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To meet the needs of automated medical analysis of brain tumor magnetic resonance imaging, this study introduces an enhanced instance segmentation method built upon mask region-based convolutional neural network. By incorporating squeeze-and-excitation networks, a channel attention mechanism, and concatenated attention neural network, a spatial attention mechanism, the model can more adeptly focus on the critical regions and finer details of brain tumors. Residual network-50 combined attention module and feature pyramid network as the backbone network to effectively capture multi-scale characteristics of brain tumors. At the same time, the region proposal network and region of interest align technology were used to ensure that the segmentation area matched the actual tumor morphology. The originality of the research lies in the deep residual network that combines attention mechanism with feature pyramid network to replace the backbone based on mask region convolutional neural network, achieving an improvement in the efficiency of brain tumor feature extraction. After a series of experiments, the precision of the model is 90.72%, which is 0.76% higher than that of the original model. Recall was 91.68%, an increase of 0.95%; Mean Intersection over Union was 94.56%, an increase of 1.39%. This method achieves precise segmentation of brain tumor magnetic resonance imaging, and doctors can easily and accurately locate the tumor area through the segmentation results, thereby quickly measuring the diameter, area, and other information of the tumor, providing doctors with more comprehensive diagnostic information. [ABSTRACT FROM AUTHOR]

Abstract To meet the needs of automated medical analysis of brain tumor magnetic resonance imaging, this study introduces an enhanced instance segmentation method built upon mask region-based convolutional neural network. By incorporating squeeze-and-excitation networks, a channel attention mechanism, and concatenated attention neural network, a spatial attention mechanism, the model can more adeptly focus on the critical regions and finer details of brain tumors. Residual network-50 combined attention module and feature pyramid network as the backbone network to effectively capture multi-scale characteristics of brain tumors. At the same time, the region proposal network and region of interest align technology were used to ensure that the segmentation area matched the actual tumor morphology. The originality of the research lies in the deep residual network that combines attention mechanism with feature pyramid network to replace the backbone based on mask region convolutional neural network, achieving an improvement in the efficiency of brain tumor feature extraction. After a series of experiments, the precision of the model is 90.72%, which is 0.76% higher than that of the original model. Recall was 91.68%, an increase of 0.95%; Mean Intersection over Union was 94.56%, an increase of 1.39%. This method achieves precise segmentation of brain tumor magnetic resonance imaging, and doctors can easily and accurately locate the tumor area through the segmentation results, thereby quickly measuring the diameter, area, and other information of the tumor, providing doctors with more comprehensive diagnostic information.

Heterogeneity has been one of the main barriers to understanding and treatment of autism spectrum disorder (ASD). Previous studies have identified several subtypes of ASD through unsupervised clustering analysis. However, most of them primarily depicted the pairwise similarity between individuals through second-order relationships, relying solely on patient data for their calculation. This leads to an underestimation of the complexity inherent in inter-individual relationships and the diagnostic information provided by typical development (TD). To address this, we utilized an elastic net model to construct an individual deviation-based hypergraph (ID-Hypergraph) based on functional MRI data. We then conducted a novel community detection clustering algorithm to the ID-Hypergraph, with the aim of identifying subtypes of ASD. By applying this framework to the Autism Brain Imaging Data Exchange repository data (discovery: 147/125, ASD/TD; replication: 134/132, ASD/TD), we identified four reproducible ASD subtypes with roughly similar patterns of ALFF between the discovery and replication datasets. Moreover, these subtypes significantly varied in communication domains. In addition, we achieved over 80% accuracy for the classification between these subtypes. Taken together, our study demonstrated the effectiveness of identifying subtypes of ASD through the ID-hypergraph, highlighting its potential in elucidating the heterogeneity of ASD and diagnosing ASD subtypes. [ABSTRACT FROM AUTHOR]

This article discusses the issue of diagnosing low-power solar power plants using the five-valued (5VL) state evaluation {4, 3, 2, 1, 0}. We address in depth how the 5VL diagnostics built upon 2VL, 3VL, and 4VL—two-valued diagnostics, three-valued logistics, and four-valued diagnostics. Logic (5VL) assigns five state values to the range of signal value changes, and these states are completely operational ({4}), incomplete ({3}), critical efficiency ({2}), and pre-fault efficiency ({1}). For the identical ranges of diagnostic signal values, all three of the applied state valence logics interpret failure as changes outside of their permitted ranges. Diagnostic procedures made use of an AI-based DIAG 2 system. This article's goal is to provide a comprehensive overview of the DIAG 2 intelligent diagnostic system, including its architecture, algorithm, and inference rules. Diagnosis with the DIAG 2 system is based on a well-established technique for comparing diagnostic signal vectors with reference signal vectors. A differential vector metric is born out of this examination of vectors. The input cells of the neural network implement the challenge of signal analysis and comparison. It is then possible to classify the object components' states in the neural network's output cells. Based on the condition of the object's constituent parts, this approach can signal whether those parts are working, broken, or urgently require replacement. [ABSTRACT FROM AUTHOR]

Modern means of information exchange are mostly built as systems with. The process of functioning of such systems consists in the interaction of software and hardware components. The architecture of such systems corresponds to the Open System Interconnection Basic Reference Model (OSI), which defines certain levels in networks, gives them standard names and indicates what functions each level should perform. Violations of the functioning of such systems can be caused by random failures and defects of the hardware component, errors and failures of the software, or as a result of external influence. The complexity of assessing the technical condition (technical diagnosis) is due to the significant territorial diversity of the system elements, the variety of functional nodes and specifications of the equipment, as well as the use of dual-purpose equipment in the systems. Given the complexity of the selected object, the task of automating control is entrusted to computer-measurement systems. Analysis of the architecture of the selected object of control (availability of formalized - standardized levels) allows to offer solutions to technical diagnostics tasks (or physical integrity control tasks) through step-by-step (step-by-step) testing of individual levels of hardware and technical means. The presented verification technique is based on organizational measures (availability of daily monitoring of functioning) and the use of standardized sets of protocols of each level of the OSI model. At the same time, at each stage of the check, two components of the diagnostic parameter (time and energy) are registered. The availability of mathematical models of the aging processes of the hardware part and the physical data transmission environment made it possible to obtain reference values for the diagnostic parameter at any time of the operation of the control object, or to determine the change of the main parameters when modeling the behavior of the system for a certain time. The set of numerical values of both components of the diagnostic parameter allows to determine the technical condition of not only the hardware part of the control object and the correct functioning of the software part at different levels of the OSI model, but also the presence (or absence) of unauthorized influence (software or hardware) in the system. [ABSTRACT FROM AUTHOR]

The article presents a continuation of research carried out to determine the effect of input parameters (changes in engine structure parameters) on selected output parameters (diagnostic measures), based on quickly changing exhaust gas temperature. A method of determining the simultaneous influence of two input factors (the structure parameter and the engine load) on one output factor was presented, as well as an evaluation of which of the analysed input factors has a stronger influence on the output parameter. The article presents the stages of the experimental research conducted and statistical inference based on the results. Three changing parameters for the structure were reviewed: the active cross-sectional area of the inlet air channel, the injector opening pressure and the compression ratio. Based on the quickly changing temperatures of the exhaust gases, three diagnostic measures were defined and subjected to statistical tests. The following data were averaged over one cycle for a 4-stroke engine operation: the intensity of changes, the specific enthalpy and the peak-to-peak value of the exhaust gas temperature. The results of the two-factor analysis are presented. Conclusions on the analysis are given and a criterion for the selection of a diagnostic measure, depending on the analysed parameter of the structural design of the diesel engine, is proposed.

Heterogeneity has been one of the main barriers to understanding and treatment of autism spectrum disorder (ASD). Previous studies have identified several subtypes of ASD through unsupervised clustering analysis. However, most of them primarily depicted the pairwise similarity between individuals through second-order relationships, relying solely on patient data for their calculation. This leads to an underestimation of the complexity inherent in inter-individual relationships and the diagnostic information provided by typical development (TD). To address this, we utilized an elastic net model to construct an individual deviation-based hypergraph (ID-Hypergraph) based on functional MRI data. We then conducted a novel community detection clustering algorithm to the ID-Hypergraph, with the aim of identifying subtypes of ASD. By applying this framework to the Autism Brain Imaging Data Exchange repository data (discovery: 147/125, ASD/TD; replication: 134/132, ASD/TD), we identified four reproducible ASD subtypes with roughly similar patterns of ALFF between the discovery and replication datasets. Moreover, these subtypes significantly varied in communication domains. In addition, we achieved over 80% accuracy for the classification between these subtypes. Taken together, our study demonstrated the effectiveness of identifying subtypes of ASD through the ID-hypergraph, highlighting its potential in elucidating the heterogeneity of ASD and diagnosing ASD subtypes.

Increasing the efficiency and reliability of modern railway transport is accompanied by an increase in monitoring and diagnostic systems for the current state of electric drives. Modern railway transport contains a large number of induction motors to ensure the operation of the drives of various mechanisms. In the article, based on the operational statistics of engine failures and the proposed scheme for diagnosing them, studies were carried out and a model was developed for assessing the reliability of a transport system equipped with an on-board diagnostic system for the current state. When building the models, the Markov method was used, including the construction of graphs for the five most relevant states of the induction electric motor during operation. The results obtained are relevant for evaluating the effectiveness of using the built-in diagnostic system and scheduling routine maintenance, which will affect the efficiency of railway transport. Based on the process of the diagnosis of railway transport systems with induction motors, five operating states of the object studied were interpreted: the state of full operation, state "S0"; the state of incomplete serviceability, state "S1"; critical serviceability, state "S2"; the state of the pre-damage condition, state "S3"; the state of unserviceability (defect), state "S4". Subsequently, a five-state model of the operation process of railway transport systems with induction motors was developed. This model is also described by equations of state: Kolmogorov–Chapman equations. The reliability quantities determined form the basis for simulation reliability studies. The effect of the simulation study is the reliability quantities determined in the form of reliability functions and probabilities of the occurrences of the operating states of railway transport systems with induction motors; an important part of the reliability study of the system examined is to estimate the times of the occurrences in the object studied of the operating states in the future. [ABSTRACT FROM AUTHOR]

Diagrammatic formats are useful for summarizing the processes of evaluation and comparison of forecasts in plant pathology and other disciplines where decisions about interventions for the purpose of disease management are often based on a proxy risk variable. We describe a new diagrammatic format for disease forecasts with two categories of actual status and two categories of forecast. The format displays relative entropies, functions of the predictive values that characterize expected information provided by disease forecasts. The new format arises from a consideration of earlier formats with underlying information properties that were previously unexploited. The new diagrammatic format requires no additional data for calculation beyond those used for the calculation of a receiver operating characteristic (ROC) curve. While an ROC curve characterizes a forecast in terms of sensitivity and specificity, the new format described here characterizes a forecast in terms of relative entropies based on predictive values. Thus it is complementary to ROC methodology in its application to the evaluation and comparison of forecasts.

In this paper, attention was paid to the problem of low controllability of marine medium- and high-speed engines during operation, which significantly limits the parametric diagnosis. The measurement of quickly changing temperature of engine exhaust gas was proposed, the courses of which can be a source of diagnostic information. The F statistic of the Fisher-Snedecor distribution was chosen as a statistical tool. Laboratory tests were carried out on the bench of a Farymann Diesel engine. The tests consisted of introducing the real changes in the constructional structure of the considered functional systems of the engine. Three changed parameters for the structure were reviewed: the active cross-sectional area of the inlet air channel, injector opening pressure and compression ratio. Based on the recorded plots of the quick-changing temperatures of the exhaust gases, three diagnostic measures were defined and subjected to statistical tests. The following data were averaged over one cycle for a 4-stroke piston engine operation, (1) the peak-to-peak value of the exhaust gas temperature, (2) the specific enthalpy of the exhaust gas, and (3) the rate of increase and decrease in the values for the quick-changing exhaust gas temperature.

The article deals with simulation tests on the reliability of the equipment of the wind farm WF in the operation process. The improvement, modernization, and introduction of new solutions that change the reliability, as well as the quality and conditions of use and operation of wind farm equipment, require testing. Based on these tests, it is possible to continuously evaluate the reliability of the equipment of WF. The issue of reliability assessment of wind farm equipment, for which intelligent systems, diagnostic systems DIAG, and Wind Power Plant Expert System (WPPES) are used to modernize the operation process, can only be tested in a simulative way. The topic of testing the reliability of complex technical objects is constantly developing in the literature. In this paper, it is assumed that the operation of wind farm equipment is described and modeled based on Markov processes. The adoption of this assumption justified the use of the Kolmogorov–Chapman equations to describe the developed model. Based on this equation, an analytically developed model of the wind farm operation process was described. The simulation analysis determines the reliability of the wind farm in terms of the availability factor Kg(t). The simulation tests are performed in two phases using the computer program LabView. In the first stage, the reliability value in the form of the readiness factor Kg(t) as a function of changes in the mean repair time value ranging {from 0.3 to 1.0} was investigated. In the second stage, the reliability value of WF devices was examined as a function of changes in the value of the average time between successive failures, ranging from 1000 to 3000 (h)}. [ABSTRACT FROM AUTHOR]

Since 1948, Shannon theoretic methods for modeling information have found a wide range of applications in several areas where information plays a key role, which goes well beyond the original scopes for which they have been conceived, namely data compression and error correction over a noisy channel. Among other uses, these methods have been applied in the broad field of medical diagnostics since the 1970s, to quantify diagnostic information, to evaluate diagnostic test performance, but also to be used as technical tools in image processing and registration. This review illustrates the main contributions in assessing the accuracy of diagnostic tests and the agreement between raters, focusing on diagnostic test performance measurements and paired agreement evaluation. This work also presents a recent unified, coherent, and hopefully, final information-theoretical approach to deal with the flows of information involved among the patient, the diagnostic test performed to appraise the state of disease, and the raters who are checking the test results. The approach is assessed by considering two case studies: the first one is related to evaluating extra-prostatic cancers; the second concerns the quality of rapid tests for COVID-19 detection. [ABSTRACT FROM AUTHOR]

The paper presents a certain class of the mathematical models of diagnostic information granules describing the fuzzy symptoms-faults relationship. A certain fuzzy diagnostic information retrieval system is described as an application of an expert diagnostic system. Symptoms and faults are fuzzy, and with some scaling of the symptom-fault concept pair values. These value pairs can be considered as intuitionistic fuzzy sets for the space of diagnosed objects. In this article, for scaling intuitionistic fuzzy sets (n-ScIFS), the deductive theory is formulated. There the intuitionistic fuzzy sets (IFSs) and the Pythagorean fuzzy sets (PFSs) are generalized to the n-ScIFS objects. The membership and non-membership values, as standard, can be described by the 1:1 scale or the quadratic function scale. However, any power scale n > 2 can be used. In this paper, any n-Sc scaling functions retaining the IFSs properties are considered. The n-ScIFS theory introduces a conceptual apparatus analogous to the classical theory of Zadeh fuzzy sets and Yager PFSs, consistently striving, for the first time, to formulate the relational structure of n-ScIFSs as a model of a certain information granule system called here the diagnostic granule system. In addition, power- and linear-repeatable diagnostic granules are defined in the n-ScIFSs structure for serial or parallel diagnosis processes. The information granule base is determined and a diagnostic granule system model produced by this information granule base is shown. Certain algorithms have been given to establish the semantic language of diagnosis describing the system of diagnostic information granules. [ABSTRACT FROM AUTHOR]

In this paper, attention was paid to the problem of low controllability of marine medium- and high-speed engines during operation, which significantly limits the parametric diagnosis. The measurement of quickly changing temperature of engine exhaust gas was proposed, the courses of which can be a source of diagnostic information. The F statistic of the Fisher-Snedecor distribution was chosen as a statistical tool. Laboratory tests were carried out on the bench of a Farymann Diesel engine. The tests consisted of introducing the real changes in the constructional structure of the considered functional systems of the engine. Three changed parameters for the structure were reviewed: the active cross-sectional area of the inlet air channel, injector opening pressure and compression ratio. Based on the recorded plots of the quick-changing temperatures of the exhaust gases, three diagnostic measures were defined and subjected to statistical tests. The following data were averaged over one cycle for a 4-stroke piston engine operation, (1) the peak-to-peak value of the exhaust gas temperature, (2) the specific enthalpy of the exhaust gas, and (3) the rate of increase and decrease in the values for the quick-changing exhaust gas temperature. In this paper will present results of the first stage of the elimination study: the one-factor statistical analysis (randomised complete plan). The next part will present the results of the second stage of studies: two-factor analysis (block randomised plan), where the significance of the effect of changing the values of the structure parameters on the diagnostic measures was analysed in the background of a variable engine load. [ABSTRACT FROM AUTHOR]

Previous studies have found that the labour market outcomes of Turkish minorities are slightly better in Germany than in the Netherlands. In this paper we test one of the explanations: differences in ethnic discrimination in hiring. We use a harmonised field experiment to test whether discrimination against job candidates of Turkish origin (age 23–25) varies across Germany and the Netherlands, while holding individual characteristics of job seekers constant. We find that, compared to majority candidates, job candidates of Turkish origin are on average eleven percentage points less likely to receive a positive call-back. Moreover, we find that discrimination against Turkish minorities is significantly higher in the Netherlands than in Germany. In Germany, job candidates of Turkish origin are five percentage points less likely to receive a call-back than equally qualified majority candidates, whereas in the Netherlands this ethnic gap is fifteen percentage points. However, the presented evidence does not support the often-mentioned argument that the amount of diagnostic information in application materials explains why discrimination against Turkish minorities is lower in Germany. Overall, adding diagnostic information has little effect on the relative employment chances of job applicants of Turkish origin, both in Germany and the Netherlands. [ABSTRACT FROM AUTHOR]

In this Letter, the authors propose a variational mode decomposition method for quantifying diagnostic information of myocardial infarction (MI) from the electrocardiogram (ECG) signal. The multiscale mode energy and principal component (PC) of multiscale covariance matrices are used as features. The mode energies determine the strength of the mode, and the PCs provide the representation of the ECG signal with less redundancy. K-nearest neighbour and support vector machine classifier are utilised to assess the performance of the extracted features for the detection and classification of MI and normal (healthy control). The proposed method achieved a specificity of 99.88%, sensitivity of 99.90%, and accuracy of 99.88%. Experimental results demonstrate that the proposed method with the multiscale mode energy and PC features achieved better output compared to the previously published work.

The paper presents a certain class of the mathematical models of diagnostic information granules describing the fuzzy symptoms-faults relationship. A certain fuzzy diagnostic information retrieval system is described as an application of an expert diagnostic system. Symptoms and faults are fuzzy, and with some scaling of the symptom-fault concept pair values. These value pairs can be considered as intuitionistic fuzzy sets for the space of diagnosed objects. In this article, for scaling intuitionistic fuzzy sets (n-ScIFS), the deductive theory is formulated. There the intuitionistic fuzzy sets (IFSs) and the Pythagorean fuzzy sets (PFSs) are generalized to the n-ScIFS objects. The membership and non-membership values, as standard, can be described by the 1:1 scale or the quadratic function scale. However, any power scale n>2 can be used. In this paper, any n-Sc scaling functions retaining the IFSs properties are considered. The n-ScIFS theory introduces a conceptual apparatus analogous to the classical theory of Zadeh fuzzy sets and Yager PFSs, consistently striving, for the first time, to formulate the relational structure of n-ScIFSs as a model of a certain information granule system called here the diagnostic granule system. In addition, power- and linear-repeatable diagnostic granules are defined in the n-ScIFSs structure for serial or parallel diagnosis processes. The information granule base is determined and a diagnostic granule system model produced by this information granule base is shown. Certain algorithms have been given to establish the semantic language of diagnosis describing the system of diagnostic information granules.

The International Classification of Diseases (ICD) not only serves as the bedrock for health statistics but also provides a holistic overview of every health aspect of life. This study aims to facilitate the computer-assisted coding of the 11th revision of the ICD (ICD-11) by leveraging the data structures of ICD-11 and semantics in WordNet. First, a computer-assisted coding framework using WordNet and ICD-11 application programming interface (API) is proposed. Secondly, a network based on entity relations in ICD-11 and synonym sets in WordNet, called CodeNet, is developed. Thirdly, an algorithm for generating ICD-11 code candidates from CodeNet with two tuning parameters on the basis of the input of disease-related text is illustrated. Finally, the discharge summaries in the Medical Information Mart for Intensive Care III database and textual information from ICD-11 entities are used to evaluate the proposed method. Experimental results indicate that the proposed coding method achieves a precision of 84% and a recall of 89% relative to a precision of 65% and a recall of 81% achieved with the existing ICD-11 API. The proposed method also outperforms other methods in the literature by reducing a failure rate of up to 8% in ICD-11 coding. The proposed thresholds of similarity and percentage can be applied to tuning the performance of our method to meet different coding needs. In sum, improving the new structures of ICD-11 with the use of semantics in WordNet can help develop more reliable computer-aided coding systems for ICD-11 coders. [ABSTRACT FROM AUTHOR]

Image fusion in neuro diagnosis is intimidating due to its complexity. The heterogeneous natures of the original brain images make intermodal transmission difficult during fusion. Medical image fusion using complementary modalities results in loss of vital salient information. Poor fusion, colour deficiencies result due to similar processing for both the modalities. A dual technique is proposed using discrete fractional wavelet transform (FRWT) and non‐subsampled directional filter banks for better extraction of salient image elements for improved diagnosis. The sparsity character of the coefficients FRWT is controlled by optimising the parity operator using Grey Wolf optimisation algorithm. Four sets of neurological multimodal magnetic resonance imaging and single photon emission computed tomography (CT) brain images are used from benchmark database for validation. The objective evaluation has been conducted using five metrics. The main values obtained from objective metrics based on the proposed technique are 6.3213 for Shannon entropy, mutual information is computed to be 2.7582, fusion factor is 1.9095, standard deviation is 0.1310, and edge strength is 0.76122 indicating improved diagnostic information and superior image quality. Subjective evaluation by a medico validates the findings with finer visual output and enhanced contrast in comparison with recent and state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

This longitudinal study was designed to test the hypothesis that the strength of metacognitive self would predict the level of motivation to obtain self-diagnostic information. We begin by defining the construct of metacognitive self as a cognitive-motivational concept that pertains to the individuals' self-awareness of biases. We then discuss the role of acquiring diagnostic information about the self in enhancing self-regulation. We predicted that stronger metacognitive self would be associated with greater motivation for seeking diagnostic information about the self, including both positive and negative feedback. More than 400 undergraduate university students participated in the 2.5-years longitudinal study. Participants were tested 5 times, with 6-months intervals, using measures of metacognitive self (Metacognitive Self Questionnaire; MCSQ-21) and the need for diagnostic information about the self (Self-Diagnostic Motive Scale; SDMS). As expected, participants with high metacognitive self sought diagnostic information about themselves significantly more than those low in metacognitive self. This effect was observed at each of the five measurement points. We conclude that individuals characterized by greater insight into their own biases are more highly motivated to obtain feedback about themselves that can be used for accurate assessment of their strengths and weaknesses and for self-improvement.

There is currently a great deal of interest in language teachers' competence in assessing language ability. Their competence in this regard, or lack of it, has much to do with their initial training and professional biases. Taking as example the teaching and learning of one specific kind of language, academic discourse, this paper discusses a number of assessment techniques that language teachers could apply to language teaching at school, or in other contexts of language tuition. Its basis is four basic principles of language assessment: reliability, validity, interpretability of results, and efficiency. These four principles are important to all assessments designed by language teachers. Some assessment techniques that have not yet widely been used to their full potential by teachers are described when different formats of language assessment are discussed. In particular, examples of effective and efficient formats of assessment will be given by referring to an analysis of a test of academic literacy administered to senior secondary school students in their pre-university year. Those examples have clear applications in other language learning settings. The paper concludes with a challenge to teachers: to experiment with new assessment designs, and to learn how to interpret the results of assessment in order to plan language instruction more effectively. [ABSTRACT FROM AUTHOR]

[18F]-FDG-PET/CT ([18F]-fluoro-deoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)) is increasingly used as a diagnostic tool in suspected infectious or inflammatory conditions. Studies on the value of FDG-PET/CT in children are scarce. This study assesses the role of FDG-PET/CT in suspected infection or inflammation in children. In this multicenter cohort study, 64 scans in 59 children with suspected infection or inflammation were selected from 452 pediatric FDG-PET/CT scans, performed in five hospitals between January 2016 and August 2017. Main outcomes were diagnostic information provided by FDG-PET/CT for diagnostic scans and impact on clinical management for follow-up scans. Of these 64 scans, 50 were performed for primary diagnosis and 14 to monitor disease activity. Of the positive diagnostic scans, 23/27 (85%) contributed to establishing a diagnosis. Of the negative diagnostic scans, 8/21 (38%) contributed to the final diagnosis by narrowing the differential or by providing information on the disease manifestation. In all follow-up scans, FDG-PET/CT results guided management decisions. CRP was significantly higher in positive scans than in negative scans (p = 0.004). In 6% of diagnostic scans, relevant incidental findings were identified. In conclusion, FDG-PET/CT performed in children with suspected infection or inflammation resulted in information that contributed to the final diagnosis or helped to guide management decisions in the majority of cases. Prospective studies assessing the impact of FDG-PET/CT results on diagnosis and patient management using a structured diagnostic protocol are feasible and necessary.

Diagrammatic formats are useful for summarizing the processes of evaluation and comparison of forecasts in plant pathology and other disciplines where decisions about interventions for the purpose of disease management are often based on a proxy risk variable. We describe a new diagrammatic format for disease forecasts with two categories of actual status and two categories of forecast. The format displays relative entropies, functions of the predictive values that characterize expected information provided by disease forecasts. The new format arises from a consideration of earlier formats with underlying information properties that were previously unexploited. The new diagrammatic format requires no additional data for calculation beyond those used for the calculation of a receiver operating characteristic (ROC) curve. While an ROC curve characterizes a forecast in terms of sensitivity and specificity, the new format described here characterizes a forecast in terms of relative entropies based on predictive values. Thus it is complementary to ROC methodology in its application to the evaluation and comparison of forecasts.

Operator performance in the main control room (MCR) depends to a great extent on the form and amount of the information on the nuclear plant process status. An unstructured form of data display may result in an increase in the data processing time and in errors in the decisions made by operators. Convenience of the NPP data flow handling is given an increased focus but the process of supplying diagnostic data to operating personnel is neglected by both the developers of technical diagnosis systems and by the MCR interface designers. The paper provides an analysis of the upper-level information from technical diagnosis systems and presents requirements to respective screen forms and implementation options.

Розглянуто основні характеристики датчиків температури, сукупність яких дозволяє порівнювати датчики між собою та цілеспрямовано обирати найбільш відповідні поставленим завданням, достоїнства та недоліки напівпровідникових датчиків температури (діоди, транзистори) зміна концентрації носіїв заряду в яких приводить до зміни струму, що протікає через напівпровідниковий прилад, температурних датчиків на основі автоєпитаксиальних та гетероєпітаксиальних структур. Крім того, розглянуто особливості виміру діагностичного параметру (температури) в методі власного випромінювання, через контакт двох поверхонь (поверхня радіоелектронного компоненту (РЕК) – пристрій зняття діагностичної інформації). Цей метод застосовано для локалізації несправного РЕК в складі цифрового блоку сучасної радіоелектронної техніки (РЕТ) засобами автономної автоматизованої системи діагностування (АА СД).

Currently, it is unclear whether pediatric multiple sclerosis (PMS) is a pathoetiologically homogeneous disease phenotype due to clinical and epidemiological differences between early and late onset PMS (EOPMS and LOPMS). Consequently, the question was raised whether diagnostic guidelines need to be complemented by specific EOPMS markers. To search for such markers, we analyzed cerebral MRI images acquired with standard protocols using computer-based classification techniques. Specifically, we applied classification algorithms to gray (GM) and white matter (WM) tissue probability parameters of small brain regions derived from T2-weighted MRI images of EOPMS patients (onset

We presented 3 studies on the relation between metacognitive self, that is self- awareness of biases, and the drive to posses information that is diagnostic about the outcome. First study (N=184) showed that high MCS participants sought self- diagnostic information significantly more often then their low MCS counter partners. Second study is devoted to the development of the new measure SDMS (N = 555), that is the motive to look for self -- diagnostic information. The last study (N = 90) presented that high MCS participants in contrary to low MCS ones, posses higher drive for looking self- diagnostic information when feedback about the outcome is negative. We discuss the results in line with Taylor & Brown (1988) reasoning on self- regulatory functions of biases. [ABSTRACT FROM AUTHOR]

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