Diagnostic and reliability model of an internal combustion engine

This article is devoted to the creation of a model of observational diagnostics for patients in dentistry using artificial intelligence as an information and measurement system. Artificial intelligence technology and algorithms in the field of clinical and radiographic image analysis in general dentistry and orthopedics have become the most common systems, contributing to accurate and timely diagnosis of a particular patient. Along with clinical practice images, radiographs are key tools for graphic visualization of the patient's dentition. A model of observational diagnostics for patients using artificial intelligence technology as the latest tool in the healthcare industry can become a structured method of information standardization and measurements in general dentistry. The model of observational diagnostics can serve as a fundamental component of functional diagnostics based on artificial intelligence algorithms, since they have a special potential for increasing the reliability and consistency of diagnostic measurements and information used by a dentist during treatment planning and restoration of the dentition. The model of observational diagnostics of dental patients presented by the authors implies a significant step forward in the development of highprecision methods of functional diagnostics, monitoring and analysis of patient condition measurements. The algorithm of the observational diagnostics model was integrated into the program, the operation of which is based on artificial intelligence technology in order to increase the efficiency, consistency, optimality and accuracy of the processes carried out in clinical practice. The purpose of the study is to determine the characteristics of the model of observational diagnostics of dental patients using artificial intelligence as an information and measuring system. Main results of the study: 1) the characteristics of the practical application of artificial intelligence technology for radiographs, clinical images of patients' teeth are provided; 2) the essential aspects of the model of observational diagnostics of dental patients, as well as the features of its practical application for planning dental treatment of patients are highlighted; 3) the results of practical testing of the model of observational diagnostics of dental patients are reflected (using the example of detection and treatment of caries). Research materials: the results of academic studies on the assessment of functional diagnostics of oral cavity and dentition pathologies using artificial intelligence technology; practical testing of the model of observational diagnostics for dental patients (using the example of detection and treatment of caries in a 49yearold woman). Research methods: systematization, comparative analysis, contrastive analysis, generalization, abstractlogical method, induction, deduction, systems approach, data conceptualization, graphical and tabular visualization of data, construction of informationmeasuring systems, artificial intelligence methods, study of clinical images and radiographs of patients' dental arches. The main conclusions of the study: 1) information and measuring instruments, the operation of which is based on artificial intelligence technology, have become the basis for the rapid processing of significant amounts of information, increasing the accuracy and efficiency of clinical decisions in dentistry. These information and measuring systems allow us to determine complex patterns on radiographs and clinical images of dental arches, to increase the degree of accuracy of functional diagnostics, in particular, in the presence of subtle pathologies and disorders; 2) artificial intelligence technology cannot replace a qualified dentist, such information and measuring systems only complement the accumulated human professional experience, allowing us to jointly carry out functional diagnostics of patients with a dental profile; 3) the potential of artificial intelligence technologies to create comprehensive plans and reports for dental treatment and dental restoration forms the basis for the joint activities of the latest tools and a dentist. In the context of the study, it is noted that artificial intelligence algorithms have become a powerful informationmeasuring tool for improving the decisionmaking process in dental practice, since digital technology promptly and quickly provides feedback to the dentist about missed anomalies or suboptimal methods of dental treatment and restorative medicine. Integration of the model of observational diagnostics for dental patients will make it possible to form an algorithm for the operation of artificial intelligence that fully complies with the recommendations for functional diagnostics and dental treatment of patients, developed on the basis of factual information. The use of artificial intelligence algorithms in dental practice will increase the efficiency of the general industry standard for providing dental services to patients.

BackgroundProtein-energy wasting (PEW) has been reported to be pretty common in maintenance dialysis patients. However, the existing PEW diagnostic standard is limited in clinical use due to the complexity of it. Bioelectrical impedance analysis (BIA), as a non-invasive nutritional assessment method, can objectively and quantitatively analyze the changes of body tissue components under different nutritional states. We aim to explore the association between PEW and BIA and establish a reliable diagnostic model of PEW.MethodsWe collected cross-sectional data of 609 maintenance dialysis patients at the First Affiliated Hospital, College of Medicine, Zhejiang University. PEW was diagnosed according to International Society of Renal Nutrition and Metabolism (ISRNM) criteria. Among them, 448 consecutive patients were included in the training set for the establishment of a diagnostic nomogram. 161 consecutive patients were included for internal validation. 52 patients from Zhejiang Hospital were included for external validation of the diagnostic model. Correlation analysis of BIA indexes with other nutritional indicators was performed. Logistic regression was used to examine the association of BIA indexes with PEW. 12 diagnostic models of PEW in maintenance dialysis patients were developed and the performance of them in terms of discrimination and calibration was evaluated using C statistics and Hosmer–Lemeshow-type χ2 statistics. After comparing to existing diagnostic models, and performing both internal and external validation, we finally established a simple but reliable PEW diagnostic model which may have great value of clinical application.ResultsA total of 609 individuals from First Affiliated Hospital, College of Medicine, Zhejiang University and 52 individuals from Zhejiang Hospital were included. After full adjustment, age, peritoneal dialysis (compared to hemodialysis), subjective global assessment (SGA, compared to non-SGA) and water ratio were independent risk factors, while triglyceride, urea nitrogen, calcium, ferritin, BCM, VFA and phase angle were independent protective factors of PEW. The model incorporated water ratio, VFA, BCM, phase angle and cholesterol revealed best performance. A nomogram was developed according to the results of model performance. The model achieved high C-indexes of 0.843 in the training set, 0.841 and 0.829 in the internal and external validation sets, respectively, and had a well-fitted calibration curve. The net reclassification improvement (NRI) showed 8%, 13%, 2%, 38%, 36% improvement of diagnostic accuracy of our model compared with “PEW score model”, “modified PEW score model”, “3-index model”, “SGA model” and “BIA decision tree model”, respectively.ConclusionsBIA can be used as an auxiliary tool to evaluate PEW risk and may have certain clinical application value.

The aims of the present study were to validate the differential diagnostic model of diabetic nephropathy (DN) and non-diabetic renal diseases (NDRD) established in 2003 and to establish a new diagnostic model suitable for the current clinical characteristics of DN. We examined 200 patients with Type 2 diabetes who underwent kidney biopsy from 2004 to 2012. The 2003 differential diagnostic model based on the data collected from 1993 to 2003 was evaluated by the diagnostic test and changes in the clinical differentiation parameters of DN and NDRD were analyzed. Logistic regression, receiver operating characteristics (ROC) curve, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) analysis were applied. The 2003 diagnostic model showed an accuracy of 77.5%. A significantly elevated incidence of hematuria, longer history of diabetes, and reduced level of glycated hemoglobin (HbA1c) were observed in the DN group from 2004 to 2012 compared with DN group from 1993 to 2003. Histories of diabetes mellitus (Dm), systolic blood pressure (Bp), HbA1c (Gh), hematuria (Hu), diabetic retinopathy (Dr), and hemoglobin (Hb) are independently related to DN. Thus, a new diagnostic model was constructed as follows: PDN = exp (0.846 + 0.022 Dm + 0.033Bp + 2.050 Gh-2.664 Hu-0.078 Hb + 2.942Dr)/[1 + exp (0.846 + 0.022 Dm + 0.033 Bp + 2.050 Gh-2.664 Hu-0.078 Hb + 2.942 Dr)].Validation tests determined that the accuracy of the new model were 90.9%. Changes in people with DN, clinical characteristics have reduced the diagnostic efficacy of the 2003 diagnostic model. The newly established model can provide a better, more current differentiation between DN and NDRD.

The specificity of various mechatronic systems functioning in a modern car gives opportuinity to analyze diagnostic signals (oscillograms) of different nature, which are read from the diagnostics characteristic points or can be obtained from the on-board diagnostics regular system (OBD). Such analysis allows achieving a higher level of automation in searching for
\nmalfunctions causes in case of certain number of diagnostic parameters received presence. Therefore, it is important to improve the system for collecting, storing and processing such diagnostic information.
\nThe paper describes the principle of diagnostic signals (oscillograms) database forming. Reference oscillograms and oscillograms of malfunctions are saved. The basic approach is that the diagnostic information is stored as separate ranges (fragments) of oscillograms in the form of fourth-order polynomials coefficients. In addition, the database contains objects of typical diagnostic features, diagnostic parameters and structural parameters, as well as possible changes of structural parameters i.e. malfunctions. Between all these objects there are «many to many» connections. As you know, when there are typical malfunctions in diagnostic signals, certain anomalies appear. Interrelations between database tables characterize the relationship between typical malfunctions and anomalies of diagnostic signals fragments. The degree of influence of the malfunctions causes on the behavior of the diagnostic signal is determined by the weighting coefficients. The determination and correction of these coefficients is the basis of the diagnostic model developed on the basis of a neuro-fuzzy network.
\nAlso the general architecture of the monitoring system is described, which is based on the implementation of diagnostic automation approaches with the implementation of intelligent-expatcher monitoring and modern IT technologies. Architecture is based on the principle of three levels. Each level is encapsulated and has an accessible communication interface. The first level contains the models of the database entities and the access functions to the database, the second level contains the system's logic and diagnostic models, the third level, that is the level of visualization and communication with the user.

ABSTRACTWe aimed to review strategies for identifying SARS-CoV-2 infection before the availability of molecular test results, and to assess the reporting quality of the studies identified through the application of the STARD guideline. We screened 3,821 articles published until 30 April 2021, of which 23 met the inclusion criteria: including at least two diagnostic variables, being designed for use in clinical practice or in a public health context and providing diagnostic accuracy rates. Data extraction and application of STARD criteria were performed independently by two researchers and discrepancies were discussed with a third author. Most of the studies (16, 69.6%) included symptomatic patients with suspected infection, six studies (26.1%) included patients already diagnosed and one study (4.3%) included individuals with close contact to a COVID-positive patient. The main variables considered in the studies, which included symptomatic patients, were imaging and demographic characteristics, symptoms, and lymphocyte count. The values for area under the receiver operating characteristic curve (AUC)ranged from 53-97.4. Seven studies (30.4%) validated the diagnostic model in an independent sample. The average number of STARD criteria fulfilled was 17.6 (maximum, 27 and minimum, 5). High diagnostic accuracy values are shown when more than one diagnostic variable is considered, mainly imaging and demographic characteristics, symptoms, and lymphocyte count. This could offer the potential to identify individuals with SARS-CoV-2 infection with high accuracy when molecular testing is not available. However, external validation for developed models and evaluations in populations as similar as possible to those in which they will be applied is urgently needed.IMPORTANCE According to this review, the inclusion of more than one diagnostic test in the diagnostic process for COVID-19 infection shows high diagnostic accuracy values. Imaging characteristics, patients’ symptoms, demographic characteristics, and lymphocyte count were the variables most frequently included in the diagnostic models. However, developed models should be externally validated before reaching conclusions on their utility in practice. In addition, it is important to bear in mind that the test should be evaluated in populations as similar as possible to those in which it will be applied in practice.

Background Inflammatory bowel disease(IBD),including Crohn's Disease(CD) and ulcerative colitis (UC), is closely linked with altered gut microbiota.Roles of bacteria in IBD have been widely investigated, whereas other microbial kingdoms, fungi, archaea, virus, are less explored. Here, we aim to investigate the multi-kingdom microbial alterations and their capabilities for IBD diagnosis. Methods A total of 968 stool samples (292 CD, 305 UC and 371 controls) of untreated patients from iHMP were collected. Differential microorganisms were identified via wilcoxon test followed by adjustment for confounders. Inter-kingdom co-occurrence interactions analysis was performed by SparCC then microorganism modules were clustered by “MCODE”. Subsequently, neural networks algorithm was used to build diagnostic models for CD or UC with single- or multi-kingdom microbial features and the best panel of biomarkers was further identified by recursive feature elimination. Moreover, diagnostic models were validated in additional independent cohorts. Differential microbial pathways were analyzed to further explore functional alterations in CD and UC. Results In CD and UC, the microbial compositional change was identified in different kingdoms. Compared to controls, we identified 41 bacteria, 37 fungi, 40 archaea and 50 viruses with differential abundance in CD. Similarly, 63 bacteria, 52 fungi, 58 archaea and 39 viruses with differential abundance were detected in UC. Moreover, inter-kingdom interactions were more complex in IBD than controls, especially the interactions between fungal Aspergillusand BeAn 58058 virus. Notably, the diagnostic models constructed with multi-kingdom microbial features performed better (AUC of 0.90 in CD and 0.91 in UC) than that with single kingdom microbial features. The diagnostic models also showed reliable accuracy in independent cohorts with an average AUC of 0.93 (CD) and 0.74 (UC), respectively. Among these biomarkers, bacterial Bacteroidesand fungalAspergillusare the main contributors to the distinguishing ability in both CD and UC diagnostic models. In addition, pathways related to lipid and terpenoids metabolism and immune response were altered in CD and UC, such as glycosphingolipid biosynthesis, carotenoid biosynthesis and IL-17 signaling pathway. Conclusions Our study investigated comprehensivepathogenic microbiota of IBD, which encompasses bacteria, fungi, virus and archaea. And the combination of multi-kingdom microbial biomarkers may serve as effective tools for CD and UC diagnosis, which outperforms single kingdom biomarkers. The alterations of microbial inter-kingdom interactions and cross-kingdom functional changes provide new insight in understanding pathological mechanisms of IBD.

IntroductionOne in four cases of asthma in adults are caused or worsened by work (work-related asthma: WRA). Early detection of WRA could prevent poor health and employment outcomes, but WRA...

We aimed to investigate the influence of patient and lesion characteristics on our diagnostic model for contrast-enhanced ultrasound (CEUS) of the breast, comparing its accuracy with that of histopathology. Conducting a study with eight medical centers, we compared 1,023 breast lesions categorized as BI-RADS 4 or 5 with the score from our newly-established CEUS-based diagnostic model, comparing the results with pathological outcomes. Univariate and multivariate logistic regression analyses were conducted to determine the influence of clinicopathological characteristics on the performance of this CEUS model. Logistic regression analysis showed that patients' age, maximum lesion diameter, and distance from the lesion's deep edge to the pectoralis major were significant independent influencing factors. The model's diagnostic accuracy was greater for patients >35 y (P=0.005), for maximum lesion diameter >20 mm, and for distance from the lesion's deep edge to the pectoralis major ≤3.05 mm. There was no significant difference in accuracy between lesions with maximum lesion diameter 10-20 and <10 mm (P=0.393). The diagnostic performance of the proposed CEUS model for breast lesions is influenced by patients' age, maximum lesion diameter, and distance from the lesion's deep edge to the pectoralis major. Consideration of influencing factors is required to optimize clinical use of the CEUS model.

This study presents an approach to the formalized construction of a diagnostic model for the TV3-117V gas turbine engine, aimed at post-flight analysis of operational data. The model is designed to detect potentially hazardous deviations in engine performance based on the comparison of actual parameters with reference values, considering the operating mode. The relevance of developing a dedicated post-flight diagnostic tool is substantiated by considering the specific features of the TV3-117V engine and limitations of existing approaches, which are mostly empirical or statistical and lack structured mode-dependent logic. A set of diagnostically significant parameters (exhaust gas temperature, compressor shaft speed, torque, fuel consumption, and logical signals) is defined and divided into three functional groups: mode-identifying, diagnostic, and auxiliary. A modular structure of the diagnostic model, comprising blocks for preprocessing, mode identification, parameter normalization, reference characteristics, normative comparison, and logical inference generation, is proposed. The mode identification algorithm is based on a combination of logical indicators and key parameter threshold values to detect engine operation in one of five defined modes: ground idle, cruise, takeoff, 2.5-minute power, and preservation/depreservation. Formulating criteria for identifying potentially dangerous deviations is given special attention. Accounting for not only absolute deviations but also inter-parameter relationships and dynamic trends enhances diagnostic reliability. Using the "ground idle" mode as an example, a set of criteria is implemented to detect temperature, torque, and rotor speed instability. The justification for using dedicated templates of reference behavior for each operational mode to enable context-sensitive comparison is provided. The proposed diagnostic model structure lays the groundwork for the further development of an automated expert system for analyzing post-flight engine data.

Existing expert systems have not improved the diagnostic accuracy of ventilator-associated pneumonia (VAP). The aim of this systematic literature review was to review and summarize state-of-the-art prediction models detecting or predicting VAP from exhaled breath, patient reports and demographic and clinical characteristics. Both diagnostic and prognostic prediction models were searched from a representative list of multidisciplinary databases. An extensive list of validated search terms was added to the search to cover papers failing to mention predictive research in their title or abstract. Two authors independently selected studies, while three authors extracted data using predefined criteria and data extraction forms. The Prediction Model Risk of Bias Assessment Tool was used to assess both the risk of bias and the applicability of the prediction modelling studies. Technology readiness was also assessed. Out of 2052 identified studies, 20 were included. Fourteen (70%) studies reported the predictive performance of diagnostic models to detect VAP from exhaled human breath with a high degree of sensitivity and a moderate specificity. In addition, the majority of them were validated on a realistic dataset. The rest of the studies reported the predictive performance of diagnostic and prognostic prediction models to detect VAP from unstructured narratives [2 (10%)] as well as baseline demographics and clinical characteristics [4 (20%)]. All studies, however, had either a high or unclear risk of bias without significant improvements in applicability. The development and deployment of prediction modelling studies are limited in VAP and related outcomes. More computational, translational, and clinical research is needed to bring these tools from the bench to the bedside. PROSPERO CRD42020180218, registered on 05-07-2020.

The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.

Background: Acute rejection (AR) remains a major issue that negatively impacts long-term allograft survival in renal transplantation. The current study aims to apply machine learning methods to develop a non-invasive diagnostic test for AR based on gene signature in peripheral blood.Methods: We collected blood gene expression profiles of 251 renal transplant patients with biopsy-proven renal status from three independent cohorts in the Gene Expression Omnibus database. After differential expression analysis and machine learning algorithms, selected biomarkers were applied to the least absolute shrinkage and selection operator (LASSO) logistic regression to construct a diagnostic model in the training cohort. The diagnostic ability of the model was further tested in validation cohorts. Gene set enrichment analysis and immune cell assessment were also conducted for further investigation.Results: A novel diagnostic model based on three genes (TSEN15, CAPRIN1 and PRR34-AS1) was constructed in the training cohort (AUC = 0.968) and successfully verified in the validation cohort (AUC = 0.925) with high accuracy. Moreover, the diagnostic model also showed a promising value in discriminating T cell-mediated rejection (TCMR) (AUC = 0.786). Functional enrichment analysis and immune cell evaluation demonstrated that the AR model was significantly correlated with adaptive immunity, especially T cell subsets and dendritic cells.Conclusion: We identified and validated a novel three-gene diagnostic model with high accuracy for AR in renal transplant patients, and the model also performed well in distinguishing TCMR. The current study provided a promising tool to be used as a precise and cost-effective non-invasive test in clinical practice.

ObjectivesComputed tomography (CT) pulmonary angiography is widely used in patients with suspected pulmonary hypertension (PH). However, the diagnostic and prognostic significance remains unclear. The aim of this study was to (a) build a diagnostic CT model and (b) test its prognostic significance.MethodsConsecutive patients with suspected PH undergoing routine CT pulmonary angiography and right heart catheterisation (RHC) were identified. Axial and reconstructed images were used to derive CT metrics. Multivariate regression analysis was performed in the derivation cohort to identify a diagnostic CT model to predict mPAP ≥ 25 mmHg (the existing ESC guideline definition of PH) and > 20 mmHg (the new threshold proposed at the 6th World Symposium on PH). In the validation cohort, sensitivity, specificity and compromise CT thresholds were identified with receiver operating characteristic (ROC) analysis. The prognostic value of the CT model was assessed using Kaplan-Meier analysis.ResultsBetween 2012 and 2016, 491 patients were identified. In the derivation cohort (n = 247), a CT model was identified including pulmonary artery diameter, right ventricular outflow tract thickness, septal angle and left ventricular area. In the validation cohort (n = 244), the model was diagnostic, with an area under the ROC curve of 0.94/0.91 for mPAP ≥ 25/> 20 mmHg respectively. In the validation cohort, 93 patients died; mean follow-up was 42 months. The diagnostic thresholds for the CT model were prognostic, log rank, all p < 0.01.DiscussionIn suspected PH, a diagnostic CT model had diagnostic and prognostic utility.Key Points• Diagnostic CT models have high diagnostic accuracy in a tertiary referral population of with suspected PH.• Diagnostic CT models stratify patients by mortality in suspected PH.

&lt;div&gt;A significant amount of chemical fuel energy in internal combustion engines is wasted through exhaust heat. Waste heat recovery (WHR) systems can transform the heat into electrical energy using thermoelectric generators (TEG). This work utilizes a 1D CFD model to demonstrate the potential of TEG-WHR in improving the thermal efficiency of mass-production, compressed natural gas (CNG) engines used in commercial 22-ton heavy-duty trucks. First, the TEG with heat exchanger experiments are performed to measure thermal and electrical performance data under different fin pitches and inlet gas conditions (Re number, temperature, gas flow rate). These data are used to develop and validate a TEG model, which considers user-defined functions of heat transfer and flow friction coefficients to reproduce measured thermal/electrical characteristics of the integrated TEG with its heat exchanger. The engine experiments are conducted based on the speed–torque map (51 test conditions) of the JE05 heavy-duty cycle using the turbocharged engine equipped with a multi-port injection system. The engine model is calibrated and validated against test data under base conditions (using production valve timings), optimal variable valve actuation (VVA), and variable compression ratio (VCR). Finally, the high-fidelity engine and TEG models are integrated to predict the electrical power generated by a compact TEG-WHR (three-layer, size: 1.3 × A4-paper). The integrated model considers a tradeoff between TEG-generated power and engine pumping loss. Simulation results show that the compact TEG can generate effective 20–701 W electrical powers, translating to about 0.03–1.07% brake thermal efficiency improvement.&lt;/div&gt;

Boiler tube leakage detection expert system