trajectory includes evaluating survey data with calculation methods like tangential, balanced tangential, average angle, radius of curvature, and minimum curvature. These methods help optimize wellbore paths. This study outlines control criteria essential for optimizing borehole trajectory management in oil and gas well drilling. The deviation correction criterion aims to maintain the borehole path within a designated trajectory, minimizing deviation from the planned design profile. Optimal control conditions are defined through mathematical criteria involving radius vectors and design trajectory alignment. The control framework incorporates efficiency measures, calculating the trajectory's costeffectiveness and operational constraints. Machine learning algorithms facilitate these control strategies, focusing particularly on zenith angle correction for trajectory stabilization. These methods provide adaptable options for deflector angle and correction length, ensuring alignment with target intervals. The approach enhances trajectory accuracy, minimizes costs, and complies with technical, geological, and technological constraints inherent to drilling. Software incorporating machine learning and these methods was developed and tested, demonstrating improvements in analyzing survey data and optimizing well trajectory, contributing to more efficient drilling operations and reduced costs. Keywords: machine learning; mathematical models; control criteria; well trajectory optimization; survey data analysis; calculation methods.

Метод долгосрочного прогноза максимального уровня воды р. Исеть на основе глубокого обучения

Numerical analysis of using deep learning in the development of long-term method of maximum water level forecast for several gauging stations of the Iset (Kataysk, Shadrinsk, Mekhonskoe) has been carried out. The possibilities of implementing two architectures of neural network model within the framework of using the same set of initial hydrometeorological observation data have been analysed in detail. It is shown that the transition to the new N-HiTS architecture allows increasing the correctness of the forecast on the validation sample in comparison with the previously used TFT architecture. Using cross-validation we obtained estimates of the classical statistical criterion of correctness of the developed method confirming the possibility of its use in operational practice for all three analysed gauging stations. Within the framework of the developed forecasting method the analysis of groundwater level observation data at groundwater wells of the Rosnedra system was carried out. It is shown that in a number of cases using such observation data allows significantly improve the correctness of forecast.

Evaluating ChatGPT’s competency in radiation oncology: A comprehensive assessment across clinical scenarios

PurposeArtificial intelligence (AI) and machine learning present an opportunity to enhance clinical decision-making in radiation oncology. This study aims to evaluate the competency of ChatGPT, an AI language model, in interpreting clinical scenarios and assessing its oncology knowledge. Methods and MaterialsA series of clinical cases were designed covering 12 disease sites. Questions were grouped into domains: epidemiology, staging and workup, clinical management, treatment planning, cancer biology, physics, and surveillance. Royal College-certified radiation oncologists (ROs) reviewed cases and provided solutions. ROs scored responses on 3 criteria: conciseness (focused answers), completeness (addressing all aspects of the question), and correctness (answer aligns with expert opinion) using a standardized rubric. Scores ranged from 0 to 5 for each criterion for a total possible score of 15. ResultsAcross 12 cases, 182 questions were answered with a total AI score of 2317/2730 (84 %). Scores by criteria were: completeness (79 %, range: 70–99 %), conciseness (92 %, range: 83–99 %), and correctness (81 %, range: 72–92 %). AI performed best in the domains of epidemiology (93 %) and cancer biology (93 %) and reasonably in staging and workup (89 %), physics (86 %) and surveillance (82 %). Weaker domains included treatment planning (78 %) and clinical management (81 %). Statistical differences were driven by variations in the completeness (p < 0.01) and correctness (p = 0.04) criteria, whereas conciseness scored universally high (p = 0.91). These trends were consistent across disease sites. ConclusionsChatGPT showed potential as a tool in radiation oncology, demonstrating a high degree of accuracy in several oncologic domains. However, this study highlights limitations with incorrect and incomplete answers in complex cases.

Gaps in knowledge regarding the diagnostic criteria and management of PCOS in Germany: An anonymous web-based survey

IntroductionPolycystic Ovary Syndrome (PCOS) is a multifactorial condition that can include a myriad of symptom complexes. A study in North America demonstrated that a significant percentage of physicians were unaware of crucial aspects of PCOS. This study aimed to examine the level of knowledge about PCOS among physicians in Germany. MethodsAn anonymous cross-sectional online questionnaire was distributed to all gynecological clinics and fertility centers in Germany. The responsible gynecologists in service were contacted and they were asked to distribute the questionnaire among the employed physicians in their clinic. Results and discussionThe questionnaire was completed 206 times. 92 (65.7 %) of all respondents were board-certified gynecologists without specialty training in reproductive medicine and gynecologic endocrinology (Non-RMGE), 48 (34.3 %) had completed this training (RMGE). RMGE were more likely to know the correct criteria for the diagnosis of PCOS (97.9 % vs. 51.3 %; p < 0.001) and were able to name a higher number of correct symptoms that may be associated with PCOS (20.8 vs. 14.0; p < 0.001) than Non-RMGE (B = 4.530, CI 1.379–7.680; p = 0.006). The preferred general treatments and fertility treatments for PCOS patients also differed significantly between the two groups (p = 0.002, p < 0.001). The participants were asked how doctors and healthcare professionals could be best supported to improve the care of PCOS patients. Over 58.3 % of participants considered the creation of a PCOS website dedicated to healthcare professionals to be valuable. ConclusionThis study is the first to identify knowledge gaps about PCOS among physicians in Germany. The findings also highlight the potential disparities in PCOS knowledge between hospital and fertility center settings, emphasizing the need for improved training to ensure consistent and high-quality care for PCOS patients. The participants preferred a dedicated website for health professionals, indicating a demand for easily accessible information and training resources.

Compositionality and Observational Refinement for Linearizability with Crashes

Crash-safety is an important property of real systems, as the main functionality of some systems is resilience to crashes. Toward a compositional verification approach for crash-safety under full-system crashes, one observes that crashes propagate instantaneously to all components across all levels of abstraction, even to unspecified components, hindering compositionality. Furthermore, in the presence of concurrency, a correctness criterion that addresses both crashes and concurrency proves necessary. For this, several adaptations of linearizability have been suggested, each featuring different trade-offs between complexity and expressiveness. The recently proposed compositional linearizability framework shows that to achieve compositionality with linearizability, both a locality and observational refinement property are necessary. Despite that, no linearizability criterion with crashes has been proven to support an observational refinement property. In this paper, we define a compositional model of concurrent computation with full-system crashes. We use this model to develop a compositional theory of linearizability with crashes, which reveals a criterion, crash-aware linearizability , as its inherent notion of linearizability and supports both locality and observational refinement. We then show that strict linearizability and durable linearizability factor through crash-aware linearizability as two different ways of translating between concurrent computation with and without crashes, enabling simple proofs of locality and observational refinement for a generalization of these two criteria. Then, we show how the theory can be connected with a program logic for durable and crash-aware linearizability, which gives the first program logic that verifies a form of linearizability with crashes. We showcase the advantages of compositionality by verifying a library facilitating programming persistent data structures and a fragment of a transactional interface for a file system.

Correct Criterion of Crustal Failure Driven by Intense Magnetic Stress in Neutron Stars

Magnetar outbursts are powered by an intense magnetic field. The phenomenon has recently drawn significant attention because of a connection to some fast radio bursts that has been reported. Understanding magnetar outbursts may provide the key to mysterious transient events. The elastic deformation of the solid crust due to magnetic field evolution accumulates over a secular timescale. Eventually, the crust fractures or responds plastically beyond a particular threshold. Determination of the critical limit is required to obtain the shear strain tensor in response to magnetic stress. In some studies, the tensor was substituted with an approximate expression determined algebraically from the magnetic stress. This study evaluated the validity of the approximation by comparing it with the strain tensor obtained through appropriate calculations. The differential equations for the elastic deformation driven by the magnetic field were solved. The results indicated that the approximation did not represent the correct strain tensor value, in both magnitude and spatial profile. Previous evolutionary calculations based on spurious criteria are likely to overestimate the magnitude of the strain tensor, and crustal failure occurs on a shorter timescale. Therefore, revisiting evolutionary calculations using the correct approach is necessary. This study is essential for developing the dynamics of crustal fractures and the magnetic field evolution in a magnetar.

Analysis of the Clinical Value of Laparoscopic Sacrocolpopexy to Support the Posterior Compartment in Women with Multicompartment Prolapse Including Rectocele.

Background/Objectives: Laparoscopic sacrocolpopexy is regarded as the gold standard treatment for apical or multicompartment prolapse, predominantly with anterior compartment descent. However, the optimal surgical approach for concurrent rectocele is still debated. The aim of this study was to evaluate the effectiveness of nerve-sparing laparoscopic sacrocolpopexy in managing multicompartment prolapse with concurrent rectocele (≥stage II), analyzing the anatomical outcomes, the necessity for concomitant or subsequent posterior repair, and the impact on bowel function in women undergoing surgery. Methods: Data from all women who underwent laparoscopic sacrocolpopexy with or without posterior repair between 01/2017 and 07/2022 for symptomatic multicompartment prolapse, including apical and posterior compartment descent ≥ stage II, were retrospectively evaluated. All women underwent a standardized urogynecological examination, including assessment of genital prolapse using the POP-Q quantification system, and completed the German-validated Australian Pelvic Floor Questionnaire before and after surgery (6-12 weeks). Preoperative anatomic support and bowel symptoms were compared with postoperative values. Results: In total, 112 women met the criteria for surgical correction. The majority (87%) had stage II posterior descent, with only 10% undergoing concurrent posterior repair during laparoscopic sacrocolpopexy. Significant (p < 0.001) objective improvement was seen for all compartments post- compared with preoperatively (Ba: 0 (-1/2) vs. -3 (-3/-2), C: -1 (-2/0) vs. -8 (-12/-7), Bp: 0 (-1/0) vs. -3 (-2/-2); (median (25%/75% quartiles)). Subsequent surgery for persistent rectocele and/or stool outlet symptoms was required in 4% of cases. Most bowel-specific questions in the German-validated Australian Pelvic Floor Questionnaire showed significant improvement (p < 0.001). Conclusions: Nerve-sparing sacrocolpopexy alone appears to be a suitable surgical approach to correct multicompartment prolapse, including a rectocele ≥ stage II, and results in a reduction of objective signs and symptoms of pelvic organ prolapse.

Optimizing Outcomes in Distal Tibial Deformity Correction: The Role of Supramalleolar Osteotomy with Computer-assisted Hexapod External Fixator.

Distal tibial deformities can significantly impact patients if left uncorrected, often leading to pain, alterations in gait, and the eventual development of post-traumatic arthritis. The criteria for surgical correction in these patients continues to be a subject of debate, while supramalleolar osteotomy (SMO) is an effective method for correcting distal tibial deformities. The purpose of this study was to evaluate and compare the clinical results of SMO using internal fixation or using computer-assisted hexapod external fixator in the treatment of distal tibial deformity. A retrospective study was conducted on 290 patients who underwent SMO between June 2015 and January 2023. Forty-four patients met the inclusion and exclusion criteria. Among the participants, 19 underwent SMO combined with a computer-assisted hexapod external fixator, while 25 received SMO with plate and screw internal fixation. The tibial anterior surface (TAS) angle, tibial lateral surface (TLS) angle, the tibiotalar (TT) angle and the talocrural (TC) angle were assessed on weight-bearing X-ray films. Functional assessments were performed according to the American Orthopedic Foot and Ankle Society (AOFAS) ankle-hindfoot score. The study followed patients for an average duration of 31.7 ± 15.3 months, with a range from 12 to 67 months. Successful bone union was achieved in all cases. For patients treated with the computer-assisted hexapod external fixator, significant improvements were observed: the mean deviation in sagittal plane deformity parameters decreased from 14.3 ± 10.4 degrees preoperatively to 2.8 ± 3.8 degrees postoperatively (p < 0.05). Similarly, coronal plane deformity parameters showed a reduction from 25.9 ± 22.5 degrees preoperatively to 5.9 ± 11.0 degrees postoperatively (p < 0.05). The AOFAS ankle-hindfoot score improved markedly from 66.0 ± 14.9 to 86.1 ± 11.7 points (p < 0.05). For patients undergoing internal fixation, the absolute difference in coronal plane parameters improved from 15.4 ± 12.6 degrees preoperatively to 3.7 ± 3.4 degrees postoperatively (p < 0.05). A significant enhancement in AOFAS ankle-hindfoot score was also noted, increasing from 68.3 ± 14.3 points to 79.4 ± 13.5 points (p < 0.05). There were no significant differences in gender, side, follow-up time, postoperative deviation of deformity, pre- or postoperative AOFAS between the two groups. In conclusion, comprehensive preoperative planning of SMO combined with either internal fixation or a hexapod external fixator for treating distal tibial deformities can achieve satisfactory outcomes. The utilization of a computer-assisted hexapod external fixator facilitates a gradual and precise correction process, which proved to be an effective and relatively safe method.

Knowledge of Narcolepsy Among Physicians in Makkah Region, Saudi Arabia: A Cross-Sectional Study.

Background Narcolepsy is a chronic sleep disorder that is characterized by excessive daytime sleepiness and cataplexy. It has been increasingly diagnosed over the years, impacting productivity and employment rates. Awareness of healthcare providers is crucial for the early identification and management of this condition. Objectives This study assessed physicians' knowledge of narcolepsy in the Makkah region of Saudi Arabia. Method This cross-sectional study was conducted from February to November 2023. An online self-administered questionnaire has been used to target physicians working in the Makkah region of Saudi Arabia. The utilized questionnaire assessed demographic and professional data as well as the participants' knowledge of narcolepsy. Statistical analysis was performed using RStudio (R version 4.3.1.). Statistical differences in knowledge were assessed using Pearson's chi-squared and Fisher's exact tests. Factors associated with knowledge of narcolepsy were investigated through univariable and multivariable regression analyses expressed using beta coefficients and 95% confidence intervals (95% CIs). Statistical significance was considered at p < 0.05. Results A total of 226 physicians participated in this study. Male physicians (54.4%, n = 123), practicing in governmental hospitals (77.9%, n = 176) and residing in Makkah City (43.4%, n = 98) were predominant. Non-surgical specialties represented 73.5% (n = 166) of the sample. Around 81% (n = 184) of the participants were aware of narcolepsy, with a significant difference according to professional status (p = 0.045). The majority of physicians have correctly identified narcolepsy as a sleep disorder (78.3%, n = 177), but only 32.3% (n = 73) have identified its typical onset age group and recognized that there are two types of narcolepsy. Almost half of the respondents indicated a lack of knowledge about the diagnostic criteria for narcolepsy in the DSM-5 (52.2%, n = 118). Only 27.4% (n = 62) recognized the correct diagnostic criteria. Half of the sample (51.3%, n = 116) recognized the use of multiple sleep latency tests for the diagnosis. For factors associated with higher participants' knowledge, non-surgical specialties showed significantly higher knowledge scores compared to surgical specialties (beta = 0.91, 95% CI, 0.13 to 1.7, p = 0.024). Conclusion This study has revealed a significant lack of knowledge about narcolepsy among physicians inMakkah region. This raises concerns about the timely identification, proper understanding, and accurate diagnosis of patients with narcolepsy. Adequate understanding of narcolepsy is crucial to avoid its misdiagnosis or delays in receiving appropriate treatment and support, ultimately impacting their quality of life.

Role of oscillometry to diagnose obstructive airway diseases in patients who are unable to perform spirometry correctly

BackgroundObstructive airway diseases (OAD) are an important contributor to the burden of respiratory diseases in India. The current gold standard to diagnose OAD is spirometry. However, spirometry is a forceful manoeuvre and many patients are unable to perform it as per correct standard acceptable criteria. Therefore, patients are sometimes misdiagnosed or undiagnosed leading to a delay in treatment. Therefore, a study was conducted with an aim to assess the role of forced oscillometry in diagnosing obstructive disease. MethodsA cross -sectional study was carried out from Jun 2019-Jun 2021 at a tertiary care chest centre, where consecutively 102 patients who were unable to perform spirometry correctly or had a contraindication to it were evaluated by oscillometry for OAD. ResultsOut of 102 patients who could not perform spirometry, OAD was detected in 84.4 %. 42.2 % of patients were diagnosed with asthma and similarly 42.2 % of patients were diagnosed to have Chronic obstructive pulmonary disease (COPD). The Mean (R5) Rrs at 5 Hz of the study population were 6.69 cmH2O/L/s for asthma, 5.91 cmH2O/L/s for COPD, and 3.16 cmH2O/L/s in a healthy population respectively. Mean R19 for Asthma, COPD and healthy individuals were 4.45 cmH20/L/s, 3.92 cmH20/L/s and 2.547 cmH20/L/s respectively. (X5) Xrs at 5 Hz of the study population were -3.16 cmH2O/Ls in asthma, −3.33 cmH2O/L/s in COPD, and −0.975 cmH2O/L/s in healthy individuals, respectively. ConclusionOscillometry is a pulmonary function test which can be performed easily and it can help in diagnosing OAD.

Reshaping free-text radiology notes into structured reports with generative question answering transformers

BackgroundRadiology reports are typically written in a free-text format, making clinical information difficult to extract and use. Recently, the adoption of structured reporting (SR) has been recommended by various medical societies thanks to the advantages it offers, e.g. standardization, completeness, and information retrieval. We propose a pipeline to extract information from Italian free-text radiology reports that fits with the items of the reference SR registry proposed by a national society of interventional and medical radiology, focusing on CT staging of patients with lymphoma. MethodsOur work aims to leverage the potential of Natural Language Processing and Transformer-based models to deal with automatic SR registry filling. With the availability of 174 Italian radiology reports, we investigate a rule-free generative Question Answering approach based on the Italian-specific version of T5: IT5. To address information content discrepancies, we focus on the six most frequently filled items in the annotations made on the reports: three categorical (multichoice), one free-text (free-text), and two continuous numerical (factual). In the preprocessing phase, we encode also information that is not supposed to be entered. Two strategies (batch-truncation and ex-post combination) are implemented to comply with the IT5 context length limitations. Performance is evaluated in terms of strict accuracy, f1, and format accuracy, and compared with the widely used GPT-3.5 Large Language Model. Unlike multichoice and factual, free-text answers do not have 1-to-1 correspondence with their reference annotations. For this reason, we collect human-expert feedback on the similarity between medical annotations and generated free-text answers, using a 5-point Likert scale questionnaire (evaluating the criteria of correctness and completeness). ResultsThe combination of fine-tuning and batch splitting allows IT5 ex-post combination to achieve notable results in terms of information extraction of different types of structured data, performing on par with GPT-3.5. Human-based assessment scores of free-text answers show a high correlation with the AI performance metrics f1 (Spearman's correlation coefficients>0.5, p-values<0.001) for both IT5 ex-post combination and GPT-3.5. The latter is better at generating plausible human-like statements, even if it systematically provides answers even when they are not supposed to be given. ConclusionsIn our experimental setting, a fine-tuned Transformer-based model with a modest number of parameters (i.e., IT5, 220 M) performs well as a clinical information extraction system for automatic SR registry filling task. It can extract information from more than one place in the report, elaborating it in a manner that complies with the response specifications provided by the SR registry (for multichoice and factual items), or that closely approximates the work of a human-expert (free-text items); with the ability to discern when an answer is supposed to be given or not to a user query.

Point of Care Ultrasounds Obtained by Novice Physician Assistant Residents (POCUS ON PAR).

The integration of Point of Care Ultrasound (POCUS) into the care of trauma patients, specifically the E-FAST, has improved the accuracy of initial diagnoses and improved time to surgical intervention in critically ill patients. Physician assistants (PAs) are critically important members of any military trauma resuscitation team and are often team leaders in a pre-hospital setting. They may receive training in ultrasound but there are little data to support their use or evaluate their effectiveness in using POCUS. We designed a study to evaluate the image quality of an E-FAST Exam performed by Emergency Medicine Physician Assistant (EMPA) Fellows and Emergency Medicine (EM) Interns following identical training. Our hypothesis is that image quality obtained by EMPAs will be non-inferior to those images obtained by EM Interns. This is a prospective single-blinded study comparing the image quality of E-FAST exams performed by first year EM interns and first year EMPA fellows. All participants completed standard POCUS training prior to enrollment in the study. A total of 8 EMPAs and 8 EM first year residents completed 10 recorded E-FAST exams to be used as study images. Participants also viewed a 15-question slide show containing images of positive (6) and negative (9) E-FAST exams and recorded their interpretations. Images were reviewed by expert reviewers who were blinded to which images were collected by which group. An image quality score was recorded for each view as well as an overall image quality score. Image quality was rated on a 1 to 5 image quality scale. For overall image quality, the mean score for EMPAs was 3.6 ± 0.5 and for EM residents was 3.2 ± 0.5 with statistical significance favoring better image quality from the EMPAs. The time to completion for the EFAST exam for EMPAs was 4.8 ± 1.3 minutes and for interns it was 3.4 ± 1.4 minutes (P value = 0.02). There was no difference in image interpretation quiz scores between the groups (mean score 92% among interns and 95% among PAs). POCUS is an imaging modality which is very portable and relatively inexpensive which makes it ideal for military medicine. PAs are essential members of military trauma teams, and often run an initial trauma resuscitation. Being able to correctly identify patients who have free fluid early in the course of treatment allows for more correct evacuation criteria to ensure the sickest patients get to care the fastest. Although there are limited data to support POCUS use by non-physicians, our data support a growing body of evidence that it is not the profession or baseline medical education that determines an individual's ability to use and incorporate ultrasound into bedside and clinical practice. Our study shows that with training and experience PAs or other members of the military health care team can use the EFAST to better care for trauma patients.

A narrative review of the phenomenon of predatory journals to create awareness among researchers in veterinary medicine.

In recent years, especially since the COVID-19 pandemic, the number of predatory journals has increased significantly. Predatory journals exploit the "open-access model" by engaging in deceptive practices such as charging high publication fees without providing the expected quality and performing insufficient or no peer review. Such behaviors undermine the integrity of scientific research and can result in researchers having trouble identifying reputable publication opportunities, particularly early-career researchers who struggle to understand and establish the correct criteria for publication in reputable journals. Publishing in journals that do not fully cover the criteria for scientific publication is also an ethical issue. This review aimed to describe the characteristics of predatory journals, differentiate between reliable and predatory journals, investigate the reasons that lead researchers to publish in predatory journals, evaluate the negative impact of predatory publications on the scientific community, and explore future perspectives. The authors also provide some considerations for researchers (particularly early-career researchers) when selecting journals for publication, explaining the role of metrics, databases, and artificial intelligence in manuscript preparation, with a specific focus on and relevance to publication in veterinary medicine.

A novel flexible exponent power-X family of distributions with applications to COVID-19 mortality rate in Mexico and Canada.

This paper aims to introduce a novel family of probability distributions by the well-known method of the T-X family of distributions. The proposed family is called a "Novel Generalized Exponent Power X Family" of distributions. A three-parameters special sub-model of the proposed method is derived and named a "Novel Generalized Exponent Power Weibull" distribution (NGEP-Wei for short). For the proposed family, some statistical properties are derived including the hazard rate function, moments, moment generating function, order statistics, residual life, and reverse residual life. The well-known method of estimation, the maximum likelihood estimation method is used for estimating the model parameters. Besides, a comprehensive Monte Carlo simulation study is conducted to assess the efficacy of this estimation method. Finally, the model selection criterion such as Akaike information criterion (AINC), the correct information criterion (CINC), the Bayesian information criterion (BINC), the Hannan-Quinn information criterion (HQINC), the Cramer-von-Misses (CRMI), and the ANDA (Anderson-Darling) are used for comparison purpose. The comparison of the NGEP-Wei with other rival distributions is made by Two COVID-19 data sets. In terms of performance, we show that the proposed method outperforms the other competing methods included in this study.

A Hybrid Approach Based on Consensus Decision Making for Green Supplier Selection in Automotive Industry

With increased global commerce, businesses must manage their supply chains while taking into account not only costs but also environmental implications. The decision-making of Green Supplier Selection (GSS) is a strategic priority for companies to survive in challenging market conditions and to effectively and sustainably manage their supply chains in the increasingly polluted and resource-depleted world. Environmental sustainability can be enhanced with the appropriate criteria when choosing green suppliers. Based on these motivations, it is necessary to determine the correct criteria, classify the chosen criteria and employ an effective evaluation method in the GSS process. In particular, evaluating each criterion at its own level is of strategic importance. In this paper, the GSS model, handled by group decision-making, is constructed with multi-sub-criteria to increase the competitive advantage of businesses in challenging market conditions for the purpose of ensuring a sustainable future. A novel hybrid methodology of the Interval Type-2 Fuzzy (IT2F) Analytical Hierarchy Process (AHP) and IT2F Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is presented for the GSS model to deal with uncertainty. This study provides decision-makers with an effective method that performs fuzzy calculations at all steps until a solution is found, especially in areas that may have a complex hierarchical structure, such as the automotive industry. In the proposed method, unlike most studies in the literature, if a criterion has sub-criteria (or multi-sub-criteria) in the hierarchy considered, each criterion is evaluated with other criteria at its own level, without the need for all other criteria to have sub-criteria (or multi-sub-criteria). The effectiveness of the proposed method has been demonstrated by testing it with an application taken from the automotive industry with a complex-structured multi-level hierarchy. Additionally, sensitivity analysis has been conducted to assess the impact of changes in subjective input by means of scenarios.

ANALYSIS OF NATURAL LANGUAGE TEXTS IN THE TASK OF ASSESSING REQUIREMENTS FOR THE DESIGN OF AIRCRAFT AVIONICS

The problem of automatic assessment of technical requirements is analyzed. One of the aspects considered is the rationale for the preparation of specific data that should be used in the training of a “transformer” neural network. For this purpose, we compare the software requirements with the design requirements. The criteria for the requirement correctness check are provided, the cases of violation of these criteria are shown. The method that allows to classify requirements according to their compliance and non-compliance with the rules is proposed. Such state-of-the-art transformer architectures as RuBERT, RuBERT-tiny, RuRoBERTa-large, RuDeBERTa, RuDeBERTa-distill are considered. The small dataset of software requirements containing both correct and incorrect examples for the “indivisibility”, “completeness”, “unambiguity” and “correctness” rules to conduct experiments was used. The F1-score to evaluate the algorithms is calculated. Experimental results show that the RuBERT-tiny model demonstrates optimal performance on this small dataset in terms of minimum inference time and the highest value of the F1-score for most rules. High values of the F1-score for the other models were also obtained. It can be concluded that the considered neural network architectures can be used for the tasks of automatic assessment and classification of technical requirements. The use of a “transformer” neural network can potentially improve the quality of preliminary analysis and reduce the time spent on verifying their correctness in the software development process.

Feature Extraction Based on Local Histogram with Unequal Bins and a Recurrent Neural Network for the Diagnosis of Kidney Diseases from CT Images.

Kidney disease remains one of the most common ailments worldwide, with cancer being one of its most common forms. Early diagnosis can significantly increase the good prognosis for the patient. The development of an artificial intelligence-based system to assist in kidney cancer diagnosis is crucial because kidney illness is a global health concern, and there are limited nephrologists qualified to evaluate kidney cancer. Diagnosing and categorising different forms of renal failure presents the biggest treatment hurdle for kidney cancer. Thus, this article presents a novel method for detecting and classifying kidney cancer subgroups in Computed Tomography (CT) images based on an asymmetric local statistical pixel distribution. In the first step, the input image is non-overlapping windowed, and a statistical distribution of its pixels in each cancer type is built. Then, the method builds the asymmetric statistical distribution of the image's gradient pixels. Finally, the cancer type is identified by applying the two built statistical distributions to a Deep Neural Network (DNN). The proposed method was evaluated using a dataset collected and authorised by the Dhaka Central International Medical Hospital in Bangladesh, which includes 12,446 CT images of the whole abdomen and urogram, acquired with and without contrast. Based on the results, it is possible to confirm that the proposed method outperformed state-of-the-art methods in terms of the usual correctness criteria. The accuracy of the proposed method for all kidney cancer subtypes presented in the dataset was 99.89%, which is promising.

Incorrect arguments of scientific discourse in pedagogical texts, or samples for non-imitation

Introduction. Modern humanities are moving towards compliance with the requirement of evidence and comprehensive validity of their conclusions, the adequacy of the methods used and re­search strategies to the results obtained. This problem can be briefly summarised as a movement towards argumentative correctness. In the sciences of education, this requirement plays an extremely important role, since it allows one to find tools for the implementation of practical solutions in various segments of education and upbringing. However, the real state of pedagogical textual discourse demonstrates a lot of serious problems. Despite the types and regulations of empirical, theoretical and contextual substan­tiation long established in classical logic, the authors of scientific pedagogical texts often violate the framework of a meaningful and logically correct discourse. The arguments put forward are often unable to either confirm or refute the conclusions of the authors; sometimes they require serious additions and corrections. This seriously undermines the degree of trust of the reading community (both theorists and practitioners) in the conclusions proposed by the authors, reduces the public authority of pedagogical science and undermines faith in its ability to constructively influence educational practice. Therefore, the need for methodological “intervention” in the argumentative field of scientific pedagogy can be con­sidered quite conditioned. Such a research initiative could be the first step on the trajectory of the return of pedagogy to meet the criterion of methodological correctness. This conclusion is supported by the studies of European philosophers of education, which, together with the authors’ analysis, determines the relevance of the chosen topic of the article.Aim. The present research aims to identify and justify the typology of incorrect arguments in ped­agogical texts; and to display the wide spread of incorrect arguments as a field for promising research activities.Results. The results of the study are a typology of incorrect arguments, including: a) arguments that are untenable and inadequate to the defended theses; b) arguments that require serious concretisation and detail; c) structurally complex arguments, which are partially adequate, but, otherwise, they require a radical revision. The typology is added by not being placed on a par with the above three types of argu­ments, denoted by the cliché “complex argumentation palette”.Methodology and research methods. The research methods were: analysis of the Russian and foreign texts containing incorrect arguments; a selection of sources containing the most widespread argumen­tation errors; inductive generalisation to construct and substantiate the typology of incorrect arguments in pedagogy; reflection of the result obtained and identification of fragments of the designated topic that are relevant for prospective research.Scientific novelty. The scientific novelty lies in the transfer of the research focus from the traditional designation by pedagogy methodologists of the criteria for the correctness of pedagogical discourse arguments to the identification of a typology of methodologically incorrect arguments, including three adjacent elements, supplemented by an element in which the three above are intertwined in a complex way. The totality of the results obtained can be displayed as a “constructive prohibition” criterion, illustrating to the scientific community ways to justify the results that must be avoided.Practical significance. The practical significance of the results lies in the specific guidelines for incor­rect arguments of scientific discourse, which can be metaphorically clichéd as an “example for non-imi­tation” and, on this basis, increase the degree of adequacy of the arguments to the thesis being defended in one’s own texts.

Correctness Notions for Petri Nets with Identifiers

A model of an information system describes its processes and how resources are involved in these processes to manipulate data objects. This paper presents an extension to the Petri nets formalism suitable for describing information systems in which states refer to object instances of predefined types and resources are identified as instances of special object types. Several correctness criteria for resource- and object-aware information systems models are proposed, supplemented with discussions on their decidability for interesting classes of systems. These new correctness criteria can be seen as generalizations of the classical soundness property of workflow models concerned with process control flow correctness.

ITERATIVE ADAPTATION ALGORITHMS IN MULTICRITERIA TASKS

This article examines using of iterative adaptation algorithms to solve the problem of determining measurement location of the carotid artery intima-media complex. The formulation of a multi-criteria decision-making problem, as the basis for determining correct criterion for proper selection and successful recognizing of the required object in an ultrasound image. The work discusses principles of constructing cascade classifiers as well as, the use of the cascade Haar classifier and the cascade LBP classifier, for which Haar primitives and local binary templates are used as a basis. The results of experimental studies in order to determine effectivity of different boosting algorithms to solve this problem are presented. The best results were shown by the Haar cascade classifier, developed using an iterative adaptation algorithm, which manages solving a multicriteria problem on a given training set more successfully and determines the most suitable areas for measuring the thickness of the common carotid artery intimate media complex.